esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 5 Experiments and Results
### Experiment Setting
According to the standard CS imaging algorithm, the experimental procedure is designed as follows. Step 1: raw data entry. The SAR raw data are transmitted from the upper machine through the Gigabit Ethernet port on the ZCU106 side to the DDR4 on the Zynq PS side for storage. After the superscalar pipeline storage exchange engine and the calculation engine on the VC709 side are initialized, they are transferred from the ZCU106 development board to the VC709 development board through SFP Cages. After the VC709 receives the data, they are converted into the AXIS protocol data stream, and the two-dimensional matrix is written into DDR3 through the storage exchange engine in the form of range. Step 2: Azimuth processing. The data are read from DDR3 in azimuth, buffered in superscalar pipeline, and output from the storage exchange engine to the calculation engine for FFT and complex multiplication calculation, which is still written to DDR3 in azimuth after the processing is completed. Step 3: range processing. The data are read from DDR3 in the range direction, buffered in the superscalar pipeline, output from the storage exchange engine to the calculation engine for FFT and complex multiplication calculation, and then written to DDR3 in the range direction after the processing is completed. Step 4: range processing. The data are read from DDR3 according to the range direction, and output from the storage exchange engine to the calculation engine for IFFT processing. After the processing is completed, they are still written to DDR3 according to the range direction. Step 5: azimuth processing. The data are read from DDR3 according to the azimuth direction, passed through the superscalar pipeline buffer, and output from the storage exchange engine to the calculation engine for complex multiplication and IFFT processing. After the processing is completed, they are still written to DDR3 in the azimuth direction. Then, all operations are completed. Finally, data are read from the VC709-side DDR3, transmitted to the ZCU106 development board through the VC709-side SFP Cages, and subsequently uploaded to the host computer to display imaging results. The experimental test process is shown in Figure 17.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 3 Performance Prediction Model
### Step 3: Training for Best-Fit Model  
The model was trained using the dataset with \(Q\) i.i.d. instances, given by \(\chi\ =\ \left\{m^{(q)},PM^{(q)}\right\}_{(q)=1}^{Q}\), where the \(q\)-th instance is represented by the superscript \(.^{(q)}\). The loss function for optimization is based on the difference between the simulated output, and the hypothesis evaluation is [20] as follows  
\[J(\beta|m)=\frac{1}{2Q}\sum_{q=1}^{Q}\Bigl{[}h_{\beta}\left(m^{(q)}\right)-PM^ {(q)}\Bigr{]}^{2}. \tag{17}\]  
The optimal solution corresponds to the values of the regression parameters that minimize the loss function:  
\[\hat{\beta}=\arg\ min_{\hat{\beta}}(\beta|m). \tag{18}\]  
The optimum solution is obtained using the gradient-descent algorithm, which starts with a random \(\beta\), and updates it with the given iterative process:  
\[\beta_{0}\beta_{0}-\frac{\alpha}{Q}\sum_{q=1}^{Q}\Bigl{[}h_{ \beta}\left(m^{(q)}\right)-PM^{(q)}\Bigr{]}, \tag{19}\] \[\beta_{1}\beta_{1}-\frac{\alpha}{Q}\sum_{q=1}^{Q}\Bigl{[}h_{\beta }\left(m^{(q)}\right)-PM^{(q)}\Bigr{]}N_{macro}^{(q)},\] \[\text{\emph{Repeat until convergence}}\quad\{\beta_{2}\beta_{2}- \frac{\alpha}{Q}\sum_{q=1}^{Q}\Bigl{[}h_{\beta}\left(m^{(q)}\right)-PM^{(q)} \Bigr{]}R_{macro}^{(q)},\] \[\beta_{3}\beta_{3}-\frac{\alpha}{Q}\sum_{q=1}^{Q}\Bigl{[}h_{\beta }\left(m^{(q)}\right)-PM^{(q)}\Bigr{]}N_{pico}^{(q)},\] \[\beta_{4}\beta_{4}-\frac{\alpha}{Q}\sum_{q=1}^{Q}\Bigl{[}h_{\beta }\left(m^{(q)}\right)-PM^{(q)}\Bigr{]}R_{pico}^{(q)},\] \[\beta_{5}\beta_{5}-\frac{\alpha}{Q}\sum_{q=1}^{Q}\Bigl{[}h_{\beta }\left(m^{(q)}\right)-PM^{(q)}\Bigr{]}P_{TD}^{(q)},\}\]  
