esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Platform Subsystem Design
### Command and Data Handling Subsystem  
The on-board computer (OBC) acts as the brain of the satellite, which allows controlling of the operation and data handling of all the subsystems by managing the different subsystem modules. It also synchronizes the CubeSat clock with the ground station. The OBC collects system data such as temperature, voltage, current, and other sensor readings. While subsystem critical health data is transmitted in beacon packets, other data is stored as housekeeping data, which is duly sent to the ground station through the communication subsystem. The OBC processor executes all commands received from the ground station. Scheduling between different tasks is managed through the OBC software, which operates by a real-time operating system. Moreover, all modes of operation are implemented in the OBC software. The command list used by the ground station operator relies on the subsystem's command requirements. Thus, each mission has a different command list in which each command has a unique code structure defined by the software developer.  
In this mission, the OBC has an integrated ADCS sensor and is compatible with ADCS actuators. As the Light-1, OBC implements the ADCS algorithms on-board, the OBC takes the ADCS sensor readings, processes them, and operates the actuators accordingly. Although the Light-1 OBC is not responsible for storing payload data on-board, a prioritization plan is implemented onboard for payload data transmission. The OBC software also accounts for different levels of authentication. It provides different privileges based on the user type level, i.e., user, superuser and manufacturer. A user can perform normal operations such as downloading satellite data. A superuser can configure certain parameters in addition to normal operations. However, certain parameters are restricted to the manufacturer, such as the parameters related to the control and determination algorithms.  
The NanoAvionics SatBus3C2 was chosen according to the detailed system requirements satisfactory evaluation. This OBC board has an integrated UHF module and ADCS sensors, magnetometers, and gyroscopes with a low power consumption of 0.287 W. Its processing unit implements the flight controller software as well as the ADCS algorithm onboard. It is an ARM 32-bit with 400 MHz processing speed. In addition, it supports different data buses such as I2C, CAN, UART, and SPI. The CAN bus is mandatory for payload interfacing with the OBC, due to its higher transmission speed compared to the I2C bus. Thus, the CAN bus is used to connect the OBC with the communication system  
Figure 6: Distribution of solar cells in each Light-1 solar panel.  
and the payload. Moreover, it supports an SD card for storage, allowing 64 GB on board for storing housekeeping data.
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 2 Hybrid VLC/RF Networks in Indoor Scenarios under Ideal Conditions
### Lighting Requirements  
Through proper room lighting design, the energy efficiency of the VLC/RF hybrid network can be improved. The goal is to achieve the desired lighting pattern with minimum energy consumption. This requires optimizing the placement and control of LED output power levels. Lighting quality is essential in these hybrid networks because all information and electronic signals are transmitted through light [32]. A bad lighting design can affect the quality of the signal; therefore, it is necessary to take into account different aspects such as the distance between the devices, the intensity of illumination, environmental interference, and power management. The illumination level stipulated by the International Organization for Standardization (ISO) for an indoor environments is 300 to 1500 \(\mathrm{lx}\). Generally, the greatest illumination is in the center of the room rather than in the corners, so careful design is required to provide uniform illumination  
subject to the shared illumination constraint. In [36], an electrical and optical power allocation scheme was proposed taking into account the lighting constraints in order to maximize the multi-user sum rate (throughput). It was shown that with increasing luminous flux the maximum summation rate exhibits a downward parabolic trend due to the limited dynamic range of LEDs. Alternatively, a higher data rate can be achieved with a higher correlated color temperature. In [37], a simple approach to the illumination design problem based on the virtual LED concept was presented. The authors formulated an optimization problem with constraints to minimize energy consumption while maintaining near-uniform illumination throughout the room. In addition, they derived the number of LEDs and the luminous intensities of the LEDs required to achieve the desired lighting constraint.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 4 Experimental Results and Analysis  
### Dataset and Parameters  
For the application and validation of the method, 85 voice recordings were used, with a sampling frequency of 8 kHz and 8 bits per sample (\(b=8\)). The duration of each recording is 10 s (80,000 samples), and they are monophonic.  
