esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 1 Introduction
Our objective is to develop a multimodal learning framework that balances high predictive accuracy, robustness, and privacy protection for complex real-world tasks. The main contributions of the paper are as follows:  
* We propose a hybrid uncertainty metric that integrates KL divergence and entropy to comprehensively evaluate modality quality, optimize cross-modal fusion, and enhance robustness in noisy or imbalanced scenarios. * We incorporate differential privacy into alternating training with gradient clipping and noise injection to effectively safeguard sensitive data and mitigate privacy attack risks. * We demonstrate through extensive experiments on the MVSA and CREMA-D datasets that PAMRL achieves a robust balance of predictive accuracy, robustness, and privacy protection, offering significant practical value. In the rest of this paper, we first provide an overview of the background and related work in Section 2. The methodology of the Privacy-Preserving Alternating MultimodalRepresentation Learning (PAMRL) framework is detailed in Section 3. Subsequently, we describe the experimental setup in Section 4, including the datasets, baseline methods, and implementation details. We present the experimental results and perform comprehensive analyses in Section 4. Finally, we summarize the findings and discuss future research directions in Section 5.
## 3 The Ionospheric Scintillation Compensation-Based Modified BP Imaging Method
### The Proposed Modified BP Imaging Algorithm
#### 3.2.1 Ionospheric Scintillation Error Extraction Based on Reference Channel Processing
According to the analysis in Section 2.3, the high-order phase error is mainly the phase error caused by ionospheric scintillation. Using polynomial fitting, the ionospheric scintillation error can be extracted from the reference phase as information for analyzing the impact of atmospheric effects. The process can be expressed as follows:  
\[ \begin{array}{ll}\phi_{ion}(t)&=\phi_{he}(t)\\ &=\phi_{ref}(t)-\phi_{he}(t)\\ &=\phi_{ref}(t)-\sum\limits_{i=0}^{3}w_{i}t^{i}\end{array}  \tag{14}\]  
where \(w_{i}\) represents the \(i\)-th order polynomial fitting coefficient. It is worth mentioning that in the proposed modified BP algorithm, the extracted reference channel Doppler phase will be used as a whole for phase compensation. But for the purpose of analyzing the impact of ionospheric scintillation error and observe the possible diurnal variation curve of ionospheric scintillation, the ionospheric scintillation  
Figure 8: The navigation signal receiving process in the reference channel for the GNSS-BSA system. error will be stripped off using Equation (14) in this paper. In the actual experimental data processing, the imaging results before and after ionospheric scintillation error compensation will be separately studied.
# Web Unevenness Due to Thermal Deformation in the Roll-to-Roll Manufacturing Process
###### Abstract  
In roll-to-roll (R2R) processing, web uniformity is a crucial factor that can guarantee high coating quality. To understand web defects due to thermal deformation, we analyzed the effect of web unevenness on the coating quality of an yttria-stabilized zirconia (YSZ) layer, a brittle electrolyte of solid oxide fuel cells (SOFCs). We used finite-element analysis to study thermal and mechanical deformations at different drying temperature levels. A YSZ layer was also coated using R2R slot-die coating to observe the effect of web unevenness on coating quality. Web unevenness was generated by thermal deformation due to conduction and convection heat from the dryer. Because of varying web unevenness with time, the YSZ layer developed cracks. At higher drying temperatures, more coating defects with larger widths were generated. Results indicated that web unevenness at the coating section led to coating defects that could damage the SOFCs and decrease yield in the R2R process. Coating defects generated by web unevenness caused by convection and conduction heat should be considered in the high-volume production of brittle electrolytes using the R2R process.  
coating defect; electrolyte layer; temperature; thermal deformation; roll-to-roll slot-die coating systems; wrinkle +
Footnote †: journal: Applied Sciences
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 3 Experiment Setup and Results  
### Data Collection  
A set of snapshots were recorded for this research using a snapshot receiver designed by Albora Technologies. A high-gain antenna (Septentrio PolaNt-x MF [37]) was used and located in an open environment at the north campus of Universitat Politecnica de Catalunya (UPC) in Barcelona, as shown in Figure 6.  
