esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Global Image-Based Local Feature Visualization, and Global and Local Image Merge Algorithm
### Training and Classification Phase
#### 3.3.1. GAN Training and Execution Stage  
In this paper, \(g^{L}_{f,i}\) is a fake local image created by GAN; and G and D are the generator and discriminator of GAN, respectively.  
To visualize \(p_{j}\) of \(m^{O}_{f,i}\), the GAN is used, as shown in Figure 3. The GAN is composed of G and D. G and D both comprise three CNN layers and one fully connected (FC) layer each. Because \(p_{j}\) cannot be extracted from \(m^{O}_{f,i}\), \(g^{L}_{f,i}\) of \(m^{O}_{f,i}\) is created using \(g^{G}_{f,i}\) of \(m^{O}_{f,i}\). Next, \(g^{G}_{f,i  
Algorithm 1 shows the image merge algorithm. \(SIZE(\ )\) is a function that returns the size of the inputted value. \(\Omega_{f,i}\) is the two-dimensional array used to create \(g_{f,i}^{M}\).  
The image merge algorithm generates \(g_{f,i}^{M}\) using \(g_{f,i}^{G}\), \(g_{f,i}^{L}\), and \(p_{j}^{*}\). The row size of \(g_{f,i}^{M}\) is the sum of the sizes of the functions \(SIZE\Big{(}g_{f,i}^{G}\Big{)}\) and \(SIZE\Big{(}g_{f,i}^{L}\Big{)}\). The column size of \(g_{f,i}^{M}\) is equal to the size of \(SIZE\Big{(}p_{j}^{*}\Big{)}\). Therefore, \(g_{f,i}^{M}\) is created by sequentially extracting and storing the pixels of \(g_{f,i}^{G}\) and \(g_{f,i}^{L}\).  
```
1 FUNCTION ImageMerger (\(g_{f,i}^{G}\), \(g_{f,i}^{L}\), \(p_{j}^{*}\))
2 OUTPUT
3\(g_{f,i}^{M}\)// Merged image
4
5 BEGIN
6\(\Omega_{f,i}\)\(\leftarrow\)2-Dimension matrix for merged image \(g_{f,i}^{M}\)
7 FOR Zero to \(SIZE\Big{(}g_{f,i}^{M}\Big{)}\)
8 FOR Zero to \(SIZE\Big{(}p_{j}^{*}\Big{)}\)
9 IF pixel from \(g_{f,i}^{G}\):
10\(\Omega_{f,i}(m,n)\)\(\leftarrow\)Extract pixel from global image \(g_{f,i}^{G}\)
11 ELSE:
12\(\Omega_{f,i}(m,n)\)\(\leftarrow\)Extract pixel from local image \(g_{f,i}^{L}\)
13 END FOR
14 END FOR
15
16 END
```  
**Algorithm 1** Local Feature Visualization Algorithm
## 4 Platform Design
### Sensor Node Design  
Early node prototypes were based on zero standby energy designs discussed in Section 2.2. Since their health status (QoS) cannot be observed, they were abandoned for a typical WSN node architecture: a microcontroller communicating with a radio device and powered by a primary cell, as shown in Figure 3.  
The prototype was designed mainly to test the operation of the low-resource microcontroller and radio in a compact format, including the radio frequency (RF) performance, and to develop the node firmware, software and in-field communication protocol. Thus, the node printed circuit board (PCB)did not include the transducer, the power supply pads did not fit a standard battery size and it had just generic soldering pads for the antenna (all of these elements were still to be decided).  
