esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment 1 Results  
All data were analysed using Jamovi version 2.0 (The Jamovi Project). Data were tested for normal distribution by [PERSON]'s test and for homogeneity of variance using [PERSON]'s test. Our data violated the normality and parametricity assumptions, thus we performed a Friedman's Test to examine for differences in ratings between bright likely, bright unlikely, dark likely and dark unlikely collision outcomes. Subsequently, pairwise comparisons (Durbin-Conover) were performed to locate differences. We report alpha levels as exact \(p\) values, without dichotomous interpretation of'significant' or 'non-significant' as advised by the American Statistical Association [26]. Effect sizes are reported using [PERSON]'s W for Friedman's Tests and rank biserial correlations (r) for pairwise comparisons. Both measures of effect are interpreted using [PERSON]'s guidelines (0.1\(-\) < 0.03 small, 0.3\(-\) < 0.5 moderate, and \(\geq\) large [27]. Figures were generated in GraphPad Prism (GraphPad Prism 8.4.3, GraphPad Software Inc, San Diego, CA, USA) and display grouped dot plots as recommended by [PERSON] [28] and [PERSON] et al. [29]. Parametric data are summarised in text as mean \(\pm\) SD and non-parametric data as median [interquartile range (IQR)]  
[PERSON]'s test revealed a difference between ratings of the four collision outcomes based on object brightness, \(\times\)2(3, n = 24) = 30.3, \(p\) < 0.001, W = 0.42 (moderate effect).  
Figure 1: From top to bottom: Top: (**A**) Dark ball likely, (**B**) Dark ball unlikely, Bottom: (**C**) Bright ball likely, (**D**) Bright ball unlikely outcomes.  
Median ratings were higher (\(p<0.001\), r = 0.94) for the bright likely (shorter distance) event (7 [2]) compared to bright unlikely (longer distance) event (2 [2]). Dark unlikely (shorter distance) event (6 [4]) were rated more likely than the dark likely (longer distance) event (4 [5]) (\(p=0.071\), r = 0.39) (Figure 2).
# Thermally Induced Displacements and Rotations of Pillars for Precise Geodetic Measurements
## 1 Introduction
Although previous studies have recognized the influence of thermal effects on such pillars, none have directly validated this relationship through simultaneous measurements of inclination and displacement.
###### Abstract  
This study explores the valorisation of alum sludge, a byproduct of water treatment processes, as a sustainable reinforcement material in Poly(butylene adipate-co-terephthalate) (PBAT) composites. The research aims to address industrial waste challenges by developing eco-friendly composite materials while promoting circular economy principles. Alum sludge particles, classified into two size distributions (<63 \(\upmu\)m and <250 \(\upmu\)m), were incorporated into PBAT matrices at varying concentrations. The composites were characterised for their mechanical, thermal, crystallographic, and moisture adsorption properties; and their biodegradation behaviour was evaluated through soil burial tests over 60 days. The results revealed that the 63 \(\upmu\)m particle size fraction exhibited superior performance compared to the 250 \(\upmu\)m fraction, demonstrating improved mechanical properties, reduced degradation rates, and enhanced interfacial bonding. Composites with 5 wt.% alum sludge achieved a balance between reinforcement and processability, outperforming the other filler concentrations examined. This innovative approach highlights the potential of upcycling alum sludge into functional materials, advancing sustainable waste management and composite manufacturing. Furthermore, the observed variation in degradation rates suggests that these composites can be tailored for applications requiring controlled compostability.  
waste valorisation; sustainable composites; alum sludge; PBAT; biodegradable Article
## 2 Problem Formulation
### Global Objectives
#### 2.6.3 Isolation Avoidance  
In multi-robot exploration missions, the individual isolation situations (when a robot becomes unconnected from any other) are non-desirable. The key motivations to avoid them are (i) When robots are unconnected they have no possibilities to coordinate their actions, hence they could visit the same regions. Therefore, keeping the fleet connected is a way to decrease inefficiency; (ii) Damages or inner failures during isolation periods can lead to information losses. Therefore, keeping the fleet connected is also a way to decrease the risk of re-work and to prevent time performance degradation, consequently.  
