esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### Model of Load Acting on Shield  
Based on the shield load model established by [PERSON] [20], the contact relationship between the shield and the stratum is blurred. The force of shield-soil interaction is decomposed along the axis direction, the normal direction, and the tangential direction of the shield, and the load model is shown in Figure 1.  
The loads acting on the shield are: \(f_{1}\), shield self-weight; \(f_{2}\), force on the shield tail; \(f_{3}\), advance load provided by the jacks; \(f_{4}\), load acting on the cutter head; and \(f_{5}\), surrounding soil load acting on the shield periphery [21].  
Figure 1: Shield load model: (**a**) Section A-A; (**b**) Section B-B; and (**c**) Section C-C.  
If the shield is regarded as a rigid body, the distance between any two points on the shield remains unchanged during the tunneling process. Six parameters are required to describe the shield attitude: cutterhead center coordinate (\(x_{0},y_{0},z_{0}\)), yawing angle \(\alpha\), pitch angle \(\beta\), and rolling angle \(\Omega\). The attitude angles are shown in Figure 2a.  
The coordinate system is established as shown in Figure 2b: in the geodetic coordinate system \(C^{E}\), the x-axis is vertically downward and the y-axis is on the horizontal plane; in the shield-structure coordinate system \(C^{M}\), the p-axis is vertically downward and the r-axis is along the shield axis; and the coordinate system \(C^{M}\) is rotated by a certain angle around the r-axis to obtain a coordinate system \(C^{\text{MR}}\).  
When the shield is considered to be in a static equilibrium state during the tunneling process, the static balance equation must be met:  
\[\begin{array}{c}\sum\limits_{n=1}^{5}f_{n}^{b}\ =\ 0\\ \sum\limits_{n=1}^{5}M_{n}^{b}\ =\ 0\end{array} \tag{1}\]  
Then:  
\[\begin{array}{c}F_{1e}^{b}+F_{2e}^{b}+F_{3e}^{b}+F_{4e}^{b}+F_{5e}^{b}=0\\ M_{1e}^{b}+M_{2e}^{b}+M_{3e}^{b}+M_{4e}^{b}+M_{5e}^{b}=0\end{array} \tag{2}\]  
where \(e=p\), \(q\), and \(r\) represent different directions of force; and \(b=E\), \(M\), and \(MR\) are expressed in different coordinate systems.
## 3 Results
### Train Tracking Stages  
To perform the vibration detection, we first annotated vibrations in a recorded raw data file individually for the two different track conditions. These ground truth data are then used to train the PCA for feature reduction and the SVM for classification. An illustration of the vibration detection can be found in Figure 2.  
The first step of the tracking algorithm is the edge detection. In Figure 3, we give an example of the result of the edge detection from the first 35 s of the dataset shown in Figure 2. After the edge detection, the assignment and Kalman filtering are performed. For the same example dataset, we show the tracking result in Figure 4.  
Figure 2: **Left**: Raw subsampled signal for visualization. **Right**: Result of the vibration detection where white pixels indicate vibration and black pixels indicate background. The gaps in the train tracks occur when a train stops since an electric train that is not moving does not emit any vibrations and is, therefore, not visible using a Distributed Acoustic Sensing (DAS) system.
## 2 Data and Methods  
### Study Area Data  
The focus of this study is on the central and western waters of the South China Sea, which have a notable tropical maritime climate characterized by high temperatures throughout the year, with an annual average temperature around 26 \({}^{\circ}\)C. The region experiences a southwest monsoon period from May to September, followed by a northeast monsoon period from October to April, as reported by [PERSON] and [PERSON] [14]. The upwelling sea in this region is a significant source of fishery resources, with an annual average catch of 340,000 tons. The coastline angle is a crucial factor that affects the wind-induced upwelling intensity, as highlighted by [PERSON] [15]. Based on the varying shoreline angles, the upwelling region is divided into three sub-regions: OU_1 (10-12\({}^{\circ}\) N), OU_2 (12-14\({}^{\circ}\) N), and OU_3 (14-15.5\({}^{\circ}\) N), as depicted in Figure 1.  
