esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 4 Experiment
### Evaluation Metrics
It is a sophisticated metric capturing more nuanced differences that are perceptible to the human eye. \[\text{LPIPS}(x,y)=\sum_{l}\frac{1}{H_{l}W_{l}}\sum_{h,w}\|\phi_{l}(x)_{h,w}- \phi_{l}(y)_{h,w}\|_{2}^{2} \tag{1}\]  
where \(x\) and \(y\) are the images being compared, \(l\) indexes layers in a pretrained network, \(\phi_{l}(x)\) and \(\phi_{l}(y)\) are the feature maps at layer \(l\) for each image, \(H_{l}\) and \(W_{l}\) are the dimensions of the feature maps at layer \(l\), and \(\|\cdot\|_{2}\) denotes the Euclidean (L2) norm. The Structural Similarity Index (SSIM) is used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, it measures the image quality based on an initial uncompressed or distortion-free image as a reference. The SSIM considers changes in structural information, luminance, and contrast, rather than aggregating errors over pixels like traditional methods such as the MSE. \[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2}+\mu_{ y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{2}\]  
where \(\mu_{x}\) is the average of
## 4 Results and Discussions
### Taguchi Analysis  
Using EF as an example, the S/N (signal/noise) ratio analysis for larger-the-better can be determined by Equation (32):  
\[\frac{S}{N}=-10\times\log\left(\frac{1}{n}{\sum\limits_{1}^{n}}z_{i}^{2}\right), \tag{32}\]  
where n is the number of data and z\({}_{i}\) is the outcome data such as EF, \(R_{A}\), \(K_{C}a\), and \(\phi\). Using No. 1 as an example, for A1 mixed solvent, the data for EF presented in Table 5 were 71.05%, 80.26%, 84.00%, 84.42%, and 88.00%, respectively. On substituting these values into Equation (32), A1 was estimated to be 38.1559. In the same method, all S/N ratios were evaluated and are listed in Table 7. The bracket words shown in this table are the maximum values for each factor, which includes the optimum condition, A5B4C5D1E5F5. The 'DELTA' value in this table is the difference between the maximum and minimum values of each factor, such as (A5-A3), yielding 1.37. According to the reported data, the parameter sequence was found to be in the order of E \(>\) D \(>\) A \(>\) F \(>\) C \(>\) B. Using the same analysis, the optimum condition and parameter sequence for other indexes could be determined and are listed in Table 8, in which the mixed amines in optimum conditions were A5, A2, A5, and A1, respectively. This supports the finding presented in Section 3.2 that MEA + PZ is the best amine.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **A1** & **A2** & **A3** & **A4** & **A5** \\ \hline EF & 3 & 4 & 1 & 2 & 5 \\ \(R_{A}\) & 5 & 3 & 1 & 2 & 4 \\ \(K_{C}a\) & 4 & 3 & 1 & 2 & 5 \\ \(\Phi\) & 1 & 2 & 3 & 5 & 4 \\ mean & 3.25 & 3 & 1.5 & 2.75 & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Points for various indicators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indexes** & **A1** & **A2** & **A3** & **A4** & **A5** \\ \hline EF & 81.54 & 82.67 & 77.40 & 80.31 & 87.20 \\ \(R_{A}\) (10\({}^{4}\)) & 11.26 & 10.52 & 9.58 & 10.51 & 10.58 \\ \(K_{C}a\) & 0.410 & 0.386 & 0.310 & 0.347 & 0.524 \\ \(\Phi\) & 0.1064 & 0.1111 & 0.1166 & 0.1385 & 0.1358 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Importance sequence analysis for various indicators.  
Based on points (0-5) analysis for six factors, the importance of parameters among the whole factors became E (4.5) > D (3.75) > C (2.5) = A (2.5) > F (1.5) > B (0.25), showing that the concentration of mixed amine (E) is the most important factor obtained. However, the optimum condition needs to be verified further.
