esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Practical Implications of XPD Variability for Satellite Communication Systems and Recommendations  
Satellite communication links, therefore, appear most greatly challenged during the summer because of the lowest XPD in Pretoria, especially during heavy rain events. This implies that the quality of radio signals is heavily attenuated due to low XPD in the summer. Winter conditions are the most favourable because XPD reaches its peak and depolarization is relatively low. It was found that seasonal XPD is more reliable, hence it can be used in high availability systems at frequencies around 10-15 GHz. Margin or adaptive modulation must be used for mid-range frequency (15-25 GHz) due to problems with rain depolarization. Satellite systems that employ the Ka and higher frequency bands (>25 GHz) require a higher XPD but also consider the increased rain attenuation at these bands. In the case of lower XPD values, adaptive modulation and coding schemes are suggested for optimal performance during summer months. Bandwidth of both options should be increased and adopted during winter months only, to provide the best link performance at the highest XPD. Link budgets ought to assume lossier polarization isolation schemes for mid-range frequencies (15-25 GHz) where depolarization conditions are most dramatic.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Percentage of**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{3}{c}{**XPD (dB)**} \\
**Time (\%)** & **(GHz)** & **Autumn** & **Winter** & **Spring** & **Summer** \\ \hline \multirow{8}{*}{0.01} & 11.7 & 14 & 15 & 14 & 13 \\  & 15 & 13 & 14 & 13 & 12 \\  & 20 & 12 & 13 & 12 & 11 \\  & 25 & 13 & 14 & 13 & 12 \\  & 30 & 14 & 15 & 14 & 13 \\  & 35 & 15 & 16 & 15 & 14 \\ \hline \multirow{8}{*}{0.001} & 11.7 & 9 & 9 & 9 & 8 \\  & 15 & 8 & 8 & 8 & 7 \\  & 20 & 7 & 8 & 7 & 6 \\  & 25 & 8 & 9 & 8 & 7 \\  & 30 & 9 & 10 & 9 & 8 \\  & 35 & 10 & 11 & 10 & 9 \\ \hline \multirow{8}{*}{0.0001} & 11.7 & 7 & 8 & 7 & 7 \\  & 15 & 6 & 7 & 6 & 6 \\ \cline{1-1}  & 20 & 5 & 6 & 5 & 5 \\ \cline{1-1}  & 25 & 7 & 8 & 7 & 6 \\ \cline{1-1}  & 30 & 8 & 9 & 8 & 7 \\ \cline{1-1}  & 35 & 9 & 10 & 9 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Seasonal XPDs at different time percentages for the selected frequencies.
# Logitwise Distillation Network: Improving Knowledge Distillation via Introducing Sample Confidence
## 3 Method
### Notations and Background  
Generally speaking, we consider classification neural networks as two modules: feature extractor and classifier. For a given input data \(x\), we use \(f^{t}\) to represent their embedded features in the teacher model, and \(z^{t}\) to represent the logits. Similarly, we use \(f^{s}\) to denote their embedded features in the student model, and \(z^{s}\) to denote the logits. The final softmax score is represented by the vector \(q^{t}=softmax(z^{t};\tau)\), where \(\tau\) is a temperature parameter.  
The final loss function of KD can be written as Equation (1) [4]:  
\[L=L_{ce}+\alpha L_{bd} \tag{1}\]  
where \(L_{bd}\) depends on the choice of distillation: logit distillation or feature distillation. \(\alpha\) is the weights for \(L_{kd}\).  
Logit distillation trains the student model by utilizing both the cross-entropy loss function \(L_{ce}\) and the knowledge distillation loss function \(L_{kd}\) to enhance the performance of the student model through the transfer of teacher knowledge. The seminal work of KD [4] utilizes KL divergence as the knowledge distillation loss function, represented as Equation (2):  
\[L_{kd}=\frac{1}{N}\sum_{i=1}^{N}KL\big{(}q^{t}_{i},q^{s}_{i}\big{)} \tag{2}\]  
Feature distillation involves extracting teacher knowledge by minimizing the differences in intermediate features before logits. The \(L_{2}\) distance between features is a loss term commonly used in feature distillation, represented as Equation (3):  
\[L_{kd}=\frac{1}{N}\sum_{i=1}^{N}L_{2}\big{(}f^{t}_{i},f^{s}_{i}\big{)} \tag{3}\]  
Class means are defined as the average feature vectors of samples belonging to a specific class in a dataset. It represents the central tendency or prototype of the features within that class. The class mean of teacher feature vectors is represented as Equation (4):  
\[c^{t}_{k}=\frac{1}{N}\sum_{i=1}^{N}f^{t}_{i} \tag{4}\]  
where \(k\) represents the class, \(N\) represents the total number of samples in that class, and \(f^{t}_{i}\) represents the teacher's feature vector.  
