esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm
## 4 Simulation Results
#### Changing Communication Radius  
* Step size 100 and changing the area from 600 to 1200;
* Node shifting.  
Changing radius from 120 to 240 with step size 20. Figures 5 and 6 show the comparison of the simulation results of APIT and DV-hop without Bat-SA methodology.  
Simulation results comparison between DV-Hop and APIT using the Bat-SA method aresh  
Figure 5: Position error vs.communication radius between DV-Hop and APIT.  
Figure 8: Latency vs. communication radius between DV-Hop and APIT with Bat-SA.  
Figure 6: Position error vs.communication radius between DV-Hop and Bat-SA.  
Figure 7: Latency vs.communication radius between DV-Hop and APIT.  
Table 1 illustrates the error tolerance of the proposed and existing methods. The error tolerance is low in the proposed method.  
**Nodes are horizontally moved by one position:** The comparison of the APIT and DV-Hop algorithms without the Bat-SA method is shown in Figure 13. Nodes move horizontally by one position. The nodes move to one position ten times [20]. The comparison of the APIT and DV-Hop algorithms with the Bat-SA method is shown in Figures 14 and 15. It is evident from the aforementioned simulation findings that the DV-Hop and APIT algorithms surpass the Bat-SA method in terms of efficiency.
## 3 Results
### Boundary Conditions
#### 3.1.2 Ventilation Airflow  
As explained in the Methods Section, the ventilation system operates at two speeds: base speed and high speed for cooking periods, i.e., for 1.5 h from 12:00 to 13:00 and 19:00 to 19:30, according to the questionnaire completed by the occupants. Ventilation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Occupants** & & **In bedroom** & **In Living Room—Open Kitchen** \\ \hline Occup. 1 + 2 & & & 7:00–8:30 \\ Parents & C1 \& C2 & 20:30–6:30 & 12:00–14:00 \\ BR1 & & & 19:00–20:30 \\ \hline Occup. 3 & & & 6:30–8:30 \\ Child & C1 \& C2 & 20:30–6:30 & 12:00–14:00 \\ BR2 & & & 19:00–20:00 \\  & & & 20:15–20:30 \\ \hline \multirow{4}{*}{Occup. 4} & & & 6:30–8:30 \\  & & 20:30–6:30 & 12:00–13:30 \\ \cline{1-1}  & & 13:30–15:30 & 15:30–19:45 \\ \cline{1-1}  & & & 20:00–20:30 \\ \cline{1-1} \cline{2-4}  & & & 6:30–8:30 \\ \cline{1-1}  & & & 12:00–14:00 \\ \cline{1-1}  & & & 19:00–19:45 \\ \cline{1-1}  & & & 20:00–20:30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Occupancy schedules compiled from questionnaires to be used for calculation of IAQ indicators.  
airflows were measured at both speeds, respectively Q\({}_{\text{BASE}}\) and Q\({}_{\text{HIGH}}\), and Table 6 shows the corresponding ACRs calculated for every room and for the whole house. The total exhausted airflow is for 80 m\({}^{3}\cdot\)h\({}^{-1}\) and is balanced with the total supplied airflow.  
All the measured airflows are much lower than the airflows required by the French building code as described in the Methods Section, resulting in a whole-house ventilation ACR of 0.25 h\({}^{-1}\), 40% lower than the required one.  
With ACRs between 0.31 and 0.56 h\({}^{-1}\), the bedrooms are relatively under-ventilated compared to the literature. Studies of ACR in bedrooms show an ACR between 0.6 and 3 h\({}^{-1}\)[52; 53; 54]. The parents' bedroom ACR is higher than the other bedrooms, 80% higher than bedroom 4.