# Ischemic Preconditioning (IPC) Enhances the Accuracy and Stability of Proprioception
## 2 Materials and Methods
### Knee Position Sense Test  
The measurements were performed using a Syntek electronic joint angle measurement device (Deqingshengtaixin Technology Co., Ltd., Deqing, China), which has a range of 0-180\({}^{\circ}\), a resolution of 0.1\({}^{\circ}\), and an accuracy of \(\pm\)0.3\({}^{\circ}\). Participants were instructed to lie supine with their eyes closed, knees flexed at 90\({}^{\circ}\), and hips flexed at 90\({}^{\circ}\), supported by a surface. The examiner positioned the goniometer with its center at the tibial tuberosity, one side aligned with the line connecting the tibial tuberosity and the greater trochanter of the femur, and the other side aligned with the line connecting the tibial tuberosity and the lateral malleolus. Participants were then asked to lift their lower leg at 40\({}^{\circ}\) without making adjustments during the process (Figure 2). Measurements were taken once the knee joint angle stabilized. The examiner repeated the measurement three times, with an interval of over five seconds between each measurement [14; 15]. The entire testing process was conducted by the same examiner to ensure consistency, with the examiner remaining silent to avoid providing verbal feedback that could influence the results. The collected data was used to calculate the average, reflecting the accuracy of the participant's knee position sense, and the variance, reflecting the stability of their knee position sense.  
\[\text{Accuracy}_{KPS}=\frac{\left(TP-\text{Trial}_{1}\right)+\left(TP-\text{ Trial}_{2}\right)+\left(TP-\text{Trial}_{3}\right)}{3} \tag{3}\]  
\[\text{Stability}_{KPS}=\frac{\left(TP-\text{Trial}_{1}-m\right)^{2}+\left(TP- \text{Trial}_{2}-m\right)^{2}+\left(TP-\text{Trial}_{3}-m\right)^{2}}{3} \tag{4}\]  
TP: target position; m: mean.
# Querying and Extracting Timeline Information from Road Traffic Sensor Data
###### Abstract  
The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data, we can obtain valuable insights into traffic behavior. Many existing applications have been proposed with limited analysis results because of the inability to cope with several types of analytical queries. In this paper, we propose the QET (querying and extracting timeline information) system--a novel analytical query processing method based on a timeline model for road traffic sensor data. To address query performance, we build a TQ-index (timeline query-index) that exploits spatio-temporal features of timeline modeling. We also propose an intuitive timeline visualization method to display congestion events obtained from specified query parameters. In addition, we demonstrate the benefit of our system through a performance evaluation using a Busan ITS dataset and a Seattle freeway dataset.  
traffic sensor data; timeline model; historical traffic sensor data; TQ-index; traffic data query processing +
Footnote †: journal: Sensors
# Analysis of Lower Limb Performance Determinants in Sport Climbing
## 4 Discussion
### Practical Applications  
Although the results of this study did not show significant associations between lower limb strength and flexibility variables and climbing level in recreational climbers, this does not imply that these capacities lack relevance. Climbing performance is highly influenced by upper body strength and endurance, particularly in disciplines such as sport climbing or bouldering. However, the role of the lower limbs in technical execution, postural control, and especially in injury prevention, should not be overlooked.  
The lower limbs play an active role in unilateral supports, pushes, dynamic movements, and jump landings, particularly in modern indoor settings where movements are increasingly acrobatic [13; 17]. Although no direct relationships with performance were observed in this study, lower limb training may help reduce the risk of musculoskeletal injuries, improve functional symmetry, and broaden the climber's technical repertoire--especially relevant for climbers progressing to higher levels or more explosive styles such as bouldering or speed climbing. Furthermore, the used tests (such as the Hop Test or the Grant Test) may have added value as functional assessment and monitoring tools--not only to detect mobility deficits or asymmetries but also to guide individualized prevention programs. Therefore, it is recommended not to exclude lower limb strength and mobility training from physical preparation programs for recreational climbers, even if its direct impact on performance was not evident in this sample. In different profiles (youth, elite, injured, or female climbers), its contribution could be even more relevant.
