esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
## 4 Comparison of 1-SMCI Learning Method and MPLE
### Numerical Comparison  
We generated \(N\) data points from a generative Boltzmann machine, \(P_{\text{BM}}(\mathbf{x}\mid\mathbf{w}^{\text{gen}})\) and then trained a learning Boltzmann machine of the same size as the generative Boltzmann machine using the generated data points. The coupling parameters in the generative Boltzmann machine, \(\mathbf{w}^{\text{gen}}\), were generated from a unique distribution, \(U[-\lambda,\lambda]\).  
First, we consider the case where the graph structures of the generative Boltzmann machine and the learning Boltzmann machine are the same: a \(4\times 4\) square grid, that is the case \"without\" model error. Figure 1(a) shows the mean absolute errors between the solutions to the MLE and the approximate methods (the MLPE and the 1-SMCI learning method), \(\sum_{(i,j)\in E}|w^{\text{MLE}}_{ij}-w^{\text{approx}}_{ij}|/|E|\), against \(N\). Here,we set \(\lambda=0.3\). Since the size of the Boltzmann machine used here is not large, we can obtain the solution to the MLE. We observe that the solutions to the two approximate methods converge to the solution to the MLE as \(N\) increases, and the 1-SMCI learning method is better than the MPLE as the approximation of the MLE. These results are consistent with the results obtained in [22] and the theoretical arguments in the previous section.  
Next, we consider the case in which the graph structure of the generative Boltzmann machine is fully connected with \(n=16\) and that in which the learning Boltzmann machine is again a \(4\times 4\) square grid, namely the case \"with\" model error. Thus, this case is completely outside the theoretical arguments in the previous section. Figure 2b shows the mean absolute errors between the solution to the MLE and that to the approximate methods against \(N\). Here, we set \(\lambda=0.2\). Unlike the above case, the solutions to the two approximate methods do not converge to the solution to the MLE because of the model error. The 1-SMCI learning method is again better than the MPLE as the approximation of the MLE in this case.  
By comparing Figure 2a,b, we observed that the 1-SMCI learning method in (b) is worse than in (a). The following reason can be considered. In Section 3.2, we replaced \(\mathcal{S}\), which is the sample points drawn from the Boltzmann machine, by \(\mathcal{D}\) in order to avoid the sampling cost. However, this replacement implies the assumption of the case \"without\" model error, and therefore, it is not justified in the case \"with\" model error.
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 3 Campaign Test and Data Description
### Raw Dataset Description  
During the campaign test, we collect two raw datasets that contain 219,814 traces and 2742 test cycles (sessions). The first dataset will be used to deeply analyze the services and to study the problem root cause (Sections 5.1 and 5.2). The second dataset is used to explore the possibility of predicting video metrics and user's QoE from the radio indicators (Section 5.3). In fact, the traces consist of a lot of more than 100 parameters subdivided into four categories in terms of data type: (i) categorical data, (ii) numerical data, (iii) temporal data (measurement instant), and (iv) spatial data (GPS coordinates, geographical area of the track, etc.). Figure 3 shows the distribution of the raw data measurements on the map in the east of the Ile-de-France region (France).  
Figure 3: A simple bitrate-based service status overview of the measured traces in the Ile-de-France region.  
Figure 2: Test cycle overview.  
Figure 3 is obtained from the \"5 Gmark\" user's dashboard at the end of the crowdsourcing phase. The color within the plot represents the service status (good, medium or bad) according only to the bit rate value. A detailed analysis based fall of the parameters is presented in the next section. Indeed, as the data are collected mainly using two transport modes (train and car), we clearly observe, in this figure, that the measurements are localized on roads, highways and rails crossed by trains/metros/buses. Having a look at the simple service status reported in the figure, it is obvious that most of the high bit rate connections are located in the city centers like \"Ivry sur Seine\", \"Mieaux\" and \"Paris\" in our region and most of the bad services are obtained on highways and rails like the rail line (SNCF Train line P) in the middle that contains a lot of red points.
