esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Ultrasound Texture Analysis to Assess the Spastic Muscles in Stroke Patients
## 2 Materials and Methods
### Image Processing  
The local binary pattern (LBP) operator is based on the local neighborhood pixel (\(P_{i}\)) by comparing the intensity difference between the gray value of the central pixel (\(P_{c}\)) from the gray values of its circular neighborhood of radius (R). The central pixel intensity is used to be the threshold value. The neighborhood pixel is allocated binary \"1\", if its intensity exceeds the threshold and binary \"0\", if it is lower. This comparison produces a binary sequence from the neighborhood pixels then a unique LBP value can be defined as follows (1): [21]  
\[\text{LBP}_{\text{value}}\ =\ \sum_{i=\ 0}^{7}(P_{i}-P_{c})\cdot 2^{i} \tag{1}\]  
The LBP operator converted each pixel into a new LBP value to form the LBP image. To obtain the multiscale analysis of the original image, the LBP image was constructed over three different scales, R = 1, 2, and 3 (Figure 2a). In this work, a statistical method was then used to extract the two useful features: entropy (ENT) and energy (ENG) from each LBP image. The formulas for computing ENT and ENG are as follows (2), (3):  
\[\text{Entropy (ENT)}=-\Sigma\ \text{(fi)}\ \times\text{log}_{2}\ \text{(fi)} \tag{2}\]  
\[\text{Energy (ENG)}=\Sigma\ \text{(fi)}^{2} \tag{3}\]  
where (fi) is the relative occurrence frequency of the histogram derived for LBP values of all image pixels [10]. The scheme of feature extraction is shown in Figure 2b. The ENT represents the randomness of the image, whereas the ENG is the uniformity of the image in the local region. Therefore, an inhomogeneous image will reveal higher entropy and lower energy or vice versa. In total, 6 LBP features (2 features \(\times\) 3 scales, namely ENT1, ENT2, ENT3, ENG1, ENG2, and ENG3) were generated per ROI. All these texture parameters were calculated by custom-written software in MATLAB (The MathWorks, Natick, MA, USA).  
Figure 1: Measurement of shear wave velocity (SWV) in the bilateral biceps brachii muscles (BBM) of a 76-year-old male at 1 month post-hemorrhagic stroke. SWV of paretic (P) BBM (2.37 m/s) was higher than that of non-paretic (NP) BBM (1.45 m/s) in the longitudinal plane.
## 4 Analytical Framework for Heterogeneous Service Provisioning
### Problem Overview and Healthcare Service Requirements
#### 4.1.8 Emergency-Aware Prioritization  
ML-RASPF supports service-level prioritization by adjusting the delay penalty coefficient \(\varphi_{s}\) in the utility model, enabling elevated prioritization for life-critical services such as ICU telemetry, cardiac alerts, and emergency broadcasts. During unexpected surges (e.g., mass casualty events), the RL agent adapts service delivery by assigning higher utility weights to delay-sensitive tasks and ensuring they are routed via low-latency paths. Additionally, integrating context-aware classification can help distinguish legitimate emergency traffic from anomalies or credential misuse by analyzing surge origin, timing, and service type.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
###### Abstract  
In this paper, a reversible data hiding method in encrypted image (RDHEI) is proposed. Prior to image encryption, the embeddable pixels are selected from an original image according to prediction errors due to adjacent pixels with strong correlation. Then the embeddable pixels and the other pixels are both rearranged and encrypted to generate an encrypted image. Secret bits are directly embedded into the multiple MSBs (most significant bit) of the embeddable pixels in the encrypted image to generate a marked encrypted image during the encoding phase. In the decoding phase, secret bits can be extracted from the multiple MSBs of the embeddable pixels in the marked encrypted image. Moreover, the original embeddable pixels are restored losslessly by using correlation of the adjacent pixels. Thus, a reconstructed image with high visual quality can be obtained only when the encryption key is available. Since exploiting multiple MSBs of the embeddable pixels, the proposed method can obtain a very large embedding capacity. Experimental results show that the proposed method is able to achieve an average embedding rate as large as 1.7215 bpp (bits per pixel) for the BOW-2 database.  
