esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Communication-Motion Planning in Networked Robotic Systems
## 2 Conventional Relay-Assisted Robot System
### Joint Communication-Motion Planning: Decide  
#### 2.3.1 Problem Formulation  
After the performance assessment, the JCMP aims at minimizing the total energy consumption. Considering that the surveillance routes are predefined, the motion costs of the sensing robots may not be optimized. Hence, in each step, the energy consumption \(E\) may contain three parts: the motion and communication costs of the relay robot, and the communication costs of the sensing robots, which can be formulated as an optimization problem given by Equation (6).  
\[\begin{array}{c}\min\limits_{x_{\text{R}},\text{R},\text{R},\text{R},\text{ R}}J=\sum\limits_{k=1}^{K}P_{\text{s},\text{k}}t_{s}+P_{\text{r},\text{k}}t_{ \text{r}}+\omega l_{\text{R}}(x_{\text{r},k},x_{\text{r},\text{k}-1})\\ \text{s.t.}\text{~{}~{}}I_{\text{R}}(x_{\text{r},k},x_{\text{r},\text{k}-1}) \leq v_{\text{max}}T,\,\forall k\in[1,K]\\ p_{snt}^{\text{e2}}(P_{\text{s},\text{k}},P_{\text{r},\text{k}},x_{\text{r},k}) \leq p_{ub},\,\forall k\in[1,K]\end{array} \tag{6}\]  
In Equation (6), \(x_{\text{r},k}\), \(P_{\text{s},\text{k}}\) and \(P_{\text{r},\text{k}}\) are the optimization variables. \(x_{\text{r},k}\) represents the position of the relay robot at the end of the \(k\)-th time step. \(P_{\text{s},\text{k}}\) and \(P_{\text{r},\text{k}}\) are the transmit power of the sensing robot and of the relay robot during their individual transmission duration, respectively. It is noted that the transmission during each time step initiatesafter the robots finish the movements, becoming stationary and synchronized. \(t_{\rm s}\) and \(t_{\rm r}\) represent the time duration of the sensing robot and of the relay robot for transmitting \(T\eta\) bits of data samples, respectively, which are given by \(t_{s}=T\eta/BR_{\rm s}\) and \(t_{r}=T\eta/BR_{\rm r}\), where \({\rm Rs}\) and \({\rm R}\) are the spectral efficiency (bit/s/Hz) of the sensing robot and of the relay robot. In addition, \(\omega b_{\rm R}(x_{\rm r,k},x_{\rm r,k-1})\) is the motion energy consumption of the relay robot, where \(l_{\rm R}(x_{\rm r,k},x_{\rm r,k-1})=\|x_{\rm r,k}-x_{\rm r,k-1}\|\) is the distance traveled by the relay robot from the current position \(x_{\rm r,k-1}\) to the goal position \(x_{\rm r,k}\) in the \(k\)-th time step. The coefficient \(\omega\) is expressed as \(\omega=\kappa_{1}+\kappa_{2}/\
u_{\rm max}\). Finally, \(p_{std}^{\rm 2e}(P_{\rm s,k},P_{\rm r,k},x_{\rm r,k})\) is the required end-to-end PER of the two-hop link from the sensing robot to the base station via the relay robot, and \(p_{\rm ub}\) is the upper bound.
## 2 Experimental and Model Configuration
A detailed description of the various configurations of the MLPNN is summarized in Table 1.  
