esa-sceva/satcom-chunk-collection huggingface dataset:
# Research of Calibration Method for Industrial Robot Based on Error Model of Position
## 2 Robot Calibration Modeling
### Calibration Model of Robot Base Coordinate System  
When measuring the pose of robot's end by measuring instrument, the data is obtained in the MICS. In order to compare the actual pose of robot with the nominal one of robot controller, it is necessary to unify it into the same coordinate system.  
Suppose that the nominal position and pose of the FCS at the end of the robot read from the controller are represented by \(P^{N}\) and \(R^{N}\) respectively when the robot is in a certain state, and the position coordinates of a point in the end of the robot which is measured by measuring instruments as \(P^{C}\). In the case of ignoring the pose of the DCP coordinate system, the following relation can be obtained from the spatial geometric transformation:  
\[\left(\begin{array}{cc}E&P^{C}\\ 0&1\end{array}\right)=\left(\begin{array}{cc}R&T\\ 0&1\end{array}\right)\left(\begin{array}{cc}R^{N}&P^{N}\\ 0&1\end{array}\right)\left(\begin{array}{cc}E&P^{L}\\ 0&1\end{array}\right). \tag{8}\]  
In the equation, \(T\) and \(R\) denote the rotation transformation and translation transformation of the MICS to RBCS, \(E\) denotes the unit matrix of \(3\times 3\), \(P^{L}\) denotes the position transformation from the FCS to the DCP, and the superscript \(N\) denotes that the value isnominal.  
Set \(T^{NL}=\left(\begin{array}{cc}R^{N}&P^{N}\\ 0&1\end{array}\right)\left(\begin{array}{cc}E&P^{L}\\ 0&1\end{array}\right)\). According to the matrix multiplication rule, the Equation (8) can be expanded into the form of equations:  
\[\left\{\begin{array}{c}n_{x}T_{1,A}{}^{NL}+o_{x}T_{2,4}^{NL}+a_{x}T_{3,4}^{ NL}+T_{x}=P_{x}^{C}\\ n_{y}T_{1,A}{}^{NL}+o_{y}T_{2,4}^{NL}+a_{y}T_{3,4}^{NL}+T_{y}=P_{y}^{C}\\ n_{z}T_{1,A}{}^{NL}+o_{z}T_{2,4}^{NL}+a_{z}T_{3,4}^{NL}+T_{z}=P_{z}^{C}\\ \end{array}\right.. \tag{9}\]  
In the equation, \(T_{i,j}^{NL}\) represents the elements of line \(j\) in column \(i\) of matrix \(T^{NL}\).  
For the solution of Equation (9), there are 12 unknowns, and at least 4 sets of data are needed. That is, at least 4 points need to be measured. In fact, more than 4 sets of positions are used to reduce errors, which makes Equation (9) an overdetermined system of equations. Set:  
\[X=\left(\begin{array}{cccc}n_{x}&o_{x}&a_{x}&T_{x}\\ n_{y}&o_{y}&a_{y}&T_{y}\\ n_{z}&o_{z}&a_{z}&T_{z}\\ 0&0&0&1\end{array}\right). \tag{10}\]  
The system of overdetermined equations can be represented as matrix:  
\[XA=B. \tag{11}\]  
In the equation, \(A\) is a coefficient matrix composed of elements \(T^{NL}\), and \(B\) is a matrix consisting of measured values \(P^{C}\).  
The least squares method is used to solve Equation (11):  
\[X=B(AA^{T})^{-1}A^{T}. \tag{12}\]From this, the transformation relation between the MICS and the RBCS can be obtained as \(X\), and then the robot end pose measured by measuring instrument and the pose of the robot end displayed in the robot controller can be unified under the same coordinate system for comparison.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 2 Data and Proposed Model
### Evaluation Indexes  
In order to test the performance of various models in TEC prediction, this paper uses two evaluation indexes to evaluate the model: the root mean square error (RMSE), the  
Figure 4: Schematic diagram of sample making process.  
Figure 5: The entire experimental process.  
