esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology  
In this experimental study, we present a structured methodology for assessing the software performance of LWSCs while encrypting resource-intensive live video feeds on an ARM11 processor. The research methodology provides the development of a benchmarking tool specifically customized for ARM11 processors, which involves a thorough analysis of requirements, the establishment of implementation criteria, rigorous testing, debugging, and optimization. LWSCs are carefully selected based on their capability to address IoT security challenges and their importance in the lightweight cryptographic community. Additionally, specific implementation considerations for ARM processors are outlined to ensure limitations in computational power and memory capacity for proving a resource-constrained IoT environment. Evaluation metrics, including encryption frame rate, throughput, processor cycles, memory footprint, flash memory usage, and energy consumption, are defined and scrutinized to comprehensively assess the efficacy, efficiency, and suitability of LWSCs in resource-intensive applications.
## 3 Routing Protocol: RFAODV
### Overview of RFAODV
#### 3.2.1 Route Discovery
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Byte** & **1** & **2** & **3** & **4** \\ \hline \multirow{8}{*}{Definition} & Type & Reserved & \(Nq\) & Hop Count \\ \cline{2-4}  & \multicolumn{3}{c}{RREQ ID} \\ \cline{2-4}  & \multicolumn{3}{c}{Destination IP Address} \\ \cline{2-4}  & \multicolumn{3}{c}{Destination Sequence Number} \\ \cline{2-4}  & \multicolumn{3}{c}{Source IP Address} \\ \cline{2-4}  & \multicolumn{3}{c}{Source Sequence Number} \\ \cline{2-4}  & \multicolumn{3}{c}{Destination Location} \\ \cline{2-4}  & \multicolumn{3}{c}{Source Location} \\ \cline{2-4}  & \multicolumn{3}{c}{Last Forwarding Node Location} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The structure of the RREQ. Node X determines whether it is in the forwarding area. And if it is not, the node drops this RREQ directly. Otherwise go to step (6);
* Determine whether the RREQ needs to continue to find the path through TTL (time to live). If not, drops this RREQ. Otherwise go to step (7);
* Node X sets a timer according to its position, energy and communication time. If the timer expires, broadcast the updated RREQ. During the waiting period, if the RREQ sent by the neighbor node is received, it indicates that a better node has carried out RREQ forwarding, and this process of route discovery is abandoned. ```
1.if the RREQ is received for the first time, then
2. establish or update the reverse route to the source node S;
3.if node X is the destination node of RREQ or has a valid route to the destination node, then
4. send RREP;
5.elseifNq < 2,then
6.if in the forwarding area and TTL > 1,then// Obtain the forwarding area using Equation (3)
7. set delay timer; // Obtain the delay using Equation (5)
8.if receive RREQ forwarded by another node before timer expired, then
9. drop this RREQ;
10. else
11. broadcast this RREQ;
12. endif
13. else
14. drop this RREQ;
15.endif
16. broadcast this RREQ;
17.endif
18.else
19. drop this RREQ;
20.endif
```  
**Algorithm 2** RREQ processing of any node  
Node Y is any node and operates after receiving RREP according to the Algorithm 3, the main steps of the algorithm are as follows. 1. Node Y determines whether it is a destination node according to the destination address in RREP, as shown in Table 3. If yes, the route is established successfully. Otherwise go to step (2);
2. Establish or update the route according to whether there is a route to source of RREP;
3. Update and forward or drop this RREP according to whether there is a route to destination of RREP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Byte** & **1** & **2** & **3** & **4** \\ \hline \multirow{6}{*}{Definition} & Type & Reserved & Hop Count \\ \cline{2-5}  & & Destination IP Address & \\ \cline{2-5}  & & Destination Sequence Number & \\ \cline{2-5}  & & Source IP Address & \\ \cline{2-5}  & & Life Time & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The structure of RREP.
