esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Security Solutions
### Service Security
#### 6.3.3 Role-based Access Control  
**Centralized RBAC:** [PERSON] et al. presented an RBAC model to define access-control policies for IoT devices in [134]. The research work also provides methods to manage access and validate authorization policies based on OpenID [164] technology. The proposed model uses trusted central entities for authorization purposes. The applicability of the proposed model cannot be ensured because the authors did not provide any proof of concept implementation of the proposed system.  
**Context-aware RBAC:** An RBAC model with context information is presented in [135]. The proposed model grants permission to a user based on the access polices defined for a requester. During policy evaluation, the proposed system also considers a set of contextual information, such as device status, device or user locations, and time of access, collected from the environment of the service device and requester. The authorization model assigns appropriate permissions to a role according to the characteristics and contextual information of the smart devices, as well as the functions of the role.
# Mobile Robotics in Agricultural Operations:
## 2 Materials and Methods
### Definition of Planning Attributes
#### 2.2.1 Reasoning Architecture  
The autonomous and intelligent operation of mobile robots requires context-aware adaptation to the particular conditions of each operating environment. Therefore, two distinct reasoning architectures (or control paradigms) which dictate the intelligent behavior of mobile robots are identified [17], namely: (i) deliberative reasoning; and (ii) reactive reasoning. Models that combine elements of both deliberative and reactive behavior comprise hybrid architectures [24].  
In the case of deliberative reasoning the robot actions are planned based on an off-line generated word map. The basic assumption under this reasoning architecture is that past and current knowledge is used for predicting the potential outcome of a robot's action [25]. In deliberative reasoning the world is defined based on incomplete information leading to uncertainty about the possible actions of a robot thus resulting in reduced system robustness. Despite the uncertainty at a spatial level, time-specific uncertainty is also prevalent due to a time gap between a priori representation and the actual world. The significance of time gaps is greater in outdoor agricultural production environments owing to the variability of time, weather conditions, ongoing evolvement of biological entities (e.g., trees, crops), etc. Except for the effect of time gaps, the reasoning process associates to high computational costs, hence generating an additional gap between reasoning and decision execution. In the reactive control paradigm, a synthesis of environment perception and robot action dictates the behavior pattern of the system behavior [17]. Practically, changes to the environment are captured through sensors, and the actuators translate the received signals to real-time response. Reactive architectures can complement a mobile robot's control system for critical tasks, in real-time, like obstacle avoidance.  
Both robot control reasoning architectures have drawbacks; however, combining and balancing these paradigms can ensure the efficient execution of the assigned tasks. The usual practice is the combination of a high-level deliberative behavior for the operation's tasks and a low-level reactive behavior for the real-time critical tasks mainly related to safety of the robot and the operating environment (including the human factor).  
Figure 1: The computational and physical sub-systems of a mobile robot and their connection to the environment.
# Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
## 3 Materials and Methods
### Optimal Workpiece Placement  
In robotic machining, the feasibility of the operation can be influenced significantly by the position and orientation of the workpiece relative to the robot base [18]. In the optimization setup, workpiece \(\{W\}\) has its local coordinate system in its center with relation to the robot's base frame \(\{B\}\), as illustrated in Figure 3. The pose of the workpiece on the worktable relative to the robot's base frame \(\{B\}\) can be determined by three variables \(\langle x,y,\gamma\rangle\), which are subject to optimization. Here, \(x\) and \(y\) represent the offsets of the workpiece in the Cartesian \(xy\)-directions from the robot base, while \(\gamma\) denotes the rotation of the workpiece about its \(z\)-axis; the \(z\)-coordinate is constant, and indicates the fixed distance between the worktable and the robot base.  
