esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Beacon-Less Routing Protocol  
The beacon-less opportunistic routing scheme increases the robustness of systems for supporting transmission decisions in a completely distributed manner. The Beacon-Less Routing algorithm (BLR) presented in [44] may be the first beacon-less routing protocol that makes use of location information to reduce routing overhead. Following BLR, geographic receiver-based beacon-less approaches have been regarded as a suitable solution for forwarding video flows in VANETs. The Maximum Progress Protocol (MPP) [45] is a beacon-less protocol that comprises alternating path-discovery phases and message-delivery phases, where the path is determined by using the Dijkstra algorithm. However, MPP cannot be adapted to a vehicular network with a dynamically-changing network topology. [PERSON] et al. [3] proposed a dynamic backbone-assisted protocol to support geocast communication on highway scenarios for different classes of vehicular applications. A distributed beacon-less dissemination protocol is introduced by maintaining backbone-based routes for video dissemination in multi-path V2V environments [4]. [PERSON] et al. [5] defined a timer-based vehicular backbone network protocol, where each vehicle can make forwarding decisions only based on the message header information, its current state and local measurements. These beacon-less routing protocols often choose the next hop according to the greedy forwarding based on the position information, regardless of the link's quality and transmission reliability.  
Apart from location information, other parameters have been also taken into account in the VANET routing protocol design [3; 4; 5]. [PERSON] et al. [46] proposed a link quality and geographical beacon-less opportunistic routing protocol for efficient video dissemination service. [PERSON] et al. [47] devised an efficient opportunistic relay protocol that exploits multiuser diversity and effectively copes with the dynamic channel, where the choice of a relay vehicle involves the tradeoff between the transmission range and throughput. [PERSON] et al. [48; 49] introduced the Quality of Experience (QoE)-driven and link-quality receiver-based protocol to allow live video dissemination, where the QoE-driven parameters are offered for the relay node selection and backbone maintenance, enhancing the capacity of the system in delivering QoE-aware videos. As far as we know, the routing path lifetime has not been taken into consideration on the multihop multimedia content transmission scheme design, which will be unfolded in this paper.
## 4 Experiment and Analysis of Experimental Results
### Objective Evaluation for Different Scenes  
We evaluate the performance of the MNSTLA model using the LCG and the BSF. The experimental results of the six actual sequences (Figures 5 and 6) are shown in Table 3. It can be seen that the method presented here can achieve the best values.  
Table 3 shows the average BSF and LCG of different methods on the six infrared image sequences. The Top-Hat and PSTNN methods have the lowest BSF and LCG values, and the corresponding background suppression capability is the worst. The IPI, RIPT, and WSNMSTIPT models have achieved good results in the six infrared image sequences, among which the RIPT and WSNMSTIPT models are slightly better than the IPI models in terms of performance; the NRAM model obtained a higher BSF value in the first sequence, but compared with the RIPT and WSNMSTIPT model, its background suppression ability is still not ideal; the MNSTLA model proposed herein achieved the highest BSF value on all six infrared image sequences, which means the robustness and efficiency of background suppression are better. In terms of LCG, this method has the highest LCG value and the best target enhancement of the six image sequences. From the evaluation results, it can be seen that the LCG and BSF values of the MNSTLA model proposed herein are much higher than those of other methods, indicating that it has great advantages in object enhancement and that the signal-to-noise ratio of images is improved effectively.  
