esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result Analysis
### Uncertainty Analysis  
According to Equation (7), the uncertainty of yaw angle can be expressed as  
\[\begin{array}{l  
optics design and APD-based angle measurement, the environmental disturbance and the rotation error in the displacement measurement were minimized. Three experiments for feasibility verification were carried out. In the differential displacement stability experiment, the displacement drift was only 16.3 nm during 100 min using DPMI. In the rotational angle comparison experiment, the standard deviations of pitch and yaw between DPMI and Renishaw interferometer were 1.68 \(\times\) 10\({}^{-5\circ}\) and 1.86 \(\times\) 10\({}^{-5\circ}\), respectively. In comparison with our previously obtained standard deviations of 6.91 \(\times\) 10\({}^{-5\circ}\) and 9.349 \(\times\) 10\({}^{-5\circ}\) using the DWS technology, standard deviations in this study were reduced to about one-third of the originals. In the differential displacement measurement with rotational angle compensation, after compensation, the maximum deviation between the measurement result of DPMI and the stage positioning was reduced from 5.207 \(\upmu\)m to about 0.719 \(\upmu\)m, and the standard deviation was reduced from 1.214 \(\upmu\)m to 0.263 \(\upmu\)m. This result is in good agreement with the positioning accuracy of the linear stage. These experimental results verify the feasibility and effectiveness of the proposed interferometer and demonstrate that it can be applied in the field of precision displacement measurement.  
Conceptualization, methodology, Writing--review and editing, L.Y.; investigation, validation, formal analysis, Y.Y.; supervision, project administration, funding acquisition, data curation, B.C.; software, investigation, writing--original draft preparation, Y.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Lower-Body Positive Pressure Treadmill Training for Pediatric Gait Disorders: A Scoping Review
## 3 Results
### Effect of Anti-Gravity Treadmill Training on Balance  
Several studies explored the effects of anti-gravity training on balance control in children with locomotor impairments [11; 18; 19; 21; 22; 23; 24; 25; 27; 29; 30; 31]. The findings of those studies are summarized in Table 2. Balance assessments included center-of-pressure (CoP) measurements (i.e., CoP sway, CoP velocity, etc.) during static (standing) [21; 29] and dynamic balance assessment using center-of-mass and center-of-pressure displacements [18; 19; 23; 25], as well as clinical tests, such as the BESTest, the Berg Balance Score (BBS) or Timed Up and Go (TUG) test [22; 24; 25; 27; 30; 31]. All included studies reported improvements in balance following anti-gravity training. Moreover, six studies highlighted a greater improvement in balance compared to conventional therapies [11; 18; 19; 21; 22; 23].
# Decision Support for Removing Fractured Endodontic Instruments: A Patient-Specific Approach
## 2 Case Report
### Virtual Treatment Planning  
The different anatomical structures were segmented using DESK, an application suited for medical images [20]. The semi-automatic segmentation is based on the attribution of pixel labels, \"seeds\", inside each anatomical structure and a growing region algorithm. Four labels were generated according to the structures of \"air\", \"tooth\", \"bone\", and \"intra-root canal material\" to produce a multi-label 3D image. This initial 3D image was then modified to simulate the procedures of the different removal strategies.  
Five clinical situations were considered by the healthcare team: the initial state of the patient, two simulated non-surgical removal strategies using a low or high root canal enlargement, and two simulated surgical removal strategies using a 3 or 6 mm apicoectomy.  
An ultrasonic tip (ET25; Satelec, Bordeaux, France) was modeled by a conical cylinder 0.5 mm in diameter and a 4% taper and recorded under Standard Tessellation Language (STL) format for VTP of non-surgical approaches. The surface of the tip was then superimposed along one-third of the instrument either on the distal side of the instrument to simulate a low root canal enlargement or on the distal and vestibular sides of the instrument to simulate a high root canal enlargement. VTP of surgical approaches was conducted with a 3 or 6 mm root shortening (Figure 2a).  
