esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary Experiments
### Flow Visualization Experiment  
Flow visualization experiments were conducted to examine the flow structures within the rotating cages. The acquired knowledge aided in determining the configurations of the catalyst pellet pockets. Figure 5(a) displays a photograph of the apparatus assembly equipped with the rotatable synchronous camera support. The rotation speed could be maintained at a specified value of up to 300 rpm in either the forward or reverse directions.  
The test enclosure had an inner diameter of 390 mm and a height of 115 mm. The vertical cylindrical surfaces of the rotor (outer diameter of 370 mm and inner diameter of 180 mm) were metal mesh with 2 mm wire spacing. The top, middle, and bottom horizontal plates of the rotor were 5-mm-thick acrylic plates evenly spaced to form 40-mm-high upper and lower decks. Each deck had eight partitions divided by 3-mm-thick vertical acrylic blades. The rotors having different blade arrangements were fabricated since the plates and blades serve as a pump impeller. The rotor displayed in Figure 5(b) had radially placed  
Figure 5: Radial pressure difference within the reactor with (**a**) partially filled pellet cage, and (**b**) fully filled pellet cage.  
Figure 6: Photographs of (**a**) flow visualization apparatus assembly, (**b**) a rotor with radial blades in both upper and lower decks, and (**c**) a rotor with twisted blades in upper deck.  
vertical blades in both the upper and lower decks. The rotor blades in the upper deck were twisted clockwise 30\({}^{\circ}\), as displayed in Figure 6c.  
The visualized flow structures revealed vigorous secondary recirculation flow within the rotor partitions. The velocity of the rotor's outer edge was approximately 4.2 m/s at a rotation speed of 200 rpm for the present test enclosure dimensions, while the net radial velocity of the liquid phase was <5.3 cm/s if the liquid flow rate was 4 LPM. The recorded results of flow visualization are provided in Supplementary Materials Video S1. The inward radial flows through the gap between the rotor's bottom plate and the bottom wall of the enclosure were also observed. The solid contaminants migrated by this flow accumulated around the rotating axis and could damage the bearing.  
The lessons learned from the flow visualization experiments employed in the design of the catalyst cage for the dehydrogenation experiment were as follows. Firstly, the bounding surfaces of the rotating catalyst bed, which neighbored the stationary walls of the reactor, needed to be highly permeable to utilize the secondary flow. The catalyst pellets needed to be located within a fairly small effective distance from this permeable wall. Finally, the bottom wall of the rotor should be an impermeable plate extended to the center of rotation and be installed with the narrowest gap with the reactor's bottom wall.
## 3 Analysis of Three-Stage Incremental Cooperative Communication
### EE Analysis
Total energy consumption also includes the energy involved in the transmission of ACK/NACK packets and is computed as follows:  
\[EE^{(3)}_{CC,ACK/NACK}=[(E_{TX_{slice}}+4E_{RX_{slice}}+\frac{P_{T}}{R}) \tag{6}\] \[+(E_{TX_{slice}}+4E_{RX_{slice}}+\frac{P_{T}}{R})PER_{SD}(1-PER_{ SR_{1}})\] \[+(E_{TX_{slice}}+3E_{RX_{slice}}+\frac{P_{T}}{R})PER_{SD}(1-PER_{ SR_{1}})(1-PER_{SR_{2}})PER_{R_{1}D}\] \[+(E_{TX_{slice}}+3E_{RX_{slice}}+\frac{P_{T}}{R})PER_{SD}PER_{ SR_{1}}(1-PER_{SR_{2}})\] \[+(E_{TX_{slice}}+3E_{RX_{slice}}+\frac{P_{T}}{R})PER_{SD}PER_{ SR_{1}}(PER_{SR_{2}})(1-PER_{SR_{3}})\] \[+(E_{TX_{slice}}+3E_{RX_{slice}}+\frac{P_{T}}{R})PER_{SD}(1-PER_{ SR_{2}})PER_{SR_{1}}PER_{R_{2}D}(1-PER_{SR_{3}})](A+H)\]  