In order to compare the above optimization methods more objectively, the comparison of the ROC curves of the sequences 1-6 is shown in Figure 11. It is found in the study that the RIPT was the worst performer and that the Top-Hat method and the PSTNN method are not satisfactory. The IPI model achieved good results on the six infrared image sequences, and the WSNMSTIPT methods are slightly better than the IPI model in terms of performance. The detection rate of the NRAM model is not as high as that of the WSNMSTIPT models, and this is because the NRAM model does not consider the temporal-spatial information. Finally, under the same false alarm ratio, the MNSTLA model proposed herein achieved the highest detection probability, which means that the proposed MNSTLA model has better performance than that of any of the other models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**a**} & \multicolumn{2}{c}{**b**} & \multicolumn{2}{c}{**c} & \multicolumn{2}{c}{**d**} & \multicolumn{2}{c}{**e**} & \multicolumn{2}{c}{**f**} \\ \cline{2-13}  & **BSF** & **LCG** & **BSF** & **LCG** & **BSF** & **LCG** & **BSF** & **LCG** & **BSF** & **LCG** & **BSF** & **LCG** \\ \hline Top-Hat & 7.73 & 5.94 & 3.28 & 6.76 & 7.86 & 1.67 & 9.66 & 7.53 & 10.25 & 3.64 & 7.34 & 3.45 \\ PSTNN & 3.85 & 1.23 & 3.86 & 8.20 & 4.16 & 1.18 & 3.67 & 2.43 & 4.14 & 3.16 & 3.14 & 2.99 \\ IPI & 3.35 & 1.70 & 2.30 & 5.65 & 3.45 & 1.06 & 3.19 & 3.18 & 5.61 & 2.37 & 2.02 & 1.94 \\ RIPT & 0.92 & 3.11 & 0.72 & 3.16 & 1.76 &
## 3 Results and Discussion
### Soil Features  
The development of subsurface erosion landform is closely related to soil properties. The soil that forms mud forest landform is a kind of loess-like soil (Figure 9). The results show that (a) all particles are smaller than 0.5 mm, (b) fine particle fraction (clay and silt) is more than three quarters, (c) a silt fraction (0.005-0.075 mm) accounts for the largest proportion, more than half. The soil was then classified as lean clay (CL) based on the Unified Soil Classification System (USCS) [57; 58].  
Table 5 shows the physical properties of loess-like soil sample, including particle size composition, plasticity index, liquid index, porosity, etc. silt content accounts for 52%, which is the highest proportion. The plasticity index value of soil sample is low, and the liquid index is less than 0. The porosity of the soil sample is 57.46%.  
Chemical composition of loess-like soil can be seen in Table 6. From the oxide composition, the content of silicon dioxide is the highest, followed by alumina. This is similar to the composition of loess. Due to the high content of calcium oxide, magnesium oxide and potassium oxide, it proves that the soil formation environment is relatively dry and cold. The calculated silicic acid coefficient (Ki = SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\)) of the sample is 7.93. The larger Ki value indicated that the loess-like soil was formed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Clay content (\%) & 24.42 \\ Silt content (\%) & 52.75 \\ Sand content (\%) & 22.83 \\ Natural density (g/cm\({}^{3}\)) & 1.44 \\ Natural moisture content (\%) & 13.23 \\ Liquid limit (\%) & 26.78 \\ Plastic limit (\%) & 18.26 \\ Liquidity index & \(-\)0.59 \\ Plasticity index & 8.52 \\ Soil classification & CL \\ Porosity (\%) & 57.46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: List of physical properties of loess-like soil sample.  
Figure 9: Grain size distribution of loess-like soil.  
when the climate was relatively arid. Therefore, the soil was formed in a dry and cold environment by the analysis of the oxide content of the sample.  
Clay dispersibility is a good indicator of the dispersion vulnerability of soil and, therefore, of the associated risks of soil erosion [57]. The soluble salt composition of soil sample investigated in the present study is given in Table 7, together with the calculated SAR, PS and TDS. The salt composition affects the properties of loess-like soil, especially dispersibility. Plotting of values presented in Table 7 in Sherard diagram clearly shows the zone of soil samples. All samples plot zone A (Figure 10). This indicates that the soil in mud forest area is dispersive.