In typical knowledge distillation methods, the student model often assigns the same weight to different samples when learning from the teacher model. This is unreasonable. Because in samples where the teacher's predictions are correct, the feature information in the teacher model usually has higher confidence. [PERSON] proposes a simple method [23]: let the student model selectively learn from the teacher model. If the teacher model can predict the ground truth, then the student model chooses to learn from the teacher model; otherwise, it learns only from the ground truth, as shown in Equation (5):  
\[L_{kd}=\frac{1}{N}\sum_{i=1}^{N}aL_{2}\big{(}f_{i}^{t},f_{i}^{s}\big{)} \tag{5}\]  
where \(a\) is 1 when the teacher's prediction is correct, otherwise \(a\) is 0.
## 4 Application Results
### Ablation Experiments  
To further validate the effectiveness of our proposed method, we conducted ablation experiments on the proposed components.  
Table 9 illustrates the results of ablation experiments conducted to evaluate the performance improvement achieved by progressively integrating the proposed components--Incremental Learning and the Pyraformer Module--into the baseline DCCA model. The table compares the FDRs and FARs across various scenarios, including TE Fault 6, CSTR Fault 4, Mold Injection Fault 1, and Mold Injection Normal 1 conditions. The baseline DCCA model achieves consistently high FDR values but exhibits relatively high FARa, particularly in the TE Fault and Mold Normal cases, indicating limited robustness to false positives. The introduction of Incremental Learning significantly reduces the FAR across all scenarios while maintaining a high FDR, demonstrating its effectiveness in addressing false alarms. Further, incorporating the Pyraformer Module enhances the model's performance by leveraging its ability to capture long-range dependencies, as evidenced by a reduction in the FAR, notably achieving zero false alarms in the CSTR Fault scenario. The combination of Incremental Learning and the Pyraformer Module in the full Incremental Pyraformer-DCCA model achieves optimal performance, with an FDR of 100% in all scenarios and a notably low FAR, including 0% in the Mold Normal case and 3.48% in the CSTR Fault scenario. These results highlight the synergistic benefits of combining Incremental Learning and the Pyraformer Module, significantly improving the model's fault detection capabilities and minimizing false alarms, thereby demonstrating the effectiveness and reliability of the proposed approach.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**TE Fault**} & \multicolumn{2}{c}{**CSTR Fault**} & \multicolumn{2}{c}{**Model Injection**} & \multicolumn{2}{c}{**Model Injection**} \\  & **FDR\%** & **FAR\%** & **FDR\%** & **FAR\%** & **FDR\%** & **FAR\%** \\ \hline DCCA & 100 & 21.12 & 91.69 & 6.97 & 80.72 & 13.21 \\ +Incremental Learning & 100 & 13.66 & 95.67 & 3.98 & 91.91 & 4.90 \\ +Pyraformer Module & 99.62 & 0.62 & 93.20 & 0 & 91.80 & 4.26 \\ **+Incremental Learning** & **100** & **4.35** & **100** & **3.48** & **97.02** & **0** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of gradually integrating the proposed components into the baseline.
# III-Nitride Short Period Superlattices for Deep UV Light Emitters
## 4 AlN/AlGa(In)N SPSL Doping and Ohmic Contacts
Similar approach was successfully used for fabrication of ohmic contacts on p-type AlN/Al\({}_{x}\)Ga\({}_{1-x}\)N SPSLs [108]. Figure 8 shows the temperature dependence of the specific contact resistances, \(\rho_{c}\), for three SPSLs with average AlN content of y\({}_{\rm{ave}}\)\(\sim\)0.7 and for the \(\sim\)300 nm thick Mg-doped p-Al\({}_{0.03}\)Ga\({}_{0.97}\)N epitaxial layer. The Mg concentration in all samples was \(\sim\)10\({}^{19}\) cm\({}^{-3}\) and room temperature \(\rho_{c}\) was \(\sim\)4 \(\times\) 10\({}^{-5}\)\(\Omega\)\(\cdot\)cm\({}^{2}\). It was shown that the temperature dependence of the specific contact resistance is primarily controlled by the activation energy of Mg acceptors in Al\({}_{x}\)Ga\({}_{1-x}\)N [123]. The \(\rho_{c}\) of metal/SPSL ohmic contact, as seen in Figure 8, also follows the temperature dependence of hole concentration in these structures, which are very similar to that shown in Figure 6b, since the density of acceptors activated in AlN barriers is the primary factor influencing the hole concentration in the well, producing a weaker temperature dependence compared to bulk materials. Thus, a weak dependence of \(\rho_{c}\) on temperature is expected, even when thermionic mechanism of current injection dominates in the metal/SPSL ohmic contact. This expectation agrees with the results shown in Figure 8.  
