esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning
## 4 Test and Analysis of the Microenvironment Monitoring System for Forest Ecology  
### Testing of System Stability  
To verify the stability of the system operation, the data of 92 consecutive days obtained from the four field ecological monitoring stations located in Beijing Jiufeng National Forest Park from 1 March 2017 to 31 May 2017 and two monitoring stations located in Inner Mongolia from 31 March 2018 to 30 June 2018. The 13 sensors of 11 types include atmosphere sensors (air temperature, air moisture, carbon dioxide concentration, precipitation, wind speed, and wind direction), light sensors (sunshine hours, total radiation, and light intensity), and soil sensors (two-layer soil moisture and two-layer soil temperature). A network is constructed to monitor the forest ecology. The monitoring data is transmitted in frame via the Beidou satellite or GPRS with each frame containing 72 bytes of data.  
Table 3 presents the result of data communication at the field ecological monitoring station in forests. A total of 13,247 frames of field ecological data were acquired with six field ecological monitoring stations in 92 days. The cloud server correctly received 13,058, 13,172, 13,194, and 13,094 frames of ecological data from Yan'er Ridge, Wangjing Tower, Luobadi tower, and Zhai'er Yu, respectively, with correct rates of 98.57%, 99.43%, 99.59%, and 98.85% respectively. The cloud server correctly received 13,232 and 13,235 frames of ecological data from Hulun Buir and Zhalantun in Inner Mongolia by GPRS, respectively, with correct rates of 99.89% and 99.90%, respectively. No data loss lasting for more than 30 min and data errors occurred. The correct rate of data transmission is high enough to meet the data communication requirements of the microenvironment monitoring system for forest ecology.
## 5 Conclusions  
The timely and accurate acquisition of SSM information is very important for agricultural water resource management and drought monitoring. Many scholars have carried out a series of studies on bare soil or sparse vegetation areas and made positive contributions to this field. However, there are relatively few studies on SSM in vegetation-covered areas such as farmland. At present, the potential of the MCARI for SSM estimation has not been exploited, and it is an important part of our research. In this paper, backscattering coefficients and spectral indices were obtained from S1 and S2 images, respectively. Then, the effectiveness of the MCARI in SSM estimation was evaluated with the WCM and compared with the other two vegetation indices (NDVI and NDWI). Finally, the model was validated with in situ measured SSM values, and the SSM of the vegetated areas in Daxing, Beijing, and Gu'an, Hebei, in two periods was plotted. The detailed conclusions of this study can be summarized as follows:  
1. Compared to VH polarization, VV polarization was more suitable for SSM estimation and achieved higher accuracy.
2. The MCARI performed well in estimating the VWC, which made it show excellent potential in estimating SSM in vegetation-covered areas.
3. Based on the MCARI, the SSM estimation model was developed. The R\({}^{2}\), RMSE, and MAE between the estimated and in situ measured SSM values were 0.867, 0.028 cm\({}^{3}\)/cm\({}^{3}\), and 0.023 cm\({}^{3}\)/cm\({}^{3}\), respectively.
4. This method can obtain SSM information with a high spatial resolution, which can be used to guide agricultural irrigation and make positive contributions to agricultural water resource management and drought monitoring.  
Methodology, R.L.; Formal analysis, R.L., H.C. and Z.W.; Data curation, R.L., H.S. and M.C.; Funding acquisition, Z.W.; Project administration, Y.L. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2021 YFB3900602), the Chinese National Science Fund (No. 52130906), the Independent Research Project of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (No. SKL2022 TS13), and the Fund of China Institute of Water Resources and Hydropower Research (No. ID0145B022021, ID0145B052021).  
**Institutional Review Board Statement:** Not applicable.
# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 3 Categories of CIAs
### Remote Desktops

Table 2: Papers on the evaluation of QoE in the three types of CIAs. server does not send updates until it receives a request for the latest version of the display. Some remote desktop services, such as Lap Link, Windows Remote Desktop, and Citrix, cache part of the screen state locally. Local caches allow for incremental screen updates, taking advantage of regions of the previous screen that could have been moved to other coordinates without receiving an unchanged portion of the screen. The second human perception that influences QoE in remote desktops is the video quality perceived by the user. Some remote desktop communication solutions, such as VNC and Sun Ray, send the screen corresponding to the user's desktop as a video stream or as a sequence of individual video frames. The video stream is compressed with losses, compromising the perceived visual quality and influencing the final QoE. Other solutions, such as LapLink, Citrix, and Windows Remote Desktop, send graphical screen primitives, such as \"plot a window of this size in that position of the screen\", to the thin client. The client receives primitives that indicate which regions of the screen must be modified and which content must be represented. The thin client reproduces screen content without any video quality degradation caused by video compression artefacts. However, video quality can be degraded in both alternatives by network packet losses. In addition, the transmission may freeze due to communication problems. This causes stuttering and affects the video quality of the remote desktop. Audio quality is generally less important in remote desktop services, but when needed, the service has to consider the effect of the compression codec, the network packet losses, and synchronisation with the video stream.