According to application specifications, the node prototype was primarily optimized for cost and size. In this regard, it is worth noting that it has significantly lower resources than a typical WSN node: the microcontroller is an ATtiny25 (2 KiB FLASH, 128 bytes SRAM, 128 bytes EEPROM, one analog to  
Figure 3: Sensor node prototype: PCB for laboratory tests (**a**); and prepared for field tests with manually soldered NTC, normal mode helix antenna (NMHA) and battery (**b**); the RF performance is limited by the balun layout (a) and the proximity to the antenna of ferromagnetic crystal case and battery seal (b).  
digital converter (ADC), one serial peripheral interface (SPI)); the radio for in-field communication is a TI CC1150 with a baseband modem (transmit-only for the 315-/433-/868-/915-MHz bands); and the external accurate resonator for the radio is a 27 MHz quartz crystal. The microcontroller uses its internal oscillators (8 MHz main clock and 128 kHz watchdog clock) to reduce the external components, the wake-up time and the energy consumption.  
The average standby current of the sensor node is about 4.7 \(\upmu\)A, largely due to the watchdog timer. It samples the temperature once per second with an NTC transducer (discussed in Section 5.1), keeping both the microcontroller and the transducer active for less than 0.05% of the time. The former consumes about 600 \(\upmu\)A in active state and the latter a few tens of microamperes; thus, their contribution to node average energy consumption is negligible. The transmission of one packet consumes about 27 mA for less than 0.3 s. Thus, in normal operation (sending one heartbeat packet per hour, as discussed in Section 4.1), the average current consumption is less than 7 \(\upmu\)A, and the node theoretical lifespan with a 1 Ah 1/2-AA size lithium battery exceeds 16 years.
## 3 Results
### SPME Optimization
#### 3.1.6 Desorption Time  
Optimizing the desorption time maximizes the transfer of analytes into the instrument for analysis. Desorption times of 30 s, 60 s, 120 s, 180 s, 240 s, and 300 s were used to determine the minimum time needed to desorb analytes from the SPME fiber (Figure 6). A desorption time of 120 s was chosen in a 260 \({}^{\circ}\)C injector. The inlet pressure was constant and determined by the pressure needed to maintain balance with the midpoint pressure.  
Figure 5: Effects of sample volume on selected volatiles extracted using HS-SPME in an Iowa Marechal Foch. Compound letters correspond to compounds found in Figure 2 (i.e., A—ethyl isobutyrate, B—isobutyl alcohol, L—ethyl palmitate). Conditions: 5 min pre-sampling desorption of 50/30 μm DVB/CAR/PDMS SPME fiber; 1, 2, 3, and 4 mL wine sample in a 10 mL threaded glass amber vial with PTFE/silicone septa; 10 min incubation at 50 \({}^{\circ}\)C; 10 min extraction time at 50 \({}^{\circ}\)C; agitation speed 500 rpm; 5 min desorption time into GC inlet. 4 mL sample volume was chosen for the method.  
Figure 6: Effects of fiber desorption time on selected volatiles extracted in an Iowa Marechal Foch. Compound letters correspond to compounds found in Figure 2 (i.e., A—ethyl isobutyrate, B—isobutyl alcohol, L—ethyl palmitate). Conditions: 30 s, 60 s, 120 s, 180 s, 240 s, 300 s pre-sampling desorption of 50/30 μm DVB/CAR/PDMS SPME fiber; 4 mL wine sample in a 10 mL threaded glass amber vial with PTFE/silicone septa; 10 min incubation at 50 \({}^{\circ}\)C; 10 min extraction time at 50 \({}^{\circ}\)C; agitation speed 500 rpm; 30 s, 60 s, 120 s, 180 s, 240 s, 300 s desorption time into GC inlet. 120 s thermal desorption time was chosen for the method.
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 4 Experiments and Evaluations
### Results of Performance Verifications  
This section presents the performance evaluation and analysis. Firstly, the performance results of different network techniques are evaluated in the experiments. Afterward, the impacts of each verification factor are discussed in terms of packet size, VM size, traffic flow, and enhanced data plane.
