esa-sceva/satcom-chunk-collection huggingface dataset:
# Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
###### Abstract  
The purpose of this article is to investigate a suitable path planning algorithm for a multi-agent-based Plug & Produce system that can run online during manufacturing. This is needed since in such systems, resources can move around frequently, making it hard to manually create robot paths. To find a suitable algorithm and verify that it can be used online in a Plug & Produce system, a comparative study between various existing sampling-based path planning algorithms was conducted. Much research exists on path planning carried out offline; however, not so much is performed in online path planning. The specific requirements for Plug & Produce are to generate a path fast enough to eliminate manufacturing delays, to make the path energy efficient, and that it run fast enough to complete the task. The paths are generated in a simulation environment and the generated paths are tested for robot configuration errors and errors due to the target being out of reach. The error-free generated paths are then tested on an industrial test bed environment, and the energy consumed by each path was measured and validated with an energy meter. The results show that all the implemented optimal sampling-based algorithms can be used for some scenarios, but that adaptive RRT and adaptive RRT* are more suitable for online applications in multi-agent systems (MAS) due to a faster generation of paths, even though the environment has more constraints. For each generated path the computational time of the algorithm, move-along time and energy consumed are measured, evaluated, compared, and presented in the article.  
adaptive RRT*; path planning; Plug & Produce; PRM; RRT*; sampling-based algorithms +
Footnote †: journal: Applied sciences
# Inferring Urban Social Networks from Publicly Available Data
## 2 Materials and Methods
### Friendship Edges
In our model, this is achieved by imposing that all vertices belonging to the same tile are at distance \(d_{\min}=\frac{l}{2}>0\) and that (with a slight abuse of notation) \(D_{\max}=D(d_{\min})<+\infty\). On the other hand, to prevent the occurrence of vertices with expected degree \(\approx\)0--at least, in a sufficiently large network--\(f_{u}\) must be bounded away from 0. Summing up, without loss of generality, we may assume that \(D(u,v)\in(0,1]\) for all \(u,v\in V\), that \(f_{u}\) is drawn from a probability distribution with support \([1,+\infty)\), and that \(\sum_{i\leq j}s_{i,j}=1\). The latter condition suggests to interpret the coefficients \(s_{i,j}\) as the probability that a randomly chosen edge of the graph connects groups \(i\) and \(j\). Selected Configurations  
Although our network model does not impose restrictions upon the choice of \(f_{u}\) and of the function \(D\), we believe that the following configuration is of special interest:  
\[ \begin{cases}f_{u}\sim 1+\mathcal{LN}(\ln(2),0.25)\\ D(u,v)=d(u,v)^{-\beta}\end{cases} \quad\text{ with }\beta\in\{0.5,2\}\,, \tag{8}\]  
where, throughout this paper, \(\mathcal{LN}(\lambda,\sigma^{2})\) denotes a Lognormal distribution such that \(\lambda\) and \(\sigma^{2}\) are the mean and variance of the associated Normal distribution. The choice for the distribution of \(f_{u}\) in (8) is motivated by the observation that the degree distribution of our friendship graph will, at least partially, follow the fitness distribution. Lognormally distributed data occur across different domains [73], and recent work suggests that the degree distribution of real-world social networks makes no exception [17; 19]. In particular, a Lognormal degree distribution would fit the intuition that only a few people have very few social links. The specific parameters have been chosen to enforce limited skewness and variance, so as to guarantee that most vertices will have a degree close to the average and that the hubs will be limited in both number and size. It goes without saying that other choices may be preferred, some of which (e.g., a Pareto, a uniform, and a constant distribution) are already supported by our simulator. As to \(D\), as extensively discussed in Section 1.1, there is a vast body of research supporting the choice of a power-law decay for the dependence of social ties upon the geographical distance between two individuals. There is no equal agreement regarding the exponent \(\beta\), with empirical findings suggesting a significant variability according to the geographical scale of the analysis and to the type of interaction object of the study. We decided to compare \(\beta=0.5\) and \(\beta=2\), which seem to lie at the two ends of the spectrum of the values observed in the literature. In both cases, as already discussed, since \(d(u,v)\) is bounded, so is \(D\). It is worth it to highlight that, for the considered set of models, the upper bound (6) for the choice of \(\mu\) is indeed rather loose in most practical cases, as we experimentally verified.