Accordingly, if \(b=8\), \(C_{SF}\) is a \(256\times 256\) matrix, where each row corresponds to an OVSF code. After scrambling, the scrambled codes (\(SC\), size: \(256\times 256\)) are obtained with a different random seed value used to encrypt each of the 85 test signals. From the matrix \(SC\), the values of the input signal were spread, replacing each input sample with a 256-bit vector. The size of the obtained binary vector (\(SB\)) is 256 \(\times\) 80,000 = \(20.48\times 10^{6}\) bits.  
Meanwhile, the number of bits chosen for the secret signal is \(w=16\). In this way, the secret signal will have \(1.28\times 10^{6}\) samples, equivalent to a signal of 160 s with a sampling frequency of 8 kHz. In other words, the expansion rate of the signal is 1:16. This expansion rate is constant as long as \(b\), \(w\), and \(f_{s}\) (sampling frequency of the encrypted signal) are constant. If \(w\) or \(f_{s}\) are doubled, the expansion rate is reduced by half, and vice versa.  
The signal obtained shows no traces of the original signal and its content is unintelligible. This signal together with the seed value of the randomization will be the input data for the recovery process. Figure 1 shows an example of the original signal, the encrypted signal and the recovered signal.
## 4 Simulations and Results Analysis
### Key Strength Probability (\(P_{f}\))
#### 4.3.2 Accuracy Analysis of \(P_{f}\)  
As \(P_{F}\) represents the probability that a secret key has not been revealed by an adversary who performs a brute force attack, we compute the accuracy by comparing the estimate given by Equation (6) with the actual fraction (obtained from the simulations) of keys not discovered by an adversary yet. This fraction is measured in each simulation as follows. Each sensor node is equipped with a key of strength \(s\) randomly selected from the total space of \(2^{s}\) keys. Then, a brute-force attack is performed in order to  
Figure 2: Key strength probability (\(P_{F}\)) _vs._ time \(t\), for different values of key strength \(s\) and rate \(f=394,875,793,722\) keys/s.  
reveal the keys of the nodes, and at each time \(t\), the fraction of nodes whose keys were not revealed yet is computed.  
We simulate scenarios with different network sizes and different values of \(t\), for a key strength of \(s=64\) bits. The results obtained are presented in Figure 3. In this figure, we can see that the accuracy remained very high, between 99% and 100%, for all values of \(t\) and network sizes.
## 4 Results and Discussion
### Case Study 1: Flexible Beam Coupling
#### 4.1.2 Direct Acceptance Components at Connection Point (Node \(\mathcal{U}_{2}\) )
Despite variations in response characteristics, the alignment on natural frequency peaks indicates consistent resonance predictions across different excitation conditions, like the observations at the tip node shown in Figure 11.
## 4 Case Study
### EV Fleet  
The industry has a diverse range of EVs, with varying capabilities and battery capacities, which enables them to effectively accomplish their tasks. The actual fleet consists of a total of 137 vehicles, and its full electrification is expected to be completed in the upcoming years. For simplicity, the case study considers a fleet of 10 _D WIDE ZE_ heavy-duty waste collection trucks, which are typically operated at night to minimize traffic disruptions.  
Fleet scheduling is generally fixed, using the same vehicle for the same route daily. Upon completing a route, vehicles are immediately recharged to 100% SOC for the next operation. Usage patterns and battery consumption depend on route characteristics, waste volume, and environmental conditions, factors that influence battery degradation over time.
