esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 4 Comparison with State-of-the-Art UWB Antennas  
Table 1 summarizes the proposed antenna's performance and its comparison with related work presented in the literature in recent years. It can be observed that the presented work offers a compact size as compared to all of the compared literature. The work reported in [15; 17] offered dual-notch characteristics, but their size is twice as big as the proposed work. In addition, Refs. [17; 18] offer a bit high peak gain value, but they suffer from structural complexity, along with the narrowband and big size as compared to the presented work. A semicircular-shaped printed antenna with two notch bands ranging from 3.3-4.2 GHz and 6.6-7.6 GHz was proposed in [19]. The lower notch band was realized using a modified dumbbell-shaped slot etched from the radiator, while the upper notch band was attained by employing a two-pair of capacitors, along with L-shaped slots carved from the ground plane. The antenna covers the complete UWB spectrum allocated globally. However, two pairs of capacitors along with narrow slots increase the structural complexity, while the physically large size of the antenna limits its applications for compact devices. In [20], a jug-shaped CPW antenna was presented. The antenna was fabricated on cheap FR4 substrate. However, the antenna does not offer any band rejection and may suffer from interference with WLAN spectrum. In [21], an EBG-backed monopole patch was presented. The antenna offers good notching characteristics. However, the antenna has a structurally large width of 25 mm. In [22], a metamaterial-backed monopole patch was presented, which offers a high gain. Although the presented design is a good candidate for high gain applications, the antenna offers no band-notched characteristics or interference rejection. Furthermore, an octagonal star-like monopole patch was presented in [23]. The antenna offers a fractional bandwidth of 120 percent. However, the antenna covers an area of 1275 mm\({}^{2}\), which is geometrically larger than the presented antenna in this manuscript. In [24], a unique geometry of an antenna was presented, offering a bandwidth of 3.581-14.1 GHz. The antenna has a peak moderate gain of 3.2 dBi. This antenna does not offer any band rejection capability and may not be suitable for applications where low interference is a vital requirement. In [25], a planer stub-loaded patch was presented. The antenna offers dual band-notched characteristics. However, the presented design is about 20% structurally larger than the presented design. Based upon the above-mentioned discussion, the proposed UWB antenna with WLAN notch band overperforms all the related work by offering a compact size, wideband, comparable peak gain, and low structural complexity.  
Figure 9: The group delay of the proposed notch band extended UWB antenna.
# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 3 Methodology
### Dataset and Preprocessing
#### 3.1.2 Preprocessing  
To ensure a fair and consistent comparison, all models were trained using a standardized preprocessing pipeline and feature space. Images were resized to 64 x 64 pixels, converted to grayscale, and then flattened into 1D vectors to serve as input features. The StandardScaler was applied to normalize the feature set, ensuring zero mean and unit variance across pixel values. To address high dimensionality and improve computational efficiency, Principal Component Analysis (PCA) was employed for dimensionality reduction. While flattening simplifies the input for traditional machine learning models, it eliminates the spatial structure of images, which may degrade performance. Nevertheless, this approach was adopted to maintain uniform conditions across all models. An exception was made for ResNet50 and VGG16, which were evaluated using RGB images resized to 224 x 224 pixels, in accordance with their pre-trained configurations optimized for color image inputs.