where \(A\) is the size of ACK/NACK in bits. Energy consumption is the same for the transmission and reception of both ACK and NACK. The first term in Equation (6) shows the energy consumption involved in the transmission of ACK/NACK by the destination in the first phase. (\(1-PER_{SD}\)) is the probability of ACK and NACK being transmitted with probability \(PER_{SD}\). In the second phase, either ACK or NACK is transmitted for the the packet decoded and forwarded by \(R_{1}\) to the destination; this happens with probability \(PER_{SD}\) (\(1-PER_{SR_{1}}\)). The second term in Equation (6) represents the energy consumption associated with the second phase. In the third phase, \(R_{2}\) forwards the packet, which is followed by another sequence of ACK/NACK transmissions. ACK/NACK is transmitted if \(R_{2}\) decodes and forwards the packet to the destination. This may happen due to the following reasons: (i) failure of direct communication, one-stage relaying, the \(S-R_{2}\) link becoming error free; and (ii) failure of \(S-D\) and \(S-R_{1}\) links, the \(S-R_{2}\) link becoming error free. In the fourth and last phase, ACK/NACK is transmitted if \(R_{3}\) decodes and forwards the packet to the destination. This may happen due to the following reasons: (i) failure of \(S-D\), \(S-R_{1}\) and \(S-R_{2}\), the \(S-R_{3}\) link is error free; and (ii) failure of \(S-D\) and \(S-R_{1}\), the success of \(S-R_{2}\), the failure of the \(R_{2}-D\) link, and the \(S-R_{3}\) link is error free. Therefore, the EE of the three-stage incremental cooperative communication is computed as follows:  
\[\eta^{(3)}_{CC}=\frac{(1-PER^{3}_{CC})xL}{E^{3}_{CC}+E^{(3)}_{CC,ACK/NACK}} \tag{7}\]  
where \(x\)=\(E_{TX_{slice}}+4E_{RX_{slice}}+\frac{P_{T}}{R}\).
# The Optimal Erection of the Inverted Pendulum
## 2 Dynamic Model  
The system is shown in Figure 1, which comprises a pendulum with length \(l\) and mass \(m\) that is mounted on a cart with mass \(M\). The pendulum has a rotational freedom \(\theta\) (with respect to the vertical axis), while the cart has a translational freedom \(y\). The acceleration due to gravity is \(g\), while there is also an external horizontal force \(F\) acting on the cart. In sum, the model have two freedoms (\(\theta\) and \(y\)), and one control input \(F\).  
Figure 1: Model of a pendulum on a sliding cart.  
The equation of motion can be derived from the kinetic energy \(T\), the potential energy \(V\) and the virtual work \(\delta W\)  
\[T = \frac{1}{2}M\dot{y}^{2}+\frac{1}{2}m\Big{(}(\dot{y}+l\dot{\theta} \cos\theta)^{2}+(l\dot{\theta}\sin\theta)^{2}\Big{)}, \tag{1}\] \[V = -mgl\cos\theta,\] (2) \[\delta W = F\dot{\delta y}. \tag{3}\]  
Using [PERSON]'s principle, one obtains  
\[\begin{bmatrix}M+m&ml\cos\theta\\ ml\cos\theta&ml^{2}\end{bmatrix}\begin{bmatrix}\dot{y}\\ \dot{\theta}\end{bmatrix}=\begin{bmatrix}ml\dot{\theta}^{2}\sin\theta+F\\ -mgl\sin\theta\end{bmatrix}, \tag{4}\]  
which can be reduced to  
\[\begin{bmatrix}1&el\cos\theta\\ \cos\theta&l\end{bmatrix}\begin{bmatrix}\dot{y}\\ \dot{\theta}\end{bmatrix}=\begin{bmatrix}el\dot{\theta}^{2}\sin\theta+gu\\ -g\sin\theta\end{bmatrix}, \tag{5}\]  
where  
\[\epsilon=\frac{m}{M+m}\qquad\text{and}\qquad u=\frac{F}{g(M+m)}. \tag{6}\]  
Equation (5) are identical to those reported in [21], when \(y\) is in the units of \(l\), and the accelerations are in units of \(g\)--as a consequence time is in units of \(\sqrt{l/g}\).  
Since most solvers cannot deal with second order differential equations, it is convenient to reduce (5) to the first-order state-space form. By introducing state variables \(v_{y}=\dot{y}\) and \(v_{\theta}=\dot{\theta}\), one obtains the following:  
\[\begin{bmatrix}\dot{y}\\ \dot{\theta}\\ \dot{v}_{y}\\ \dot{v}_{\theta}\end{bmatrix}=\begin{bmatrix}v_{y}\\ v_{\theta}\\ \frac{(v_{\theta}^{2}+\cos\theta)\epsilon\sin\theta+u}{1-\cos\cos^{2}\theta}\\ -\frac{\epsilon v_{\theta}^{2}\sin\theta\cos\theta+\sin\theta+\cos\theta}{1- \epsilon\cos^{2}\theta}\end{bmatrix}, \tag{7}\]  
which is in standard state-space form. From now on \(x=[y,\theta,v_{y},v_{\theta}]^{T}\) represents a vector of the scaled state variables.
# Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model
## 2 System Architecture
### Key Motion Feature Assessment  
#### 2.3.1 Depth of Compression  
OpenPose can identify the coordinates of the key points of the human hand, so we use the max displacement of the coordinates to determine compression depth. During CCs, both hands are crossed together and pressed downwards vertically, and they have the same moving distances. Thus, we only consider the compression depth of a single hand. The initial position of the right wrist is selected as the starting position of the compression. Since the \(x\)-coordinate of the corresponding key point has no reference meaning of depth, only its \(y\)-coordinate needs to be considered when calculating.  
As shown in Figure 5, the depth of compression means the distance from the starting compression position to the deepest position in a cycle. Therefore, we took the order number of the frame as the abscissa and the \(y\)-coordinate of the right wrist key point in the corresponding frame as the ordinate, and we drew the curve by the plt.plot function in python. Then, we found the compression depth in a cycle by the argrelextrema function of scipy.signal., which calculates the relative extrema of input array data of \(y\)-coordinates and returns a tuple with maximum value. As shown in Figure 6, in actual experiments, it was found that local minimum points appear in one cycle due to the fast frame rate and the accuracy of OpenPose. As a result, multi values are selected incorrectly in one cycle. To avoid this problem and to prevent missing extreme points, after testing, the \(y\)-value is selected to output every two frames to prevent the problem of local minimum points. Example curve is provided in Supplementary Material (Figure S2). Since the point coordinates in the image are in pixels, it is necessary to transform the depth to the actual depth through a corresponding scale transformation to discriminate whether the compression depth is 5-6 cm.  
Figure 4: Illustration of the depth of one compression.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & **Desired Value Range** & **Sub-Requirement** & **Overall Requirement** \\ \hline \multirow{3}{*}{Operating position} & The midpoint of the & \multirow{3}{*}{The correct rate is more} & \multirow{3}{*}{Every indicator should be qualified} \\  & connection between the & & \\  & two nipples & & \\ \hline \multirow{3}{*}{Elbow straightness} & \multirow{3}{*}{171–180\({}^{\circ}\)} & The correct rate of the left and & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\  & & right elbows should both & \\  & & reach more than 70\% & \\ \hline Frequency & 100–120 times/min & & \\ \hline Depth & 5–6 cm & & \\ \hline Chest rebound & Must be fully rebound & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Criteria for chest compressions.
# Geotechnical Property Modeling and Construction Safety Zoning Based on GIS and BIM Integration
## 2 Research Methods
### Geotechnical Investigation Data/Information  
The geotechnical investigation of the construction site is necessary for every civil engineering and construction project [60]. The site investigation aims to identify the distribution of soil layer, groundwater table location, and to know the mechanical and physical characteristics of the site soil. The geotechnical investigation is performed to transfer the nature and behavior of the subsurface information to the designers and managers so they can use it to prepare technical solutions such as planning for the construction or design of the foundation. Information about the soil type, available quantities, and the location of each type of underground geotechnical layer are the key factors to be considered during the planning stage of the construction. Furthermore, accurate information about the geological and geotechnical investigation is important regarding the geographic location. The geotechnical investigation data can be obtained from the results of field tests and laboratory tests. It is the method of gathering, analyzing, managing, and presenting the site geotechnical-related information in the form of geotechnical investigation reports.  
The in-situ and laboratory test data provide all the geotechnical information that characterizes the soil. The cone penetration test (CPT), and standard penetration test (SPT) are two widely adopted field tests for the representation of the geotechnical condition [59]. The values of these test data can be obtained from the borehole information. The infor  
Figure 1: Concept Overview.  
mation obtained from the laboratory test can be divided into different categories such as strength, compaction, and classification. Table 1 lists all the laboratory test parameters significant to be included that should hold benefits for the users involved in it. In the context of this paper, any geotechnical parameters obtained from the geotechnical site investigation can be used and not limited to certain parameters to develop a geotechnical model. For example, the borehole investigation data used in geotechnical site investigation includes the subsurface geotechnical information such as SPT, water table, etc., can be used to develop a geotechnical model.  