The sea surface temperature data set utilized in this study was obtained from the high-resolution SST data set ([[https://psl.noaa.gov/data/](https://psl.noaa.gov/data/)]([https://psl.noaa.gov/data/](https://psl.noaa.gov/data/)) (accessed on 1 January 2022)) provided by the National Oceanic and Atmospheric Administration (NOAA). The data set has a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), a temporal resolution of 1 day, and was selected based on its reliability and accuracy [16]. The wind field data used in this study were sourced from the IFREMER CERSAT Global Hybrid Mean wind field data set ([[https://resources.marine.copernicus.eu/](https://resources.marine.copernicus.eu/)]([https://resources.marine.copernicus.eu/](https://resources.marine.copernicus.eu/)) (accessed on 1 January 2022)), provided by Copernicus-Marine Environmental Monitoring Service (CMEMS). The data set has a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a temporal resolution of 6 h. The data used in this study used in this study has undergone a thorough data quality assessment, which has ensured that it is suitable for our analysis [16; 17; 18; 19].
## 3 Simulations of System Measurement Method in Noise Conditions
### Analysis of Anti-noise Performance of the Temperature/Strain Demodulation Algorithm
Figure 8: The maximal cross-correlation coefficients after adding the Gaussian noise with an amplitude of 0.4 times to the initial signal: (**a**) 21 frequency points; (**b**) 31 frequency points; (**c**) 51 frequency points; (**d**) 101 frequency points. Figure 10: The maximal cross-correlation coefficients after adding the Gaussian noise with an amplitude of 0.8 times to the initial signal: (**a**) 51 frequency points; (**b**) 101 frequency points. Figure 9: The maximal cross-correlation coefficients after adding the Gaussian noise with an amplitude of 0.6 times to the initial signal: (**a**) 31 frequency points; (**b**) 51 frequency points; (**c**) 101 frequency points. It can be seen from Figure 11 that the array with a length of 101 has much better anti-noise ability, but when the noise amplitude added is larger than 0.9, the demodulation results appear with two errors. So, it can be inferred that the array length decides the temperature/strain measurement accuracy. However, the array length has a great influence on the system measurement time and efficiency, which is an important issue to be discussed.
## 8 Conclusions  
Several mixed-criticality domains, such as automotive and automated industries, require deterministic latency performances. Missing latency deadlines in such applications may generate dangerous situations for humans or considerable economic waste in highly time-sensitive applications. To support such applications, TSN technology is defined based on several TT-Ethernet amendments. Most of these enhancements have been introduced mainly to serve time-triggered traffic targeting zero jitter delay and bounded end-to-end latency. However, there is a concern that soft real-time traffic could miss their requirements under strict timing constraints in GCL designs.  
This paper proposed a flexible window-overlapping scheduling (FWOS) algorithm with complete timing analysis for the WCD using the network calculus approach considering relative window offsets between adjacent nodes. Our model expresses all overlapping situations between TT windows, leading to a more generic timing analysis. Based on a realistic vehicular use case, the numeric results confirm that the impact of higher-priority overlapping on the WCD is larger than that from lower-priority overlapping. Further, overlapping at the opening edge generates bigger WCDs than at the closing edge. Thus, under guaranteed latency deadlines, the overlapping ratio between TT windows must be bounded accordingly. Based on his, the FWOS model can be applied to optimize the maximum-allowable overlapping ratio (OR) that ensures TT latency requirements and maximizes the bandwidth available for unscheduled transmissions. For example, to meet the 400 us latency deadline under specific timing assumptions, the highest-allowable OR is 38.62% with higher priority (opening edge), 54.62% with higher priority (closing edge), 54.31% with higher priority (opening and closing edges), 76.84% with higher priority (opening edge) and lower priority (closing edge), 85.2% with lower priority (opening edge) and higher priority (closing edge), and 100% with any case of lower priority. Accordingly, more relaxed GCLs can be implemented to fit the requirements of incoming data from vehicle-related devices, such as sensors, cameras, and actuators. Compared with the previous works, FWOS obtains less pessimistic WCDs, even under fully isolated window scheduling.  