Thus, in addition to (5), the last of possible individual disconnections should be minimised. To this end, concepts of graph theory are borrowed in order to model a time-varying network topology of mobile robots. Such network is represented employing an undirected graph defined as \(\mathcal{G}(t)=(\mathcal{V},\mathcal{E}(t))\) where the nodes \(\mathcal{V}=\{1\dots M\}\) represent the robots \(R_{i}\mid i\in[1..M]\) and the edges \(\mathcal{E}(t)=\{i,j\mid i,j\in\mathcal{V},j\in\mathcal{N}_{i}(E_{known}(t),t)\}\) represent the operative communication links between any pair of robots \(\big{(}R_{i},R_{j}\big{)}\).  
The function \(\mathcal{N}_{i}(E_{known}(t),t)=\{j\mid\Gamma_{i}(j,E_{known}(t),t)>0\}\) computes the neighbours of a robot \(i\) at time \(t\). From this it is possible to define the isolation situations of any robot \(i\) like the periods when the corresponding node \(i\) has no incident edges (\(degree(i)=0\)). Furthermore, isolation situations may repeat several times along the exploration.  
In Figure 4 an example of an exploration timeline concerning disconnections is depicted.  
From this model, the expression for the disconnection last optimisation may be obtained as follows:  
\[\min\sum_{i\in\mathcal{V}}\sum_{k}\Delta E_{k} \tag{6}\]  
Figure 4: Disconnection events representation. The disconnection events \(dE_{k}\) can appear distributed along the exploration timeline. Its last is variable and depends on the movements realised by the fleet during the exploration. The starting and ending times of each disconnection are represented by the timestamps \(t_{sk}^{i}\) and \(t_{sk}^{i}\), respectively.  
where : \(k\) indexes the disconnection events \(dE\)  
\(\Delta dE_{k}=t_{ek}^{i}-t_{sk}^{i}\), represents the last of the disconnection event \(dE_{k}\)  
\(t_{sk}^{i}=\min t_{k}\mid\mathcal{N}_{i}(E_{known}(t),t_{k})=\emptyset\), represents the starting time of the disconnection \(dE_{k}\)  
\(t_{ek}^{i}=\max t_{k}\mid\mathcal{N}_{i}(E_{known}(t),t_{k})=\emptyset\), represents the ending time of the disconnection \(dE_{k}\)
# PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks
## 2 Related Work
### State of Art
Furthermore, the integration of drones creates a connection between the offloading decision-making process of mobile edge computing and the drone deployment problem, which is frequently disregarded. The optimization of drone-assisted edge computing networks is vital for network performance and necessitates thorough research. _DRL in Edge Computing_: Currently, the research into applying deep reinforcement learning to address the computation offloading and resource allocation issues in edge computing systems primarily focuses on the decision-making of computation offloading [27]. [PERSON]. [28] proposes a deep Q-learning-based offloading scheme for IoT devices with energy harvesting to optimize the server selection and discrete action of the offloading rate. [PERSON]. [29] introduces a method based on Deep Q Network (DQN) to estimate the number of tasks processed locally, at ECSs, and at cloud servers. This method improves the privacy level of user devices while reducing offloading costs such as latency and energy consumption. A solution based on the deep deterministic policy gradient is proposed to optimize the power allocation for continuous local execution and offloading. Ref. [30]considers multiple users in edge computing systems, where multiple user devices can offload computations to ECSs through wireless channels. By discretizing the continuous offloading decision and allocating computing resources, this method minimizes the total delay cost and energy consumption of all terminals in the edge computing system. Note that existing work typically considers discrete variable planning or integer planning for edge computing optimization models. However, this differs significantly from the challenges presented in UAV-assisted edge computing networks, where the problem includes both discrete and integer variables. Unfortunately, current deep reinforcement learning methods are inadequate for scenarios where these variables intersect. Therefore, the design of a novel optimization framework that can cope with UAV-assisted edge computing networks necessitates in-depth discussion.
# Flexural Behavior of a 30-Meter Full-Scale Simply Supported Prestressed Concrete Box Girder
## 4 A Nonlinear Numerical Model for the Tested Box Girder  
### Assumptions  
Full-scale testing of a large structural member involves high cost; therefore, it is necessary to establish a reasonable numerical model on the basis of test results. A nonlinear numerical model was developed using the FSM. The basic assumptions are as follows:  
1. The plane section assumption is applied, and normal strains along the depth are distributed linearly.
2. The reinforcement and concrete are well bonded without relative sliding.
3. The shear deformation is negligible.
4. The failure characteristics of the girder are the crushing of concrete in the compression zone or fracture of the prestressing tendon.