To evaluate the machining performance suitable for optimization, we introduce the machining time index (MTI) concept, which provides a reference metric that estimates the machining time based on the calculated \(V_{\text{max}}^{opt}\) (18) and the density of design points on the workpiece:  
\[\tau=\sum_{i=1}^{N}\frac{d_{i}}{V_{\text{max}}^{opt}} \tag{23}\]  
where \(\tau\) stands for MTI, \(d_{i}\) is the so-called standard distance between points (standard measure), \(N\) is the number of surface points under consideration, and \(V_{\text{max}}^{opt}\) is the maximum feasible linear speed in the tangent plane attached to the point on the workpiece surface.  
The MTI can also be calculated for each region individually by adjusting the surface points and speeds for that region. The total workpiece MTI is then the sum of the regional MTIs, where lower values are better and correlated to shorter machining times.  
Again, an optimization algorithm is used to find the optimal workpiece pose based on minimizing the total workpiece MTI (23). The objective function is as follows:  
\[\min_{\langle x,y,\gamma\rangle}F(\tau) \tag{24}\]  
which is subject to the following constraints:  
Robot joint angles \(\theta_{i}\) are within limits:  
\[\theta_{i,\min}\leq\theta_{i}\leq\theta_{i,\max,i}=1,\ldots n \tag{25}\]  
* The workpiece must be located within a working area:  
\[\begin{array}{l}x_{\min}\leq x\leq x_{\max},\\ y_{\min}\leq y\leq y_{\max},\\ \gamma_{\min}\leq\gamma\leq\gamma_{\max}.\end{array} \tag{26}\]
* All points on the workpiece must be reachable, i.e., the inverse kinematic (IK) solutions must exist for all points.
* The robot must not be in self-collision, collision with the workpiece, or collision with the movable platform.
* The workpiece must be on the working table with its full area.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 2 Mathematical Models
### Modulation Principle  
The ChPN signal can be expressed as the following equation:  
\[s(t)=\sum_{-\infty}^{\infty}c_{m}p(t-mT_{c}) \tag{1}\]  
with the spread spectrum symbol waveform, \(p(t)\), as:  
\[p(t)=\sqrt{2}\cos\left(2\pi f_{c}t\pm\pi\frac{B_{s}}{T_{c}}t^{2}\right)g\left( \frac{t}{T_{c}}\right),\;-\frac{T_{c}}{2}<t<\frac{T_{c}}{2} \tag{2}\]  
where \(\{c_{m}=\pm 1\}\) is the PRN sequence, \(T_{c}\) is the chip duration, \(f_{c}\) is the center frequency of chirp, \(B_{s}\) is the sweep bandwidth, \(g(t/T_{c})\) is the gate function with a duration of \(T_{c}\). The amplitude of \(p(t)\) is assumed as \(\sqrt{2}\) to ensure the power is 1, without loss of generality. A chirp with frequency increasing is called an up-chirp, otherwise is called a down-chirp [17]. While only up-chirp is taken as an example hereinafter, the techniques and conclusions in this paper are also applicable to the down-chirp case. The waveform of the ChPN signal is illustrated in Figure 1.  
Henceforth, the notation ChPN(\(p\),\(q\)) will denote a ChPN signal with a sweep bandwidth of \(B_{s}=p\times f_{0}\) and a code rate of \(q\times f_{0}\), respectively, with \(f_{0}=1.023\) MHz. The main-lobe bandwidth (MLB) of the ChPN signal is determined by the chipping rate and the sweep range. As most signal energy is concentrated on the band \(-(p+q)f_{0}\)-\((p+q)f_{0}\), the MLB can be expressed as:  
\[B_{chp}=2(p+q)f_{0} \tag{3}\]  
According to Equation (3), there exist various modulations with the same MLB if we change parameters \(p\) and \(q\). It is necessary for us to make comparisons for them that will be carried out in Sections 4 and 5.