# Linear Plasma Device for the Study of Plasma-Surface Interactions
###### Abstract  
At the research and production company \"PlasmaScience\" (Ust-Kamenogorsk, Kazakhstan), a linear plasma generator installation, KAZ-PSI (Kazakhstan Plasma Generator for Plasma Surface Interactions), has been developed and constructed for the study of the interaction of plasma and materials. This article outlines some features of the developed experimental installation designed for the investigation of surface-plasma interactions. The primary components of the linear plasma installation include an electron-beam gun with a LaB6 cathode, a plasma-beam discharge chamber, an interaction chamber, a target device, and an electromagnetic system comprising electromagnetic coils. The KAZ-PSI unit enables continuous plasma generation using hydrogen, deuterium, helium, argon, and nitrogen. The electron density of the plasma is in the range of about \(10^{17}\)-\(10^{18}\) m\({}^{-3}\) and the electron temperature is in the range of 1 to 20 eV. The incident ion energy is regulated by applying a negative potential of up to 2 kV to the target. Experiments on the irradiation of tungsten with helium plasma were carried out using the KAZ-PSI installation for the first time. This article presents the research findings on the structure and properties of tungsten relative to the temperature of helium plasma irradiation. Alterations in roughness, microstructure, hardness, modulus of elasticity, and erosion of the tungsten's surface following helium plasma irradiation at varying temperatures were examined. The study's results indicate that helium plasma irradiation induces changes in the morphology of the tungsten's surface, creating surface relief due to sputtering by helium ions, as well as the formation of blisters. Mechanical testing revealed that after irradiation at T = 500 \({}^{\circ}\)C, there was an increase in hardness of up to 10%, and a slight decrease in modulus of elasticity. And after irradiation at T = 900 \({}^{\circ}\)C and T = 1300 \({}^{\circ}\)C, both hardness and elastic modulus decreased with rising temperature. The tungsten surface erosion evaluation results showed that the degrees of surface erosion increase with increasing target temperature.  
Keywords:plasma; linear plasma devices; plasma-surface interactions; tungsten; hardness +
Footnote †: journal: Applied
## 4 Hydraulic Fracturing
### Well Completion  
[PERSON] et al. explored methods to enhance hydraulic fracturing in the Vaca Muerta shale, emphasizing the critical role of rock characteristics and stress factors. They employed a 'hybrid' fracturing approach that merges the benefits of both 'plug and perf' and 'ball and sleeve' techniques [67].  
[PERSON] et al. focused on technological advancements in well completion, utilizing machine learning to forecast well performance based on past data and geological attributes. Their findings indicate that machine learning offers a reliable method for predicting well performance and selecting the most effective fracturing fluids, especially in geologically complex areas such as Vaca Muerta [68].  
[PERSON] et al. assessed well performance in Vaca Muerta using contemporary rate transient analysis (RTA). They examined 168 wells and sorted them into three design categories (A, B, C) based on variables such as proppant intensity and fluid per cluster. Design Group A featured moderate proppant intensity with a broad range of fluid per cluster values (Figure 5). Design Group B had reduced proppant intensity and lower fluid-per-cluster values, while Design Group C significantly increased proppant intensity but reduced fluid-per-cluster values. Their analysis led to statistically sound recommendations concerning the economic benefits of various completion designs [69].  
Figure 5: Classification of completion designs based on the fluid per cluster and proppant intensity.  
The approach to well completion in Vaca Muerta is continually evolving, with an emphasis on tailoring hydraulic fracturing methods to the formation's intricate geology. Technological advancements like machine learning and updated RTA techniques are pivotal in this optimization effort. These methods not only enhance the economic feasibility of well completions, but also promote more sustainable and efficient resource extraction.