## 3 Normalization Algorithms
### Pseudo\(-\)Invariant Polygon Features\(-\)Based Normalization
#### 3.3.2 Pseudo\(-\)Invariant Polygon Features with Multiple Bands (Polygon\(-\)Multi)  
Multivariate regression equations have similar high accuracy as single\(-\)band regression, even obtaining more precise correlations (Table 4). Bands 4, 5, and 6 are strongly and positively influenced by corresponding bands while the others are greatly affected by adjacent bands.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sentinel\(-\)2A Landsat8** & **Band\(-\)1** & **Band\(-\)2** & **Band\(-\)3** & **Band\(-\)4** & **Band\(-\)5** & **Band\(-\)6** \\ \hline Band\(-\)1 & 0.6945 & \(-\)0.0268 & \(-\)0.0758 & 0.2168 & \(-\)0.1613 & 0.1875 \\ Band\(-\)2 & 0.2421 & 1.2244 & 0.7760 & 0.1973 & 1.0132 & 0.5372 \\ Band\(-\)3 & \(-\)0.1027 & \(-\)0.1630 & 0.4461 & \(-\)0.0332 & \(-\)0.4453 & \(-\)0.3343 \\ Band\(-\)4 & \(-\)0.0455 & \(-\)0.0540 & \(-\)0.0878 & 0.9290 & 0.0004 & 0.0350 \\ Band\(-\)5 & 0.1315 & 0.1467 & 0.1451 & 0.2432 & 0.8925 & \(-\)0.0506 \\ Band\(-\)6 & \(-\)0.1596 & \(-\)0.1671 & \(-\)0.1581 & \(-\)0.3065 & 0.0135 & 0.8808 \\ Bias & \(-\)0.0070 & \(-\)0.0113 & \(-\)0.0123 & \(-\)0.0240 & \(-\)0.0368 & \(-\)0.0293 \\ R2 & 0.9667 & 0.9743 & 0.9785 & 0.9956 & 0.9977 & 0.9972 \\ RMSE & 0.0020 & 0.0021 & 0.0028 & 0.0043 & 0.0035 & 0.0029 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transfer coefficient and error of polygon\(-\)multi between Sentinel\(-\)2A and Landsat\(-\)OLI.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sentinel\(-\)2A Landsat8** & **Band\(-\)1** & **Band\(-\)2** & **Band\(-\)3** & **Band\(-\)4** & **Band\(-\)5** & **Band\(-\)6** \\ \hline Band\(-\)1 & 0.6386 & 0.5386 & 0.5773 & \(-\)0.0735 & 0.5868 & 1.0106 \\ Band\(-\)2 & \(-\)0.5458 & \(-\)0.3355 & \(-\)0.8125 & 0.0166 & \(-\)1.2052 & \(-\)1.4804 \\ Band\(-\)3 & 0.1608 & 0.1515 & 0.7489 & \(-\)0.1694 & 0.0999 & 0.1242 \\ Band\(-\)4 & 0.0768 & 0.1005 & 0.0639 & 0.9756 & 0.1713 & 0.1599 \\ Band\(-\)5 & \(-\)0.5442 & \(-\)0.5553 & \(-\)0.5363 & \(-\)0.3156 & 0.1266 & \(-\)0.6149 \\ Band\(-\)6 & 0.6779 & 0.6665 & 0.6427 & 0.4385 & 0.8774 & 1.5224 \\ Bias & 0.0286 & 0.0443 & 0.0432 & 0.0182 & 0.0440 & 0.0412 \\ R\({}^{2}\) & 0.2982 & 0.3356 & 0.4222 & 0.7169 & 0.7733 & 0.6855 \\ RMSE & 0.0441 & 0.0445 & 0.0486 & 0.0460 & 0.0497 & 0.0535 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transfer coefficients and error of point\(-\)multi between Sentinel\(-\)2A and Landsat\(-\)8 OLI.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 2 System Modeling
### RF Link Modeling  
The Nakagami-\(m\) channel model is a generalized model and mathematically tractable [20]. Here in this section, we deal with the detailed description of the RF link modeling. At \(R_{1}\), the CDF of the instantaneous SNR, \(F_{\gamma RF}(\gamma)\), is given by Equation (2), as in [27], as  
\[F_{\gamma RF}(\gamma)=1-\frac{1}{\Gamma(m)}\Gamma\Bigg{(}m,\frac{mL_{RF}\gamma} {\hat{\gamma}_{RF}}\Bigg{)} \tag{1}\]  
where \(\hat{\gamma}_{RF}\) represents the average SNR, \(\gamma\) is the instantaneous SNR of the RF link, \(\Gamma(.,.)\) stands for the upper incomplete Gamma function, \(L_{RF}\) is the RF channel path loss given as \(L_{RF}=Ad_{RF}^{\pi}\), where \(\alpha\) represents the path loss exponent, \(A\) is assumed to be a constant related to the transmission environment and frequency of the information signal, and \(d_{RF}\) represents the distance between \(S\) and \(R_{1}\). The parameter \(m\) denotes the Nakagami-  
Figure 1: Three-hop mixed RF/FSO/UWOC system.  