Here, \(\alpha\) is the step size of the algorithm, taken as 0.01. On convergence, \(C\)P and \(ASE\) are estimated using Equations (20) and (21), respectively,  
\[\hat{C}\hat{P} =\hat{\beta}_{CP}^{T}m, \tag{20}\] \[A\hat{S} =\hat{\beta}_{ASE}^{T}m. \tag{21}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Attributes** & \(C\)**P** & **ASE** \\ \hline \(N_{macro}\) & 0.51 & 0.59 \\ \(R_{macro}\) & \(-\)0.58 & 0.48 \\ \(N_{pico}\) & 0.15 & 0.12 \\ \(R_{pico}\) & \(-\)0.12 & \(-\)0.11 \\ \(P_{TD}\) & 0.41 & 0.18 \\ \hline \end{tabular}
\end{table}
Table 3: Correlation table.  
Here, \(\hat{\beta}_{CP}\) and \(\hat{\beta}_{ASE}\) are the regression parameter vectors after convergence for ASE and \(CP\), respectively.
## 3 Root-Cause Detection and Identification Algorithm
### Classification Pipeline  
Figure 6 outlines the classification process: sensor data are preprocessed, features selected via Relieff, and classified using an Optimized Feed-Forward Neural Network (OFFNN) with a moving window approach [25].  
The core of the proposed approach lies in its hybrid structure, which integrates physical simulation with data-driven classification. A bond graph-based physical model is used to simulate time-series sensor data under various degradation scenarios by modifying parameters such as shunt resistance, series resistance, and photocurrent. These simulations are carried out across a range of environmental conditions to mimic realistic operational diversity. The resulting synthetic signals serve as labeled inputs to the machine learning stage. Specifically, the data are preprocessed, normalized, and used to train the OFFNN classifier. This simulation-to-classification pipeline enables effective root-cause detection, even in the absence of large-scale real-world fault datasets, and leverages the strengths of both physics-based interpretability and data-driven adaptability.  
**Preprocessing:** Features (\(T_{cell}\), \(T_{amb}\), irradiance, \(V_{cell}\), \(I_{cell}\)) are normalized to [0, 1] using MinMaxScaler (scikit-learn version 1.2.2) [26]. Relieff [14], validated by [27], ranks feature relevance (Table 2), showing \(T_{cell}\) and \(T_{amb}\) as dominant, with irradiance, \(V_{cell}\), and \(I_{cell}\) correlated (Figure 7).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **ReliefF Score** \\ \hline \(T_{cell}\) & 0.012122590549920763 \\ \hline \(T_{amb}\) & 0.012122590549920752 \\ \hline Irradiance & 0.00481230342158903 \\ \hline \(V_{cell}\) & 0.0047244059597221525 \\ \hline \(I_{cell}\) & 0.004724262104104376 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relieff scores of each feature.  
Figure 6: Workflow of the root-cause classification pipeline. Raw sensor data are preprocessed, with relevant features selected via Relieff, and fed into an optimized feed-forward neural network (OFFNN) for degradation mode identification.  
**Model Design**: The OFFNN, optimized via Genetic Algorithm (GA) over 50 generations, features two Dense layers (128 and 64 neurons) with ReLU activation, Dropout (0.3) for regularization, and a softmax output for five-class classification. GA optimizes neuron counts and layers, outperforming manual tuning and aligning with neural architecture search trends [28].  