The RBFNN, whose architecture was a form of feed-forward neural network that was robust in addressing issues with function [23], universal approximation and quicker learning pace of RBFNN set them apart from other neural networks. The RBFNN consisted of the input layer, the hidden layer, and the output layer [24]. Similar to the MLPNN, the RBFNN configuration involved the selection of a variable, the partitioning of the dataset, and the setting of the architecture. The dataset was split into two proportions for: training (70%) and testing (30%). During the training, once the computed error reaches the appropriate values or the required number of training iterations has been reached, the RBFNN model's training will be stopped. Two configurations of the RBFNN, which utilized the ordinary radial basis activation function and the standard radial basis activation function, were employed in this study. Equation (1) was used to determine the output of  
Figure 2: The architecture of a multilayer perceptron and radial basis function neural networks. the \(i\)th activation function in the hidden layer of the network as a function of the distance between the input vectors and the center [25]. \[\varnothing_{i}(\|x-c_{i}\|)=\exp\left(-\frac{\|x-c_{i}\|^{2}}{2\sigma_{j}^{2}}\right) \tag{1}\]  
where the symbol \(\|\ \|\) denotes the Euclidean norms, \(c_{i}\) denotes the center of the hidden neurons, \(\sigma\) denotes the width of the hidden neuron [26]. The RBFNN was often trained in using two steps. The first step entailed the determination of centers and widths using a pre-defined algorithm. The second step involved the determination of interconnected weights between the hidden layer and the output layer by minimizing the root mean square over the entire data set. The coefficient of determination (R\({}^{2}\)), root mean squared errors (RMSE), and the sum of squares error (SSE) were used to model performance. The level of importance (\(\theta_{ik}\)) analysis using the [PERSON] modified algorithm (Equation (2) was performed on the best model to determine to what extent the input variables influence the predicted model output [27]. \[\theta_{ik}=\ \frac{\sum_{j=1}^{L}\left(\frac{w_{ij}w_{ij}}{\sum_{l=1}^{N}w_{ ij}}\right)}{\sum_{i=1}^{N}\left(\sum_{j=1}^{l}\left(\frac{w_{ij}w_{ij}}{\sum_{l=1}^{ N}w_{ij}}\right)\right)} \tag{2}\]  
where \(i\) and \(k\) are the input and output vectors, respectively, the sum of the weights linking the input layer (\(i\)) and the \(j\) neuron can be represented as \(\sum_{i=1}^{N}w_{ij}\), the total number of vectors input into the model is represented as \(N\), and the total number of neurons in the hidden layer is denoted as \(L\). The weights that represent the connection between \(j\) neuron and the \(k\) input are denoted as \(wv_{rj}\).
# Towards Understanding Clustering Problems and Algorithms: An Instance Space Analysis
## 3 Methodology of Instance Space Analysis for Clustering
### Clustering Instances and Algorithms  
The first stage of the experiments used 380 synthetic clustering datasets from the literature to compose the instance set \(I\). A total of 80 Gaussian datasets of low dimension and 80 ellipsoidal datasets of high dimension were collected from [PERSON] [25]. Another 220 datasets of various shapes and different number of attributes, clusters and instances from different sources were included [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. In the selected datasets, the number of examples ranges from 100 to 5000, the number of attributes ranges from 2 to 1024 and the number of embedded clusters ranges from 1 to 40. Although we could include some labeled classification datasets into the analysis, as done in previous work [22; 23], their structure is not always suitable for clustering purposes. For instance, there may be classes spanning different regions or manifolds of the input space. Additionally, a dataset might present more than one valid structure, which is not reflected in the assigned data labels. By employing synthetic datasets dedicated to the evaluation of clustering problems, we are able to circumvent these issues and build an instance space more suitable to clustering problems.  
For composing the set of algorithms \(A\), we analyzed 10 different partitional and hierarchical clustering algorithms widely used in the literature: K-Means (KME), K-Medians (KMD), K-Medoids (KMO), Mini-Batch K-Means (MBK), Fuzzy C-Means (FCM), Single Linkage (SL), Complete Linkage (CL), Average Linkage (AL), Closest Centroid (CC) and Ward's Method (WM). The algorithms were executed using the implementations contained in the following R packages: stats, Gmedian, kmed, ClusterR and e1071. Appendix B presents the parameters used for each of the algorithms.
## 2 Online Measurement System Overall Architecture
### Detecting System Fixed Device  
The fixing devices of this measurement system mainly include a camera cooling system, universal joint, fixed bracket, camera fixing board, and shock absorption damping. Since both the inlet and outlet of the straightening machine are equipped with centering devices, the measurement system introduced in this article has a certain inclination angle when measuring the cross-cutting board. Therefore, the installation of the measurement system has some characteristics. Firstly, the industrial camera fixing plate is used to fix the camera inside the camera cooling system device. At the same time, the camera cooling system device is fixed with the universal joint using screws. Secondly, the universal joint is fixed on the fixing bracket with bolts, and the fixing bracket is fixed on the straightener platform from above with bolts. Bolts are reinforced by spot welding. In addition, shock absorption damping is installed at all fastening points to reduce vibrations that may occur on site. Furthermore, the installation of the measurement system on the production site can be achieved, and the measurement system can be adjusted at any angle through universal joints.
