esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Modeling  
### Dynamic Model of Robot  
We first establish a structural dynamics model for the industrial robot to analyze the vibration characteristics. For this purpose, an \(n\)-DOF industrial robot with \(n\) links and \(n\) rotary joints is illustrated  
Figure 1: The robotic milling system under study.  
in Figure 2. A cutter tool is mounted on the robot end-effector. The local body-fixed coordinate systems {\(o_{i}\_x}x_{i}y_{i}z_{i}\) (\(i=1,\,2,\,\ldots,n\)) and the tool coordinate system {\(o_{t}\_x}x_{i}y_{i}z_{t}\)} are established at each joint and at the cutter tool tip, respectively. The position vector of the \(i\)th joint and the origin of tool coordinate system in the inertial coordinate system {\(o\_xyz}\) are denoted by \(\mathbf{P}_{i}\) and \(\mathbf{P}_{t}\) respectively. During the milling operation, the cutter is subjected to an external machining force \(\mathbf{F}_{E}\).  
For industrial robots, the main source of deformation is joint flexibility [1; 39]. Thus, to simplify the robot model, we assume that all the links are rigid and that all the joints are equivalent to linear elastic torsion springs. If the joint angles for a given configuration are denoted as \(\mathbf{q}=(q_{1},\,q_{2},\,\ldots,\,q_{n})^{T}\), then the dynamic model of the stationary robot under forced vibrations in this configuration can be written as  
\[\mathbf{M}\Delta\ddot{\mathbf{q}}+\mathbf{C}\Delta\dot{\mathbf{q}}+\mathbf{K} \Delta\mathbf{q}=\mathbf{J}_{v}^{T}\mathbf{F}_{E} \tag{1}\]  
where \(\mathbf{M}\) represents the \(n\,\times\,n\) symmetric generalized mass matrix, \(\mathbf{K}\) is the \(n\,\times\,n\) diagonal stiffness matrix with its diagonal entries equal to the torsional stiffness of the corresponding joints, and \(\mathbf{C}\) is a damping matrix. Both \(\mathbf{K}\) and \(\mathbf{C}\) are defined in the joint space. Vector \(\Delta\mathbf{q}\) is the perturbations in the joint angles from an equilibrium configuration and represents the joint deflections. The \(3\,\times\,n\) linear velocity Jacobian matrix \(\mathbf{J}_{v}\) represents the mapping from the joint rates to the velocity of the tool tip such that \(\mathbf{v}=\mathbf{J}_{v}\dot{q}\), in which \(\mathbf{v}\) is the linear velocity of tool tip written in the inertial coordinate system and \(\dot{q}\) is the joint rates. Matrices \(\mathbf{M}\) and \(\mathbf{J}_{v}\) depend on the robot configuration, and the force \(\mathbf{F}_{E}\) depends on the specific machining task.  
The dynamic model described as Equation (1) is a linear time invariant system. Force \(\mathbf{F}_{E}\) can be considered as the external input, and \(\Delta\mathbf{q}\) is the output response of the robot. In addition, if the joint deflection is small, the linear displacement of tool tip offset can be written as  
\[\Delta\mathbf{d}=\mathbf{J}_{v}\Delta\mathbf{q}, \tag{2}\]  
Thus, once the joint deflection is obtained, the offset of the cutter tool caused by the robot vibration can be calculated through Equation (2), which can be used to evaluate the quasi-static performance of the robotic machining system under a specific machining force.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 4 Proposed Method  
Hollow convolutional prototype learning for interference OSR is composed of a feature exactor and a prototype classifier, as shown in Figure 1. The feature exactor \(f(\cdot;\theta)\) transforms the input \(x\) to d-dimensional deep feature representation, where \(\theta\) denotes the parameters in the feature extractor. The prototype classifier \(C(\cdot;M)\) consists of prototypes \(\left\{\mathbf{m}_{ij}|j\in\left\{1,\dots,J\right\}\right\}\) corresponding to each class \(i\in\left\{1,\dots,N\right\}\), and the label is assigned to the input \(x\) according to the nearest prototype, where \(M\) is the set of all prototypes, and \(J\) is the number of prototypes for each class. Below, we maintain one prototype for each class by default [22]. The way of HCPL to realize interference OSR is to convert the similarity between the signal features and prototypes into probability and to judge whether a signal is a KIS through the threshold in the prototype classifier.  
