esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Sources of Uncertainties  
The eddy covariance measurement methodology is subject to uncertainties due to systematic errors [75] or random errors [76] that are difficult to assess. Despite the gap-filling methods developed for flux uncertainty, problems still exist [63; 77; 78]. Although these gap-filling errors affect the magnitude of the fluxes, all seasons and years should be affected similarly, allowing us to make comparisons over time.  
In addition, our LAI is derived from remote-sensing data products. We caution the readers on using satellite-based LAI, as this approach may underestimate or overestimate LAI [79; 80; 81]. These uncertainties may occur due to overlapping or clumping between leaves and light obstruction from branches, boles, and stems [82].
## 3 Results and Discussion
### Fundamental Analysis of the SC Ni-Mn-Ga Alloys
#### 3.1.2 \(\theta\)-2\(\theta\) X-ray Diffraction Measurements  
SC Ni-Mn-Ga particles, which were subjected to the processes of the annealing treatment of 1073 K for 3.6 ks followed by an ordering treatment at 873 K for 158.4 ks as described in Section 2.2, were used for the phase identification by using an \(\theta\)-2\(\theta\) X-ray diffractometer. The X-ray diffraction pattern measured at RT is shown in Figure 2. All the characteristic peaks corresponded well to the 5M martensite phase without a second phase within the detection limit. This suggests that SC Ni-Mn-Ga alloys are composed of a single 5M martensite phase. In addition, the apparent phase at RT of the Ni\({}_{50}\)Mn\({}_{28}\)Ga\({}_{22}\) alloy is consistent with the findings of a single 5M martensite phase in the literature [35]. The identified diffraction peaks have been indexed with respect to the coordinate system of the cubic austenite phase (parent phase). The unindexed characteristic peaks are from the modulated superstructure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Applied Fields**} \\  & **Compressive Field** & **Magnetic Field** \\ \hline Quantity & 0–50\% & 0 and 3.7 kOe \\ Methodology & In-house compression cell & Magnet \\ \hline \hline \end{tabular}
\end{table}
Table 2: The applied field types, applied quantities, and methodologies for the observations of the field-driven shape deformation of the composites.  
Figure 1: Heating and cooling curves of the HT Ni-Mn-Ga alloys. Phase transformation temperatures were specified via a tangent method. (Forward martensitic transformation start temperature: \(M_{\rm s}\); forward martensitic transformation finish temperature: \(M_{\rm f}\); reverse martensitic transformation start temperature: \(A_{\rm s}\); reverse martensitic transformation finish temperature: \(A_{\rm f}\)).
## 1 Introduction
Some new applications of spherical (R-S) in a three-dimensional sphere were provided. In [15], the first-order infinitesimal bending of a curve in three-dimensional Euclidean space is considered to obtain an (R-S). The properties of this kind of (R-S) were described and the conditions for (R-S) by bending to be developable were obtained. In [16], the dual expression of [PERSON] concept for the parallel p-equidistant (R-S) in Euclidean space was investigated, utilizing the Study mapping. In addition, the dual part of the dual angle on the unit dual sphere corresponded to the p-distance and was defined by (R-S). Furthermore, the dual parallel equidistant (R-S) was obtained. In [17], the parallel q-equidistant (R-S) was defined such that the binormal vectors of two given differentiable curves are parallel along the striction curves of their corresponding binormal (R-S). In addition, the distance between the asymptotic planes is constant at certain points. Some properties were specified and plotted for these surfaces. In the case of closed surfaces, the integral invariants such as the pitch, the angle of the pitch, and the drill of them were given. It is known, see, e.g., [18], that surfaces of revolutions characterize inner conditions, i.e., there exists an equidistant vector field. For (R-S), the similar inner conditions do not exist. Therefore, it is relevant to examine the characteristics of (R-S) in our work. In [19; 20; 21; 22], the features and applications of generated surfaces across various mathematical fields have been specified. The investigation of equiform Bishop spherical image governed surfaces in Minkowski 3-space yields important minimality and developability requirements, with consequences for computer-aided geometric design and physics. Simultaneously, research on inextensible (R-S), which are especially important in computer vision and animation, provides insights into tangential, normal, and binormal (R-S). These surfaces are formed by a curve with constant torsion. Furthermore, the study of circular surfaces in the Euclidean 3-space provides geometric analysis, minimality criteria, and systematic parametrization, all of which are valuable applications in computer-aided design and architecture. Our study focuses on (R-S) constructed from the W-curve in \(\mathbb{E}^{3}\). We determine some quantities of the constructed (R-S) such as mean, Gaussian, second mean, and second Gaussian curvatures. We provide some special (R-S) with their properties. Also, the sufficient conditions for the constructed (R-S) to be minimal, flat, II-minimal, and II-flat surfaces are determined. In addition, the sufficient conditions for the base curve for constructed (R-S) to be a geodesic, an asymptotic line, and a principal line are determined. The outline of the present research is organized as follows: In Section 2, we present some geometric concepts about (R-S) in the Euclidean 3-space. In Section 3, we construct (R-S) from the W-curves. In Section 4, we investigate some special (R-S) and describe their properties. In Section 5, we provide an application of (R-S). Finally, we present our conclusions.
