esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spectroscopy Measurements  
The measurement protocol involved pre-conditioning the samples for two hours prior to analysis, ensuring they reached a room temperature of 20 \(\pm\) 2 \({}^{\circ}\)C, to guarantee the quality and repeatability of the recorded spectra [1]. Subsequently, four slices from each package were segregated and stacked in alignment. The samples subjected to NIR spectroscopy were placed on a black cardboard covered with a PVC plastic film to make the background uniform for analysis.  
The measurements were taken using four portable NIR spectrometers: MicroNIR 1700 ES (VIAVI, Milpitas, CA, USA), Enterprise Sensor (TellSpec, Toronto, ON, Canada), SCiO Sensor (Consumer Physics, Tel Aviv, Israel), and microPHAZIR (Thermo Fisher Scientific, Waltham, MA, USA). A benchtop spectrometer, the NIRFlex N-500 (Buchi, Flawil, Switzerland), chosen for its robustness and good performance in various applications, was also used as a reference for the evaluation of the portable devices [34; 35; 36]. Each instrument features distinct technical specifications, levels of portability, and key functional elements. From the optical point of view, predominantly they have different wavelength selectors, which largely define the operational spectral ranges and overall sizes of the sensors. It should be noted that all the instruments used are equipped with a tungsten halogen lamp and InGaAs detector except the SCiO Sensor, which features an LED lamp and Si photodiode detector. The recording configurations used were those recommended for each device as reported by [PERSON] et al. [27], who also provide detailed characteristics of the key attributes of these instruments.  
For all the devices, the detector window was applied directly to the surface of the same slice for static recording. To avoid the possible evaporation of water in the samples, the measurements were taken consecutively with all the equipment. Spectra were measured at six points on the lean muscle and four points on the fat for each slice. For each recording area, the mean of all replicates was calculated to obtain an average spectrum of the fat and an average spectrum of the lean meat for each of the 60 sliced ham samples.
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 3 Solution
### DeepRanking Neural Network  
Image-to-image comparison is a DL solution applied to foreground object ROIs detected in images. We measure the Euclidean distance between two embeddings produced by the CNN from the DeepRanking [8] approach. \"FaceNet\" was introduced in [22] and proposed the triplet loss function as a learning criterion. The triplet loss minimizes the distance between the anchor (query image) and the positive example image and maximizes the distance between the anchor and the negative example image. DeepRanking applies this triplet loss idea to learn an image-to-image similarity model. The triplet loss implementation in DeepRanking is as follows [8]:  
\[\begin{split}\min\sum_{i}\epsilon_{i}+\lambda\|\mathbf{W}\|_{2}^{2}& s.t.:\;\max\bigl{\{}0,g+D\bigl{(}f(p_{i}),f\bigl{(}p_{i}^{+}\bigr{)} \bigr{)}-D\bigl{(}f(p_{i}),f\bigl{(}p_{i}^{-}\bigr{)}\bigr{)}\bigr{\}}\\ &<\epsilon_{i}\forall_{p_{i}p_{i}^{+},p_{i}^{-}};r\bigl{(}p_{i}, p_{i}^{+}\bigr{)}>r\bigl{(}p_{i},p_{i}^{-}\bigr{)}\end{split} \tag{1}\]  
Figure 4: Illustration of car mask extraction for car images from the COCO database.  
where \(r\) is the image-pair similarity score. This score is responsible for ensuring that the positive image is more similar to the \"average\" one than the negative image. In our solution, we take positive images from the given class of model-view and negative images from another class of model-view. DeepRanking metric \(D\) is the Euclidean distance between two feature vectors. \(\lambda\) is a regularization parameter that controls the margin of the learned ranker to improve its generalization [8]. The \(g\) is a gap parameter that regularizes the gap between the distance of the two image pairs: (p\({}_{i}\), p\({}_{i}\)\({}^{+}\)) and (p\({}_{i}\), p\({}_{i}\)\({}^{-}\)). Function \(f(p)\) is the image embedding function. \(W\) is the parameters of the embedding function. It is considered to be one of the best neural network architectures for image matching (based on published results). Hence, we selected DeepRanking to obtain image-pair similarity scores.