Below, we introduce HCPL from two phases. In the inference phase, we propose a feature extractor that uses HARFNet and a prototype classifier based on similarities. In the training phase, we design a hybrid loss function to update the parameters of the feature extractor and prototypes.
## 3 Experiments and Analysis
### Relative Rotational Angle Measurement Experiments  
The sensor achieves a relative rotational angle measurement resolution of 30 degrees. To verify this, experiments were conducted, and the results are shown in Figure 5. As shown in Figure 5a, during the experiment, the peak or trough of any arbitrarily selected electrode was chosen as the zero-angle reference point (0 degrees). The sensor was then rotated through one complete revolution, yielding six output waveforms. Therefore, the angular difference between consecutive waveforms is 60 degrees. As illustrated in Figure 5b, within a single waveform, the output voltage signal exhibits successive peaks and troughs. Peaks correspond to positive voltage, and troughs to negative voltage. Thus, a voltage polarity change occurs with each tooth of the rotor. Therefore, in the subsequent data processing, a voltage threshold is set to detect peaks and valleys, thereby improving the sensor's angular measurement resolution to 30 degrees, wherein the threshold voltage is determined based on the experimental conditions of this study. Since the sensor uses the same output signal for measuring both rotational speed and angle, differing only in the subsequent signal processing, the measurement error for relative rotational angle is the same as that for rotational speed, namely 3.2%. Figure 5c includes a screenshot of the sensor data processing software, displaying the actual data collected by the sensor.  
Figure 4: Rotational speed measurement experimental results. (**a**) Open-circuit voltage waveforms at different rotational speeds; (**b**) short-circuit current waveforms at different rotational speeds; (**c**) fitted voltage pulse curves at different rotational speeds; and (**d**) measurement errors at different rotational speeds.
## 5 Experiments
### Effectiveness of Multi-Scale SAMA Bilinear Fusion  
In our study, we propose combining multiple \(SAMA\) indicators of different scales for time series prediction. Tables 2 and 3 report the comparison results between each individual \(SAMA\) indicator and the fusion of multi-scale ones in different ways, from which we can make the following observations:  
* Overall, combining multi-scale \(SAMA\) indicators offers better performance than utilizing one of them alone, leading to, for example, at least 3.19% and 2.85% relative improvements in terms of \(F_{1}\) score on the stock and air quality datasets, respectively. The results suggest the necessity of simultaneously exploiting the \(SAMA\) indicators of different scales for enhancing time series prediction. In addition, the performance of time series trend prediction by combining the three indicators \(SAMA_{5}\), \(SAMA_{20}\), and \(SAMA_{60}\) deteriorates, which may be caused by the following two reasons. The \(SAMA_{20}\) indicator is between the \(SAMA_{5}\) and \(SAMA_{60}\) indicators, which can reflect the fluctuations of time series in the past month. Since the \(SAMA_{20}\) indicator is not sensitive to the short-term fluctuation of the time series and cannot adequately reflect the long-term trend change, some redundant information is introduced when the three indicators are combined for trend prediction, which reduces the accuracy of the prediction. On the other hand, we simply splice the three indicators to obtain a unified representation through the fully connected layer as an excessively simple combination method may lead to performance deterioration.  
It can be seen that the bilinear fusion of \(SAMA_{5}\), \(SAMA_{20}\), and \(SAMA_{60}\) achieves the best result among all multi-scale competitors. This may be because \(SAMA_{5}\) could reflect the recent short-term fluctuations of time series: on the contrary, \(SAMA_{20}\) and \(SAMA_{60}\) capture the overall mid-term and long-term dynamics of the past period. In the process of fusion, we interact with the three different indicators and comprehensively consider the dimensions of different scale indicators to better predict the future trend of the time series.