Keywords:reversible data hiding; high capacity; encrypted image; multi-MSB embedding strategy +
Footnote †: journal: Accepted: 15 March 2020; Published: 18 March 2020
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 4 Discussion  
Contributions of this paper are presented in this section. Existing load balancing schemes usually focus on the offloading algorithms, which are executed after overloading. Note that once overloading occurs, data rates of users decrease significantly. Therefore, we utilized FEM to learn and evaluate users' impacts on overloading in the future, and propose an active load balancing scheme to ensure data rates. Furthermore, the proposed FDDPG adds an adaptive neuro fuzzy network before the original DRL network. Compared with the widely utilized PPO and DDPG, FDDPG pre-filters the data relations that the DRL network needs to learn, thereby reducing the training difficulty of the DRL network and obtaining better training results. On the other hand, the proposed FL-OL makes FL-LB retain the ability of reactive load balancing on the basis of active load balancing. When the ANFN is trained, the complexity of calculating FEM is only related to the number of input  
Figure 11: The satisfied ratio with different joint schemes.  
Figure 10: The overload ratio with different joint schemes.  
variables and the number of membership functions in the ANFN. Therefore, the computational complexity of calculating FEM will not increase with increases in base stations and users. Compared with the existing method, FL-OL maintains the same complexity \(O(MN)\) while utilizing FEM to consider the impact of offloading selection on overload in the future and extending the next overload interval.  
However, FDDPG still needs more than \(10^{6}\) training steps even after pre-filtering by FEM. This is because the proposed scheme hopes to run on the central control cell and give the access decision for all of the users. This inevitably leads to a large state space and action space. On the other hand, compared with the existing algorithms that only focus on reactive load balancing, the FLRL-AC in FL-LB obviously increases the computational complexity, even though it is thought acceptable compared with the QoS gain. In order to solve these problems, multi-agent deep reinforcement learning (MADRL) based load balancing is a future research area. If FLRL-AC is distributed to each cell, the complexity of training will be greatly reduced. Additionally, in this case, since the decision dimension is the same, the access algorithm and the offloading algorithm can be more deeply combined so that to reduce the additional computing overhead brought by the access algorithm.
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
## 5 Training and Validation of the Neural Network  
For data-driven damage classification, a multi-layer ANN framework was established. The neural network consisted of input, hidden, and output layers. In this section, the effect of the number of hidden layers was investigated first. Then, the activation function effect between tanh and ReLU was analyzed. Lastly, strong noise was considered to verify the robustness of MLP for damage classification.
## 3 Full-Scale PCS Model Development and Validation
### Full-Scale PCS Model Validation  
Figure 10a presents the stress distribution derived from the FE simulation, while Figure 10b compares the stress measurements obtained from both the FE simulation and onsite experiments. The stress values at the measured points show strong consistency between the simulation and experimental results. Although the FE simulation generally predicts slightly lower stress values compared to the onsite measurements, the similar trends and closely aligned stress values validate the model's accuracy. It is important to note that variations in material properties and uncertainties in boundary conditions can impact both field test outcomes and numerical simulations [44; 45]. Future studies should incorporate statistical considerations to account for these variabilities, enhancing the reliability of the findings. Discrepancies between the test and simulation results may also be attributed to environmental factors, such as temperature, humidity, and wind, which can influence onsite tests. Incorporating probabilistic analyses in future work could help evaluate the effects of load variations and diverse environmental conditions on the structure by integrating long-term monitoring data, providing deeper insights into the fatigue and durability of prefabricated cantilever systems under real-world traffic conditions.  
Figure 9: Constructed PCS roadbed model: (**a**) beam–column dimension diagram; (**b**) full-scale model; (**c**) interaction details diagram; and (**d**) mesh size diagram and boundary conditions diagram.
## 4 Submitted Systems
### Speaker Diarization
#### 4.2.1 Open-Set Condition Systems
In relation to the diarization system, several systems based on four different types of embeddings extractors: x-vector-basic, x-vector-factored, i-vector-basic, and bottleneck features (BNF) i-vector were used. All the systems followed the structure: parameter extraction, voice activity detector, embeddings extraction, probabilistic linear discriminant analysis (PLDA) scoring, fusion and grouping of speakers.
## 4 Experimental Results and Analysis
### Comparison with Existing Methods
#### 4.3.2 Classification Results on the UP Dataset  
Using the UP dataset, 30 samples of data for each class of features were randomly used as the training set, and the rest of the samples were used as the test set. Accordingly, the classification results obtained via the nine dimensionality reduction algorithms are shown in Table 4. Figure 5 visualizes the classification feature maps obtained via these nine dimensionality reduction algorithms.  