R-square and the mean absolute percentage error (MAPE). The calculation formulas are shown in Equations (3)-(5):  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(ytrue_{i}-ypre_{i})^{2}} \tag{3}\]  
\[R-square=1-\frac{\sum_{i=1}^{n}(ytrue_{i}-ypre_{i})^{2}}{\sum_{i=1}^{n}(ytrue_{i} -ymean)^{2}} \tag{4}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{ypre_{i}-ytrue_{i}}{ytrue_{i}} \right|\times 100\% \tag{5}\]  
where \(n\) is the number of test samples, \(ytrue_{i}\) is the true value of test sample \(i\), \(ypre_{i}\) is the predicted value of test sample \(i\), and ymean is the average value of all the test samples. RMSE is used to describe the prediction error. The smaller the RMSE, the better the prediction performance of the model; the R-square is used to describe the fitting degree between the predicted value and the real value. The closer it is to 1, the better the fitting ability of the model to the TEC observation data; MAPE represents the error accuracy between the predicted and true values, the closer MAPE is to zero, the higher the prediction accuracy of the model.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results  
Before analyzing each modality, a brief state-of-the-art review is presented to identify current trends. In a recent study, [PERSON] et al. stressed that \"mixed reality is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies\" [58]. The impact of augmented reality on a user's satisfaction in numerous applications in the fields of engineering [59], archaeology [60], medicine [61], or education [62] cannot be questioned. Nevertheless, separating the physical world from the virtual has a significant impact on the user's immersion level. This enforced the development of mixed reality environments and upgraded augmented reality interactions to become more natural and include more aspects of the physical world. [PERSON] et al. in 2017 [63] proposed a framework to boost the user's immersion experience in augmented reality through material-aware interactions by training a neural network for material recognition. In 2018, [PERSON] et al. [64] mentioned that semantic understandings of the scene are necessary for mixed reality interactions to become realistic. The importance of structural information of physical objects is inextricably connected with proper augmentation and placement, but natural interactions between virtual and real objects require semantic knowledge. We have noticed a lack of publications related to material-aware MR interactions, and it seems that more research needs to be done in this field. Context-aware and material-aware interactions can lead to realistic physically-based sound propagation and rendering. In 2018 [65], [PERSON] et al., in their work on sonic interactions in virtual reality, concluded that the auditory outcomes of sound synthesis are not yet indistinguishable from real ones. Sonic interactions involve the techniques of sound propagation [66] and binaural rendering for binaural hearing [67] to provide immersive action and environmental sounds. An example includes the sound of the footsteps of a virtual man walking in a grass field. Through context-aware and material-aware interactions, the sound propagation algorithm would \"consider\" the material of the grass and the open area to generate the sound. The same material would sound differently inside a cave.
# Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol
## 2 Materials and Methods
### Multiple Instance Learning by Support Vector Machine  
The purpose of this research was to verify whether it is possible to train a model that distinguishes with a high probability between prostate cancer patients and the healthy ones. As input, the TW2-MRI scans with annotations were taken. This resulted in several images describing the same patient that should be analysed together. For the region of interest, a large number of textural features was calculated. Additionally, the data were supported with PIRADS information given per patient. The PIRADS protocol took values in the range of 1 to 5. However, values PIRADS < 4 described the healthy person and, thus, all values were assigned to one class (label 0), while PIRADS \(\geq\) 4 defined the PCa, and, also, both those values were classified together (label 1). Therefore, this problem can be treated as a binary classification. Because the data set was not balanced, there were 62 healthy patientsand 30 ill ones, the option for automatic balancing (calculating results by weighting) during training was set on in the classifier.  
In the dataset, as mentioned above, each patient was described with several images. However, in the case of changed tissue, it was not known whether it was visible only in one image or in more of them. Therefore, we could not train a classifier on a separate image from the series, because the change was not always visible while the PIRADS value was one for all images. That would confuse the model. Therefore, we used the multiple-instance approach to address this problem. In this case, a subject may be described by several images and it is sufficient that one of them represents the PCa tissue to state that the patient's PIRADS should be \(\geq\)4. The model was obtained using a support vector machine (SVM) as a binary classifier. The classification was performed on each image. However, the output was constructed considering all data describing one patient. This method was implemented using'mil' Python library.  
Since there were 92 samples (patients), a leave-one-out (LOO) methodology was applied to assure the generality of the results obtained. This method assumes training the dataset with N-1 samples and testing with the remaining one, and then repeating this experiment as many times as there are samples, selecting each time a different sample to the test set. In consequence, we used 91 samples for training and one for testing and repeated this experiment 92 times.