## 4 AMGs with the Potential to Impact Central Carbon Metabolism
In the host _Pseudoalteromonas_ infected by cyanophage HP1, the most highly expressed genes (-3-fold) were _aceA_ and _aceB_ involved in the glyoxylate shunt [91]. The glyoxylate shunt, which is used to increase cellular ATP synthesis and reducing power, is commonly observed under various stressors [94; 95]. In addition, two viral gene families were identified from the Pacific Ocean Virome dataset. The fatty acid oxidation complex (_fadB_), the long-chain fatty acid transporter (_fadL_) involved in the fatty acid metabolic pathway, acetyl-CoA carboxylase (_acc_), propionyl-CoA carboxylase (_pcc_), and methylmalonyl-CoA epimerase/mutase (_mcm_) are involved in the 3-hydroxypropionate (3 HP) cycle. Considerably more energy can be generated by fatty acid oxidation, and TCA cycle intermediates can be balanced during the infection process. Carbon flux can also be redirected from fixation to energy production by the genes _acc_, _pcc_, _mcc_, and _mcm_ involved in the 3 HP cycle. Through this reprogramming, the carbon and nitrogen cycles may also be influenced (Scheme 2B) [96]. All these changes in central carbon metabolism are consistent with the hypothesis that viruses can redirect carbon flux away from carbon fixation pathways towards dNTP and energy production, which can be used for the generation and assembly of cyanophages.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 4 Results
### Variation in ET in the GRB  
The spatial distribution of the average ET in the GRB from 2002 to 2018 is presented in Figure 3b. Figure 3b shows that the average annual ET of the GRB during the study period varies from 600 mm to 1000 mm, with an average of 780 mm. The regions with maximum ET are concentrated mostly in the middle and south, while the ET in the northwest is lower than those in other regions, and the spatial difference of annual average ET is obvious.  
Figure 4c,d show the variation in annual and monthly mean ET in the 172 sub-basins from 2002 to 2018. From Figure 4c, we notice that the ET in most sub-basins presents a relatively consistent fluctuation during the study period, but the annual ET values of different sub-basins in the same year are quite distinct, and the difference in annual ET among the sub-basins can reach 300 mm. Figure 4d shows that the monthly average ET of different sub-basins presents highly consistent fluctuations. The monthly ET increases from January, reaches a peak in July, and then begins to decrease. The average monthly ET of different sub-basins varies from 10 mm to 150 mm. Noting that precipitation and ET show different seasonal variations, the precipitation peaks from May to June, but the peak of ET occurs from July to August, and ET may be affected more by temperature in the GRB.
# A Construction Method of Lower Limb Rehabilitation Robot with Remote Control System
## 2 Materials and Methods
### Design of Rehabilitation Robot System
#### 2.1.2 Key Factors  
At present, rehabilitation robots have some points for improvement such as large size, small quantity of products in high performance, prescribed training site, few users, and insufficient humanity design. At the same time, the patients' rehabilitation environment is mostly indoors, they require a high flexibility of the rehabilitation robot because of the aging society. In order to make the patients have good mood to train, all components or functional units of the robot are integrated in the movable robot body, so this robot is a compact and mobile independent training platform that can be used in open and flat places such as living rooms, courtyards and parks. Patients generally have motor dysfunction and insufficient self-protection abilities. The safety protection of patients is more important and needs to be considered at every stage of robot design. Combining the above factors, under the premise of achieving the goal of patient rehabilitation training, the design of lower limb rehabilitation robots first needs to consider safety issues, followed by mechanism optimization issues. Rehabilitation robots not only meet safety requirements but also be used flexibly in the workspace. At the same time, patients of different races, heights, and ages can use this robot, its structural design can adapt to changes in users.  
According to the average body size standard data of the Asian and European population [17; 18; 19], the body height is about 170-175 cm, the body width is about 45-55 cm, and the weight is about 60-75 kg. Some patients have insufficient lower limb strength and cannot directly use the rehabilitation robot, and need wheelchairs for assistance. The width of a wheelchair commonly used in daily life is about 70 cm. The width of a single door in public buildings is generally about 100 cm, the width of a double door is about 120-180 cm, and the height is generally not less than 200 cm. According to the international elevator industry standards, the load of the elevator is more than 1100 kg, and the standard door opening size is not less than 1100 \(\times\) 2100 cm\({}^{2}\)  
To meet the mobility of robots in such environments and the convenience of wheelchair patients, refer to the above data. The robot structure design has an inner dimension of 76 cm, an outer dimension of 98 cm in width, 120 cm in length, and 180 cm in height. This design ensures that the robot can move between rooms, elevators, and buildings in a public environment, and has good pass ability. Additionally, for some narrow doors that cannot be passed, the robot solves such problems through disassembly and assembly.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology
### Importance of a Node  
In the proposed scheme, a node's importance within the network topology is quantified using centrality. Centrality identifies nodes that serve as key connectors or communication hubs. This plays a critical role in ensuring the efficiency, resilience, and robustness of the overall network. A node with higher centrality typically participates in more routing paths and data exchanges, which makes it both more influential and more valuable for maintaining network performance.  
