esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sensor Principle
### Response of Multiple Fiber Optic Sensor Probes
For investigating the multiplexing operation of two sensor probes, three different configurations are prepared with A-A, B-B, C-C type probes connected on one optical fiber line. The experiment is performed by dropping water on the vessel of sensor probe #1 at time intervals of 1 minute eight times and, after dropping water on the vessel of the probe #1, water continuously is dropped on the vessel of the probe #2 at the time interval of 1 minute eight times. The responses of A-A, B-B, and C-C probe multiplexing are shown in Figures 11-13 for the three probe types. Figure 11 shows the result of sensor output \(S\) for the multiplexing response of two A-type probes. As shown in the graph, the probe #1 starts to respond gradually from 3 minutes to 8 minutes. The response rate of the probe #1 is determined as 0.8 dB/min in the linear response range of the graph, then, the sensitivity of the probe #1 can be found as 0.267 dB/mL which is calculated by using 3 mL of water instead of 1 minute of time. After showing the response of the probe #1, water is dropped on the vessel of the probe #2 at the time of 8 minutes in \[P_{out}=C_{1}P_{in}\exp(-C_{2}\gamma s) \tag{5}\]0 The response of the probe #2 shows slightly bigger than that of the probe #1. In the meantime, the multiplexing response of two B-type probes is also shown in \[P_{out}=C_{1}P_{in}\exp(-C_{2}\gamma s) \tag{5}\]1 In this figure, the response rate of probe #1 is 0.7 dB/min, and then, the sensitivity is 0.233 dB/mL. The probe #2 responds slightly quicker than the probe #1, also the response of this probe is bigger than the probe #1 caused from the probe uncertainty. In Figure 13, there is an unexpected point between 10 and 12 min. We think that it comes from the uncertainty of the expansion time of WCS material. Finally, in Figure 14, the response rate of probe #1 is 0.2 dB/min, and the sensitivity is 0.067 dB/mL. However, the response rate of the probe #2 appears to be slightly quicker than that of the probe #1, which is caused by the uncertainty of probe #2. \[P_{out}=C_{1}P_{in}\exp(-C_{2}\gamma s) \tag{5}\]2 Sensor outputs according to injected water ratio of three types of probes. \[P_{out}=C_{1}P_{in}\exp(-C_{2}\gamma s) \tag{5}\]3 Sensor output of C-type as a function of water injection time. \[P_{out}=C_{1}P_{in}\exp(-C_{2}\gamma s) \tag{5}\]4 Sensor output of A-type probe as a function of water injection time. \[P_{out}=C_{1}P_{in}\exp(-C_{2}\gamma s) \tag{5}\]5 Sensor output of B-type as a function of water injection time. The sensitivity of the A type probe is almost same as that of the B type probe, however, the C type probe has the smallest sensitivity in this study. This work suggests the idea to develop a novel fiber optic sensor probe using two highly reflected connectors and a vessel of WCS in order to apply the sensors to detect some water leakage by managing the sensitivity of the probe by controlling the bending loss mechanism with WCS and the dynamic range of the probe by controlling the connector reflectivity.
# State of the Art: Bipedal Robots for Lower Limb Rehabilitation
## 2 Bipedal Locomotion  
### Hardware and Structure  
The bipedal locomotion of the robot imitates human walking, so the robot also mimics the human leg in the design of the mechanical structure, including the hip, knee, and ankle [50]. Figure 3a shows the classic lower limb structure with 12 degrees of freedom, where each leg has 6 degrees of freedom. Usually, the rotation pair (Figure 3b) will be used to simulate the joints of the human leg. The actuator is used instead of the muscle to drive the various joints. Currently, widely-used drives include motors, hydraulic pumps, and pneumatic muscles. A large number of sensors installed on the robot are equivalent to the human senses used to obtain their own state of motion and external environment information, for example, torque sensors, angle sensors, and contact switches [51]. This provides the basis for the control of the bipedal locomotion. This hardware provides the possibility for the bipedal robot to walk on the flat, slopes, or even up the stairs like a human. The imitation of human lower limb structure laid the foundation for the application of bipedal robot in rehabilitation.  
Figure 2: Popular bipedal robots at current stage. (**a**) HRP-4C used for entertainment; (**b**) SAFFiR used for firefighting; (**c**) Atlas walking on an uneven ground. ((**a**) was reproduced with permission from [36]; (**b**) was reproduced with permission from [48], Copyright Elsevier, 2015; (**c**) was reproduced with permission from [49], Copyright Springer, 2016.)