## 3 Simulation Results and Discussion
### Generation of CP Waves  
Because of the new feed shape and symmetrical structure, it is possible to generate circular polarization and increase bandwidth. Few studies on the similar idea of increasing S11 bandwidth and generating CP BY employing symmetrical designs have been published [19; 20; 21]. By improving the sizes, two smidgins S11 points that vibrate at close frequencies can be generated. As a result, two degenerate modes are formed, and because they are orthogonal to one another, they produce C.P. The degenerate mode pair TEx13 at 3.85 GHz and TEx13 at 4.5 GHz were thrilled to generate the Cp wave. For the creation of the C.P signals, the initial criterion of orthogonal modes is met. Furthermore, design-2 has  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline
**H2** **(mm)** & **H3** **(mm)** & **H4 (mm)** & **W2 (mm)** & \
\begin{tabular}{c} **Width of** \\ **Strip (mm)** \\ \end{tabular} &
\begin{tabular}{c} **10-dB S11 Bandwidth** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{5}{*}{11} & \multirow{5}{*}{1.5} & 7 & 0 & 1 & 22.43 \\ \cline{3-6}  & & 7.5 & 1 & 1 & 22.18 \\ \cline{3-6}  & & 8 & 2 & 1 & 23.92 \\ \cline{3-6}  & & 8 & 2 & 1 & 23.65 \\ \cline{3-6}  & & & 1 & 23.40 \\ \cline{3-6}  & & & 1 & 24.14 \\ \cline{3-6}  & & 8.5 & 3 & 1 & 25.67 \\ \cline{3-6}  & & & 1 & 25.42 \\ \cline{3-6}  & & 9 & 4 & 1 & 26.16 \\ \cline{3-6}  & & & 1 & 27.63 \\ \cline{3-6}  & & & 1 & 28.38 \\ \hline \multirow{5}{*}{12} & \multirow{5}{*}{2} & \multirow{5}{*}{10.5} & 1 & 31.89 \\ \cline{3-6}  & & 10.5 & 5 & 1 & 32.97 \\ \cline{3-6}  & & & 1 & 32.38 \\ \cline{3-6}  & & 11 & 5.5 & 1 & 29.13 \\ \cline{3-6}  & & 11.25 & 5.75 & 1 & 28.51 \\ \cline{3-6}  & 2.5 & 11.50 & 6 & 1 & 28.90 \\ \hline \multirow{5}{*}{13} & \multirow{5}{*}{3} & 12 & 7 & 1 & 26.42 \\ \cline{3-6}  & & & 1 & 26.20 \\ \cline{3-6}  & & & 1 & 23.97 \\ \cline{3-6}  & & & 1 & 23.75 \\ \cline{3-6}  & & & 1 & 23.54 \\ \cline{3-6}  & & & 1 & 22.34 \\ \cline{3-6}  & & & 1 & 21.91 \\ \hline \end{tabular}
\end{table}
Table 3: Optimization of the Novel conformal strips (unit: mm).  
Figure 6: Impedance-matching bandwidth of Design-2.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 4 Critical Auxiliary Data Necessary to Support Detecting SCE from SAR Data
### Utillization of Temperature and the Need for Snow Record Data  
Although most SAR-based algorithms such as \"Nagler's method\" ([PERSON], 126) did not require ground information as an input, meteorological data about temperature and precipitation makes estimating the snow cover conditions easier. For example, it is possible to infer the retreat of snow between two observations if the daily mean air temperature is rising (133) and the existence of wet snow can be postulated when surface temperature is observed to be near 0 \({}^{\circ}\)C (68). Thus, many studies included temperature information gathered by ground surveying or meteorological stations (118; 136; 149; 163; 169; 188).  
However, the often sparsely distributed meteorological stations hardly satisfy the required spatial resolution necessary to derive an areal inventory of surface temperatures. Hence, [PERSON] et al. (151) calculated a temperature map by interpolating the data derived from meteorological station network. Another approach is utilizing spaceborne thermal imagery. [PERSON] and [PERSON] (85) used atmospheric profiles of temperature and water vapor at the sensed time to derive the surface temperature based on recorded satellite radiance. Moreover, snow record data also helps to analyze the snow cover conditions. [PERSON] et al. (189) utilized the snow accumulation recorded by snow stations to decide when the snowmelt period ended.
## 3 Present PV Technologies
### The Module Assembly Technologies  
The modules are assembled from cells of almost the same parameters and the efficiency of the module is lower than the efficiency of the cells from which it is assembled. Cell-to-module (CTM) losses are due to the reflectivity of the outer protective layer, the absorption of light in the encapsulating layers, unabsorbed radiation, ohmic losses at the connections of the cells into strings, etc. [44]. The present module design and technology can be improved through improving light management (e.g., reducing the reflectivity of the glass by means of antireflective coatings, optimization of cell spacing, increasing the reflectance of internal inactive areas, and reducing the optical shadowing of metal) and reducing resistance losses (e.g., increasing number of busbars, using half-cut cells or strip cells for shingling, optimizing ribbon cross-section according to the width, optimizing junction box and cabling design). By combining these tools, CTM losses can be reduced below 2%. In combination with the PV cells' efficiency improvements, the PV module efficiency increases, as shown in Figure 14 for average c-Si module efficiency. The top module efficiency has reached 23% [45] using P-type PERC cells. Another trend stems from the ability to produce larger solar cells. The larger cell area allows the production of large area modules with high power that help reduce BOS costs [46]. The increase in maximum module power is shown in Figure 15. However, increasing the area of the module places greater demands on handling and on the resistance of the modules to mechanical stress.
