esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Architecture
### Software Defined Transport Network Controller  
The Software-Defined Transport Network Controller is a functional block with the following purposes[21]; It is The main entry point from the OSS/BSS systems to the network. It is in charge of coordinating/providing services through several domains and layers. It has the multi-layer/multi-domain topological view of the network. The SDTN controller can split requirements based on the technological requirements. During this process, the SDTN controller can add/assign logical resources to be used by the network at the service implementation. The SDTN controller have two RESTCONF interfaces, one to  
Figure 1: Overall proposed architecture.  
process the OSS/BSS systems' requirements and one to send the specific requests to the domain controllers. TeraFlow project [24] is proposing the development of a cloud-native SDN controller that will serve as SDTN.  
The SDTN controller can have incorporated the Network Slice Controller as a working piece of its implementation.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results  
### Implementation Details  
The experiments conducted utilized the DEIF-Net on a single NVIDIA RTX 3090 GPU. Optimization of the network parameters was achieved using the SGD optimizer. The experiments were run with a batch size of four across 150 epochs. The initial learning rate was set at 0.01, which was reduced by multiplying it by 0.9 at regular intervals during the training process. All inputs were uniformly resized to \(224\times 224\) pixels. We applied simple data augmentations such as random rotation and flip. The hyperparameters within the network were set as \(\alpha=\beta=\gamma=0.5\). In the Transformer block, the channel dimension \(C^{\prime}\) was set to 320.
# Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules
## 6 Conclusions  
This work describes an evolutionary MPPT method dedicated to the thermoelectric panel of multi-string sections of low voltage devices connected in series. The simple boost, self-oscillating converters are dedicated to the specific sections, have outputs configured in parallel, and they are controlled by a common central unit equipped with ultra-low power microcontroller. Firstly, details of the proposed evolutionary MPPT regulation technique were explained and it was compared to the popular P&O and INC ones. The results obtained confirm that the proposed solution allows to track optimal operational power point of TE strings much faster than the referenced ones. Convergence speed of the designed algorithm was proved by means of simulations made for ten Thevenin, two-connector equivalent circuits which model different individual internal parasitic resistances present in real thermoelectrics. Matching was assured by a boost converters PWM signals optimization. Secondly, the practical experiment effects are presented, for which the panel with the string of sixteen thermoelectrics passively cooled is tested. The proposed MPPT evolutionary method was implemented to the ultra-low power ARM microcontroller and used for the optimization of the thermal to electric transformation process. The measurements obtained for different representative resistive loads proved the usefulness of the evolutionary optimizations. Future studies on the proposed solution are desirable and, for example, a low power active cooling dedicated method may be applied to improve the efficiency of electric energy generating process.  
This work was supported by the Polish Ministry of Science and Higher Education funding for statutory activities on Silesian University of Technology (BK-246/RAu-11/2022).  
Not applicable.  
Simulation data and practical measurements were saved to files with formats compatible with the software and hardware environment used during the described research performed by the author.  
The author declares no conflict of interest.
# A Novel Particulate Matter 2.5 Sensor Based on Surface Acoustic Wave Technology
## 2 Design and Simulation
### PM\({}_{2.5}\) Mass Detection  
When the VI and TP on the front end are working at the steady flow and temperature fields, the SAW propagation along the piezoelectric substrate is perturbed by the PM\({}_{2.5}\) mass loading, causing a linear velocity shift of SAW. This characteristic is used to detect the particulate mass concentration. The corresponding response mechanism was analyzed theoretically by solving the piezoelectric medium equations of motion and the surface effective permittivity in [23], indicating the threshold detection limit of the PM\({}_{2.5}\) sensor using a SAW sensor chip was 0.17 ng.  
To obtain the sufficient deposition area, a SAW sensor chip configuring a three-transducer-structure resonator was adopted. The schematic of the resonator structure is shown in Figure 9, which consists of three interdigital transducers (IDTs) and two identical shorted grating reflectors [24].  
Prior to the fabrication of the SAW sensor chip, the design parameters of the SAW resonator are determined by using the COM model [25]. The aim is to obtain low insertion loss and high-quality value of the resonator. By using the cascading mixed P-matrixes of the IDTs, reflectors and gaps, where the acoustic ports are cascaded and the electrical ports are in parallel, the frequency response \(S_{12}\) is obtained as [26]:  
\[S_{12}=\frac{-2Y_{12}\sqrt{Y_{01}Y_{02}}}{(Y_{01}+Y_{11})(Y_{02}+Y_{22})\ -\ Y_{12}Y_{21}} \tag{3}\]  
In Equation (3), \(Y_{01}\) and \(Y_{02}\) are the input and output impedance respectively, \(Y_{11}\), \(Y_{12}\), \(Y_{21}\) and \(Y_{22}\) are the admittance matrix elements.  
