esa-sceva/satcom-chunk-collection huggingface dataset:
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
## 6 Limitation and Future Work  
When a packet is received, a router checks whether it is an acknowledgment or a new packet. If it is a new packet, then it is sent to the high-priority link. If this is an acknowledgment, then it is sent to the same link from whence it came. When going towards the internal server, a packet reaches the WLAN Access Point (AP), and if the AP finds that the link from the AP to the server is down, it will route traffic towards the AP to the LAN switch. The LAN switch will send the traffic towards the server. We designed the logical diagram VRPR (shown in Figure 6), which is a new concept proposed and tested for the LAN only. In the future, we can propose a network model for the MAN and WAN.  
We have tested VRPR in the prior monitoring of the link model. This means that when a packet enters a switch, the switch checks the status of the outgoing link. If the link is available, it sends a packet on the main link; otherwise, it sends the packet on an alternative link. If the switch is sending a packet on the main link and the transmission of the packet link is broken, there will be a packet loss. This is an infrequent possibility; however, we plan to develop a solution for this limitation in the future.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Packet size & 64 K \\ Wired link delay max & 5 ms \\ Wireless link delay max & 20 ms \\ Wired link bandwidth & 50 Mb \\ Wireless link bandwidth & IGE \\ Wireless link type & 802.11g \\ Wired media type & Fiber \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameter: wireless and wired.  
**Author Contributions:** Conceptualization, T.M.; data curation, S.K.; formal analysis, T.M., F.A., A.A. and J.A.; investigation, T.M. and A.H.; methodology, T.M.; project administration, F.A. and J.A.; resources, A.H.; software, A.A.; supervision, F.A. and J.A.; visualization, F.A.; writing--original draft, T.M., A.A. and S.K.; writing--review and editing. J.A., T.M. and F.A.; proofreading and writing the paper script in overleaf, T.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported partially by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education (NRF5199991514504) and by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2018-0-01431) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Fundamental Study on Circularly Polarized Double-Loop Antennas with Quasi-Two Sources
###### Abstract  
Two concentric double-loop antennas were analyzed using the moment method to increase the 3 dB axial ratio bandwidth. Each antenna has a driven loop connected to a branched feedline that is vertical to the ground plane. First, a parasitic loop was added outside of the driven one with a spacing of \(S\). It was found that the axial ratio bandwidth has a maximum value of 23% for \(S=0.02\) wavelengths. Next, we moved a parasitic loop inside of the driven one. It was revealed that the antenna shows an axial ratio bandwidth of 40%, which is three times as wide as that of a single-loop antenna. The analysis results were verified with experimental work.  
loop antenna; wideband antenna; circular polarization +
Footnote †: journal: Physics Letters B
## 6 Conclusions and Future Perspectives
### Challenges
#### 6.2.3 Lack of Uncertainty Quantification  
Existing DL studies in ocean and climate research primarily focus on improving performance metrics such as accuracy and RMSE. Only a few studies incorporate uncertainty quantification, which is essential to evaluate the reliability and robustness of DL models in real-world applications. Uncertainty in DL models generally comes from two sources, i.e., aleatoric uncertainty and epistemic uncertainty. Aleatoric uncertainty, also known as data uncertainty, arises from inherent noise in the input data, such as sensor errors or cloud cover. In contrast, epistemic or model uncertainty stems from limited network knowledge, typically caused by the lack of training data. Neglecting uncertainty can lead to overconfident predictions, posing significant risks in critical decision-making scenarios. For example, in SST prediction, DL models are often evaluated using RMSE without reporting confidence intervals across different regions or seasons. In high-variability areas like the equatorial Pacific, model outputs may exhibit high epistemic uncertainty due to sparse observational data. Without uncertainty quantification, users cannot distinguish between reliable and unreliable predictions, which limits the practical use of such DL models in climate monitoring or early warning systems.