-fading parameter and it can be calculated as \(m\approx\frac{(K+1)^{2}}{2K+1}\); \(K\) here stands for the Rician factor [28].
# Engineering Challenges Ahead for Robot Teamwork in Dynamic Environments
## 3 Teamwork Challenges
### Heterogeneity  
Heterogeneity in an MRT may refer to the hardware and software features of the individual team members. Different application domains require different robot functionalities. Thus, capabilities related to robot mobility (e.g., static, wheels, legs, aerial), sensors (e.g., optical, acoustical, temperature, air quality parameters, laser, lidar, infrared, etc.) and actuators (e.g., arm, drill, kicker, extension rails, etc.), compute power, storage capacity, operating system software, communication type and range (e.g., WiFi, Bluetooth, LoRaWAN), access to cloud computing resources, and many more will be different for different robot types. In a smart factory, the degree of heterogeneity will probably be limited and known in advance at design time, while, in autonomous driving scenarios, we cannot anticipate completely what kind of traffic participants appear and what their specific properties and capabilities are.  
From the perspective of teamwork, robots in a team need to be capable of interacting with each other. Thus, not only must a common communication architecture be in place, but team-wide understood application level protocols for information exchange, coordination, and decision-making are also needed. On the one hand, the heterogeneity of robots certainly contributes to the complexity of the teamwork application design, particularly since, so far, there are no dominating standards for masking the heterogeneity through transparency solutions. On the other hand, from an application perspective, heterogeneity may be beneficial if a team is able to make use of specific capabilities of team members. Then, the whole can truly be more than its parts.  
The key challenge is as follows:  
* Will it be possible to agree on a small set of standardized hardware and software interfaces for the wide range of diverse robot hardware components and software services?  
The situation is similar to the evolution of computing hardware and software where commonly agreed interface standards--de facto or official ones--made it possible to mix and match components from different manufacturers to prevent vendor lock-in and to respond to the variety of user requirements. If the emergence of multi-robot applications continues at the speed that we are witnessing today, more standardization is needed in order to enable a flexible combination of heterogeneous robots for application specific robot teamwork. Consequently, more practical experience and applied research are needed to fuel such a standardization.
## 5 The Proposed Routing Method
### Trust-Based Clustering Process (T-Clustering)
If a specified time period (\(T_{end}\)) is expired, and the CH does not receive any beacon packet from itscluster member node, it deletes the identification of this node from its cluster member list to cancel its membership to the cluster. * **Supporting the node:** It is a very important step in any clustering algorithm. If the trust level corresponding to the CH node decreases, or its energy is lower than an energy threshold, it is necessary to replace this CH. In CTSRD, when the cluster is formed, the CH node records the cluster members in a list and saves their chances and trust levels. Then, CH chooses a backup CH node, which is a node with the maximum chance from CMs with the highest trust level. When the CH node is exposed to failure, the backup CH is responsible for playing the role of CH. Algorithm 2 shows the pseudo-code related to T-Clustering in CTSRD. ```
0:\(n_{i}\): IoT nodes (\(i=1,...,N\)) \(\textit{Timer}=0\)
0: Clustered network Begin
1:if\(\textit{Timer}=\textit{Beacon period}\)then
2:for\(i=1\) to \(N\)do
3:\(\textbf{n}_{i}\): Broadcast a beacon message in the network;
4:\(\textbf{n}_{i}\): Update a neighboring table in the network;
