esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Analysis of GNSS Stations Affected by Samos Earthquake
## 2 Materials and Methods
### Data and Process  
Data from 21 continuously operating GNSS stations in Turkey and Greece (Figure 1) were gathered to analyze the deformation field. Permanent stations in Turkey are from the CORS-TR network, and a local network works in the earthquake's vicinity. In Greece, data from NOA (National Observatory of Athens) and the private company Metrica S.A. are used. The data are continuously operating GNSS data which are available 24 h a day, with 30 s intervals of gathering signals from GNSS satellites. Figure 1 illustrates the locations of the continuous stations with brown triangles. As seen in Figure 1, even though the earthquake happened in the sea, there are lots of GNSS stations that make it possible to analyze the earthquake's characteristics because of the islands surrounding the area.  
GNSS data were processed using GAMIT/GLOBK (v10.71) GNSS software [32] with the strategy of [PERSON] et al. [33]. At first, GNSS phase observations from each day were used to estimate station coordinates with loose constraints. Estimation of these coordinates involved IGS data that link regional and global networks. In the next step, loosely constrained estimates were used as quasi-observations to obtain consistent coordinates by combining IGS stations. A coordinate was determined from data collected throughout the day in 30 s intervals, and this is referred to as a daily coordinate. After estimating the daily coordinates of GNSS stations, the coseismic offsets associated with the Samos earthquake were calculated as the differences between coordinates before and after the earthquake (before 30 October and after). The standard deviations were computed using the error propagation law.  
Figure 1: Map of the 2020 earthquake along with continuous GNSS stations. The yellow star indicates the epicenter of the Mw 7.0 Samos earthquake and the green beachball illustrates the point source mechanism solution of the U.S. Geological Survey (USGS). Brown triangles illustrate the GNSS stations and the thin black lines show active faults in Turkey [34].
# Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs
## 3 Experiment Result and Analysis
### Experimental Preparation
#### 3.1.3 Evaluation Metrics  
This paper selects three commonly used evaluation metrics for assessing the effectiveness of relation reasoning tasks: Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits at rank n (Hits@n). MR is used to measure the average position of the correct answers among all possible answers; MRR aims to calculate the average of the reciprocal ranks of the correct entities; and Hits@n is designed to measure the percentage of correct entities that appear in the top n ranks. By selecting Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@n as evaluation metrics, we can comprehensively and accurately assess the performance of the patent knowledge graph inference algorithm. These metrics not only reflect the overall effectiveness of the algorithm but also take into account user experience in practical scenarios, ensuring that the algorithm performs excellently across different dimensions.  
The calculation method for MR is as follows in Equation (18):  
\[MR=\frac{1}{|N_{l}|}\sum_{i=1}^{|N_{l}|}rank_{i} \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Entity**} & \multirow{2}{*}{**Relation**} & \multicolumn{3}{c}{**Edge**} \\ \cline{3-6}  & & & **Training Set** & **Validation Set** & **Test Set** \\ \hline Dataset 1 & 40,943 & 11 & 86,835 & 3034 & 3134 & 2.12 \\ \hline Dataset 2 & 14,541 & 237 & 272,115 & 17,535 & 20,466 & 18.71 \\ \hline Dataset 3 & 75,492 & 200 & 149,678 & 543 & 3992 & 1.98 \\ \hline Dataset 4 & 10,094 & 251 & 368,868 & 46,302 & 46,159 & 36.54 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of information on the three different datasets.  
where \(|N_{t}|\) represents the index of the triplet, and \(rank_{i}\) indicates the rank of the \(i\)-th triplet in the reasoning task. Additionally, the calculation method for the MRR metric is as follows in Equation (19):  
\[MR=\frac{1}{|N_{t}|}\sum_{i=1}^{|N_{t}|}\frac{1}{rank_{i}} \tag{19}\]  
The Hits@n (where \(n=3\)) metric is shown in Equation (20):  
\[Hit@n=\frac{1}{|N_{t}|}\sum_{i=1}^{|N_{t}|}1(rank_{i}\leq n) \tag{20}\]  
where \(1(\cdot)\) is an indicator function, such that the value of \(1(\cdot)\) is \(1\) when \(rank_{i}\leq n\) is true; when \(rank_{i}\geq n\) is the case, the value of \(1(\cdot)\) is \(0\).
