esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Evaluation
### Performance Comparison  
Finally, we performed several experiments to compare the performance of the proposed controller with the ones obtained by (i) the legacy MQTT-SN QoS levels and (ii) the method proposed in [20] (indicated as Rocha2019). In order to do this, we set up a network topology with 20 subscribers: again, the PER of each subscriber is set randomly, according to a normal distribution with mean \(\bar{p}\) and standard deviation 0.1. We performed several experiments, each time increasing the value of the average PER \(\bar{p}\). For each scenario, we extracted from the simulation the mean hitrate and latency observed by each subscriber over the entire simulation length, either forcing the QoS to a fixed level or using the controller in  
Figure 11: Hitrate-constrained mode scenario.  
Figure 12: Latency-constrained mode scenario.  
different operational modes. The input parameters of the different operational modes are the same that have been used in the runtime behavior experiments.  
To perform a fair comparison with the method in [20], we performed the following assumptions: (i) the QoS controller is applied on the subscribers, (ii) the subscribers all request QoS 1 and (iii) the network latency estimation is performed end-to-end, exactly as in our case. The latency thresholds for normal network conditions (when the controller in [20] leaves the QoS level untouched) are set to 1 s and 3 s. Note that if the estimated latency is below the lower thresholds or above the higher, the method in [20] will force the subscribers to QoS 0 level.  
At the end of each simulation, we aggregate the results obtained using their average and standard deviation. The results are illustrated in Figure 13. As one can see, such results again highlight the tradeoff present between hitrate and latency. At very low PER values, all configurations tested result in good performance. However, as the PER increases, it is clearly visible how the case where all subscribers have been forced to QoS 0 departs from all other configurations, with a rapidly decreasing hitrate. Conversely, the proposed QoS controller has performance that is between the ones showed by the QoS 1 and QoS 2 fixed scenarios, both in terms of hitrate and latency, with the plus of respecting the delivery performance requested by clients. Finally, the method in [20] works as expected, with the average hitrate and latency behaving as QoS 0 when the estimated latency is outside the interval corresponding to normal network conditions and with performance closer to QoS 1 (the original level requested by the subscribers) otherwise.
## 5 Conclusions
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ACM & Adaptive Coding and Modulation \\ AWGN & Additive White Gaussian Noise \\ BER & Bit Error Rate \\ CCSDS & Consultative Committee for Space Data Systems \\ CER & Codeword Error Rate \\ CPU & Central Processing Unit \\ CUDA & Compute Unified Device Architecture \\ CW & CodeWord \\ ECCS & European Cooperation for Space Standardization \\ EGSE & Electrical Ground Support Equipment \\ EO & Earth Observation \\ ESA & European Space Agency \\ FD & Frame Descriptor \\ FER & Frame Error Rate \\ FM & Frame Marker \\ GP-GPU & General-Purpose Graphics Processing Unit \\ HW & Hardware \\ LEO & Low-Earth Orbit \\ LLR & Log-Likelihood Ratio \\ MAP & Maximum A-Posteriori \\ MHOMS & Modem for High Order Modulation Schemes \\ ModCod & Modulation and Coding \\ NewSpace & New Space Economy \\ PL & Physical Layer \\ SCCC & Serial Concatenated Convolutional Codes \\ SDR & Software Defined Radio \\ SIMD & Single Instruction Multiple Data \\ SISD & Single Instruction Single Data \\ SISO & Soft-In Soft-Out \\ SME & Small Medium-Size Enterprise \\ \end{tabular}
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### Access Models for IoT-over-Satellite
#### 3.3.4 Case IV: Scheduled Round Robin Transmission  
In this scheme, we assume that the network is capable of allocating time-slots to different IoT devices while at least one satellite is available. Note that this access method is not random in nature since time-slots are being deterministically allocated. Prior to transmitting, an IoT device will be allocated to a transmitting queue that ends when all IoT devices within the satellite footprint transmit their signals. As such, the IoT devices are assumed to be operating in an ideal condition where they are capable of dividing the time slots among each other with the aid of round robin scheduling algorithms. The round robin scheduling method had been widely used in wireless communication to provide fair sharing of the radio resources [51; 52], where co-channel interference is mitigated. Nevertheless, this access method significantly increases the complexity of IoT requiring two way communication and protocols.