# Comparison between Laser and Stamping without Die (SWD) for Micro Tapered Hole Forming
## 2 Finite Element Software DEFORM-3D  
### Computer Aided Design Geometric Modeling  
The workpiece and support base are both put in the simulation analysis in order to simulate the buckling effect generated by the designed mechanism. Meanwhile, in order to reduce the number of grids, the DEFORM-3D grid fining function is used for contacting area of the punch head and workpiece. The DEFORM-3D grid fining function for the punch and workpiece are as shown in Figure 2. The minimum edge length in grid fining is 0.001 mm.  
Since the mesh element number influences the simulation time and result, the grid convergence analysis is applied before simulation. Convergence analysis is based on the \(Z\)-axis loading. When the difference between this loading and previous loading is less than 0.1%, it means the convergence has been reached. The mesh element number is set as 50,000-150,000 with an increment of 10,000. The analysis result shows that when the mesh element number reaches 100,000, the load difference is less than 0.01%, and the result has converged. The mesh element number is 100,000 for the subsequent simulation.  
First, Solidworks was used to build the entire model for stamping, including the punch head, the workpiece and the support base, as shown in Figure 3. Since this study aims at the process of micro stamping without lower die, the lower die is replaced by a support base. Therefore, the effect of support base bearing distance on the workpiece must be considered. First, CAD software, Solidworks (Version 2015, Dassault Systemes SOLIDWORKS Corporation, Massachusetts, MA, USA, 2014), is used to draw the punch, workpieces, and lower die size for stamping. The geometry of stamping model is as shown in Figure 3. In this process of SWD, the deformation or buckling of workpiecees resulted from the support base is examined. The support base is an 8 mm \(\times\) 8 mm \(\cap\)-shaped structure. There are two types of workpieces, one is a 0.3 mm thick flat plate; the other is a 1 mm thick flat plate with a groove. The groove size is 6 mm \(\times\) 6 mm and 0.7 mm deep. Three punch head angles are used, 24\({}^{\circ}\), 54\({}^{\circ}\) and 90\({}^{\circ}\). The simulation parameters are listed as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Condition** \\ \hline Workpiece & Al6061 \\ Punch angle & 24\({}^{\circ}\), 54\({}^{\circ}\), 90\({}^{\circ}\) \\ Workpiece/Die & Plastic/Rigid \\ Stamping speed & 0.5 mm/min \\ Stamping depth (\(Z\)) & 0.38 mm \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of DEFORM-3D.  
Figure 2: Schematic diagram of workpiece with fine mesh elements (**a**) mesh refinement (**b**) partially enlarged mesh.
## 1 Introduction
### Contributions and Paper Organization  
In this article, we study the impact of different cyber attacks on the LFC system equipped with AC/HVDC transmission lines and bulk ESS. Two major types of data availability and integrity attacks--DoS and FDI attacks--are introduced and explored. The FDI attack scenario has been introduced in our previous work [32], where only the stationary FDI attack is considered. In this article, we move a step forward to include the dynamic (time-variant) FDI attack. We propose optimal strategies that the attacker can exploit to launch DoS or FDI attacks to corrupt the system stability. Our contributions are reflected through three aspects: (i) We have enabled to model the studied LFC system under DoS attacks as a switched linear system. Then theoretical results are obtained for switching strategies that an advanced attacker could exploit to make the targeted system unstable. (ii) The FDI attack scenario is extended to include the dynamic FDI attack. The optimal FDI attacks that can be stealthy and disruptive are characterized by optimization programs. Particularly, we introduce a type of dynamic FDI attack called zero-dynamics attack that can remain stealthy with respect to an arbitrary anomaly detector, if certain conditions are met. (iii) A comparison study is performed especially in simulation part to learn DoS and FDI attacks on different LFC models of normal AC system, AC/DC system and AC/DC system with virtual inertia. To be noted in the end, to the best of our knowledge, it is the first time that the DoS attack and the dynamic FDI attack are introduced and learned in the context of new LFC considering a hybrid AC/DC grid with virtual inertia.  