5:\(\textit{Timer}=0\);
6:endfor
7:endif
8:\(\textbf{n}_{i}\): Calculate the CH chance (\(S_{i}\)) using Equation (18);
9:\(\textbf{n}_{i}\): Share the CH chance (\(S_{i}\)) through a beacon message with its neighboring nodes;
10:if\(S_{i}\) is higher than the CH chance of the neighboring nodes then
11:\(\textbf{n}_{i}\): Broadcast a CH-candidate message in the network;
12:else
13:\(\textbf{n}_{i}\): Wait to receive the CH-candidate messages from neighbors;
14:if\(n_{i}\) receives various CH-candidate messages from different neighbors then
15:\(\textbf{n}_{i}\): Join the CH with highest trust;
16:CH: Select a trust node with highest CH chance from its member list as the backup CH;
17:else
18:\(\textbf{n}_{i}\): Join the CH;
19:CH: Select a trust node with highest CH chance from its member list as the backup CH;
20:endif
21:endif
22:if CH does not receive the beacon message from a cluster member node after \(T_{end}\)then
23:CH: Revoke this cluster member node from its member list;
24:endif
25:if\(T_{CH}<T_{threshold}\)or\(E_{CH}<E_{threshold}\)then
26:CH: Replace the backup CH as the new CH;
27:endif
28:\(\textit{Timer}=\textit{Timer}+1\);
29:go to Line 1; End
```  
**Algorithm 2** T-Clustering
# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
## 3 Results and Discussion
### Evaluation  
In this section, the performance of the proposed classifier is evaluated in terms of accuracy, sensitivity, and error rate. This study can also indicate that this evaluation part is the final classifier performance as the study already trained the model before. Table 3 shows that ECG 1 and ECG 2 from (MIT-BIH SVDB) and AHA recordings obtained remarkable performance. The ECG 1 lead achieved 98% accuracy, whereas ECG 1 and AHA recordings obtained 97% and 96% accuracy, respectively. ECG 1 achieved the highest result because this lead is modified which is good enough to distinguish the beats very efficiently. The detailed performance of our proposed model is described in Figures 8-11 where the confusion matrix shows the total number of classified beats and the graph represents the performance statistics based on percentage in terms of accuracy and sensitivity.  
Figure 8: Confusion matrix of the ECG1 classified beats.  
Figure 10: Confusion matrix of the AHA classified beats.  
Figure 9: Confusion matrix of the ECG2 classified beats.  
It is noticed that the complexity of RC is higher in random connections, whereas the cycle-based ESN model has low computational cost and it is suitable for implementation in real time scenarios. In the learning phase, this study used Ridge regression to optimize the desired output weights and avoid overfitting, whereas in the testing phase, it only involves sparse connectivity which helps to reduce the number of heavy multiplications and additions. In this study, all experimental work was conducted using MATLAB (2018a) on a desktop computer running with 128 GB RAM and 16 cores. Once all scaling parameters were adjusted in the model, the system only required eight minutes in training with a 30 min duration ECG dataset. However, the testing runtime was observed that it takes 1.5 s for feature extraction or selection from ECG beats and the model takes 5 s to classify the heartbeat either as (N + SVB) or (A + VB). The total approximation time is estimated as 2.0 s and total time complexity is observed \(\boldsymbol{O}(\boldsymbol{F}(\boldsymbol{n})^{2}\boldsymbol{E})\) where E denotes the training examples and F(n) is the number of features.  
In contrast, this study examined the limitation of our proposed ESN in implementation and the finding is that the classifier needs to wait until the reference beat is computed. Once the template beat is computed, the system is ready to implement the real-time beats for classification. The system is able to compute the new template beat when more than 10 beats were classified properly and the model starts retraining by using the new template beat and it only consumes 2.0 s for the training model.