## 2 Background
### Data Center Network  
The Fat-Tree architecture is currently the most popular network architecture in a data center. Figure 1 shows a typical Fat-Tree architecture. Sixteen groups of servers (S1-S16) are linked to the edge switches (E1-E8), which is the bottom layer of Fat-Tree. The second layer is composed of eight aggregation switches (A1-A8), and the top layer is four core switches (C1-C4).  
An edge switch and a set of servers connected to it are called a pod. Therefore, the topology in Figure 1 contains eight pods. According to whether the traffic crosses the pod, the traffic in the data center can be divided into two types: Inter-pod flows and internal pod flows. The sender and receiver of an inter-pod flow belong to two different pods. Internal pod flow is the flow of communication between two servers under the same edge switch.  
Generally, the traffic collection function is deployed on the edge switches. Their independent work has made entire network traffic collection very inefficient. However, we have found that an inter-pod flow is forwarded by at least two edge switches and two aggregation switches. This means that packets belonging to one flow have at least four chances to be sampled ideally. So if we can allow each switch to run a single layer of software sampling, and the switches can exchange their own sampling information, then the overall network sampling effect should be improved [14].  
In a data center, there is a large number of optional paths from a source server to a destination server. These optional paths generally have the same cost metric in the routing table of the switch. Therefore, switches typically use ECMP mechanisms (IP 5-tuple hashes) to spread traffic across different optional paths.
## 2 Problem Definition  
The path planning problem involves solving the task of finding an optimal collision-free path between a starting point and the target point. It also considers the characteristics of the obstacle, for example, the size and shape of the object, or landmarks within a map.  
Figure 1 shows the top view illustration of an \(\mathbb{R}^{3}\) space.\(\mathbb{R}^{3}\) contains two objects, \(C_{1}^{obs}\) and \(C_{2}^{obs}\) that occupy the free space, each assumed as a landmark. A collision-free path will allow the vehicle to traverse pass these landmarks along the traversable trajectory from point \(S_{0}\) to \(S_{g}\).  
To address the aforementioned problem, continuous space is discretized. However, this will result in the shape of the object being constrained by the pixel that it occupies. For example, in Figure 2, the cell being marked as \(\times\) has blocked the direct path between the two points.  
By transforming the map, and discretizing it again, the occupied cell will yield a different shape compared to the original orientation. Figure 3 illustrates a map rotated at an angle of 90\({}^{\circ}\) clockwise. Note that object \(C_{2}^{obs}\) occupies a 2-by-2 cell, similar to its non-rotated counterpart in Figure 2, while the object \(C_{1}^{obs}\) has a different shape. The path between the point \(S_{0}\) to \(S_{g}\) is also not blocked by any cells.  
In a discretized space, the generated path may not be smooth. However, it is common for the path smoothing to be performed after a path is found [17].  
Figure 1: Landmarks within the map before being discretized.  
Figure 3: Discretized map with the transformation applied.  
During APF path planning, when the obstacles and the goal are in close proximity, the computation of the potential map might result in one global minimum (where the goal is located) and multiple local minima. Figure 4 illustrates the difference between local and global minimum.  
In this case, it is possible for the planner to become stuck between the local minima. Consider the following configuration in Figure 5 where this issue is depicted.  
Since the goal is close to both landmarks, the planner will fall into a local minimum as it heads towards them. As the planner is aware that it has yet to arrive at the true goal, it will keep on repeating the same previous subroutine. However, as it is unable to escape the area of the local minimum, the computation will keep repeating infinitely, therefore causing infinite loop problem.  
The modification of grid's shape by rotation such as shown in Figure 6, allows the possibility for the planner to escape the local minimum and break from the infinite loop.  
Figure 4: Illustration of local minimum and global minimum.  
Figure 5: The planner suffers from infinite-loop.  
