esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
There is recent research that analyses myoelectric games to facilitate the transfer to a robotic prosthesis [16; 18]. However, we were not able to find the literature where myoelectric games are tested with a homogeneous group of patients with SCI with no limb amputation. The performance indexes obtained in this paper are dynamic values influenced by the user's feedback while operating the myoelectric platform. The user sees in real-time the direct effects of his or her myoelectric activity with a minimum of intermediary systems in the human-machine interface. Furthermore, the value of these indexes is their embedded biofeedback when compared with a posteriori analyses with no real-time myoelectric feedback.  
Patients showed fast adaptation to the _state navigator_ in the first trial. The improvement was less remarkable in further trials when compared with the CS, so both populations reached a similar performance. Results depended on the tool: Patients gained a faster adaptation to the _state navigator_ and CS perform better with a more dynamic tool like the _Myo-Pong_ game. Despite these differences, the range of timings and scores was not markedly dissimilar between patients and CS. For most of the users, myoelectric gaming was motivating, which is crucial to keeping a regular, yet occasionally burdensome rehabilitation routine. Hence, it can be a complementary aid during rehabilitation sessions or an amusing inclusion in the daily activities of the user with SCI.  
**Author Contributions:** This investigation was conducted by two teams: One from the UVA and one from the NHPT. The contributions to this research paper were: conceptualization, R.d.l.R. and A.C.; methodology, R.d.l.R., A.A. and A.G.-A.; software, R.d.l.R.; formal analysis, A.C.; investigation, A.C. and B.P.-M.; resources, A.G.-A. and A.C.; writing--original draft preparation, R.d.l.R. and A.C.; writing--review and editing, R.d.l.R., A.C., A.A., A.G.-A. and E.J.A.; visualization, A.C. and R.d.l.R.; supervision, A.A. and E.J.A.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 4 Proposed System
### System Workflow
#### 4.2.3 Face Decryption by Owner  
To initiate the face decryption process, [PERSON], the image owner, must provide both the image identifier (\(a\)) and her password (\(pwd\)) to the server, as illustrated in Figure 4. Although [PERSON] is authenticated with the server via a JSON Web Token (JWT) during registration or login, her password is required for each face decryption operation. This password is used to derive a decryption key, which is subsequently employed to decrypt [PERSON]'s private key stored on the server. This approach ensures that only [PERSON], as the sole holder of the password, can access her private key and decrypt the facial data.  
Upon receiving and validating the request, the server retrieves [PERSON] identifier \(ID_{alice}\), the encrypted image \(I_{a}^{enc}\), and associated face regions \(F_{a}\) from its storage and forwards them as a request to the CSP alongside [PERSON]'s password \(pwd\).  
Upon receiving the request from the server, the CSP then retrieves [PERSON]'s salt \(salt_{alice}\), encrypted [PERSON]'s private key \(sk_{alice}^{enc}\), and initialization vector \(IV_{a}\) from its storage. Using \(pwd\) and \(salt_{alice}\), the CSP calls function \(\mathbf{KDF}(pwd,salt_{alice})\) to derives a decryption key \(K_{\text{dec}}\), which is then used to decrypt \(sk_{alice}^{enc}\) using function \(\mathbf{D}\). This decrypted private key, \(sk_{alice}\), is then used to decrypt \(K_{a}^{enc}\), producing \(K_{a}\). With \(K_{a}\), \(IV_{a}\), and \(F_{a}\), the CSP decrypts face regions within \(I_{a}^{enc}\), producing the original image \(I\). The CSP then returns the processed image to [PERSON] through the server. This process ensures that only [PERSON], as the image owner, can access and view the protected facial data.