The different virtual removal strategies were analyzed on the 3D modified image. The latter offers the operator the possibility to add or suppress masks of bone, ultrasonic tip or instrument to plan his procedure. For non-surgical strategies, the high enlargement was associated with a long perforation. VTP of surgical strategies was also informative for the reduction of the crown-root ratio (Figure 2a). The 3D modified image could also be used to simulate the clinical point of view of the dental practitioner. For non-surgical strategies, the location of the instrument and the long perforation were difficult to perceive on the simulated clinical view. The clinical view of surgical strategies also enables planning possible access ways that avoid sinus perforation (Figure 2b).  
Figure 1: The process for a patient-specific biomechanical analysis and detailed steps for virtual treatment planning and finite element analysis: (**a**) radiograph of the initial situation presenting a fractured instrument, (**b**) cone beam computed tomography axial view with temporary intracanal medication, (**c**) segmentation based on a growing region algorithm, (**d**) transformation of the initial 3D image to simulate a 3 mm apicoectomy, (**e**) analysis of the 3D simulated treatment, and (**f**) meshing of the 3D transformed image to get a finite element model and application of boundary conditions.
# Experimental Study on the Straw Flexible Space Assembly Flood Control System
## 3 Proposal and Validation of the Straw Flexible Space Theory
### Force Analysis of a Straw Flexible Baffle under the Uniform Water Flow  
When flooding occurs, the flow force of the flood water on the water baffles is calculated as follows [28]:  
\[F_{w}=C_{w}\frac{\rho}{2}v^{2}A \tag{1}\]  
where \(F_{W}\) is the water flow force, \(A\) is the area of the baffle board, \(V\) is the design flow velocity, \(C_{W}\) is the water flow resistance coefficient, and \(\rho\) is the density of water.  
Figure 2: ABAQUS finite element model for the flood control system. (**a**) Main view. (**b**) Top view.  
The hydrostatic pressure on the water baffle is given by  
\[S_{0}=P_{c}A \tag{2}\]  
where \(P_{c}\) is the pressure at the center of the water baffles shape, \(P_{c}=\rho gh_{c}\), \(g\) is gravitational acceleration, and \(hc\) is the depth below the liquid surface at the center of the water baffles shape.  
The support force of the straw on the baffle under hydrostatic conditions is given by  
\[F=\sigma A_{1} \tag{3}\]  
where \(\sigma\) is the compressive strength of the straw material and \(A_{1}\) is the cross-sectional area of the straw between the water-bearing and backing plates.  
The support force of the straw on the baffle at a constant flow velocity together with the water flow force and the water pressure form a pair of flat constant forces, whose relationship is expressed as  
\[F=F_{w}+s_{0} \tag{4}\]  
When the oncoming board is subjected to wave impact loads due to the deformation of the straw, the support force of the straw on the oncoming board increases and it will exceed the hydrostatic pressure of the floodwater on the oncoming board after the wave, thus pushing the oncoming board to its original position. This cycle is repeated. Between the oncoming board and the backing board, an impact load is formed and it can be deformed, and after unloading, it can be restored to the flexible space. Thus, the straw flexible space theory satisfies the formula derived.
## 3 Results
### Spine Dataset Results  
As the benchmark comparison of tested architectures in 2D vs. 3D versions is done on brain dataset, we trained and evaluated only 3D versions of the networks. A comparison of results achieved during benchmark phase using all three architectures can be seen in Table 6 and visualisation of the segmented spine models can be seen in Figure 9. This dataset does not contain second set of labels done by a human expert and therefore the results cannot be compared with human precision.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Dense-U-Net** & **Res-U-Net** & **U-Net** \\ \hline P.A. & 0.99732 & 0.99727 & 0.99721 \\ Dice c. & 0.96784 & 0.96733 & 0.96635 \\ Lo.U. & 0.93770 & 0.93672 & 0.93490 \\ A.H.D. [voxel] & 0.21982 & 0.08754 & 0.09226 \\ A.u.R.C. & 0.98589 & 0.98539 & 0.98262 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of tested 3D U-Net versions on spine dataset in benchmark training phase. The training was limited by 50 epochs for each. Used metrics for evaluation are: pixel accuracy, Dice coefficient, intersection over union, average Hausdorff distance [voxel] and area under ROC curve.  