Considering the area of the collection plate in TP, the center frequency of the resonator was set to 311.6 MHz, where the period of electrode \(\lambda_{0}\) is 10 \(\upmu\)m. The electrode width is \(\lambda_{0}/4\). The electrode thickness is 1600 A. The aluminum (Al) fingers and the ST-X quartz piezoelectric substrate were used. The acoustic aperture is 150\(\lambda_{0}\). The numbers of electrodes of IDT\({}_{1}\), IDT\({}_{2}\), IDT\({}_{3}\) and adjacent gratings are 45, 90, 45 and 400, respectively. The gaps between the IDTs and reflectors are all 1.25\(\lambda_{0}\), and the distances between the IDTs are both 22.25\(\lambda_{0}\)[27]. The frequency response of the SAW resonator was deduced and shown in Figure 10. The insertion loss was 5.2 dB and the quality value was 4500.  
Figure 10: Frequency response of the SAW resonator.  
Figure 9: Schematic of a three-transducer-structure surface acoustic wave (SAW) resonator.
## 6 JCS Sidelink Performance Evaluation Using CCM-SPS
### JCS Sidelike Performances with Dynamic Vehicle Density
metrics effectively across different density scenarios, especially in high vehicle density. The maximum communication ranges in low-, mid-, and high-density scenarios are increased by 22.2%, 30%, and 50%, respectively. This analysis indicates that the CCM-SPS scheme demonstrates robustness and effectiveness in various traffic density scenarios. In short, as vehicle density rises, available resources in the pool decrease, increasing the probability of access collisions and resulting in consecutive resource collisions, leading to JCS performance degradation. The FD-enhanced method employs full-duplex to detect collisions and achieve early termination of consecutive collisions. However, an overly aggressive reselection mechanism leads to an excessive number of vehicles entering the reselection stage in high-density scenarios. According to (13), an increase in the number of vehicles in the reselection process within the same time slot raises the probability of new access collisions. The advantages of the FD-enhanced scheme over traditional SB-SPS are more pronounced at mid and low densities but diminish at high densities. However,  
Figure 8: PRR over distance using SB-SPS, FD-enhanced and CCM-SPS. Figure 9: The maximum distance allowing PRR larger than 0.95 is evaluated using conventional SB-SPS, FD-enhanced methods and CCM-SPS. the CCM-SPS scheme outperforms FD-enhanced in a variety of density scenarios. It can be demonstrated that the Q-learning-based scheme significantly enhances performance, particularly in high-density environments. This is due to the reward function defined in the proposed Q-learning algorithm, i.e., vehicles that do not undergo resource collisions will receive rewards and strive to maintain the current state as much as possible, ensuring the stability of the resource pool. Vehicles that experience resource collisions are penalized based on the current \(RC\) and the times of consecutive collision, which encourages vehicles to learn the best strategy to minimize consecutive collisions and avoid new collisions. The results demonstrate that the proposed CCM-SPS scheme outperforms the comparison methods in terms of CRLB and PRR across various density scenarios. These advantages have strong application potential in fields of high-accuracy sensing. For example, in high-density environments, it maintains a CRLB accuracy of 0.1 m and a reliable communication range of 50 m, ensuring both accurate target sensing and reliable communication, which are critical for autonomous driving decision-making.
## 4 Experimental Evaluation
### Scenario 3: Multiple STAs
#### 4.4.2 Results of IP Network Layer in Scenario 3  
Figure 14 shows the throughputs of TCP and UDP communications. The x-axis label indicates each STA for the four measurements. \"M[1-4]\" indicates the measurement number. The first distance is STA1, and the second is STA2. That is, M1_10m, M2_30m, M3_50m, and M4_70m are for STA2. In TCP communication, two STAs have almost the same throughput trend, even if the distance of the STA2 increases. On the other hand, there is no consistent trend for UDP communication. Therefore, in TCP communication, an STA with poor communication affects another STA with stable communication.  
Table 7 shows the retransmitted TCP packets and the connection failures in the TCP communication. Compared to the results of Section 4.3.2, the retransmissions of the TCP packets occur at relatively close distances. The occurrence can lead to a decrease in the throughput. On the other hand, no connection failures occur in the measurements.  