## 2 Production of CNTs
### Single-Walled CNTs (SWCNTs)  
Single-walled carbon nanotubes (SWCNTs) are cylindrical nanostructures composed of a single layer of carbon atoms arranged in a hexagonal lattice, forming a tube-like configuration, which has a diameter of \(\sim\)1-2 nm, and its length can reach tens of micrometers [33; 34; 35]. SWCNTs were first reported in 1993 in the work of [PERSON] and [PERSON], which synthesized SWCNTs with a 1 nm diameter [35]. The unique structure of SWCNTs allows them to have exceptional mechanical, electrical, and thermal properties. They possess tensile strength and elastic modulus, far surpassing those of other materials, including steel [33]. Electrically, SWCNTs can be either metallic or semiconducting, depending on their chirality and diameter, which is defined by the orientation of the graphene sheet rolling direction, characterized by the chiral vector, which is determined by how the graphene sheet is rolled up to form the tube (Figure 1). The electronic properties of SWCNTs are highly sensitive to these formations, enabling a wide range of applications from nanoelectronic devices to conductive composites. SWCNTs are known for their low resistance and high current carrying capacity of up to 10\({}^{9}\) A cm\({}^{-2}\)[34; 35; 36; 37]. They are also characterized by high thermal conductivity, allowing them to absorb heat effectively [34; 35; 36; 37].  
Various methods, including arc discharge [39; 40], laser ablation [41; 42], and chemical vapor deposition (CVD) [43; 44; 45], can be used to synthesize SWCNTs. Each method has advantages and limitations in terms of yield, purity, and control over the structural properties of the nanotubes. However, CVD has been most commonly used to synthesize SWCNTs in recent years due to the flexibility in producing large amounts of products and using various substrates, allowing growth in various forms, such as powder and thin films [45; 46].
## Generation of 25-TW Femtosecond Laser Pulses at 515 nm with Extremely High Temporal Contrast  
**Marco Hornung \({}^{1,2,\ast}\), Georg Alexander Becker \({}^{2}\), Andreas Seidel \({}^{2}\), Jan Reislohner \({}^{2}\), Hartmut Liebetrau \({}^{2}\), Lennart Bock \({}^{2}\), Sebastian Keppler \({}^{2}\), Alexander Kessler \({}^{1}\), Matthew Zepf \({}^{1,2}\), Joachim Hein \({}^{1,2}\) and Malte Christoph Kaluza \({}^{1,2}\)**  
\({}^{1}\) Helmholtz Institute Jena, Frobelstieg 3, 07743 Jena, Germany;  
E-Mails: alexander.kessler@uni-jena.de (A.K.); m.zepf@uni-jena.de (M.Z.);  
joachim.hein@uni-jena.de (J.H.); malte.kaluza@uni-jena.de (M.C.K.)  
\({}^{2}\) Institute of Optics and Quantum Electronics, Friedrich-Schiller-University Jena, Max-Wien Platz 1, 07743 Jena, Germany; E-Mails: georg.becker@uni-jena.de (G.A.B.);  
seidel.andreas@uni-jena.de (A.S.); jan.reisloehner@uni-jena.de (J.R.);  
hartmut.liebetrau@uni-jena.de (H.L.); lennart.bock@uni-jena.de (L.B.);  
sebastian.keppler@uni-jena.de (S.K.)  
* Author to whom correspondence should be addressed; E-Mail: marco.hornung@uni-jena.de;  
Tel.: +49-3641-947613; Fax: +49-3641-947282.  
Academic Editor: Totaro Imasaka  
_Received: 7 September 2015 / Accepted: 14 December 2015 / Published: 19 December 2015_  
**Abstract:** We report on the frequency doubling of femtosecond laser pulses at 1030 nm center wavelength generated from the fully diode-pumped laser system POLARIS. The newly generated pulses at a center wavelength of 515 nm have a pulse energy of 3 J with a pulse duration of 120 fs. On the basis of initially ultra-high contrast seed pulses we expect a temporal intensity contrast better \(10^{17}\) 200 ps before the peak of the main pulse. We analyzed the temporal intensity contrast from milliseconds to femtoseconds with a dynamic range covering more than 20 orders of magnitude. The pulses were focussed with a f/2-focussing parabola resulting in a peak intensity exceeding \(10^{20}\,\mathrm{W/cm^{2}}\). The peak power and intensity are to the best of our knowledge the highest values for 515 nm-laser-pulses achieved so far.  