# Optimal Deployment of Vector Sensor Nodes in Underwater Acoustic Sensor Networks
## 2 Communication Performance Based on Single-Vector Sensors
### Underwater Acoustic Communication Using Single-Vector Sensors
The particle velocity is given by [24]:  
\[v(t)=\ \frac{1}{\rho_{0}}\ \int\limits_{0}^{t}\frac{P_{2}(\tau)-\ P_{1}(\tau)}{d}d\tau \tag{1}\]  
where \(P_{1}\) and \(P_{2}\) are the acoustic pressure signals at the two adjacent sensors, \(d\) is the sensor spacing between two sensors, \(\rho_{0}\) is the water density, and \(\tau\) is the time variable. The acoustic pressure is equivalent to the particle velocity in a particular direction multiplied by the acoustic impedance, which is \(\rho_{0}c\), where \(c\) is water sound speed. Then, the acoustic pressure \(P_{M}\) at the center between two adjacent sensors, and the horizontal and vertical components (\(P_{r}\), \(P_{z}\)) of the pressure equivalent particle velocities are calculated by:  
\[P_{M}(t)=\ \frac{P_{1}(t)+P_{2}(t)}{2},\ \ P_{r}(t)=\ -\rho_{0}c\ v_{r}(t),\ \ P_{z}(t)=\ -\rho_{0}c\ v_{z}(t) \tag{2}\]  
where \(v_{r}\) and \(v_{z}\) are the horizontal and vertical components of the particle velocities, respectively. For the simulation of particle velocities, two points, which were 3 cm apart both horizontally and vertically, were selected, and their center was considered to be the receiver point. Then the pressure channel impulse responses for these two adjacent points were predicted by the BELLHOP propagation model, and finally, \(P_{M}\), \(P_{r}\), and \(P_{z}\) were calculated by Equation (2). The distance between the two adjacent points was equivalent to one fifth of the acoustic wavelength.\(\Phi\). For the comparison of BER performances shown in Figure 3, two different kinds of bottom types were considered. The hard bottom was a coarse sandy sediment with a mean grain size of 1.0 \(\Phi\) and the soft bottom was a silty sediment with a mean grain size of 6.0 \(\Phi\) where \(\Phi=-\log_{2}(d/d_{0})\), \(d\) and \(d_{0}\) are the grain diameter in millimeters and a reference length of 1 mm, respectively]. In general, an acoustically hard bottom causes severe ISI due to strong reflection from the water-bottom interface, whereas in case of a soft bottom, the number of dominant multipaths is limited due to a large bottom loss. Overall, the BER performances obtained by the single-vector sensor outperformed those obtained by the vertical-pressure sensor array in both the hard- and soft-bottom cases (Figure 3). In addition, the performances in silty sediment were better than those in sandy sediment due to the effect of ISI.
###### Abstract  
This study investigates differences in power and velocity at different loads and power and force at different velocities during trunk rotations in athletes who practice sports with rotational demands on the trunk. Athletes of combat (n = 23), fighting (n = 39), ball (n = 52) and water sports (n = 19) with a mean age of 23.8 \(\pm\) 1.5 years performed standing trunk rotations on each side with bars of different weights (from 1 kg up to 50 kg) placed on their shoulders. The findings showed significant between-group differences in mean power in the acceleration phase of trunk rotations, especially at higher weights (\(\geq\)10.5 kg) or lower velocities (\(\leq\)334.2 rad/s). The power at 10.5 kg was significantly higher in fighting than water (\(p\) = 0.035; d = 0.86), combat (\(p\) = 0.001; d = 1.53) and ball sports athletes (\(p\) = 0.001; d = 1.48), with no significant differences between the two latter groups; at 15.5 kg, it was higher in water than combat (\(p\) = 0.027; d = 0.91) and ball sports athletes (\(p\) = 0.009; d = 1.17) but not those in fighting sports; and at 20 kg, it was higher in water than combat (\(p\) = 0.013; d = 0.98) and ball sports athletes (\(p\) = 0.006; d = 1.33), with no significant differences with those in fighting sports. This testing is sensitive in discriminating between athletes of various sports, which may reflect the specificity of their training, including trunk rotations at various velocities under different load conditions.  
**Keywords:** angular velocity; athletes; muscle power; rotational movement of the trunk
## 1 Introduction
Therefore, the method proposed in our study applies to most non-deformed targets, unless the target is deformed violently, causing the normalized parameter-matching error. The main contributions of this study:  
1. A method for establishing benchmarks for object tracking with motion parameters (OTMP) was proposed. This dataset is constructed by the image obtained by the monocular camera, the motion trajectory of the monocular camera, the motion trajectory of the sample to be trained, and camera internal parameters. This data set can be used for visual scientific research such as visual slam of indoor dynamic environments, spatial positioning of moving targets, and dynamic target-recognition and classification. 2. A network structure of a Fast Depth-Assisted Single-Shot MultiBox Detector was designed. With a data set with depth information (such as the OTMP in this article), the original SSD and the target-depth information were fused to train, enabling the network to learn the correlation between the depth of a target and the feature detectors at various scales. The normalized depth information obtained by combining target-positioning and -tracking algorithms can highly reduce the amount of computing on the SSD network when FDA-SSD is employed for actual detection. 3. Based on the FDA-SSD network and the author's previous work [10], a target-detection-and-tracking algorithm framework for a monocular motion platform was established using the spatial-positioning method of geometric constraints. In this study, multiple sets of indoor target-tracking experiments were conducted with the indoor dynamic capture system as the ground truth. The test data demonstrate that the average RMSE of the tracking trajectory was 4.72 cm, which verifies the effectiveness of the target-tracking algorithm framework. The rest of this article is organized as follows. In Section 2, the research work related to this study is introduced. Then, benchmarks for object tracking with motion parameters are briefly described in Section 3. Next, the design and training method of the FDA-SSD neural network is detailed in Section 4. The methods of target depth extraction and spatial positioning based on geometric constraints are illustrated in Section 5. Afterward, the comparison of related algorithms and experimental verification is presented in Section 6. Finally, the conclusions are drawn in Section 7. Besides, the related fund projects that supported the research of this study are listed.
# Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud
## 3 Center Axis Estimation
### Straight Line Modeling  
As shown in Section 3.1, directionality can be estimated through the set of trajectories using spheres, which can be assumed to be the center axis of the cylinder. Therefore, the central axis estimation models the straight line and uses principal component analysis (PCA). PCA is a statistical approach [18; 19] that estimates the direction of e2, e3, etc., which are perpendicular to e1 and e1 with the largest variance of data, as shown in Figure 3. Therefore, if e1 is found, the direction of the cylinder's center axis can be known.  
PCA constructs a covariance matrix as shown in Equations (4) and (5), and eigen-decomposition of the covariance matrix yields a direction vector.  
\[cov(x,y)=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{(n-1)} \tag{4}\]  
\[C_{m}=\left|\begin{array}{cccc}cov(x,x)&cov(x,y)&cov(x,z)\\ cov(y,x)&cov(y,y)&cov(y,z)\\ cov(z,x)&cov(z,y)&cov(z,z)\end{array}\right| \tag{5}\]  
Figure 2: Center estimation using the trace of a sphere.  
The eigenvalue decomposition can be used to find a nonzero vector \(x\) and an eigenvalue \(\lambda\) that \(Ax=\lambda x\) can hold. Here, a way to determine if the given data is meaningful (straight/curved) is presented.  
\[\omega=\frac{\lambda_{2}}{\lambda_{1}+\lambda_{2}+\lambda_{3}},\ \lambda_{1}\geq \lambda_{2}\geq\lambda_{3}\geq 0 \tag{6}\]  
Equation (6) means the ratio of the eigenvalue \(\lambda_{2}\). If \(\omega\) is close to 0 (\(0<\omega<\mathcal{X}\) ), it is a straight line, and if it is within a certain range (\(\mathcal{X}<\omega<\varepsilon\)), it is a curve. Using a discriminant based on neighboring points at one point can reduce execution time.
## 5 Thermal Balance Test of the Deployable Reflector Antenna in STM Phase
The temperature data for cold/hot balance condition measured by the thermal balance test will be used to construct a reliable thermal analysis model. In QM, using the well-correlated thermal analysis model, the orbital thermal analysis will be conducted to define the operational temperature range. Figure 8: Concept of the thermal balance test; (**a**) Test configuration with thermal vacuum chamber; (**b**) test procedure.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 1 Introduction
The choice of ConvMixer is motivated by its effectiveness in challenging transformers in computer vision tasks, as well as its unambiguous and consistent architecture. This paper introduces a deep learning architecture tailored for LSTF tasks, specifically addressing the challenges posed by non-stationary time series and aiming to unravel the inter-series and intra-series dependencies. The proposed architecture comprises two novel components, a \"weak-stationarizing\" block and \"non-stationarity restoring\" block, designed to handle non-stationary input time series. The \"weak-stationarizing\" block operates by duplicating the original time series and aligning each data point to similar phase positions as the original series. Subsequently, the resulting time series undergoes differencing to render it weak-stationary. This block utilizes the power spectral density (PSD) of the time series at individual frequencies to determine the appropriate number of roll backs needed before differencing, aligning the time series based on the dominant frequency. The weak-stationary time series is then transformed using fast Fourier transform (FFT) [24] to obtain its spectral decomposition, which serves as the basis for feature learning using the ConvMixer architecture for forecasting. To account for the loss of information due to the \"weak-stationarizing\" block, the \"non-stationarity restoring\" block is employed to restore the non-stationary properties of the time series. The resulting architecture, formed integrating these methods, surpasses previous state-of-the-art (SOTA) results across six benchmark datasets. Our contributions are summarized as follows:  
* We propose a generalized deep learning model capable of addressing both univariate and multivariate forecasting problems. * We present a novel \"weak-stationarizing\" block, which utilizes PSD values at different frequency levels to determine the appropriate number of rollbacks before differencing, effectively rendering the time series weak-stationary. The \"non-stationarity restoring\" block is employed to restore non-stationarity, ensuring information preservation for final predictions. Ablation studies demonstrate the significant performance improvement achieved with these blocks. * We modify the ConvMixer architecture for use in TSF, which operates on the spectral decompositions of the time series to produce high-quality forecasts. * The proposed overall architecture obtains an average of 21% and up to 64.6% of relative improvements compared to the previous state-of-the-art methods on six real-world datasets, ETT, electricity, traffic, weather, ILI, and exchange, in various settings.