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 3 Methods for Identification of _Thiothrix_ in Wastewater Treatment Plants
### Nitrogen Removal
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & \multicolumn{4}{c}{**Dissimilatory Nitrate Reduction**} & \multicolumn{4}{c}{**Assimilatory**} \\  & & **NO\({}_{3}\)\({}^{-}\)\(\rightarrow\) NO\({}_{2}\)\({}^{-}\)** & **NO\({}_{2}\)\({}^{-}\)\(\rightarrow\) NO** & **NO\({}\rightarrow\) N\({}_{2}\)O** & **N\({}_{2}\)O\({}^{-}\)\(\rightarrow\) N\({}_{2}\)** & **NO\({}_{3}\)\({}^{-}\)\(\rightarrow\) NO\({}_{2}\)\({}^{-}\)\(\rightarrow\) N\({}_{4}\)** \\ \hline MAG _Thiothrix_ sp. 207 & _nurGHil_, _nurS_ & _norBC_ & _nosZ_ & _nasA_, _nasD_ & _nirBD_ \\ MAG _Thiothrix_ sp. STA 22 & _nurGHil_ & _nirS_ & _norBC_ & _nosZ_ & _nasA_, _nasD_ & _nirBD_ \\ _T. litorhals_ AS\({}^{\Gamma}\) & _nurGHil_ & _nirS_ & _norBC_ & - & _nasA_, _nasD_ & _nirBD_ \\ _T. fructosaurus_ QI\({}^{\Gamma}\) & _nurGHil_ & _nirS_ & _norBC_ & - & _nasA_, _nasD_ & _nirBD_ \\ _T. calidfonts_ GI\({}^{\Gamma}\) & _nurGHil_ & _nirS_ & _norBC_ & - & _nasA_, _nasD_ & _nirBD_ \\ _T. iniothgrasshadi_ CT\({}^{\Gamma}\) & _nurGHil_ & _nirS_ & _norBC_ & - & _nasA_, _nasD_ & _nirBD_ \\ _T. iniothria_ AI\({}^{\Gamma}\) & _nurGHil_ & _nirS_ & _norBC_ & - & _nasA_, _nasD_ & _nirBD_ \\ _Ca. Thiothrix singaporensis’ SSD2 & _nurGHil_ & _nirS_ & _norBC_ & - & _nasA_, _nasD_ & _nirBD_ \\ _Ca. Thiothrix moscovensis’ RT & _nurGHil_ & - & _norBC_ & - & _nasA_, _nasD_ & _nirBD_ \\ _Ca. Thiothrix pulet_ GSK-02 & _nurGHil_ & - & _norBC_ & - & - & - \\ _T. subterman_ Ku-S\({}^{\Gamma}\) & _nurGHil_ & - & - & - & _nasA_, _nasD_ & _nirBD_ \\ _T. lucustris_ BL\({}^{\Gamma}\) & _nurGHil_ & - & - & - & _nasA_, _nasD_ & _nirBD_ \\ _Ca. Thiothrix sulfatifontis’ KT & - & - & - & - & _nasA_, _nasD_ & _nirBD_ \\ _T. nircu_ JP\({}^{\Gamma}\) & _nurAB_ & - & - & - & _nasA_, _nasD_ & _nirBD_ \\ _Ca. Thiothrix annitrata’ A52 & - & - & - & - & - & - \\ _Ca. Thiothrix namsaraevi\({}^{\Gamma}\) & _nurGHil_ & - & - & - & _nasA_ & _nirBD_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of nitrogen metabolism genes in _Thiothrix_ genomes. Genes: _narG_, membrane-bound nitrate reductase; _napAB_, periplasmic nitrate reductase; _nasA_, assimilatory nitrate reductase; _nasB_, assimilatory nitrite reductase; _nirS_, dissimilatory nitrite reductase; _nirBD_, assimilatory nitrite reductase; _norBC_, nitric oxide reductase; _nosZ_, nitric oxide reductase. Table was based on [PERSON] et al. [5] and [PERSON] et al. [84] under the terms of the Creative Commons Attribution 4.0 International License. [PERSON] et al. found a large number of bacteria of the _Thiothrix_ morphotype in biofilm reactors with simultaneous nitrification-denitrification processes, where pomelo peel, which can biodegrade to form sulfide, served as a carrier [123]. This explains the appearance of dominant representatives of the _Thiothrix_ morphotype in the system and the presence of denitrification associated with the oxidation of reduced sulfur compounds.
## 3 Experiments
### Evaluation Metric  
This section introduces the commonly used evaluation metrics in depth completion, mainly including root mean square error (RMSE) that quantifies the overall level of difference between predicted and true values. The physical significance of RMSE can be understood as the average distance between each predicted value and its corresponding true value, mean absolute error (MAE), that quantifies the average level of difference between predicted and true values. The physical significance of MAE can be understood as the average absolute distance between each predicted value and its corresponding true value, inverse-depth root-mean-squared error (iRMSE) and inverse-depth mean absolute error (iMAE). These metrics are used to measure the error between the predicted depth map and the ground truth, and thus to evaluate the performance of the depth completion algorithm.  