## 3 Results
### Complexity Analysis
#### 3.1.2 CoordNorm Preprocessing Algorithm  
* Description: This algorithm normalizes the keypoint coordinates by adjusting them to a reference point and normalizing them within a bounding box.
* Complexity: The complexity is \(O(k)\), where \(k\) is the number of keypoints (21 in this case). Since the algorithm processes each keypoint sequentially, it results in a linear time complexity relative to the number of keypoints.
# Enhancing Friction Pendulum Isolation Systems Using Passive and Semi-Active Dampers
## 5 Results and Analysis
### Performance Discussion  
The performance of the different configurations is further studied hereof. Firstly, the performance for a range of concave plate radii is studied. Secondly, the performance for the same concave plate is analyzed, and finally, the robustness of the isolator configurations under variation of the friction coefficient \(\mu\) is studied. Figure 16: CDF curves under the Northridge earthquake. Figure 17: Performance function \(\phi_{2}\) of the configurations studied for the eight earthquakes. Figure 18 shows the performance function \(\phi_{2}\) and the normalized structure mechanical energy \(J_{4S}\) for the serviceability earthquake and for the design earthquake, considering a range of concave plate radii (in consonance with the minimum and maximum value of the pendulum natural frequency \(f_{a}\)). It can be concluded that the semi-active damper of the FPS acts when needed, avoiding  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Earthquake** & **System** & \(J_{1,P}\) & \(J_{1,RMS}\) & \(J_{2,P}\) & \(J_{2,RMS}\) & \(J_{3,P}\) & \(J_{3,RMS}\) & \(J_{4,S}\) & \(J_{4,T}\) & \(\phi_{2}\) \\ \hline \multirow{6}{*}{Livermore} & Non-isolated & 1.000 & 1.000 & - & - & 1.000 & 1.000 & 1.000 & 1.000 & 0.833 \\  & FPS\({}_{\mu=0.05}\) & 0.760 & 0.554 & 0.071 & 0.013 & 0.492 & 0.544 & 0.408 & 0.318 & 0.486 \\  & FPS+VD & 0.776 & 0.580 & 0.056 & 0.009 & 0.495 & 0.566 & 0.395 & 0.336 & 0.495 \\
5.8 Mw & FPS+SD\({}_{Phase}\) & 0.648 & 0.433 & 0.078 & 0.018 & 0.482 & 0.455 & 0.369 & 0.233 & 0.434 \\ USA & FPS+SD\({}_{Energy}\) & 0.655 & 0.547 & 0.050 & 0.008 & 0.482 & 0.532 & 0.345 & 0.297 & 0.458 \\  & FPS\({}_{\mu=0.20}\) & 1.133 & 1.072 & 0.010 & 0.003 & 1.059 & 1.068 & 1.287 & 1.190 & 0.900 \\ \hline \multirow{6}{*}{Imp.
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 4 Experimental Results and Discussion
### Results and Analysis
#### 4.7.2 Confusion Matrix  
A confusion matrix is a vital tool when evaluating the performance of a classification algorithm. It is a table that is used to describe the performance of a classification model on a set of test data for which the true values are known. The matrix itself is easy to understand and provides a great deal of insight into the performance of the model. A confusion matrix for a binary classification problem is typically structured as follows:  
\begin{tabular}{c c c} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline Actual Positive & True Positive (TP) & False Negative (FN) \\ \hline Actual Negative & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}  
A breakdown of the terms is provided below:  
**True Positive (TP)**: The number of positive instances correctly predicted by the classifier.  
**False Negative (FN)**: The number of positive instances incorrectly predicted by the classifier as negative.  
**False Positive (FP)**: The number of negative instances incorrectly predicted by the classifier as positive.  
**True Negative (TN)**: The number of negative instances correctly predicted by the classifier.  
Several performance metrics can be derived from the confusion matrix. These metrics are part of what is known as a classification report.
## 2 Results and Discussion
### Optimization of HS-SPME Procedure
#### 2.1.5 Effect of Sample Volume  
For the HS-SPME extraction procedure, sample volumes of 1, 2 and 4 mL were tested, using 15-mL vials. Figure 5 shows the effect of sample volume in the extraction procedure. The number of adsorbed analytes is generally proportional to sample volume. Therefore, higher sample volume lead to higher analyte extraction. However, fiber overload can have negative effects due to reverse diffusion of analytes from the fiber to the sample. In this experiment, an increase in sample volume from 1 to 2 mL lead to analyte extraction enhancement. However, a further increase from 2 to 4 mL had negative effect in the extraction procedure resulting in optimum sample volume of 2 mL for the examined analytes [24].