For a graph \(G=(V,E)\), where \(V\) is the set of nodes and \(E\) is the set of edges, the harmonic centrality of a node \(i\in V\) is defined as follows:  
\[Centrality_{i}=\sum_{\begin{subarray}{c}j\in V\\ j\
eq i\end{subarray}}\frac{1}{d(i,j)} \tag{8}\]  
where \(d(i,j)\) is the shortest path distance between node \(i\) and node \(j\).  
Harmonic centrality is particularly useful in our scenario because the efficiency of information dissemination and robustness of the network are important. Nodes with high harmonic centrality can quickly disseminate information to other nodes.
# Abrasion-Resistant and Temperature Control of
## 2 Anti-Scouring and Abrasion-Resistant Performance Test on Concrete
### Test Results and Discussions
#### 2.3.2 Silica Fume Effect
As shown in Figure 6c, the volumetric dry shrinkage rate of concrete increased with increasing dosage of silica fume. At the test age of 90 day, compared to the dosage of 5%, the volumetric dry shrinkage rates of the concrete specimen at the dosages of 7% and 9% increased by 5.50% and 9.39%, respectively. Meanwhile, due to the extremely rapid development of the early strength of silica fume concrete, the hydration heat release of concrete was extremely rapid as well, resulting in the high hydration heat-induced temperature rise of concrete, and could very easily produce a relatively high temperature stress in concrete. The easy concentration of this stress at the top of the shrinkage crack caused the crack to propagate and extend, and a penetrated crack was likely to develop. For this reason, the dosage of silica fume in concrete should be optimal to ensure the compliance of the anti-scouring and abrasion-resistant strength of concrete with engineering requirements and reduce the risks of shrinkage cracking and concrete cracking. Figure 6: Effect of silica fume on concrete properties. (**a**) Compressive strength; (**b**) abrasion resistance; (**c**) volume shrinkage.
The connections of silver particles to each other as well as the ohmic contact between Ag-HJT solar cells should be obtained by a new kind of curing system, which is mainly composed of organic binding agents, such as epoxy resin and a suitable latent curing agent. Increasing the mass fraction of the curing system in the HJT silver paste will improve the adhesion strength. However, a higher mass fraction of the curing system will reduce the electrical performance to some extent on account of the mass fraction of Ag decreasing. Meanwhile, the types of organic binding agents in the curing system also affect the adhesion strength as well as other characteristics of the HJT solar cell. Therefore, a high-performance curing system is indispensable to HJT silver paste, and it is necessary to investigate the curing mechanism and adjust the curing conditions of silver paste to improve the performance of the curing system and thus improve the adhesion strength of HJT silver paste. It is obvious that the effects of curing conditions and the curing mechanism are of great significance to HJT silver paste to improve the adhesion strength. However, most of the experimental results concerning various curing systems were obtained with a single-factor method [19; 20; 21]. In this way, the interactions among those processing conditions are difficult to analyze. Especially, the dynamic effect of all the factors on the mechanical property of HJT silver paste cannot be determined. Response surface methodology (RSM) is an experimental design method for multiparameter analysis with interaction determination, which is very effective to build statistically significant empirical models with a minimum number of experiments. [PERSON] et al. demonstrated that RSM could be used for optimization of the preparation of silver powders with a model that accorded well with the experimental data [22]. [PERSON] et al. found that RSM could be used for predicting aluminum permeation with a model correlating with the experimental data [23]. Therefore, RSM shows the potential to analyze the interactions among the operation conditions as well as to determine the dynamic effect of all the factors on the mechanical property of the HJT silver paste. The object of this work was to optimize the curing conditions of HJT silver paste and to build a model to evaluate the interactions among them by RSM. The processing conditions were curing time (t, min), treatment temperature (T, \({}^{\circ}\)C), and curing agent dosage (m, wt%). The optimization index was adhesion strength, which is an important index for paste property. Curing mechanism analysis and validation experiments were also carried out in this study.