## 2 LiCSAR System Architecture
### LiCSAR Processing Chain
#### 2.1.1 Preparation of Frame Epoch SLC  
In the first step, we identify Sentinel-1 SLC data covering a given temporal epoch (a given date) and containing all or at least some bursts of the given frame definition. Frame burst data are extracted from related files available from some of the source data stores (see Section 2.5) and merged to form a frame epoch SLC. We perform several operations at this stage of processing. Firstly, we apply precise (21 days delay) or restituted orbit ephemerides data generated by the Sentinel-1 Quality Control Subsystem ([[https://qc.sentinel1.eo.esa.int](https://qc.sentinel1.eo.esa.int)]([https://qc.sentinel1.eo.esa.int](https://qc.sentinel1.eo.esa.int))) and downloaded daily from ASF DAAC. We perform a basic set of checks and corrections, such as padding of missing burst data with zeroes (in case of burst data unavailability for the given epoch), or correction of some known issues, e.g., an azimuth phase shift of -1.25 pixels in the first swath for older data (typically before April 2015) processed by Instrument Processing Facility (IPF) software version 2.36. Finally, we generate an MLI image using our default parameters, e.g., numbers of looks (4 for direction in azimuth and 20 in range), leading to a pixel spacing of \(56\times 46\) m (azimuth x range) in the LOS direction.
## 3 Results
### Traditional CT Models Based on Measured Samples and Validation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**Predicted**} \\ \cline{3-10}  & & & **OW** & **EV** & **SV** & **FV** & **CA (\%)** & **OA (\%)** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Traditional CT \\ model for July \\ \end{tabular} } & \multirow{4}{*}{Measured} & \multirow{4}{*}{\begin{tabular}{c} OV \\ \end{tabular} } & \multirow{4}{*}{17} & 0 & 0 & 0 & 100.00 & \multirow{4}{*}{82.14} \\  & & EV & 0 & 10 & 0 & 1 & 90.91 & \multirow{4}{*}{82.14} \\  & & SV & 6 & 0 & 38 & 5 & 77.55 & \\  & & FV & 3 & 0 & 5 & 27 & 77.14 & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Traditional CT \\ model for August \\ \end{tabular} } & \multirow{4}{*}{Measured} & \multirow{4}{*}{\begin{tabular}{c} OV \\ \end{tabular} } & \multirow{4}{*}{30} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{3} & \multirow{4}{*}{90.91} \\  & & EV & 0 & 9 & 0 & 2 & 81.82 & \\ \cline{1-1}  & & SV & 21 & 0 & 46 & 0 & 68.66 & \\ \cline{1-1}  & & FV & 2 & 0 & 1 & 29 & 90.63 & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Traditional CT \\ model for September \\ \end{tabular} } & \multirow{4}{*}{Measured} & \multirow{4}{*}{
\begin{tabular}{c} OV \\ \end{tabular} } & \multirow{4}{*}{40} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{100.00} & \multirow{4}{*}{84.36} \\  & & EV & 0 & 9 & 0 & 2 & 81.82 & \\ \cline{1-1}  & & SV & 21 & 0 & 75 & 2 & 76.53 & \\ \cline{1-1}  & & FV & 3 & 0 & 0 & 27 & 90.00 & \\ \hline \hline \end{tabular} Note: \(\mathrm{OW}=\mathrm{open}\) water; \(\mathrm{EV}=\mathrm{emerg}\) vegetation; \(\mathrm{SV}=\mathrm{sub}\mathrm{merged}\) vegetation; \(\mathrm{FV}=\mathrm{floating}\)-leaved vegetation; \(\mathrm{CA}=\mathrm{classification}\) accuracy; \(\mathrm{OA}=\mathrm{overall}\) accuracy.  
\end{table} 
Table 5: Confusion matrix for the traditional classification tree models developed from the three image dates.