As can be seen in Table 4, when using the UP dataset, the dimensionality reduction method based on the multi-manifold feature extraction strategy is more effective at classification than the traditional algorithm. Among these algorithms, the proposed SSAMMA  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **PCA** & **LPP** & **MFA** & **NSPE** & **MLE** & **MMDA** & **SSMRPE** & **SPCA** & **SSAMMA** \\ \hline C1 & 83.20 & 85.86 & 87.38 & 85.96 & 82.09 & 92.45 & 92.64 & 79.64 & 95.61 \\ C2 & 90.05 & 91.5  
achieves the highest classification accuracy, and the values of OA, AA, and Kappa are 96.53%, 94.12%, and 95.38%, respectively. Compared to SSMRPE, which is also based on spatial-spectral feature extraction, the proposed method improves the OA, AA, and Kappa by 4.18%, 2.93%, and 4.06%, respectively. Since the proposed method fuses the multi-manifold map of spatial and spectral dimensions, it can extract more discriminative features of each type of ground object for the spatial-spectral features of HSIs, thus achieving the highest classification accuracy after the dimensionality reduction. As can be seen in Figure 5, compared to other dimensionality reduction algorithms, the proposed method SSAMMA obtains a more detailed classification result map using the PU data set with the least number of misclassified samples. In contrast, the classification maps obtained via the two-dimensionality reduction algorithms, i.e., NSPE and MLE, have the most misclassified samples and a large amount of noise in the classification result maps. In addition, the classification result maps obtained via the dimensionality reduction algorithm, which consider the spatial-spectral features obtained using the PU dataset, have clearer classification boundaries, and the classification maps have the least noise.
# Evaluation of Structural Behavior of Hysteretic Steel Dampers under Cyclic Loading
## 5 FE Analysis
### Comparison of Experimental and Analytical Results  
Figure 11 shows the stress distribution of each damper by FE analysis when the target displacement was reached. In the figure, the red color refers to the plastic region after yielding, and the stress decreases as the color turns blue. From Figure 11 it can be seen that the region reaching yield stress in each damper was almost the same as it was in the experimental result in Section 3.3. In the case of SD1, the yield and energy dissipation zones were concentrated in the vertical members. The analysis confirmed that some yielding occurred in the semicircular connecting member adjacent to the vertical member, which was similar to the experimental results obtained from the horizontal member adjacent to the vertical member in SD2.  
As shown in Figure 11c, in SD3 with an additional horizontal member, the yield region was concentrated on the vertical and lower horizontal members, the same as it was in the experimental results. In particular, most of the area of the lower horizontal member reached yield stress. In addition, by adding the lower horizontal member, a larger area of the damper engaged in stress transmission and energy dissipation than did the areas of SD1 and SD2 damper.
# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 5 Experiments
### CrowdPose  
We demonstrated the proposed method has a state-of-the-art human pose estimation performance on the CrowdPose [38] dataset, which contained crowd scenes to make it more challenging. The training, validation, and testing subset contained 10K, 2K, and 8K images, respectively. The CrowdPose dataset also used the AP from the COCO dataset as an evaluation metric and split it into three crowding levels: easy, medium, hard. In this section, for metrics, we mainly use AP, AP\({}^{0.5}\), AP\({}^{0.75}\), AP\({}^{E}\) (for easy images), AP\({}^{M}\) (for medium images), and AP\({}^{H}\) (for hard images). We trained the models on the training and validation subsets and reported the results achieved on the testing subset. The experimental setup follows that of COCO exactly.  