Not applicable.  
The authors declare no conflict of interest.
# Smart IoMT Framework for Supporting UAV Systems with AI
## 3 Architecture of the System
### Data Collection Stage  
The data channel is constrained by superframe structures, similar to those in the IEEE 802.15.6 standard. A Random Access Period (RAP), a beacon, and an optional Contention Free Period (CFP) make up each superframe. Beacons are utilized to transmit control data such as superframe borders and packet sizes. The cores use a prioritized Carrier Sensor Multiple Access with Collision Avoidance (CSMA/CA) protocol to transfer data to the UAV using the RAP. Variable time periods are set aside during the optional CFP period for multimedia traffic.  
The priority-based system in this work's CSMA/CA adaptation has three priority tiers. The priority levels distinguish cores that contain life-critical information; this is significant in disaster zones because several cores may have life-critical information and  
Figure 4: IoMT service architecture.  
require immediate access to the channel. Following receipt of the WUR signal as well as the beacon frames, all cores adjust their back-off counter represented by K, to a random number between (1, CW), where the contention window is represented by CW and is chosen from the least and highest contention bounds in the range \((CW_{min},CW_{max})\). When K is 0, the core transmits information to the UAV and decrements it for every unoccupied slot. The value of the CW is doubled for every even quantity of failures, by the IEEE 802.15.6 standard. The high-priority core will have a tiny CW, allowing it to transfer the data just before the low-priority core. After a successful transmission, the backoff stage, indicated by B, does not reset. The amount of CW may be scaled effectively because the present backoff stage indicates the total number of effective transmissions or collisions of the UAV data.  
The data flow schema for uplink transmission is such that the UAV initially sends a WUR signal to the core. Once the core is operational, the UAV emits a beacon frame that specifies the period bounds. The core transmits data to the UAV through the previously specified prioritized CSMA/CA protocol. When the data have been received, the UAV transmits an acknowledgment back. A similar data flow schema is used for multimedia transmission, which includes the live patient video, and just like uplink transmission, after the request is received in the beacon frames, the cores transfer the multimedia information in the CFP slots. The data flow schema for downlink transmission is such that, in the beacon, the UAV may provide downlink control data such as synchronization and configuration frames. Figure 5 provides the suggested protocol's schema.
# Privacy-Preserving Monotonicity of Differential Privacy Mechanisms
## 6 Privacy-Preserving Monotonicity of Several Differential Privacy Mechanisms
### Privacy-Preserving Monotonicity Based on Expected Estimation Error
According to the Theorem 2, the expected estimation error of Gaussian mechanism decreases as privacy budget increases. The privacy-preserving level of Gaussian mechanism decreases as privacy budget increases. By Corollary 2, the modulus of characteristic function of Gaussian mechanism increases as privacy budget increases. Thus, the data utility using Gaussian mechanism increases as privacy budget increases. According to Definition 8, Theorem 2 demonstrates the privacy-preserving monotonicity of Gaussian mechanism. Privacy-preserving monotonicity of Gaussian mechanism demonstrates the trade-off between privacy and utility based on Theorem 2 and Corollary 2.
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 6 Conclusions and Future Work  
### Adievements and Contributions  
The main achievements and contributions of our work include the following:  
(1) This paper proposes a method for estimating atmospheric light intensity using scene polarization state and applying it to the dark channel prior method to achieve haze removal in hazy images.  
(2) To achieve real-time haze removal, an imaging and processing system for hazy scenes is designed and fabricated. The cameras in the system capture frames at a maximum rate of 50 Hz. We used CUDA programming on the NVIDIA GTX 3060 GPU to achieve a single-frame picture dehazing processing time of less than 5 ms, which is far faster than the 20 ms image capture time. This signifies that the system is capable of real-time image-dehazing processing.  