## 5 Simulation Results and Discussion
The median RSSs obtained using Equation (15) for the outlier-free observations generated using the MAD and \(S_{n}\) scale estimators are presented in Table 3 and compared with the median RSS of the raw RSS observations of each dataset. Figure 11: RSS Dataset 2 with and without the artificial outliers. (**a**) Without artificial outlier (**b**) With artificial outliers. Figure 12: RSS Dataset 4 with and without the artificial outliers. (**a**) Without artificial outlier (**b**) With artificial outliers. of a free-space propagation model with no environmental effect and using a Wi-Fi AP operating at a frequency of 2.4 GHz. A distance error of 2 m translates to a very large localization error. As such, when dealing with RSS time-series observations that are known to be asymmetric in nature, it is recommended to use the \(S_{n}\) scale estimator for outlier detection and the median approach to determine the aggregated RSS representative. One of the limitations of using the \(S_{n}\) scale estimator when compared to MAD is the computational time. Figure 15 shows a computational time comparison between the MAD and \(S_{n}\) scale estimators for varying observation sizes. The run time of the MAD is \(O(n)\), while that of the \(S_{n}\) scale estimator is \(O(nlogn)\), making it the slowest. The \(S_{n}\) scale estimator becomes exponentially slower as the number of RSS observations increases. For example, for an observation size of 32, the \(S_{n}\) scale estimator is approximately 3.5 times slower than the MAD. However, based on our previous analysis, the MAD has approximately 50% average false detection of an outlier. In summary, both estimators can be used to detect outliers in time-series RSS observations. However, if the observation is known to be skewed, that is, asymmetrical, or the presence of outliers is expected, then the \(S_{n}\) scale estimator may be a more appropriate choice. On the other hand, if computational efficiency is a concern, then MAD may be a better choice. However, if computational efficiency is not a concern and the observation is known to be skewed, a hybrid of the two techniques can be used, and the \(S_{n}\) scale estimator should be applied first, followed by the MAD scale estimator.
## 3 Results and Discussion
### Impact of Grazing Management on Vegetation Phenology  
Figure 8 shows that the EVI continued increasing (as high as 0.6) in P13 though the pasture was grazed during June-July in 2015 with stocking rate of 0.39 head-ha\({}^{-1}\) (average cattle weight of 480 kg). This increase most likely occurred due to new vegetative growth, facilitated by good amount of precipitation, was higher than the removal of biomass by cattle grazing. However, the EVI plateaued at around 0.4 or declined when the pasture was grazed from May to mid-June 2016 with a stocking rate of \(\sim\)0.7 head-ha\({}^{-1}\) (average cattle weight of 567 kg). This response indicated that new vegetation growth, hampered by poor precipitation, did not exceed the amount of biomass removed by grazing cattle when the stocking rate was substantially higher. The results indicate the confounding effects of several factors (i.e., cattle size, stocking rate, precipitation) on vegetation phenology.  
Figure 7: Seasonal dynamics of the enhanced vegetation index (EVI) for two pastures with different management (i.e., grazing vs. having). Pasture 15 was grazed and pasture 20 was hayed in early July.
## 8 Results and Discussion
### Example 3  
In Equation (1), we take  
\[\mathbf{A}=\left[\begin{array}{cccc}1&2&3&4&5\\ 2&3&4&5&1\\ 3&4&5&1&2\\ 4&5&1&2&3\\ 5&1&2&3&4\end{array}\right],\ \mathbf{x}=\left[\begin{array}{c}1\\ 1\\ 1\\ 1\\ 1\end{array}\right].\]  
\(\mathbf{A}\) is a symmetric positive definite matrix. When \(\mathbf{A}\) is a positive definite matrix,  
\[w_{\mathrm{opt}}=\frac{2}{1+\sqrt{1-\rho^{2}(\mathbf{I}_{n}-\mathbf{D}^{-1} \mathbf{A})}} \tag{53}\]  
is the optimal value for the SOR method [38], where \(\rho\) is the spectral radius of \(\mathbf{I}_{n}-\mathbf{D}^{-1}\mathbf{A}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AOR** & **AOR (op. _r_)** & **A1-AOR** & **Al-AOR (op. _r_)** & **ROR** & **Al-ROR (op. _r_)** \\ \hline NI & 96 & 62 & 47 & 45 & 46 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Example 2, NI for AOR and ROR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\boldsymbol{\alpha}\) & **1** & **1.2** & **1.3** & **1.4** & **1.5** & **1.6** \\ \hline NI & 62 & 51 & 49 & 48 & 46 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Example 2, comparing NI for A1-PROR with different values of \(\alpha\).  
We find that the spectral radius of \(\mathbf{I}_{n}-\mathbf{D}^{-1}\mathbf{A}\) is 2.5233, which according to Equation (53) means that the SOR method for this problem is divergent. Indeed for this problem other algorithms AOR, QAOR, ROR, POR, and PROR are divergent.  
We take \(\mathbf{x}_{0}=\mathbf{0}\). Under \(\varepsilon=10^{-5}\), Table 12 demonstrates the NI of the A2-SOR method for different values of \(\alpha\). \(w=0.20078\) is computed from Algorithm 3.  
Table 13 demonstrates the A2-PROR method for different values of \(\alpha\). \(r=0.865643\) is computed from Algorithm 6 with \(w=0.5\) and \(\sigma=0.5\).  
There exists an optimal value of \(\alpha\), for which NI is minimal. In general the relation between \(\alpha\) to NI is not simple.  