The experimental results are shown in Table 4. Our method outperforms traditional top-down methods (Mask-RCNN and AlphaPose) and bottom-up method (CMU-Pose) by a large margin in terms of AP. SPPE is an efficient crowded scene pose estimation method which is a global refinement of AlphaPose; the performance of our method is comparable to AlphaPose without additional optimization. Multi-scale testing can improve the precision of predictions for small people, especially in crowd scenes. After multi-scale testing, HigherHRNet achieves the best performance on the CrowdPose dataset. While, without the optimization of multi-scale testing, the performance of our method is on par with HigherHRNet even the latter significant advantages in terms of the backbone used and the input size. The experimental results in Table 4 show the great potential of our method in complex environments and challenging scenes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AP** & **AP\({}^{0.5}\)** & **AP\({}^{0.75}\)** & **AP\({}^{\mathbf{M}}\)** & **AP\({}^{\mathbf{L}}\)** & **AR** \\ \hline PCP Network (TPR) & 0.648 & 0.854 & 0.715 & 0.603 & 0.700 & 0.697 \\ PCP Network (PPR) & **0.660** & **0.869** & **0.725** & **0.608** & **0.742** & **0.704** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study results: traditional pose representation (TPR) vs. proposed PPR on the COCO2017 val dataset. Superscripts M, L of AP stand for medium and large objects. The highest values are indicated in bold.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AP** & **AP\({}^{0.5}\)** & **AP\({}^{0.75}\)** & **AP\({}^{\mathbf{M}}\)** & **AP\({}^{\mathbf{L}}\)** & **AR** \\ \hline PCP Network & 0.657 & 0.867 & 0.722 & 0.607 & 0.728 & 0.701 \\ (original loss) & **0.660** & **0.869** & **0.725** & **0.608** & **0.742** & **0.704** \\ PCP Network (improved loss) & **0.660** & **0.869** & **0.725** & **0.608** & **0.742** & **0.704** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results: original \(l_{1}\) loss vs. improved \(l_{1}\) loss on the COCO2017 val dataset. Superscripts M, L of AP stand for medium and large objects. The highest values are indicated in bold.
## 2 Numerical Model
### Numerical Model
#### 2.2.2 DPM Model Theory  
The volume fraction of sand particles in the water body is very small, so the collision between particles is not considered in the analysis of sediment erosion. In the fluid, the force of particles can be expressed as [33; 34]:  
\[\frac{\text{d}u_{p}}{dt}=F_{D}\big{(}u-u_{p}\big{)}+\frac{g\big{(}\rho_{p}- \rho\big{)}}{\rho_{p}}+F_{y} \tag{4}\]  
\[F_{D}=\frac{18\mu C_{D}\text{Re}_{p}}{24\rho_{p}d^{2}{}_{p}} \tag{5}\]  
\[\text{Re}_{p}=\frac{\rho d_{p}|u-u_{p}|}{\mu} \tag{6}\]  
\[C_{D}=\alpha_{1}+\frac{\alpha_{2}}{\text{Re}_{p}}+\frac{\alpha_{3}}{\text{Re} _{p}} \tag{7}\]  
In the formula, \(u_{p}\) is the particle velocity, \(\rho\) is the fluid density, \(\rho_{p}\) is the particle density, and \(F_{y}\) is the force in the Y direction. \(C_{D}\) is the drag coefficient, and \(\text{Re}_{p}\) is the relative Reynolds number. \(d_{p}\) is particle diameter, \(u\) is the fluid velocity and \(\mu\) is the hydrodynamic viscosity; \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) are constants. The Erosion model agreed to the Erosion/Accretion model.  
The Erosion amount of this model is related to fluid velocity and particle parameters, and its Erosion model equation is [35]:  
\[\text{Re}=\sum_{p=1}^{N_{p}}\frac{m_{p}C\big{(}d_{p}\big{)}f(\alpha)u_{p}^{b( v)}}{A_{face}} \tag{8}\]  
In the formula, \(Re\) is the wall wear, \(N_{p}\) is the number of particles, \(m_{p}\) is the mass of particles, \(C(d_{p})\) is the particle diameter function, \(F\) (\(\alpha\)) is the invasion angle function, \(u_{p}\) is the velocity of particles relative to the wall, and \(b(v)\) is the relative velocity function. In general, the empirical constant is 2.41, and \(A_{face}\) is the wall area of the calculation region.