Figure 8: Comparison of ground truth brain model (**left**) and brain model segmented by Dense-U-Net (**right**) after the final phase of training.  
Figure 9: Visualisation of segmented spine models from the benchmark training phase. From left to right—Dense-U-Net, Residual-U-Net, U-Net. Please notice that the abnormal vertebrae adhesions exist also in original ground truth masks as can be seen in Figure 10.  
Dense-U-Net network has achieved the highest results in all metrics except for average Hausdorff distance. Reason for this is that the model does not perform well on borders of the image. In the dataset there were labelled only thoracic and lumbar vertebrae, but the CT scans contained also the first cervical vertebra and the network did include it in its segmentation results. You can see a part of the first cervical vertebra on top of the segmented spine in Figure 10.  
After accuracy of Dense-U-Net was verified to outperform the other architectures in the benchmark phase, we trained the network in the fine-tuning phase to achieve the best results possible, now using 99 epochs with weights initialized using pre-trained model. 3-folds cross-validation was used for evaluation. Results of the Dense-U-Net network in fine-tuning phase is depicted in Table 7. The visualisation of fine-tuned Dense-U-Net network result can be seen in Figure 10. Please notice that the abnormal vertebrae adhesions exist also in ground truth masks on the model in Figure 10 and therefore it is not a failure of the segmentation algorithm.  
Results on testing data which the network had not seen during the training process show, that the algorithm is capable of precise segmentation of human spine on CT images. The network has achieved
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 4 Experimental Results
### Convergence Analysis  
To provide an insight into the training behavior of the proposed CS-SISR model, we present convergence graphs illustrating the training loss and validation PSNR across epochs. These graphs demonstrate the stability and efficiency of the training process, as well as the consistent performance improvement over the course of training.  
* Training Loss: As shown in Figure 16, the training loss steadily decreases, reflecting the model's effective optimization. The use of the L1 loss function contributes to the preservation of fine details while mitigating over-smoothing.
* Validation PSNR: Figure 16 shows the validation PSNR values improving consistently across epochs, highlighting the model's ability to generalize effectively to unseen data. This result further validates the robustness of the proposed approach.  
By comparing these metrics with other baseline models, it is evident that the CS-SISR model achieves superior convergence properties, balancing computational efficiency and high performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Scale**} & **Bicubic** & **VDSR** & **EDSR** & **Proposed Method** \\  & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline \multirow{3}{*}{ISIC} & \(\times\)2 & 40.9848/0.9956 & 42.1513/0.9956 & 43.7888/0.9983 & 44.0720/0.9983 \\  & \(\times\)3 & 38.7052/0.9870 & 39.9154/0.9892 & 41.4544/0.9922 & 41.4387/0.9932 \\ \cline{1-1}  & \(\times\)4 & 35.2591/0.9823 & 37.0129/0.9866 & 39.5213/0.9887 & 39.7872/0.9907 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average PSNR/SSIM for scale factors \(\times\)2, \(\times\)3, and \(\times\)4 (the boldened values indicate the best performance).  
Figure 16: Training loss and validation PSNR comparison across models and scales.