Figure 15 shows the jitter results for UDP communication. While the left graph shows that the jitter outliers of STA2 grow as the distance increases, there is no characteristic trend in the distribution in the right figure.  
Figure 16 shows the _ping_ results of STA1, STA2, and the AP. Each STA sends ICMP packets to the AP, whereas the AP sends ICMP packets to each STA, i.e., the AP executes two _ping_ commands for the two STAs. Compared to the throughputs in Figure 14, the RTTs increase as the throughputs decrease. There seems to be a correlation. On the other hand, for UDP communication, the RTT is relatively stable, except for the STA2 of 70 m. Note that there are no lost packets in the _ping_ results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Measurement 1**} & \multicolumn{2}{c}{**Measurement 2**} & \multicolumn{2}{c}{**Measurement 3**} & \multicolumn{2}{c}{**Measurement 4**} \\ \cline{2-9}
**Distance** & **STA1** & **STA2** & **STA1** & **STA2** & **STA1** & **STA2** & **STA1** & **STA2** \\
**(m)** & **10 m** & **10 m** & **10 m** & **30 m** & **10 m** & **50 m** & **10 m** & **70 m** \\ \hline \begin{tabular}{c} Retry \\ Failure \\ \end{tabular} & \begin{tabular}{c} 7.1 \\ 0 \\ \end{tabular} & \begin{tabular}{c} 8.6 \\ 0 \\ \end{tabular} & \begin{tabular}{c} 6 \\ 0 \\ \end{tabular} & \begin{tabular}{c} 5.3 \\ 0 \\ \end{tabular} & \begin{tabular}{c} 9.6 \\ 0 \\ \end{tabular} & \begin{tabular}{c} 7.6 \\ 0 \\ \end{tabular} & \
\begin{tabular}{c} 24.4 \\ 0 \\ \end{tabular} &
\begin{tabular}{c} 18.9 \\ 0 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: The average number of retransmitted TCP packets and connection failures (Scenario 3).  
Figure 13: Measurement environment for two STAs.
## 4 Results and Discussion
### Graphite Nodules Contours  
Figure 6 shows the results of the different steps that were followed, depicted in Figure 4, for the coordinates extraction of the graphite nodule contours from the DCI original micrograph (Figure 6a). A binarization process was applied to the micrograph of Figure 6a, where an image (Figure 6b) was obtained in white (graphite nodules) and black (ferritic matrix). Subsequently, the nodules at the edges and the smaller particles were removed, as shown in Figure 6c. In this image, a particular  
region that is enclosed in a yellow rectangle can be observed, to which a zoom was applied to show a pair of nodules (red and green) that are overlapped, when two nodules are very close in a DCI microstructure, they visually generate overlapping morphologies that could be considered as one, however in the industrial field, there are different works that recommend to separate them [29; 33]. For this reason, in Figure 6d, the image shows that the overlapping nodules have been separated (region contained in the blue rectangle). Afterwards, a labeling was applied (Figure 6e), and finally in Figure 6f, the contours that were extracted from the graphite nodules of the original micrograph can be seen. Once the aforementioned procedure is completed, a file in a.txt format is generated. This file contains the number and coordinates of the points that form the graphite nodules contours, as well as the \(CSF_{\text{dig}}\) values that were obtained at the image processing level, since these information are necessary to apply the proposed methodology based on genetic algorithms to optimize the selection of the Bezier curves degree that approximate the contours of each one of the obtained nodules at pixel level.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 1 Introduction
The manuscript concludes by asserting the practicality and necessity of a post-quantum security framework for coherent optical networks, underscoring the benefits of layering quantum key distribution with optical encryption to protect critical data transmission over the long term.