The collected snapshot data were sent to the Albora cloud platform and were then processed by the SRTK positioning engine proposed in this manuscript. The base station data were collected using the Virtual Reference Station (VRS) service provided by Institut Cartografic i Geologic de Catalunya (ICGC). Several streams of VRS data were collected simultaneously by setting a series of reference points that have a different distance to the snapshot receiver; in this way, reference data for different baseline distances were collected. The GPS navigation data bits were collected from the GFZ server to address data bit ambiguity issue and compensate the half cycle phase biases properly. A total of 240 snapshots were collected over 4 h, with each snapshot datum separated by one minute. The original data contain signals from the L1 and G1 bands; however, only L1 signals were used for this research. The default front-end sampling rate was 63.6 MHz and each snapshot had an original duration of 100 ms.
## 2 Cooperative SCARA System
### Synchronization of SCARA Robots for Cooperative Tasks
This can happen if the coordination and synchronization of their movements are not precise, particularly  
Figure 6: Pick-and-place process in the cooperative system: (**a**) Initial position of the robots; (**b**) Robot 1 grasps the object; (**c**) Robot 1 transfers the object; (**d**) Robot 1 releases the object and Robot 2 grasps the object; (**e**) Robot 2 transfers the object; and (**f**) task completion. if they do not exchange information about the positions of their links in real time. Collisions can occur when one of the robots moves into a position occupied by another robot or when both attempt to access the same point in the common working area simultaneously. Figure 7 shows an example of a collision scenario. Such collisions can result in damage to robots and their components, causing significant losses and prolonged downtimes during the production process. To avoid collisions and ensure precise movements when the robots jointly transfer an object, the algorithm illustrated in Figure 8 was implemented. Figure 8: Algorithm for coordinating cooperative robots to move an object together. Figure 7: Cooperative SCARA system in collision: (**a**) Collision with the links of the two robots, and (**b**) collision with the end effectors of both robots. This algorithm ensures exact synchronization and coordination, allowing robots to exchange information regarding the positions of their links in real time and dynamically adjust their trajectories to avoid interference and collisions. The objective of the algorithm is to coordinate the movement of the two robots to transfer an object from point A to point B. Initially, the system determines the position of the object and verifies whether its center is within the intersection of the working areas of both robots. If this condition is not satisfied, the movement of the object is not feasible, and the process ends. If the center of the object is correctly positioned, the trajectory planner generates an optimal path from point A to point B. Subsequently, the grasp positions for each robot at the ends of the object are calculated, defining specific contact points (TR1 and TR2) adjusted to the object length. See Figure 9.  
The robots move to these contact points, and a collision check is performed. If no collisions are detected, the robots proceed to grasp the object synchronously. During the transfer, the system continuously monitors whether both robots maintain the correct grip on the object and verifies that the planned movement is completed. Finally, once the transfer is completed, the robots release the object and return to their initial positions, thereby safely and efficiently concluding the process. See Figure 10.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 5 Discussion  
Figures 11-14 show the influence of the noise on the transmitted and received signals. The noise floor in the transmitted signal changes with the SNR value. In the received signal, it changes with both the SNR value and the data rate value.  
When using a higher data rate, we must either increase the output power, reduce the noise, or reduce the range. Advanced satellite communication subsystems are in favor of adaptive systems, because in non-adaptive approaches, only the lowest data rate can be used. If this new design can achieve a 9600 bps data rate for 30% of the communication window's duration, this will allow the delivery of 90% more data than 2400 bps systems, and 30% more than 4800 bps systems.  
Selecting the patch size is important, as the data rate is not changed while the patch is being transmitted. For large patches, the channel condition will change, and this might cause packet loss. In our design, we opted for the patch transmission duration of the slowest data rate to not exceed 1 min. As the transmission duration test showed, one packet needs about 0.29 s, 0.57 s or 1.13 s for 9600 bps GMSK, 4800 bp GMSK or 2400 bps GMSK, respectively. Therefore, for our system design, the patch size was selected to be 50 packets per patch.  
By virtue of the socket buffer, the period required to send 1000 packets to the transmitter is not affected by the data rate, as the sent packets are saved in the buffer while being modulated and transmitted to a queue. It is important to mention that the buffer size is crucial when implementing the system in embedded systems. The transmission duration test was performed in order give available data for comparison when testing the system in the real hardware.  
The results of the useful data bit rate showed that it was a little below the ideal value; given that the AX.25 frame can be 330 bytes, the ideal percentage of useful data in the AX.25 packet is 77.57% (256/330). Our results deviated by 7% from the ideal percentage. This deviation was mainly due the introduced delays and the time between packets being received and processed.  
The system minimizes the possibility of losing the feedback by transmitting it in the most robust mode. If the satellite cannot receive the uplink, the ground station will probably not receive the downlink, because it is common practice to make the uplink margin higher than the downlink margin in the link budget design.  