## 5 Simulation Calculation Model of Grouting
### The Impact of Various Factors on the Slurry Diffusion Radius
#### 5.4.3 The Influence of Different Slurry Viscosity on Slurry Diffusion Radius  
The study employed fixed parameters of 0.3 MPa grouting pressure and 0.3 m/min drilling speed while varying slurry viscosity between 83.3-833.3 mPa s to investigate its influence on slurry diffusion radius. The corresponding simulation results are presented in Figure 13.  
It can be seen from Figure 13 that when the slurry viscosity is 83.3 mPa s, the maximum slurry diffusion radius is 82.5 cm, and when the slurry viscosity is 416.6 mPa s, the slurry diffusion radius is 28.4 cm. As the slurry viscosity increases, the slurry diffusion radius gradually decreases. When the slurry viscosity increases to 833.3 mPa s, the slurry diffusion radius decreases to 23.1 cm, the slurry viscosity increases by 9 times, and the slurry diffusion radius decreases by 72%. The relationship between slurry diffusion radius and slurry viscosity is obtained by regression analysis of the results:  
\[\begin{cases}R=16.93\mu^{-0.689}\\ r^{2}=0.983\end{cases} \tag{12}\]  
Figure 12: Slurry diffusion at different drilling speeds levels: (a) 0.5 m/min; (b) 0.3 m/min; (c) 0.1 m/min.  
Figure 13: The correlation between the slurry diffusion radius and slurry viscosity.  
Equation (12) establishes a power-law relationship between slurry diffusion radius and viscosity, with Figure 14 illustrating the corresponding diffusion patterns across different viscosity values. The results demonstrate significant variations in diffusion range, where the highest viscosity (833.3 mPa-s) produces the most constrained diffusion, primarily localized near the outlet with minimal radial penetration. These findings indicate that highly viscous slurries exhibit poor permeability during field applications, ultimately compromising anchoring effectiveness due to insufficient grout dispersion.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## Materials and Methods
### Methods
#### 3.2.1. Wheat Surface Area Extraction  
Identifying crop surface area is the first step to mapping irrigated and rainfed areas based on remote sensing images. NDVI time series are commonly used for the classification of different crops, as time series are generally able to represent seasonal rhythms and phenological variations in crops [35; 36; 37].  
During the growth period of winter wheat, NDVI increases from seeding to dormancy. It then decreases from dormancy to greening, increases again during greening and heading and finally decreases from heading to maturity (Figure 3). For the winter wheat, NDVI exhibits a distinct changing pattern during two periods. The first is from early October to the dormancy period. For other land-use types, NDVI gradually decreases with decreasing temperature. Wheat NDVI, however, gradually increases and reaches a peak in greenness as a result of vigorous growth after sowing. During this period, the time-NDVI slope of wheat is greater than zero, but the corresponding slopes of other land cover types are less than zero. The second occurs from May to June (heading to maturity), which usually corresponds to the late growth period of wheat. Nutrients are gradually transferred from organs, such as roots, stems and leaves, to grains, resulting in a gradual decrease in greenness and NDVI. During this period, the time-NDVI slope of wheat is less than zero, but the slopes for other land-cover types are greater than zero. Therefore, winter wheat can be classified based on the slopes during these two stages.  
Generally, the peak NDVI for irrigated wheat is larger than that for rainfed wheat. However, sometimes, the peak NDVI for rainfed wheat may be nearly almost the same as irrigated wheat, or even lightly higher. After rainfed wheat reaches its peak NDVI, the NDVI value decreases rapidly. On the contrary, the peak NDVI for the irrigated wheat appears later and decreases more slowly compared to rainfed wheat.  
In this study, we used DEM (digital elevation model) information to extract the wheat surface area. The information of wheat planted areas at the county level was derived from the Shanxi Bureau of Statistics. The relationship between planted areas and their corresponding elevations showed that the elevation of most counties where wheat is planted is generally below 1000 m. Therefore, we defined a 1000-m height as the upper elevation limit for wheat. This means that elevations above 1000 m are not considered suitable for growing wheat.  
Figure 3. NDVI temporal variations for major land cover types in the south central Shanxi Province.