## 2 Materials and Methods
### Datasets
#### 2.1.1 GLH-Water Dataset Construction  
This study constructed a benchmark testing platform based on the publicly available GLH-water dataset [44]. This dataset is composed of high-resolution satellite images distributed globally. Each image has a size of 12,800 \(\times\) 12,800 pixels, a spatial resolution of 0.3 m, and a geographical coverage area of approximately 3686 square kilometers. The original data has been processed through a strict quality control process, and it contains precise semantic annotations of 40.96 billion pixels, covering diverse hydrological environments, including rivers, lakes, forest ponds, irrigated fields, bare land, and urban zones, as shown in Figure 1. To be compatible with the deep learning framework, we cut the original images into 4193 sub-tiles with a size of 1024 \(\times\) 1024 pixels each. Then, we divided the dataset into a training set and a validation set at a ratio of 8:2. After performing standardized preprocessing on the images, the pixel values of their labels were normalized to 0 (background) and 1 (water body). After the model training was completed, we performed inference on each block of the prediction set, and then we reconstructed the full-size image by stitching together the sub-images to achieve the final evaluation.  
Figure 1: Example images and corresponding segmentation masks from the GLH-water dataset.
## 2 Related Research
### Size Recommendation System  
There are several studies associated with the customized recommendation services in e-commerce. Studies recommending taste and affinities based on the customer's past interactions are being carried out widely [17; 18; 19; 20]. However, such a system just makes recommendation of a taste on a certain style, thus, the recommendation models dealing with sizes are rare. [PERSON] et al. (2020) proposed a method of recommending the most suitable clothing sizes based on the customer's movement and the distance to the point where he/she is standing using a 3D virtual clothing (simulation) technology [21]. The 3D virtual technology is one of the rising technologies being integrated with the clothing services currently and used for the online VFMs (virtual fitting rooms) where customers can check the clothes either in 2D or 3D [22; 23; 24; 25].  
Although 3D virtual technology is a flashy technology that can attract customers greatly, it is still a heavy technology to be used for the clothing services as far as the accuracy of size recommendation is concerned [21]. The accuracies (men: 81%, women: 69%) show that it may be difficult for this technology to serve customers in making a quick decision in this fast moving contemporary society and according to the studies conducted  
Figure 2: A method of acquiring body sizes from images.  
for the fitness of 3D virtual body-based services [26; 27; 28], many services overly relied on the database used when the body was constructed [29] or used a method that extracts a human form from the images [30]. Thus, to deal with such limitations, [PERSON] and [PERSON] (2017) proposed [31] a method that recommends right sizes by inferring the customer's body information through the feedback provided on the online platform.  
However, as this method requires the feedback to be completed through two or more purchases, it only applies to the customers who visit the platform more than twice, leaving yet another problem.
# Effects of Nonlinear Damping on Vibrations of Microbeam
## 1 Introduction  
A study of the dynamics and energy dissipation of microbeams is necessary, since slender beams are frequently used in micro-scale devices and systems, such as microelectromechanical systems (MEMS) [1; 2; 3].  
A crucial issue of the mechanical properties of microbeams is the size-independent behavior observed in micro-scale experiments [4; 5; 6]. [PERSON] and [PERSON] observed that the microbeam's flexural rigidity is at least four times larger than the one predicted by the classical beam theory [4]. [PERSON] found that the dimensionless torque increases the classical torque theory by three times when the wire diameter decreases from 170 to 12 \(\upmu\)m [5]. [PERSON] and [PERSON] found that the dimensionless flexural rigidity increases significantly as the beam thickness decreases from 50 to 12.5 \(\upmu\)m [6]. However, the classical slender beam models based on the classical elasticity cannot describe this size effect due to the lack of material length scale parameters. This disadvantage motivated the development of beam theories using nonlocal elasticity theories to model nanobeams [7; 8] and higher-order strain gradient theories to model microbeams [3; 9; 10; 11]. Moreover, almost all of the existing microbeam models are based on linear constitutive relations. However, the material nonlinearity also influences the mechanical properties of nanobeams [12].  