The structure of this article is as follows. In Section 2, we show how the conventional LFC system model is adapting to handle the modern scenario where controlled areas are interconnected with AC/HVDC links and there is emulated inertia by ESS. Section 3 focuses on DoS attacks which corrupt the availability of the wide-area measurements data. We enable modeling of the LFC system under DoS attacks as a switched linear system and the switching strategy to make the system unstable is proposed. The dynamic FDI attacks are introduced and studied in Section 4 where the optimal stealthy and disruptive FDI attacks are characterized by optimization programs. In Section 5, we provide simulation results and discussions, and conclusions are drawn in Section 6.
# A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems
## 2 The Sector Combination Optimization Algorithm to Obtain Initial Solutions
### Group the Sectors  
After obtaining the sector sequence, the sectors in the sector sequence need to be grouped and customers must be assigned to vehicles. Starting from the first sector of the obtained sector sequence, the sectors are grouped according to customer demand. The grouping rule is: if the total demand of customers in the current sector and its latter sector does not exceed the vehicle capacity, then the customers in the two sectors are allocated in the same vehicle, and the next sector is grouped similarly. Otherwise, the customer in the latter sector is added to the current sector in order; if the sum of the demand of one customer and the total customer demand of the current sector exceeds the vehicle capacity, skip this customer and select the next customer in the latter sector, and so on, maximizing the  
Figure 4: (**a**) One sector sequence result; (**b**) Illustration of the method of grouping the sectors; (**c**) The rearranged order using CWS method.  
Figure 3: Illustration of the threading-needle method.  
vehicle load without exceeding the vehicle capacity. The remaining customers in the latter sector are then grouped into one sector as the current sector, continuing to group its latter sector.  
As shown in Figure 4b, the total customer demand of the sector [12; 8] is 29.0, the total customer demand of the sector [4; 9; 13] is 27.0, and the sum of two sectors is 56.0, which does not exceed the vehicle capacity, 80.0. Therefore, the two sectors are grouped into one sector [12; 8; 4; 9; 13]. The total customer demand of the next sector [10; 7, 1; 3], 48.0, 56.0 + 48.0 = 104.0, exceeds the vehicle capacity, 80.0, so the customers in the sector [10; 7, 1; 3] are selected to add to the sector [12; 8; 4; 9; 13] in order. The result is that 10, 7, 1 have been added to the sector [12; 8; 4; 9; 13]; therefore, customers 12, 8, 4, 9 13, 10, 7, 1 are assigned to a vehicle and the sector [3] is left as the current sector, and so on. As a result, as shown in Figure 4b, the customers 12, 8, 4, 9, 13, 10, 7, 1 are assigned to the first vehicle, and the customers 3 and 6 are assigned to the second vehicle, and the customers 2, 11, 5 are assigned to the third vehicle.
# Monitoring Autonomous Mowers Operative Parameters on Low-Maintenance Warm-Season Turfgrass
## 2 Materials and Methods  
The trial was carried out at the Centre for Research on Turfgrass for Environment and Sports (CeRTES) of the Department of Agriculture, Food, and Environment of the University of Pisa (San Piero a Grado, Pisa, Italy--43\({}^{\circ}\)40\({}^{\prime}\) N, 10\({}^{\circ}\)19\({}^{\prime}\) E, 6 m a. s. l). The trial was performed on a permutadagrass (_Cynodon dactylion_\(\times\)_Cynodon transvaalensis_ cv 'Patriot') mature stand severely infested with dallisgrass (_Paspalum dilatatum_ Poir.). Both species' initial cover was visually estimated to be around 48% for Dermudagrass and 51% for dallisgrass. The turfgrass was established on a plot of calcaric fluvisol, which had a coarse silty texture (mixed, thermic, typic xerofluvents with a pH of 7.8 and 2.2% organic matter). Since April 2020, the whole area of 400 m\({}^{2}\) was managed with a Husqvarna Automower(r) 535 AWD (Husqvarna AB, Stockholm, Sweden), with a height of cut of 5 cm, 3 h per day and 3 days per week. Irrigation and fertilization were applied as necessary, and no weed or pest control were performed. The amount of water irrigation was provided only to supplement rainfall and to avoid any potential turfgrass stress. The weather conditions during the test period were collected from the local weather station, with a focus on September and October 2021, San Piero a Grado (PI) (Table 1). The workflow diagram of the research is showed in Figure 1.