The borehole dataset can be collected from several preselected spatial locations along with different depth intervals. However, it may be required to assess the soil geotechnical properties at the location other than the predefined locations. The geotechnical data analyst is required to assess the data before digital mapping in such a way that a useful and meaningful conclusion can be drawn for practical uses [61]. In this way, the site investigation creates a significant volume of soil information from field tests and laboratory tests. All this information is stored in a database that aids the further processing of the geotechnical property modeling and safety zonation.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
###### Abstract  
A Vehicular Ad-hoc Network (VANET) comprises a group of moving or stationary vehicles connected by a wireless network. VANETs play a vital role in providing safety and comfort to drivers in vehicular environments. They provide smart traffic control and real-time information, event allocation. VANETs have received attention in support of safe driving, intelligent navigation, emergency and entertainment applications in vehicles. Nevertheless, these increasingly linked vehicles pose a range of new safety and security risks to both the host and its associated properties and may even have fatal consequences. Violations of national privacy and vehicle identities are a major obstacle to introducing forced contact protocols in vehicles. Location privacy refers to the privacy of the vehicle (driver) and the location of the vehicle. Whenever a vehicle sends a message, no one but authorized entities should know their real identity and location of the vehicle. All the messages sent by the vehicle must be authenticated before processing, hence location privacy is an important design aspect to be considered in VANETs operations. The novelty of this paper is that it specifically reviews location privacy in VANETs in terms of operational and safety concerns. Furthermore, it presents a critical analysis of various attacks, identity thefts, manipulation and other techniques in vogue for location privacy protection available in state-of-the-art solutions for VANETs. The efforts in this paper will help researchers to develop a great breadth of understanding pertaining to location privacy issues and various security threats encountered by VANETs and present the critical analysis of the available state-of-the-art solutions to maintain location privacy in VANETs.  
intelligent transportation system; VANET; location privacy; VANET security +
Footnote †: journal:  
known as VANETs. In addition to running challenges, the rapid proliferation of vehicle connections has also created critical security and information confidentiality concerning the evolution and expansion of VANETs design. From the literature review, it can be asserted that some threats related to privacy and infringing location privacy are more dangerous as they can lead to more advanced physical attacks such as trail and robbery.  
In the next three sub-sections, we first explain the generic VANET architecture and then its unique characteristics. We also present the motivation behind the survey and the contributions of this work.
# THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts
###### Abstract  
In recent years, the use of techniques based on electromagnetic radiation as an investigative tool in the agri-food industry has grown considerably, and between them, the application of imaging and THz spectroscopy has gained significance in the field of food quality control. This study presents the development of an experimental setup operating in transmission mode within the frequency range of 18 to 40 GHz, which was specifically designed for assessing various quality parameters of hazelnuts. The THz measurements were conducted to distinguish between healthy and rotten hazelnut samples. Two different data analysis techniques were employed and compared: a traditional approach based on data matrix manipulation and curve fitting for parameter extrapolation, and the utilization of a Self-Organizing Map (SOM), for which we use a neural network commonly known as the Kohonen neural network, which is recognized for its efficacy in analyzing THz measurement data. The classification of hazelnuts based on their quality was performed using these techniques. The results obtained from the comparative analysis of coding efforts, analysis times, and outcomes shed light on the potential applications of each method. The findings demonstrate that THz spectroscopy is an effective technique for quality assessment in hazelnuts, and this research serves to clarify the suitability of each analysis technique.  
1  
THA Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts.
# Blur Kernel Estimation by Structure Sparse Prior
## 3 Method
### Sparse Representation  
Sparse representation assumes that natural images can be sparsely represented under a basis or an overcomplete dictionary, which has been used in the field for several years. Let \(x=[x_{1},x_{2},\ldots,x_{n}]\in\mathbb{R}^{m\times n}\) denote a clean image. Image \(x\) can be represented as  
\[x=D\alpha, \tag{2}\]  
where \(D=[D_{1},D_{2},\ldots,D_{n}]\in\mathbb{R}^{m\times n}\) is an over-complete dictionary with \(n\) atoms and \(\alpha\in\mathbb{R}^{n\times n}\) is the sparse coefficient matrix. As shown in Figure 4, the sparse coefficient matrix \(\alpha\) can be recovered by:  
\[\begin{array}{l}\underset{\alpha}{min}\left\|\alpha\right\|_{\alpha^{\prime }}\\ \text{s.t.}\left\|x-D\alpha\right\|_{2}^{2}\leq\varepsilon,\end{array} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Explanation** \\ \hline \(x\) & the latent sharp image \\ \(y\) & the input blurred image \\ \(k\) & the blur kernel \\ N & the additive noise \\ \(*\) & the convolution operator \\ \(D\in\mathbb{R}^{m\times n}\) & the over-complete dictionary \\ \(\alpha\in\mathbb{R}^{n\times n}\) & the sparse coefficient matrix \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation and symbols.  