For the packet success rate test, the timeout of reading the packets influences the success of the reception. In the first attempts, this delay was set lower than the required time, so the test resulted in a high packet loss, even with a strong SNR. The test gave the expected results after the timeout delay was adjusted to be 300 milliseconds higher than the average time required to receive one packet (10 milliseconds in the first attempt). The resulting graph was structured as expected.  
As expected, and as shown in Figure 17, the system's data rate doubles whenever the SNR is raised by 3 dB.
## 5 Experiments
### Results
#### 5.5.1 Anomaly Detection Results
Although these buoys are on the direct path of the Katrina, anomaly scores generated by IsolationForest are not stable and exhibit high values for data points when Katrina center is not very close. Meanwhile, the anomaly scores generated by MC-CNN-LSTM is smoother than LDBSCAN and IsolationForest. In other words, the anomaly scores generated by MC-CNN-LSTM do not exhibit sharp drops and high peaks in consecutive data points. The proposed framework outperforms LDBSCAN and IsolationForest in capturing the overall anomalous trend in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Buoy ID** & **IsolationForest** & **LOF** & **LDBSCAN** & **ST-Outlier** & **MC-CNN-LSTM** \\  & 2005-08-29 & 2005-08-28 & 2005-08-28 & 2005-08-28 20:00 \\  & 09:00 & 20:00 & 20:00 & 08:00 & 2005-08-28 20:00 \\ \cline{2-5}  & 2005-08-29 & 200  
the test data. In summary, MC-CNN-LSTM generates fewer false alarms and does not miss real alarms, as generated anomaly score values show higher correlation with the distance between buoys and the center of the Katrina. Figure 8: Anomaly score comparison of the proposed framework, IsolationForest and the LDBSCAN algorithm. Normalized anomaly scores are plotted against the normalized distance between buoys and the center of the Katrina.
## 3 Results
### Performance and Biomechanical Variables during the 6-Week Training Period  
#### 3.7.1 Performance  
Performance time that was recorded in the first session of each week during the 4 \(\times\) 50 m training set was similar among G-MS, G-ME, and G-CON during the 6-week training period (\(F_{(2,21)}\) = 1.05, \(p\) = 0.37, \({\eta_{p}}^{2}\) = 0.09 [medium], Figure 5). Furthermore, all groups decreased (indicating improvement) their performance time in the 4 \(\times\) 50 m training set in weeks 4, 5, and 6 compared with week 1 (\(F_{(5,105)}\) = 11.73, \(p\) = 0.01, \({\eta_{p}}^{2}\) = 0.36 [large], Figure 5). In addition, the calculated DS was similar among G-MS, G-ME, and G-CON during the 6-week training period (\(F_{(2,21)}\) = 0.09, \(p\) = 0.90, \({\eta_{p}}^{2}\) = 0.00 [small], Figure 5). Furthermore, all groups decreased their DS in week 4 compared with weeks 1 and 2 (\(F_{(5,105)}\) = 2.78, \(p\) = 0.02, \({\eta_{p}}^{2}\) = 0.12 [medium], Figure 5).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{Bench Press (kg)} \\ \hline  & G-MS & G-ME & G-CON \\ \hline Pre & 52.50 \(\pm\) 17.11 & 48.13 \(\pm\) 12.80 & 45.63 \(\pm\) 14.00 \\ Post & 59.81 \(\pm\) 19.94 * & 55.31 \(\pm\) 14.79 * & 46.50 \(\pm\) 14.90 \\ \(\%\Delta\) & 15.08 \(\pm\) 18.92 & 16.31 \(\pm\) 17.00 & 1.85 \(\pm\) 7.80 \\ \hline  & \multicolumn{3}{c}{Seated pulley rowing (kg)} \\ \hline  & G-MS & G-ME & G-CON \\ \hline Pre & 57.50 \(\pm\) 18.90 & 50.63 \(\pm\) 9.03 & 53.75 \(\pm\) 15.06 \\ Post & 70.44 \(\pm\) 18.34 *\# & 66.87 \(\pm\) 9.23 *\# & 54.69 \(\pm\) 15.61 \\ \(\%\Delta\) & 24.42 \(\pm\) 12.28 & 34.72 \(\pm\) 25.60 & 1.80 \(\pm\) 6.68 \\ \hline  & \multicolumn{3}{c}{Half squat (90\({}^{\circ}\)) kg} \\ \hline  & G-MS & G-ME & G-CON \\ \hline Pre & 73.13 \(\pm\) 30.93 & 67.50 \(\pm\) 14.39 & 61.25 \(\pm\) 18.66 \\ Post & 90.00 \(\pm\) 30.24 *\# & 78.75 \(\pm\) 16.85 *\# & 60.94 \(\pm\) 18.51 \\ \(\%\Delta\) & 25.62 \(\pm\) 13.50 & 17.76 \(\pm\) 14.85 & 0.01 \(\pm\) 8.64 \\ \hline \(\%\Delta\): Post- vs. pre-measurements, *\(p\) < 0.05; post- vs. pre-measurements, \# \(p\) < 0.05, between groups. & \\ \hline \end{tabular}
