esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Unmanned air vehicle placement and user association is an important challenge to enable emergency communications. In this paper, we handled the problem of optimizing the user sum rate by associating the prioritized users to the deployed emergency aerial base stations. Simulation results verified that the performance under the proposed scheme significantly increased in terms of sum rate and the reduction in the number of users experiencing outages compared to the conventional SNR-based user association scheme. Moreover, we noticed that the sum rate increased as the number of ABSs increased, but after deploying more than four ABSs, the sum rate began to decrease. The reason is that as the number of ABS increases, interference among them also increases, which lowers the sum rate. By comparing these two schemes, we achieved a mean sum rate of around 23 bps/Hz compared to the conventional SINR-based user association scheme. In the future, we plan to further improve the system performance using state-of-the-art deep learning algorithms and also compare the proposal with game- and graph-theory-based approaches.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithms** & **Mean Sum Rate (bps/Hz)** \\ \hline Benchmark & 30.5 \\ Proposed prioritized user association & 24 \\ SINR-based user association & 1.5 \\ Distance-based user association & 1.3 \\ Random user association & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sum-rate comparison of different algorithms.  
Figure 6: Outage performance comparison.  
Writing--review & editing, Z.K. All authors have read and agreed to the published version of the manuscript.  
This work is funded by Higher Education Commission (HEC) Pakistan under the NRPU 2021 Project with grant no. 15687.  
The authors declare no conflict of interest.
## 3 Current Remote Sensing Data  
Remote sensing data employed in peatlands can be broadly categorised into passive and active sensors. Passive sensors detect radiation naturally emitted or reflected by a target, while active sensors emit energy and then measure how much of it is reflected or scattered back by the target. Passive sensors are sensitive to the electromagnetic spectrum's visible, near-infrared, and thermal infrared regions and help collect information regarding the target's reflectance properties and surface temperature. Active sensors like LiDAR and radar capture terrain information and subsurface characteristics. The choice of sensor type depends on the specific application and required information. For example, passive sensors are typically used for collecting spectral information, e.g., multispectral and hyperspectral imagery, which provide valuable insights into vegetation dynamics and environmental variables, while active sensors are used to determine structural and height information. Combining both sensor types can provide a more comprehensive view of the target.  
Figure 3 presents a comprehensive overview, encapsulating research studies focusing on remote sensing applications in peatlands. This SLR is thoughtfully organised based on the chosen platform for remote sensing, including satellite, airborne, unmanned aerial vehicle (UAV), in situ, and mixed (a fusion of platforms). Furthermore, the studies are systematically categorised by the sensing they employ, which can be active (active remote sensing), passive (passive remote sensing), or \"Act+Pas\" (a combination of active and passive sensing methods).  
These included studies cover a spectrum of years from 2017 to 2022, with Figure 3b revealing the number of articles or studies related to each category. These valuable insights shed light on the distribution of research efforts within the remote sensing applications in peatlands. This resource equips researchers and practitioners with a profound understanding of evolving trends and focal points across different platforms and sensing types over the years.  
The trends highlighted in Figure 3 indicate technology's adaptability in addressing the unique challenges posed by peatland environments. These include the transition from active to passive sensing and its dominance in satellite applications, the increasing prevalence of active airborne sensing, and the growing adoption of the fusion of sensors in \"Act+Pas\" multiplatform applications. Moreover, they underscore the global relevance of peatland research, with many countries such as Canada, Finland, Great Britain, and Poland contributing to this dynamic field (Figure 3a). This insight is a critical resource for anyone interested in the advancements and shifts in remote sensing methodologies applied to peatlands, allowing for more informed research and decision-making in this vital environmental domain.
## 4 Flow Pattern Recognition Results and Discussion
### Data Processing
Random sampling was applied to the images represented by every kind of label. The training set to the validation set to the test set ratio was 8:1:1. The ECT flow type samples are shown in Figure 15; these three different transverse images are obtained at different time instantaneous tomography images. According to the strict requirement of the data set for feature fusion above, 650 copies of 1000 sets of vibration signals of each flow type are also selected as a data set. It is not necessary to divide the data set. In the data processing phase, the image and vibration signal data are under the same label. Compared with two-dimensional convolutional neural network, the one-dimensional convolutional neural network has the advantages of small parameter number, easy training, and high computational efficiency. In the model training process, for each image trained, the vibration signal data corresponding to the label of this image will also be trained. The image data sets are divided as shown in Table 5.  