## 2 Modeling of Offshore Oil and Gas Platform Cluster System
### Stability Analysis and Cluster-Partition Principles of Offshore Oil-Gas Platform Cluster System  
#### 3.1.1 Stability Analysis  
In [22], [PERSON] proposed a method to judge the stability of power flow, mainly by studying the power and the line-impedance between the nodes of the distribution-network branch to judge whether the power flow had a solution. However, in the derivation process, the simple equivalent of the complex distribution network, from the perspective of loss, led to large errors and the influence of voltage fluctuation was not considered. Therefore, some scholars proposed an improved derivation of power-flow stability [23], which can more comprehensively reflect the power-flow relationship between nodes and more accurately judge the existence of power-flow solutions. The expression of static voltage-stability indicator \(L_{ij}\) of the branches between nodes \(i\) and \(j\) is as follows:  
\[L_{ij}=\frac{4}{U_{i}^{4}}\Big{[}\big{(}P_{j}X_{ij}-Q_{j}R_{ij}\big{)}^{2}+ \big{(}P_{j}R_{ij}+Q_{j}X_{ij}\big{)}U_{i}^{2}\Big{]}, \tag{12}\]  
where \(U_{i}\) is the voltage amplitude of node \(i\); \(P_{j}\) and \(Q_{j}\) are the injected active power and reactive power, respectively, of node \(j\); and \(R_{ij}\) and \(X_{ij}\) are the resistance and reactance, respectively, between nodes \(i\) and \(j\). When \(L_{ij}<1\), the branch power flow is solvable and  
Figure 2: Combined power supply structure of renewable and traditional power sources.  
To simplify the expression, let  
\[a_{i}=\frac{2P_{i}R_{i-1,i}}{U_{i}^{2}}, \tag{19}\]  
Then, the relationship between the change-of-system loss and the change of power can be obtained as follows:  
\[\Delta P_{\text{loss}}=\begin{bmatrix}\frac{\partial P_{\text{loss}}}{ \partial P_{1,2}}&\frac{\partial P_{\text{loss}}}{\partial P_{1,3}}&\dots& \frac{\partial P_{\text{loss}}}{\partial P_{1,n}}\end{bmatrix}\begin{bmatrix} \Delta P_{L2}\\ \Delta P_{1,3}\\ \vdots\\ \Delta P_{Ln}\end{bmatrix}, \tag{20}\]  
where  
\[\frac{\partial P_{loss}}{\partial P_{Ln}}=\sum_{i=2}^{n}a_{i}+\sum_{\begin{array} []{c}i,j\in[2,n]\\ i<j\end{array}}^{j=n}a_{i}a_{j}+\sum_{\begin{array}{c}i,j,k\in[2,n]\\ i<j<k\end{array}}^{k=n}a_{i}a_{j}a_{k}+\dots\approx\sum_{i=2}^{n}a_{i}. \tag{21}\]  
From Equations (19) and (20), it can be determined that in a distributing network with radial topology, the loss sensitivity is strongly related to the node injection power. The greater the absolute value of the node injection power, the greater the sensitivity and the stronger the ability to adjust the loss. In addition, Equation (18) shows that the loss has a quadratic relationship with the injection power of the node. The smaller the absolute value of the injection power, the smaller the loss.
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 2 Materials and Methods
### Hyperspectral Image Acquisition  
Hyperspectral images were acquired in a dark room using the visible-near-infrared hyperspectral imaging system that consists of three components. These components are the imaging part, the light source, and a mobile platform. The imager was the main part and was composed of an imaging spectrograph (ImSpector Pro-V10E; Spectral Imaging Ltd., Oulu, Finland), a charge-coupled device (CCD) camera and a camera lens (HSIA-OL25). Spectral resolution of the imaging spectrograph was 2.73 nm. The light source consisted of four tungsten halogen lamps placed on both sides of the camera at a 45\({}^{\circ}\) angle, with a light controller. The mobile platform was a conveyer belt driven by a stepping motor. The whole system was controlled by a computer with SpectraView software. Scanning of rice samples was completed by moving the conveyer belt while the imager was fixed. A white panel was used as the reference every time the rice samples were scanned under the same illumination conditions.  
Hyperspectral images were obtained on 31 July and 22 August. Three data sets were extracted from these images. The first data set consisted of 136 images with different development stages of leaf blade lesions, the second data set consisted of 44 images with different levels of ShB severity  
Figure 1: Symptom development of sheath blight (ShB) on the rice leaf blade and the leaf sheath.  
on the whole leaves, and the third data set consisted of 58 images with different development stages of leaf sheath lesions.
