esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### GPP and Climate  
GPP production has a close relationship with the local climate. The climate in current and previous years influences the growth of plant tissues and, hence, the efficiency of photosynthesis. There are three candidate drivers to affect GPP: pure temperature, pure precipitation, and the combined effects of temperature and precipitation.  
Pure precipitation could affect GPP absorption (Figure 6A). Precipitation in June had a positive relationship with GPP using real data and combined data (R forest combined June = 0.483; R grass combined June = 0.516; R forest real June = 0.511; R grass real June = 0.592). Precipitation in the previous June had a positive correlation with GPP in grassland using real data (R grass real PreJune = 0.205), but in the same month, the  
Figure 5: The simulated GPP maps and the GPP tendency curves. Note: The PDSI in the parenthesis was the average PDSI of the growing season. In (**E**), “All mean value” indicates the average GPP values of all forests and grasslands in our study areas. The lower-case letters in (**F**) corresponded to the GPP values in specific years whose GPP spatial distribution could be found in (**A–E**). The dash lines in (**F**) were the tendencies using real data, while the solid lines were the tendencies using combined data. The red lines separate the three phases of analysis.  
correlation of forest and grassland using combined data did not meet the significant level (R forest combined PreJune = 0.039; R = grass combined PreJune = 0.122).  
Figure 6: The correlation between GPP and climate factors. Note: The top red line (\(p\) = 0.05) and bottom red line (\(p\) = 0.05) show the significance levels (\(p\) < 0.05).  
Temperature could influence our GPP absorption (Figure 6B). The temperature in July had negative relationships with GPP in the whole study area, including forest and grassland (R forest combined July = \(-0.317\); R grass combined July = \(-0.233\); R forest real July = \(-0.623\); R grass real July = \(-0.467\)). The temperature in March had positive correlations with GPP using real data (R forest real March = \(-0.537\); R grass real March = \(-0.398\)), but in the same month, the correlation using combined data did not show significant low values (R forest combined March = \(-0.130\); R grass combined March = \(-0.098\)).  
The combined effects of temperature and precipitation could affect GPP absorption (Figure 6C). PDSI in June, July, August, and September had positive relationships with GPP in the whole study area including forest and grassland using combined data and real data. PDSI in the previous September had a positive correlation with GPP in grassland using real data (R grass real PresSep = 0.191), but in the same month, a high correlation could not be found in GPP in forest and grassland using combined data (R forest combined PresSep = 0.043; R grass combined PresSep = 0.096).
## 4 Results and Discussion
### Pros and Cons of Using Machine Learning
#### 4.9.4 Justification for Discussing Pros and Cons  
The pros and cons are as shown below:  
Transparency and Decision-Making. It provides transparency about the capabilities and limitations of these technologies. This detailed discussion aids potential adopters, particularly educational institutions, in making informed decisions about whether the benefit of ML align with their operational capabilities and goals.  
Contextual Suitability. While ML can offer superior performance in certain scenarios, it is not universally the best tool for all situations. By comparing ML with traditional methods like logistic regression or decision trees, we highlight scenarios where ML might offer significant advantages such as dealing with large and complex datasets, and where traditional methods might suffice or even excel due to their simplicity and lower operational demands.  
Encouraging Robust Analytical Approaches. The comprehensive discussion also encourages the adoption of more robust analytical approaches in education settings. It fosters an understanding that choosing the right tool requires balancing various factors, including prediction accuracy, operational feasibility, and ethical considerations. We have enhanced the clarity on the following aspects;  
* Data Collection: We detailed the process of gathering data from institutional databases, including student demographics, academic records, and survey responses.
* Feature Selection: We employed statistical methods and domain expertise to select relevant features, such as socio-economic background, psychological factors, and academic performance indicators.
* Machine Learning Algorithms: We utilized random forests due to their robustness and ability to handle large datasets with missing values. Other algorithms considered include Support Vector Machines and Neural Networks.
* Hyperparameter Optimization: We implemented grid search and cross-validation techniques to fine-tune hyperparameters, ensuring optimal model performance and avoiding overfitting.  