In the process of flow pattern conversion, there are some similar flow patterns in the flow pattern captured by capacitance tomography technology. For example, in the process of flow conversion from stratified flow to slug flow (Figure 14(1-5)), similar flow patterns as shown in Figure 16 appear. At this time, the label definition of the flow pattern needs to be confirmed from the time series of the ECT image. The first flow pattern is named  
Figure 15: Samples of ECT flow pattern. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Raw Data Set** & **Training Sets** & **Verification Sets** & **Testing Sets** \\ \hline Stratified flow & 650 & 520 & 65 & 65 \\ Slug flow & 650 & 520 & 65 & 65 \\ Annular flow & 650 & 520 & 65 & 65 \\ Bubbly flow & 650 & 520 & 65 & 65 \\ Total & 2600 & 2080 & 260 & 260 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical tables for image data sets. stratified flow, and the second one is slug flow. Then, in the recognition process, the ECT image features of two similar flow patterns become one of the reasons for misjudgment. Secondly, the vibration signal characteristics of similar flow patterns are also similar in the process of flow pattern conversion. Therefore, the eigenvalues that combine two different features make the flow pattern misidentified. In order to avoid misidentification of gender as far as possible, the accuracy of sample labeling needs to be confirmed manually. At the same time, the current excellent Adam optimizer is used to optimize the classification model, which enhances the recognition ability of similar flow patterns in the samples and improves the classification of similar flow patterns.
## 5 Results
The diagrams in Figure 5 show correlations between the experimental and model values. It can be seen that the lines approximate to each other, confirming the good agreement of results. Figure 4: Main effect plot for cutting temperature with different tools: (**a**) HM and (**b**) CBN. Figure 5: Correlation between experimental and model values of cutting temperature for tools: (**a**) HM and (**b**) CBN. The resulting ANFIS model has the ability to display three-dimensional surfaces. From the 3D diagrams shown in Figure 6, the influence of individual processing parameters can be determined, such as cutting speed, cutting position, and cutting depth, in relation to cutting temperature. According to the dispersion analysis, the depth of cut had the least influence; therefore, it was maintained at a constant value and only the influence of the cutting speed and feed are displayed on the 3D diagrams. Diagrams are shown for a center point with a depth of cut of 0.22 mm. Also, from these diagrams, the optimum processing parameters can be determined to obtain the minimum cutting temperature for both tool materials. The minimum limit temperature for both tool materials could be achieved when the cutting speed and feed were maintained at minimum values. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Input of ANFIS**} & \multicolumn{3}{c}{**Temperature \(\Theta\) (\({}^{\circ}\)C)**} \\ \hline
**Tool Material** & \begin{tabular}{c} **v\({}_{\text{c}}\)** \\ **(m/min)** \\ \end{tabular} & \begin{tabular}{c} **f** \\ **(mm/rev)** \\ \end{tabular} & \begin{tabular}{c} **a** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Experimental** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **ANFIS** \\ **CBN** \\ \end{tabular} \\ \hline \multicolumn{8}{c}{_Training data_} \\ \hline  
& 120 & 0.1 & 0.7 & 156 & 161 & 130.23 & 161.0 \\ \hline \multicolumn{8}{c}{_Average error for training data: 2.1\%_} \\ \hline
1. & \multicolumn{3}{c}{_Test data_} \\ \hline
1. & 37. & 41. & 80 & 0.1 & 0.22 & 115 & 202 & 97.0 & 201.0 \\ \hline
1. & 38. & 42. & 180 & 0.1 & 0.22 & 145 & 195 & 140.9 & 183.3 \\ \hline
1. & 39. & 43. & 120 & 0.045 & 0.22 & 102 & 165 & 113.0 & 164.6 \\ \hline
40. & 44. & 120 & 0.25 & 0.22 & 145 & 210 & 139.0 & 208.9 \\ \hline \multicolumn{8}{c}{_Average error for test data: 5.1\%_} \\ \hline
1. & \multicolumn{3}{c}{_Validation data_} \\ \hline
1. & 47. & 120 & 0.25 & 0.22 & 145 & 160 & 139.3 & 165.6 \\ \hline
1. & 48. & 120 & 0.1 & 0.07 & 130 & 250 & 127.2 & 229.4 \\ \hline \multicolumn{8}{c}{_Average error for validation data: 4.5\%_} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental plan and results. The above analysis indicates that among the three turning machining parameters and one attributive factor discussed in this study, i.e., cutting speed, feed, depth of cut, and tool material, the cutting speed and feed had the most substantial effect on cutting temperature, followed by the depth of cut. The analysis of the influence of input processing parameters was confirmed using dispersion analysis of empirical models, main effect plots, and 3D surfaces of the ANFIS model.