## Abbreviations  
The following abbreviations are used in this manuscript:  
**3G**: **Third Generation**
**4G**: **Fourth Generation**
**5G**: **Fifth Generation**
**xG**: **Next Generation**
**6 LoWPAN**: **I**V**6 over **LoW**-Power Wireless **P**ersonal **A**rea **N**etworks**
**AE**: **A**uthenticated Encryption**
**AIS**: **Automatic **I**dentification **S**ystem**
**AoDV**: **Ad-hoc **on**-demand **D**istance **V**ector**
**AoMDV**: **Ad-hoc **on**-demand **M**ultipath **D**istance **V**ector**
**AMI**: **Advance **M**etering Infrastructure**
**AMQP**: **Advanced **M**essage **Q**ueuing **P**rotocol**
**AUV**: **Autonomous **U**nderwater **V**ehicles**
**BYOD**: **B**ring **Y**our **O**wn **D**evice**
**BYOK**: **B**ring **Y**our **O**wn **K**ey**
**CoAP**: **Constrained **Application **Protocol**
**CP-OFDM**: **Cyclic Prefix **Orthogonal **F**requency **Division **M**ultiplexing **CPSS**: **Cyber-Physical-Social **S**ystems**
**D2D**: **Device-to-**D**evice**
**DARPA**: **Defense **A**dvanced **R**esearch **P**rojects **A**gency **DM**: **Device **Manager**
**DRS**: **Demand **R**esponse **S**ystems **DSC**: **Digital **S**elective **C**alling **EC**: **Emergent **C**onfiguration **ECM**: **Emergent **Configuration **Manager**
**ETC**: **Expected **Transmission **C**ount **GEO**: **GE**ostationary **Orbit **M**ariComm**: **MARI**time **broadband **COMM**unication **MIMO**: **Multiple **Input** Multiple **Output**
**MQTT**: **Message **Queue **T**emperty **MUE**: **M**erine **User** Equipment **NAVDAT**: **NA**V**igation **DAT**a **NFC**: **Near **Field** Communication **NFV**: **Network Function **Virtualization**
**OLSR**: **Optimized **Link-state **R**outing **Protocol**
**OSV**: **Offshore **S**upply **V**essel **O**WL**: **O**il on **Water** Locator **PBS**: **Pair **Broadcast **S**ynchronization **PSV**: **Platform **S**upply **V**essel **QoE**: **Quality **of Experience **QoS**: **Quality **of **S**ervice **RFID**: **Radio **Frequency **ID**entification **ROV**: **Remotely **O**perated **U**nderwater **V**ehicle **RPAS**: **Remotely **P**iloted **A**ircraft **S**ystem
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 5 Experimental Results and Discussion
### Analyzing the Computational Costs  
This section examines the computational costs of using different combinations of descriptors and classifiers. The extraction times of each local descriptor were measured by extracting a feature vector with a sub-region size of 13 \(\times\) 13 pixels and 56-bin histograms. The same parameter setting was used in all descriptors to make the comparison fair. On  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CK+** & \multicolumn{3}{c}{**RFD**} \\ \hline
**Descriptor + Classifier** & **6-Class** & **7-Class** & **6-Class** & **7-Class** & **8-Class** \\ \hline WLD + SVM & 96.0 & 92.4 & 96.7 & 95.9 & 91.5 \\ LDP + SVM & 96.1 & 93.3 & 97.0 & 96.8 & 94.0 \\ ALDP + SVM & 97.5 & 91.2 & 97.0 & 96.8 & 94.1 \\ LBP + SVM & 96.4 & 93.0 & 96.6 & 97.0 & 93.0 \\ CLBP + SVM & 95.3 & 93.0 & 97.5 & 97.0 & 93.5 \\ HOG + SVM & 97.4 & 93.4 & 96.6 & 97.0 & 93.8 \\ WLD + MLP & 96.3 & 94.2 & 96.9 & 95.9 & 91.9 \\ LDP + MLP & 95.9 & 93.2 & 96.9 & 96.2 & 93.4 \\ ALDP + MLP & 96.7 & 92.7 & 95.4 & 95.3 & 93.0 \\ LBP + MLP & 97.2 & 93.1 & 96.7 & 96.4 & 94.0 \\ CLBP + MLP & 97.5 & 93.5 & 96.8 & 97.4 & 93.1 \\ HOG + MLP & 98.3 & 95.1 & 97.4 & 96.4 & 94.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of recognition rates on the CK+ and RFD datasets with a varying number of expressions.  
Figure 6: FER results for the best of sub-regions on the CK+ dataset (6 expressions).  
Figure 7: FER results for the best of sub-regions on the RFD dataset (6 expressions).  
the other hand, when measuring the prediction time, the optimum parameters (see Table 3) were used to extract feature vectors before making predictions on those feature vectors. Tables 5 and 6 give the extraction and prediction times, respectively, where each value is the average time over 500 iterations. Results show that the best extraction time was achieved by WLD (0.54 s), followed by HOG (1.72 s). The longest times are seen in LBP and CLBP. NB was the fastest among the considered classifiers, with an average prediction time of 197 ms, followed by MLP, which took an average of 438 ms. SVM came in the third position, and KNN was the last.  