## 4 The Proposed Real-Time Monitoring System
### Feature Selection Based on PCA Algorithm  
We primarily employed the Principal Component Analysis (PCA) algorithm to reduce the dimensionality of our dataset. PCA helps prioritize the most crucial features that contribute to the variance in the data. This process not only simplifies the dataset but also removes redundant or less significant features, allowing the model to learn more effectively from the available data. The statistical analysis of applying the PCA as a preprocessing step assisted in making the proposed encoding-based algorithm more robust, flexible, and applicable to various datasets. The analysis presented in Figure 4a revealed that the PCA retained 12 principal components, which together explained 80% of the variance in the dataset. Consequently, only the most informative components were considered, leading to significant improvements in classification outcomes for cardiac patients. Notably, the  
Figure 3: Accuracy vs. number of neurons in the hidden layer of the developed ANN model structure.  
Figure 2: The structure of the developed ANN classifier.  
features contributing most significantly to the PCA components were found to be SPO2, GC, RR, HR, blood pressure (including diastolic and systolic (Dia. and Sys.)), CP, and SoB.  
On the other hand, the clustering shown in Figure 4b demonstrates that the first two principal components (PC1 and PC2) effectively capture the key variations in the dataset concerning criticality and stability. Specifically, PC1 represents primary indicators of stability and criticality, such as HR and blood pressure (both systolic and diastolic). In contrast, PC2 reflects secondary factors, including SPO2, GC, and RR. As seen in Figure 4b, these five vital signs are insufficient to accurately differentiate the model classes. Therefore, we encoded the CP and SoB, along with the previously determined vital signs, to realize outstanding classification accuracy, and we discuss it further in the next section.
## 2 System Model and Configuration
### Allowable Atmospheric Attenuation Value of G/S  
Ignoring the atmospheric attenuation, for the eight MCSs selected in Section 2.2, the maximum \(E_{\mathrm{S}}/N_{0}\) available at the G/S and the demodulation threshold values considering total degradation are shown in Figure 4. It can be seen that as the elevation angle increases from 5\({}^{\circ}\) to 90\({}^{\circ}\), the maximum \(E_{\mathrm{S}}/N_{0}\) in clear sky available at the G/S increase from 36.01 dB to 48.39 dB, which are beyond the threshold values of all MCSs. The variation range of \(E_{\mathrm{S}}/N_{0}\) in clear sky reaches 12.38 dB. Therefore, under this ideal condition, the reliable data transmission can be achieved through the satellite-to-ground link.  
In the actual satellite-to-ground data transmission link from LEO EO satellites to G/S, the atmospheric attenuation is inevitable, and is closely related to the G/S location and the link availability. If only pursuing high link availability, sufficient margins need to be reserved to compensate the atmospheric attenuation. This will result in a waste of resource for LEO EO satellites, which do not need to transmit at any time. In practical engineering applications, it is necessary to make tradeoff between implementation cost and link availability.  
From Figure 4, the maximum allowable atmospheric attenuation values of the eight selected MCSs at different elevation angles can be obtained, as shown in Figure 5, which can be used for link availability analysis.
## 2 Experimental Section
### Characterisation  
The morphology of the samples was observed by field emission scanning electron microscopy (FESEM; Zeiss Supra 40 VP). X-ray powder diffraction (XRD) was carried out on a Rigaku X-ray diffractometer (Cu K\(\alpha\), k= 1.54059 A) over Bragg angles ranging from 10\({}^{\circ}\) to 45\({}^{\circ}\) to characterize the crystallinity and \(\beta\) phase formation of the samples. The relative proportion of the \(\beta\) phase formation was assessed from XRD by calculating the ratio of the peak intensity for \(\beta\) (I\({}_{\beta}\)) and sum of peak intensities of \(\alpha\) (I\({}_{\alpha}\)) and \(\beta\), respectively, as given below [37].  