Table 14 demonstrates the A2-ROR method for different values of \(w\) but \(\alpha=1.3\) fixed. \(r\) is computed from Algorithm 6 at each iteration step.  
This problem shows that the factor \(\eta_{k}\) in the splitting iterative schemes can stabilize the original splitting iterative schemes, which are unstable for this problem.
## 3 Results
### GPP and Growth Relationships  
The LAI showed a phase of accelerated increase from sprouting to around 80 DDP, when the LAI maximums were reached. The maximum LAI in Irrigated (4.7) was 23.9% higher than in Rainfed (3.5). In Irrigated, the daily GPP had the same trend as LAI, increasing up to 115 kg C ha\({}^{-1}\) d\({}^{-1}\), when the LAI reached its maximum, then decreased to values around 70 kg C ha\({}^{-1}\) d\({}^{-1}\) by the end of tuber bulking stage. In Rainfed, the daily GPP had lower values compared to Rainfed, therefore, the daily GPP did not show the same trend as the LAI. On the day when the LAI was maximum, the daily GPP showed a maximum value of 38.7 kg C ha\({}^{-1}\) d\({}^{-1}\) (Figure 6).  
Figure 4: Half-hourly variation of gross primary production (GPP) of (**a**) Rainfed and (**b**) Irrigated potato crop in four different growth stages (sprouting, vegetative, tuberization, tuber bulking). Half-hourly averages are plotted. Vertical bars indicate the standard deviation.  
Figure 5: Half-hourly gross primary production (GPP) Versus incident PAR in four different growth stages (sprouting, vegetative, tuberization, tuber bulking) in (**a**) Rainfed and (**b**) Irrigated potato crop.  
In Irrigated, the plants reached an averaged maximum DW and TDW of 1103 g and 1004 g, respectively. AGR increased progressively until 114 DPP (when the crop reached 82% of the canopy life cycle) with a maximum of 20.95 g d\({}^{-1}\). From 115 to 138 DPP, the daily dry biomass accumulation rate decreased progressively until a minimum value of 0.05 g d\({}^{-1}\). In Rainfed, DW and TDW values were lower than in Irrigated. Averaged maximum DW and TDW were 256.8 g and 186.8 g, respectively. Maximum AGR (5.25 g d\({}^{-1}\)) was observed 72 DPP, when the crop reached 64% of the canopy life cycle. From 73 DPP to 113 DPP (the end of the canopy life cycle), AGR dropped to 1.94 g d\({}^{-1}\) (Figure 7).  
Higher leaf area durations (LAD) were observed in Irrigated, compared to Rainfed. In Irrigated, LAD was 1.3, 1.5, and 3 times LAD in Rainfed for vegetative, tuberization, and tuber bulking stages, respectively (Table 2).  
Figure 6: Gross primary productivity (GPP) and leaf area index (LAI) versus days post planting (DPP) in (**a**) Rainfed and (**b**) Irrigated potato crop.  
Figure 7: Dry weight (DW) and tuber dry weight (TDW) in (**a**) Rainfed and (**b**) Irrigated; absolute growth rate (AGR) in (**c**) Rainfed and (**d**) Irrigated.
## 2 Materials and Methods
### Evaluation of Bone Density  
Drilling was carried out in a 3-step sequence, with a difference of 1 mm in diameter, according to the manufacturer's instructions. Osstem tapered burs (TPD3C, OSSTEM IMPLANT Co. Ltd., Seoul, Korea) with a final 3.8 mm, 4.8 mm, and 5.8 mm major diameter and 10 mm length were used for CD of bone density test, and Versah universal tapered burs (VT, Versah LLC, Jackson, MI, USA) with a final 3.5 mm, 4.5 mm, and 5.5 mm major diameter and 10 mm length were used for OD. The bone density of the implant bed was scanned using a micro-positron emission tomography/computed tomography (CT) scanner (Quantum GX, PerkinElmer Inc., Waltham, MA, USA). Image scanning conditions were as follows: 90 kV voltage, 80 uA current, 45 mm field of view (FOV), 4 min exposure time, and 90 um voxel size. Scanned images were obtained using Quantum Image Viewer software (Quantum GX SimpleViewer, PerkinElmer Inc., Waltham, MA, USA). BMD of the samples was measured using biomedical imaging analysis software (Analyze 12.0, AnalyzeDirect Inc., Stilwell, KS, USA). The total mean BMD value of the sternum including the cortical and trabecular bone was 131.57 \(\pm\) 10.14 mg/cm\({}^{3}\) (Figure 1).  
Figure 1: Micro-positron emission tomography/computed tomography images of the sternum: (**A**) total bone; (**B**) trabecular bone.  