The time costs for training each classifier using different descriptors were also measured. We used the same extraction parameters for all the considered classifiers to ensure a fair comparison. We set the sub-region size to 19 \(\times\) 19 pixels and the number of bins to 48. The features were extracted from the RFD dataset with six expressions. As seen in Table 7, KNN and NB had the fastest training times (a few milliseconds), followed by SVM, which took 29 ms. MLP was the slowest, taking up to 6 seconds during training. All our experiments were performed on a Windows 10 PC with an AMD Ryzen 7 (3700U) processor and 12 GB of RAM.
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 4 Discussion and Insights
### Future Directions  
Several directions for future work emerge from this study. First, further optimization of the spatiotemporal feature extraction process could lead to even more efficient models. Exploring more advanced temporal modeling techniques, such as recurrent neural networks or attention-based mechanisms, could provide a deeper understanding of temporal relationships in the data. Additionally, integrating other sensor modalities, such as thermal imaging or radar, could further enhance the model's robustness and accuracy in complex, real-world environments. While our current approach uses uniformly spaced frames, future work could explore more adaptive frame selection strategies--such as motion-aware sampling, key-frame detection, or content-aware heuristics--which may further enhance efficiency and accuracy by focusing on the most informative temporal segments.  
Another potential avenue for improvement is the handling of dynamic elements in the environment, such as moving vehicles or changing terrain conditions. Further research could investigate methods for real-time adaptation to these dynamic changes, enabling more effective and accurate segmentation in ever-evolving scenarios.  
In addition, enhancing the gating mechanism used in the spatiotemporal clue-gated encoder presents a promising direction. While the current design employs a simple sigmoid activation over a linear projection to maintain computational efficiency, more dynamic strategies--such as time-aware modulation or residual-based gating--could better capture evolving spatiotemporal patterns and adapt to varying feature contexts. Furthermore, although the current model has shown stable performance under moderate noise levels in historical frames, future work could investigate the sensitivity of the gating unit to moresevere sensor noise or outlier contamination, potentially guiding the development of more robust and adaptive feature fusion mechanisms.
# Carbon-Isovalent Dopant Pairs in Silicon: A Density Functional Theory Study
## 3 Results
### Carbon Interstitial-Isovalent Dopant Defect Pairs  
The carbon atoms in the Si lattice can also reside in interstitial positions. Figure 2 represents the energetically favorable carbon interstitial-isovalent dopant substitutional  
Figure 2: Schematic representation of the energetically favorable (**a**) C\({}_{\text{i}}\)Ge\({}_{\text{s}}\), (**b**) C\({}_{\text{i}}\)Sn\({}_{\text{s}}\) and (**c**) C\({}_{\text{i}}\)Pb\({}_{\text{s}}\) defects in Si. Important distances and angles in the vicinity of the defects are given. Dimmer atoms represent atoms in the background.  
\(\rm C_{i}D_{s}\) (D = Ge, Sn, Pb) defects. The binding energy of a \(\rm C_{i}D_{s}\) defect can be calculated using the following equation:  
\[\rm E_{b}(C_{i}D)=E_{CID_{s},supercell}+E_{Si\_supercell}-E_{CieSi\_supercell}-E_{D_{s},supercell} \tag{2}\]  
where \(\rm E_{CID_{s},supercell}\) is the total energy of a Si supercell containing a \(\rm C_{i}D_{s}\) defect and \(\rm E_{CieSi\_supercell}\) is the total energy of the Si supercell containing a \(\rm C_{i}\).  
The energetically favorable \(\rm C_{i}D_{s}\) defects were derived using DFT calculations for all the possible carbon interstitial configurations in the vicinity of the D substitutional within the supercell. The repulsion of larger isovalent dopants with interstitials is due to the local distortion of the oversized isovalent atoms that effectively limits the space that is required for the interstitial \(\rm C_{i}\) defects.  
Figure 3 summarizes the dependence on the isovalent dopant radius on the carbon-dopant binding energies. The isovalent dopant radius is a key criterion, given that the lattice is locally strained by the introduction of the dopant. These local strains contribute significantly to binding energies, particularly in defect clusters consisting of isovalent dopants where there is no contribution from the dopant charge states. In that sense, the dopant radius is a common criterion used to discuss energetics of defect clusters. The main trend of this figure is that the larger the dopant, the more stable the defect pair (i.e., more negative binding energy). Also, it is clear that the \(\rm C_{s}D_{s}\) defects are more bound than the \(\rm C_{i}D_{s}\) defects.