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 1 Introduction
Using a drone is a simple and cost-effective solution that can achieve high precision. IoT devices have several advantages and challenges when it comes to agriculture and viticulture [11]. In this work, a low-cost hardware implementation for real-time data acquisition and processing in a vineyard is presented. Wireless nodes consisting of small and low-cost Internet of Things (IoT) devices are used for monitoring the soil moisture and the soil temperature. If the monitoring data exceed some predefined values, a drone flies over the area towards the node that reported these data and takes pictures. Then, the drone returns to the base station and forwards the pictures to a control room for further processing. The novelty of this work is on the introduced system framework and system implementation. Commercially available, low-cost IoT devices were used to build wireless monitoring nodes. A framework is also designed for data transmission. To minimize duplicate messages, a routing protocol was implemented in each node. The energy consumption of each type of node, monitoring and relay node, was measured in and was minimized. Three wireless technologies were examined and compared in terms of energy consumption. A drone was used to locate specific nodes in the field and the localization accuracy was also measured. Finally, two feasibility tests were conducted to examine the performance of the proposed system. The rest of this paper is organized as follows: in Section 2, some background information on vineyards and the diseases that can occur are presented. In Section 3, the related work is reviewed. Section 4 describes the architecture of the proposed system followed by a description of the different hardware components in Section 5. Section 6 presents results from experimentation with the system, and Section 7 concludes this work.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 2 Materials and Methods
### Enzyme-Linked Immunosorbent Assay (ELISA)  
To verify the authenticity of the Feta cheese samples, a cow milk-specific ELISA was performed using the Bio-Shield Cow Cheese ELISA Kit (ProGnosis Biotech S.A., Larissa, Greece, Cat. No. B0348), which is designed for the detection of cow's milk in brined soft cheeses. The assay targets bovine IgG and was carried out in duplicate for each sample, following the manufacturer's instructions. Absorbance was measured at 450 nm using a microplate spectrophotometer (Multiskan(tm) FC, Thermo Fisher Scientific Inc., Waltham, MA, USA).  
The method exhibits a limit of detection (LOD) of 0.04% and a limit of quantification (LOQ) of 0.10%, with 100% recovery, according to the product specifications. The intra-assay precision at the 1% adulteration level was 5.5%. The assay demonstrates very high specificity, with cross-reactivity below 0.01% for both sheep and goat immunoglobulins, and 3.2% for buffalo IgG, effectively minimizing false positives from non-target milk species. These characteristics render the method highly suitable for detecting bovine adulteration in Protected Designation of Origin (PDO) products such as Feta. Therefore, any sample exhibiting bovine milk protein levels above the LOQ (0.10%) was considered adulterated with cow's milk. While the assay exhibits 3.2% cross-reactivity with buffalo IgG, the likelihood of interference from buffalo milk is negligible, as this milk type is not widely produced or used in Greece and is significantly costlier than cow's milk, which is the primary source of economically motivated adulteration [12; 28; 29; 30].
# RTL-DEVS: HDL Design and Simulation Methodology for DEVS Formalism-Based Simulation Tool
## 2 Hardware Representation Requirements
### HDL Requirement  
To perform HDL simulation on a DEVS-based simulator, the following requirements must be satisfied. To simulate a combination and sequential circuit in a digital system, (1) the output to the input must be representable, and (2) the transport delay of the circuit is required to express and simulate of the combination circuit. (3) For the representation and simulation of sequential circuits, it should be possible to express the state representation,  
Figure 2: Abstract specification of RTL-DEVS co-simulator.  
state transition, output transition, and timing properties for state transition. Table 1 shows the requirements of HDL simulation to be guaranteed by RTL-DEVS.  
HDL-based design begins by specifying the interface port of the module. The operation of the module is then determined by calculations based on input signals and internal variables, and the output is allocated from calculated or stored values. The hierarchical model framework and interface underlying DEVS formalism are compatible with hardware technical languages such as Verilog HDL. For HDL simulations, abstraction levels must be able to guarantee representations of concurrency and state transitions, and hierarchical and modular methodologies are required to invoke pre-designed modules [8].  