## 1 Introduction
In [19], the authors developed a classification system able to distinguish five types of physical activity, such as rest, daily activities, living, running, biking, and resistance training, along with models to estimate the energy expenditure (EE) associated with diabetes therapy. Deep neural networks using the long-short-term-memory (LSTM) architecture had 94.8% classification accuracy, while various other ML techniques had varying success rates in classifying the five different PAs. In [17], PA (sedentary, treadmill running, and stationary bike) and APS (non-stress state, mental stress, and emotional anxiety) were simultaneously detected and discriminated using various classical ML algorithms. Accuracy scores over 99% for detecting PA and 92% for discriminating APS were achieved for the concurrent presence of PA and APS. Clinical data collected using the Empatica E4 wristband [21] (ACC, BVP, HR, GSR, ST) were used to assess the performance of various ML methods (Figure 1). PA categories included sedentary state, treadmill running, and stationary bicycling, while APS events included non-stress states, mental stress, and emotional anxiety. Motivated by [17; 19], this paper focuses on alternative techniques for the selection of the most informative set of features, balancing the data for various groups of activities with different data lengths, and the use of deep neural networks (NN) for detection and classification of PA and APS. The performance of principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and sequential forward selection (SFS) in identifying the most informative features is investigated. Then, the effects of four different approaches used for handling different data lengths of various classes by up- and down-sampling (the Adaptive Synthetic Sampling (ADASYN), random oversampling and downsampling, and cost-sensitive/weighted balancing techniques) are compared. Lastly,we explored the performance of a neural network model using the LSTM architecture for each combination of feature selection and data balancing technique. The remainder of the paper is structured as follows. In the Materials and Methods section, the data sources used in this work are described along with the techniques for generating features from these data, selecting the most informative features, and balancing the effects of the different data lengths for various groups of activities, and the deep neural networks (CNN-LSTM) used for detecting and classifying various types of PA and APS events are presented. The Results section reports the performance of the algorithms developed when PA or APS occur individually or concurrently. The Discussion section presents the impact of the proposed techniques on the treatment of chronic disease and assesses the performance of various alternative techniques tested to carry out the sequence of activities involving signal processing, data rearrangement, PA and APS detection, and classification of the results. The Conclusions section summarizes the results and the potential benefits of the proposed approach for the treatment of diabetes.