# A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks
## 4 Experiments
### Real-Life Example
These FLC results validate the theoretical assumption and experiments conducted with synthetic datasets and show that the consistency-based special RNN transition formula shown in Equation (4) is applicable in a real-world scenario. Although the predictions thatare obtained from the ConsRNN-based NN architecture are a little off from the ground truth, it is certainly consistent based on the changing number of increments. The reason for the accuracy decrease is still a significant point that needs further investigation. Figure 9: Bilinear Forming Limit Curve (FLC) results. As the number of increments increases, predictions from the GRU-built NN architecture increasingly deviate from the actual FLC curve, in contrast to those from the ConsRNN-built architecture. **Stress-Strain Deviatoric Elastoplastic Behavior Estimation:** In the work of [PERSON] and [PERSON] [20], the authors generated a numerical stress-strain dataset using an elastoplastic model incorporating the von Mises yield criterion. They developed a linearized minimal state cell (LMSC) as a surrogate model in Finite Element simulations to capture the deviatoric response of elastoplastic behavior. Since our claim about the proposed RNN transition formula, ConsRNN, aims to provide consistent estimates across a wide range of engineering fields, we trained a ConsRNN-based NN architecture on the given dataset from the work of [PERSON] and [PERSON] [20], using 125 strain increments. We then tested the trained architecture's predictions on a larger number of increments, as shown in Figure 10. We compared the MSE results based on the changing number of increments in the validation and test sets. It can be seen from the MSE results that while the ConsRNN-based NN architecture remains stable regardless of the changing number of increments, the GRU-based architecture's error increases. Strain inputs with 2000 increments from the validation set and 5000 increments from the test set also validate that our approach provides consistent estimates. Although there is still some accuracy loss in strain prediction, this is mostly related to the engineering aspect. The theoretical formulation we are proposing captures consistent estimates due to its solid mathematical construction. However, for direct application in engineering contexts, the proposed approach or formula needs further development from the engineering side.
## 5 The Group-Aware Query Region Tree (GQR-Tree)
### GQR-Tree Manipulations
Then, SplitNonLeaf sets \(Conceptual\_Leaf\) stored in \(N\) for \(g\) to \(False\) (Line 2) and according to two cases, it proceeds as follows:  
* **Case (1):** If \(N\)'s children are non-leaf, SplitNonLeaf copies the tuple \(\langle g.bv,full\_q\_list\rangle\) maintained in \(N.full\_qid\_table\) and pastes it into \(full\_qid\_tables\) of \(N\)'s children. (Line 4). Next, given the tuple \(\langle g.bv,partial\_q\_list\rangle\) maintained in \(N.partial\_q\_table\), for each query \(q\) referred to by each query identifier contained in \(partial\_q\_q\_list\), SplitNonLeaf checks for each child node \(\hat{N}\) if \(q.R\) covers or equals \(\hat{N}\). If so, it inserts the query identifier of \(q\) into \(full\_qid\_list\) of the tuple \(\langle g.bv,full\_qid\_list\rangle\) maintained in \(\hat{N}.full\_qid\_table\) (Lines 5-9). On the other hand, if \(q.R\) is covered by or partially intersects \(\hat{N}\), SplitNonLeaf increases \(|g\_N|\) by \(1\), creates a new tuple \(\langle g.bv,partial\_q\_list\rangle_{new}\) for \(g\) in \(\hat{N}.partial\_q\_table\) (if it does not exist), and inserts the query identifier of \(q\) into \(partial\_q\_list\) (Lines 10-13). Then, SplitNonLeaf deletes the tuple \(\langle g.bv,partial\_q\_list\rangle\) from \(N.partial\_q\_table\) (Line 14). Finally, for each \(N\)'s child node \(\hat{N}\), SplitNonLeaf checks if \(|g\_N|\leq\theta\). If so, it sets \(Conceptual\_Leaf\) stored in \(\hat{N}\) for \(g\) to \(True\) (Lines 15-17). Otherwise, SplitNonLeaf invokes itself with \(\hat{N}\) and \(g.bv\) as an input (Lines 18-19). * **Case (2):** If \(N\)'s children are leaf, similarly to the case (1), SplitNonLeaf copies the tuple \(\langle g.bv,full\_qid\_list\rangle\) maintained in \(N.full\_qid\_table\) and and pastes it into \(full\_qid\_table\) of \(N\)'s children (Line 21). Next, given \(\langle g.bv,partial\_q\_list\rangle\) maintained in \(N.partial\_q\_table\), for each \(q\) referred to by each query identifier contained in \(partial\_q\_q\_list\), SplitNonLeafchecks for each child node \(\dot{N}\) if \(q.R\) covers or equals \(\dot{N}\). If so, it inserts the query identifier of \(q\) into \(full\_qid\_list\) of the tuple \(\langle g.bv,full\_qid\_list\rangle\) maintained in \(\dot{N}.full\_qid\_table\) (Lines 22-26). On the other hand, if \(q.R\) is covered by or partially intersects \(\dot{N}\), SplitNonLeaf increases \(|g\_\dot{N}|\) by \(1\), inserts a new tuple \(\langle g.bv,partial\_qid\_list\rangle_{new}\) for \(g\) into \(\dot{N}\) (if it does not exist), and inserts the query identifier of \(q\) into \(partial\_qid\_list\) (Lines 27-30). Then, SplitNonLeaf deletes the tuple \(\langle g.bv,partial\_qid\_list\rangle\) from \(N.partial\_qid\_table\) (Line 31). In case that each \(N\)'s child node \(\dot{N}\) overflows (_i.e._, \(|g\_\dot{N}|>\theta\)), SplitNonLeaf invokes SplitLeaf with \(\dot{N}\) and \(g.bv\) as an input (Lines 32-34).