## 4 Experimental Results  
The performance assessment of our proposed technique based on smartphone authentication is given in detail in this section, in which the TOPSIS methodology-based feature ranking has been combined with different ML algorithms. The evaluation of our proposal is mainly concentrated on the performance criteria, a comprehensive analysis of the confusion matrix to evaluate the classification accuracy, and robustness testing under real-world conditions and circumstances. The proposed framework was implemented on three main layers to correctly perform and authenticate each participating user. First of all, the touch-based dynamic pattern recognition data, including the gyroscope, 3D touch sensor, and accelerometer, were used for data collection and generation layer. This layer was leveraged to extract real-time features, such as coordinate curves for trajectories, 2D spatial coordinates, touch pressure, acceleration, and velocity. Data preparation and processing was the second layer that was used to carry out data preprocessing, including standardization, normalization, and noise mitigation or reduction. During data processing, the TOPSIS method was implemented and applied to carefully rank the extracted valuable features. The ranked valuable features were then fed as input into three different ML models--RF, KNN, and GBM--for user classification purposes. Finally, the application layer was used to classify the findings to the authenticated persons or users in order to give immediate feedback and input for denying or granting access.  
The proposed framework processes the incoming dynamic touch information in real-time, in which optimized algorithms have been leveraged for low-latency performance in order to ensure a better, smooth operation. The primary sequence of the given events was as follows: the personal touch data was first entered and preprocessed; then, the ranked affected features were fed into the trained ML models; and next, the classification results of the participated persons were pulled up, and the system either refuses or allows the user to enter.
# Trends and Challenges in Network Covert Channels Countermeasures
## 4 Covert Channel Detection
### Timing Channels
A further idea could be developed by considering the timing statistics of segments containing an acknowledgment. Typically, the elimination of a network timing channel can be considered simple in some network scenarios or under some hypotheses (e.g., the absence of traffic with real-time constraints) and possible optimizations can be borrowed from the literature dealing with timing channels targeting the single node (see [21] for a comprehensive survey on timing channels not limited to the network case). However, an attacker could be able to slow the rate of the covert channel as to make the attack not detectable or the communication impossible to disrupt without impacting legitimate traffic. When designing a warden against timing network covert channels, the detection may be more desirable than limiting or eliminating the threat [16] as identifying compromised hosts or the range of contacted IP addresses could lead to neutralize some \"assets\" of a cybercriminal, such as a botnet. Lastly, when in the presence of methods reordering the sequence of packets or messages (e.g., IP datagrams, TCP segments or HTTP requests), a promising trend is to borrow techniques for data mining and apply them to the captured traffic [22]. This can be also done for implementing distributed wardens able to perform detection by considering observations gathered in different portions of the network. Moreover, being able to implement protocol-agnostic schemes is an important requirement, since the attacker can use different layers of the network stack to exfiltrate information. For instance, [23] proposes a protocol-independent approach and demonstrates that the detectability of a channel is proportional to the number of packets altered by the secret sender.