## 6 Applications of RF Energy Harvesting
### Industrial IoT  
In industrial environments, the integration of RFEH into remote sensors has significantly advanced predictive maintenance strategies and smart factory operations. Wirelesssensors equipped with RF harvesters can continuously monitor key parameters such as machine vibration, temperature, humidity, environmental conditions, and structural integrity without relying on wired connections or frequent battery replacements. This capability enables real-time data collection and analysis, resulting in improved operational efficiency, reduced downtime, and cost savings [98]. For instance, a study demonstrated the deployment of RF-powered low-energy sensor nodes for predictive maintenance in electromagnetically harsh industrial environments. The system utilized 2.45 GHz RF energy to power sensor nodes monitoring machinery conditions, effectively eliminating the need for battery replacements and enhancing maintenance efficiency [53]. Integrating RF energy harvesters into sensors placed on rotating machinery or in inaccessible locations facilitates continuous health monitoring without the logistical challenges associated with traditional battery-powered systems. This proactive approach supports predictive maintenance strategies, minimizing the risk of sudden equipment failures and the costly unplanned downtime that accompanies them [99]. Moreover, EnOcean's energy-harvesting technology has been adopted in industrial automation to create maintenance-free wireless sensors that operate without batteries. These sensors harvest energy from their environment, drawing on sources such as motion, light, or temperature differences, and are used in various applications, including the monitoring of equipment status and environmental conditions in industrial settings [89]. Recent research demonstrated that RF-powered predictive maintenance systems are capable of detecting mechanical faults approximately 25% earlier than conventional battery-operated sensors, leading to a substantial reduction in equipment downtime and maintenance costs [100]. In support of this, a comprehensive survey by [PERSON] et al. (2022) highlighted the performance determinants of RF energy-harvesting systems in industrial applications, emphasizing their role in enhancing predictive maintenance by providing continuous power to sensor networks, thereby improving fault detection and reducing maintenance costs [13]. Moreover, the scalability of RFEH technologies enables the deployment of thousands of wireless sensors across expansive industrial sites, paving the way for the realization of Industry 4.0, where machine-to-machine communication, real-time monitoring, and advanced data analytics drive unprecedented levels of industrial automation and efficiency.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 4 Sensitivity Analysis  
In this section, the global sensitivity analysis (SA) of the model output is briefly described. This allows for an evaluation of the relative importance of inputs when they are varied generously, that is, when their uncertainty is acknowledged over a wide range. The most common approach to global SA is the analysis of variance of the model response originally proposed by Reference [33]. This approach, known also as Variance-based, can capture the fraction of the model's response variance explained by a models input on its own or by a group of model inputs. In addition, this approach can also provide the total contribution to the output variance of a given input, that is its marginal contribution and its cooperative contribution. There are several methods, as discussed in References [33; 34; 35], to compute this SA approach. In this paper, the GP-based SA method [36; 37], which is computationally more efficient, will be adopted and used to compute the sensitivity measures.  
In order to carry out the SA using the methods proposed in References [36; 37], we need to examine how a function of interest, \(f(\mathbf{\mathsf{x}})\), depends on its input variables. For the case of this study, \(f\) will typically be the function that computes, for example, collision as a function of a vector of input parameters (speed of ego car, speed of pedestrian stepping onto street, distance between pedestrian and ego car at the time the pedestrian steps out, sensor range, and horizontal field-of-view of sensor) as described and illustrated in Section 6.1.  
We first need to introduce some important notations. We denote a \(d\)-dimensional random vector as \(\mathbf{\mathsf{X}}=(X_{1},\ldots,X_{d})\), where \(X_{i}\) is the \(i_{\text{th}}\) element of \(\mathbf{\mathsf{X}}\), the sub-vector \((X_{i},X_{j})\) is shown by \(\mathbf{\mathsf{X}}_{i,j}\). In general, if \(p\) is a set of indices, then \(\mathbf{\mathsf{X}}_{p}\) can be written for the sub-vector of \(\mathbf{\mathsf{X}}\) whose elements have those indices. \(\mathbf{\mathsf{X}}_{-i}\) is defined as the sub-vector of \(\mathbf{\mathsf{X}}\) containing all elements except \(X_{i}\). Similarly, \(\mathbf{\mathsf{x}}=(x_{1},\ldots,x_{d})\) denotes the corresponding observed random vector \(\mathbf{\mathsf{X}}\). In this study, \(\mathbf{\mathsf{X}}\) is considered as an input vector consisting of all input parameters.  
By considering the sensitivity of the input parameters in \(\mathbf{\mathsf{X}}\), we can determine which input parameters are most influential in inducing uncertainty in \(f\). The main effects as the function over the input range, which are introduced in Appendix A, provide a visual tool to investigate how the logical scenarios respond to variations in each individual input parameter. In the next section, the variance-based SA indices, including first-order and total effects indices will be introduced.