esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### _Rfo-Svm_
#### 3.5.1 RFO-SVM-Based Data Fragmentation  
In the pre-processing step of the RFO-SVM algorithm, the input database is processed to remove noise data and handle missing or null values. The missing values and null values are filled with mean and median values, respectively. Subsequently, the features of the database are analyzed. Once the missing and null values are handled, the features of the database that typically involve learning the statistical properties of the features are analyzed, like distributions, central tendencies, variances, and correlations with the target label. The characteristics and variability of the features are analyzed and the relations between the features and the target label are determined. In the feature analysis step of the RFO-SVM algorithm, a subset of features is selected for processing and the feature values are denoted as x and the target values as y. Let the obtained 6 features be \(\left(x_{1},\ x_{2},\ x_{3},\ x_{4},\ x_{5},\ x_{6}\right)\) and 1 target value be (y), which are selected for analysis. The RFO-SVM model is trained with the selected features \(\left(x_{1},\ x_{2},\ x_{3},\ x_{4},\ x_{5},\ x_{6}\right)\) and the corresponding target values (y) are then divided into training sets, denoted as \(x_{train}\) and \(y_{train}\), respectively. The training set is used to develop the SVM classifier. The RFO-SVM model combines the Red Fox Optimization (RFO) algorithm with the SVM algorithm for classification. The RFO algorithm optimizes the SVM classifier parameters, including the kernel type \(\gamma\), kernel parameter \(\kappa\), and boundary parameter \(C\) values to find the optimal values for these parameters that maximize the accuracy of the SVM classifier. The kernel function determines the type of decision bound ary used by the SVM classifier. The kernel parameter determines the shape and flexibility of the decision boundary and the boundary parameter determines the balance between achieving a larger margin (less misclassification) and allows handling misclassification in complex or overlapping data. The RFO algorithm is used to optimize these parameters by iteratively searching for the parameter values that yield the highest accuracy metric. The accuracy metric measures the performance of the SVM classifier on the training set to ensure that the SVM classifier is trained with the most suitable parameter values for the air quality dataset. The SVM classifier is trained with the optimized parameters and is used to classify new, unseen data based on the learned decision boundaries.
# Research on Nanoparticle-Enhanced Cooling Technology for Oil-Based Drilling Fluids
## 3 Results and Discussion  
### Thermal Conductivity of Nanoparticle-Enhanced OBDFs  
Thermal conductivity is a critical parameter for evaluating the thermal management performance of drilling fluids in high-temperature downhole environments, which determines the efficiency of drilling fluids in absorbing and emitting heat. The enhancement effect of different nanoparticles on the thermal conductivity of OBDFs is illustrated in Figure 1. The introduction of all three nanoparticles enhanced the thermal conductivity of OBDFs. The addition of CNTs enhances the thermal conductivity by 7.97%, while a 3% concentration of SiO\({}_{2}\) nanoparticles enhances the thermal conductivity by 8.54%. Al\({}_{2}\)O\({}_{3}\) showed the highest thermal conductivity increase of 9.41%. The higher the thermal conductivity, the faster the formation transfers heat to the drilling fluids, which is not conducive to wellbore cooling. Given that an increase in thermal conductivity may adversely affect bottomhole cooling, it is also important to consider changes in specific heat capacity, which plays a critical role in the overall thermal management of OBDFs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline displacement & 28 L/s \\ \hline density & 1860 kg/m\({}^{3}\) \\ \hline ground temperature & 50 \({}^{\circ}\)C \\ \hline rotation speed & 50 rpm \\ \hline well depth & 6500 m \\ \hline \end{tabular}
\end{table}
Table 2: Key parameters for model inputs.  
Figure 1: Thermal conductivity of OBDFs with the addition of different nanoparticles.
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 4 Discussion
### Limitations and Outlooks  
A classic problem in signal denoising is its fidelity. How to keep most of the useful signals when effectively wiping out errors is always challenging. We proposed a SR denoising method for optically complex water spectra. Even when it is dependent on the general prior information of the \(R_{\text{rs}}\) signal--the bio-optical model--it ruined some useful information when the SNR level was high enough (Figures 6 and 9). This means the current algorithm is still improvable in separating the noise from the signal. We believe that, with the help of fast-developing deep learning algorithms, the performance of denoising might be further improved.  