# Study of the Load-Bearing Characteristics of Bolts under Asymmetric Freezing Conditions
## 3 Results and Analyses
### Study on the Bearing Characteristics of Bolts in a Cold Environment
#### 3.2.4 Influence of Melting on the Ultimate Pull-out Force  
The relationship between the melting and sinking action and the anchor pull-out force is shown in Figure 17, and the freezing temperature was selected as \(-7\)\({}^{\circ}\)C.  
Analysis of Figure 17 shows that:  
1. The ultimate pull-out force of soil in the frozen state was the largest, and that of soil in the molten state was the smallest, under the same water content working condition. The pull-out force under frozen conditions was much greater than that before freezing and after thawing, the freezing and thawing effect greatly weakened the pull-out force of frozen soil, and the pull-out force after thawing was less than that before freezing. Taking the moisture content of 10% as an example, the pull-out force in the frozen state and after thawing was 16.8 times and 0.71 times that before freezing. When the soil around the anchor solid entered the frozen state, the water in the soil condensed into larger ice crystals and migrated continuously in the direction of the cold source. However, freezing and expansion increased the spacing of soil particles and was irreversible. The frozen soil melted when the temperature rose, and the melting sequence was from top to bottom; a large amount of liquid phase water appeared at the top right of the anchor. The melting of ice crystals in the soil body into pore water caused the volume of the liquid phase to shrink, but the spacing of the soil particles could not shrink. Frozen expansion caused cracks inside the soil body, which collapsed under self-weight stress. However, the part of the soil that froze and swelled  
Figure 16: Extremes of pull-out force versus freezing temperature curve.  
Figure 17: The relationship between the melting action and the ultimate pull-out force of bolts.  
adsorbed a large amount of water from the surrounding area in the process of freezing and had a large water content after thawing, which caused drainage consolidation and settlement under the action of self-weight stress; the stability and integrity of the anchored soil was damaged. A schematic diagram of water migration during freeze-thaw cycles is shown in Figure 18.
2. The effect of freeze-thaw damage increased significantly with increasing moisture content. The thawing and sinking resistance for 10, 12 and 14% water content conditions were 71, 68 and 52% of that before freezing, respectively. Water migration and swelling during freezing and thawing became more significant at greater water content, causing more loosening of soil particles and more severe loss of shear capacity at the anchor-soil interface.
## 2 Background
### The Privacy Problem  
The expansion of LPR systems usage has raised questions about protecting the privacy of citizens because such systems enables the monitoring of any vehicle identified. Several organizations and news agencies have been concerned about these issues.  
The EFF (Electronic Frontier Foundation), a nonprofit organization based in San Francisco, California (CA), working on digital rights advocacy, requested information regarding the use of LPRs in the United States. Utilizing the transparency law, they assessed that LPR systems from 2016 and 2017 performed more than 2.5 billion license plate recognitions. Furthermore, according to the study, 99.5% of the monitored vehicles were not associated with criminal investigations [14].  