# Polystyrene-Sepiolite Clay Nanocomposites with Enhanced Mechanical and Thermal Properties
## 3 Results and Discussion
### Mechanical Properties  
The mechanical properties of pristine PS and PS/m-sepiolite nanocomposites showing the yield strength (YS), tensile strength (TS), Young's modulus (E) and elongation at break (Eb) are summarized in Table 3. The comparison among the tensile strengths of pristine PS and fabricated nanocomposites is shown in Figure 6a. The graphs indicate an increasing trend in tensile strengths of the nanocomposite. The tensile strength of pristine PS was 38.74 MPa, which increased to 46.29 MPa by adding 4 wt.% of modified sepolite clay, indicating a better compatibility between the matrix and the filler at the interface. This can be attributed to siloxane (Si-O-C) linkages, as evident from the FTIR spectra. Similarly, the Young's modulus (E) results of pristine PS and the nanocomposites were compared in Figure 6b, indicating the highest value with 4 wt.% loading of m-sepiolite. This increase in the mechanical properties of the nanocomposites indicates robust intermolecular interaction and better dispersion, as evident from the SEM analyses. In contrast to TS and E, Eb  
Figure 5: SEM images of S0 (pristine PS) in (**a**), S1 (PS/1 wt.% sepiolite) in (**b**), S2 (PS/2 wt.% sepiolite) in (**c**) and S4 (PS/4 wt.% sepiolite) in (**d**).  
Figure 6: Mechanical properties of pristine PS and PS/Sepiolite composites: (**a**) Tensile Strength (TS) and (**b**) Young’s Modulus (E).  
decreased from 2.16 to 1.61% upon addition of the m-sepiolite clay in the nanocomposites as compared to pristine PS. The decrease in Eb depicts a transition of amorphous PS toward a semicrystalline structure of the polymer nanocomposites with the addition of the filler. It was observed that the increase in filler content in the polymer matrix might have led to a rigid interphase, thereby reducing the deformability.
## 1 Introduction
Using a huge test bench involving real wheel and rail elements, they also studied how various factors, such as normal load, current intensity, and oxide layer thickness, affect contact resistance. Subsequent studies conducted in parallel on a 1/4 scale test bench and on site with an instrumented regional train, both stationary and running at low speed, have further refined the understanding of the wheel-rail electrical contact [18; 19]. These studies demonstrated that the results obtained at a reduced scale are fully representative of those obtained at full scale. This important observation validated the relevance of working on a scaled-down model to facilitate the study of the influence of the many possible parameters (load force, imposed current intensity, surface properties, rolling speed, etc.). Consequently, the authors recently developed a flexible experimental device to accurately replicate the wheel-rail contact conditions in the laboratory, along with a protocol to achieve different oxidation states on demand [20]. It is this versatile platform allowing studies on a much smaller scale than those previously proposed that is used in the present work. As has been demonstrated in the literature, the presence of rust causes non-linear current-voltage characteristics [21; 22]. Therefore, the subject of oxidation was the primary focus of the experimental platform, with initial emphasis placed on the simplest cases of static contact and direct current stresses [23]. In order to more accurately reflect the context of rail traffic, new investigations are reported hereafter, based on a series of tests conducted using rolling conditions and alternating current. The work presented in this paper is the follow up work of this previous publication. This paper will therefore focus on the influence of a controlled thickness oxide layer on the wheel/rail contact resistance under dynamic conditions, to better understand its non-ohmic behavior and with the application aim of estimating the maximum admissible rust state of a railway track. The measurements were carried out under reproducible conditions (speed, load, and oxidation state) on our reduced-scale bench. They required the development of specific analysis tools, mainly based on statistical analysis, to obtain the maximum information on the electrical behavior of the wheel/rail contact. After a brief reminder of the structure and implementation of the bench, these new tools will be detailed before presenting the results obtained.
# FMNSCF: Fine-Grained Multi-Domain Network Interconnection Security Control Framework
## 3 Implementation
### Match Tree Building of Packet Filter  
The transmitted security rules are built into a packet filter match tree in the gateway machine. The implementation method is to build an 8-layer filter matching tree. The root node is in the first layer, the second layer is the source IP range of child nodes, and the third layer is the source IP range of which the parent node is IP range node. By analogy, the fourth floor is protocol type interval, the fifth layer is the source port interval, the sixth layer is the destination port, the seventh layer is the time interval, and the eighth floor is URL characteristics. As you can see from step 2, the security rule script has a very similar structure to the filter match tree. This procedure can be understood as the gateway machine \(\mathcal{M}_{rf}\) executing the _load security rule_ instruction.  