## 6 Real Data Applications
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Observations** & **Sources** \\ \hline \multirow{6}{*}{Dataset 1.} & 10, 33, 44, 56, 59, 72, 74, 77, 92, 93, 96, 100, 100, 102, 105, 107, 107, 108, & \\  & 108, 108, 109, 112, 113, 115, 116, 120, 121, 122, 124, 130, 134, 136, & \\  & 139, 144, 146, 153, 159, 160, 163, 163, 168, 171, 172, 176, 183, 195, 196, & Bjerkedal [34] \\  & 197, 202, 213, 215, 216, 222, 230, 231, 240, 245, 251, 253, 254, 255, 278, & \\  & 293, 327, 342, 347, 361, 402, 432, 458, 555 & \\ \hline \multirow{6}{*}{Dataset 2.} & 12.20, 23.56, 23.74, 25.87, 31.98, 37, 41.35, 47.38, 55.46, 58.36, 63.47, & \\  & 68.46, 78.26, 74.47, 81.43, 84, 92, 94, 110, 112, 119, 127, 130, 133, 140, & \\  & 146, 155, 159, 173, 179, 194, 195, 209, 249, 281, 319, 339, 432, 469, 519, & \\  & 633, 725, 817, 1776 & \\ \hline \multirow{6}{*}{Dataset 3.} & 3.1091, 3.3825, 3.1444, 3.2135, 2.4946, 3.5146, 4.9274, 3.3769, 6.8686, & \\  & 3.0914, 4.9378, 3.1091, 3.2823, 3.8594, 4.0480, 4.1685, 3.6426, 3.2110, & \\  & 2.8636, 3.2218, 2.9078, 3.6346, 2.7957, 4.2781, 4.2202, 1.5157, 2.6029, & \\  & 3.3592, 2.8349, 3.1348, 2.5261, 1.5806, 2.7704, 2.1901, 2.4141, 1.9048 & \\ \hline \multirow{2}{*}{Dataset 4.} & 1.1, 1.4, 1.3, 1.7, 1.9, 1.8, 1.6, 2.2, 1.7, 2.7, 4.1, 1.8, 1.5, 1.2, 1.4, 3.0, 1.7, & \\  & 2.3, 1.6, 2.0. & \\ \hline \end{tabular}
\end{table} 
Table 5: Medical datasets. \[CM=\sum_{i=1}^{n}\left[F(x_{i};\Theta)-\frac{2i-1}{2n}\right]^{2}+\frac{1}{12n}.\]
* The AD (Anderson Darling) test statistics: \[AD=-n-\frac{1}{n}\sum_{i=1}^{n}(2i-1)\times[\log(1-F(x_{i-n+1};\Theta))+\log F( x_{i};\Theta)].\]
* The KS (Kolmogorov Smirnov) test statistic: \[\sup x[F_{n}(x;\Theta)-F(x;\Theta)].\] The DMs were computed as
* The Akaike IC (information criterion): \[AIC=2w-2\ell(\Phi).\]
* The Bayesian IC: \[BIC=w\log(n)-2\ell(\Phi).\]
* The Consistent Akaike IC: \[CAIC=\frac{2 nw}{n-w-1}-2\ell(\Phi).\]
* The Hannan-Quinn IC: \[HQIC=2w\log(\log(n))-2\ell(\Phi).\]  
where \(\ell(\Phi)\) is the maximized LF evaluated on the MLEs, \"\(n\)\" is the sample size, and \"\(w\)\" is the number of probability model parameters. The proposed distribution was implemented in R software using an (Adequacy Model) package with the \"BFGS algorithm\" method for analysing the datasets. In general, a statistical model that has lower values of these AMs and a higher \(p\)-value will be considered a good probability (or competitor) model for the underlined (or considered) datasets. By implementing these AMs, it is observed that the NGLog-Weib model gives a superior fit to the considered datasets as compared to the other distributions.