As predictive analytics in educational settings involves collecting and analyzing vast amounts of student data, including personal, academic, and socio-economic information, ensuring the privacy of this sensitive data was paramount. Ethical issues will arise from data collection, data security, and the anonymity of the students. In addition, there may be bias present such as algorithmic bias possibly favoring majority groups over minority groups if they are not adequately balanced.
## 4 Performance Evaluation of the Proposed Framework
### Data Locality in HDFS Cluster  
Within the HDFS cluster, the core nodes work as input or output channels for the created clusters, and it also provides data storage. The tasks performed by the task tracker play a vital role on the core nodes taking advantages of the data storage. Core slaves on the physical cluster have more capacity and storage to perform the scalability. Moreover, core instances cannot be terminated even they are idle. As per the above results, the scaling cluster used the private cloud as AWS and scaled based on the Covering HDFS methods, whereas the fixed clusters use the traditional infrastructure. As displayed in Figures 3 and 4, the proposed Covering HDFS in the AWS does not cause any issues without any performance degradation. The auto-scaling methods are more flexible in balancing the cost and cluster's performance. In some cases, applications use a small amount of core nodes to avoid the cost of the resources when the data is low. To achieve the best performance, the data nodes can be added when the data is high. The fourteen-slave cluster setup on the traditional Hadoop infrastructure performs well with the big data but it wastes more resources when it has less data to process. The seven-slave cluster in Figure 4 displays the best resource use rate when compared to the fourteen-slave cluster. Also it can be noticed that the performance is degraded in the seven-slave cluster in case of heavy data load.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**EC2 Instances** & **CPU Cores** & **Memory (GB)** & **Unix/Linux Usage** \\ \hline t2.medium & 2 & 4 & \$0.05 per Hour \\ c3.xlarge & 4 & 7.5 & \$0.239 per Hour \\ \hline \hline \end{tabular}
\end{table}
Table 1: Amazon EC2 instances pricing.  
Figure 5: Time consumption for all workload.  
Overall, the auto-scaling cluster showed the best performance vs. cost trade-off and better resource use by dynamically adjusting the virtual computing resources on the Hadoop cluster.
## 7 Technical Considerations for Implementing 6G Technology
### Propagation of Electromagnetic Waves at THz Frequencies  
One of the core technologies in 6G is communication in the THz band (0.1-10 THz). Unlike lower frequencies used in 5G, THz waves exhibit unique propagation characteristics, including the following:  
* High Path Loss: THz frequencies are highly susceptible to free-space path loss and atmospheric absorption, particularly by water vapor and oxygen molecules. This limits their effective range, often requiring line-of-sight (LOS) propagation [259].
* Limited Diffraction: The reduced wavelength of THz waves results in poor diffraction, making them less capable of bending around obstacles. This increases the need for direct LOS paths or reflection-enhancing technologies [260].
* High Data Rates: Despite these challenges, the large bandwidth available in the THz spectrum supports extremely high data rates, making it ideal for applications such as holographic communications and ultra-high-definition video streaming [52].
# Testing Procedure of Unmanned Aerial Vehicles (UAVs) Trajectory in Automatic Missions
## 1 Introduction
While taking into account the rapid development of UAVs, it should be noted that the research has been carried out in different directions. The studies of mathematical models of a vehicle movement trajectory, structural models of machines, testing their reliability by developing appropriate procedures and methods of the research are among the most important ones [30; 34; 35]. The results of such analyses include the parameters that can be used to optimise the performance and durability, to determine the reliability, safety, and effective operation of the systems. In the case of unmanned aerial vehicles, it is essential to identify the possibilities of their control. The studies that were conducted in this research paper have aimed to develop a new set of tests for measuring the position of UAVs in space. In the set of tests that were proposed by the author, the navigation using the GNSS system, magnetometer, IMU assisted with baro-sensors, has been subject to the studies. It should be noted that these elements are the basic components that are used by on-board computers when controlling UAVs. The performed tests will allow for the use of UAVs in specific missions that require flights with small horizontal and vertical separations [31; 36], at precisely determined altitudes [12; 25; 33] and in areas with a complex terrain morphology [37; 38]. In addition, the procedures that were proposed in this research paper will allow verifying the algorithms that optimise the test flights of the objects in reality. The targets of these explore-and-exploit approaches are many fold, such as maximizing the coverage of a target object or minimizing the acquisition time or energy consumption [36]. They will also allow for the introduction of additional parameters in the models, resulting from the measurement of the actual flight trajectory. In [39], the authors propose a method that can be used to model and analyse the energy consumption of a specific drone as a function of its speed and operating conditions.
# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
## 3 Proposed Methodology  
As demonstrated in Figure 2, in the proposed framework, the hyperspectral images are passed by IPCA to reduce the dimension and keep important information only. After this, the image is divided into 11 \(\times\) 11 patches, which undergo a 3D convolution layer to extract shallow features. These extracted features are used by Capsule-Net to get classification done. The details regarding IPCA and Capsule-Net are discussed below.  
Figure 1: Ground truth of datasets.
## 4 Discussion
In order to investigate responses and obtain more precise information on their components, origin and variations in scoliotic children, the subjects stood on a specially constructed platform that could be suddenly tilt either to the right or to the left. Thoracic and lumbar paraspinal muscle activity was recorded with pairs of self-adhesive surface bipolar EMG electrodes. The responses were analyzed to detect components and study their time course and relative amplitude in successive trials; characteristics common to different subjects were sought. The presence of short-latency responses and later activities following postural perturbation was confirmed. In a given subject, these components vary in amplitude and time course from one trial to another. On the other hand, the differences found across subjects are not significantly different from those found within the various subjects. Results exhibit some differences with previous data. They lead to a different neurophysiological interpretation and indicate that the stimulus and the responses need more precise analysis before being used as a diagnostic and prognostic tool in evolutive scoliosis [37]. The aim of another study was to evaluate electromyographic (EMG) responses of erector spinae (ES) and lower limb muscles to dynamic forward postural perturbation (FPP) and backward postural perturbation (BPP) in patients with adolescent idiopathic scoliosis (AIS) and in a healthy control group. Muscle responses were measured over a 1s time window after the onset of perturbation. In the FPP test, the EMG responses of right EST10, ESL3, and biceps femoris (BF) muscles in the scoliosis group were respectively about 1.40, 1.43, and 1.45 times greater than those in the control group. Additionally, in the BPP test, right ESL3 muscle of the scoliosis group, the EMG activity was 1.64 times higher than that in the control group. During FPP, the scoliosis group displayed asymmetrical muscle responses in the EST10 and BF muscles. This asymmetrical muscle activity in response to FPP is hypothesized to be a possible compensatory strategy rather than an inherent characteristic of scoliosis [38]. The uneven tension of the erector spinae in children with scoliosis, visible in SEMG amplitude, causes its destabilization and abnormal development.
## 2 Preliminaries
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & \(\varphi(t)\) & \(C(x,y)\) & **Parameter** \\ \hline [PERSON] & \((-\ln t)^{\theta}\) & \(\exp(-[(-\ln x)^{\theta}+(-\ln y)^{\theta}]^{\frac{1}{\theta}})\) & \(\theta\geq 1\) \\ [PERSON] & \(t^{-\theta}-1\) & \((x^{-\theta}+y_{xy}^{-\theta}-1)^{-\frac{\theta}{\theta}}\) & \(\theta>0\) \\ [PERSON] & \(\ln\frac{1-\theta(1-t)}{t}\) & \(\frac{1-\theta(1-x)(1-y)}{1-\theta(1-x)(1-y)}\) & \(-1\leq\theta<1\) \\ [PERSON] & \(-\ln[1-(1-t)^{\theta}]\) & \(1-[(1-x)^{\theta}+(1-y)^{\theta}-(1-x)^{\theta}(1-y)^{\theta}]^{\frac{1}{ \theta}}\) & \(\theta\geq 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: In ref. [24] Archimedean Copulas. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Fuzzy Negation** & **Parameter** \\ \hline Sugeno class & \(n_{\lambda}(x)=\frac{1-x}{1+\lambda x}\) & \(\lambda\in(-1,\infty)\) \\ Yager class & \(n_{\lambda}(x)=(1-x^{\lambda})^{1/\lambda}\) & \(\lambda\in(0,\infty)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: In ref. [32] Fuzzy Negations. **Definition 6**. : _Let \(\alpha=\langle\mu_{\alpha},\