After the matching tree has been built, when the gateway machine executes the instructions \(filter\)_packet_, it will start from the root node, from top to bottom, and select the matching subtree from the eligible subtree, until it finds the action of the leaf node. In particular, if no eligible subtrees are found at a certain level, it jumps out of the tree traversal and performs the default action.
## 5 Feasibility and Evaluation
### Architectural Comparison
#### 5.5.5 Security  
We have already established in Section 1 how traditional controls are not feasible for resource-constrained things and how they lack analysis of a context holistically, though they do provide security to a certain level. We have also detailed how EDAS addresses these issues by transferring computations required at a thing level to a resourceful machine (EDAS platform) and correlating different types of events in time to provide grounds for accurate analysis that reduces potential false alarms. Its cross event correlation feature can analyze a spectrum of threats, such as power exhaustion, confidentiality, intrusions, _etc._ Traditional controls can only defend against a defined set of threats. These can be the network, the web, local files or OS-related risks. Hence, their security aptitude is very restricted, and due to this lack of scope and context, they usually result in false alarms [5, 11].
## 4 Results and Discussion
### Results of the Decision Trees  
The Decision Tree model was also applied to the same training data. Table 7 presents the result obtained. The parameters Gini and entropy index were used in its analysis.  
The best accuracy of 71.83% was obtained using entropy. This parameter defines how to measure the purity of each subset in each decision tree. In other words, it measures the probability of obtaining an occurrence of a positive event (hospital discharge) from a random selection of the data subset. It is observed that the precision obtained was 74.87%, the sensitivity (recall) was 0.8544, and the harmonic mean between these two variables (F1 score) was 0.79808.  
The value reached for the ROC curve analysis was 0.69 (with the entropy parameter), as shown in Figure 7. There is a 69% chance of correctly classifying the patient's clinical evolution using the Decision Tree model prediction.
# Removal of Metal Ions in Spin-on Hardmask Using Functionalized Porous Silica Adsorbents
## 3 Result and Discussion
### Synthesis and Characterization of Mesoporous Adsorbents
\begin{table}
\begin{tabular}{c c c c} \hline
**Plot Data** & **Specific Surface Area (m\({}^{2}\) g\({}^{-1}\))** & **Pore Size (nm)** & **Pore Volume (cm\({}^{3}\) g\({}^{-1}\))** \\ \hline MCM-41-OH & 1120.4 & 3.5605 & 0.9973 \\ MCM-41-CH\({}_{3}\) & 927.21 & 3.4795 & 0.8066 \\ MCM-41-NH\({}_{2}\) & 656.81 & 3.1381 & 0.5153 \\ MCM-41-SH & 785.3 & 3.2415 & 0.6364 \\ SBA-15-OH & 593.75 & 7.737 & 1.1455 \\ SBA-15-CH\({}_{3}\) & 462.63 & 7.3737 & 0.8528 \\ SBA-15-NH\({}_{2}\) & 376.28 & 7.1297 & 0.6707 \\ SBA-15-SH & 500.89 & 7.6897 & 0.9629 \\ \hline \end{tabular}
\end{table} 
Table 1: Textural parameters of pristine and functionalized mesoporous silica materials. Figure 3: N\({}_{2}\) adsorption–desorption isotherms of functionalized (**a**) MCM-41 and (**b**) SBA-15. B]H pore size distributions of functionalized (**c**) MCM-41 and (**d**) SBA-15. samples, the amine-modified (-NH2) materials exhibited the smallest pore sizes in both MCM-41 and SBA-15. This can be attributed to the nucleophilic or alkaline nature of the amine, which is presumed to facilitate surface modification by promoting additional layer formation within the pores. Consequently, both the specific surface area and pore volume were reduced [22]. To confirm the successful functionalization of mesoporous silica, zeta potential measurements were conducted to evaluate the surface charge variations resulting from the introduction of different functional groups. As shown in Figure 4, MCM-41-OH and SBA-15-OH exhibited a negative zeta potential of approximately \(-43.6\) mV and \(-21.9\) mV at pH 7, which is attributed to the presence of deprotonated silanol (-Si-OH) groups [23]. Upon methyl functionalization (-CH3), the zeta potential values increased to around \(-19\) mV for both materials and thiol-functionalized silica (-SH) also showed a moderate increase (\(-28.7\) mV for MCM-41 and \(-22.3\) mV for SBA-15), consistent with the partial ionizability of thiol groups. Notably, amine-functionalized mesoporous silica exhibited a significant shift to positive zeta potentials, reaching +11.3 mV (MCM-41) and +14.3 mV (SBA-15), due to the protonation of surface amine groups at neutral pH. This confirms the successful grafting of positively charged -NH3+ groups and highlights the utility of zeta potential analysis for distinguishing surface functionalization. The magnitude and direction of potential shifts across different functional groups demonstrate the varying surface charge environments, thereby validating the presence and identity of introduced functional groups [24].