## 5 Conclusions  
In this study, using time-series and high-resolution remote sensing images, we extracted the water-surface area in the Honghu Lake of China from the 1990s to 2013, documented its intra-and inter annual change characteristics, and analyzed quantitatively the influence of climate factors and human activities induced by land use change based on water balance and statistical analysis method. The relative effects of climate factors and human activities were also compared. Results show the water-surface area of the Honghu Lake presents significant inter-annual and seasonal variability and tends to decrease generally since the 1990s. Meanwhile, spring drought may occur in this area. The degree to which climate factors and land use structure affect the water-surface area varies between different stages. In the April-May period, the comprehensive effect of precipitation and evaporation has positive contribution to the water-surface area. The sum of the water demands of paddies and aquaculture has a negative effect, and such effect is greater than the former. In the June-October period, the precipitation features a positive impact that is greater than the negative influence of the water demand of agriculture, except in August and September. Meanwhile, climate factors and human activities have no influence on the lake area in the November-March period. Evidently, the increase in pond area heightens the water demand of agriculture, which leads to quick reduction of the water-surface area.  
The long-term and high-resolution data record of monthly water-surface area can serve as base data to explore other studies such as quantifying and reflecting the flood/drought condition in Honghu Lake in future. The outcomes may provide scientific foundation for understanding the causes of water-surface area variations and keeping effectively Honghu area stable by changing regional land use practices. Certainly, deficiencies exist in the present study. For instance, lag effects observed between climate factors and water-surface area changes in individual years should be further analyzed via mathematical methods in ongoing research. However, the results of a quantitative analysis are more persuasive than the aforementioned qualitative studies.
# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
## 3 Methodology  
### Simulation Setup  
The current research aims to assess the sensors using cluster-based routing protocols. The evaluation will investigate throughput, reliability, packet latency time, and energy consumption with four mobility models. The study compares the results of different sink positions, ranging from one to eight static and mobile sinks. To ensure accuracy and reliability, the simulation will be repeated 100 times for each scenario in different topologies. The Castalia/OMNET++ simulator [50] will be utilized for the simulation process. The \"Throughput Test\" application is implemented and used for this purpose.  