Figure 6: Augmentation of Figure 5 rotated at \(71^{\circ}\) clockwise.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 4 Results and Discussion
### Performance Metrics on Test Set  
* R2 and Adjusted R2: Random Forest exhibits enhanced predictive capability, evidenced by significantly elevated R2 and Adjusted R2 values relative to Linear and Lasso models. Both Linear and Lasso regressions exhibit comparable performance; however, Lasso slightly underperforms Linear Regression due to the effects of regularization. On Figure 18, the blue bar represents the RFR, the orange bar represents the LASSO regression, and the green bar represents Linear regression.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **R2** & **Adjusted R2** & **MSE** & **RMSE** & **MAE** & **AIC** & **BIC** \\ \hline Linear Regression & 0.6199 & 0.6194 & 0.0303 & 0.1741 & 0.1376 & \(-\)23,063.5 & \(-\)22,970.5 \\ Random Forest Regression & 0.7608 & 0.7606 & 0.0294 & 0.1715 & 0.1320 & \(-\)34,355.5 & \(-\)34,284.8 \\ Lasso Regression & 0.6110 & 0.6108 & 0.0313 & 0.1769 & 0.1399 & \(-\)29,847.0 & \(-\)29,825.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of metrics of three regression models.  
Figure 18: Comparison of regression models by metrics (R2 and Adjusted R2).  
MSE, RMSE, and MAE: The Random Forest model exhibits significantly lower MSE, RMSE, and MAE, underscoring its enhanced accuracy and diminished prediction errors. Linear Regression and Lasso have similar performance, while Lasso demonstrates somewhat inferior outcomes due to its penalization of certain characteristics. The differences are minimal, as evidenced by the proximity of each bar's heights in Figure 19, but they have a significant impact.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 3 Problem Formulation
### Mathematical Formulation  
A VRP-PD instance consists of a single depot and \(N\) customer nodes, comprising \(N_{\mathcal{L}}\) delivery nodes and \(N_{B}\) pickup nodes. The objective is to minimize the total travel distance for a fleet of identical vehicles while satisfying the following constraints: (1) each customer node must be visited exactly once; (2) all customer demands must be fulfilled; (3) the vehicle's load must not exceed its maximum capacity; (4) delivery nodes must be served before pickup nodes within each route. Formally, we define the VRP-PD as a directed graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\), where \(\mathcal{V}=\{0,1,\cdots,N\}\) represent the set of all nodes and \(\mathcal{E}\) denotes the set of edges connecting them. Based on this representation, the mathematical formulation of VRP-PD is detailed as follows:  
\[\min\sum_{k=1}^{K}\sum_{i=0}^{N}\sum_{j=0}^{N}e_{ij}^{k}d_{ij}^{k}, \tag{1}\] \[\text{s.t.}\sum_{k=1}^{K}\sum_{i=0}^{N}e_{ij}^{k}=1,\;\forall k \in\mathcal{K},\;j\in\mathcal{V}^{\prime},\] (2) \[\sum_{i=0}^{N}e_{ij}^{k}=\sum_{i=1}^{N}e_{ji}^{k},\;\forall k\in \mathcal{K},\;j\in\mathcal{V}^{\prime},\] (3) \[\sum_{k=1}^{K}\sum_{i\in\mathcal{B}}\sum_{j\in\mathcal{L}}e_{ij}^ {k}=0,\] (4) \[\sum_{k=1}^{K}\sum_{i\in\mathcal{B}}e_{0j}^{k}=0,\] (5) \[\sum_{i=0}^{N}u_{ji}^{k}-\sum_{i=0}^{N}u_{ij}^{k}=q_{j},\;\forall j \
eq 0,\] (6) \[0\leq u_{ij}^{k}\leq e_{ij}^{k}\mathcal{D},\;\forall i,j\in \mathcal{V},\;k\in\mathcal{K},\] (7) \[e_{ij}^{k}\in\{0,1\},u_{ij}^{k}\geq 0,\;\forall i,j\in \mathcal{V},\;k\in\mathcal{K}, \tag{8}\]  
where the notations of parameters and decision variables are summarized in Table 1. The objective function, as shown in Equation (1), aims to minimize the total traversal cost across all vehicles. Constraints (2) and (3) guarantee that each node is visited merely once and that each route is assigned to a single vehicle. Constraints (4) and (5) enforce the service order, requiring that pickup nodes be visited only after their corresponding delivery nodes. In specific, constraint (4) prohibits a vehicle from visiting a pickup node before completing all deliveries within its route, while constraint (5) prevents a vehicle from traveling directly to a pickup node upon departing from the depot. Constraint (6) represents the load variation of the vehicle after serving different types of customer nodes. Constraint (7) enforces that the vehicle's freight load does not exceed its maximum capacity. For the improved VRP-PD variant, Equation (4) is omitted, relaxing the strict service order constraint and allowing greater flexibility in route planning.