The modified couple stress theory is widely accepted to model the mechanical properties of microbeams [13]. The couple stress tensor is symmetric and only involves one internal material length scale parameter in the modified couple stress theory. Based on this theory, the linear and nonlinear Bernoulli-Euler and Timoshenko microbeam models have been proposed [14; 15; 16; 17; 18; 19; 20]. Nevertheless, most of the studies on microbeam vibrations have overlooked the nonlinear terms, which are induced by damping. For example, the authors of [3; 11; 19] only considered the linear term of damping, while the authors of [11; 16] completely overlooked it. The energy dissipation is related to the vibration frequenciesof the structures [21]. In macrostructures, nonlinear damping is almost smaller than the geometrical nonlinear, which is induced by the finite deformations [22]. However, since the vibration frequencies of microbeams are significantly higher than the macrobeams, nonlinear damping significantly affects the microstructure vibrations. Based on the modified couple stress theory, the present paper will propose a new Bernoulli-Euler model that includes the small-size effect and nonlinear terms, which are induced by the axial elongation of the beam and Kelvin-Voigt damping. Then, the effects of nonlinear damping are studied for the non-resonance and primary resonance.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 4 Methods
### Shannon Entropy  
Entropy is a statistical concept first introduced by [PERSON] in 1949. It allows the quantification of data uncertainty of random variables, and the equation for entropy is as shown in Equation (9).  
\[H(Y)=H(P_{\text{L}},\ \cdots\ P_{n})=-\sum_{i=1}^{n}P_{i}log_{2}P_{i} \tag{9}\]  
where \(H(Y)\), \(Y\) and \(P_{i}\) denote the information entropy, a random variable and the \(i\)-th probability, respectively. An occurrence with high probability can be considered a natural event; thus, little information is gained from the observation. In contrast, significant information can be gleaned from observing an uncommon occurrence; thus, it can be considered that the amount of information and probability are inversely proportional [32]. Therefore, low and high entropy indicate concentrated and dispersed data distributions, respectively. Based on this, it was predicted that under normal conditions the entropy value will be small because of the small variation in the data; under fault conditions, on the other hand, the entropy value will increase, exhibiting a direct correlation with the severity of the fault.  
Since the PCA results obtained in this study were the results of visualizations after dimension reduction to 3D or 2D, human input was required for the classification of faults. To simplify this process and to reduce the time required for fault detection and diagnosis, the PCA results were quantified via the entropy method for comparison.
# New Materials and Effects in Molecular Nanomagnets
## 8 Recent Trends in Molecular Nanomagnets  
Besides the aforementioned topics, which are often related to basic research, some recent trends should be mentioned that are currently in the focus of research in this area, often aiming at multifunctional molecular magnets.  
One of these topics is related to luminescent SMMs based on lanthanides for possible application in quantum computing, bio-labeling, or LEDs. [PERSON] et al. recently reviewed the design strategies for such luminescent lanthanide-based SMMs, pointing out the necessary combination of a strongly UV absorbing ligand environment that efficiently populates excited states in the metal ion, with a filled-up coordination sphere of the Ln(III) ions to reduce radiation-less deactivation processes [170]. [PERSON] et al. investigated the isostructural dimers [Ln(hfac)\({}_{3}\)(PyNO)]\({}_{2}\) (with the lanthanides Eu, Gd, Tb, and Dy) and found luminescence for Eu, Tb, and Dy [171]. For Yb\({}^{\text{III}}\), Er\({}^{\text{III}}\), and Eu\({}^{\text{III}}\), [PERSON] et al. found luminescence in different mononuclear Ln\({}^{\text{III}}\) complexes [172]. [PERSON] et al. suggested using a near-infrared emissive SMM as a highly sensitive luminescent thermometer [173]. A review of luminescent Schiff-base lanthanide SMMs can be found in [174].  
Other authors have concentrated on the electric properties of SMMs. [PERSON] et al. investigated the magneto-electric coupling in a paramagnetic ferroelectric lanthanide complex and found a strong interaction between both electric and magnetic properties, making this material possibly useful for data storage or spintronics applications [175]. The electro-conductive properties of a Dy(III) double-decker SMM were investigated by [PERSON] et al. and [PERSON] et al. who found conductivity at temperatures below the spin blocking temperature and magneto-resistance in the form of a magnetic hysteresis curve, possibly enabling the utilization of such systems as spin valves [176].  