# Influence of Isometric and Dynamic Fatiguing Protocols on Dynamic Strength Index
## 4 Discussion
Overall, the interplay of different fatigue mechanisms, such as central fatigue during IMTP and peripheral and metabolic fatigue during CMJ, can explain the task-specific effects on the DSI observed in this study. These distinct fatigue responses between isometric and dynamic movements underscore the importance of considering the specific demands of each task when assessing an athlete's strength capacity and interpreting DSI values. Understanding these fatigue-related nuances in motor unit recruitment and energy systems can aid in designing more effective and targeted training strategies to optimize athletic performance based on DSI assessments. The notable differences in the DSI values observed after IMTP fatigue (from 0.79 \(\pm\) 0.12 to 1.30 \(\pm\) 0.21) compared to CMJ fatigue (from 0.79 \(\pm\) 0.12 to 0.68 \(\pm\) 0.09) were not attributed to differences in perceived exertion (RPE) during the two fatigue protocols. Previous studies have demonstrated that human motor control systems can adapt the coordination of lower limbs to maintain task performance despite fatigue, and dynamic movements like CMJ can also affect other factors such as muscle stiffness [39; 40]. In contrast, during isometric contractions, both the muscles and the central nervous system are more heavily affected by fatigue. The findings also suggest that IMTP fatigue predominantly affected slow muscle fibers, while the fast-twitch fibers, responsible for generating peak force during CMJ, were only slightly affected after IMTP fatigue. Additionally, the longer isometric contractions in the IMTP-fatigue protocol likely led to greater metabolic fatigue due to greater blood flow obstruction [41], resulting in a higher accumulation of metabolites compared to the dynamic CMJ-fatigue protocol. This greater metabolic fatigue could contribute to the larger change in the DSI observed after the IMTP-fatigue task. Another crucial difference is the presence of eccentric muscle actions during dynamic contractions (such as CMJ), which are associated with higher force production capabilities compared to isometric actions. As the IMTP lacks an eccentric component, the peak force during CMJ (eccentric force) was not significantly affected by IMTP fatigue. In contrast, the peak force during IMTP was substantially impacted, leading to a greater change in the DSI after IMTP fatigue.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
### Sustainability Aspects of Flying Taxis  
Flying taxis, especially those powered by electric propulsion systems, produce significantly lower emissions compared to traditional internal combustion engine vehicles. By replacing a portion of ground-based transportation with electric flying taxis, urban areas can achieve substantial reductions in greenhouse gas emissions. Studies have shown that the adoption of electric vehicles, including flying taxis, can play a crucial role in achieving carbon neutrality goals [21].  
One of the primary advantages of flying taxis is their ability to bypass congested roadways. By taking to the skies, flying taxis can reduce the number of vehicles on the ground, thus alleviating traffic congestion. This not only improves travel times but also reduces the environmental impact associated with idling and stop-and-go traffic conditions. Research indicates that a significant portion of urban air pollution is attributable to traffic congestion, which flying taxis can help mitigate [22].  
Flying taxis are part of a broader movement towards eco-friendly urban mobility solutions. They offer a new mode of transportation that can be seamlessly integrated with other sustainable transport options, such as electric buses and bikes. Additionally, the infrastructure required for flying taxis, such as vertiports, can be designed with sustainability in mind, incorporating green technologies and renewable energy sources. This holistic approach can contribute to more sustainable urban environments [23].  
Several cities and companies are already pioneering the integration of flying taxis with sustainable urban planning. For instance, Volocopter's initiatives in Singapore and Germany emphasize the use of renewable energy sources for charging infrastructure, highlighting the potential for sustainable implementation [24]. Similarly, Uber Elevate's plans in Los Angeles include partnerships with local governments to develop eco-friendly vertiports [25].
## 4 Training and Performance Evaluation of the CCBA Model
### Two-Stage Training Strategy
#### 4.2.1 Stage I: Comprehensive Distillation and Generalization  
In the first stage of training, the student model is designed to learn both high-level feature representations and the overall behavior of the pre-trained teacher model. The teacher model, trained on the UrbanSound8K dataset using SPWVD-MFCC features and a dual-stream ResNet50-CNN-LSTM architecture, is a reference for high-quality feature extraction and classification. To ensure the student model can generalize effectively across different sound environments, this training stage incorporates three datasets, which are UrbanSound8K, UrbanSoundPlus, and DCASE2016, as introduced in Section 3.2.  
During this phase, the training loss is driven by feature and knowledge distillation, with a loss weight ratio of 0.9:0.1, respectively, as described in Equation (4). Through feature distillation, which accounts for 90% of the total loss, the student model's feature extraction layers (CNN-Audio) are guided to mimic the intermediate representations of the teacher's SPWVD-MFCC feature extractor. Simultaneously, knowledge distillation with a smaller weight of 10% is crucial in helping the student model align its output distribution with the teacher model. The temperature \(\tau\) in Equation (2) is set to 4 to soften the logits of the prediction distribution. This alignment occurs even in cases where the teacher model may not always produce accurate results. The rationale behind this strategy is that replicating the teacher's behavior, regardless of its correctness, allows the student to capture the nuances of the teacher's decision-making process. This comprehensive distillation process ensures that the student model learns effective feature representations and adapts to the teacher model's decision-making behavior across different datasets, enhancing its feature generalization capabilities. As a result, the need for extensive annotated data is reduced while simultaneously improving the student model's ability to generalize across diverse audio domains.
