esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Static Count Assessment  
In static count testing for cotton bolt detection, 122 images were randomly selected from the test set, and detections were conducted using the YOLOv8 and the improved COTTON-YOLO models. The detection results displayed in Figure 14 demonstrate that the improved COTTON-YOLO model excels in detecting varying numbers of cotton bolls. The original YOLOv8 model exhibited a coefficient of determination (\(R^{2}\)) of 0.543, which increased to 0.853 with the improved COTTON-YOLO model. This significant improvement suggests a substantial enhancement in the COTTON-YOLO model's ability to explain variability in the data. Moreover, compared to the original YOLOv8 model, the improved COTTON-YOLO model demonstrated significant advantages in terms of root mean square error (RMSE) and mean absolute error (MAE). The MAE was reduced from 1.231 to 0.587, and the RMSE decreased from 1.779 to 1.008. These improvements in metrics, by 52.34% and 43.39%, respectively, further demonstrate the efficacy of the COTTON-YOLO model in enhancing cotton bolt detection performance. Overall, the COTTON-YOLO model not only significantly improves the accuracy of cotton bolt detection but also highlights its practical application value in cotton field management, thus providing important technical support for the management and monitoring of cotton growth. The results are depicted in Figure 15 and Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(AP_{50}\) & \(AP_{50:95}\) & Parameters & Weight Size \\ \hline Faster-RCNN & 92.6 & 54.0 & - & 315.2 MB \\ Cascade-RCNN & 92.6 & 54.8 & - & 527.6 MB \\ FCOS [32] & 93.1 & 54.7 & - & 244.7 MB \\ YOLO-tiny & 90.0 & 53.5 & 12.1 M & 24.4 MB \\ YOLOv5s & 89.1 & 53.8 & 9.1 M & 18.5 MB \\ YOLOv6s & 89.7 & 55.3 & 16.4 M & 32.8 MB \\ YOLOv8m & 89.9 & 56.4 & 25.9 M & 52.0 MB \\ YOLOv8-DETR & 78.1 & 42.6 & 12.9 M & 26.2 MB \\ YOLOv9-gelan [33] & 95.2 & 61.7 & 25.2 M & 51.5 MB \\ COTTON-YOLO & 95.8 & 65.7 & 6.1 M & 12.4 MB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different mainstream models.
## 2 Materials and Methods
### SAR-SL Production and Quality Control Indicators
#### 2.4.2 Shoreline Filtering Based on Distance from the Reference Line
Once the optimal number of components is determined, the Gaussian distribution with the largest population is considered, and its mean (\(\mu_{d}\)) and standard deviation (\(\sigma_{d}\)) are calculated in a way that only distances (\(d_{i}\)) that satisfy the following expression are considered:  
\[\mu_{d}-2\sigma_{d}\leq d_{i}\leq\mu_{d}+2\sigma_{d} \tag{1}\]  
An example of GMD filtering is shown in Figure 6. When the SLs are well concentrated within a polygon, a GMD with a single component is sufficient to statistically describe their distances, as demonstrated in Figure 6a,b. In contrast, for more dispersed distributions, a GMD with multiple components is necessary, as showcased in Figure 6c,d. The final stage involves computing the mean distance value (\(d_{M}\)) for each SL by grouping all filtered distance points associated with each SL within individual polygons, thus assigning a distinct value to each SL. This value is assumed to indicate the SL position, and from all SLs, we obtain a time series of \(d_{M}\) which is suitable for detecting shoreline changes over time. Additionally, in each polygon, a linear regression is applied to the filtered SLs' mean \(d_{M}\). The slope of the regression line represents the change rate for that  
Figure 6: Illustration of Gaussian mixture distribution (GMD) technique for two examples showing a single distribution **(a,b)** and a GMD with multiple components **(c,d)** for the Start Bay study case. Panels **(a)**,**(c)** show the polygons as light-orange-shaded areas and the SL points after filters. The points after the heatmap and polygon filters are indicated as blue circles, and the the ones after the GMD as pink circles. Panels **(b,d)** show the histograms of the distances from the reference line distribution for the examples shown in panels a and c, respectively. Source of aerial imagery: Google Satellite Hybrid. polygon, expressed in m/year. The positive or the negative sign of the slope indicates accretion or erosion over time, respectively.