# Universal Behavior of the Image Resolution for Different Scanning Trajectories
## 3 Results and Discussion
### Image Reconstruction
Similar results have been observed concerning the quality of images generated by confocal microscopes, where the pixel scanning duration or pixel dwell time primarily controls the signal-to-noise ratio rather than the frequency of scans. Thus, a scanning trajectory that produces a very dense and/or irregular pattern, such as the RL, may not be the best choice due to frequent but very short visits of the pixels, which has a detrimental impact on reconstruction outcomes. These findings seem to suggest that the scanning trajectory's density or pixel size is not the main factor that leads to an accurate reconstruction of the true image. The data clearly demonstrate the inverse correlation between similarity (PSNR and SSIM) and error (RMSE and NTE). The regularity and pixel size of the scanning pattern have a significant impact on the quality of the reconstructed image. Our findings imply that the scanning trajectory needs to generate a fairly regular pattern as well as a small pixel size in order to produce a high-quality reconstructed image.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
###### Abstract  
The integration of Internet Protocol version 6 over Low-Power Wireless Personal Area Networks (6 LoWPANs) provided IP technologies within wireless sensor networks that dramatically increased the Internet of Things (IoT). Therefore, to facilitate efficient mobility management for resource-constrained IP-based sensor nodes, the Proxy Mobile IPv6 (PMIPv6) standard has been introduced to reduce communication overhead. However, the standard has addressed security and mobility authentication challenges in 6 LoWPANs, although recent solutions have yet to focus much on facilitating secure group handovers. Considering these issues, a Secure and Lightweight Group Mobility Authentication Scheme (SL_GAS) is proposed for 6 LoWPAN's highly constrained sensor nodes. SL_GAS innovatively utilizes one-time alias identities, temporary IDs, tickets, and an aggregated MAC with tags to ensure mutual authentication while maintaining sensor anonymity, providing a balanced security and privacy approach. SL_GAS's robustness against a variety of security threats is validated through formal automated verification using the Scyther tool alongside SVO logic, while an informal analysis demonstrates its resilience to known attacks. Comparative analysis with existing schemes highlights SL_GAS's advantages in reducing signal cost, transmission delay, communication, and computation overhead. SL_GAS stands out for its combination of security, privacy, and efficiency, making it a promising approach for enhancing IoT connectivity in resource-constrained settings.  
PMIPV6; group handover; secure authentication; 6 LoWPAN IEEEexample:BSTcontrol  
31 January 2025  
Revised: 24 February 2025  
Accepted: 25 February 2025  
Published: 27 February 2025  
**Citation:** [PERSON],[PERSON]; [PERSON]; [PERSON] Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks. _Sensors_**2025**, 25, 1458  
[[https://doi.org/10.3390/s25051458](https://doi.org/10.3390/s25051458)]([https://doi.org/10.3390/s25051458](https://doi.org/10.3390/s25051458))
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 4 Machine-Learning-Based Models  
This section presents various ML-based models for predicting Path Loss for future indoor wireless communications. Eight prediction models are adopted in this work, namely MLR, PR, DT, RF, SVR, KNN, ANN, and RNN-LSTM. The following subsection will represent these models in detail.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 1 Introduction
### Packet-Switching
#### 1.2.3 Wormhole  
This method extends the idea of cut-through routing by introducing the concept of virtual channels (VC). It is based on the idea of splitting a packet into smaller pieces called flits (flow control digits). The first flit of a packet (the header flit) contains the data needed for routing with or without a part of real data. It can be followed by 0, 1 or more than 1 data flits. Several VCs can be associated with 1 physical channel. Each of these VCs is dedicated to the transmission of a unique packet.  