# Method for Improving the Reliability of SRAM-Based PUF Using Convolution Operation
###### Abstract  
This paper introduces a novel and efficient physical unclonable function (PUF) extraction method for SRAM. The proposed one-layer convolution scheme is based on a convolution operation, which significantly enhances the reliability of the PUF. To further reduce the hardware resources, a lightweight solution is presented based on a one-layer convolution scheme at the cost of a higher redundancy coefficient and a larger range for the inter-chip Hamming distance (HD). Both the above schemes only use certain hardware resources in the initial stage and the hardware resources are automatically released after PUF verification. The two schemes were verified using SRAM cells in three stm32407 chips to output a 256-bit PUF response. The experimental results show that the one-layer convolution scheme required 8 KB SRAM, while the lightweight scheme used only 0.5 KB SRAM. The reliability of the one-layer convolution was found to be 100% when the redundancy coefficient was 0.08 and the inter-chip HD was 50.8073%. The reliability of the lightweight scheme was 100% and of the inter-chip HD was 50.195%.  
SRAM PUF; convolution; Hamming distance; Internet of Things (IoT) security Article
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 4 Common Preparation Methods
### Liquid Phase Method
#### 4.4.2 Precipitation Method  
The precipitation method is one of the most common methods for synthesizing nanomaterials. In the precipitation method, substances with different chemical components are mixed, and a precipitant is added to obtain a precursor precipitate. The precursor precipitate is then dried or calcined to produce the corresponding nanoparticles [201; 202; 203; 204; 205; 206]. When the particle diameter is about 1 \(\upmu\)m, a precipitate is formed. The particle size of the particles generally depends on the solubility of the precipitate and the supersaturation degree of the solution. The smaller the solubility of the precipitate is, the smaller the diameter of the particle produces, and the smaller the supersaturation degree of the solution is, the larger the diameter of the particle forms.  
The photocatalyst prepared by the precipitation method has good light absorption capability and can promote the separation of holes, and it can thereby improve photocatalytic performance. In the precipitation process, many of the heavy metals added do not readily react in solution and may introduce impurities into the solution, which limits the application of the precipitation method. During the operation of the precipitation method, the reaction temperature, time, pH value, ratio of reactants, titration rate, and other factors have a certain influence on the material preparation. The precipitation method shows the characteristics of low cost, simple process, and safety.
## 4 Experiment and Analysis  
### Experimental Data and Evaluation Indexes  
We use the dataset called BCAT to validate the performance of our model. The important information of BCAT is shown in Table 1. BCAT is a taxi parking lot departure statistical dataset from Beijing Capital International Airport. The statistics interface counts the number of taxi departures from the parking lot every half hour. BCAT records the departure data of the terminal taxi parking lot from 15 August 2022 to 31 December 2022, with a total of 6600 entries. We standardized the original data, and then decomposed the original data into modal components with different frequencies by using the VMD method. We verified several times that the center frequency of the last modal data tends to be smooth when the decomposition number K is equal to 5. As shown in Table 2, when the decomposition number is less than 5, the last central frequency decreases, indicating that there may be hidden information not unearthed; when the decomposition number is greater than 5, there are similarities between the modes, leading to modal duplication. Therefore, we set the decomposition number K to 5. Part of the original data, the data after standardization and the imfs after the variational modal decomposition are shown in Figure 6. We divided the BCAT dataset into training set, validation set, and test set with the ratio of 6:2:2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Time** &
\begin{tabular}{c} **Acquisition** \\ **Frequency** \\ \end{tabular} & **Total Number** & \multicolumn{3}{c}{**Splitting Strategy**} \\ \cline{3-6}  & & & & **Train Validation Test** \\ \hline \hline BACT & 15 August 2022–31 December 2022 & 30 min & 6600 & 0–3960 & 3960–5280 & 5280–6600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of BCAT.  
Figure 5: Encoder and decoder structure.  
We use mean square error (MSE) and mean absolute error (MAE) to verify the performance of the model, with smaller values of both metrics indicating better model prediction performance. These two indicators can be defined as:  
\[\begin{split}\text{MSE}=\frac{1}{n}\sum_{t=1}^{n}(y_{t}-\widehat{y }_{t})^{2},\\ \text{MAE}=\frac{1}{n}\sum_{t=1}^{n}|y_{t}-\widehat{y}_{t}|.\end{split} \tag{7}\]