## 4 Results
### Ablation Experiments and Loss Curves
Nonetheless, the overall trend suggests that the model was still able to effectively capture change patterns in the data and gradually converge to a stable state, demonstrating strong generalization capabilities. However, room remains for further optimization, particularly in addressing the fluctuations in the validation set and improving stability. Overall, the proposed model demonstrated outstanding performance across different datasets, effectively adapting to varying data distributions and complex feature variations while showcasing robust change detection capabilities. Despite the potential for further optimization, especially in enhancing stability on complex datasets, the current results strongly validate the model's excellent performance in the farmland change detection task. In addition to analyzing the loss curves of the training and validation sets, we further evaluated the effectiveness of the proposed multi-scale supervision strategy. As shown in Table 4, introducing multi-scale loss consistently improved performance across both the HRSCD and CLCD datasets. When using only the single-scale loss \(L(P_{\text{s}_{4}})\), the F1 scores for the two datasets were 63.74% and 74.14%, respectively. By incorporating an additional loss at the second scale, \(L(P_{\text{s}_{8}})\), the recall on the HRSCD dataset increased to 69.36%, while the recall on the CLCD dataset increased significantly to 81.20%. This indicates that multi-scale supervision effectively enhances the model's ability to detect change regions. Furthermore, after introducing the third-scale loss \(L(P_{\text{s}_{16}})\), the F1 scores improved to 65.48% and 75.02% on the HRSCD and CLCD datasets, respectively, with the IoU scores also increasing to 48.68% and 60.02%, demonstrating the contribution of multi-scale supervision to both detection accuracy and spatial coverage. Figure 6: **(a)** CLCD; **(b)** HRCD. These results support the initial design motivation: features extracted at different scales possess unique advantages in capturing change patterns. Specifically, multi-scale features not only provide complementary information but also capture changes at different spatial resolutions and semantic abstraction levels. Therefore, explicitly imposing loss constraints at these intermediate scales effectively guides the model to progressively learn more discriminative and robust multi-scale representations, ultimately improving both the accuracy and stability of the change detection process.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 2 Related Works
The main challenge for web service providers [41] is to provide internet access inside moving vehicles with the best speed possible. The cloud radio vehicular ad hoc network architecture is a sensible solution for data broadcasting from the cloud to moving vehicles within a specific radius for road safety and traffic supervision. Traditional radio network architectures [42] are less efficient with respect to energy consumption, so a multilayer system with the elimination of intermediate points is proposed to improve energy efficiency. The creation of a 6G environment with dynamic objects is possible by setting dedicated paths with higher speeds and energy efficiency. A neuro-fuzzy system is proposed [42; 43] as an efficient tool to reduce intermediate points, and a comparison with various parameters is performed to validate its effectiveness. VANETs can benefit from the Cloud Radio Network(C-RAN), which provides a durable platform for ad hoc networks. Node selection can be improved with Learning Vector Quantization (LVQ) and neuro-fuzzy techniques such as back-propagation. Next-generation technology, such as a sixth-generation virtual network, may be introduced in the next decade using advanced techniques such as learning vector quantization and a neuro-fuzzy forwarder scheme. The cost-effective interchange and viability of buses used as mRSUs [1] have been investigated, and the results show that the performance was improved after the sRSU was replaced by the mRSU, but the costs increased and remediation was difficult, so both static and moving RSUs were implemented in accordance with all relevant requirements: static, installed on public transportation, and mobile. The simulation results [44] corroborate the analytical model's findings, which show that the greater the number of mRSUs, the greater the probability of communication in VANETs. According to the findings, adopting a 5 per cent rate in mRSUs may increase the communication efficiency by two times while decreasing the cost. Because the automobile is not under fixed RSU radio coverage, it may communicate with adjacent mRSUs to obtain traffic information. The simulation findings of the social-aware mobile roadside unit scheme [45] for content distribution in vehicular social networks show that it improves the source vehicle's mean throughput performance. It is a cross-layer RSU/mobile relay station (RS) detection method for VANETs that takes into account many variables from various protocol levels, such as the connection status, bandwidth, and latency aspects, and user services.