## 3 Results
### Network Metrics Analysis  
The global topological alterations of the estimated brain network were quantitatively examined via graph theoretical analysis. Particularly, small-world network properties--including clustering coefficient, characteristic path length, and small-worldness--were estimated. The network metrics (averaged across participants) for each task and workload level are shown for 30% sparsity in Figure 3. Some differences in values are observed for each task, due to their different demands. It seems there is little difference across frequency bands. However, in almost all cases, it is obvious that metric values are decreasing with increased workload level. Similar patterns were observed in other sparsity levels.  
Statistical analysis was performed via a repeated-measures two-way ANOVA to assess the workload effects on brain network reorganization. The AUC of each metric over sparsity level was calculated and was used for statistical analysis for all participants together. Data were normally distributed according to Shapiro-Wilk test (\(p>0.05\)). Given that our primary objective was to reveal task-independent mental workload influence on brain activities, we focus mainly on the workload effects (Table 1). Specifically, for the clustering coefficient, a significant workload effect was found for all frequency bands (\(p<0.05\)) and the post-hoc paired \(t\)-test revealed significant decrement for all bands for the \(n\)-back task (\(p<0.05\)) and in \(\theta\) and \(\alpha\) for the mental arithmetic (\(p<0.05\)). For the characteristic path length, a significant workload effect (F\({}_{1,316}=4.50,p=0.04\)) as well as significant interaction of task and workload factors (F\({}_{1,316}=4.75,p=0.03\)) were detected in \(\beta\) band, although the \(t\)-test analysis presented significant decrement only for the \(n\)-back task (t\({}_{79}=3.20,p=0.002\)). Finally, the small-worldness metric displayed significant workload effects in \(\theta\) (F\({}_{1,316}=8.61,\)\(p=0.004\)) and \(\alpha\) bands (F\({}_{1,316}=7.69,p=0.006\)), with the post-hoc analysis further confirming the significant decrement for both tasks (\(p<0.05\)). In all statistically significant cases, a common trend was observed--i.e., a decrement of the network metric values--revealing the way of brain network reconfiguration with the increase in workload levels.  
Figure 2: The top 100 PLI functional connections per frequency band, for \(n\)-back (NB) and arithmetic tasks (AT) in low workload (LW) and high workload (HW) conditions.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 3 Aquatic Platform
### Software Architecture
Then, the data message is sent through a ROS topic to the _Path Planner_ module, to update the state machine that controls the device and then it is sent to the _mobile Opportunistic VEhicular (mOVE)_ module to be delivered to any DTS. The _mOVE_ module is a Delay Tolerant Network (DTN)-based architecture developed in our group [29]. It supports several technologies such as IEEE 802.11n (Wi-Fi technology) and IEEE 802.11p (Wireless Access in Vehicular Environments, which is not used in this platform). This module is responsible for delivering data messages to the server via Wi-Fi. It has a Store-Carry-and-Forward Mechanism to cope with the connection disruption that can occur. It integrates routing mechanisms, from the simplest ones, such as Epidemic, to the more complex ones based on connection link quality, social heuristics or others. It also integrates a neighbouring table, so each device has the entire network information spread among devices by advertisement messages. When there is no connection to any Wi-Fi DTS, then this module forwards the _Data messages_ to the _LoRa_ module. The _LoRa_ module is responsible for handling the delivery and reception of LoRa packets. Once a _Start Mission message_ is received, the same will be forwarded to the _Task Provider_ module to process and deploy the mission. Additionally, this module listens the _mOVE_ module for Sensor Data payloads that needs to be structured with the LoRa header and dispatched to the LoRa DTS. When the _LoRa_ module receives a _Start Mission message_, it forwards it to the _Task Provider_ module to store the information until it is time to start the mission. When the mission has to start, the _Task Provider_ sends the navigation information to the _Path Planner_ module. Each time this module receives a new task, it stops the navigation mechanism of any old task still in motion (not deleting data packets) and starts the new process, with a new grid map, targets and nodes. The _Path Planner_ module implements single and team navigation mechanisms. The single navigation mechanism is responsible to allocate targets regarding the type of sensors each vehicle has and the requirements of the mission and to find a path to that specific target avoiding static obstacles. The team navigation mechanism is responsible for maintaining connectivity between the team vehicles and is responsible to avoid collisions between them. These mechanisms are described in more detail in Reference [30]. The _Bluetooth_ module is responsible for saving the last gathered values in a database. When a Bluetooth device with our App is near and chooses the USV, the USV starts to directly deliver all the database information to that device by Bluetooth technology.