# Thermal and Physical Properties of Crude Palm Oil with Higher Oleic Content
###### Abstract
African palm oil (APO) has a balanced composition between saturated and unsaturated fatty acids; hybrid palm oil (HOPO), in turn, has a high content of unsaturated fatty acids, especially oleic acid [9]. Furthermore, palm oil is widely used in non-food industries, including its application other than food as hydraulic oil, drilling fluid, transmission fluid and lubricants, being fully biodegradable [19]. Several physical properties which infer process design also depend on the fatty acid composition. For instance, density and viscosity, essential fluid properties for the design of pipes, accessories, and equipment utilized in the oil industry, are highly dependent on the length and degree of fatty acids' unsaturation [20; 21]. The composition of fatty acids attached to a triacylglycerol molecule determines its thermal behavior. The different molecular structures of triacylglycerols, with their different chemical characteristics, manifest their physical states at different temperatures, giving the oil crystallization and melting behavior [18]. The more saturated the fatty acids adhered to the triacylglycerol, the faster the crystallization processes. Besides the importance of palm oil for the industry and the growing interest in cultivating hybrid oil palms, there is a lack of physical and thermal properties for palm oils with higher oleic content. In this context, this work aims to determine the physicochemical and thermal properties of commercial hybrid palm oil obtained from an interspecific hybrid cultivar (_E. oleifera_\(\times\)_E. guinensis_). The results were compared to the better-known and widely commercially exploited African palm oil (_E. guinensis_).
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
## 4 Results and Discussion  
In order to evaluate the performance of the MSB-SE approach in monitoring gear wear progression, the recorded vibration data were processed using MSB method in which 90 averages were achieved based on the data length and 50% overlap ratio between successive data frame. In addition, a Hanning window was applied to the data frame during the fast Fourier transform to suppress spectral leakages.
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 4 Results and Discussion
### Full Field Scale Modelling
Furthermore, this chemical interaction between the low salinity brine, crude oil, and the rock can also lead to a reduction in the residual oil saturation by displacing the oil from the small pore throats and rock surfaces towards the larger pores. As a result, the mobility of the oil phase increases in the flooded regions, which enhances the overall sweep efficiency compared with the conventional high salinity waterflooding. Moreover, the conventional water injection scenario often leads to an early water breakthrough. Despite the positive simulation results of the Qamchuqa reservoir, an intensive evaluation program is necessary to determine the effective brine composition, and to assess the effect of the clay content, the composition of the crude oil, pH, and rock characteristics on the performance of the low salinity injection, as well as to reduce the uncertainty and to execute this technique effectively. Eventually, a significant improvement in the sweep efficiency and reservoir performance may require a longer time frame to observe its impact and to confirm the observed results of laboratory experiments. It is worth mentioning that additional uncertainty due to the existence of bitumen in the Qamchuqa reservoir can impact the performance of the low salinity waterflooding significantly. Therefore, it is important to study the bitumen characteristics, its distribution, and how that can impact on the fluid flow in the Qamchuqa reservoir. The negative role of the bitumen might nullify the expected improvement in the oil recovery using a low salinity water injection scenario as the bitumen occasionally behaves like a flow barrier between the aquifer and the oil zone if it existed as a uniform layer. However, the bitumen may have less impact if it exists as scattered patches.
## 4 Conclusions  
This study demonstrated the effectiveness of an integrated approach to the problem of soil salinity forecasting and segmentation using Sentinel-2 satellite data and ERA5-Land climate parameters. The developed model combines unsupervised methods (K-Means, Agglomerative Clustering, DBSCAN), supervised learning (XGBoost), and a multi-task neural network, which achieved high accuracy and robustness when working with heterogeneous spatial data. Key clustering metrics showed quality improvement: the Silhouette Score reached 0.8156, and the Davies-Bouldin Score decreased to 0.3022. In the classification task, the model's accuracy was 99.99%, with a single error in more than 10,000 test observations.  
The proposed method can be adaptive, taking into account the spectral features of different soil types, minimizing boundary errors, and considering the influence of climatic factors on salinization processes. Due to ensemble techniques and the probabilistic enrichment of features, achieving a more precise separation of soil classes was possible than with traditional algorithms.  