# Historically Based Room Acoustic Analysis and Auralization of a Church in the 1470s
## 3 Digital and Historical Reconstruction
### Room Acoustic Simulations
#### 3.3.1 Adaptation of the Digital Model  
The high-detail model used for visual presentation was transformed into a digital model for acoustic simulation, primarily by simplification of various surfaces. The structure of the ceiling vaults was simplified significantly, and detailed models of sculptures and altar decorations were replaced by simple geometric forms. This improves the performance of the acoustic simulations with regards to accuracy and to calculation speed. Examples of simplifications are shown in Figures 6 and 7. The simplified model is exported from Google Sketch-up using ODEON's exporting tool and imported to ODEON. The exported model counts 5600 surfaces and takes around 1.5 s to export to ODEON format.  
Figure 6: Simplification of vaults and chapels in the acoustic model, view from below with floor layer being hidden. The elaborate wooden walls around the altars and graves (compare to Figure 3) are rendered with boxes which are assigned a 45% transparency in ODEON. Screenshot from Google Sketch-up.  
Figure 7: Simplification of statues from the visual model to the acoustic model. Screenshot from Google Sketch-up.
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 3 Energy Efficiency Analysis  
The achievable rate of the \(k\)-th UE is given as  
\[\begin{split} R_{k}&=\mathbb{E}\left\{\log_{2} \left(1+\gamma_{k}\right)\right\}\\ &=\mathbb{E}\left\{\log_{2}\left(1+\frac{P\theta_{k}}{\left\| \mathbf{a}_{k}\right\|^{2}\sigma^{2}}\right)\right\}.\end{split} \tag{8}\]  
Then, the sum-rate of the uplink massive MIMO system can be given as  
\[R_{\text{sum}}=\sum_{k=1}^{K}R_{k}. \tag{9}\]  
Denoted as \(P_{\text{sum}}\), the total power consumption of the system consists of four parts; i.e.,  
\[P_{\text{sum}}=P_{0}+NP_{c}+KP_{c}+\sum_{k=1}^{K}\frac{P\theta_{k}}{\xi}, \tag{10}\]  
where \(P_{0}\) denotes the basic power consumption of the system, \(P_{c}\) is the circuit power consumption per the radio frequency (RF) chain, and \(\xi\) is the power amplifier efficiency. The EE \(\eta_{\text{EE}}\) can be expressed as  
\[\eta_{\text{EE}}=\frac{R_{\text{sum}}}{P_{\text{sum}}}. \tag{11}\]  
**Theorem 1**.: _The EE \(\eta_{\text{EE}}\) of the distributed massive MIMO system can be tightly approximated by the following deterministic value_  
\[\eta_{\text{EE}}=\frac{\sum_{k=1}^{K}\log_{2}\left(1+\frac{P\theta_{k}N}{ \sigma^{2}}\frac{\left(r_{k}^{2}+r^{2}\right)^{\frac{n}{2}-1}}{\left|r_{k}^{2 }-r^{2}\right|^{n-1}}\right)}{P_{0}+NP_{c}+KP_{c}+\sum_{k=1}^{K}\frac{P\theta_{ k}}{\xi}}. \tag{12}\]  
**Proof.** Similarly to [26], the achievable rate of the \(k\)-th user falls in the interval of \([R_{k1},R_{k2}]\) or \([R_{k2},R_{k1}]\) when \(N\) is large, where  
\[R_{k1}=\log_{2}\left(1+\frac{P\theta_{k}N}{\sigma^{2}}\frac{\left(r_{k}^{2}+r^{ 2}\right)^{\frac{p}{2}-1}}{\left|r_{k}^{2}-r^{2}\right|^{v-1}}\right), \tag{13}\]  
and  
\[R_{k2}=\log_{2}\left(1+\frac{P\theta_{k}N}{\sigma^{2}}\frac{\left(r_{k}^{2}+r ^{2}\right)^{\frac{p}{2}-1}}{\left|r_{k}^{2}-r^{2}\right|^{v-1}}\cdot\frac{2^ {3\left(\frac{p}{2}-1\right)}\Gamma^{2}\left(\frac{p}{2}-\frac{1}{2}\right)}{ \pi\Gamma\left(v-1\right)}\right). \tag{14}\]  
In addition, we have \(\left|R_{k1}-R_{k2}\right|<0.6\). Thus, the sum-rate \(R_{\text{sum}}\) can be tightly approximated by  
\[\tilde{R}_{\text{sum}}=\sum_{k=1}^{K}\log_{2}\left(1+\frac{P\theta_{k}N}{ \sigma^{2}}\frac{\left(r_{k}^{2}+r^{2}\right)^{\frac{p}{2}-1}}{\left|r_{k}^{2 }-r^{2}\right|^{v-1}}\right). \tag{15}\]  
Combining (10), we can conclude the proof. \(\square\)
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 4 Simulations
### Node Density
This leads to a rapid decrease in the probability of nodes discarding generated data, providing more data transmission opportunities and significantly enhancing the likelihood of forming long-distance transmission paths. Consequently, both link complexity and interference risks increase, elevating the probability of data transmission failure. When \(\rho\) continues to increase, the average distance between nodes decreases significantly, reducing the network coverage area. Under such conditions, each newly established transmission path occupies a larger number of silent nodes, resulting in a notable decrease in the number of parallel transmission paths and