Figure 17: Failure case. (**a**) Foggy image without sky and with very dark region. (**b**) Dehazed image using original DCP. (**c**) Dehazed image using the proposed method. (**d**) DOP map of the foggy image.  
(3) We performed an analysis of the natural scene's polarization characteristics and the influences of atmosphere estimation in the dehazing method, which is introduced in Section 3.3.  
(4) The effectiveness of the proposed method was validated on both publicly available datasets and real-world collected data. The method's performance was analyzed through subjective perception and objective evaluation techniques. The results demonstrate that the proposed method outperforms the original DCP in terms of haze removal, particularly in hazy images containing sky regions. Compared to the original DCP, the proposed approach showed a 28% improvement in SSIM and an increase of over 20% in contrast. The results demonstrate that the proposed method outperforms the original DCP in terms of haze removal, particularly in hazy images containing sky regions.
## 2 Materials and Methods
### Physical and Psychological Well-Being Tracking Component
* StressIdentified is codified with 1 if the maxRate is registered with a higher value than the avgRate in the working interval for working days (Monday-Friday). Otherwise, the value is set to 0. Figure 1 presents a sample from the database to facilitate a better understanding of the data representation methodology. Thus, the highlighted records, which correspond to 27 February 2023, include the following metrics: an average heart rate (avgRate) of 77, a minimum heart rate (minRate) of 45, and a maximum heart rate (maxRate) of 113. Additionally, the individual recorded 7391 steps, covering a distance of 4830 m. The running distance was 441 m, with a calorie expenditure of 139 calories. The sleep data indicate no deep sleep or shallow sleep recorded for that day, as both deepSleep and shallowSleep show 0 min, with a wakeTime also of 0 min. The stress identified for that day was marked as 1. The research explores multiple variations of the well-being indicator calculation to identify the most versatile approach. Each variant was tested against certain criteria, including accuracy and the ability to capture the nuances of physical and psychological health metrics.
# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
###### Abstract  
This study presents a semi-random mobility model called Semi-Random Elliptical Movement (SREM), developed for relay-oriented Flying Ad Hoc Networks (FANETs). In FANETs, node distribution has a major impact on network performance, making the mobility model a critical design element. While random models offer simplicity and path diversity, they often result in unstable relay paths due to inconsistent node placement. In contrast, planned path models provide alignment but lack the flexibility needed in dynamic environments. SREM addresses these challenges by enabling nodes to move along elliptical trajectories, combining autonomous movement with alignment to the relay path. This approach encourages natural node concentration along the relay path while maintaining distributed mobility. The spatial characteristics of SREM have been analytically defined and validated through the Monte Carlo method, confirming stable node distributions that support effective relaying. Computer simulation results show that SREM performs better than general mobility models that do not account for relaying, offering more suitable performance in relay-focused scenarios. These findings suggest that SREM provides both structural consistency and practical effectiveness, making it a strong candidate for improving the realism and reliability of FANET simulations involving relay-based communication.  
Flying Ad Hoc Network; mobility model; unmanned aerial vehicle; relay network
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 3 Camouflage Effect of an RB RAIM Detector in Multi-Outlier Mode
### Baseline of an RB RAIM Detector
The Chi-square test statistic of an RB RAIM detector is computed as:  
\[T_{RB}=\sqrt{r^{\mathrm{T}}\mathbf{W}\mathbf{r}/(n-m-3)}\sim\chi^{2}(n-m-3, \lambda_{RB}^{2}). \tag{7}\]The test statistic \(T_{RB}\) follows a non-central Chi-square distribution with \((n-m-3)\) degrees of freedom and a non-centrality parameter \(\lambda_{RB}^{2}\), which is function of the fault vector \(\mathbf{b}\):  
\[\lambda_{RB}^{2}=\mathbf{b}^{\mathbf{T}}\mathbf{DSb}. \tag{8}\]  
The threshold \(T_{D}\) of the Chi-square test can then be calculated by the number of satellites and the possibility of a false alarm. The detector rejects the observation with the largest test statistic in the outlier exclusion. If multiple outliers exist, then the single outlier test is applied iteratively until all outliers have been removed. Equation (5) shows that if we can obtain an absolutely accurate estimation of the state parameters then we obtain \(\mathbf{r}=(\epsilon+\mathbf{b})\). However, it is impossible to achieve the expected result. However, the more accurate the estimated state parameters, the more reliable the FDE result of an RB RAIM.