The obtained results confirm the scientific and applied significance of the proposed approach. It can be used to monitor land degradation, assess fertility decline risk, and support agroecological systems' decision-making. The method is planned to develop in the direction of deep segmentation using convolutional neural networks, integrating data from uncrewed aerial vehicles, and expanding the analysis to other regions and seasonal intervals. This will provide a more universal and scalable system for monitoring soil salinity in the context of climate change.  
Conceptualization, G.A., D.K., A.K. and A.B.; methodology, G.A., D.K., A.K. and G.N.; software, M.A. and M.Y.; validation, G.A., D.K. and A.K.; formal analysis, G.A., D.K., G.N. and M.Y.; investigation, G.A., D.K., A.K., A.B. and G.N.; resources, Y.A.S. and R.B.; data curation, A.B. and M.A.; writing--original draft preparation, D.K., A.K. and M.Y.; writing--review and editing, D.K., A.K., G.N. and M.Y.; visualization, M.A. and M.Y.; supervision, G.A. and D.K.; project administration, G.A. and D.K.; funding acquisition, G.A. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR28712473).  
Not applicable.  
The dataset is available upon request from the authors.  
Author [PERSON] is employed by the company \"Information Technology and Urban Development of Non-profit limited company Semey University named after [PERSON]\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 9 Conclusions  
We simulated microlensing parallax curves with different orbital phases using CS techniques. Our CS simulation results show an error of less than 0.5% over all time samples and an average error of less than 1.1% at \(t_{0}\), while using 25% of traditional detector measurements for microlensing parallax light curves with a range of \(\Phi\) from [0, \(\frac{7\pi}{8}\)]. Despite different microlensing parallaxes at the three orbital radii generating different photometric curves with significant difference in flux magnitude, CS worked well for all  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Traditional Detector** & **CS Detector** \\ \hline
1 & Data acquisition (ADC) interface & Data acquisition (ADC) interface \\ \hline
2 & Data storage module & Data storage module \\ \hline
3 & Data compression & Spatial modulation implementation \\ \hline
4 & Data packetization and transmission & Data packetization and transmission \\ \hline \hline \end{tabular}
\end{table}
Table 13: FPGA modules comparison for a traditional detector and CS based detector.  
the cases. The CS error at peak magnification for R = 7000 km, 42,000 km, and 1 AU, at each \(\Phi\) value is less than the error between the parallax curve generated with that particular \(\Phi\) value and any other parallax curve generated using the \(\Phi\) value ranges. This shows that CS reconstruction should not cause any significant errors in detection of a microlensing parallax curve for any given \(\Phi\) value. Using CS, we can significantly reduce the data storage volume, as well as data downlink bandwidth--both of which can be a limitation for SmallSat type instruments. CS shows potential for implementation in a SmallSat constellation for detecting microlensing parallax events.  
Conceptualization, A.K.-P. and R.K.B.; methodology, A.K.-P.; writing--original draft preparation, A.K.-P.; writing--review and editing, R.K.B.; supervision, T.M. and R.K.B.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We have used simulated data using simulation parameters described in this work. This study has not used any publicly available datasets.  
The authors declare no conflict of interest.
## 3 The Proposed Algorithm
### Phase I: Objective Color Correction (OCC)  
#### 3.1.1 The Architecture of the OCC Network  
The architecture of the OCC network is shown in Figure 3. OCC network includes three parts: input layer, nonlinear transform layers, and an output layer. They work together to perform feature learning between distorted color map and original color map. For a regression model, we only utilize the convolution layers without pooling in our network.  
In the input layer, the first operation is feature extraction. The convolutional layers transfer the image from the spatial domain to the feature domain. To train the OCC network for color correction, the input image is the captured color patches of the color checker, which is tiled into the \(30\times 30\) pixel color patches. The target output is the standard chroma value of the color checker. In the input layer,  
Figure 3: The framework of the OCC network.  