thus reducing the overall number of transmission paths established per second. Simultaneously, when the total number of nodes is fixed, the reduction in inter-node distance decreases the generation rate of long-distance transmission paths but improves the reliability of data transmission, as shown in Figures 7 and 8. In summary, according to the theoretical model of Equation (11), there exists a close functional relationship between the network throughput, the number of parallel transmission paths \(N_{\text{path}}\), and the transmission bandwidth per path \(B_{\text{path}}\). Although both the path generation rate and the transmission failure probability initially increase and then decrease with the increase of \(\rho\), the growth rate of the path generation rate significantly exceeds that of the transmission failure probability. Consequently, the network throughput exhibits a characteristic of first increasing and then decreasing as \(\rho\) increases, as shown in Figure 9.  
Based on this analysis, the following conclusions can be drawn: When the initial \(\rho\) value is low, as \(\rho\) increases, nodes gain more transmission opportunities, leading to a rapid rise in the transmission path generation rate. Although the increase in long-distance transmission paths elevates the transmission failure probability, the growth rate of the path generation rate far surpasses that of the failure probability, resulting in an upward trend in network throughput. As \(\rho\) continues to increase, the network topology further contracts, shortening the inter-node distances. This contraction increases the number of silent nodes occupied by each transmission path, thereby reducing the path generation rate. Although the reduction in long-distance paths when \(\rho\) is sufficiently large decreases the transmission failure probability, the decline in the path generation rate outweighs the decrease in failure probability, leading to a reduction in the overall network throughput.
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 3 Results and Discussion
### Variography
#### 3.3.2 Indicator Variography for Spatial Variability Analysis and Modeling  
The study of directional indicator variograms considers the spatial correlation between indicator variables, which are binary variables that show the existence or absence of a particular characteristic in a specific area. In the presented scenario, directional indicator variograms were utilized to evaluate the geographical continuity of four possible cut-offs, namely 0.3, 0.6, 0.9, and 1.5 parts per million (ppm).  
The lag spacing and the angular tolerance had to be adjusted so that there would be sufficient numbers of samples. Lag spacing is the distance between the data points used to make the variogram, and angular tolerance is the biggest angle between them. In the variogram analysis, an accurate representation of the spatial correlation of the data is essential, and these factors play a significant role in that.  
Table 5 lists the parameters of the variogram models fitted to the experimental variograms, while Figure 6 displays experimental variograms and their corresponding fitted models. The spatial correlation and variability of the data can be better understood by examining the variograms and their associated parameters, allowing for more accurate predictions of gold values in areas that were not sampled.  
Using an exponential variogram model, Table 5 shows the directed variogram parameters for four different cut-off values, from 0.3 to 1.5. The nugget effect is a representation of the data's variation when there is no distance involved. In this scenario, the nugget effect drops marginally with increasing cut-off value, which may indicate that measurement or sampling error reduces data variability.  
The value of sill represents the entire variation in the data. The relative consistency of the sill values across the various thresholds suggests that the spatial correlation of the data is robust.  
The range is the distance where spatial correlation becomes minimal. Range values fluctuate across cut-off values, demonstrating that spatial data correlation varies with cut-off value.  
Last but not least, the azimuth and dip values indicate, respectively, the direction and angle of the connection that is the strongest. Variation in the directional dependency of the data is also seen in the azimuth and dip values as a function of the cut-off value.
# Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms
## 3 Skin Metabolites  
### Skin Metabolites Sources  
Analysis of unconventional biological samples, such as skin, has been associated with a significant focus of attention lately, to obtain clinically applicable information, in addition to conventional biofluids such as urine, saliva, and blood.  
Interstitial fluid (IF), the fluid filling spaces around cells, provides body cells with nutrients and helps in waste removal. Although its composition is not fully specified, it has been reported by some researchers that it is similar to plasma, due to the presence of lipids and proteins, with lower component concentrations because of their transcapillary flow from the blood to IF. Protein degradation that takes place in the skin's outer layers, interstitial fluid, sebum, and sweat is the source of low-molecular-weight compounds present within or on the skin. Products secreted from exocrine glands and volatile compounds released by the skin reflect metabolic changes due to cancer progression [26].  
Secretory cells of the eccrine gland secrete electrolytes, inorganic ions, amino acids, and elements, across luminal cell membranes as an aqueous fluid, without disintegrating the cells. On the other hand, lipids, proteins, and steroids are secreted as a viscous fluid from the apocrine gland's secretory cells. Both eccrine and apocrine glands' secretions contain  
Figure 1: Metabolomic analysis advantages over other omics technologies.  
skin metabolites that can be detected using analytical techniques. Furthermore, other metabolites that are not excreted onto the skin surface can be detected in the epidermis and interstitial fluid. Complete degeneration of glandular cells leads to the holocrine secretion of sebum, which is a complex combination of lipids formed and secreted by sebaceous glands [27].  
A wide collection of microorganisms such as bacteria, protozoa, fungi, and viruses represent skin microbiota, which can interact with many components such as topical drugs on the skin, cosmetics, as well as sebaceous, eccrine, and apocrine gland secretions on human skin including volatile organic compounds (VOCs). This interaction may affect the composition of skin metabolites. In addition, VOCs emitted from human skin form odors and provide significant information on body metabolism. Additionally, they are affected by nutritional status, environmental factors, and age [26]. The volatile profile of human skin and body odor changes at an older age, due to decreased hormone levels, lead to decreased secretions from sebaceous and apocrine glands. Other skin metabolites produced from topical drugs such as creams or transdermal patches and cosmetics are available in their outer layers [27].
# Monitoring of Possible Activities of Yangsan Fault Zone Using GNSS
## 3 Methodology  
GNSS data processing is composed of both relative and absolute positioning. The precise point positioning (absolute positioning) is a method to determine the position of an observation point independently using direct signals from GNSS satellites, which is used for initial approximation of the receiver with an accuracy of several centimeters in this study. Relative positioning is a method of determining the coordinates of an unknown point with respect to the base, usually at millimeter-level accuracy. The time series analysis of the daily solution was performed through the baseline analysis. The normal equation generated because of the daily processing was accumulated to calculate the velocity of displacement over a specific period as well as the final solution.  
The resulting daily three-dimensional Cartesian coordinates were converted into a local reference frame to break the coordinates down to north, east, and up components. We analyzed the results in three ways to figure out the movement of the Yangsan Fault Zone. First, the relative movement of the Yangsan Fault Zone was calculated based on the IGS  
Figure 3: The 25 geometrically distributed IGS stations used in the paper.  
\begin{table}
\begin{tabular}{c c} \hline Reference frame & IGS14 \\ Toposphere model & Dry/Wet GMF \\ Gravity model & EGM2008\_SMALL \\ Nutation model & JAU2000R06 \\ Celestial ephemeris & JPL DE405 \\ Cut-off angle & 10\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 3: Option of the Bernese Software V5.2.  
stations to understand the global movement. The latest IGS14 was used for the reference coordinates and velocity information for the IGS stations. In the second method, baseline analysis was performed using the GKPG station located near the Yangsan Fault as a known point to confirm the distortion of the crust. Thus, all coordinates are calculated as a relative displacement with respect to GKPG in the Yangsan Fault based on.  
Lastly, the pattern on the changes in principal strain and/or stress acting on the fault was investigated together, which was accomplished by the Matlab code provided by UNAVCO. Since the degrees of stress on the fault and crust are essential for fault monitoring, we analyzed the characteristics and changes of stress on the crust around the Yangsan Fault Zone in this study.