# Protecting Private Communications in Cyber-Physical Systems through Physical Unclonable Functions
## 3 Proposed Encryption Scheme
### Encryption Mechanism
The proposed key generator in the next subsection employs a lightweight PRNG connected to a PUF in order to create pseudorandom sequences with a very large period, including also a dynamic mechanism to dynamically modify this sequence in a secret manner. This encryption scheme also enables us to easily calculate the synchronization signal through the proposed correlation algorithm. As key streams have a random behavior, their correlation with any  
Figure 2: Encryption scheme. other signal tends to be negligible (8). To prove that, we are obtaining a correlation estimator \(c^{*}[r]\) between the receiver key stream \(k^{*}[n]\) and the encrypted signal \(e[n]\). \[ \begin{array}{l}c^{*}[r]=\sum\limits_{n=0}^{N-1-r}e[n+r]\cdot k^{*}[n]=\sum \limits_{n=0}^{N-1-r}(m[n+r]\ \oplus\ k[n+r])\cdot k^{*}[n]=\\ =\sum\limits_{n=0}^{N-1-r}(m[n+r]\cdot k^{*}[n])\oplus\ (k[n+r]\cdot k^{*}[n])\approx\sum \limits_{n=0}^{N-1-r}k[n+r]\cdot k^{*}[n]=c[r]\end{array}  \tag{8}\]
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 5 Simulation and Evaluation of DEC and DEC-KM Protocols  
In this chapter, simulations conducted for the DEC and the modified DEC-KM protocol with K-means have been presented and compared. Both simulations were performed for the same parameters, as shown in Table 1.
## 3 Integrated Disaster Resource Scheduling Modeling
### User Satisfaction-Oriented Dynamic Scheduling Model  
#### 3.2.1 Spatiotemporal Scheduling Scheme  
In the integrated power grid and transportation network system, the scheduling of mobile energy storage devices is determined by both their charge/discharge states and transportation states, exhibiting strong spatiotemporal coupling characteristics. Under disaster scenarios, power grid failures and road blockages significantly hinder traditional static resource scheduling, making it difficult to respond effectively to sudden disruptions. To address this challenge, this paper proposes a spatiotemporal dynamic scheduling model that comprehensively considers the transportation time \(T_{\text{new},j,m,n}(t)\) between node \(m\) and node \(n\), the real-time traffic conditions, and the installation and configuration time of the equipment \(T_{\text{place},i}\). By incorporating these factors, the model enables the optimization of route selection and dispatch strategies, ensuring efficient and timely delivery of resources to affected areas during disasters. This enhances the efficiency of transportation path selection and energy supply responsiveness, ensuring that resources are deployed rapidly and accurately to the disaster-stricken regions.  
The results of the proposed spatiotemporal scheduling model are visualized in Figure 4, which demonstrates the optimal spatial deployment and coordinated routing of mobile water/energy storage vehicles, charging stations, and reservoirs throughout the community network under ice disaster conditions. Each community zone is equipped with strategically sited charging stations (black icons) and reservoirs (blue icons), while mobile resource vehicles (green icons) are flexibly dispatched across nodes based on real-time network conditions and demand. This dynamic zone-aware scheduling approach enables balanced and efficient distribution of electricity and water resources even in the presence of extensive transmission line damage, thereby greatly enhancing the self-sufficiency and resilience of community energy systems during disasters.  
To achieve this, our study incorporates two different optimization strategies, described below.  
Path Planning and Dynamic Dispatch OptimizationBecause ice disasters may cause road closures or traffic restrictions, route selection for mobile energy storage devices must be dynamically adjusted in real time based on changing traffic conditions to ensure that vehicles can select the optimal path and quickly deliver resources to the target communities.  
Multi-Energy Coordination and Priority-Based SupplyDuring disasters, different communities exhibit varying demands for electricity, thermal energy, and water resources; therefore, we introduce a multi-energy coordination strategy to ensure that critical nodes receive priority in resource dispatch. Special emphasis is placed on essential facilities such as hospitals and fire stations to ensure their operational continuity. This prioritization improves the overall fairness and effectiveness of resource allocation.