u_{\alpha}\rangle\) and \(\beta=\left\langle\mu_{\beta},\
u_{\beta}\right\rangle\) be two IFVs and \(k\geq 0\) a parameter._  
1. _Addition operation_ \[\alpha\oplus_{\mathbb{C}}\beta=\left\langle C_{\lambda}^{*}(\mu_{\alpha},\mu_{ \beta}),C(\
u_{\alpha},\
u_{\beta})\right\rangle=\left\langle n_{\lambda}( \varphi^{-1}[\varphi(n_{\lambda}(\mu_{\alpha}))+\varphi(n_{\lambda}(\mu_{ \beta}))]),\,\varphi^{-1}[\varphi(\
u_{\alpha})+\varphi(\
u_{\beta})]\right\rangle.\]
2. _Multiplication operation_ \[\alpha\otimes_{\mathbb{C}}\beta=\left\langle C(\mu_{\alpha},\mu_{\beta}),C_{ \lambda}^{*}(\
u_{\alpha},\
u_{\beta})\right\rangle=\left\langle\varphi^{-1}[ \varphi(\mu_{\alpha})+\varphi(\mu_{\beta})],\,n_{\lambda}(\varphi^{-1}[ \varphi(n_{\lambda}(\
u_{\alpha}))+\varphi(n_{\lambda}(\
u_{\beta}))])\right\rangle.\]
3. _Scalar Multiplication operation_ \[k\alpha=\left\langle n_{\lambda}(\varphi^{-1}[k\varphi(n_{\lambda}(\mu_{ \alpha}))]),\varphi^{-1}[k\varphi(\
u_{\alpha})]\right\rangle.\]
4. _Power operation_ \[\alpha^{k}=\left\langle\varphi^{-1}[k\varphi(\mu_{\alpha})],n_{\lambda}( \varphi^{-1}[k\varphi(n_{\lambda}(\
u_{\alpha}))])\right\rangle.\]  
By replacing the \(\lambda\) parameter with the appropriate value, the [PERSON] et al. 's [9] operations of IFVs are produced. Moreover, a further investigation is held for the properties of the proposed operations. **Theorem 1**. : _Let \(\alpha=\left\langle\mu_{\alpha},\
u_{\alpha}\right\rangle\) and \(\beta=\left\langle\mu_{\beta},\
u_{\beta}\right\rangle\) be two IFVs and \(k\geq 0\) a real valued parameter. The novel operations of IFVs are closed or their values are also IFVs, i.e., \(\alpha\oplus_{\mathbb{C}}\beta,\alpha\otimes_{\mathbb{C}}\beta,k\alpha,\alpha ^{k}\in V\)._  
Proof of Theorem 1.: We have \(\mu_{\alpha}\in[0,1]\) and \(0\leq n_{\lambda}(\mu_{\alpha})\leq 1\).
# UAV Charging Station Placement in Opportunistic Networks
## 4 Application Scenario  
In our application scenario, there are three types of communicating pairs: UAV-to-UAV, node-to-node, and UAV-to-node. The pairs will communicate whenever they encounter each other. The system consists of people moving who carry electronic devices with WiFi connectivity. These people serve as the ground nodes for the opportunistic network with a set of UAVs that support the network in order to achieve high network performance in terms of low message delays and high success rate.  
Nodes create text messages every 30 min with a 1-min error rate and distribute them in a hop-by-hop manner. These messages have a three-hour lifetime, starting from the initial creation of the message, after which the nodes stop receiving messages.  
Both UAVs and nodes on the ground are using the IEEE 802.11 Wi-Fi protocol. The maximum connectivity distance is 250 m, which is appropriate for the IEEE 802.11 standard.  
The UAVs, however, are only responsible for distributing the messages through the network. The charging station is a circle with a one-meter diameter. The UAV stays at the charging station for 30 s to charge its battery. As charging a battery will likely take longer than 30 s, we assume that instead of charging the battery, the drained battery will be replaced with a fully charged one in 30 s.  