**Training**: The dataset is split (80% training, 20% validation), using categorical cross-entropy loss, Adam optimizer, and EarlyStopping (patience 10 epochs) [29]. Training runs for 100 epochs with internal validation.
## 6 Results
### Data Congruency in Emotion Norms  
As the first step in the experiment, distinctive pairs of discrete emotions from the NAPS BE dataset are considered to establish a correlation between them. In this regard, Pearson (\(r\)) and Spearman's rank correlation (\(r_{s}\)) coefficients were obtained for 15 distinct pairs of 6 emotion norms. The results are shown in Figure 6.  
The results show that a moderate negative correlation can be observed for norm pairs {happiness, sadness}, {happiness, anger}, and {happiness, disgust} with \(r=-0.67\), \(-0.62\), \(-0.62\), respectively. Additionally, there is a strong positive correlation of {sadness, anger} 0.81, {fear, surprise} 0.76, and of {surprise, disgust} 0.71, as detailed in Figure 7. The highest Spearman's rank coefficient is observed for three tuples {sadness, anger}, {happiness, sadness} and {happiness, anger} with \(r_{s}=0.85\), \(-0.81\), and \(-0.81\), respectively. This indicates strong monotonic relationships where stimuli with a high value of sadness also have high value of anger, and oppositely, when happiness is high, sadness and anger are low. Interestingly, the relationship in tuples {fear, anger} and {anger, disgust} is more continuously growing than in the other pairs. Some negative polarity emotions are mutually less co-linear in absolute terms than they are with the positive emotion of happiness. Likewise, although negative polarity emotions are not all strongly co-linear, sadness and anger tend to increase together the most of all discrete emotion pairs.  
Having the correlations between discrete emotion pairs, the same analysis is performed for all 12 possible combinations of discrete emotions and emotion dimensions. As can be seen in Figure 7, there is a very strong positive correlation between happiness and valence (\(r=0.93\)). Strong positive correlations also exist for fear and arousal (\(r=0.79\)), and surprise and arousal (\(r=0.74\)). On the other hand, strong negative correlations are found between valence and four negative polarity norms: sadness, fear, anger, and disgust, the highest being for sadness (\(r=-0.85\)) and anger (\(r=-0.78\)). The findings confirm previously reported results about the relationship between different discrete emotions in affective processing [61]. These observations highlight interesting pairs of basic emotions for further in-depth analysis regarding clustering in dimensional space.
## 1 Introduction
This further helps to increase the robustness of the system, because the UAV can re-detect the landing platform autonomously in case the latter disappears from the field of view of the UAV's camera or if the relative error between the landing platform and the UAV in the immediate moments before landing is greater than a threshold. Furthermore, a novel finite state machine is presented in this work, which together with our re-localization module allows for life-long operation, as we will demonstrate in Section 4. The proposed system in its two versions has been extensively tested on a realistic three-Dimensional (3D) simulated environment (Gazebo [8]) and deployed for qualitative evaluation on real robotic platforms. Figure 1 shows the proposed aerial-ground robot fleet. We employ Robotnik's Summit XL as the UGV and the Parrot's AR Drone 2.0 as the UAV. In the simulated environment, we use the corresponding Robot Operating System (ROS) [9] packages, namely the _summit_xl_sim_ and _tum_simulator_ packages. This paper is organized as follows. Section 2 analyzes previous work. In Section 3, an overview of our system is presented, and the robotic frameworks and platforms used are described. Section 4 presents an extensive evaluation of the system in the simulated environment and qualitative tests in the real robotic platforms. Section 5 concludes this work.