Figure 2: The framework of the proposed algorithm.  
the feature map is composed of a set of three-dimensional tensors. There are 64 filters of size \(3\times 3\times 3\) for generating 64 feature maps. The nonlinear activation function is ReLU. The input layer can be expressed as the following equation:  
\[F_{1}(X_{1})=\max(0,W_{1}X_{1}+B_{1}), \tag{1}\]  
where \(W_{1}\) and \(B_{1}\) are filters and biases, respectively. The size of \(W_{1}\) is \(3\times 3\times 3\times 64\). Subsequent feature learning plays an important role in learning the nonlinear model, which is essential for color recover. In the nonlinear transformation layers, ReLU is used for nonlinearity. They can be expressed as  
\[F_{i}(X_{i})=max(0,W_{i}F_{i-1}(X_{i-1})+B_{i})i=2,3,4, \tag{2}\]  
where \(W_{i}\) and \(B_{i}\) are the convolution filters and biases in the 2 nd to 4 th layers. The kernel size of the convolution filter is \(3\times 3\times 64\times 64\). Except for the output layer, the feature map of each layer is 64 channels. All feature maps have the same spatial resolution as the input image.  
In the output layer, the size of filter is \(3\times 3\times 64\times 3\). These filters reconstruct the output by a convolutional operation given by Equation (3):  
\[F_{5}(X_{5})=W_{5}F_{4}(X_{4})+B_{5}. \tag{3}\]  
To demonstrate the architecture of the OCC network, the parameter details are listed in Table 1. For all convolutional layers, the padding is zero, and the stride is set at 1.  
The number of hidden layers of the OCC network model is defined by experimental performance. We tested the numbers 1 to 10 of the hidden layers and chose the best number of hidden layers. For more details, see Section 4.1.
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 5 Configuration of the Proposed Double Level ANN Architecture
### Dataset Normalization  
Before passing the inputs and targets to the neural network architecture, it is necessary to preprocess the dataset values for improving the performance of the training process. The normalization process is important for neural network training because it adjusts the different inputs and outputs ranges to a normalized range before applying them to the neural network. In MATLAB, the normalization process is set by default and the values are adjusted to fall in the range of \([-1,1]\). However, in this case, a small bug is found that is associated with the default normalization process. It is found that the default normalization process has problems with inputs or targets that are too small, producing errors in the training process. For instance, the common range for the saturation current \(I_{sat}\) is in the order of micro and nano amperes. These ranges of values prevent the training process from finding a suitable fit for the targets.  
Figure 5: Proposed ANN architecture.  
For solving this issue, the normalization process is implemented manually, and the inputs and targets are adjusted in the following way:  
\[input= \left[\frac{G}{G_{max}},\frac{T}{T_{max}}\right] \text{for ANN level \#1}\] \[input= \left[\frac{G}{G_{max}},\frac{T}{T_{max}},\frac{V_{i}}{V_{max}}, \frac{I_{i}}{I_{max}}\right] \quad i\in[1,2,3] \text{for ANN level \#2}\] \[target= \left[\frac{I_{ph}}{I_{ph,max}},\frac{log_{10}(I_{sat})}{log_{10} (I_{sat,max})},\frac{\eta}{\eta_{max}},\frac{R_{s}}{R_{s,max}},\frac{R_{sh}}{R _{sh,max}}\right]\]  
For the saturation current, the normalization is given on a logarithmic scale for better representing the large range of variations of this parameter.  
Since the neural network approximates its outputs inside the same range, it is also necessary to convert the ANN results back into the same range rather than into the originals inputs and targets.