## 5 Experimental Results and Analyses  
In this section, eight typical grayscale images with a size of \(256\times 256\) pixels are chosen to validate the denoising performance of the Lp-OSG-TV-FAST method. The test images are as shown in Figure 3. The image \"House\" is downloaded from [[http://sipi.usc.edu/database/database.php?](http://sipi.usc.edu/database/database.php?)]([http://sipi.usc.edu/database/database.php?](http://sipi.usc.edu/database/database.php?)) volume%92=%92 misc&image=5 top. The images \"Lena\" and \"Pepper\" are from [[http://decsai.ugr.es/cvg/dbimagenes/](http://decsai.ugr.es/cvg/dbimagenes/)]([http://decsai.ugr.es/cvg/dbimagenes/](http://decsai.ugr.es/cvg/dbimagenes/)). The images \"Woman\", \"Girl\", and \"[PERSON]\" are from [[http://www.hlevkin.com/default.html#testimages](http://www.hlevkin.com/default.html#testimages)]([http://www.hlevkin.com/default.html#testimages](http://www.hlevkin.com/default.html#testimages)). The images \"Milk drop\" and \"Shoulder\" are from [[http://www.cs.cmu.edu/~cil/v-images.html](http://www.cs.cmu.edu/~cil/v-images.html)]([http://www.cs.cmu.edu/~cil/v-images.html](http://www.cs.cmu.edu/~cil/v-images.html)). The versions of the images in our paper are other special formats which are converted by Photoshop from the sources above.  
The method proposed here is compared with ATV, ITV, TGV, OSG-L1, and OGS-Lp methods, and is evaluated objectively in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), runtime, and other experimental indicators [45]. Simulations are performed on MATLAB R2014a platform running in a hardware environment of Inter(R) Core (TM) i7-6700%3.4 GHz CPU and 16 GB memory.
## 5 DSS Techniques and Generalized Finite State Machine
### Dss1--Collaboration Protocol
#### 5.1.2 Collaboration Protocol Structure  
CP is used to disseminate at least the following type of messages:  
* Position of a node (mandatory message, generated by all transmitter nodes or their respective GSs/GWs);
* Active transmission characteristics (mandatory message, generated by all transmitter nodes or their respective GSs/GWs);
* FWA alarm message in case of detected interference (optional message, generated only by receivers in terrestrial networks or their respective GWs).  
The position of a node can be defined in multiple ways, depending on the coordinate system employed. In our study, we used the Earth Centered Earth Fixed (ECEF) coordinate system [30]. Each node advertises its location periodically. To reduce the overhead of the GS-satellite control link, we assume that satellites rely on their GSs to advertise their position in the CP network.  
Regarding the active transmission characteristics message, it is used to describe any active transmission from any interface of a transmitting node. Active transmission characteristics messages are advertised periodically upon change in transmission characteristics, if the change is substantial enough to influence other entities (i.e., additional spectrum allocated). Furthermore, the CP architecture is designed so that each active satellite and ESIM (or their GS) should have all relevant information only about transmissions that could affect them (in the case of FSM only running at GS, transmissions that could affect GS's belonging satellites and ESIMs), their power levels, and their timing information, and therefore could build a synthetic interference map. For the transmission to be clearly described, and for receivers to be able to calculate the possible interference to them, the following information must be included in active transmission characteristics message for each interface:  
* Transmission power setting of the interface;
* Destination focal center point coordinates;
* Gain as a function of transmission cone azimuth and elevation;
* Time information about the transmission (continuous, periodical, start and end of transmission, allocated MF-TDMA slot[s], etc.).  