Among them, the KITTI benchmark is using RMSE as the main basis for the ranking of depth completion. Under the circumstances of calculating model error sensitivities or data assimilation applications, MAEs are definitely not preferred over RMSEs. The values of iRMSE and iMAE are affected by the average of the true and predicted values, so there may be a risk of exaggerating or minimizing the actual performance of the model.  
RMSE is one of the main evaluation metrics and is calculated as shown in the following equation:  
\[RMSE(d-\hat{d})=\sqrt{\frac{1}{m}\sum_{i-1}^{m}(d_{i}-\hat{d_{i}})} \tag{17}\]  
where \(d\) denotes the predicted depth map, \(\hat{d}\) the ground truth of the depth map, \(i\) denotes the depth of the \(i\)-th pixel and \(m\) denotes the total number of pixels. A lower value of RMSE where the error between the predicted depth and the ground truth is smaller and the performance of depth completion is better.  
In addition to the RMSE, there are other evaluation metrics that can be used to assess the depth completion algorithm. The MAE is one of them and is calculated as shown below:  
\[MAE(d-\hat{d})=\frac{1}{m}\sum_{i-1}^{m}\Bigl{|}d_{i}-\hat{d_{i}}\Bigr{|} \tag{18}\]  
The iRMSE and the inverse-depth mean absolute error iMAE are metrics that are evaluated against the inverse depth and are calculated as follows, respectively:  
\[iRMSE(d-\hat{d})=\sqrt{\frac{1}{m}\sum_{i-1}^{m}(\frac{1}{\hat{d_{i}}}-\frac{ 1}{\hat{d_{i}}})} \tag{19}\]  
\[iMAE(d-\hat{d})=\frac{1}{m}\sum_{i-1}^{m}\Bigl{|}\frac{1}{\hat{d_{i}}}-\frac{ 1}{\hat{d_{i}}}\Bigr{|} \tag{20}\]  
For these metrics, lower values indicate better performance results for depth completion.
## 6 Conclusions  
Trajectory-based message delivery with the help of roadside units have been considered as an efficient message dissemination method on VANETs under the assumption that message senders know the driving routes of message receiver vehicles. However, from a security viewpoint of location privacy, users of VANETs want to limit sharing of their driving locations to the desired message senders by themselves to prevent the location information from being illegitimately exposed to others. Therefore, in this paper, we propose a secure location sharing system in which vehicles control what roadside units can access vehicles driving locations on their own decision for trajectory-based message delivery services. To effectively share vehicle's trajectory data with the socialspot roadside units designated by the vehicle on the cloud, we devised a secure trajectory data sharing mechanism by taking advantage of a certificateless proxy re-encryption scheme in which the role of maintaining and distributing encrypted trajectory data can be delegated to a semi-trusted service manager but the access rights to the trajectory data are controlled by vehicles themselves. Therefore, even though vehicles trajectory data are managed by a service manager on the cloud, the trajectory data are hidden from not only unauthorized entities but also the service manager. In addition, whenever vehicles change their preferred driving routes, vehicles can efficiently revoke the access rights of unwanted roadside units to the updated trajectory data just by updating re-encryption keys without involvement of the service manager and roadside units. Consequently, we can design a more flexible and self-controllable secure trajectory data sharing system on VANETs.  
[PERSON] and [PERSON] have been involved in all stages of this work including protocol design, validation, and writing & editing the manuscript; [PERSON]-[PERSON] performed simulation and experiment analysis; [PERSON]-[PERSON] supervised the work.  
This work was partially supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2015-0-00403) supervised by the IITP (Institute for Information & communications Technology Promotion), and by the IITP grant funded by the Korea government (MSIT) (No. 2017-0-00156, The Development of a Secure Framework and Evaluation Method for Blockchain).  
The authors declare no conflict of interest.
# Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms
## 3 Methodologies
### The Improved ABC Algorithms
#### 3.2.1 Paralleled Full-Dimensional ABC Algorithm  
Different from randomly updating only one dimension in traditional ABC algorithm, the full-dimensional search strategy (fdABC) is introduced in this section, where all dimensions of the solution are traversed with Equation (7) and the optimal dimension is kept for further exploration, therefore the search could be extended and the possibility of obtaining the optimal solution will be improved, but the cost of time will increase inevitably. In order to improve its efficiency, a master-slave parallel mode [27] is applied to the most time-consuming stages, such as the calculation of initial fitness values and the updating procedure in the employed bee phase, in which the population is divided into different parts and the repeated calculations are executed in multi-core processor. Thus the corresponding tasks could be finished in parallel, meanwhile the process of the ABC algorithm will not be affected. The following are the main steps of PfdABC algorithm:  
Step 1: Initialization. The parameters of ABC, including the food source, the population, and the maximum number of iterations are initialized, and the initial population is divided into different parts. Then, each of them is evaluated in different CPU cores.  