## 1 Introduction
This rotation of the steered wheel is provided by the introduction of a steering torque from the steering system of the vehicle by the driver about the kingpin axis and can be carried out both during the movement of the vehicle and while it is parked. It follows from the above that the kinematics and dynamics of a steered wheel are a general case of the motion of an elastic wheel, and we consider the motion of an unsteered wheel as a special case of the motion of a steered wheel when there is no rotation of it about the kingpin axis. The steered wheel is rotated by the driver about the axis, which is usually called the kingpin axis. The kingpin axis is inclined relative to the vertical in the lateral and longitudinal directions. The inclination of the kingpin axis in the lateral direction produces the kingpin inclination angle \(\alpha_{k}\), and in the longitudinal direction--the caster angle \(\beta_{k}\)[1,9]. Due to the presence of a lateral inclination angle and a caster angle of the kingpin, such an inclination is called a combined angle of the kingpin. Resultant normal reactions of the support surface acting on the steered wheel, in the presence of kingpin inclinations, create a weight-aligning torque [1,2,10]. To rotate the steered wheel about the kingpin axis in static state, it is necessary to overcome the steering resistance torque caused by the tire, kingpin inclinations, and friction in the bearings of the kingpin unit [1,2,10,11,12,13]. In references [1,14], it is also recommended to take into account the inertia torque. Based on this, we determine the static steering resistance torque of the steered wheel by the following expression:  
\[M_{w}(\theta)=M_{\varphi}(\theta)+M_{dl}(\theta)+M_{f}(\theta)+M_{j}(\theta) \tag{1}\]where \(M_{w}(\theta)\) is the static steering resistance torque of the steered wheel about the kingpin axis; \(M_{\varphi}(\theta)\) is the static tire steering resistance torque; \(M_{al}(\theta)\) is the weight-aligning torque; \(M_{f}(\theta)\) is the friction torque in the bearings of the kingpin unit; and \(M_{j}(\theta)\) is the inertia torque. An analysis of the components in Expression (1) proved that the torque created by the tire \(M_{\varphi}(\theta)\) on the asphalt concrete surface reaches 75-90% of the steering resistance torque of the steered wheel \(M_{w}(\theta)\); the weight-aligning torque together with the friction torque in the bearings of the kingpin unit \(M_{al}(\theta)+M_{f}(\theta)\) in total represent 10-25% of the torque \(M_{w}(\theta)\), and the inertia torque \(M_{j}(\theta)\) is so insignificant that it can be equated to zero. Since \(M_{\varphi}(\theta)\) is the determinant in Expression (1), literary sources thus often equate the torque \(M_{w}(\theta)\) and \(M_{\varphi}(\theta)\) with each other, considering \(M_{w}(\theta)=M_{\varphi}(\theta)\).
## 5 Empirical Results and Discussion  
As seen in Table 5, the researchers used the K-nearest neighbors (K-NN) method to construct the spatial weight matrix because K-NN can generate a weight matrix regardless of the absolute distance. Phuket, being an island, does not share a border with any other province in Thailand. The Moran's I statistical test for PM\({}_{2.5}\) data across the three years (2012, 2017, and 2022) found Moran's I values of 0.945, 0.928, and 0.935, respectively. These values are close to 1 and are statistically significant at the 1% level (\(p\)-value \(<\) 0.001). Thisindicates that PM\({}_{2.5}\) concentrations in each province exhibit a spatial clustering pattern. In other words, the PM\({}_{2.5}\) concentration in one province influences the PM\({}_{2.5}\) concentration in nearby provinces, forming a clustered distribution. Therefore, this study employs spatial econometric methods, including the Spatial Lag Model (SLM), Spatial Error Model (SEM), and Spatial Durbin Model (SDM), to analyze the spread and spatial effects of PM\({}_{2.5}\) pollution across provinces.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### Particle Filter  
PDR is a self-contained algorithm that provides an accurate position in a short time, but it depends on the previous position and suffers from cumulative error. BLE-based positioning does not rely on the previous position, but the accuracy varies due to signal interference. To improve the positioning accuracy, continuity, and stability, we propose integrating the two methods using a particle filter. The key strategy of the particle filter is to use a set of particles to represent the posterior distribution of the positioning process, replace integral operations with the sample mean, and obtain the minimum variance distribution of the state. The process of particle filtering consists of the following steps.
## 4 Results
### Complexity and Cost Analysis of OrthoDETR
#### 4.4.1 Computational Complexity  
OrthoDETR's core modifications, including the substitution of the ResNet backbone with an MLP-Mixer and refinement of the Multi-Head Self-Attention mechanism, impact its  
Figure 8: Sample images and detection results. In the image, letters A–J correspond to different models of bone plates.  
computational complexity. By utilizing the MLP-Mixer, we reduce the number of parameters involved in the architecture while maintaining comparable performance. Additionally, the improved Multi-Head Self-Attention mechanism allows for better context comprehension, increasing efficiency by focusing on relevant local and global features. Consequently, OrthoDETR displays a reduced computational complexity compared to the original DETR model, which leads to faster detection times.