Figure 8: Specific contact resistances of Mg-doped AlN/Al\({}_{x}\)Ga\({}_{1-x}\)N SPSLs (y\({}_{\rm{ave}}\)\(\sim\)0.7) and Al\({}_{0.03}\)Ga\({}_{0.97}\)N epitaxial layer at different temperatures.
# A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
## 4 Scenario and Problem Definition
Not only are these directional characteristics stable over time, but they can also be measured at the base station antenna array without requiring too much complex process ing [30; 31; 32]. Moreover, the directional properties of the nodes' channels are critical for the performance that can be obtained, in terms of the data rate and/or bit error rate. With MRC specifically, the energy of a transmission (on the downlink) is focused at the target node; however, since this focusing is imperfect, especially in the presence of imperfect CSI, interference is caused in relation to nearby nodes. This means that grouping nodes in such a way as to spread out the nodes by angle leads to improved SINRs for the nodes. For these reasons, node partitioning based on directional channel characteristics has the potential to provide a simple partitioning method with minimal information collected from node devices, thus allowing them to sleep longer, transmit less, and save energy.
# Optimal CNN-Hopfield Network for Pattern Recognition Based on a Genetic Algorithm +
## 6 Results and Discussion
### Evaluation of the Performance  
Herein, the performance of the proposed architecture for the classification task with respect to the number of sets of \(K\) Hopfield networks is presented. The results in Figures 17-19 are based on the three versions of the Noisy MNIST Dataset. The proposed architecture is compared with the naive approach.  
The increased number of parallel networks used to store training patterns had a direct impact on the classification accuracy. In fact, in our approach, we needed about two to three networks to achieve the best performance on the three versions of the Noisy MNIST Dataset. The performance remained relatively stable even after using more than three networks.  
Figure 16: GA fitness vs. the number of generations for motion-blur noise.  
Figure 15: GA fitness vs. the number of generations for AWGN.  
The total number of trainable parameters was 324,858, of which 200,514 parameters belonged to the last three FC layers, which means that 61.72% of the parameters were in the last three FC layers. Our architecture achieved remarkable results in terms of accuracy and memory requirements, as shown in Table 5. The inference time performance (CPU Intel Core i3) was also kept and improved in our hybrid architecture. In order to classify a single  
Figure 19: Classification’s accuracy on the MNIST dataset with motion-blurred noise.  
Figure 17: Classification’s accuracy on the MNIST dataset with added white Gaussian noise.  
Figure 18: Classification’s accuracy on the MNIST dataset with reduced contrast + AWGN.  
test AWGN image, Lenet took 0.8368 ms, while our hybrid version only took 0.7954 ms; the same observations were observed on the other noisy versions of the datasets (see Table 6).  
Table 7 illustrates the accuracy obtained by the proposed hybrid architecture using the Hopfield network. One can see that the results obtained exceeded those of the state of the art by 0.75% in the case of added AWGN noise, by 0.54% in the case of added motion noise (motion blur), and 0.28% in the case of contrast+AWGN noise, and our architecture gave the best classification accuracies of 99.18%, 99.74%, and 97.53%, respectively.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 4 Results and Experiment
### Class Imbalance and Model Performance
#### 4.3.1 Logistic Regression  
As shown in Figure 3, the strengths and weaknesses of the LR model are as follows:  
* **Strengths:** The LR model correctly predicts 113,195 instances for the \"Very Low\" class and 6621 instances for the \"Very High\" class, showcasing its strong performance on the dominant classes.
* **Weaknesses:** The model struggles with mid-range classes, misclassifying 625 instances of \"Very High\" as \"Very Low\" and 124 instances of \"Very Low\" as \"Very High\". This indicates limited capacity to differentiate between these mid-range stress levels.