## 4 Data Analysis and Post-Processing
\[L_{diff+spec}=L_{w}-10 Log\Bigg{(}\left(\frac{S_{panet}}{4\pi r_{1}^{2}}\right) \frac{(1-\alpha)s}{2\pi r_{2}^{2}}\Bigg{)}\] (7) For the equations above, clarifications on nomenclature are required, in particular:
* \(r_{1}\) is the distance between the sample and the central microphone;
* \(r_{2}\) is the distance between the sample and the sound source. \(S_{panet}\) is the surface area of the sample, in square meters. Equations (5)-(7) describe a theoretical model in function of \(\alpha\) and \(s\). This result allows for the construction of the reference curves at each band of frequency, based on the theoretical model explained above. Figure 6 shows the reference curve trend at 500 Hz based on the disposition of the microphones around the test sample. The values of \(\alpha\) and \(s\) have been considered when their results, describing a curve along all the 25 microphones, indicate little variations with respect to the theoretical model. It should be noticed that the reference curve is different for each frequency band, becoming narrower at high frequencies related to the noise levels centered on the microphones close to the sound source. Making the theoretical model as a reference curve, it is possible also to calculate the directional diffusion coefficient as one of the essential requirements of the  
Figure 6: Curve trend of the theoretical model at 500 Hz. standard ISO 17947-2: 2004. The procedure to obtain the directional diffusion coefficient (\(d_{\vartheta}\)) is described in Formula (8). \[d_{\vartheta}=\left( \begin{array}{c}\dfrac{\left(\Sigma_{i=1}^{n}10^{L_{i}/10} \right)^{2}-\Sigma_{i=1}^{n}(10^{L_{i}/10})^{2}}{(n-1)\;\Sigma_{i=1}^{n}(10^{L_ {i}/10})^{2}}\end{array} \right) \tag{8}\]  
where \(n\) is the number of microphones.
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 2 Materials and Methods
### Pre-Processing RGB-D Data
#### 2.4.1 Alignment of Frames  
The FRAMOS D415e camera had its origin reference in the left IR imager sensor and the RGB imager on the right side, as shown in Figure 8. As the sensors are different, an alignment between the colour image and the information was performed.  
The alignment was performed in relation to the colour imager, i.e., the depth information was aligned to the colour frame. Since the alignment changes the relation of the pixels in the depth frame to match the colour sensor, the intrinsic matrix used to reproject the point cloud was also derived from the colour imager.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 3 Implementation
### Data Report Generation  
\(CP_{i}\) performs signcryption on the \(EV_{i}\)'s charging and discharging data \(m_{i}\). Subsequently, based on the signcrypted message \(\delta_{i}\), a data report \(P_{i}\) is generated and uploaded to the respective local \(FN_{w}\).  
The CP first selects a random number \(r_{i}\in Z_{q}^{*}\) and generates \(C_{i}\) according to the following formula:  
\[\left\{\begin{aligned} R_{i}=r_{i}P\\ h_{1\text{c}}=H_{1}(ID_{CSO},X_{\text{c}},Y_{\text{c}})\\ U_{i}=r_{i}(X_{\text{c}}+Y_{\text{c}}+P_{pub}h_{1\text{c}})\\ h_{3i}=H_{3}(PID_{i},ID_{C\text{P}i},X_{i},Y_{i},U_{i},R_{i})\\ C_{i}=m_{i}\oplus h_{3i}\end{aligned}\right. \tag{1}\]  
Then, according to the following formula, a signature \(s_{i}\) is generated, and the final signature message \(\delta_{i}=(R_{i},C_{i},s_{i})\) is obtained.  
Subsequently, the charging and discharging data report \(P_{i}\) is uploaded to the local \(FN_{w}\), where \(P_{i}=\{PID_{i},ID_{C\text{P}_{i}},\delta_{i},PK_{i},TS_{i}\}\). Here, \(TS_{i}\) is a timestamp used to prevent replay attacks.  