### Ensemble Learning Approaches  
Ensemble learning, a technique that integrates the strengths of multiple models, has emerged in recent years as a key approach to addressing the limitations of single models [24; 25]. Combining the predictions of multiple base learners, ensemble learning not only enhances the generalization ability of models but also effectively reduces the bias and variance of individual model predictions, thereby achieving higher prediction accuracy and stability. Common ensemble methods, including Bagging [26; 27], Boosting [28; 29], Stacking [30; 31], and Voting [32; 33], offer different strategies for combining base learners, each contributing to improved model performance in various applications. In the complex scenario of channel prediction, ensemble learning has demonstrated significant potential due to its ability to integrate multi-source data features, improve model robustness, and enhance noise resistance. Research indicates that ensemble learning has been widely applied in channel prediction tasks.  
For instance, random forest has shown strong performance in path loss prediction tasks due to its low computational complexity and good prediction accuracy [34]. Nonetheless, its relatively simple model structure may struggle to capture the nonlinear characteristics of complex channel environments. Particularly in intricate propagation scenarios such as high-speed train carriages, the random forest's limited ability to handle the interaction of multiple influencing factors can lead to reduced prediction accuracy. The study in [35] systematically evaluated the performance of ensemble methods such as voting, bagging, and gradient-boosting trees for predicting path loss in rural environments. Ultimately, a stacking ensemble method combining five different base learners was adopted, achieving significant improvements in prediction performance. That said, the stacking approach employed a three-layer neural network, whose high model complexity may result in prolonged training times and overfitting.  
In [36], the authors proposed an artificial neural network based on double-weighted neurons (DWN-ANN). The proposed model exhibited satisfactory alignment between prediction outcomes and measured data, outperforming traditional empirical models. Nevertheless, as it employs a sequential integration strategy, it may face challenges related to local optima. The layer-by-layer training and updating approach might not fully exploit the global information within the data and could reduce computational efficiency. In [37], the authors trained multiple neural networks with varying hyperparameters to enhance model diversity and combined their predictions to obtain the final results. Yet, the study did not provide an in-depth analysis of the output weights of each neural network, which could affect the optimization of the ensemble results.
# Neural Network-Informed Lotka-Volterra Dynamics for Cryptocurrency Market Analysis
## 3 Deep Neural Network Model for Crypto Dynamics
### Feed-Forward Propagation  
A feedforward neural network processes data points through a sequence of operations, as outlined in [29]. Given an input data point \(t^{(m)}\), the forward propagation begins by initializing the first layer activation as \(r_{1}^{[0]}=t^{(m)}\). To obtain the activations of layer \(l\), denoted \(\mathbf{r}^{l}\), from those of the previous layer \(\mathbf{r}^{l-1}\), two main steps are followed.  
Figure 1: An example of a deep neural network with one input neuron, three output neurons, and three hidden layers, with 10.6 and 9 neurons.  