## 4 Proposed Hybrid (InC-DFO) Technique for PV Energy Systems
### Partial Shading Detection Method to Transfer from InC to DFO
So, the values of G-1 and G-2 are calculated using (27) at \(I_{0.8 Voc}\) and \(I_{0.8 Voc-array}\) sequentially as follows:  
\[ \begin{split} G-1&=\frac{I_{0.8 Voc}}{I_{sc,STC}}G_{STC}\\ &=\frac{6.001}{6.14}\times 1000=977\\ \\ G-2&=\frac{I_{0.8 Voc-array}}{I_{mpp,STC}}G_{STC}\\ &=\frac{5.78}{5.76}\times 1000=1003\\ \end{split}  \tag{28}\]  
It is concluded from (28) and (29) that G-1 and G-2 values are near to the real value of G like 1000 W/m\({}^{2}\). Though, (27) is estimated on the basis such that \(I_{mpp}\) is situated at 0.9 \(I_{sc}\). This consideration is not mandatory correct for all values of G because of creating a mismatch value. In above discussed computation, mismatch value is 23 W/m\({}^{2}\) among G-1 and G-2 at 1000 W/m\({}^{2}\) at 1000 W/m\({}^{2}\). However, G mismatch value at 700 W/m\({}^{2}\) between G-1 and G-2 is reduced to 12 W/m\({}^{2}\), whereas at 300 W/m\({}^{2}\) G mismatch value within G-1 and G-2 is decreased closely to zero. Based on the mismatch values of G, an important conclusion could be constructed that in stage one, InC techniques with variable step size, act as UI conditions for a below specific threshold value of irradiance. This threshold value 40 is calculated by authors [42] on I-V curves using an extensive survey of ten poly and mono-crystalline different PV modules. Therefore, it could be deduced that if the value of mismatch is greater than 40 W/m\({}^{2}\) then the PS condition is prevailing and it acts as a UI condition for less than 40 W/m\({}^{2}\). \[ \begin{split}|G1-G2|&>40 PS\quad Condition\\ |G1-G2|&<40 UI\quad Condition\\ \end{split}  \tag{30}\]  
To overcome the problem of power loss, the bio-inspired DFO technique is used in the second stage for searching the GM. InC technique with variable step size initiate in stage one and force the PV energy system to converge to 80 percent of \(I_{sc}\). once the InC algorithm achieved the \(I_{sc}\) then it triggers the DFO algorithm which settles the PV array to exact MPP. Thus, InC-DFO not only removes the cons of both methods but also searches the MPP at PS and CPS conditions. The flowchart of the proposed InC-DFO algorithm is given in Figure 8.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 4 Performance Characterization
### Effect of Error Sources  
The main error sources that affect the positioning using LEO SV Doppler shift measurements are the thermal noise, the satellite position and velocity errors, and the satellite clock drift. In this subsection, we study their effects on the user position and attitude determination.  
We run the filter with the inertial sensors, magnetometer, and Doppler shift data described in Section 4.1. The filter states are initialized as follows. The position error is \(10\text{ km}\left(1-\sigma\right)\), the velocity error is 3 m/s (\(1-\sigma\)), and the receiver clock bias error is 0.5 s (\(1-\sigma\)). They are all set randomly with a Gaussian distribution. The attitude angles are randomly selected using a uniform distribution. The processing of the filter outputs permitted us to derive the performance of the filter, which will be analyzed in the following paragraphs.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{SV numbers} \\ \hline
104 & 107 & 114 & 116 & 123 & 126 & 135 & 148 & 150 & 163 & 165 \\ \hline \multicolumn{11}{c}{} & \multicolumn{6}{c}{Satellite availability (\%)} \\ \hline
36 & 100 & 96.6 & 100 & 99.7 & 100 & 28.8 & 73.7 & 100 & 100 & 51.1 \\ \hline \multicolumn{11}{c}{} & \multicolumn{6}{c}{Received power (dBw)} \\ \hline \(-136\) & \(-131.1\) & \(-135.5\) & \(-128.1\) & \(-135.7\) & \(-129.7\) & \(-136.1\) & \(-134.6\) & \(-133.0\) & \(-134.7\) & \(-136.3\) \\ \hline \end{tabular}
\end{table}
Table 2: Satellites availability and received bursts power (averaged over 200 simulations).  