In this study, we only focused on hyperspectral data. In practice, multispectral remote sensing data, such as Landsat series image [41; 59; 60], Sentinel-2 and 3 image [61; 62], Moderate Resolution Imaging Spectroradiometer (MODIS) image [63; 64; 65], Medium Resolution Imaging Spectrometer (MERIS) image [66; 67; 68], Geostationary Ocean Color Imager (GOCI) image [40; 69; 70], and Gaofen series image [71; 72], are more frequently chosen, due to their higher spatial and temporal coverage, and more importantly, their higher SNR that benefits from their limited spectral bands. On the other hand, for these data, the accurate retrieval of the atmosphere parameters, such as aerosol optical depth, water vapor content, and gas content, are more challenging. Therefore, accurate atmospheric correction has long been a problem for the optically complex water color multispectral remote sensing missions. Theoretically, if the atmospheric correction error is randomly distributed along the wavelengths, our proposed SR method can improve the atmospheric correction results. However, for the systematically biased errors, the performance will need a thorough discussion in the future. If possible, the atmospheric-correction-then-denoise scheme will improve the image quality for inland optically complex water monitoring.
## 3 Results
### Ablation Experimental Design and Results
#### 3.1.3 Simulation Accuracy Results  
In this paper, we designed a variety of experimental schemes as ablation experiments described in the previous section, and the experimental comparison results are shown in Figure 7, and the experimental accuracy comparison is shown in Table 2.  
Based on Table 2, the comparison is illustrated in the following aspects: (1) the recommended model integrates the dual advantages of traditional machine learning  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial No.** & **Model Type** & **Kappa** & **FoM** & **OA** \\ \hline I & RF-SNSCNN-CA & 0.7683 & 0.3836 & 0.9782 \\ II & RF-CNN-CA & 0.7663 & 0.3798 & 0.9780 \\ III & RF-TN-CA & 0.7278 & 0.3128 & 0.9744 \\ IV & SNSCNN-TN-CA & 0.7272 & 0.3118 & 0.9743 \\ V & CNN-TN-CA & 0.7257 & 0.3094 & 0.9742 \\ VI & RF-NN-CA & 0.7424 & 0.3374 & 0.9758 \\ VII & SNSCNN-NN-CA & 0.7354 & 0.3255 & 0.9751 \\ VIII & CNN-NN-CA & 0.7294 & 0.3155 & 0.9745 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation accuracy comparison.  
Figure 8: Comparison scheme between the real situation in 2017 and the results of various models predicting 2017 in Chongqing 21. (**a**) RF-SNSCNN-CA simulation results; (**b**) RF-CNN-CA simulation results; (**c**) RF-TN-CA simulation results; (**d**) SNSCNN-TN-CA simulation results; (**e**) CNN-TN-CA simulation results; (**f**) RF-NN-CA simulation results; (**g**) SNSCNN-NN-CA simulation results; (**h**) CNN-NN-CA simulation results; (**i**) 2017 real situation.  
and deep learning models, where SNSCNN improves the learning effect of neighborhood features through the combined effect of channel and spatial attention mechanisms; (2) the machine learning-like urban sprawl CA modeling process, regardless of the approach taken to obtain development suitability, cannot completely replace the learning of the neighborhood part; (3) when deep learning or decision tree learning models are used to extract developmental suitability, the use of traditional neighborhood modeling methods instead has an inhibiting effect on the model simulation results.
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### Attitude Control
#### 4.3.6 Magnetic Torque Coil Endurance Tests  
The magnet coils were the next parts that were tested. There were two aspects of testing the endurance of the magnet coil: checking that the hardware could handle quick and frequent changes in PWM and checking that the system could stay on for long durations. The Teensy was connected to the H bridge which was connected to one torque coil for each of these tests. The IMU was not used and the PWM values were chosen randomly or periodically instead. The electrical diagram of the setup is shown in Figure 37.  