## 2 Proposed Method
### Gait Recognition
#### 2.2.1 Gait Verification Using Joint Bayesian  
Joint Bayesian [28] technique was widely and successfully used for face verification [18; 19; 32]. In this paper, we modeled the extracted DeepGait (after mean-subtracted) by summing two independent Gaussian variables as:  
\[x=\mu+\varepsilon \tag{4}\]  
where \(x\) represents a mean-subtracted DeepGait vector. For a better performance, \(L_{2}\)- normalization was applied for DeepGait. \(\mu\) is gait identity following a Gaussian distribution \(N(0,S_{\mu})\). \(\varepsilon\) stands for different gait variations (e.g., view, clothing and carrying bags etc.) following a Gaussian distribution \(N(0,S_{\varepsilon})\). Joint Bayesian models the joint probability of two gait representations using the intra-class variation (I) or inter-class variance (E) hypothesis, \(P(x_{1},x_{2}|H_{I})\) and \(P(x_{1},x_{2}|H_{E})\). Given the above prior from Equation (4) and the independent assumption between \(\mu\) and \(\varepsilon\), the covariance matrix of \(P(x_{1},x_{2}|H_{I})\) and \(P(x_{1},x_{2}|H_{E})\) can be derived separately as:  
\[\Sigma_{I}=\begin{bmatrix}S_{\mu}+S_{\varepsilon}&S_{\mu}\\ S_{\mu}&S_{\mu}+S_{\varepsilon}\end{bmatrix} \tag{5}\]  
\[\Sigma_{E}=\begin{bmatrix}S_{\mu}+S_{\varepsilon}&0\\ 0&S_{\mu}+S_{\varepsilon}\end{bmatrix} \tag{6}\]  
\(S_{\mu}\) and \(S_{\varepsilon}\) are two unknown covariance matrices which can be learned from the training set using the Expectation Maximization (EM) algorithm. During the testing phase, the likelihood ratio (\(r(x1,x2)\)) is regarded as the similar score (\(SimScore\)):  
\[SimScore(x_{1},x_{2})=r(x_{1},x_{2})=log\frac{P(x_{1},x_{2}|H_{I})}{P(x_{1},x_ {2}|H_{E})} \tag{7}\]  
\(r(x_{1},x_{2})\) is efficiently obtained with the following closed-form process:  
\[r(x_{1},x_{2})=x_{1}^{T}Ax_{1}+x_{2}^{T}Ax_{2}-2x_{1}^{T}Gx_{2} \tag{8}\]  
where \(A\) and \(G\) are two final result models, which can be obtained by using simple algebra operations between \(S_{\mu}\) and \(S_{\varepsilon}\). Please refer to [28] for more details. We also make public our trained model (\(A\) and \(G\)) and testing codes in Supplementary Materials for further comparison.  
Euclidean distance is also adopted as a baseline method for comparison and the similar score (\(SimScore\)) can be calculated as:  
\[SimScore(x_{1},x_{2})=-||\frac{x_{1}}{||x_{2}||}-\frac{x_{1}}{||x_{2}||}|| \tag{9}\]  
Finally, \(SimScore\) is compared with a threshold value to verify whether \(x_{1}\) and \(x_{2}\) belong to the same subject.
# A Review of Transformer-Based Approaches for Image Captioning
## 4 Transformers in Captioning
### Attention on Attention  
[PERSON] et al. [84] introduce an attention on attention module (AoA) as illustrated in Figure 4. In the encoder they extract feature vectors of objects in the image and apply self attention. The AoA module is then applied to determine how the objects are related to each other. Essentially, self-attention is used to model the relationships among objects in the input image. In the decoder, the AoA module helps determine to what extent the attention results are related to the queries. The model first generates an information vector and an attention gate by employing the attention result and the context vector. A second attention module is then added through an element-wise multiplication of the attention gate and the information vector to yield the final attended information. Thus, the irrelevant or misleading results are filtered out to keep only the useful ones.  