According to the authors of [15], police agencies or private corporations that collect and store vehicular license plate data could track the location of a vehicle by inferring a wide range of information about private life, history, religion, or personal beliefs of an individual.  
Two of the most critical aspects of privacy concerning data captured by LPR systems are the life-time of the records in the LPR database and the control access to the database. The concern for privacy primarily focuses on readings maintained in the LPRs database that were not associated with activities of interest to the police when they occurred. These records can be explored later, at the discretion of who owns the property and access to the database because, in most cases, LPR systems are sold to government institutions by private companies, and it is unclear to what kind of use such data may be susceptible.  
Nevertheless, each country has different laws protecting the privacy of its citizens. In general, these laws protect the citizen against the storage of personal data captured without explicit consent. Some exceptions are allowed, such as the storage of personal data by public interest, public security, and others. The problem is compliance with these lawsby the technologies employed in the LPR systems in use today. In the system proposed in this article, we meet these requirements to offer a solution that fits most countries possible.
# A Construction Method of Lower Limb Rehabilitation Robot with Remote Control System
## 2 Materials and Methods  
### Design of Rehabilitation Robot System  
#### 2.1.1 Requirements  
The lower limb rehabilitation robot provides rehabilitation training for patients with stroke or lower limb walking dysfunction. Personalized rehabilitation training can help patients to recover lower limb muscle movement function gradually and adapt to their walking. Doctors can evaluate and analyze the patients' rehabilitation effect quantitatively and objectively using the feedback data from the robot sensor system. Patients can use lower limb rehabilitation robots to train at home or in communities, and doctors can adjust the value of the robot's body-weight support (BWS) system to keep the patient in a balanced and upright state in order to help the patient to reduce the burden of lower limb muscle walking and gradually restore the lower limb movement strength. The force sensing system in the BWS system monitors the changes in the strength of the lower limbs during the patient's rehabilitation training. With the improvement of the rehabilitation treatment effect, the patient's lower limb strength increases gradually. The force sensing feedback data value in the BWS system will show a decreasing trend, meanwhile, the data can be used to evaluate the rehabilitation effect of patients' lower limbs objectively and quantitatively. When the patient falls with insufficient lower limb strength, the force sensor data increases instantly and the emergency stop protection state of the robot is triggered so that to protect the patient injured. The robot with human-computer interaction system can company the patient to walk around in real time, this movement mode can enhance the autonomy of artificial intentions in the lower limb rehabilitation training, and improve the patient's self-confidence and active rehabilitation willingness in the rehabilitation process. Compared with the current training robots used in-hospital rehabilitation, the robot system designed in this paper has three main functions: (1) BWS system; (2) multi-sensor information feedback system; and (3) human-computer interaction system.
###### Abstract
TE mode can provide a better image laterally compared to TM mode because the current flows continuously along the strike. Electric current interacts more strongly with conductive material than resistive material; thus, the response of the TE mode will be more affected if any conductive layer is present. Prior to any knowledge of strike, \(x\) is usually associated with north-south, while \(y\) is associated with east-west of geographical coordinates during the MT field survey. The impedance \(Z\) is a complex number, where one can derive two values: (1) apparent resistivity (\(\rho_{a}\)) and (2) phase (\(\varphi\)). The resistivity information can be derived from \(Z\) in the form of:  
\[\rho_{ij}=\frac{1}{\omega\mu_{0}}\big{|}Z_{ij}\big{|}^{2} \tag{3}\]  
where \(\omega\) is the angular frequency and \(\mu_{0}\) is the magnetic permeability of free space (\(4\pi\times 10^{-7}\) H/m). The \(E\) and \(H\) components will have a phase difference \(\varphi\) of:  
\[\varphi_{ij}=arg\big{(}Z_{ij}\big{)} \tag{4}\]  
In the assumed homogeneous Earth, the apparent resistivity reflects the true resistivity, and the phase value is \(45^{\circ}\). An increase in resistivity corresponds to a phase decrease below \(45^{\circ}\). Meanwhile, a decrease in resistivity will have a larger phase over \(45^{\circ}\). EM waves that penetrate the Earth experience an amplitude attenuation as the depth increases, and used to estimate the skin depth (\(\delta\)), also known as penetration depth. This parameter represent the distance that the signals diffuse as a function of period \(T\):  
\[\delta(T)=\sqrt{\frac{2\rho_{a}}{\omega\mu_{0}}} \tag{5}\]  
that can be simplified into:  
\[\delta(T)=503\sqrt{\rho_{a}T} \tag{6}\]
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Multi-Scale Feature Adaptive Enhancement (MS-FAE) Module
#### 3.2.2 Small-Object Adaptive Attention Mechanism  
To further enhance the feature representation of small objects, we propose the Small-object Adaptive Attention Module (SAAM). This module dynamically adjusts channel and spatial attention weights based on the size of the target, with the core idea of applying stronger feature enhancement to smaller objects. The SAAM first computes target size-related attention weights as follows:  
\[\alpha=\sigma(\text{MLP}([\log(w),\log(h)])) \tag{4}\]  
where \(w\) and \(h\) are the width and height of the bounding box, \(\sigma\) is the Sigmoid function, and MLP is a two-layer perceptron. The SAAM includes the following two branches: channel attention and spatial attention. The channel attention branch generates channel weights  
Figure 2: Architecture of MS-FAE.  