The system's ability to run for a long time was tested in the longevity test. When the CubeSat leaves the ISS, the ACS system might need to run nonstop for several hours to stop tumbling and achieve its spin target. To check the reliability of the ACS subcomponents, especially the H bridge and the coil, a longevity test was conducted for two days. In this test, the PWM values were gradually increased from 0 to 255 for both polarities of the coil. Each PWM value for each current direction lasted for ten microseconds. The coil would change its polarity roughly every 2.5 s. A mechanical gyroscopic compass and timer were  
Figure 37: Circuit setup (**a**) Wire diagram of the ACS endurance testing; (**b**) Live picture of setup as tested.  
used to perform verification. Figure 38 below shows the verification of the functioning coil using the gyroscopic magnet.
# Optimizing Service Placement for Microservice Architecture in Clouds
## 4 Placement Algorithm
### Application Partition  
In order to make the values of different resources comparable to each other and easy to handle, we first normalize the amount of available resources on machines and the resources that services demands to be the fraction of the maximum ones. We define the term \(v_{max-j}\) to be the maximum amount of available resources \(r_{j}\) on a machine.  
\[v_{max-j}=\max_{i\in\{1,2,...,M\}}\left(v_{i}^{j}\right) \tag{6}\]  
Then the vector \(V_{i}\) of available resources on machine \(m_{i}\) and the vector \(D_{i}\) of resource demands of service \(s_{i}\) are normalized as:  
\[V_{i} =(\frac{v_{i}^{1}}{v_{max-1}},\frac{v_{i}^{2}}{v_{max-2}},..., \frac{v_{i}^{R}}{v_{max-R}}) \tag{7}\] \[D_{i} =(\frac{d_{i}^{1}}{v_{max-1}},\frac{d_{i}^{2}}{v_{max-2}},..., \frac{d_{i}^{R}}{v_{max-R}}) \tag{8}\]  
After normalization, we start partitioning the service-based application. The key question we ask first is how many parts the application is partitioned into. Considering multi-resource demands of different services, we introduce a threshold \(\alpha\) to determine the number of parts when performing partition algorithms. The threshold \(\alpha\) denotes the upper bound of the resource demands of partitioned parts, which means we perform partition algorithms continuously until the total resource demands from each part do not exceed \(\alpha\) or no part contains more than one service. With a threshold \(\alpha\in[0,1]\) (as the resource demands have been normalized), it assures that each part after partition can be packed into a machine. Figure 3 shows an example of an application partition with threshold \(\alpha=0.5\). In the figure, the total CPU demands and memory demands from each part do not exceed \(0.5\). Given a threshold \(\alpha\), we propose two partition algorithms: _binary partition_ and _k partition_, which are based on the contraction algorithm.  
Figure 2: An example process of the contraction algorithm (\(k=2\)).
###### Abstract  
This study examines the spatial relationship of PM\({}_{2.5}\) concentrations across provinces in Thailand and explores the relationship between socio-economic factors and PM\({}_{2.5}\) levels from 2012 to 2022. The study results indicate that PM\({}_{2.5}\) pollution in Thailand is spatially clustered, meaning that PM\({}_{2.5}\) spills over into nearby provinces and is not confined to a single area. The factors that positively affect PM\({}_{2.5}\) concentrations include population density and energy consumption per capita, while industrial density has a negative effect on PM\({}_{2.5}\) levels. Additionally, an Environmental Kuznets Curve (EKC) analysis found that the Gross Provincial Product (GPP) per capita has a U-shaped relationship with the PM\({}_{2.5}\) concentration. In the initial stage of economic growth, as the GPP per capita increases, PM\({}_{2.5}\) concentrations gradually decrease. However, once income reaches USD 56,715 and the economy becomes significantly large, further increases in GPP per capita lead to rising PM\({}_{2.5}\) concentrations. In other words, during the early phase of economic development, PM\({}_{2.5}\) pollution does not intensify significantly. However, once Thailand's economy reaches a certain scale, continued economic expansion exacerbates PM\({}_{2.5}\) pollution, leading to greater economic and social consequences. The study highlights the importance of integrated collaboration among various organizations in mitigating the widespread impacts of PM\({}_{2.5}\) pollution.  