## 3 Results and Discussion
### Validation of the Test Setup
#### 3.1.4 Evaluation of the Electrode Arrays
Another explanation could be an increased number of short circuits as a result of the contact between a vertically oriented fibre and an electrode at height level two. The specimens with the second electrode array, with angular distances of 90\({}^{\circ}\), showed the same tendency, where the expected behaviour could not be detected for the highest fibre content only (see Figure 10). In combination with the fact that the total resistivity for PC was smaller for the specimens tested with electrodes with angular distances of 180\({}^{\circ}\), while for the SFRC the opposite behaviour was observed, and the changes in the electrical conductivity of the electrodes in the model did not lead to significant changes in the results, the conclusion was drawn that the small number of specimens was the reason for the partially unexpected results and thus, the model is probably applicable for a larger test series of drilling cores. Nevertheless, based on the results of the validation series, the fibre contents of the single specimens and the fibre orientations inside the specimens were calculated using Equation (2) (see Section 2.4). For this purpose, each series of three specimens, the first with six electrodes with angular distances of 180\({}^{\circ}\) and the second with twelve electrodes with angular distances of 90\({}^{\circ}\), was analysed separately. For the specimens with only six electrodes, only one horizontal direction of the specimens was tested, so it was assumed that the other direction was almost identical and that the global value of the resistance was calculated as the mean value of the vertical resistivity and two times the horizontal resistivity. For the specimens with twelve electrodes, both horizontal directions were used for the calculation of the global value. Due to the influence of the embedded electrodes at height level two on the vertical current flow, the vertical resistivity was calculated as the mean value of the vertical arrays from level one to level two, level one to level three and level two to level three to prevent any direct connections between the fibres and the electrodes and thus, a short circuit. Each plain concrete specimen was used as the reference for the conductivity of the matrix for the corresponding SFRC specimens.
## 3 A Brief Review of the Related Literature
According to [PERSON] [61], Latent Semantic Analysis is one of the most popular approaches in this field (e.g., [62; 63]); its fundamental idea is to map high-dimensional count vectors to a lower-dimensional representation in a so-called latent semantic space. Its purpose is to find a data mapping that provides information beyond the lexical level and reveals semantical relations between the entities of interest (e.g., [64; 65; 66]). Within the scope of short-text classification, there are several studies in the area of NLP focused on classifying news according to contents. [PERSON] and [PERSON] [67] processed a corpus of news published from 1999 to 2002 in order to explore the impact of several factors (term frequency, grammatical structure, and context) on the actual news classification. Furthermore, [PERSON], [PERSON], and [PERSON] [68] proposed a hybrid approach that enriches the traditional BoW representation with background knowledge for the semantic analysis of texts. The results indicated that their concepts-based approach adds information that improves classification performance for news items. As mentioned before, Twitter has served as a platform to easily extract content and apply classification techniques on it. There are in the literature studies focused on classifying news based on retrieved tweets. [PERSON] et al. [59] took advantage of newsgroups that started to share their headlines on Twitter and worked on classifying news into different groups to help the users to identify the most popular newsgroups by time, depending on the country. The classification was performed using SVM, since they considered it to be more effective facing such a high-dimensional problem. Their proposal displayed satisfactory performance detecting content related to entertainment and health, whereas other categories had acceptable effectiveness (more than 70% for most cases). On the other hand, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [69] developed a news processing system that showed tweets reporting the latest breaking news. Unlike [PERSON] et al. [59], [PERSON] et al. [69] did not take the tweets of accounts related to newsgroups, but taking any tweets since tweets regularly report the news before the conventional news media does. Although most of the studies in the literature are applied for classifying English news (e.g., [70; 71; 72]), some researches have recently enriched the related literature by focusing on other languages, employing machine learning techniques (e.g., [73; 74; 75; 76; 77]). The related studies on Spanish are still limited even though short-text classification becomes one of the main applications of NLP. Here, we tackle a real-world problem of Spanish news classification with imbalance by using (a) a BoW representation, (b) a MI feature selector, (c) a sampling method, and (d) a supervised learner.
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 4 Modeling Channel Occupation Using SCHC  
### Channel Occupancy Efficiency Estimate  
In [11], the author presents the efficiency estimation for the ARQ protocols (see Chapter 5.2.4). This definition can be extended to other protocols using a clear and precise concept, such as the effective transmission rate.  