\[\frac{I_{\beta}}{I_{\alpha}+I_{\beta}}=\frac{I_{200}/I_{110}}{(I_{100}+I_{020}+ I_{110}+I_{021})\alpha+(I_{200}/I_{110})\beta} \tag{1}\]  
where I\({}_{100}\), I\({}_{020}\), I\({}_{100}\), I\({}_{021}\) and I\({}_{200}\)/I\({}_{110}\) correspond to the peak intensities of (100), (020), (100), (021), and (200/110) planes, respectively.  
In order to confirm the chemical structures and bonding configuration of the PVDF and nanocomposite fibers, FT-IR spectra (Paragon 1000 Spectrometer, Perkin Elmer) were obtained. The relative proportion of \(\beta\) phase in the samples were also calculated from the FTIR as per the equation [37]:  
\[\frac{I_{\beta}}{I_{\beta}+I_{\alpha}}=\frac{I_{513}+I_{839}+I_{1272}}{I_{491}+ I_{530}+I_{615}+I_{764}+I_{796}+I_{978}+I_{513}+I_{839}+I_{1272}} \tag{2}\]  
where I\({}_{513}\), I\({}_{839}\), I\({}_{1272}\), I\({}_{491}\), I\({}_{530}\), I\({}_{615}\), I\({}_{764}\), I\({}_{796}\), I\({}_{978}\), I\({}_{513}\), I\({}_{839}\), and I\({}_{1272}\) designate the peak intensity of adsorption bands at 513, 839, 1272, 491, 530, 615, 764, 796, 978, 513, 839, and 1272 cm\({}^{-1}\), respectively.  
The differential scanning calorimetry (SDT Q600 V20.9 Build 20) thermograms of the PVDF nanocomposite were obtained to estimate the crystallization behaviour. The degree of crystallinity (X\({}_{\rm C}\)) was calculated by the following equation  
\[X_{\rm C}=\frac{\Delta H_{m}}{\Delta H_{100\%}}\ast 100 \tag{3}\]  
where \(\Delta\)H\({}_{\rm m}\) and \(\Delta\)H\({}_{100\%}\) refer to the heat of fusion of the sample and 100% crystalline.  
Micro-Raman spectroscopy (Nanofinder 30) with an argon ion laser at an excitation wavelength of 532 nm was used to confirm the different structural phases of the GO in the samples.
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 5 Role of Network Slicing in 6G Technology  
Network slicing is a technique for partitioning a physical or virtualized multi-domain network into smaller, more manageable subnetworks. Mobile network operators may easily design network slices that serve a certain application, service, group of customers, or network by using SDN, NFV, orchestration, analytics, and automation. Slices of a network may span several operators and span many network layers, including the access, core, and transport layers. By slicing a network, each piece may support a particular use case with its own design, administration, and security. Although functional elements and resources may be shared across different network slices, aspects like data speed, capacity, connection, quality, latency, reliability, and services can be customised to a particular service-level agreement (SLA) for each slice. Automation will play a big role in network slicing since MNOs are expected to build and manage hundreds or even thousands of network slices. MNOs can't manually handle this quantity of slicing at the speeds that their customers want. Instead, end-to-end automation should be used to execute the zero-slice lifecycle flexibly at scale and in real-time when traffic loads, service needs, and network resources change. However, once this technology is operational, it will open up a wealth of new income opportunities for MNOs. 6G network architectures will open up new revenue streams and allow for the sharing of network and spectrum resources for a variety of different enterprises with varying needs. 6G networks will provide unparalleled openness, customization, automation, and softwarization based on the evolving ORAN (Open Radio Access Network) paradigm. 6G networks will be a reality in the not-too-distant future, and they will be used for a variety of purposes in many walks of life. Network slicing enables service providers and significant consumers to make the most efficient use of network resources. An analysis of the current slicing approaches provides a better and more efficient artificial intelligence-based network slicing framework, which will be used in the next 6G communication network [56]. Figure 6, discusses the network slicing in 6G networks.