Even ferroelectricity was found in SMMs [177; 178] and SCMs [179; 180].  
As this short overview shows, multifunctional molecular nanomagnets enable combining magnetic with different optical or electrical properties, making them highly useful for diverse recent applications.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm
#### 3.4.3 Posture Evaluation Metrics  
To determine the subject's posture, several geometric metrics are computed based on keypoint coordinates:  
* _Shoulder and Hip Centers_ \[shoulders=\frac{P_{5}+P_{6}}{2},\] (2) \[hips=\frac{P_{11}+P_{12}}{2},\] (3)  
where P\({}_{i}\) = (x\({}_{i}\),y\({}_{i}\)) is the position of the ith keypoint.
* _Trunk Orientation (Body Angle)_ The orientation of the upper body is evaluated using the ratio of vertical to horizontal displacement between shoulders and hips: \[body\_angle=\left|\frac{y_{hips}-y_{shoulders}}{x_{hips}-x_{shoulders}+\in}\right|,\] (4) where \(\epsilon=10^{-5}\) is a small constant to avoid division by zero.
* _Relative Hip Position to Feet_ A person is considered to be above ground level if \[y_{hips}<y_{feet},\text{ where }y_{feet}=\frac{y_{15}+y_{16}}{2}\] (5)
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 5 Performance Evaluation
### Analysis of Clustering Result  
The reasonable CHs are expected to be selected during the selection procedure and one of the significant standards to evaluate the reasonability is the average communication distance between the CH and its members. The same simulation parameters are used and the number of sensors is set as 100. The presented algorithm is compared with LEACH-AP [19] and the simulation result is shown as Figure 7. From Figure 7, it can be seen that the presented APSA can greatly reduce the intracluster communication distance and improves about 30.5% performance compared to LEACH-AP.
## 3 Video Application Based on Deep Learning Algorithm
### Generative Deep Architecture  
DBN is a probability generation model which is obtained by a multi-RBM stack and layer-by-layer greedy training. RBM is a shallow neural network model based on energy. Its structure, shown in Figure 2, consists of a visible layer and a hidden layer. The nodes between layers belong to two-way connections, while the nodes within layers do not produce connections. The visible layer, also known as the input layer, is used to describe data features. The hidden layer will not send and receive signals through the outside; its function is to extract the abstract dependency relationship between data features so that it can be better linearized.  
Because of the structure of multi-layer neural networks, deep neural networks lead to some problems, such as a low learning efficiency, a high dataset sample size and a difficulty with parameter selection. However, DBN can deal with the above problems well by its novel training methods, so deep learning has been widely studied and applied.  
The existing routing and caching strategies in vehicular networks cannot effectively cope with time-varying network conditions; thus, [PERSON] et al. [27] proposed an intelligent routing algorithm based on DBN (IRA) which aims to provide high-quality video streaming services in vehicular networks. Its model structure is shown in Figure 3. In order to discover the deep abstract relationship between the order of requesting video content and the time spent requesting video content, the DBN-based learning model [28] optimizes itself through back propagation regret after layer-by-layer pre-training (regret is defined as the difference between the predicted results and the actual results). Simulation results show that IRA achieves better network performance than other algorithms in terms of lookup data delay and cache hit rate.  
Figure 3: Structure of DBN.  
Figure 2: RBM structure schematic diagram.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies
### Data Rate  
The data rate (DR) is the maximum transfer rate of bits per unit of time that a protocol achieves to keep its services running. A high data rate for a communication link between vehicles means that more services can be provided. In general, the data rate can be analyzed based on the bandwidth and overhead ratio. Calculating the DR is fundamental to determining channel capacity, spectral efficiency, and suitability for practical applications in autonomous driving. In this study, MCS is considered to analyze the maximum data rate achieved by the 802.11p, 802.11 bd, LTE-V2X, and NR-V2X protocols. The average data rates obtained through simulations should be regarded as effective data rates as they consider the probability of successful transmission between vehicles.