Our research aims to support front-end-level System IC design between specifications and HDL simulation during HDL Design. The RTL-DEVS simulation component is divided into two components: testbench design for RTL Design and verification and synthetic RTL modeling.
# Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT
###### Abstract
In addition, a LoRa sensitive analysis was conducted by the authors in [25] for PV short-time forecasting accuracy estimation. By considering this preliminary background, as well as the lack of contributions focused on monitoring self-consumption PV solar installations and providing information to prosumers in terms of consumption/production profiles [26], the main contributions of this paper are described below:  
* A novel monitoring solution for the operation and maintenance of self-consumption PV systems is proposed and assessed. * The solution is based on Internet of Things (IoT) applications by using Narrowband IoT (NB-IoT). * The new design uses the Pycom IoT platform with NB-IoT as data transmission technology. The Pycom development board FiPy and sensor shield Pysense have recently been proposed to monitor environmental variables such as humidity, temperature, altitude, pressure, or light [27]. The rest of the paper is structured as follows: Section 2 introduces NB-IoT technology; Section 3 discusses materials and methods; Section 4 describes the use case; Section 5 discusses both collected data and results; finally, conclusions are given in Section 6.
## 2 Methods
### The Values of Evaluation Indicators and Influential Factors  
#### 2.2.1 The Values of Evaluation Indicators  
(1) Average number of stops  
This refers to the number of stops made by vehicles passing through the green wave on a road in a coordinated direction. The value is denoted by \"\(S\)\" and is measured as number of stops/vehicle. The average number of stops was selected as the evaluation indicator to reflect the coordination effect of green wave control [6].  
(2) Average travel time  
This refers to the time taken by vehicles to pass through the green wave in a coordinated direction. The value is denoted by \"\(T\)\" and is measured in hours/vehicle. The average travel time is selected as the evaluation indicator to reflect the traffic efficiency of the green wave [19].  
(3) Average delay  
This refers to the time lost by vehicles while passing through signalized intersections on the green wave road. The value is denoted by \"\(D\)\" and is measured as s/pcu. The average delay is selected as the evaluation indicator to reflect the traffic benefit of the green wave [19].  
\(S\), \(T\), and \(D\) can be obtained through Vissim simulation [14].  
We needed to calculate the value of influence \(E\) based on S, T, and D data on the three indicators above to facilitate subsequent association analysis. The smaller the \(E\), the smaller the influence on the operational effect of the green wave. This implied a better operational effect of the green wave. The \(E\) is calculated as follows.  
We used Equations (1)-(3) to process the S\({}_{ij}\), \(T_{ij}\) and \(D_{ij}\) data relating to the \(S\), \(T\), and \(D\) to obtain non-dimensional \(S^{\prime}_{ij}\), \(T^{\prime}_{ij}\) and \(D^{\prime}_{ij}\) data. Then, the \(E_{ij}\) of \(E\) was calculated using Equation (4):  
\[S^{\prime}_{ij}=\frac{S_{ij}}{S}, \tag{1}\]  
\[T^{\prime}_{ij}=\frac{T_{ij}}{T}, \tag{2}\]  
\[D^{\prime}_{ij}=\frac{D_{ij}}{\overline{D}}, \tag{3}\]  
\[E_{ij}=S^{\prime}_{ij}\omega_{S}+T^{\prime}_{ij}\omega_{T}+D^{\prime}_{ij} \omega_{D}, \tag{4}\]  
where \(i\) indicates that there are multiple influential factors, and \(j\) indicates that each influential factors has multiple values. \(S\), \(T\) and \(\overline{D}\) are the average values of \(S\), \(T\) and \(D\), and \(\omega_{S}\), \(\omega_{T}\) and \(\omega_{D}\) are the weights of \(S\), \(T\) and \(D\), which was set to \(1/3\) to ensure simplicity of calculation. The range for value of influence \(E\) is (0, +\(\infty\)).