The difference in HU between the implant hole and peripheral bone was measured in each group of bone density tests and subdivided into coronal (1 mm distance from the upper surface), middle (5 mm distance), and apical (9 mm distance) areas. The region of interest was identified as the surrounding bone to a distance of 1 mm from the surface of the hole, and it was measured 10 times per group (Figure 2).
## 2 Materials and Methods
### The InSAR and UAV Collaborative Monitoring Method  
The D-InSAR technique is utilized to measure surface deformation by solving the phase, achieving monitoring accuracy at the millimeter level. Consequently, it is well suited for tracking points at the periphery of the subsidence basin. However, this method faces limitations when it comes to accurately monitoring regions with substantial deformation gradients at the center of the subsidence basin. In the relevant literature [34], it is pointed out that, when the deformation volume of two pixels exceeds half of the radar's wavelength, computation in the radar's LOS direction becomes unfeasible. The Sentinel-1A sensor, equipped with C-band, has a wavelength of approximately 0.05 m, resulting in a theoretical maximum detectable deformation of 0.025 m for image-pair interferograms. As such, the maximum deformation observed in the image-pair interferogram is approximately 0.025 m. In this study, a total of 35 interferometric image pairs were analyzed, resulting in a theoretical cumulative maximum detectable deformation of 0.875 m using D-InSAR. However, due to factors such as atmospheric delays and natural scatterer attenuation, the actual detectable deformation with D-InSAR is generally much smaller than the theoretical value. This study utilized Sentinel-1A imagery and the D-InSAR technique to monitor surface subsidence in mine subsidence basins. However, the D-InSAR technique's effectiveness is limited when the coherence factor falls below 0.3. The coherence coefficient demonstrates a notable decrease in proximity to the central area of the subsidence basin, as observed in the monitoring data. Based on the coherence values of interferometric image pairs, the D-InSAR monitoring results are valid if the cumulative subsidence at a monitoring point is less than 0.42 m. Conversely, for cumulative settlement exceeding 0.42 m at a monitoring point, the UAV technique was employed for monitoring. This study demonstrates the complementary advantages of both techniques. D-InSAR is suitable for monitoring the subsidence basin's edge area, while UAV oblique photogrammetry effectively monitors  
Figure 6: 3D model.  
Figure 7: Two\(-\)phase DSM data obtained from UAV: (**a**) 10 April 2021; (**b**) 28 June 2022.  
regions with significant gradient deformation at the basin's center. Through the integration of InSAR and UAV approaches, this study achieved comprehensive and synergistic monitoring of surface subsidence in the mining area.
# Multi-Means Comprehensive Forecasting Technology for Water Inrush in Long Tunnels
## 3 Comprehensive Geophysical Exploration Techniques
### Compared with Existing Methods  
Traditional geophysical approaches for tunnel hazard detection predominantly employ standalone techniques such as seismic reflection, ground penetrating radar, or transient electromagnetic surveys, each constrained by intrinsic limitations. For instance, while seismic reflection methods demonstrate exceptional capability in long-distance identification of large-scale geological interfaces, they exhibit limited resolution in delineating small-scale water-bearing structures or disentangling overlapping signals within heterogeneous media. Similarly, transient electromagnetics, though effective in mapping conductive water-rich zones at intermediate depths, suffer from reduced accuracy in environments plagued by electromagnetic interference (e.g., construction sites) or within highly conductive lithologies. Ground penetrating radar, despite its unparalleled precision in characterizing near-surface anomalies, is fundamentally restricted by its shallow penetration depth (<30 m), a critical drawback for deep-seated or extensive water innyh risk assessment.  
In contrast, our proposed multi-physics framework pioneers a hierarchical integration of seismic reflection waves, electroseismic coupling, transient electromagnetic method, and ground penetrating radar into a unified detection system. Unlike conventional single-method workflows that operate in isolation, this approach strategically synergizes the complementary strengths of each technique: seismic-electroseismic coupling enhances long-distance sensitivity to subtle fluid-rock interactions, the transient electromagnetic method provides robust medium-range conductivity mapping with adaptive noise suppression, and the ground penetrating radar delivers high-resolution near-field verification. By orchestrating these methods across spatial scales (long-medium-short), the system systematically eliminates the inherent \"blind zones\" of singular approaches--such as undetected small fractures by seismic methods or overlooked deep anomalies by ground penetrating radar--while enabling cross-validated, multi-dimensional risk characterization. This tiered integration not only addresses the resolution-depth trade-off but also establishes a dynamic detection continuum tailored to the evolving demands of tunnel excavation.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 3 Overview of MPTCP Congestion Control Algorithms
### Lia  
In the LIA algorithm [32], when an ACK is received for a subflow \(r\), in the congestion avoidance phase, the congestion window grows by the following equation.  