# A Telepresence Wheelchair with 360-Degree Vision Using WebRTC
## 2 Methods
### QoE Estimation of Wireless Video Streaming Using Neural Networks
MOS has commonly been used as the subjective metric during human subject experiments. To evaluate the video quality, The International Telecommunication Union (ITU) defined a five-level scale, called an ITU-5 point impairment scale, as shown in Table 1. To train the NN, the collection of MOS rankings in compliance with the ITU-T standard [18] during voting periods of human subject experiments was carried out. \begin{table}
\begin{tabular}{c c c} \hline
**MOS** & **Quality** & **Impairment** \\ \hline
5 & Excellent & Imperceptible \\
4 & Good & Perceptible but not annoying \\
3 & Fair & Slightly annoying \\
2 & Poor & Annoying \\
1 & Bad & Very Annoying \\ \hline \end{tabular}
\end{table} 
Table 1: ITU five-point quality scale measurement of mean opinion score (MOS). Figure 4: Quality of experience (QoE) estimation model based on the neural network. Figure 5: Block diagram of QoE estimation based on neural network. As presented in [18], there is a close relationship between the MOS, the PSNR, and SSIM, as described in Table 2.  
After training the NN, the weights and bias parameters in the NN are determined and, then, the closed-form expressions for online prediction of user QoE can be obtained. The estimated QoE in terms of MOS is a function of online measurable network parameters (Jitter, SBR, PLR, and RTT) obtainable from wireless streams at monitoring points in the network. To develop the models, a series of well-designed objective and subjective test cases have been executed for the experiments. The ultimate goal of the objective and subjective testing is to come up with a set of user QoE training data from human subject experiments that can be fed to a neural network tool.
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Gaussian Process Regression (GPR)
#### 2.4.3 Target, Algorithms, and Features for Machine Learning Models  
All the features chosen for each machine learning model can be seen in Table 3. Besides the GSE, Z-Coordinate, GWL, SPT-N Value, and % Finer than 0.075 mm Sieve, a set of features named Location Information was included to represent the spatial relation in this modeling process. There are three forms of Location Information features to identify query point location: (1) EDF features (Section 2.3.1), (2) Raw X-Y Cartesian coordinate, and (3) EDFB features (proposed by [PERSON] et al. [20]).  
Machine learning models with EDF as the features were separated into three models based on the applied PCA on that model: (1) without PCA, (2) with 80% threshold for the PCA variance ratio, and (3) with 90% threshold for the PCA variance ratio. As a result, seven machine learning models were generated for each target in this study.
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 1 Introduction
The effectiveness of the technique in replacing dielectric resonating reflectors in mitigating large back radiations associated with aperture-coupled patch antennas has been proven with measured results using conductive screen-printed antennas. This research aims to develop practical and high-performing antennas at a commercial level with seamless integration into everyday clothing with little or no discomfort to the wearer. The authors believe this to be a step in the right direction for smart clothing technology. Figure 1: Illustrated view of fabric-based antennas integrated into fabric clothing. Section 2 elaborates on the materials used and the distinction between the use of ink-jet/direct printing and screen-printing techniques. Section 3 describes the antenna design and measured results of the direct printed and screen-printed techniques. Section 4 introduces the aperture-coupled cable feeding technique. This antenna feeding method, the first of its kind to be used on fabric antennas, was tested with a patch antenna and measured results have been presented as well.
## 3 Optical Characterization Measurement Results
### Hybrid External Cavity Laser Operation
As the SOA current increased every longitudinal cavity mode present in the resonance peak bandwidth had enough amplification to win over total round-trip losses, even if they were reflected less compared with the main peak; this lead to very fast switching of the lasing mode between the available cavity modes, which was then captured by the time-averaged spectra on the OSA, whose resolution was limited to 50 pm, as spectral broadening. Figure 6: Measured Light-Current (LI) curve for the external cavity laser. Can see that threshold current is approximately 22 mA and the output power from the laser is 4 mW at 100 mA for RSOA (Reflective Semiconductor Optical Amplifier) current. Slope efficiency of the laser is 0.054 W/A. When the current increases further, the gain is so high that even other resonances with lower Q factors are in an over-threshold condition and all the cavity modes overlapping with these resonances start lasing. In particular, it can be seen that a PhC resonance of around 1559 nm, shown in Figure 4, starts lasing for SOA currents greater than 40 mA.