According to [11], the efficiency \(\eta_{0}\) is the **ratio** between the effective transmission rate \(R^{0}_{eff}\) and the transmission rate of the channel named \(R\), as indicated in the following equation:  
\[\eta_{0}=\frac{R^{0}_{eff}}{R}=\frac{\frac{\pi_{bits}}{t_{0}}}{R} \tag{1}\]  
Thus, the effective transmission rate \(R^{0}_{eff}\) is calculated as the **ratio** between the number of bits of information delivered to the recipient and the total time required to transmit that number of bits. Due to the possibility of transmission errors, \(t_{0}\) in Equation (1) has a random behavior defined in Section 4.3. \(R\) is defined for each band in the document LoRaWAN(tm)Regional Parameters [12].  
For the case of the SCHC protocol using the ACK-on-Error mode, the number of bits of information delivered to the recipient is called \(n_{bits}\) and is given by Equation (2):  
\[n_{bits}=n_{tiles}\cdot tile_{size} \tag{2}\]  
If we represent this equation as a function of the number of SCHC Fragments, then the new expression will be Equation (3):  
\[n_{bits}=n_{fragments}\cdot n_{tiles\_per\_fragments}\cdot tile_{size} \tag{3}\]  
where \(n_{fragments}\) is the number of SCHC Fragments within a transmission window in ACK-on-Error mode, and \(n_{tiles\_per\_fragments}\) is the number of tiles in a SCHC Fragment. In this model, we assume that all SCHC Fragments have the same size. \(tile_{size}\) is the size of a tile in bits.
## 7 Open Issues and Research Challenges
### Health Hazards  
6G offers an exceptionally high data rate (in terabytes) for high-speed communication networks over the THz frequency band, which is hazardous for human eyes and skin tissues. Human safety from THz waves is a challenging task in the 6G environment. To address the same, super-narrow beams need to be deployed that minimize the propagation loss of fading channels and the interference of inter-symbols in data patterns, which is a challenging task.
# Ten Years of TerraSAR-X Operations
## 3 Evolution of SAR Product Portfolio
### TerraSAR-X-Like Products  
In the pursuit monostatic campaign of the TanDEM-X mission in 2014/2015 (see Section 4.2), scientific and operational TanDEM-X mission acquisitions were commanded on both satellites to acquire the same swath on ground with a time lag of about 10 s corresponding to their along-track separation in space. Unlike the bistatic acquisitions that were performed in the nominal TanDEM-X close formation that require specific commanding with synchronization pulse exchange and additional calibration sequences, this raw data is very similar to the one of nominal TerraSAR-X mission acquisitions. The TSX/TDX pairs were thus screened and processed by an upgraded TMSP to generate the so-called TerraSAR-X-like products and make them accessible to the TerraSAR-X user community.  
The TerraSAR-X-like products are not subject to the basic product specification, since they do slightly differ in terms of product performance. TerraSAR-X-like products are available in all nominal TerraSAR-X mode variants, and in most cases, the deviations in performance parameters are negligible. For Stripmap data, the TanDEM-X elevation beams have been used in some conditions that areoptimized for coverage and not for radiometric performance. In TanDEM-X commanding, also the near range beams use a 100 MHz range bandwidth instead of the nominal 150 MHz. In combination with different approaches for radar timing (PRF selection), these products may thus miss the specified radiometric looks and ground range resolution of 3 m for small incidence angles. Despite these small discrepancies, the available data opens new fields of application exploiting the time lag with nominal TerraSAR-X product analysis tools e.g., for ship motion measurements and other change detection methods.