## 2 Results
### The Hidden Momentum, Refraction, Ray-Wave Tilt, and Bound Charge Waves
In source-free regions, the induction fields \(\mathbf{D},\mathbf{B}\) do not have divergence \((\
abla\cdot\mathbf{D}),(\
abla\cdot\mathbf{B})=0\), which means that waves in isotropy-broken materials carry effective bound electric and magnetic charge waves:  
\[\rho_{be}=-\
abla\cdot\mathbf{P}=\frac{1}{4\pi}\
abla\cdot\mathbf{E}=\frac{1}{4\pi}( i\mathbf{k}\cdot\mathbf{E})e^{i(\mathbf{kr}-\omega t)}=-(i\mathbf{k}\cdot\mathbf{P})e^{i(\mathbf{kr}- \omega t)} \tag{12}\]  
\[\rho_{me}=-\
abla\cdot\mathbf{M}=\frac{1}{4\pi}\
abla\cdot\mathbf{H}=\frac{1}{4\pi}( i\mathbf{k}\cdot\mathbf{H})e^{i(\mathbf{kr}-\omega t)}=-(i\mathbf{k}\cdot\mathbf{M})e^{i(\mathbf{kr}- \omega t)} \tag{13}\]Since the fields \(\mathbf{D}\) and \(\mathbf{B}\) are solenoidal and are transverse to the phase propagation direction \(\mathbf{k}\),  
\[ \begin{split}\overline{\mathbf{\Delta g}_{\perp}}& =\frac{1}{c}\text{Re}\Big{\{}P_{z}^{*}\hat{\mathbf{k}}\times\mathbf{B}+\mathbf{D}^{*} \times M_{z}\hat{\mathbf{k}}-4\pi P_{z}^{*}\hat{\mathbf{k}}\times\mathbf{M}_{\parallel}-4 \pi P_{\parallel}^{*}\times M_{z}\hat{\mathbf{k}}\Big{\}}\\ &=\frac{1}{c}\Big{(}\text{Re}\Big{\{}P_{z}\hat{\mathbf{k}}\times\mathbf{H }_{\parallel}^{*}+\mathbf{E}_{\parallel}^{*}\times M_{z}\hat{\mathbf{k}}\Big{\}} \Big{)}=-\frac{k_{0}}{ck^{2}}(\text{Re}\{i\rho_{b}\mathbf{D}^{*}+i\rho_{bm}\mathbf{B}^ {*}\})\end{split}  \tag{14}\]  
The transverse component of the hidden momentum density \(\overline{\mathbf{\Delta g}_{\perp}}\) is responsible for the ray-wave tilt in isotropy-broken media. The tilt appears because the [PERSON] momentum density \(\mathbf{g}_{Abr}\) is directed along the ray vector \(\mathbf{s}\), while the [PERSON] momentum density \(\mathbf{g}_{Min}\) is directed along the wave vector \(\mathbf{k}\). We see from Equation (14) that the difference between the directions of \(\mathbf{g}_{Abr}\) and \(\mathbf{g}_{Min}\) is due to the existence of the transverse component of the hidden momentum \(\overline{\mathbf{\Delta g}_{\perp}}\), which requires non-zero longitudinal polarization \(P_{z}\) and magnetization \(M_{z}\) of the material. According to Equation (10), the longitudinal polarization and magnetization are only possible in isotropy-broken media and are related to the propagation of electric and magnetic bound charge waves described by Equations (12) and (13).
## 1 Introduction
Several shapes have been suggested for wearable antennas, like planar Inverted-F antennas (PIFA) [21; 22], magneto-electric dipole antennas [23], substrate-integrated waveguide (SIW) antennas [24; 25], electromagnetic band gap (EBG)-based antennas [26], dipole antennas [27], fractal-based patch antennas, circularly polarized integrated filtering (CPIF) antennas, surface wave parasitic array (SWPA) antennas, and cavity slot monopoles [28]. In addition, Yagi-Uda antennas were proposed as candidates for on-body millimetre-wave communications, representing a good prototype in terms of size and gain performance [29]. Theses antennas should be stable electrically and mechanically robust to not to get affected by movement [17]. Considering these requirements, several problems occur during the fabrication of wearable antennas [30; 31; 32; 33; 34]. Few wearable ultra-wideband (UWB) antennas have been designed specifically for use in breast cancer imaging. One of the most relevant wideband antennas was designed on a flexible polyimide substrate (\(\varepsilon r=3.5-3.8\)) and obtained bandwidth (BW) of 2-5 GHz with dimensions of 20 mm \(\times\) 20 mm. Then, it was embedded in a bra to detect a tumour in the breast [35; 36]. Due to its narrow bandwidth, the system could not detect the tumour properly and with precision, and several clutters that appeared around tumour. Spatial resolution and range resolution (\(\rho R\)) (inversely proportional to the effective bandwidth of the pulse) are two factors that indicate whether a specific antenna design and imaging system can detect tumours and show the minimum dimension of the tumours they can detect. In addition to those, some flexible wearable antennas are designed for 5G and IoT applications [37; 38; 39; 40; 41]. The UWB antennas presented in the literature, both wearable and non-flexible, showed limitations on the resolution, high SAR, and low performances having larger dimensions. A wearable antenna intended for use in detecting breast tumours should have broad BW, low SAR, and small dimensions to detect tumours effectively [35; 36].