**Keywords:** second harmonic generation; diode pumped lasers; frequency conversion; high-power lasers; high-intensity lasers; femtosecond lasers
###### Abstract  
In this paper, we present the Software PARIS Interferometric Receiver (SPIR), a high-speed GNSS reflectometry recording receiver which has been designed and implemented with the primary goal of demonstrating the synoptic capabilities of the interferometric technique in GNSS Reflectrometry. Thanks to the use of large bandwidth GNSS signals, this technique is advantageous in comparison to the so-called _clean-replica_ processing, when sea surface altimetric applications are pursued. The SPIR receiver down-converts, samples, and records the GNSS signals acquired by the sixteen elements of two antenna arrays. It can operate at any of the common GNSS L1, L2, or L5 bands. Digital beam-forming and signal processing is performed off-line by its dedicated signal processor, so that the GNSS reflectometry can be applied to different transmitting satellites using the same set of recorded signals. Alternatively, different processing techniques can be compared by applying them to exactly the same signals. This article focuses on the SPIR instrument hardware and software, as well as the remote sensing observables that can be obtained using this equipment.  
Global Navigation Satellite System Reflectometry (GNSS-R); Passive Reflectometry and Interferometry System (PARIS); Signals of Opportunity (SoOP); Bistatic radar; Hardware; High-speed Recorder; Software-defined; Software correlator; Digital Beamforming; GEROS-ISS; Sea-surface Altimetry 20179 9 450 10.3390/rs9050450
# A Study on Design and Control of the Multi-Station Multi-Container Transportation System
## 4 Discussions
Figure 29: The installation position of RFID readers to help identify containers entering or leaving the modules. exceed the boundaries, which will lead to a collision between the sliding actuator and the sprocket. * In addition to the experiment (a), we also test the delay between power off and reversing the conveyor direction and how it would affect the operation of the conveyor. To extend the operating life of the motor and guarantee electrical safety, the function for reversing the motor is only active after the motor has been turned off. From that, the operation of the lifting module and the line switching module are all sequential, so we use the PLC's sequential function charts (SFC) [14] programming to build the function. * As for the experiment (b) in Section 3.3, we provide a series of figures regarding the experimental process of transporting with three containers corresponding to any three routes. It shows that the algorithm solved the problems, such as waiting when another container was already in position and the lifting module or switching module is unavailable. The algorithm takes the priority order at the 2-line and the 4-line switching modules into account, in which the containers that come first will be moved in first. The result shows that the proposed control algorithm is effective. * As for the control model from the experiment (b) in Figure 4, the experiment also shows that the data will be updated to the database whenever a signal is detected. The signal from the proximity sensors is sent by the PLC to the server via TCP/IP protocol [15] for processing. The server, after synthesizing the data from the proximity sensor and PLC, will process and send it back to the PLC to execute the system's operation. After that, the data continue to be updated to the database, and the cycle repeats over again. * As emphasized above, the database only requires updates when there are changes in the data. Because the data synchronization speed is set by MySQL by default to 200 ms, the server would be delayed during data processing if updates are continuously required, as the system is not multi-threaded. Therefore, within 200 ms of transmitting data, the server cannot process the data. * In addition, experiment (b) also helps us to check if the system could function as normal again after being turned off. Based on the recorded data on the database, whenever there is a system power shutdown and then turned back on again, the previous data stored in the database could be obtained. Therefore, the system could continue activating the corresponding mechanical modules, and then update new information to the database, such as the ID of containers and their respective routes, etc.