Step 2: Employed bee phase. A full-dimensional search is performed in this phase. All the employed bees are equally distributed into different CPU cores, and the neighborhood searches using Equation (7) are executed in all dimensions, where \(k\) varies from zero to the number of dimensions. If the fitness value of the updated solution is better than the previous one, it will be preserved for further searches.  
Step 3: Onlooker bee phase. Onlooker bee selects a food source with Equation (8) and executes a full-dimensional search around the selected solution.  
Step 4: Scout bee phase. If the number of iteration reaches the threshold and there is no better solution, a new solution will be generated randomly.  
Step 5: Record the global optimal solution obtained so far and jump to step 2 for further exploration until reaching the maximum iteration number.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 3 CR-WSN Characteristics and Architecture
### Node Deployment
#### 3.3.1 Deterministic Deployment  
This is also called structured deployment, or regular deployment. This type of deployment is done in an indoor environment or in a small and favorable environment. All types of CR-WSNs (see Section 3.2) can be considered for this type of deployment, which provides the best possible coverage, and allows easier clustering. Common types of deterministic deployment strategies are the triangular grid, the rectangular grid, and the hexagonal grid. The coverage area of each node in triangular grids, rectangular grids, and hexagonal grids are \(\left(\sqrt{3}/4\right)\)\(R^{2}\), \(R^{2}\), and \(\left(3\sqrt{3}/4\right)\)\(R^{2}\), respectively, where R is the distance between any adjacent nodes. The total number of nodes required to cover a given area, A, in a triangular grid is \(\left\lceil\frac{4A}{\sqrt{3R^{2}}}\right\rceil\), in a rectangular grid, is \(\left\lceil\frac{A}{R^{2}}\right\rceil\), and in a hexagonal grid, is \(\left\lceil\frac{4A}{3\sqrt{3R^{2}}}\right\rceil\). Deterministic deployment is recommended for a fixed location where fewer sensors are deployed. It is easy to maintain, and has lower maintenance costs. For uninterrupted performance, multiple CHs should be deployed.
# A TEE-Based Federated Privacy Protection Method: Proposal and Implementation
## 1 Introduction
Methods based on cryptography, such as multi-party secure computation, are impeded by communication consumption and, therefore, affect the performance of federated learning. Methods based on TEE are limited by the size of hardware memory and the lack of support for large memory applications and distributed computing. In order to solve the problem of attacks in federated learning, this study proposes a privacy-enhancing method to implement a common federated learning framework based on TEE for the imperfect privacy protection of existing schemes, such as large losses in accuracy and training efficiency. Specifically, the main contributions of this study are as follows:  
1. The proposed method has obvious advantages over the existing privacy inclusion methods. The proposed method has less accuracy loss than the method based on differential privacy and less performance loss than the method based on secure multi-party computation. Conventional privacy protection methods based on TEE do not consider the protection of the overall longitudinal federated model training process. The method proposed in this study considers the end-to-end privacy protection in the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attack Name** & **Attack Pattern** & **Attack Process** & **White/Black Box** & **Attack Target** \\ \hline \multirow{3}{*}{Model inversion attack} & For a model with a simple structure, dynamic analysis is used or the similarity between samples is calculated. & & & \\ \hline \multirow{3}{*}{Inference attack} & Trains a factor or target attribute binary classifier. & & & & \\ \cline{1-1}  & A backdoor model is trained by poisoning samples and so on and implanting them into the global model. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of the main attacks on FL. federated learning process, and it can cover horizontal federated learning and vertical federated learning. 2. The proposed method can effectively resist gradient leakage, model inversion, poisoning, and backdoor attacks with less performance loss under the premise of ensuring accuracy. 3. The method proposed in this study is based on Intel SGX technology, which could realize the migration of the general federated machine learning framework and end-to-end privacy protection for the complete federated learning training process. 4. The proposed method enhances the privacy protection ability for the general federated machine learning framework; therefore, the model developer does not need to perceive the privacy protection technology, which reduces the difficulty of use for developers. 5. The proposed method supports cloud-native architecture deployment and is suitable for deployment in the cloud environment.