Castalia is a discrete-event simulator specifically designed for WSNs and is built on the OMNeT++ simulation framework. To evaluate the energy consumption and the QoS in a large-scale WSN using Castalia, we follow these steps to set up the simulation, define parameters, and select appropriate metrics:
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
## 2 Materials and Methods
### Reliability Analysis
This function \(F(t)\) is used to indicate the successful response of the system and is given by:  
\[F(t)\ =\ P\ (T\ \leq\ t)=\int_{-\infty}^{t}\!\!f(k)dk\]  
Since the reliability function \(R(t)\) is the probability of \(T\) while the system is working successfully after time \(t\), it is given by:  
\[R(t)=1\ -\ F(t)=1-\int_{-\infty}^{t}\!\!f(k)dk\]  
\[R(t)\ =\ \int_{t}^{\infty}\!\!f(k)dk \tag{4}\]  
From (4), the following statistics can be calculated for assessment purposes:  
Reliable life: the time to which a specified reliability \(x\) can still be achieved. It is calculated by solving \(R(t)\ =\ x\) to obtain \(t\). For example, the time at which a reliability of 90% can still be achieved is mathematically expressed by:  
\[Reliable\_Life\ =\ (t|R(t)\ =\ x)\ Month \tag{5}\]  
Mean life: the average time before failure of the unrepairable systems (or average time between consecutive failures for repairable system). Thus, the mean life is the time at which the reliability decays to the minimum acceptable reliability \(y\). Thus,  
\[Mean\_Life\ =\ (t|R(t)\ =\ y)\ Month \tag{6}\]  
Failure rate: the reciprocal of the mean life. Thus,  
\[Failure\_Rate\ =\ \frac{1}{Mean\ Life}\ Month^{-1} \tag{7}\]  
The system response that depends on more inputs and device factors can be used for the reliability study to predict the optimal usage of the system. In this work, we used the ReliaSoft Weibull++ software to predict \(R(t)\), reliable life, mean life and failure rate of the system based on the pdf of the time of a selected system response. The data of the chosen system response collected from the simulation step were fed into the ReliaSoft Weibull++ software to process and fit it into a mathematical formula as an estimate of \(f(t)\). The method set to the software to estimate \(f(t)\) and its parameters is the rank regression method (RRM). To determine how well the parameters of the estimated distribution fit the data, a goodness-of-fit (GOF) was used. This GOF utility uses three different statistical tests for this purpose. From the estimated function \((t)\), the reliability function \(R(t)\), \(Reliable\_Life\), \(Mean\_Life\) and \(Failure\_Rate\) can be obtained according to Equations (4)-(7), respectively.
# Efficient Key-Value Data Placement for ZNS SSD
## 4 Design
### Garbage Collector for ZNS SSD
#### 4.2.1 Dynamic Victim Selection  
Since the overhead of the zone deletion command is large, the garbage collector dynamically determines the number of zones that needs to be deleted. When calling garbage collection, the maximum number of deletions is defined by Equation (1). \(Z_{t}\) means the total number of zones that the disk has and \(Z_{e}\) means the number of empty zones.  
\[Z_{t}\times\frac{1}{1+Z_{e}}\left(Z_{t}>0;\;Z_{t}\geq\;Z_{e}\geq 0\right), \tag{1}\]  
If \(Z_{t}\) is 512, tracing the number of changes shows a graph as shown in Figure 10. Equation (1) selects an average from one to four zones as victims if there are enough empty zones. This prevents the problem that too many zones are deleted at once and the garbage collection delay increases even when the number of empty zones is sufficient. However, if the number of empty zones is less than 10%, it is determined that there is insufficient space in the disk and the number of victim zones for garbage collection is increased in the form of an exponential function to create more empty zones.  
Figure 10: As obtained from Equation (1), the graph shows the maximum number of zones to be deleted with varying empty zones ratio. Here, \(Z_{e}\) and \(Z_{t}(=512)\) are the number of empty zones and the total number of zones, respectively.  
Figure 9: Overall architecture of garbage collector.  
To minimize the amount of valid data copying and reduce the overhead in the process of searching for queues, the number of queues that can be selected according to the empty space ratio, determined by the result of Equation (2).  
\[L_{max}\times\frac{1}{1+(E/5)}\left(L_{max}\geq 1;\,100\geq E\geq 0\right), \tag{2}\]  
Figure 11 shows the gradual change in the maximum level of queues determined by the empty space ratio in Equation (2). The solid line indicates the maximum level of queues that can be selected to search for victim zones. Note that queue selection for garbage collection is only allowed when the remaining space is less than 25%. The dotted line indicates the maximum level of queues that can be selected without the threshold for remaining space. As a result, the overhead of traversing queues is reduced by the garbage collector.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 4 Data-Cleaning Methods
### Cleaning Method for Noisy Data
Based on the HMM model, the OPI step firstly constructs a transition graph of candidate trajectories, and queries the SP (shortest path pair) distance among candidate trajectories. Then, it derives the optimal path of the trajectory. In the CPC step, the SPs of continuous candidate paths in the optimal path will be connected to construct a complete path. The GC step constructs corresponding geometry. Finally, after the above processes, the original GPS trajectory can be corrected onto the road network of the digital map. In this paper, the accuracy rate is used as a metric to evaluate the algorithm. It is the ratio of the number of successfully matched GPS points to the total number of GPS points. For a given matched GPS point, it must satisfy the following conditions: (a) The GPS point should be located on the road network or very close to the road section; (b) The matched road segments are attached to an actual driving route; (c) The matching error is less than a predefined threshold.