## 2 Materials and Methods
### Sample Preparation  
After optimization, the specific SBSE procedure employed was as follows: 10 mL of sample diluted to 7% (\(v/v\)) with ultrapure water was pipetted into a 50 mL Erlenmeyer flask closed with parafilm and spiked with 100 \(\mu\)L of a solution of thiophene (IS, 0.1043 g/L  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & **Molecular** & **Boiling** & **Melting** & **Partition** & \\  & **Weight** & **Temperature** & **Temperature** & **Coefficient** & **Aromatic Descriptor** \\  & **(g/mol)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(Log K\({}_{\text{ow}}\))** & \\ \hline Ethyl methyl sulphide (EMS) & 76.16 & 65.0–67.0 & \(-\)104.8 & 1.54 & cabbage, garlic, sulphur \\ Diethyl sulphide (DES) & 90.19 & 92.1 & \(-\)103.9 & 1.95 & garlic, ethereal \\ Thiophene (T) & 84.14 & 84 & \(-\)39.4 & 1.81 & benzene \\
1-Pentanethiol (P) & 104.22 & 126.6 & \(-\)75.69 & 2.74 & meaty, strong garlic \\ Dimethyl disulphide (DMDS) & 94.20 & 109.72 & \(-\)84.67 & 1.77 & garlic, onion, sulphurious \\ Dipropyl sulphide (DPS) & 118.24 & 142–143 & \(-\)102.5 & 2.84 & unpleasant \\ Diethyl disulphide (DEDS) & 122.3 & 152 & \(-\)101.5 & 2.41 & cabbage, carbide, roasted \\  & & & & & onion, rubber, sulphur \\ Dibutyl sulphide (DBS) & 146.3 & 182–189 & \(-\)80 & 4.50 & green, unpleasant \\ Dimethyl trisulphide (DMTs) & 126.3 & 165–175 & \(-\)85 & 1.87 & cabbage, fish, onion, sulphur \\ Dipropyl disulphide (DPDS) & 150.3 & 193–195 & \(-\)85.6 & 3.84 \({}^{\circ}\) & onion, garlic \\
2-Thiophenecarboxaldehyde (TC) & 112.15 & 198 & \(<\)10 \({}^{\circ}\)C & 1.56 & sulphurious \\ \hline \hline \end{tabular}  
* Data obtained from PubChem, [[https://pubchem.ncbi.nlm.nih.gov](https://pubchem.ncbi.nlm.nih.gov)]([https://pubchem.ncbi.nlm.nih.gov](https://pubchem.ncbi.nlm.nih.gov)), accessed on 10 January 2025. National Institutes of Health, National Center for Biotechnology Information, U.S. National Library of Medicine, USA.
* Estimated.  
\end{table}
Table 1: Some of the physical and chemical properties of the volatile sulphur compounds studied \({}^{1}\).  
in ethanol). NaCl at 10% (\(w/v\)) and 1% (\(w/v\)) ethylenediaminetetraacetic acid (EDTA) were also added. A 10 mm commercial PDMS stir bar (0.5 mm film thickness) was employed for the extraction. Each sample was agitated at 1100 rpm at a temperature of 35 \({}^{\circ}\)C for 47 min on a multi-position magnetic stirrer (Mulheim a/d Ruhr, North Rhine-Westphalia, Germany). After the extraction, the PDMS stir bar was removed, washed with distilled water, and gently dried. Then, it was transferred into a glass thermal desorption tube for thermal desorption.
# A P2P Scheme for Debating and Voting with Unconditional Flexibility
## 4 Analysis  
In this section, the security of this scheme is analyzed in the light of critical assumptions. The first of these is that the time required for adversaries to break asymmetric keys is longer than the time required to carry out the poll, and that in which any consequences to the vote owners might accrue. The assumption is based on using adequate key lengths regarding the computational capability of any adversary. The second assumption is that the voter group has fewer adversaries than honest members. The limit is explained below. The third is that devices are free of any malware. This can be achieved by following the appropriate security policies, such as using live CDs recorded with verified software, so that users start with a secure operative system (OS) and debate application. Hackers should be deterred by having to find vulnerabilities in a system tested by experts, and dedicated solely to the process of voting (with no additional services or ports open). Should vulnerabilities exist, the opportunity to exploit them should be limited to the short interval in which voting is carried out. The fourth assumption is that network services guarantee communications among users. Problems related to service failures are not considered in the protocol. Lastly, the selected anonymous channel does not provide any information about senders. Features of messages received (time, frequency, size, sequence, etc.) cannot attribute more probability to one user than another. As mentioned above (Section 2), there are different tools to avoid physical traceability whose strength is not considered in this paper. Users can choose these anonymous channels according to security level and speed.  
Figure 2: Phase 2: Users show their alias certificate \(AC_{i}\) to establish anonymous TLS channels (1). Then they can debate (2). When the time is over they sign their list of contributions (3). Signatures are checked to solve inconsistencies (4) and the tally is computed.