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 3 Graph-Enhanced Multimodal Recommendation Method: GEMRec
### Interpretable Methods for Graph-Semantic Enhancement
#### 3.4.2 Graph-Semantic Enhancement with Large Models  
After understanding feature importance, generating readable recommendation explanations becomes crucial. To achieve this, we designed a multi-stage recommendation explanation generation algorithm, combining template-based methods with natural language generation techniques to provide explanations for each recommendation. First, features that impact the recommendation decision are ranked based on SHAP values, and the Top-K most important features (typically 3 to 5) are selected as the basis for generating the recommendation explanation, balancing completeness and conciseness.  
Once the key features are identified, the system maps each feature to a corresponding text template. For example, for \"knowledge graph concept similarity\", a high similarity reason might be: \"This course covers concepts that are highly relevant to your current knowledge base\". In cases of low similarity, the explanation focuses on expanding the learner's knowledge domain. Additionally, by incorporating user background information, the system uses a context-aware mechanism and the user's learning history to select the most appropriate template, with a pretrained language model used to polish the explanation, ensuring a natural and fluent flow. Based on user preferences, the system also fine-tunes the explanation to maintain personalization and brevity.  
To avoid repetitive explanations, the system includes a dynamic template pool that records recently used templates and prioritizes varied expressions. This approach ensures that the final generated recommendation explanations are personalized and evidence-based and that they enhance the interpretability of recommendations.
## 4 Results and Discussion
### Performance Evaluation  
The performance of the newly developed text line extraction method is quantitatively evaluated using standard metrics such as the MatchScore, Detection Rate (DR), Recognition Accuracy (RA) and F-measure (FM) [31].
###### Abstract  
Laser doping, though able to improve cell characteristics, enables the formation of a selective emitter without the need for additional processing. Its parameters should be investigated to minimize laser defects, such as the heat-affected zone (HAZ), and to obtain a low contact resistance. Herein, the laser fluence and speed were changed to optimize process conditions. Under a laser fluence of 1.77 J/cm\({}^{2}\) or more, the surface deteriorated due to the formation of the HAZ during the formation of the laser doping selective emitter (LDSE). The HAZ prevented the formation of the LDSE and impaired cell characteristics. Therefore, the laser speeds were changed from 10 to 70 mm/s. The lowest contact resistivity of 1.8 m\(\Omega\)-cm\({}^{2}\) was obtained under a laser fluence and speed of 1.29 J/cm\({}^{2}\) and 10 mm/s, respectively. However, the surface had an irregular structure due to the melting phenomenon, and many by-products were formed. This may have degraded the efficiency due to the increased contact reflectivity. Thus, we obtained the lowest contact resistivity of 3.42 m\(\Omega\)-cm\({}^{2}\), and the damage was minimized under the laser fluence and speed of 1.29 J/cm\({}^{2}\) and 40 mm/s, respectively.  
c-Si solar cell; laser doping selective emitter; heat-affected zone; contact resistivity 2020 10 4554 10.3390/app10134554
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 4 Results
### Korshunov et al. Database  
The Korshunov et al. database is the less selective database (cf. Figure 11). Most of the metrics have high correlation coefficients and the choice of color space has close to no impact on the performances especially on color-blind metrics. Even using non-perceptually linear color space like the \(Y^{\prime}C^{\prime}Cb^{\prime}\) color space impacts only moderately the performances of MS-SSIM, FSIM, PSNR-HVS-M and PSNR-HMA. For this database, the best performing color-blind metrics are \(\text{FSIM}_{I_{2}a_{2}b_{2}}\), \(\text{FSIM}_{HDR-Lab}_{1000}\) and \(\text{MS-SSIM}_{I_{2}a_{2}b_{2}}\).