# Empowering Tourism Accessibility: A Digital Revolution in Pieria, Greece
## 2 Materials and Methods
### Defining the Study Area  
The first step in the methodology was the definition of the focus area, which was performed using Google Maps to outline the boundaries for data collection. The pilot study focused on key tourist destinations within the Municipal District of Platamonas, ensuring that areas with high tourist traffic, such as cultural and natural landmarks, were prioritized. This is depicted in Figure 3, which shows how the study area was demarcated using the Seek & Go auditing tool and Google Maps as a base layer. By defining these boundaries, the team was able to target the most relevant and high-traffic tourist spots for detailed accessibility assessments. These locations were prioritized based on their importance to tourists, including popular points of interest, such as archaeological sites, beaches, and tourism-related services.  
In addition to the geographical delineation, Figure 3 also emphasizes the integration of the Seek & Go auditing tool, which was used to collect accessibility data within this defined area. The auditing tool allowed the research team to systematically map out the infrastructure, such as sidewalks, pedestrian routes, and other points of interest (POIs), while also assessing the accessibility features and potential barriers for people with mobility challenges. As a first step, a segment is drawn between two points (Point A and Point B). The user specifies whether the segment represents a pedestrian pathway or a road and indicates the presence of sidewalks on one or both sides of the road (depicted as parallel lines adjacent to the segment). Subsequently, these segments are connected to form a unified route within the study area. Points of interest (POIs) are then added to the map (represented as pins) along with their entrances (depicted as door icons) within the defined segments. Connections between segments are established via sidewalks or directly if the segment is a pedestrian route. Similarly, the entrances of POIs are linked to sidewalks or directly to the pedestrian route, with the flexibility to assign multiple entrances to a single POI.
## 1 Introduction
In addition, the delay bound was analyzed and compared with some broadcast protocols selected from each of the different categories explained above. The rest of this paper is organized as follows. In Section 2, the preliminaries are given. Then, the proposed protocol is presented in Section 3, followed by Section 4 which analyzes and evaluates the protocol comparatively with some related approaches. Concluding remarks are given in Section 5.
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 1 Introduction
In [8], the authors introduced a new circuit to digitally adjust the SOP of the dual-polarization signal, used to extract clock tone, to avoid losing lock and to improve timing recovery. In [9; 10], the authors studied the impact that coupling has on synchronization in multi-mode coherent optical systems. They showed how for even a small group delay (0.1 ps/km) will weaken the strength of the clock tone. The authors of [11] investigated various clock phase error detectors for different values of excess bandwidth using similar architecture as in [8] and adding signal extracting after the 2\(\times\)2 adaptive filtering. Additionally, they discussed hardware complexity and its implication on the tracking bandwidth. Phase detection in Nyquist signals with small pulse shaping roll-off factors, to limit out-of-band energy, was considered in [12]. The latter showed, using simulation and lab experiment versus link DGD and chromatic dispersion (CD), how Gardner detector fails as the roll-off gets smaller than 0.1 and using the power of the signal improves the quality of the detection. However, the impact of the neighboring channels in single-mode link, i.e., multi-channel propagation as in our work, on jitter was not discussed. In [13; 14; 15], authors discussed advantages of SDN and Network Function Virtualization (NFV) in reducing Capital Expenditures (CAPEX) and Operational Expenses (OPEX). In our work, we are interested in SDN as orchestration mechanism where the controller is aware of the conditions of the link, mainly the frequency of the channels in service. In this paper, we present a new methodology to optimize the channel spacing in gridless configurations by adaptively controlling the coherent receiver settings based on link conditions, such as baseline jitter collected from programmable optical transponders and available optical signal to noise ratio (OSNR) margin. The method, along with using a new clock recovery scheme, aims to support as  
Figure 1: An example of gridless configuration. much bitrate on optical transport line system to maximize the CAPEX of network operators. In other words, our method improves the spectrum efficiency by exploiting the hardware architecture and link margin through the lifecycle of the optical links. An SDN controller is required to leverage various information captured from the data-center interconnect (DCI) link to increase the overall capacity. To the best of our knowledge, the study of clock recovery in gridless coherent systems has not been presented in the literature prior to this work. The paper is organized as follows. In Section 2, we introduce the typical timing recovery in optical coherent receivers where we describe the source of noise in optical links and clock recovery circuity. The theory along with simulations are presented in Section 3. Finally, in Section 4, we present the experimentations results measured.