The difference between flits and packets is that two packets with the same source and destination may go through different paths and may not be received in the same order as they have been sent. However, two flits of a packet always pass through the same path and in the exact order as they have been sent. Also, a packet, unlike a non-header flit, has everything needed for routing. It is the VC mechanism that handles the routing for header-less flits. On reception of a header flit in an intermediate node, proper network resources are allocated before relaying. The same resources are used for the rest of the flits of that packet.  
This is the algorithm of choice for many modern HPC systems like Cray's 3D torus Gemini, Dragonfly-based Aries, and Flattened Butterfly technologies. It reduces the overall software latency and increasing the throughput. Cut-through and wormhole methods try to decouple the relation between the overall transmission latency and the number of hops.
## 1 Introduction
[PERSON] [12], through numerical simulation, clarified the positive impacts of isolation walls and in-pit reinforcement in controlling the deformation of foundation pits in split-pit excavation schemes. They concluded that the split-pit excavation scheme effectively mitigates the impact of the simultaneous construction of adjacent deep and large foundation pits. [PERSON] et al. [13] employed numerical methods to investigate the influence of different arrangements of double foundation pit excavation on existing tunnels. They determined that in vertical arrangements, the impact of the far tunnel pit on tunnel displacement and deformation is minimal, while the overall horizontal displacement is influenced. Notably, the excavation of the near tunnel pit plays a decisive role in tunnel deformation. [PERSON] et al. [14] observed that after pit excavation, the excavated foundation pit tends to move as a whole, with the soil between the two foundation pits exhibiting a tendency to extrude toward the middle of the pit. While previous studies have offered valuable insights into the impact of foundation pit excavation on existing lines, several issues warrant further exploration in the current discourse. Presently, numerous studies focus on conventional single pit excavation, with limited attention given to double pit or pit group excavation. Notably, there is a dearth of cases examining double pit excavation in the form of heterogeneous pits connected to circular end shafts. Moreover, existing research on double pit cases often involves pits distributed on both sides of the tunnel, with a tendency to concentrate on the mutual influence of these pits. Unfortunately, there is a notable gap in discussions regarding the excavation sequence of double pits on existing subway tunnels, resulting in an elusive understanding of the underlying patterns. In this study, we draw upon the foundation pit project of the Beijing Urban Subcenter Comprehensive Transportation Hub. Specifically, we employ the Plaxis3D numerical simulation method to investigate the deformation response of adjacent existing subway tunnels in the circumferential direction, longitudinal direction, and tunnel interval. This exploration is conducted under three distinct excavation sequences for double pits: sequential, synchronous, and comprehensive excavation. The objective is to unveil the patterns governing the displacement changes within the tunnel under varying working conditions. The findings elucidate the systematic variations in tunnel displacement under these different scenarios.
## 4 Experiment and Result Analysis
### Ablation Experiment Results and Analysis
#### 4.4.3 Multi-Head Self-Attention Ablation Experiment  
Existing models use a combination of CNN and RNN to make full use of the local and global semantic information of text, but RNN has a high time cost due to the problem of its model structure. We use a combination of CNN and the multi-head self-attention mechanism to extract local and global semantic information of text, shortening the training time of the model. To verify the effect of the multi-head attention mechanism and RNN on the time cost of the model, we conducted four sets of ablation experiments using four common RNN models, GRU, LSTM, Bi-GRU, and Bi-LSTM. All model parameters were kept constant except for the change to the multi-head self-attention part of the models. Accuracy, F1 value, time used to train a single epoch of the model, and the number of epochs used for model convergence were used as evaluation metrics.  
As shown in Table 8, the time used to train a single epoch and the number of epochs used for model convergence for the models in ablation experiments 1-4 are higher than those of the DCCMM model in this paper, but their model performance is slightly lower than that of the DCCMM model. It can be concluded that the extraction of global semantic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & \multicolumn{2}{c}{**Weibo\_Senti\_100k**} & \multicolumn{2}{c}{**SMP2020-EWECT**} \\ \cline{2-5}  & **ACC (\%)** & **F1 (\%)** & **ACC (\%)** & **F1 (\%)** \\ \hline Ablation experiment 1 & 97.41 & 97.31 & 78.88 & 74.12 \\ Ablation experiment 2 & 97.48 & 97.48 & 78.71 & 73.77 \\ Ablation experiment 3 & 97.56 & 97.56 & 79.03 & 74.53 \\
**DCCMM** & **98.49** & **98.52** & **80.07** & **75.49** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experimental model performance comparison.  
information using the multi-head self-attention mechanism takes less time compared to the RNN model, without degradation in the model performance.