## 1 Introduction
### A Review of Existing Wireless LPG Level Sensors for Domestic Stationary Tanks
The sensor takes one measurement per day and is powered by non-rechargeable AA-size lithium/iron disulfide (Li/FeS\({}_{2}\)) batteries (not included), with an estimated lifespan of two years. Manufacturer: Cosas Web, S.A. de C.V., Jalisco, Mexico. Price: 80.30 USD. * Redgas Touch(r) [11]: It is a sensor to wirelessly measure the gas level in stationary tanks autonomously through Wi-Fi communication. It informs you directly on your phone how much gas you have and warns you when you are going to run out of gas. It sends alerts if it detects a low gas level in the tank. The device displays the data only to the tank owner and cannot be shared with the gas refilling plant. It is not mentioned if it uses any cybersecurity strategy. The sensor makes one measurement per day and uses a rechargeable Li-ion (Rechg.-Li-ion) battery (size not specified) and a small photovoltaic (PV) cell. However, according to the product's reviews and ratings, it stops working after five months because the small PV cell is not able to recharge the battery. Manufacturer: Redtapp S. de R.L. de C.V., San Luis Potosi, Mexico. Price: 85.00 USD. * SensiGas(r)[12]: This is a device designed with a sensor that sends the gas level of your tank remotely to an app. It works with 4G connectivity, so coverage must be checked to be able to use the device. The app is free. The device displays the data only to the tank owner and cannot be shared with the gas refilling plant. It is not specified (N/S) if it uses any cybersecurity strategy. The Rechg.-Li-ion battery (size not specified) can have a lifespan of up to five years, transmitting once a day in excellent signal conditions. However, if the tank is located in an area with poor signal or if it has a lot of activity and there are more transmissions due to low level alerts, the battery life will decrease significantly. Manufacturer: Mobile IoT Latam, S.A.P.I. DE C.V., Jalisco, Mexico. Price: 98.50 USD. * Gaszen(r) (Discontinued since 2020 [13]): This used Wi-Fi connectivity and an app to indicating the LPG level in the tank. The device showed the data only to the tank user, which could not be shared with the gas refilling plant. It did not use any cybersecurity strategy. The business model consisted of charging a commission to the gas distribution company every time users requested a refill service through their app, and there was the option of rating the service of the filling plant. Manufacturer: Gaszen Tech S.A.P.I., Guanajuato, Mexico. Price: 100.35 USD. It is worth mentioning that the information presented above was obtained from the website of each device and neither in these nor in the available literature of each have technical data been found, such as energy consumption levels, specifications of main electronic components, or electronic design diagrams.
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 3 Methodology
### Long Short-Term Memory Network for Sequence Inputs  
This LSTM [41] is a recurrent module designed for sequence processing. Generally, CNNs are capable of extracting only the local spatial information of images. However, they fail to capture the long-distance dependency of temporal learning. Thus, we apply an LSTM network, which addresses this limitation by sequentially modeling each image across multiple input data. The major contribution of LSTM is its memory cell, which accumulates state information over a long duration. As shown in Figure 7, a standard LSTM unit comprises an input gate, output gate, and forget gate. All of the gate units have  
Figure 6: The schematic diagram of the proposed model that synergistically combines CNN and LSTM.  
a sigmoid non-linearity which squashes the real value input into a range of [0, 1], while the input units can have any squashing non-linearity. The gate's equation can be computed using the following set of equations. The \(\mathrm{i}_{t},\mathrm{o}_{t}\), and ft represent the input gate, output gate, and forget gate, respectively.  
\[\mathrm{i}_{t} =\sigma(\mathrm{W}_{xi}\mathrm{X}_{t}+\mathrm{W}_{hi}\mathrm{h}_{t -1}+\mathrm{b}_{i}), \tag{2}\] \[\mathrm{f}_{t} =\sigma(\mathrm{W}_{xf}\mathrm{X}_{t}+\mathrm{W}_{hf}\mathrm{h}_{ t-1}+\mathrm{b}_{f}),\] (3) \[\mathrm{o}_{t} =\sigma(\mathrm{W}_{\mathrm{so}}\mathrm{X}_{t}+\mathrm{W}_{ho} \mathrm{h}_{t-1}+\mathrm{b}_{o}),\] (4) \[\mathrm{c}_{t} =\mathrm{f}_{t}\circ\mathrm{c}_{t-1}+\mathrm{i}_{t}\circ\tanh( \mathrm{W}_{xi}\mathrm{X}_{t}+\mathrm{W}_{hi}\mathrm{h}_{t-1}+\mathrm{b}_{i}),\] (5) \[\mathrm{h}_{t} =\mathrm{o}_{t}\circ\tanh(\mathrm{c}_{t}), \tag{6}\]  
where \(\mathrm{c}_{t},\mathrm{h}_{t}\), and \(\sigma\) stand for the memory cell, hidden state, and sigmoid function, respectively. For a particular time step \(t\), the forget gate decides which information to clear or keep from the memory state cell by observing the new input sequence \(\mathrm{x}=\mathrm{x}_{1},\mathrm{x}_{2},\ldots,\mathrm{x}_{t}\). Here, at time step \(t\), the feature \(\mathrm{x}_{t}\) is extracted from the pretrained VGG16 architecture and passed to the LSTM unit. Then, the input gate determines how the extent of information from the input will alter the data contained in the cell state. Lastly, the output gate is in charge of determining what output from the internal cell state is used as the output \(\mathrm{h}_{t}\). Then, the hidden unit state is calculated by \(\tanh\) activation and the memory cell. W, b, and \(\circ\) denote the weight matrix, bias vector, and element-wise product, respectively. Our proposed LSTM layer is constructed with 256 memory cells to process each frame of the sequence and store only the crucial features of each input image while learning.