\end{table}
Table 6: Post vs. prior to one-repetition maximum (1 RM) strength in the bench press, the seated pulley rowing, and the half squat in the group of maximum strength (G-MS), the group of muscular endurance (G-ME), and the control group (G-CON).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variables** & **G-MS** & **G-ME** & **G-CON** \\ \hline Performance time (\%) & \(-\)3.1 \(\pm\) 4.6 & \(-\)4.0 \(\pm\) 2.3 & \(-\)1.2 \(\pm\) 4.8 \\ SR (\%) & \(-\)0.6 \(\pm\) 8.7 & 4.7 \(\pm\) 5.5 & \(-\)0.5 \(\pm\) 5.8 \\ SL (\%) & 4.5 \(\pm\) 6.9 & \(-\)0.2 \(\pm\) 5.6 & 2.0 \(\pm\) 4.0 \\ SI (\%) & 8.0 \(\pm\) 8.3 & 4.0 \(\pm\) 7.6 & 3.4 \(\pm\) 7.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance time, stroke rate (SR), stroke length (SL), and stroke index (SI) percentage changes (\(\%\Delta\)) in the 4 \(\times\) 50 m sprints at post and prior to the 6-week training period. G-MS: group of maximum strength, G-ME: group of muscular endurance, and G-CON: control group.
# An Approach to the Dynamics and Control of Uncertain Robot Manipulators
## 3 Tracking Control of Robot Manipulators with Uncertain Dynamics  
We suppose this framework in the joint space of the robot. The task description is given by the end-effector of robot manipulators trajectory as constraint with \(h(q,t)=f(q(t))-\mathbf{x}_{d}(t)=\mathbf{x}(t)-\mathbf{x}_{d}(t)=0\), in which \(\mathbf{x}=f(q)\) denotes the forward kinematics [30]. We make use of the differential forward kinematics,  
\[\dot{\mathbf{x}}=J(q)\dot{q},\quad\ddot{\mathbf{x}}=J(q)\ddot{q}+\dot{J}\dot{q}, \tag{18}\]  
and we bring Equation (18) into Equation (8) with  
\[A(q,\dot{q},t)=J,\quad b(q,\dot{q},t)=\ddot{\mathbf{x}}_{d}-\dot{J}\dot{q}. \tag{19}\]  
We considered the uncertainties in the modeling of the moments of inertia of the robot manipulators by using the concept of a sliding surface [15; 16; 17]. The equation of motion of the controlled \"actual\" system is  
\[M_{a}\dot{q}_{c}=Q_{a}(q_{c},\dot{q}_{c},t)+Q^{c}(t)+Q^{u}(q_{c},\dot{q}_{c},t), \tag{20}\]  
in which \(q_{c}\) is the generalized coordinate vector of the controlled actual system; the subscript \(a\) denotes the actual, real-life system whose knowledge is uncertain, but bounded; the actual \"given\" force vector is \(Q_{a}\); \(Q^{c}\) is obtained from Equation (16) and \(Q^{u}\) is the additional control force vector that compensates for the actual system. The tracking errors (position and velocity) between the actual and the nominal system is  
\[e_{xa}(t)=\mathbf{x}_{a}(t)-\mathbf{x}_{d}(t),\quad\dot{e}_{xa}(t)=\dot{ \mathbf{x}}_{a}(t)-\dot{\mathbf{x}}_{d}(t). \tag{21}\]  
For a sliding surface defined as  
\[s(t)=\dot{e}_{xa}(t)+k\epsilon_{xa}(t), \tag{22}\]  
when the sliding surface \(s=0\), it tracks the desired trajectories of the manipulator system exactly, using a smooth function [15] (instead of the traditional signum function or saturation function), and we can only ensure that the actual system stays within a small region around the origin, \(s\in\Omega_{\epsilon}\). This region \(\Omega_{\epsilon}\) defined as  
\[\Omega_{\epsilon}:=\{s\in R^{n}\|s\|\leq\epsilon\} \tag{23}\]  
can be made arbitrarily small. The method requires the computation of the following estimates:\[(i)\lambda_{\min}:=\min(eigenvalues\quad of\quad AM_{a}^{-1}A^{+}), \tag{24}\] \[(ii)\beta\geq\frac{\|A\|\|\delta\dot{q}\|+(\|A\|+k\|A\|)\|\dot{e}_{a }\|}{\lambda_{min}},\quad\forall t>0.\]  
in which  
\[\delta\dot{q}=M_{a}^{-1}(Q_{a}+Q^{c})-M^{-1}(Q+Q^{c}). \tag{25}\]  
In the above relations, \(\|*\|\) means the \(L_{2}\) norm. The additional control force is given as  
\[Q^{u}=-\beta A^{+}(s/\epsilon), \tag{26}\]  
where \(\epsilon\) is a positive number, which can be chosen by the user. The tracking errors boundary is given by (for a Proof, see Ref. [15]):  
\[A(q,\dot{q},t)=J,\quad b(q,\dot{q},t)=\ddot{\mathbf{x}}_{d}-\dot{J}\dot{q}. \tag{19}\]0  
Proof Equation (26).