The attention on attention mechanism is formulated as the element-wise multiplication of an attention gate \(\mathbf{g}\) and an information vector \(\mathbf{i}\) as shown in Equation (3). The attention gate and information vector are results of two linear transformations, which are both conditioned on the context vector (the query) and the attention result. In the decoder, the AoA module is used, coupled to an LSTM module.  
\[\text{AoA}(f_{att},\mathbf{Q},\mathbf{K},\mathbf{V})=\sigma(\mathbf{W}_{q}^{S}\mathbf{Q}+\mathbf{W}_{v }^{S}f_{att}(\mathbf{Q},\mathbf{K},\mathbf{V})+\mathbf{b}^{S})\odot\ (\mathbf{W}_{q}^{i}\mathbf{Q}+\mathbf{W}_{v }^{i}f_{att}(\mathbf{Q},\mathbf{K},\mathbf{V})+\mathbf{b}^{i}) \tag{3}\]  
where \(f_{att}\) is an attention function that operates on the queries, keys and values, denoted by the matrices \(\mathbf{Q},\mathbf{K}\) and \(\mathbf{V}\), respectively; \(\mathbf{W}_{q}^{i},\mathbf{W}_{v}^{i},\mathbf{W}_{q}^{S}\) and \(\mathbf{W}_{v}^{S}\) are learnable weight matrices;\(\mathbf{b}^{s}\) and \(\mathbf{b}^{i}\) are bias vectors of the attention gate and information vector, respectively; \(\sigma\) is the sigmoid activation function and \(\odot\) denotes element-wise multiplication.
## 7 Hardware Complexity Optimization for MIMO
### Filter Tap Number for MIMO  
The structure of an MIMO equalizer as an extension of the standard CMA is depicted in Figure 24, which is based on multiple FIR filters in a butterfly structure. The hardware complexity of such an architecture per transmitted bit is only doubled for the 4 \(\times\) 4 MIMO configuration compared to standard single-mode operation, for sixteen adaptive filters are prerequisite to process 2 \(\times\) 100 Gb/s with a 4 \(\times\) 4 MIMO compared to four filters for 1 \(\times\) 100 Gb/s with a 2 \(\times\) 2 MIMO as a reference. On the  
Figure 23: The BER vs. OSNR for three different modes for a PDM-64 QAM signal using the STBC-RLS scheme [27].  
Figure 22: The BER vs. OSNR for three different modes for a PDM-16 QAM signal using the STBC-RLS scheme [27].  
right graph of Q\({}^{2}\)-factor versus the FIR filter tap number, which is used to estimate the required length of the MIMO equalizer allowing the detection of all the polarization and mode tributaries, an almost flat factor from nine taps to 15 taps can be observed for both 100 Gb/s PDM-QPSK and 2 \(\times\) 100 Gb/s MDM after a 40-km transmission [42].  
The required number of taps per carrier used as a function of distance in recent MDM transmission experiments is illuminated in Figure 25, where the DMGD increases linearly with distance in the weak coupling regime [19]. Fiber management is expedient in decreasing the maximum DMGD in an FMF. In addition, as different forms of signals, fibers, and spatial channels are likely to cohabit in future SDM transport networks, it is also significant that MIMO DSP has sufficient processing capability to handle all received signals.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 3 VRITHI: System Design and Characteristics
### Communication Characteristics of Control Channel  
Before concluding this section, we would like to derive specific characteristics of the control channel in terms of the maximum DTMF tone transfer rate in the voice channel and latency for sending the tone. Figure 6(a) shows the transfer rate of DTMF tones on encoded data in the channel. The plot reflects that average of four different test cases for sent versus received tones. A cubic polynomial-fit is found suitable in this plot, with \(p\) value less than \(2.2\times 10^{-16}\), and with \(R^{2}\) value as 0.8927.  
Initially, the tones are tested for unidirectional communication. It is observed that, after the rate of 58 tones transmitted unidirectionally, there is a decline of tone rate due to the mixture of DTMF tones at the receiver side of the modem. The resultant packets were corrupted and, hence, could not be used for communications. However, the channel can be reset by sending an ACK bit back to the sender. The test is conducted without a Cyclic Redundancy Check (CRC) mechanism to know the maximum tone rate that we could achieve with the modem.  