Keywords:PM\({}_{2.5}\): satellite data; Google Earth Engine; spatiotemporal characteristics; spatial econometrics; Environmental Kuznets Curve; sustainable development; Thailand +
Footnote †: journal: Urban Sci.
# Study on Compaction Characteristics and Compaction Process of an Unsaturated Silt Based on PFC3D
###### Abstract  
In order to reduce the construction cost of excessively wet silt roadbeds and improve the compaction quality, this paper studies the compaction characteristics and compaction technology of excessively wet silt through theoretical derivation, indoor experiments, numerical simulation, engineering verification, and other research methods. Based on the pendulum-type liquid bridge structure, this paper uses the Hertz-Mindlin theory, particle motion equation of state, and other theories to modify the force between particles of an unsaturated soil. Through the contact angle, which is a medium, the matrix suction is linked to the water content and is used to approximate the water content state of soil samples. Using PFC3D (particle flow code 3D) numerical simulation software, an unsaturated silt model was established based on Hill contact, and the model parameters were calibrated through basic geotechnical tests and triaxial compression tests. The correctness of the theory is verified by comparing the simulated triaxial compression test of wet silt silt with the indoor test. By simulating subgrade rolling, considering factors such as the distribution of contact force chain and the variation of compaction degree, the effects of rolling times, strong vibration, and weak vibration on the compaction effect were compared and analyzed. Finally, the optimal rolling and compaction process under the optimal water content is obtained through on-site engineering tests. The results show that: (1) The Hill contact model is reasonable for simulating the wet silt. (2) During the simulation of the roadbed compaction process using PFC3D software, it was found that the compaction degree change during the entire compaction process can be roughly divided into the initial compaction stage and the re-compaction stage. The reasonable number of compaction times was determined to be five through discrete element simulation. (3) It is found from the numerical simulation results that compared to static compaction, both strong and weak vibration compaction can effectively improve the compaction effect of subgrade compaction. The larger the vibration amplitude, the more obvious the improvement of the compaction effect. The compaction effect of strong vibration followed by weak vibration is stronger than that of weak vibration followed by strong vibration. (4) The optimal compaction process obtained under the optimal moisture content of 15% is static pressure once + strong vibration twice + weak vibration twice.  
unsaturated silt; discrete element; hill contact; compaction characteristics; compaction process
# Spinal Cord Segmentation in Ultrasound Medical Imagery
## 3 Proposed Method
### Semantic Segmentation and U-Net Architecture  
The semantic segmentation is the task of classifying every pixel inside an image into a meaningful category. In medical image analysis, semantic segmentation is a highly pertinent task. Computer-aided diagnosis relies heavily on the accurate segmentation of the organs and structures of interest in the captured medical image modalities (MRI, CT, Fluoroscopy, Ultrasound...). The success made by Convolutional Neural Networks had profoundly influenced the area of semantic segmentation. Many architectures had been proposed. Among the most used architectures in medical image segmentation, U-Net [26] is a state of the art model, Figure 5 represents the architecture of U-Net. U-Net model is an encoder-decoder architecture. The encoder is the contracting part on the left, and the decoder is the expansive path on the right side. The encoder part contains a series of 3 \(\times\) 3 convolutions followed by a ReLU (Rectified Linear Unit) activation function. A 2 \(\times\) 2 max-pooling operation is done after a series of consecutive convolutions for downsampling. The decoder part consists of upsampling the feature vector obtained at the end of the encoder part by a series of 2 \(\times\) 2 up-convolutions to reconstruct the segmentation map at the size of the input image at the end. Between every up-convolutions operations, there are a series of 3 \(\times\) 3 convolutions followed by ReLu similarly to the decoder part. Skip connections are added by copying the feature map of the encoder part with the correspondingly feature map of the decoder part. In the end, a sigmoid activation function is used to generate for each feature vector the desired class category. The network is trained end to end using back-propagation and stochastic gradient descent. As being discussed in the experimental part of this study, U-Net has proven efficiency where the spinal cord pattern is not previously learned by in the train set.
## 2 Materials and Methods
### Experimental Setup  
The operation of the proposed system was tested with subjects in a sitting position in front of the radar. The starting position of the hand before performing the gesture was at a distance of 10-40 cm from the antenna. AWR1642 was placed vertically with the transmit and receive antennas pointed towards the human subject (see Figure 2).  