## 5 Experiments and Results
### Discussion
#### 5.3.2 Comparison Experiment in Square Tracking  
A comparison experiment was conducted in order to verify the advantages of the proposed method. Because the square tracking including four line tracking and three yaw angle changes, is a more difficult tracking task, the comparison experiment is only conducted in square tracking. In the experiment, the BSR tracked the same reference trajectory based on the proposed MPC algorithm, the traditional MPC, and the backstepping algorithm. Figure 24 depicts the comparison results. The asterisk designates the start point of the reference trajectory. The backstepping trajectory tracking algorithm is almost able to drive the robot to track the reference trajectory. As a sudden change in the yaw angle at the corner, a huge increment in the control input will be generated based on the backstepping method, which will damage the motors of the thrusters. In the experiment, a hard constraint was added to prevent such damage. Consequently, the backstepping algorithm performs somewhat poorly in the second and forth line stages. The traditional MPC can drive the robot to track and land roughly on the desired trajectory. While, as compared with the proposed MPC, there is a little oscillation in the second and last line stages. In addition, at the corner, the real trajectory based on the traditional MPC deviates the reference trajectory more seriously than that based on the proposed MPC. The lack of restriction on the control input increments is the main reason for the above results. Meanwhile, the proposed MPC performs well and stably in every line stage. Although the real trajectory slightly overshot the reference trajectory during the change in yaw angle, the robot was finally able to converge to the desired trajectory at the end. The mean square errors (MSEs) of the robot states (including \(x,y\) and yaw angle \(\theta\)) during the tracking of the desired trajectory are shown in Table 4, which clearly reflects the better tracking effect of the proposed algorithm. It is clear that the real trajectory that is based on the proposed MPC is closer to the desired trajectory.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Line** & **Square** \\ \hline \(x[m^{2}]\) & 0.208 & 0.080 \\ \(y[m^{2}]\) & 0.210 & 0.067 \\ \(\theta[rad^{2}]\) & 0.246 & 0.437 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean square errors (MSE) of Tracking Different Trajectories.  
Figure 24: Results of the comparison experiments in square tracking.
# Fail-Safe Topology Optimization Using Damage Scenario Filtering
## 3 Numerical Benchmarks
### T-Shaped Beam  
To test whether this method could verify more complex scenarios, the T-shaped beam mentioned by [PERSON] et al. [42] is considered in this case. As shown in Figure 15, the design space of the T-shaped beam is discretized, encompassing a total of 5100 elements. The bottom of the T-shaped beam is supported, and vertical loads (F = 1) are applied at the lower corners of the left and right boundaries. The volume fraction of the solid material to be preserved is constrained to 0.4. Each failed patch covers 10 \(\times\) 10 elements. The blue regions in the figure represent inactive areas, with each inactive area covering 5 \(\times\) 30 elements. The density threshold is set to A = 10.  
Firstly, the T-shaped beam is optimized so that the patch size is 5 \(\times\) 5. In addition, the local damage near the loading position, that is, at the red arrow, is not considered in the optimization process. The loading conditions are kept unchanged, and the influence of stress concentration at the loading point is avoided.  
As shown in Figure 16, the aforementioned T-shaped beam still obtains a clear configuration under the corresponding conditions. Simultaneously, under these conditions, the optimally worsened damaged area of the structure is represented by a red square, corresponding to a strain energy of 184.02. In order to observe and locate weak areas within the structure (stress concentration), a stress analysis of the aforementioned structure is conducted.  
As shown in Figure 17, the maximum stress is \(\sigma_{\text{max}}\ =\ 1.564\). By comparing the two optimized result diagrams, it is evident that the most severe damaged areas are identical. Filtering through density thresholds not only ensures reduced sensitivity to failure but also enhances the computational efficiency. Consequently, fail-safe topology optimization filtered by density thresholds can efficiently achieve a highly robust fail-safe design.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{L = 4} & A & 0.01 & 0.1 & 1 & 2 & 4 \\ \cline{2-7}  & Rate & 34.7\% & 37.2\% & 42.4\% & 45.2\% & 49.2\% \\ \hline \multirow{2}{*}{L = 6} & A & 0.01 & 0.1 & 1 & 2 & 4 \\ \cline{2-7}  & Rate & 26.1\% & 27.0\% & 29.5\% & 31.3\% & 33.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reduction rate in the average iteration time during the optimization process of the L-shaped beam.  
Figure 14: Comparison of average iteration times for each method when L = 6.  
Figure 16: The optimized T-shaped beam model.  
Figure 17: The stress model of the optimized T-shaped beam.  
Figure 15: The initial design space of the T-shaped beam.