## 4 Results
### Combined Scenarios  
In this section, we simulated two scenarios consisting of two groups of strategies. Scenario 1 consists of the application of solar shading, TrpLoE and active measures, while Scenario 2 consists of the application of daylighting, TrpLoE and active measures in the building model. Results from the simulations of these scenarios can be observed in Figure 12. In the Figure 12a, scenario 1 showed an annual energy consumption of 75,203 KWh  
Figure 11: Annual energy saving percentages for cooling and total loads obtained from each proposed scenario (authors elaboration).  
Figure 10: Annual energy consumption by end-use estimated for each proposed scenario (author’s elaboration).  
(EUI = 69.22 kWh/m\({}^{2}\)-year) and scenario 2 of 63,713 kWh (EUI = 58.64 kWh/m\({}^{2}\)-year). Figure 12b showed the percentage savings in total energy consumption and in cooling in the modeling of the two scenarios. Scenario 1 reduced total energy consumption by 35% and scenario 2 by 23% compared to the base case. The impact of both scenarios on building cooling is about 43%.
# Secret Image Sharing Revisited: Forbidden Type, Support Type, and Their Two Approaches
###### Abstract  
In this paper, we introduce two new image-sharing types to extend the applicability of sharing. Type 1 is our so-called forbidden type. In its sharing system, any \(t\) of the \(n\) shares can recover the secret image, unless the \(t\) shares form a forbidden group listed in a forbidden list. Type 2 is our so-called cross-department support type. If a government has 3 departments {\(DEP_{\text{H}}\), \(DEP_{\text{M}}\), \(DEP_{\text{L}}\)}, then 3 thresholds (\(t_{\text{H}}\), \(t_{\text{M}}\) and \(t_{\text{L}}\)) exist. Any \(t_{\text{H}}\) number of officers from department \(DEP_{\text{H}}\) can unveil the secret image, and likewise for any \(t_{\text{M}}\) and \(t_{\text{L}}\) number of officers from departments \(DEP_{\text{M}}\) and \(DEP_{\text{L}}\), respectively. Type 2 image sharing allows a secret to be disclosed not only in an intra-department meeting but also in a cross-department meeting. In this study, both types are implemented through two approaches: the polynomial and linear-equations approaches. Hackers can be confused when two approaches are mixed. As for the applications, use Type 1 to protect sensitive information in medical or military images or legal documents; and use type 2 to support cross-department crime investigation, industrial production, etc.  
secret image sharing (SIS); shadow image; forbidden type; cross-departments support; polynomial approach; linear-equations approach +
Footnote †: journal: Accepted: 25 May 2020; Published: 28 May 2020
# Tailorable and Broadband On-Chip Optical Power Splitter
###### Abstract  
An on-chip optical power splitter is a key component of photonic signal processing and quantum integrated circuits and requires compactness, wideband, low insertion loss, and variable splitting ratio. However, designing an on-chip splitter with both customizable splitting ratio and wavelength independence is a big challenge. Here, we propose a tailorable and broadband optical power splitter over 100 nm with low insertion loss less than 0.3%, as well as a compact footprint, based on 1\(\times\)2 interleaved tapered waveguides. The proposed scheme can design the output power ratio of transverse electric modes, lithographically, and a selection equation of a power splitting ratio is extracted to obtain the desired power ratio. Our splitter scheme is close to an impeccable on-chip optical power splitter for classical and quantum integrated photonic circuits.  
integrated beam splitter; silicon photonics; tailorable power splitting ratio Article