## 4 Experimental Validation
### Comparison of the Predictions with the Measurements  
The results of the first test, the constant-speed test, are shown in Table 1. This table contains, for the twelve tested speeds, the speed of the motor measured by the tachometer and the error in the speed estimation. The average error and the standard deviation error were less than 1 rpm, and their relative values were less than 0.01%.  
The three tests at constant-speed, slowly changing speed, and rapidly changing speed, performed between 2000 and 3000 rpm, showed an average error less than 1 rpm and a standard deviation error less than 1.5 rpm. These results indicate that the proposed method works correctly for constant speeds, slowly changing speeds, and rapidly changing speeds.
## 5 Existing Security Mechanisms
### Trust Management  
Trust signifies the mutual association of any two trustworthy nodes (_i.e._, sensor node and data aggregator node), that are sharing their information. In [89] trust is defined as \"the degree to which a node should be trustworthy, secure, or reliable during any interaction with the node\". Wireless healthcare applications depend on distributed cooperation among the network nodes. The key aspect of healthcare applications is a trust evaluation on the behavior of a node (_i.e._, data delivery and quality), so trust management systems are useful to detect the degree of trust of a node. [PERSON] [89], evaluated the trust for mobile healthcare system. However, trust management must still be implemented in real-time healthcare application using WMSNs, to ensure a clearer picture of trustworthiness of the parties involved (_i.e._, medical sensors, _etc._).
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 4 Methodology
### Feature Fusion Module  
To get the spatial and temporal features of the coarse trajectory, we use a feature fusion module to obtain the integrated representations of the data; the architecture of this module is presented in Figure 3.  
To fuse the time feature \(\mathbf{X}_{time}\in\mathbb{R}^{b\times c\times d_{1}}\) and the position features \(\mathbf{X}_{loc}\in\mathbb{R}^{b\times c\times d_{2}}\), the formulas of the fusion module are as follows.  
\[\mathbf{X}_{res}=\mathbf{W}_{2}\cdot(\text{Feedforward}(\mathbf{X}_{time}||\mathbf{X}_{loc}))+ \mathbf{b}_{2}, \tag{10}\]  
\[\mathbf{X}_{norm}=\text{LayerNorm}(\mathbf{X}_{res}), \tag{11}\]  
\[\mathbf{X}_{mid}=\text{Dropout}(\phi(\mathbf{X}_{norm})), \tag{12}\]  
\[\mathbf{X}_{fused}=\mathbf{X}_{res}+\text{Dropout}(\mathbf{W}_{3}\cdot\mathbf{X}_{mid}+\mathbf{b}_{ 3}). \tag{13}\]  
In these formulas, \(\mathbf{W}_{2},\mathbf{b}_{2},\mathbf{W}_{3},\mathbf{b}_{3}\) are all learnable parameters; we use LayerNorm to catch the local embedding of the spatial-temporal features, and \(\mathbf{X}_{res}\) is the original global features of the network. The above two parts can help smooth the training process, and we use the ResLayer to preserve information across layers. \(\phi\) is the activation function of the model. In this module, we use ReLU [40] as the activation function. This module will be used for the fusion of the features of the output of the graph embedding module and the fused features of the position encoder and time encoder.