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 2 Study Area and Data  
### Study Area  
A 625 km\({}^{2}\) study area (centered at 66.3 N, \(-\)147.4 W) was selected within the Yukon Flats, a dynamic wetland system in north central Alaska surrounding an extended low-relief section of the Yukon River (Figure 1). This defined area contains >450 lakes and a 30 km section of the Yukon River, as well as the small settlement of Beaver, AK (Figure 1, box a). We also performed lake area validation in two other subareas in the Yukon Flats (Figure 1, box b,c). The Yukon Flats are characterized by extremely cold winters (mean January air temperature of \(-\)23 \({}^{\circ}\)C) and warm, dry summers (mean July air temperature of 17 \({}^{\circ}\)C) with low annual precipitation (26.7 cm water equivalent on average) [23]. The study area is primarily covered by spruce and birch forest and marshland and is underlain by discontinuous permafrost [23; 30; 31; 32]. Lakes in the Yukon Flats typically depend on recharge from spring river ice breakup flooding events [23]. In recent years several studies have analyzed long-term inter-annual trends in surface water extent to assess a potential role of thawing permafrost [22; 23; 24; 25; 26].  
Figure 1: Map of the Yukon Flats, Alaska. Box (a) is the primary study area (Planet tile No. 669714); (b,c) are the location of concurrent WorldView/Planet images used to validate our lake extraction method. Background is an RGB Landsat-8 image from 12 July 2016. Inset shows location within Alaska.
## 4 Key Parameters of Cavitating Jet
### Injection Pressure  
To show the effect of injection pressure on the processing capability of the cavitating jet, Figure 14 reveals the processing capability \(\beta\) at (a) the 1 st peak, i.e., water jet peening, and (b) 2 nd peak, i.e., cavitation peening, at the constant downstream pressure condition [92]. The processing capability \(\beta\) is defined by the arc height \(h\) of band steel made of the same material as Almen strip, considering the width of the steel \(w_{s}\) and the peening width \(w_{p}\), as follows [92].  
\[\alpha=\left\lvert 1-\frac{w_{p}}{w_{s}}\right\rvert \tag{9}\]  
Figure 12: Weight loss, i.e., the aggressive intensity of the jet, as a function of standoff distance changing with cavitation number at constant injection pressure. The peak at the near side of the nozzle, i.e., the 1 st peak, was caused by water column impacts. The peak at the far side from the nozzle, i.e., the 2 nd peak, was produced by cavitation impacts. The standoff distance of 2 nd peak was changed by the cavitation number [77].  
Figure 13: Classification map for cavitation peening and water jet peening considering standoff distance and cavitation number [90].  
\[\frac{1}{\rho}\ =\ \frac{8h}{L^{2}} \tag{10}\]  
\[\beta=\ \frac{1}{\rho}\ (1+\alpha) \tag{11}\]  
Here, \(L\) is the length to measure \(h\). In Figure 14, the processing capability, i.e., a kind of aggressive intensity, was obtained by the arc height of the peened plate, as the arc height using the Almen strip is commonly used to measure the peening intensity [93]. In the case of water jet peening, \(\beta\)increases with the injection pressure \(p_{1}\), as the peening effect is produced by water column impacts, which increases with \(p_{1}\). On the other hand, in the case of cavitation peening, \(\beta\)has a maximum at \(p_{1}=40\) MPa at a constant downstream condition. When the maximum values of the 1 st peak and 2 nd peak are compared: \(\beta\)at the 2 nd peak is 1.7 times larger than that at the 1 st peak. As the jet power of 60 MPa is 1.8 times larger than that of 40 MPa, the peening efficiency of cavitation peening is about three times higher than that of water jet peening. Note that too high an injection pressure reduces the peening intensity of cavitation peening, as shown in Figure 14b. The reason the peening intensity of cavitation peening decreases at \(p_{1}>40\) MPa is discussed in \"5. Estimation of Aggressive Intensity of Cavitating Jet\".