\[\left\{\begin{aligned} h_{2i}=H_{2}(ID_{CP_{i}},X_{\text{c}},Y_{ \text{c}},R_{i},C_{i},TS_{i})\\ s_{i}=d_{i}+x_{i}h_{2i}\end{aligned}\right. \tag{2}\]
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 5 Simulation Results
### Achievable Data Rate
consider the \(\mathcal{N}\) and \(N_{t}\) are equal. Figure 4, shows the achievable data rate of the different SMTs at the same modulation order, the X-axis in Figure 4 represents the different number of antennas transmitting \(N_{t}\) and the length of the spreading code \(\mathcal{N}\). The number of antennas transmitting \(N_{t}\) will be affected on the SM, GSM, QSM, FGSM, EFGSM, and the proposed SS-FGSM while the different length of the spreading code \(\mathcal{N}\) will be affected on the GCIM and the proposed SS-FGSM scheme as well. In Figure 4 the achievable data rate of the proposed SS-FGSM is weighted against the maximum achievable data rate of SM, GSM, FGSM, EFGSM, QSM, and GCIM provided by (4)-(8), and (11), respectively. The proposed SS-FGSM is significantly outperformed all of the conventional schemes in the achievable data rate. For example, at \(N_{t}=\mathcal{N}=4\), the achievable SE of SS-FGSM is 9 bpcu compared to 4, 4, 5, 6, 7, and 6 bpcu of SM, GSM, FGSM, QSM, EFGSM, and GCIM, respectively. orthogonal spreading codes used in the proposed SS-FGSM scheme. Although, these spreading codes used in the GCIM, it has a poor ABER performance in underwater communication. Because of the limited achievable data rate of GCIM, the modulation order should be increased to have the same spectral efficiency as in the proposed SS-FGSM. To avoid that, the GCIM can use long orthogonal codes with less mapped bits to provide a better performance, but unfortunately, that increases the system complexity also. The proposed SS-FGSM based on ML-detector provide high ABER performance compare to the SS-FGSM using a DL-based detector, but unfortunately, the ML-detector based has high competition complexity. In addition, it's shown that the upper bound and the results of asymptotic ABER in Equation (26) of proposed SS-FGSM using ML-detector are matched to the simulation result especially for a wide range of SNR values. Figure 5: The performance of ABER of proposed SS-FGSM and conventional SMTs at 10 bpcu (over a statistical UWA channel). Figure 6: The performance of ABER of proposed SS-FGSM and conventional SMTs at 12 bpcu (over a statistical UWA channel).
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**} \\ \hline \multicolumn{3}{c}{**Descritives**} \\ \hline \multicolumn{3}{c}{Mean} & 306,376.7500 & 53,249.06184 \\ \hline
95\% Confidence Interval for Mean & \
\begin{tabular}{c} Lower Bound \\ Upper Bound \\ \end{tabular} &
\begin{tabular}{c} 194,925.1827 \\ 417,828.3173 \\ \end{tabular} & \\ \hline
5\% Trimmed Mean & \
\begin{tabular}{c} 301,791.6667 \\ 278,897.5000 \\ \end{tabular} &
\begin{tabular}{c} \\ \end{tabular} \\ \hline \multicolumn{3}{c}{Variance} & 56,709,251,742.829 \\ \hline \multicolumn{3}{c}{Std. Deviation} & 238,137.04404 \\ \hline Minimum & & 76,225.00 \\ \hline Maximum & & 619,060.00 \\ \hline Range & & 542,835.00 \\ \hline Interquartile Range & & 396,095.00 \\ \hline Skewness & & 0.146 & 0.512 \\ \hline Kurtosis & & \(-1.984\) & 0.992 \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._The results show that all data distributions are not normal since the prevailing tests of normality--i.e., Kolmogorov-Smirnov and Shapiro-Wilk--lead to reject the normal distribution hypothesis. Therefore, parametric tests--i.e., one-way ANOVA--were not conducted, whilst nonparametric ones were conducted: the Kruskal-Wallis H test was applied [110] since it is more appropriate than the Mann-Whitney U one. In fact, in our analysis, all independent categorical variables--i.e., both area- and project-specific factors--have more than two levels. In Table 8a,b, the results of the Kruskal-Wallis H tests are reported. The results of the first statistical tests conducted--from the Kolmogorov-Smirnov to the Shapiro-Wilk one--are aimed at proving the reliability and suitability of the second step of the statistical analysis--i.e., Kruskal-Wallis H test [110]--which challenges the research hypotheses with corresponding null hypotheses. The final results prove that the only null hypothesis rejected is the one related to the bike model, thus confirming that the hypothesized dependence is true and significant. On the other hand, the across-the-region hypothesis is not supported, thus showing that there are no specific regional features affecting profit and profitability results more than others.
## 3 Results and Discussion
### Effect of Plasma Treatment on POD Activity  
As shown in Figure 6c, the POD activities of both control and plasma-treated groups increased at the initial stage and then showed a decreasing trend, however, the POD activity of the plasma-treated group was higher than that of the control group throughout the process. After 16 d of potato storage, the POD activity of the treated group increased by 8.69% compared with the control group. POD enzyme utilizes hydrogen peroxide as a substrate to oxidize phenolic compounds, and effectively scavenges hydrogen peroxide during the storage process [33]. The increase of POD and other antioxidant enzyme activities can reduce the level of reactive oxygen species (ROS) and inhibit the sprouting of potato tubers, thus prolonging dormancy. The increase in the activities of POD and other antioxidant enzymes can decrease the level of reactive oxygen species (ROS), and inhibit the sprouting of potato tubers, thereby prolonging the dormancy period [34]. The POD activities of the treatment group increased by 8.69%, compared with the control group.