###### Abstract  
The objective of this research was to investigate the effect of pulsed electric field (PEF) processing on the volatile compounds produced in onion cultivars. The changes in the volatile compounds of onions were assessed comparing results observed while measured immediately and 24 h after PEF treatment using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS). Significant increases in the concentrations of propenathial S-oxide, propenyl propyl thiosulfinate, 2-methyl-2-pentenal, dipropyl disulfide, propenyl propyl disulfide, methyl propyl disulfide, and methyl propenyl disulfide were observed immediately after PEF treatment. The concentrations of propenyl propyl thiosulfinate, dipropyl disulfide, methyl propyl disulfide, methyl propyl disulfide, methyl propyl trisulfide, methyl propyl trisulfide, and propenyl propyl trisulfide increased after 24 h compared to initial concentrations. It is postulated that these changes are due to PEF-induced cell permeabilisation that facilitates enzyme-substrate reactions after the PEF treatment.  
pulsed electric field; onions; HS-SPME-GCMS; volatile sulfur compounds +
Footnote †: journal: Physics Letters
## 4 Types of Robots
### Marine Robots  
Marine robots are used for the inspection of marine structures such as bridge piers, dam embankments, underwater pipes, and offshore structures. There are two types of marine robots found in the literature--(a) submersible robots, also known as unmanned underwater vehicles (UUVs) [111] or autonomous underwater vehicles (AUVs) [112]; and (b) unmanned surface vehicles (USV), sometimes called surface water platforms (SWPs) [113]. Both USV and UUV are sometimes clubbed together under a single term of unmanned marine vehicles [114].  
Submersible robots, as the name suggests, can be fully submerged in water and can perform inspection under water. They are also sometimes referred to as underwater drones [115]. Underwater inspection is conventionally performed by human divers, which is costly, labor-intensive, and unsafe [116, 117]. With UUVs, positioning and navigating the robot is a huge challenge because most sensor and positioning techniques (GPS, fiducials, UWB, visual odometry, etc.) used on the ground are inviable underwater [111]. Dead reckoning techniques using inertial navigation, as explained later in Section 6.1.1, can be used for underwater navigation, but its accuracy deteriorates with time due to drift [111]. Optic and acoustic systems are also used that measure distance from the reflected light or sound waves [8]. However, inertial systems do not rely on information from outside and therefore are not affected by underwater rocks or marine life, as is the case with optic and acoustic systems [8].  
Unmanned surface vehicles, on the other hand, operate on the surface of a water body. They are low-cost devices that can facilitate safer inspection of marine structures. USVs are often designed as large devices so they are stable in rough waters, though small USVs have also been developed and tested. The use of waterproof cameras and other equipment is a crucial design consideration for USVs [118]. Although USVs have been primarily used in military applications before, the construction industry can also benefit largely from their application. Bridge inspection is one of the largest areas where USV can be utilized because out of 575,000 bridges in the US, 85% of them span waterways [119].
# Fixation Stability and Stiffness of Two Implant Systems for Proximal Femoral Variation Osteotomy
## 1 Introduction  
Proximal femoral variation osteotomy is a well-established surgical procedure in children with different hip problems, such as developmental dysplasia of the hip (DDH), containment in Perthes disease, and hip dislocation or subluxation in severe cerebral palsy (CP) [1; 2; 3; 4; 5; 6; 7; 8; 9]. In the literature, older implant systems had higher complication rates and longer time to bone union [8].  
To simplify the procedure and to stabilize the fixation, new blade plate systems were developed [9]. Given the low complication rates and postoperative mobilization under full weight bearing, fixed-angle blade plates became the most used implant over time [2; 4; 5; 6].  
Clinical studies with the new implant systems comparing blade plate and screw-side plate show generally good results [2; 10; 11; 12]. [PERSON] et al. described a rate of implant-related fractures of 2.5% in all patients, and they found no significant difference between the blade plate and screw-side plate [11]. Trainee surgeons had preference for cannulated blade plate when learning proximal femoral osteotomy, analyzing cannulated blade plate versus fixed angled blade plate. The fixed angled blade plate group had a higher prevalence of technical errors. However, radiological results were similar regarding migration and neck shaft angle [12]. In contrast, little is known about the biomechanical stability of the new implant systems used in proximal femoral variation osteotomy [1; 2; 3].  