## 4 Cloud Server
### Data Processing Architecture
#### 4.1.5 Web API  
The web application programming interface (API) enables external services to access data stored in the database, with a format suitable for the required task. Such APIs were developed using the Flask [59] python library.  
Figure 10: Database ER diagram—aggregation tables.  
As the Grafana [35] tool is the main client of the APIs, the API endpoints must follow a format supported by Grafana. In the developed system, the selected format is as follows:  
\[\text{base\_url}/\{\text{board\_id}\}/\{\text{measure\_name}\}?\text{avg}=\{ \}\&\text{sts}=\{\}\]  
where \"board_id\" is the multi-sensor device identifier, as stored in the _BOARD_ database table, and \"measure_name\" identifies which measurement should be retried, e.g., temperature and TVOCs. The \"avg\" query parameter is the time aggregation of the retrieved data points, which can be selected amongst real time (RT), three hours, twenty-four hours, three days, one week, and one month. The \"ts\" query parameter is the length of the time interval, starting from the instant of the request, for which the data must be retrieved. It can be specified via a number followed by a time descriptor, e.g., 3 s, 4 m, 2 h, 5 d, 1 w, 6 M, and 1 y. Table 3 reports the database pre-aggregation used by the API to compute each available time aggregation, distinguishing between measurements and comfort indexes. The 24-h aggregation alignes with midnight, whereas others aggregations align with the request instant. This allows the user to always receive up-to-date information, without the need to wait for the next aggregation to be computed. Real-time data remain unaggregated, as they are retrieved directly from the database table.  
An important aspect is that averages of indoor parameters should not be calculated during periods when the indoor space is not in use, e.g., at night, during holidays, or weekends. Doing so could distort comfort evaluations, as the monitored environment may not be maintained under habitable conditions, e.g., no heating, lighting, or air circulation. However, it can still be valuable to track how conditions evolve during these unused periods. To address this point, real-time and 3-h aggregations are calculated using all available data, while other aggregations only consider measurements collected during the actual utilization periods. This distinction is achieved by configuring a calendar, which the API consults before computing the aggregations.
# A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis
## 4 Results and Discussion
### Determination of Parameters  
As discussed before, our proposed algorithm has the following parameters:  
* TH\({}_{D}\): The spherical radius used for the comparison of a newly entered pixel and the color categories.
* TH\({}_{match}\): The required number of samples allowing the joint category to be regarded as the background.  
TH\({}_{D}\) represents a perceived color difference. In all our tests, TH\({}_{D}\)= 75 generated the best detection results. In order to determine the optimal TH\({}_{match}\), we monitored the change in PCC at different TH\({}_{match}\) values in images detected with the EBBE method. As shown in Figure 6, TH\({}_{match}\geq 20\) generated satisfying PCC results, while the computational cost of the algorithm increased with the increase in TH\({}_{match}\). Therefore, we decided that the optimal setting is TH\({}_{match}=20\).  
Figure 6: Percentage of correct classification (PCC) for TH\({}_{match}\) ranging from 1 to 30.
## 5 Matching-Iteration-Based Joint User Access and Resource Allocation Algorithm  
The formulated problem (18) is a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard. Thus, we design a matching-iteration-based joint user access and resource allocation algorithm to obtain a sub-optimal solution of the problem.
## 5 Experimental Results and Analysis  
### Operation Environment  
The simulation environment is MATLAB R2015a and executed on a 3.40 GHz Intel Core i7-2600U CPU equipped with 4 GB RAM and Windows 10. The main parameters are shown in Table 4. In this paper, the final result of each algorithm is the average result of 20 independent experiments. The number of nodes \(N\) is 100.  
In the LEACH protocol, the threshold \(T(i)\) for cluster head selection is defined as:  
\[T(i)=\left\{\begin{array}{cc}\frac{p}{1-p|\text{rmod}(1/p)|}&i\in G\\ 0&\text{other}\end{array}\right. \tag{14}\]  
where \(p\) is the probability to be selected as cluster head in each round. \(G\) is the nodes set including those nodes which have not yet been selected as cluster head in recent \(1/p\) rounds. \(r\text{mod}(1/p)\) means the remainder for division of \(r\) and \(1/p\).