Figure 3: Ground-truth blur kernel.  
where \(\left\|\cdot\right\|_{0}\) is the \(l_{0}\) norm and \(\varepsilon\geq 0\) is the error tolerance. Generally, the optimization of \(l_{0}\) norm problem is NP-hard. When \(\alpha\) is sparse enough, Equation (3) can be solved through Lagrange multiplier, namely the following \(l_{1}\) problem:  
\[\hat{\alpha}=\underset{\alpha}{argmin}\;||x-D\alpha||_{2}^{2}+\lambda||\alpha|| _{1}. \tag{4}\]
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 1 Introduction
### Contributions  
Motivated by discussion above, this paper focused on control movement ability of the AGV on curve path and localizing the AGV on the localization system in the structure-less environment (long corridor). The main contributions of our work are as follows:  
* For trajectory tracking, we adopt the PP algorithm and improves it. The traditional PP algorithm often causes errors when it encounters a turn because it is overdue to decelerate speed. Therefore, an improved PP algorithm is proposed that incorporates turning prediction-based deceleration to reduce the impact caused by late attempts at deceleration.
* To solve the kidnapped-robot problem, we combine 2D LiDAR point-cloud features with a deep convolutional network-based classifier to distinguish the current situation for selecting SLAM or odometry localization system. Thus, if the AGV is in a situation where SLAM fails to determine robot position, the task can still be continued.
* In addition, practical experiments in the long corridor terrain are carried out to verify the feasibility of the proposed system.  
The remain of this paper is organized as follows. In Section 2, the hardware platform and vehicle kinematic of robot system are described. In Section 3, the improved Pure Pursuit algorithm using turning prediction-based speed adjustment is introduced. the deep learning-based selection strategy using 2D LiDAR point-cloud features for localization task is discussed Section 4. In Section 5, the practical experimental results and verification of the proposed method is reported. Finally, in Section 6, the conclusions are presented.
# Coastal Zone Classification Based on U-Net and Remote Sensing
## 3 Results and Analysis  
### Classification Results by Different Methods  
Deep learning-based classification heavily relies on training data. To prevent overfitting and improve the generalization ability of the network, we applied data augmentation techniques such as random rotation and mirroring of the training sets. As shown in Figure 4, as the number of iterations increases, the training accuracy improves and the training loss decreases, gradually stabilizing until the model reaches a state of convergence.  
To verify the effectiveness of high-resolution remote sensing imagery classification using the U-Net method, we compared the U-Net model with SegNet, DeepLab v3+, RF, and SVM, using the same training and validation data. For each model, evaluation metrics were averaged over multiple experiments (three repetitions) to minimize random errors. These five models were applied to five distinct areas, as shown in Figure 5a, with the corresponding experimental results presented in Figure 5b-f.  
Figure 4: Training accuracy, verification accuracy, and loss value of U-Net model.  
Figure 5: Classification results of different models. (**a**) Original RGB image. (**b**) Classification results based on the U-Net model. (**c**) Classification results based on the SegNet model. (**d**) Classification results based on the DeepLab v3+ model. (**e**) Classification results based on the SVM model. (**f**) Classification results based on the RF model.  
The classification accuracy results in Table 2 indicated that the U-Net model outperformed the others in terms of OA, Kappa, and F1-score. All five algorithms achieved an OA value above 80%, with the three deep learning algorithms exceeding 85%, surpassing the two traditional statistical learning models. When analyzing Figure 4 and Table 2 together, it is evident that the three deep learning models effectively suppressed the \"noise\" phenomenon and significantly improved the classification of mixed pixels and minor objects. This advantage arises because traditional classification methods focus only on shallow features, whereas deep learning can extract contextual and semantic information from high-resolution remote sensing images. A comprehensive analysis demonstrated that the U-Net model provided superior robustness in classification performance and was particularly well-suited for coastal classification tasks.