Synthetic bones are often used as a substitute for human tissue in biomechanical in vitro investigations. In certain applications, they show good agreement in terms of macroscopic mechanical properties and anatomy [13]. A further advantage is their standardization and the associated low variance.  
This study aimed to evaluate fixation stability and stiffness of two new implant systems (PediLoc Locking Proximal Femur Plate System versus PediLoc Locking Cannulated Blade Plate System) using a synthetic bone model.
## 2 Morphological Reconstruction and Multi-feature Analysis for TIR Ship Images
### Gray-Level Morphological Reconstruction for Sea Clutter Suppression and Saliency Detection
#### 2.2.2 Pre-Processing and Intensity Foreground Saliency Detection
As can be seen from Figure 6d-f, for these three complex maritime scenes, the IFSM can greatly suppress most sea clutter and highlight the ship targets at the same time. This is because the OGMR or CGMR uses region merging and iterative approach method to automatically estimate the gray level of backgrounds, just like a boundary constrained region merging method, as presented in Figure 6a-c. Then according to the distinctive gray intensity characteristics between ship targets and sea background clutter in TIR image, the square of the difference between the original image and the estimated background is used to highlight the intensity foreground saliency map. Therefore, even for chaotic sea clutter, the GMR can estimate the local gray level of the background and IFSM can well highlight the foreground map of potential ship targets.
## 2 System Model
The signal received by the \(j\)th mMTC pair \(M\)U\({}_{j}\) is  
\[z_{j}=\sqrt{P_{j}^{d}}\delta_{j}x_{j}+\sum_{i\in\mathcal{N}}\alpha_{ij}\sqrt{P_{i }^{c}}h_{i,j}x_{i}+\sum_{ \begin{subarray}{c}j\to 1\\ j^{\prime}\
eq i\end{subarray} }^{M}\sum_{i\in\mathcal{N}}\alpha_{ij^{\prime}} \sqrt{P_{j^{\prime}}^{d}}f_{f,j}x_{j^{\prime}}+n_{0}, \tag{5}\]  
where the first term is the signal receiver \(M\)U\({}_{j}^{t}\) received from mMTC transmitter \(M\)U\({}_{j}^{t}\). The second term is the interference from the eMBB device \(C\)U\({}_{i}\). The third term is the interference signal from the transmitter of the mMTC pair \(M\)U\({}_{j}\) that occupies the same subcarrier with mMTC pair \(M\)U\({}_{j}\). According to (5), the SINR of the mMTC receiver \(M\)U\({}_{j}^{t}\) can be derived as  
\[\gamma_{j}=\frac{P_{j}^{d}\left|g_{j}\right|^{2}}{\sum_{i\in\mathcal{N}} \alpha_{ij}P_{i}^{c}\left|h_{i,j}\right|^{2}+\sum_{ \begin{subarray}{c}j\to 1\\ j^{\prime}\
eq i\end{subarray} }^{M}\sum_{i\in\mathcal{N}}\alpha_{ij^{\prime}}P_ {j^{\prime}}^{d}\left|f_{f,j}\right|^{2}+\sigma_{0}^{2}}. \tag{6}\]  
Since mMTC communication is mostly aimed at periodic monitoring data in IoT applications such as smart cities, the length of data packets transmitted is usually very short. According to information theory, the rate of short packets cannot achieve the Shannon limit. Therefore, the transmission rate of the mMTC devices is represented by the short packet rate [27], as shown in (7):  
\[R_{j}=\log_{2}(1+\gamma_{j})-\sqrt{\frac{1}{m}}\log_{2}(e)Q^{-1}(\epsilon) \sqrt{1-\frac{1}{(1+\gamma_{j})^{2}}} \tag{7}\]  
where \(m\) is the block length, \(\epsilon\) is the transmission error probability, and \(Q^{-1}(\mathrm{x})\) is the inverse of the Gaussian \(Q\) function.
# Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
## 4 Results
### Experimental Results
#### 4.3.2 Experiment Comparisons  
As shown in Table 1, TransUNet fuses multimodal features by simple element-level additive concatenation, and its multimodal fusion results are not as good as the fusion results using a single RGB modality. This demonstrates that the simple element-wise additive concatenation strategy is not effective in mitigating the effect of noise, which leads to performance degradation. EAEFSeg fuses multimodal features with a fusion module designed based on an attention or design strategy, and its multimodal fusion results are not as good as the results using a single RGB modality. This suggests that the noise in the depth image affects the fusion results and even reduces the fusion results of some networks. MAFNet uses an attention-based fusion module to achieve more effective feature fusion by dynamically adjusting the importance of different features. InconSeg employs a feature-complementary strategy to improve the model's performance in the fusion process by treating RGB and depth information as complementary feature sources. PotCrackSeg further develops this idea by adopting a fusion module based on semantic feature complementarity that effectively enhances the resistance to noise. The results show that all three methods perform significantly better than the conventional method of fusing only RGB images when processing RGB-D images, which supports the effectiveness of multimodal feature fusion in noisy environments. In contrast, EAEFSeg also uses an attention-based fusion module, but its fusion results fail to achieve the performance of a single RGB image. This phenomenon suggests that relying on the attention mechanism  
Figure 6: Comparing the segmentation results of different algorithms for negative obstacles.  
alone is not sufficient for solving the noise problem, but may instead lead to information loss or confusion. Therefore, an appropriate fusion strategy not only overcomes the negative effects of noise but also improves the overall performance of the model, making the feature fusion process more efficient and accurate.  
Compared with existing networks such as MAFNet, InconSeg, and PotCrackSeg, our network achieved the best performance in all indicators. We speculate that this achievement is mainly due to our proposed new data enhancement strategy and model improvement method. These innovations significantly enriched the diversity of features, allowing the encoder to extract more discriminative features. In addition, the design of the feature extraction module effectively expanded the perception domain, thereby improving the expressiveness of semantic features. In the final feature fusion stage, we were able to efficiently integrate the features of the two branches, further improving the detection performance. Overall, NROSegNet shows the best results among all compared networks, which fully verifies the effectiveness and superiority of our method.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 5 Related Work
### Privacy Preserving in Mobile Sensing Applications  
In terms of the applications about mobile sensing, most works focus on researching the various methods of a user's privacy protection or discussing the operation of aggregated data, like [19; 20; 21; 22; 23].  
Figure 11: Computing time spent by different steps in PPDC.  
Figure 10: Computing time of PPDC with different value of \(q\).  
Both [15; 24], consider the protection of user's privacy and then to seek the minimum computation in the aggregated dataset. They study how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple mobile users, without compromising the privacy of each user. Their scheme [15], which is based on [24], utilizes the redundancy in security to decrease the communication cost caused by each user's joining and/or leaving activities. Their protocol traverses the entire data space to find the minimum value on the basis of summation protocols rather than bitwise XOR operations.  
[PERSON] et al. in [25] propose a scheme for mobile crowdsensing sevices. In this scheme, a differential privacy mechanism is utilized through allowing different users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally, uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. However, the cost of transmission and evaluation is relatively non-negligible.  
[PERSON] et al. describe a lightweight framework for mobile crowd sensing systems in [26]. The framework can achieve the protection of each participating worker's sensory data and reliability information, and introduce little overhead to the workers. It is implemented by involving two non-colluding cloud platforms and adopting additively homomorphic cryptosystem, where on the workers' side, their jobs are perturbing the data with some random numbers rather than directly encrypting the data to be uploaded. This method, however, has more requests for the participating clouds which are proposed to be non-colluding but can communicate with each other.  
In [27], [PERSON] and [PERSON] propose semi-honest protocols to calculate the minimum and \(k\)th minimum values in mobile sensing systems. The data can be a time-series. By using probabilistic coding schemes and a cipher system, they construct two protocols that allow homomorphic bitwise XOR computations for their problems. The homomorphic bitwise XOR algorithm ensures privacy during the whole process. As the interaction times increase, the bits sent or received by users and the aggregator are much more.