\[min(\beta B\frac{MSS_{r}}{Wnd_{sum}},B\frac{MSS_{r}}{Wnd_{r}}) \tag{2}\]  
where \(B\) is the number of the acknowledged bytes, \(MSS_{r}\) is the maximum segment size of subflow \(r\), \(Wnd_{r}\) is the congestion window of subflow \(r\), \(Wnd_{sum}\) is the sum of the congestion window of all subflows, and \(\beta\) is the aggressiveness parameter of the subflow given by  
\[\beta=Wnd_{sum}\frac{max(\frac{Wnd_{r}}{RTT_{r}^{2}})}{\sum\limits_{r=1}^{n} \frac{Wnd_{r}}{RTT_{r}^{2}}} \tag{3}\]  
where \(n\) is the number of subflows and \(RTT_{r}\) is the round trip time of the subflow \(r\). LIA meets the goals of improved throughput and friendliness. However, in order to achieve responsiveness, LIA departs from optimal load balancing.
## 3 Materials and Methods
### NN Architecture  
This study aims to identify the best MT output out of the two provided. Two linguists annotated the sentences with 1 if the NMT output is better than the SMT one and with 0 if the SMT output is better than the NMT. A low annotation percentage is observed for the SMT class (EL: 37% for _C1_, 48% for _C2_, IT: 43% for _C1_, 48% for _C2_) compared with the NMT class (EL: 63% for _C1_, 52% for _C2_, IT: 57% for _C1_, 52% for _C2_). A low annotation agreement rate is observed (_C1_: 5% for EN-EL/6% for EN-IT, _C2_: 3% for EN-EL/5% for EN-IT). For the few different answers, the annotators had a discussion and finally agreed on one common label. The NN model takes as input the tuple (_S1_, _S2_, _Sr_). These sentences are passed to the embedding layer. Two ways for extracting embeddings are applied (described in Section 3.3) producing _EmbS1_, _EmbS2_, _EmbSr_. The _EmbS1_, _EmbS2_, _EmbSr_ vectors are concatenated in a pairwise fashion as (_EmbS1_, _EmbS2_), (_EmbS1_, _EmbSr_), (_EmbS2_, _EmbSr_), and they form the input to the similarity-based hidden layers _h1z_, _htr_, _h2z_. As extra inputs, the hidden layers are fed with the matrices _H1z[i,j]_, _H1z[i,j]_, _H2[i,j]_ (where \(i\) is the number of sentences and \(j\) the number of features), containing the second category features (NLP set). The hidden layer outputs form the input to the output layer. Moreover, an extra input to the output layer is used: the matrix _A[i,j]_, containing the first category features (described in Section 3.2). The DL NN schema is shown in Figure 1.  
The binary classification problem is modeled as a Bernoulli distribution (Equation (3)):  
\[Y\sim Bernoulli(Y\setminus by), \tag{3}\]  
where _by_ is the sigmoid function \(\sigma(w^{T}x+b)\), \(w^{T}\) and \(b\) are the network's parameters.
## 3 Discussion
The characterization and classification of VOCs from Chinese herbal medicines by HS-GC-IMS coupled with an appropriate multivariate analysis has the potential to be used as a non-destructive way to evaluate Chinese herbal medicines from different origins. This gives us a new idea to distinguish Chinese herbal medicines in different regions. A number of studies have indicated that HS-GC-IMS has the capacity to confirm geographical and botanical origin [35]. For example, HS-GC-IMS was successfully used for reliable classification of geographical origins for both olive oil (EVOO) [38] and wine [39]. Furthermore, the HS-GC-IMS method was applied to quickly identify Ophiopogonis Radix from different regions [40]. Moreover, HS-GC-IMS has also been used to determine the geographical origins of \"Chenpi\" [41]. The compounds identified by HS-GC-IMS were subjected to multivariate analysis. A total of 14 volatiles that could explain the separation of SS samples into six regions were identified based on VIP scores and RF. Present observations showed that HS-GC-IMS and HS-SPME-GC-MS can better characterize volatile components in TCM. The application of HS-GC-IMS can better distinguish between different sources of SS and improve the effectiveness and efficiency of the process. HS-GC-IMS has good potential in identifying TCM from different regions. However,the HS-GC-IMS database is not perfect, so it is still limited in the comprehensive characterization and accurate quantitative analysis of samples. Therefore, a large number of experiments are still needed to enrich the database for the application of HS-GC-IMS in distinguishing TCM from different origins.