First, an intermediate vector, \(\mathbf{c}^{l}\), is calculated by multiplying the weight matrix \(W^{l}\) with \(\mathbf{r}^{l-1}\) and then adding the corresponding bias vector \(\mathbf{b}^{l}\):  
\[c_{j}^{l}=\sum_{k=1}^{l^{\prime}-1}W_{jk^{\prime}}^{l}r_{k}^{l-1}+b_{j}^{l}. \tag{5}\]  
This operation can be compactly written in matrix form as follows:  
\[\mathbf{c}^{l}=W^{l}\mathbf{r}^{l-1}+\mathbf{b}^{l}. \tag{6}\]  
The second step applies the activation function \(\sigma^{l}\) element-wise to \(\mathbf{c}^{l}\), producing the output activations \(\mathbf{r}^{l}\):  
\[\mathbf{r}^{l}=\sigma^{l}(\mathbf{c}^{l})=\sigma^{l}\Big{(}W^{l}\mathbf{r}^{ l-1}+\mathbf{b}^{l}\Big{)}. \tag{7}\]  
Thus, for each input \(t^{(m)}\), both the pre-activation values, \(\mathbf{c}^{l}\), and the activated outputs, \(\mathbf{r}^{l}\), are computed using Equations (6) and (7). To highlight the dependence on the input sample, \(t^{(m)}\), we can denote these layer-wise computations as \(\mathbf{r}^{(m)}=\sigma^{l}(\mathbf{c}^{l(m)})\). In matrix notation, the forward propagation for the entire dataset is summarized as follows:  
\[T = [t_{1},\dots,t_{N}],\] \[c^{l} = [\mathbf{c}^{l(1)},\dots,\mathbf{c}^{l(N)}], \tag{8}\] \[A^{l} = [\mathbf{r}^{l(1)},\dots,\mathbf{r}^{l(N)}].\]  
This leads to a recursive formulation for the forward pass:  
\[A^{0} = T,\] \[c^{l} = W^{l}A^{l-1}+\mathbf{b}^{l}, \tag{9}\] \[A^{l} = \sigma^{l}(c^{l}),\quad\text{for }l=1,\dots,L.\]  
The final network output, represented as \(A^{L}\), is a function of the input and the learned parameters:  
\[N(T,W^{L},\mathbf{b}^{L})=A^{L}. \tag{10}\]  
Alternatively, we may express the output in component form as follows:  
\[N_{j}(t^{(m)},W^{L},\mathbf{b}^{L})=\pi_{j}(A^{L})=A^{L}_{j}(t^{(m)},W, \mathbf{b}), \tag{11}\]  
where \(\pi_{j}\) in (11) denotes the extraction of the \(j\)-th component from the final output vector \(A^{L}\).
# Independent System Calibration of Sentinel-1B
## 4 Geometric Calibration  
The geometric connection of the SAR system with the earth surface is enabled by geometric calibration. This task is split into two categories: the azimuth shift caused by the uncertainties between radar system time and orbit time (GPS reference time) in-flight direction and the range delay offset mainly influenced by the internal electronic delay of the instrument, and propagation delays due to atmospheric effects. Both offsets are estimated by calculating the difference between the measured time information derived from the product annotation and the geometric related elapsing time which is derived from orbit data and the position of the point target.  
The geometric accuracy is mainly defined by the accuracy of the orbit data and the deployed point target position. The distance between the satellite and the reference point targets was calculated from the precisely known geometry: the targets were surveyed using differential GPS down to an accuracy of a few centimeters, and precise orbit data of Sentinel-1B with an accuracy of about 5 cm were used. The continent drift is considered by converting the geographic positions into the International Terrestrial Reference Frame (ITRF) 2008.  
In order to avoid additional error sources related to the reference targets, which could potentially arise from active electronic components within the transponders, only corner reflectors are used for the geometric calibration as they have no further internal delay. The geometric calibration has been performed using Level-1 Single Look Complex (SLC) data which provide the highest spatial resolution.
# Edge and Cloud Computing in Smart Cities
## 4 Application Domains
### Edge-Assisted Augmented and Virtual Reality  
The adoption of AR/VR applications demands ultra-low-latency processing and high computational efficiency, making edge-cloud integration essential for immersive experiences. Edge computing accelerates real-time rendering by offloading processing from end-user devices, ensuring seamless motion tracking, adaptive scene generation, and AI-driven interaction modeling [143; 144; 145].  
Cloud services complement edge computing by handling computationally intensive physics simulations, DL-based content generation, and large-scale data synchronization. This balance ensures that AR/VR experiences remain fluid and responsive, avoiding delays that could degrade user immersion [146; 147].  
Edge-assisted AI prediction enhances AR/VR experiences by anticipating user movements, reducing perceived latency, and improving interactivity. Optimized network bandwidth allocation further ensures smooth multi-user collaboration in virtual environments, preventing congestion and maintaining synchronization in real-time simulations [148; 149; 150].
## 4 Simulation Results and Discussion
### Performance Comparison under High-Frequency DoS Attacks  
In this subsection, we will make a comparison between our method and the approach described in [32]. It is supposed that the attacker conducts DoS attacks with the same high frequency on the communication links (2, 3) and (3, 4) after the secondary control scheme is applied. The simulation results are shown in Figures 14 and 15.  
As can be seen in Figure 14, with the increase of the DoS attacks' frequency, the secondary control with the approach described in [32] fails to realize proper reactive power sharing among DGs and regulate the output voltages to the wrong values. Since the frequency of the DoS attacks is larger than the frequency of communication attempts between the agents, all transmissions are blocked on communication links (2, 3) and (3, 4), and DG3 becomes an isolated node. Thus, the secondary control  
Figure 12: Evolution of control inputs for \(V^{AVE}\) and \(Q^{AVE}\) under DoS attacks: (**a**) control inputs for \(V^{AVE}\); (**b**) control inputs for \(Q^{AVE}\).  