The UAVs perform random spiral scans on the environment at a 100 m altitude. We assume that the UAV's maximum speed is 20 m per second and 30 min of cruising time with a single charge. The UAV's specifications are appropriate for a commercial drone, for example a DJI Matrice 300 RTK [55]. The UAV scanning strategy is autonomous, so there is no need for the central station. How far UAVs the move away from a charging station is determined by whether their battery time is enough to reach another charging station or not. In this case, it is also possible that the charging stations may also include control stations.  
The main application scenario of the system is for disaster cases where the Internet is either not available, or the connection is not stable. The proposed system can broadcast messages through the campus area with the UAVs considering a university landscape where the dataset is collected.
## Materials and Methods
### Remote Terminal Unit
#### 2.4.5 RTU Algorithm  
The control algorithm of the RTU is shown in Figure 7. At the start of the program, the geographical and field positions of the heliostat are read from a database. Afterwards, all the parameters of the different components of the MCU and the program variables are initialized. Four timers are used to execute interrupts in the main program at specific time intervals, as shown in Table 3. Besides, a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Rated Voltage & 24 & V \\ No Load Current & 1 & A \\ Rated Current & 5 & A \\ Maximum Current & 25 & A \\ Rated Speed & 5 & rpm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter of the DC motors.  
Figure 6: H-bridge driver circuit.  
watchdog timer resets the MCU at any error in the main program. Finally, the functions of the external components of the MCU are initialized: two universal asynchronous receiver-transmitter (UART) modules for the serial communication with the XBee RF module and the Bluetooth radio module, an I2C module for the serial communication with the RTC, an ADC (Analog-to-Digital Converter) module for the manual control with the analog joystick, and the parallel communication with the \(20\times 4\) LCD.  
In the main loop of the program, the control data is received from the XBee RF module, the Bluetooth radio module, or the analog joystick. The manual control has a higher priority than the data received from the Bluetooth radio module, which has a higher priority than the data received from the XBee RF module. However, if an emergency stop is received from the SU, the OS only can be changed by the SU. If no data is received in 20 s, a flag restarts the MCU, in order to reset the communication modules at any error in the program.  
The orientation control algorithm modifies the PWM signals (with a dead time of 1 ms) when the control timer (Timer 3) overflows, in order to orient the heliostat to the desired position, which is determined by the received OS. Afterwards, every second the program reads the date and time from the RTC module, calculates the position of the sun, sends the data to the SU, and shows the OS in the LCD. If one minute has elapsed, the program calculates the angles of the heliostat; this is because the sun changes its position in the sky by \(0.25^{\circ}\) every minute. Therefore, it is not necessary to change the angular position of the heliostat every second of the day.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Timer** & **Function** & **Frequency** & **Period** \\ \hline
1 & Encoder clock signal & 200 kHz & 5 μs \\
2 & PWM period & 1 kHz & 1 ms \\
3 & Controller sampling time & 100 Hz & 10 ms \\
4 & 1-s timer & 2 Hz & 500 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Timers of the remote terminal unit algorithm.  
Figure 7: Control algorithm of the remote terminal unit.
# Simplified Calculation Model for Contact Resistance Based on Fractal Rough Surfaces Method
###### Abstract  
Electrical contact resistance (ECR) is critical to evaluate the stability and reliability of electrical connections. In this paper, a simplified contact model is established for rough surfaces based on the fractal theory and Monte Carlo method, which can overcome the difficulty of constructing the resistance networks for traditional contact models. The model reveals the influence of fractal parameters \(D\) and \(G\) on the surface morphology and contact characteristics. The established surface method can simulate Gaussian and non-Gaussian isotropic surfaces. Then the contact resistance considering a contaminated film is calculated, which provides a quantitative analysis of the change and the influencing factors. The accuracy of the calculation method in this paper is ensured by comparing the existing experimental data and finite element results. The results show that the contact surface with \(D\) of 1.5 has the largest real contact area and the smallest contact resistance. The model has accurate calculation results when dimensionless contact load \(F^{*}\) is less than \(4\times 10^{-3}\).  
fractal; rough surface; contact characteristics; contact resistance Article