Figure 9: The Skyplot showing the positions of the tracked satellites in a polar projection (elevation and azimuth) centered on the user’s path. The numbers in the legend are satellite numbers.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 4 Nanocellulose-Based Composites
### Graphene/Nanocellulose  
Graphene is a thin, thermally, and electrically conductive transparent sheet. It is the fundamental building component of allotropes of carbon, including fullerenes, graphite, and carbon nanotubes [101; 102; 103]. Without the chemical functionalization of graphene, incorporating cellulose nanoparticles improves the graphene nanoparticle's dispersion in the aqueous environment and restricts aggregation [104]. The fabrication of nanocellulose/graphene composites begins with water-based dispersion. Different approaches such as filtration, filtration with hot pressing, freeze-casting, and freeze-drying can be employed for composite fabrication. Furthermore, other fabricating methods include incorporating graphene during the bacterial synthesis of nanocellulose and depositing graphene on a nanocellulose layer [105; 106; 107; 108; 109; 110; 111; 112]. Several types of nanocellulose and graphene can be used for generating nanocellulose/graphene composite, and these composites can be supplemented with different compounds, for example, ceramic nanoparticles, carbides, oxides, enzymes, and polymers, to modify their characteristics for the particular application. An enhanced osteogenic differentiation is observed in human umbilical cord mesenchymal stem cells after adding graphene oxide to the electrospun cellulose acetate nanofibrous scaffold. It also improved these cells' growth and adhesion properties [113]. Nanocellulose/graphene composites offer considerable promise for developing wound dressing and antibacterial textiles due to their mechanical and antibacterial properties. Antibacterial textile fabricated via electrospinning a solution of graphene oxide sheets, cellulose acetate, and TiO\({}_{2}\) results in increased antibacterial activity against _Bacillus cereus_ and _Bacillus subtilis_[114].
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 3 Methods
### Prediction Model Construction
#### 3.4.1 1 DCNN  
A 1 DCNN is suitable for analyzing signal and time-series data. A 1 DCNN, 2 DCNN, and 3 DCNN all have the same characteristics and follow the same processing method. The main difference lies in the dimensions of the input data and the manner in which the convolution kernel (also known as the filter) moves within the data. For example, the convolution kernel of the 2 DCNN moves along two directions, and the input and output data are 3D. However, the convolution kernel of the 1 DCNN moves in one direction, and the input and output data are 2D. A CNN comprises convolutional layers, pooling layers, and fully connected layers. These layers are constructed differently according to the CNN architecture.  
Commonly used CNN architectures such as AlexNet and GoogleNet have well established network architectures that can be easily applied to or modified for 2D or 3D data; however, a 1 DCNN has no established network structure. The network structure of a 1 DCNN is built and modified according to the type and size of the data.  
We used Keras in Python to construct a 1 DCNN model (see Table 3 and Figure 5, taking data set 2 as an example). There were eight layers in total: a 1D convolutional layer, a maximum pooling layer, a 1D convolutional layer, another maximum pooling layer, a dropout layer, a fully connected layer, another dropout layer, and an output layer. The sigmoid activation function was used for solving binary classification problems. The equation is as follows:  
\[f(x)=\frac{1}{1+\mathrm{e}^{-x}} \tag{2}\]  
The parameter settings are listed in Table 3. The number of convolution kernels (filters) were set to 16 and 32 in the two 1D convolutional layers; the convolution kernel size was set to 3; the maximum pooling layer window size was 2; the strides were all set to 1; the padding was set to the same value, which means that the input and output data are of the same size; and the dropout was set to 0.2.  