To prevent the collection of clutters behind the human hand, the \(X\) and \(Y\) thresholds have been established. The range of motion when capturing a gesture may vary from person to person. Therefore, we set the \(y\)-axis cut-off at 60 cm, which allows a sufficient margin of 20 cm while performing the hand movement. The software cut off all targets beyond 0.6 m in \(Y\) axis and outside the range from \(-\)0.3 m to 0.3 m in \(X\) axis. As the interface works in close range, the Best Range Resolution mode of AWR1642 was chosen. The frame rate was set up to 30 frames per second, which is considered optimal for the investigated application. A lower value can reduce the accuracy of the neural network, and a higher value creates too much data to process. Other important parameters are shown in Table 1.  
Figure 1: Pipeline of the proposed system. The hand gesture is captured by the radar and transformed by the AWR1642 hardware into samples consisting of frames that are eventually used with the LSTM neural network.  
Figure 2: Position of the hand against the radar.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 2 Communication Architectures
### Decentralized Communication Architecture  
Because UVAs operate at high speeds and missions may cover large areas, UAVs frequently connect and disconnect to the network. As a result, UAV swarm ad hoc networks are considered the best choice. Under decentralized communication architecture, UAVs conduct real-time interactive communication in an ad hoc manner, thereby eliminating dependence on the infrastructure and eliminating the communication range restrictions [38].
## 3 Results and Discussion
### Sensorial Properties
#### 3.3.2 Electron Microscopy  
Light microscopy is not able to reach a resolution higher than 1000\(\times\) according the to the Abbe theory. For this reason, SEM microscopy was used for a detailed description of formed meat alternative (Figures 5 and 6).  
SuA and SuM confirmed the light microscopy results. Both products form compact matrices with included fat particles (Figure 5--c, C). SuA exhibited smaller fat particles with higher density. Partial air bubbles were also included in both products (Figure 5--b, B).  
StM was formed from meat fibres (Figure 5--A) connected with collagen fibres, as shown Figure 3. A low content of fat was also shown. In comparison, Sta was formed from texturised protein, as shown in Figure 1; the binder for this protein is shown in Figure 5--d. The formed matrix is compact with air bubbles homogenously distributed in (Figure 5--b).  
KA and KM exhibited similar results to those from light microscopy. Figure 5 (KA and KM--a and A) shows differences in the structure of fibrillar proteins. In KM, muscle myofibrils were observed, while KA also exhibited a fibrous structure but with different characteristics, including greater thickness and porosity. The variation in porosity is influenced by the production process of the extruded protein, as well as the addition of starch or oil, which affects the final structure [44; 46]. Differences between texturised protein and cooked meat fibres were also previously confirmed [23].  
MM was formed as typical meat product, where muscle myofilaments were also shown (Figure 6--A). MA was formed from parts of plant-origin raw material and demonstrated starch particles included in the cells. The cell wall was also present, and binder was present in small amounts (Figure 6--a, b).  
SaA and SaM showed similar results to those from light microscopy, with a protein matrix typical of these products (Figure 6--b, B). A small amount of muscle fibre was also observed, which is characteristic of meat binders and does not significantly impact the final structure [47]. Fat was included in the matrix, whereas SaA contained smaller particles than SaM (Figure 6--c, C).  
MM displayed the typical structure of a minced meat product, with visible meat fibres, including myofilaments (Figure 6--A) and collagen forming areolar connective tissue (endomysium) (Figure 6--D). MA, on the other hand, exhibited a fibrillar structure derived from plant proteins, though with a distinct organisation (Figure 6--d). The microstructure of MA also revealed plant raw materials (Figure 6--e) mechanically integrated with the fibrillar protein.  
At the microscopic level, the addition of salt and hydrocolloids to plant-based meat analogue products increases cross-linking bonds and structural compactness while also enhancing the development of a fibrous structure [29].  
Figure 5: Microstructure of Frankfurter sausage (analogue vs. conventional), steak (analogue vs. conventional), and Hungarian sausage (analogue vs. conventional) products.