The latency of the tones is measured from sender to receiver. The experiment is repeated eight times, and the distribution is plotted in Figure 6(b) using a box plot for five values of the number of tones sent. It can be observed that, to send 30 tones, it incurs an average latency of 46.5 s.
## 3 Methodology
### Data Processing
#### 3.2.1 Trajectory Segmentation  
Based on the AIS data structure, AIS data are divided into several trajectory sets according to the unique ship identification number, MMSI, represented as \(Traj^{mmsi}\). Since AIS data for each ship consist of multiple segments of voyage data, it is necessary to segment different voyages for the same ship. This paper performs trajectory segmentation at two different levels of granularity based on ship status and ship speed from the AIS data. Trajectory segmentation is expressed as shown in Figure 3.  
Firstly, the trajectories in the AIS data are divided into navigation states (NSs). In the method proposed in this paper, NSs mainly include navigation and anchoring, represented by 0 and 1, respectively. Based on these two states, the original set of navigation trajectories can be divided into multiple navigation trajectory segments. Then, further divide the trajectories based on the ship's speed. When trajectory points with speed \(sog_{i}=0\) and \(ns_{i}=1\) appear, mark them as stopping points (\(Po_{stop}\)) for new sailing segments. Then, cut the trajectory based on \(Po_{stop}\), and the cut trajectories \(\{Traj_{1},\cdots,Traj_{n}\}\) are added to the navigation trajectory set, represented as \(Traj^{nav}\). This results in finer-grained trajectories.  
\[Po_{stop}=\{Po_{i}|sog_{i}=0,ns_{i}=1\} \tag{4}\]  
Since trajectories with a smaller number of points have less significance, trajectories in the new trajectory set with point counts less than the specified threshold length \(l_{min}\) are removed; we obtain \(Traj^{nav}\) as Formula (5). The final set of ship trajectories serves as the foundation for the subsequent processing, analysis, and dataset construction.  
\[\left\{Traj_{j}|len(Traj_{j})\geq l_{min}\right\}\sigma Traj^{nav},j=1,\cdots,n \tag{5}\]
## 1 Introduction
At this time, it is necessary to rationally schedule the sensor nodes to work, rather than allowing all the nodes to work, which can greatly reduce the energy consumption of the network. As a consequence, it is usually necessary to consider the partial coverage problem; by slightly reducing the quality of coverage in exchange for the network's ultra-long lifetime, we obtain a higher packet delivery rate (PDR) and less delay because, for some applications such as fire monitoring and mudflow monitoring, these applications' coverage rate can be changed based on the actual project requirement, so as to save energy. In this paper, we divide the monitoring area into inner and outer areas, where the sink moves in the inner area and uses a tree-based routing protocol for network updating to save energy, and the outer area uses an improved geographic routing protocol, which adequately equalizes the energy consumption of the network. Based on this, we make the following contributions. * We propose a link detection and repair scheme where each node periodically detects the state of its own transmission link and selects a valid neighboring node with higher energy as the next hop when the link is invalid, and when the node fails to find a valid link, the node stops the data transmission in order to conserve the energy, which greatly improves the reliability of the data transmission and saves energy to some extent. * We combine routing protocol and coverage control technique to propose a node collaborative scheduling algorithm, which greatly reduces the number of packets generated and transmitted, largely conserves the energy of the network, and reduces the delay. This paper is organized as follows: We will present some of the previous research works and analyze the advantages and disadvantages of these works in Section 2. In Section 3, the network model used for the study and the technical details and flows of the proposedalgorithms will be described. In Section 4, the experimental methodology and parameters are provided, and the experimental simulation results are shown in terms of several important network metrics, and the simulation results are analyzed in detail. Finally, the work conducted in this paper and its value is summarized in Section 5.