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
## 3 Anomaly-Based Intrusion Detection in Internet-Integrated CoAP WSN
### Misbehavior Detection  
We find it important to start by identifying the threat model considered, and in this context we focus on internal attackers, thus devices that are able to participate in 6 LoWPAN and CoAP communications [11], while at the same time trying to subvert the normal usage patterns, rules or  
Figure 1: Architecture for intrusion detection in the context of Internet-integrated CoAP sensor networks.  
semantics of the CoAP protocol. More precisely, we currently consider the detection of anomalies of four different (and complementary) classes of attacks against 6 LoWPAN and CoAP communication environments and devices. We also label each class of attack, for the purpose of its employment with supervised machine learning algorithms, as follows:  
* Label 1--Refers to CoAP requests sent to a CoAP server at a rate above a particular threshold, thus traducing an attack which we subsequently designate as \"DoS FREQ\";
* Label 2--Refers to CoAP acknowledgements sent to a CoAP server when no corresponding CoAP requests exists, which we subsequently designate as \"DoS ACK\";
* Label 3--Refers to requesting resources that are not supported (available) by the CoAP server, which we subsequently designate as \"WRONG URI\";
* Label 4--Refers to sending requests to a CoAP server with an invalid ACCEPT option, which we subsequently designate as \"WRONG ACCEPT\".  
Other than the previous anomalous situations, the NORMAL class, using the label \"0\", refers to the absence of an attack in the CoAP communications environment. The aforementioned situations are evaluated in the context of our framework and are known for not being detected by simple Signature-based IDS approaches. As such, this motivates our focus on the application of anomaly-based intrusion detection to CoAP communication environments. The presence of an attack reveals the compromised nodes, and as examples of anomalous behaviors we may consider an attacker trying to drain all the energy from the batteries of the compromised node, or forcing the remaining nodes to process unnecessary messages. Our discussion proceeds with an analysis of the machine learning methodology and of the algorithms employed for the purpose of implementing anomaly-based intrusion detection in the context of our framework.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 4 Functional Blocks of the Proposed System
### The Adder Block  
The output of this block is the evaluation of one Fourier transform coefficient. For this reason, N blocks are required. The generic \(k\)-th block makes possible the computation of the \(k\)-th Fourier coefficient by adding the \(k\)-th Fourier coefficients of each bit slice weighted by a suitable power of two.  
Figure 3: Diagram of functional blocks composing the low \(-\) latency solution.
## 2 Description of Anode Recirculation and Gas Composition  
Figure 1 shows a simplified schematic diagram of the hydrogen recirculation loop in PEM fuel cell system. After throttling by a pressure regulator and an injector, the hydrogen from the hydrogen tank is mixed with the unreacted hydrogen recirculated by hydrogen pump and enters the fuel cell stack as anode fuel. The vast majority of the hydrogen entered into the stack is consumed to generate electric power, and the rest hydrogen is discharged from the stack together with water which is formed by the electrochemical process in the stack. Some contents of nitrogen diffusing from the cathode is usually in the fluid discharged from the anode of the stack, since the oxygen is supplied to the stack by means of compressed air. To limit the performance deterioration of the stack, the hydrogen purge valve is opened periodically if the content of the nitrogen exceeds a certain value. In the water separator before the pump, the liquid water is separated by cyclone or collision separation. Thus, the fluids that are recirculated by the hydrogen pump include hydrogen, nitrogen and water vapor. The effects of all these contents on the performance of the hydrogen pump should be investigated; this is the objective of this study.  
In practice, the nitrogen concentration in the real system is difficult to determine because the temperature, relative humidity, current density and stoichiometric ratio could affect the concentration of nitrogen [33]. According to the research of [PERSON] and [PERSON] [34], the nitrogen concentration in the PEMFC systems is below 3%. [PERSON] et al. [35] performed a numerical study on the purging scheme of the anode cycle, and the nitrogen concentration at the outlet position changes between 0-60%. [PERSON] et al. [36] measured experimentally the nitrogen fraction, which varies between 10-22% at the stack inlet for an 8-kW stack. It can be seen that the nitrogen concentration of different studies varies greatly, so the range of nitrogen concentration is considered as 5-25% in this study.  