## 4 Enabling Technologies for FSO Communication
### Aperture Averaging
#### 4.5.2 Advantages of Aperture Averaging  
To achieve effective FSO communication through continuous transmission link establishment, it is crucial to maintain a larger aperture area, resulting in a wider FoV. A narrow FoV limits the coverage area, which is not ideal for FSO communication systems. Although a larger collection aperture increases ambient light background noise, this can be mitigated by various methods that allow the FSO Rx to capture a larger portion of the incoming optical beam. Conversely, a higher FoV provides a larger coverage area and eliminates the need for additional circuitry for pointing and tracking mechanisms, ensuring reliable wireless connectivity between transmission and reception apertures. Moreover, FSO systems with a larger FoV encounter reduced misalignment errors, reducing the impact of obstacles between the source and destination points and easing the alignment control mechanism [46].
## 2 Feature Enhancement Point Cloud Data Resampling
### Small-Scale Noise Removal  
For the filtering of small-scale noise, traditional filtering methods are used such as mean filter [24], median filter [25], Gaussian filter [26], and bilateral filter [27]. The essence of mean filtering is to distribute the outliers around each point equally to all points, and point clouds are irregularly distributed in most cases, which causes mean filtering to potentially degrade accuracy when denoising point clouds. Median filtering method has the drawback of losing details and connectivity on the point cloud surface, as it is hard to determine the optimal size of the neighborhood for denoising. Gaussian filtering method blurs the edges more obviously, and the protection of high frequency details is not obvious. The bilateral filtering method needs to use the K-neighborhood in the process of filtering and solves the bilateral filtering factor of all data points in the neighborhood at that point, which is too long in computation time and low in efficiency and cannot be well applied to field experiments. Therefore, a fast bilateral filtering algorithm based on threshold segmentation is proposed in this paper. The specific algorithm is as follows.  
Firstly, the point cloud data are divided into grids along \(u_{1}\) axis and \(u_{2}\) axis direction, respectively, and the set of \(u\) coordinates in the grid is \(\widetilde{Q}^{u}[z^{\prime\prime},\lambda]\), \(\lambda\) denotes the length of the divided grid, and \(\widetilde{Q}^{u}[z^{\prime\prime},\lambda]\) is  
To improve the operational efficiency, the improved bilateral filtering factor can be expressed as:  
\[\alpha^{\prime}=\frac{\sum\limits_{j=1}^{N}w_{c}(\left\|q_{i}-q_{j}\right\|)w_{s }(\left\|z_{i}-z_{j}\right\|)(z_{i}-z_{j})}{\sum\limits_{j=1}^{N}w_{c}(\left\|q _{i}-q_{j}\right\|)w_{s}(\left\|z_{i}-z_{j}\right\|)} \tag{14}\]  
where \(N\) is the number of neighboring points, \(w_{c}(x)=e^{-x^{2}/(2\sigma_{c}^{2})}\) is defined as the smooth filtering power function, \(w_{s}(x)=e^{-x^{2}/(2\sigma_{c}^{2})}\) is the feature retention power function, \(\sigma_{c}\) and \(\sigma_{s}\) are the Gaussian filtering factors, \(\sigma_{c}\) is the length of the grid, \(\sigma_{s}\) is the length of the points in the grid, and \(q\) is the standard deviation of the distance from the point in the grid to the point.  
The filtered point cloud data are:  
\[q^{\prime}=q-\alpha^{\prime}n \tag{15}\]  
where \(q\) is the pre-filtered point cloud data, \(q^{\prime}\) is the post-filtered point cloud data, and \(n\) is the unit vector (0,0,1).