Optimization in neural network learning implies determining the ideal combination of parameters with minimal loss. Various optimizers, such as stochastic gradient descent (SGD), Adam, and RMSprop, have been developed. The learning speed of the SGD optimizer is low. Therefore, we used Adam and RMSprop with the grid search method (GridSearchCV) in Python to determine the most suitable optimizer for this model, and the loss function was set to binary_crossentropy to deal with the problem of two-group classification. The batch_size was set to 10, 20, and 30, and the epochs were 50, 100, and 150 with fivefold cross-validation.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 5 Performance Evaluation
### MANET with RandomWaypoint
#### 5.2.1 Throughput vs. Network Density  
This test analyzes the average throughput level that can be achieved with different density nodes (25, 36, and 100 nodes) and with a nodes' average speed of 5 km/h. Figure 5 summarizes the results.  
Figure 3: Analysis of throughput in a linear topology.  
The power control mechanism will have favorable results as long as it is applied to a density not too high of nodes. When reaching the 100 nodes there is no improvement in the average throughput, this behavior is because the minimum transmission power calculated for 100 nodes is very low. This limits the successful transmissions by increasing the number of jumps (forwarding) and therefore the probability of collisions and delay.  
Figure 6 shows the average transmission power for all node densities, with and without power control.  
Figure 4: MANET with RandomWaypoint mobility model.  
Figure 5: Analysis of throughput vs network density. DPCM 1 uses an upper bound of five. DPCM 2 uses an upper bound of six. Both DPCM configurations use a lower bound of four.  
The results indicate that the average transmission power was reduced when the mechanism was used. The throughput increased in two out of the three cases but, DPCM 1 using an upper bound of five nodes showed better performance. This means that the mechanism can save energy that could deteriorate the network performance.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 2 Background of Process Automation
### Process Control Automation
#### 2.1.2 Closed-Loop Control  
Closed-loop control is a conventional system that has a controller maintaining the desired set-point of the process. Here, dead-time and external noise cause significant issues, while the maximum allowed latency varies from 10 to 100 ms, with a less critical rate in comparison with emergency class systems.
# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 5 Experiment and Analysis
### Data Processing  
Due to the limited fault vibration data collected, it may be difficult to retrieve fault features. Therefore, the acquired vibration signals were enhanced in this paper. Data enhancement is a way to increase the value of limited data without substantially increasing it. In this paper, the original one-dimensional vibration signal was augmented using the overlap sampling method. The original data were first segmented using sliding windows of size 1024 and a sliding stride of 300. The segmented signals were then transformed into 224 \(\times\) 224 feature images using GASF transformation. Figure 4 depicts the schematic diagram of the overlap sampling. The gearbox dataset consisted of five classifications for both 0 Nm and 7.32 Nm loads. For each classification, 3500 feature images were generated. In each iteration, 3000 feature images were randomly selected and split into a training set and a testing set in an 8:2 ratio. The distribution of specific fault samples is shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fault Label** & **Fault Types** \\ \hline
0 & normal \\
1 & Tooth defect \\
2 & Tooth broken \\
3 & Tooth root crack \\
4 & Tooth surface wear \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault Types.  
Figure 4: Overlap sampling.
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 3 Link Quality Based Routing Metrics for Tactical MANETs  
Selection of good routing metric plays a key role in achieving data transmission efficiency with power savings and rate adaptation. Different link-quality assessment strategies have been suggested [19]. For example, channel quality can be estimated at the receiver with the help of known pilot symbols or training sequences. Blind and semi-blind channel estimation methods have been also considered [20]. Our intention is to explore commonly used link-quality metrics which can be used to make reliable routing decisions over dynamic topology of tactical MANETs [21]. In particular, physical-layer reports can be used to estimate the instantaneous or average link quality over certain time period. Such metrics are attractive, since they are readily available without any additional costs or modification of existing protocols, and they can be used for continuous monitoring of link quality. The key measurements readily available at different stages of the receiver are outlined in Figure 1[22]. Next, we will review these metrics in more detail.