Figure 3: Network architecture of CP consisting of different entities with their respective user and control links.  
The FWA alarm message should contain the location of the receiver node and time-frequency resource usage information (i.e., utilized MF-TDMA slot[s]).
## 3 Epidemiology and Diagnosis
### Diagnostic Test for MRONJ  
Several serum-based tests have been explored to assess the risk of MRONJ, including markers such as osteocalcin, alkaline phosphatase, N-terminal telopeptide, and C-terminal telopeptide cross-link of type I collagen (CTX) [16]. CTX is released during the bone remodeling process and has been considered a potential diagnostic indicator for MRONJ [51]. However, the serum level of CTX has its limitations, failing to predict MRONJ development in more than 40% of patients [16]. In contrast, recent advancements in technology have led to the development of a machine learning model demonstrating a 100% sensitivity and 83.3% specificity in MRONJ risk assessment [52]. For a test to be considered effective in diagnosing MRONJ, it is generally expected to meet a minimum specificity of 95% and sensitivity of 75% [16]. However, CTX falls short of these benchmarks, with a sensitivity of 56.8% and specificity of 72%, indicating the need for more accurate and reliable diagnostic methods [16].  
The medication-induced salivary changes are also considered for the assessment of MRONJ risk [21]. The study highlights those various pharmacological agents, including BPs, DS, corticosteroids, and chemotherapy, known to alter salivary composition, secretion levels, and function [21]. The review underscores the protective and functional properties of saliva in maintaining oral homeostasis, including tooth remineralization, buffering of acids, inhibition of harmful microorganisms, prevention of xerostomia, and facilitation of speech and swallowing. Any disruption in salivary function could lead to a range of complications, impacting the quality of life.  
The review illuminates the potential role of salivary alterations as risk factors in the development of MRONJ [21]. It identifies a decrease in salivary flow and changes in -salivary protein levels as key indicators that may predispose individuals to MRONJ [21]. The intricate relationship between salivary changes and MRONJ development is further explored, with particular emphasis on how bone-modifying agents affect the microstructure of saliva [53]. These agents, by altering saliva's microbiome profiles [54], cytokine profiles [55], interleukins [56], hypotaurine [57], and RNA-binding proteins such as RMBS3 [58], have been associated with an increased risk of MRONJ due to their influence on salivary composition and function.  
Despite these insights, the review does not present specific sensitivity and specificity values for using salivary changes as diagnostic markers for MRONJ [21]. Therefore, while the review highlights a promising avenue for early detection and risk assessment, the actual diagnostic utility of salivary alterations in MRONJ remains to be validated through further research. This calls for more in-depth studies to substantiate the diagnostic value of these salivary changes, ideally through standardized, large-scale, prospective research efforts.
## 3 Results
### Comparative Analysis
#### 3.2.6 Efficiency Analysis of Parallel Computing  
Optimization of algorithmic efficiency becomes critical when processing large-scale image datasets. This section systematically evaluates the temporal performance of the STSU-SAR-SIFT method under three computational architectures: single-thread CPU, multi-thread CPU, and CPU-GPU heterogeneous computing. The feature point extraction and descriptor construction processes involve complex logical decisions and full-image traversal operations, which exhibit fundamentally different computational characteristics compared to conventional image processing tasks. Experimental results demonstrate that while GPUs show limited efficiency in logical operations, they provide substantial advantages in scale-space acceleration computations, whereas CPU architectures are better suited for logic-intensive processing. Benchmark tests using Sentinel-1 data covering Hunan Province (Table 5) indicate a baseline processing time of 2.7 h without acceleration. With multi-thread CPU optimization, the processing time was reduced to 1.3 h. The  
Figure 11: Matching results of different algorithms.  
Figure 12: Test results of the three methods (**a**) NCM of the three methods on each pair of images; (**b**) RMSE of the three methods on each pair of images.  
implementation of CPU-GPU co-acceleration further decreased the processing time to 0.75 h, achieving a 3.6-fold efficiency improvement. For large-scale GF-3 satellite data processing, the CPU-GPU heterogeneous architecture achieved a 2.6-fold speedup ratio compared to CPU serial processing. The data in Table 5 conclusively demonstrate the significant acceleration benefits of heterogeneous computing.