through a two-layer convolutional network following global average pooling, while the spatial attention branch produces pixel-level weight maps via 3 \(\times\) 3 convolution. The final feature enhancement is achieved as follows:  
\[F_{out}=F_{in}\cdot(\alpha\cdot W_{ch}+(1-\alpha)\cdot W_{sp}) \tag{5}\]  
Here, \(W_{ch}\) and \(W_{sp}\) represent the channel attention and spatial attention weights, respectively. When the target size is small, \(\alpha\) increases, and channel attention dominates; conversely, spatial attention becomes more influential for larger targets.
## 3 Thermal-Acoustic Nonlinear Response Analysis  
A 2D plain-woven C/SiC ceramic matrix composite panel is used in this work. The panel measures 350 mm \(\times\) 230 mm \(\times\) 1.5 mm with all ends clamped; see Figure 2. The material properties are shown in Table 1. Owing to the wide range of temperaturesconsidered in this paper, the effect of temperature on the material properties is taken into account. The panel is modeled by 400 S4R shell elements. The ABAQUS/Explicit solution is used to analyze the nonlinear response to obtain the displacement and stress time histories for all loading conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Property** & \multicolumn{2}{c}{**T\({}^{\circ}\)C**} \\  & **27** & **600** \\ \hline E11/GPa & 120 & 94.008 \\ E22/GPa & 120 & 94.008 \\ E33/GPa & 60 & 47.004 \\ G12/GPa & 44.4 & 34.817 \\ G13/GPa & 24 & 18.815 \\ G23/GPa & 24 & 18.815 \\ \(\alpha\)11/(10\({}^{-6}\)/\({}^{\circ}\)C) & 0 & 3.72 \\ \(\alpha\)22/(10\({}^{-6}\)/\({}^{\circ}\)C) & 0 & 3.72 \\ \(\alpha\)33/(10\({}^{-6}\)/\({}^{\circ}\)C) & 0 & 4.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material property of 2D plain-woven C/SiC ceramic matrix composite.  
Figure 2: Panel subjected to thermal-acoustic loadings with full clamped boundary conditions. (**a**) Panel with full clamped boundary conditions; (**b**) Panel subjected to thermal-acoustic loadings.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 3 Model
### Attacker Model
#### 3.1.2 Path Filtering  
Let \(P\) be the set of all paths in the network. The attacker will target one or more links and find all bots combinations that pass through these links via the shortest path. The attacker only selects and retains those paths that pass through the target in the attack direction.