Figure 13: Triggering time instants of DG1 and DG4 under DoS attacks: (**a**) triggering time instants for average voltage restoration control; (**b**) triggering time instants for reactive power sharing control.  
scheme becomes invalid since the convergence performance of the approach in [32] is destroyed, and the state observer cannot acquire the correct average information. On the contrary, as seen in Figure 15, our method is more robust to high-frequency DoS attacks, because it can ensure successful information exchanges when the sleep period of attacks is detected. By exploiting the ACK-based monitoring mechanism, the correct average information of voltage and reactive power can be obtained and the proper compensation signal is sent to the primary control level of each DG. Therefore, the consensus of agents can still be achieved and the secondary control objectives can be realized regardless of the DoS attacks' frequency.
## 3 Review of WBASN MAC Protocols for IEEE 802.15.4 and IEEE 802.15.6
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Technology** & **Data Rate** & **Frequency** & **Modulation** & **Channels** & **Topology** & **Range** & **Setup** & **Current** & **Market Adaptability** \\  & & & & & & & & **Time** & **Values** & **for WBASNs** \\ \hline Bluetooth Classic & 1–3 Mbps & 2.4 GHz & GPSK & 79 & Scatternet & 1–10 m & 3 s & \(-\)45 mA & Low due to high \\  & & & & & & & & & & power requirements \\ \hline Bluetooth & 1 Mbps & 2.4 GHz & GPSK & 3 & \begin{tabular}{l} Piconet, \\ Star \\ \end{tabular} & 1–10 m & \(<\)100 s & \(-\)28 mA & Low due to power & Low due to power & Low due to power & requirements and \\  & & & & & & & & & & requirements \\  & & & & & & & & & fewer channels \\ \hline NB-IoT & 234 Kbps & 180 kHz & GPSK & 13 & Star & 35 Km & 120–300 mA & Low \\ \hline LoRa & 290 bps- & \begin{tabular}{l} 433 MHz, \\ 868 MHz \\ 915 MHz \\ \end{tabular} & SS chip & \begin{tabular}{l} 13 channels \\ for 915 MHz \\ \end{tabular} & Star & 10 Km & 32 mA & Low as it is not & open-source \\ \hline IEEE 802.15.4 & 250 Kbps & \begin{tabular}{l} 2.4 GHz \\ 915 MHz \\ \end{tabular} & O-QPSK & 16 & Star, Mesh & 10–100 m & 30 s & \(-\)16.5 mA & High for its suitability \\  & & & & & & & & in terms of QoS \\ \hline IEEE 802.15.6 & 10 Kbps - & Narrowband & DPSK, & \begin{tabular}{l} Multiple \\ channels \\ \end{tabular} & \begin{tabular}{l} Two hop \\ \end{tabular} & 1–5 m & \(<\)3 s & \(-\)1 mA & Still in the adoption \\  & 10 Mbps & HBC and UWB & DPSK, & \begin{tabular}{l} DPSK, \\ decording to \\ frequency \\ \end{tabular} & \
\begin{tabular}{l} Noep \\ Star, Mesh \\ \end{tabular} & 1–5 m & \(<\)3 s & \(-\)1 mA & also involves \\  & & & & & & & implanted sensors \\ \hline ANT & 1 Mbps & 2.4 GHz & GPSK & 125 & Star, Mesh & 10–30 m & \(-\)22 mA & Low due to high \\  & & & & & & & & & impaired QoS \\ \hline Sensium & 50 Kbps &
\begin{tabular}{l} 868 MHz \\ 915 MHz \\ \end{tabular} & BPSK & 16 & Star & 1–5 m & \(<\)3 s & \(-\)3 mA & Low due to its low \\  & & & & & & & data rates \\ \hline Zaralink & \multirow{2}{*}{50 Kbps} & 402–405 MHz & \multirow{2}{*}{2 PSK/4 PSK} & \multirow{2}{*}{10} & \multirow{2}{*}{P2P} & \multirow{2}{*}{1–5 m} & \multirow{2}{*}{\(<\)3 s} & \multirow{2}{*}{\(-\)3 mA} & Low due to its low \\  & & & & & & & & data rates \\ \cline{1-1} ZJ/0101 & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Characteristics and comparison of available technologies [67]. The existing literature regarding the optimisation of the IEEE 802.15.4 / IEEE 802.15.6 MAC protocols is categorised in Figure 7.