# Quantum LDPC Codes Based on Cocyclic Block Matrices
## 3 Quantum Codes Based on Cocyclic Quasi-Jacket Block Matrices
### Cocyclic Matrix Derived from Jacket Block Matrices
According to the property of the Kronecker product, it obeys the following formula  
\[(A_{u}\otimes B_{v})^{-1}=[(a_{ij}b_{st})^{-1}]_{uv}^{T}/(c_{a}c _{b})\]2  
for any square matrices \(A,B,C\) and \(D\). Hence,  
\[(A_{u}\otimes B_{v})^{-1}=[(a_{ij}b_{st})^{-1}]_{uv}^{T}/(c_{a}c _{b})\]3  
It means that Equation (24) is equal to Equation (26), i.e., the case of \(n=m+1\) also holds. In terms of the above conclusion, based on its decomposition factors, we can form a _factor group_\(\mathcal{A}=\{A^{1}_{N},A^{2}_{N},\cdots,A^{m}_{N}\}\). Resorting to the factor group \(\mathcal{A}\), we consider constructing the stabilizer with two isomorphic classes of groups. **Lemma 1**. : _Take factor group \(\mathcal{A}\) in Equation (23) and its isomorphic factor group \(\mathcal{B}=\{B^{1}_{N},B^{2}_{N},\cdots,B^{m}_{N}\}\) with matrix \(J^{\prime}_{p}\). In terms of the property of the Kronecker product, if matrices \(J_{p}\) and \(J^{\prime}_{p}\) satisfy the commuting condition, i.e., their symplectic inner product is equal to zero, it is easy to check that any pair of factors \(A^{i}_{N}\) and \(B^{i}_{N}\) (\(1\leq i\leq m\)) in the two groups also satisfies the commuting condition (7)._  
It means that any pair of \(A^{i}_{N}\) and \(B^{i}_{N}\) can be taken as the generator matrices \(G^{x}\) and \(G^{z}\) of quantum codes, respectively, when two matrices \(J_{p}\) and \(J^{\prime}_{p}\) commute. **Example 1**. : _If the prime integer is given \(p=2\), let \(\omega=e^{\sqrt{-1}\pi/2}\). It has vectors \(\vec{i}_{0}=(1=\omega^{0},\omega)\) and \(\vec{i}_{1}=(\omega,1)\) in group \(G=\{1,\omega\}\)._
# Quark Powder Valorisation in White Self-Compacting Concrete: Mortar Level Study
## 1 Introduction
### Research Significance and Objectives  
Due to colour requirements, white cement concrete formulations are usually required for architectural applications. Architectural concrete generally has a lower aggregate content, and its maximum size is reduced. Moreover, the dosage of fine powder material is increased and filled with cement and SCM to meet a high-quality finishing appearance. Self-compacting concrete brings immense advantages for architectural applications manufactured in different styles and forms in construction. Its self-compacting property eliminates the compaction stage, which potentially translates into cost and time savings, better quality of the final product, higher surface finishing homogeneity, and work sites with less noise for workers and the surroundings.  
The partial replacement of white Portland cement by white coloured admixtures is beneficial from an economic, technical and environmental point of view. However, in Portugal and many other countries, (white) SCM is scarce. The white colour of QP can be an asset for architectural purposes. This solution contributes in an integrated way to achieve more sustainable and circular concretes.  
In Portugal, the exploration of quartz as an industrial mineral is carried out in the open air due to the nature of the formations in which the quartz occurs, generally at the extremes of lenticular masses or formations. The fronts are usually low (in the order of a few tens of metres) and small in extent, which is why these deposits only allow small-scale mining [45]. In 2020, about 184000 tons of industrial mineral quartz were extracted in mainland Portugal [46]. Considering the technological evolution observed in the photovoltaics, electronics and fibre optics market, quartz exploration has been increasing and, consequently, the residue generated by such exploration. Quartz powder, from the production and processing of quartz, is a powdered material with a particle size above 125 microns, has a white colouration, and a chemical composition of about 99% silica.  
This research addresses the valorisation of quartz powder from a Portuguese company by incorporating white cement-based materials for architectural purposes. Firstly, the QP was characterised by chemical, physical and morphological properties. Afterwards, 10% of the white PC was replaced with QP on SCWM, and the following engineering properties were evaluated, flowability and viscosity, electrical resistivity, porosity and mechanical strength. The performance of SCWM-QP was compared to equivalent mortars, including three different LF commercially available and widely used in Portugal and Europe. In equivalent pastes samples, the heat of hydration and setting times were accessed. Finally, a prototype of an architectonic element was produced using SCWM-QP, and colour and aesthetics were evaluated.