## 4 Physical Audio Features Extraction Techniques
### Frequency Domain Physical Features
#### 4.2.3 Brightness-Related Physical Frequency Features  
Brightness is an attribute that is closely related to the balance of signal energy in terms of high and low frequencies (a sound is said to be bright when it has more high than low frequency content). The most relevant brightness-related physical features found in the literature are the following:  
* **Spectral centroid:** or SC for short, this feature describes the center of gravity of the spectral energy. It can be defined as the first moment (frequency position of the mean value) of the signal frame magnitude spectrum as in the works by [PERSON] _et al._[40], or by [PERSON] and [PERSON] [28], or obtained from the power spectrum of the entire signal in MPEG-7. SC reveals the predominant frequency of the signal. In the MPEG-7 standard definition [45], the audio spectrum centroid (ASC) is defined by computing SC over the power spectrum obtained from an octave-frequency scale analysis and roughly describes the sharpness of a sound. SC has been applied in musical onset detection ([PERSON] _et al._[57]), music classification ([PERSON] _et al._[41], [PERSON] _et al._[40], [PERSON] _et al._[12], [PERSON] _et al._[42], [PERSON] _et al._[9]), environmental sound recognition (like in [PERSON] _et al._[18], [PERSON] and [PERSON] [47], [PERSON] and [PERSON] [48]) and, more recently, to music mood classification ([PERSON] _et al._[86]).
* **Spectral center:** this feature is defined as the median frequency of the signal spectrum, where both lower and higher energies are balanced. Therefore, is a measure close to spectral centroid. It has been shown to be useful for automatic rhythm tracking in musical signals (see [PERSON] _et al._[83]).
# Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
## 3 Cartesian Constrained STOMP
### Noise Generator  
The standard noise generators Normal Distribution Sampling and Goal Guided Multivariate Gaussian work in robot joint space, but they should be generated in cartesian space due to requirements of cartesian constraints. Therefore, in this article, Cartesian Normal Distribution Generator is introduced.  
The implementation is based on the virtual plugin method generateNoise, which uses noiseless trajectory input. An output is noise and has noisy trajectory. Since all parameters are in joint space, a conversion between joint space and cartesian space is needed using forward and inverse kinematics. The generator works as follows:  
1. Convert each timestep of noiseless joint input trajectory into cartesian waypoints using forward kinematics;  
Figure 2: An example of valid trajectory (blue line), initialization trajectory (red line) and rollouts (grey lines) (Reprinted Ref. [22]).  
Each waypoint is noised by a random generator in the interval of stddev parameters by (1);
* New timestep is computed by inverse kinematics from each generated waypoint;
* If inverse kinematics for a particular waypoint has a solution, the new computed timestep is added to the resulting trajectory; otherwise, noiseless timestep from input trajectory is added to the resulting trajectory.  
The following equation calculates the noised waypoint position:  
\[P=P^{\prime}*T_{xyz}(r_{1},r_{2},r_{3})*R_{x}(r_{4})*R_{y}(r_{5})*R_{z}(r_{6}) \tag{1}\]  
where \(P^{\prime}\) is a 4 \(\times\) 4 transformation matrix (from robot base link to robot TCP) of noiseless waypoint and \(T_{xyz}\) is a 4 \(\times\) 4 translation matrix, where the rotation part is the identity matrix and the translation part is variable. \(R_{x}\), \(R_{y}\) and \(R_{z}\) are 4 \(\times\) 4 rotation matrices, where the rotation part is variable and the translation part is zero vector. Values \(r_{1}\), \(r_{2}\), \(r_{3}\), \(r_{4}\), \(r_{5}\) and \(r_{6}\) are random numbers with amplitudes defined by parameter stddev.  
Figure 3 shows the space around a waypoint in which a new noised waypoint could be created. This space is user-defined by the parameter stddev. It consists of six values that represent the amplitude of the translation noise on axes \(x\), \(y\) and \(z\) and rotation noise on axes \(x\), \(y\) and \(z\). For example, parameter values (0.1, 0.1, 0.5, 0.7, 0.7, 1.5) mean that the TCP could be translated up to 0.1 m on axes \(x\) and \(y\) and 0.5 m on axis \(z\), and rotated up to 0.7 rad around \(x\) and \(y\) axes and 1.5 rad around z axis in one iteration. Higher values could explore greater space, but there is a risk that generated noise will be out of required cartesian constraints. Cartesian Normal Distribution Generator does not guarantee that the new rollout satisfies constraints. The first reason is that a user could set stddev parameter values higher than the allowed constraints. The second problem is that in the next iterations different rollouts are used, which could be outside the tolerance.