Figure 1: Schematic diagram of the hydrogen supply loop in a proton exchange membrane (PEM) fuel cell system.
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 3 Design of AMP-RTR Model
### Implementation of AMP-RTR Model
#### 3.2.2 Anomaly Monitoring of Process
Formula (9) means that when one of the conditional judgments \(R(A_{i})\geq N(A_{i})\) is valid, model iteration stops accordingly in the face of multiple iteration processes. \(R(A_{i})\) is used to record the number of times greater than the upper limit during each iteration, and the threshold \(N(A_{i})\) set for each attribute is different. At the same time, the number of times that the record update value is greater than the upper limit \(R(A_{i})\) and satisfies the set threshold \(N(A_{i})\) is different. Therefore, it is necessary to determine the conditions under which model iteration stops. In fact, if the timeliness score iteration of any attribute is abnormal, the production line is more likely to be abnormal. Therefore, it is reasonable for us to use Formula (9) to select the attribute with the first exception as the condition for stopping the model iteration. Record the number of times that the timeliness score value after data update is greater than 1 with \(R(A)\). The setting of the threshold value \(N(A)\) can generally be calculated by the following formula:  
\[N(A)=100\times fall(A)\times\frac{1}{\alpha}\times\frac{1}{T-t} \tag{10}\]  
When \(R(A)=N(A)\), the anomaly monitoring model will issue a warning. \(T\) represents the ideal data update cycle, and \(t\) represents the time when the data are actually updated. When the formula is applicable to the actual time series, \(T-t\) takes 1 (i.e., \(t\) is one unit ahead of the predetermined update time). Due to the statistical analysis of the actually generated time series, it is found that the probability of the time when the data update occurs is one unit around the set period.
# Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities
## 3 Results and Discussion  
### Cart Design  
Figure 1 shows the cart and a schematic view indicating measurements used to model the effects of camera and laser placement. Figure 2 shows the stepper motor configuration in more detail. The choice of motor and gearing was governed by the need for sufficient torque to allow the cart to move from a dead stop up a slight incline on loose surface material (Figure 3). The tradeoff was the top speed of the cart, which is about 2.2 km/h. After testing, it was decided that the design constraints were overly restrictive and, if needed, sprocket configuration could be altered to allow the top speed to be doubled, which would also double the distance moved in response to a single pulse. Alternatively, a gearbox could be added or, if only continuous motion was desired, the stepper motors could be replaced by servo motors.  
Videos of the cart movement on pavement indicated that the motion was uniform by step. The only quirk was the finding that one motor started and stopped a fraction of a second before the other motor. It is assumed that this is due to the sequencing of events by the controller. Alternating motor starts on a step by step basis was considered, but was not implemented as there did not appear to be a resulting directional bias in movement over a number of steps. The goal is to be able to manually position the cart at the start of a crop row and then have the cart move down the row. It is not clear if active steering using differential control of the individual motors will be necessary to keep the cart on course. In case there is a problem, a prototype control algorithm was developed that uses the imaging data to alter motor commands to follow the edges of a crop row. Testing will require field deployment of the cart and optics system. Another problem that needs to be addressed for actual use in produce fields is the variety of widths of crop rows across different fields and crops. The width of the cart can be changed by altering the length of the cross bars.  
The cart was designed with the potential to be fully autonomous. The front section with the motors could be replaced with a single axle that allows steering control. Alternatively, the rear wheels are mounted so as to facilitate the addition of two additional stepper motors to allow independent four-wheel drive. Observations in actual produce fields suggest that full autonomy would be difficult to achieve due to area restrictions at the end of some rows and occasional instances of rough terrain (e.g., large deep grooves made by tractors when the ground was muddy).