## 4 Results and Discussion
### Case 3: Minimization of Voltage Drops  
In this scenario, the proposed algorithms are intended to reduce transmission voltage drop at each bus, hence Equation (23) is chosen as the objective function for the OPF algorithm. Table 8 illustrates the optimal control parameter values for ESDO and how they compare to other approaches. The findings reveal that the recommended strategies work better in terms of achieving the lowest voltage drop, demonstrating more optimization capabilities in this specific scenario. Figure 8 shows the convergence curve for Case 3. PSO, MVO, and HO all converge to stable voltage drops of approximately 0.20 p.u. within the first few iterations, showing fast optimization. HHO has similarly shown a gradual decrease, with a drop at the 40 th iteration and stabilization slightly around 0.214 p.u. ESDO maintains a large voltage drop initially but lowers rapidly after the 35 th iteration, finally stabilizing at approximately 0.146 p.u., indicating improved voltage stability. GOA converges slowly and stabilizes at about 0.3 p.u. Overall, ESDO and MVO have the highest ultimate voltage stability and so are the most successful algorithms in this context. Figure 9 is a bar chart of voltage drop in p.u. for all algorithms including ESDO beta dog. It can be depicted from the figure that the best performance is obtained by ESDO Alpha Dog with a voltage drop of 0.1466 p.u., thus showing very good voltage stability. Also, the PSO and ESDO Beta Dog performances are also outstanding, with voltage drops of 0.1834 p.u. and 0.1831 p.u., respectively. In contrast, MVO, HHO, and HO perform moderately, with their ranges of voltage drop being from 0.2127 p.u. down to 0.2146 p.u. GOA seems to be the worst performer in this regard, yielding a voltage drop of 0.3048 p.u. That is to say, ESDO Alpha Dog attained the best values to minimize voltage drop in this scenario, followed by ESDO Beta Dog and PSO. Table 9 compares the optimized voltage drop (V.D.) by different optimization algorithms and their respective cost and power loss. The best performance in voltage stability can be seen with ESDO Alpha Dog, which has the
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 3 Proposed Methodology
### Implementation Details  
The histogram representations for all of the datasets are created by following the same sequence of steps as shown in Figure 3. As a preprocessing step, the images are converted to gray-scale mode by using the available standard resolution, and the dense SIFT features are extracted on six multi-scales, i.e., {2,4,6,8,10,12} for the computation of codebook [38]. The step size of 5 is applied to compute the Dense SIFT features [38]. Dense features are selected, as the dense regular grid has shown to possess better discriminative power [16]. To save computation time for clustering, 40% of the features (per image) are selected by applying a random selection on a training set to compute the codebook.  
To quantize the descriptors, _k-means_ clustering is applied to generate visual vocabulary. Since the size of the codebook is one of the major factors that affects the performance of image retrieval, the proposed approach is evaluated by using different sizes of codebook to sort out the best retrieval performance. The visual vocabulary is constructed from the training set and the evaluation is done using the test set. The experiments are repeated in 10 trials to remove the ambiguity created by the random initialization of cluster centers by _k-means_. For each trial, the training and test images are stochastically selected and the average retrieval performance is reported in terms of precision and recall values, which are considered as standard image retrieval measures [8; 39].  
The calculation of RGSIR involves computing subsets of pairs from sets of identical visual words. To accelerate computation, a threshold value is set and a random selection is applied to limit the number of identical words used for creating the pair combinations. We use a nine-bin RGSIR representation for the results presented in Section 4. Figure 4 gives the empirical justification for the number of bins on two different image benchmarks used in our experiments. Support Vector Machine (SVM), a supervised learning technique, is used for classification. The SVM Hellinger Kernel is applied to the normalized RGSIR histograms. The optimal value for the regularization parameter is determined by applying 10-fold cross validation on the training dataset. As we have used a classification-based framework for image retrieval, the class of the image is predicted by using the classifier labels; similarity among the images of the same class is determined on the basis of distance in decision values [8]. The results obtained from the evaluation metrics are normalized and average values are reported in tables in graphs. MATLAB is used to simulate the research by using Corei7, a 7 th generation processor with 16 GB RAM.  
Figure 3: Block diagram of the proposed work.