5. The section of concrete is divided into finite strips in which the strain is assumed to be uniformly distributed.
# Molecular Dynamics-Based Study of Graphene/Asphal Mechanism of Interaction
## 2 Models Establishment and Simulation Methods
### Molecular Dynamics Simulation Methods
#### 2.2.2 Radial Distribution Function (RDF)  
The radial distribution function (RDF) is the probability of other particles appearing in the space around a given particle at a distance \(r\). It is often used to characterize the spatial aggregation of particles and the orderliness of molecular arrangement in the molecular model of asphalt [15], and the calculation formula is shown in Equation (2).  
\[g(r)=\frac{\rho(r)}{\rho} \tag{2}\]  
where \(g(r)\) is the radial distribution function; \(r\) is the distance (A) from the given particle; \(\rho(r)\) is the particle density (g/cm\({}^{3}\)) at a distance of \(r\) around the given particle; and \(\rho\) is the average particle density (g/cm\({}^{3}\)) of the molecular simulation system.
# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
## 2 Microservices and Grayscale-Based Image Transform Features
### Grayscale-Based Transform Features
Malware samples were executed in a dynamic environment, and behavior was captured in different time frames ranging from 5 to 30 seconds, such that hardware-based features were collected at the time of their occurrence via a VTune tool. Those features were normalized and inputted into a 1D vector, padded with zeros, and converted into a 2D vector, represented by a grayscale image of size 16 \(\times\) 16, which was fed into a NN. A voting network system on CNN and LSTM gave an accuracy of 91.63%. The majority of malware detection solutions are based on static analysis and dynamic analysis, and these methods exhibit an optimal rate of detecting known malware. However, these methods have limitations in detecting advanced malware, such as encrypted, obscured, or zero-day malware [33]. In [34], the author proposed a novel approach based on structural feature extraction methodology (SFEM). It is based on machine learning for XML-based document malware detection. The analysis was conducted for 830 malicious files and 16180 benign files. The results showed that the SFEM can achieve a TPR of 97%. In [33], the author proposed a new approach, including data extraction and image conversion, to identify MS-DOC malicious files and benign files using three CNN models. The experimental results showed a detection rate of 94.09% on the test samples. In [35], the author proposed a technique for PDF malware detection using image processing. The PDF files were converted to grayscale images through image visualization methods. Then features were extracted for malware and benign PDFs. In the end, learning algorithms were applied to classify the files as benign or malicious. Optimum results were achieved using a byte plot with a Gabor filter and RF, which resulted in a 99.48% F1 score. In [36], the author used a PDF malware detection technique based on malware visualization and image classification. VGG19 with CNN architecture was used to train the model, which resulted in an accuracy of 97.3% and an F1-score of 97.5%. Based on the gaps identified in this review, the proposed work is based on a single CNN model, which will be trained to detect three file types: MS, PE, and PDF. This presents an advantage over existing works in terms of complexity and different file type detection. Aside from this, our detailed experimentation aims to improve the overall accuracy as well. In addition to that, we aim to detect the encrypted malware in MS, which is considered a limitation in machine learning methods.
## 3 Algorithms
### Group Delay and Phase Delay Formulation  
The group delay \(\tau_{g}\) and the phase delay \(\tau_{\phi}\) are computed from the unwrapped phase shift \(\phi(\omega)\), as given in (6) and (7), according to [2, 28], as follows:  
\[\tau_{g}(\omega)=-\frac{\partial\phi(\omega)}{\partial\omega} \tag{6}\]  
\[\tau_{\phi}(\omega)=-\frac{\phi(\omega)}{\omega} \tag{7}\]  
The physical interpretation of (6) establishes that group delay represents the negative frequency derivative of the phase response. This formulation ensures that positive group delay values correspond to physically realizable signal delays, as the negative phase slope versus frequency in (7) combines with the negative sign in (6) to produce this causal behavior.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 6 Conclusions and Future Work  
This research addressed the problem concerning the optimal placement and sizing of PV generation units in monopolar DC distribution grids with a radial structure by applying a master-slave optimization approach. In the master stage, the GNDO approach was used to define the nodal location and sizes of the PV sources by using a discrete-continuous codification, whereas the slave stage corresponded to an efficient DC power flow approach that allowed evaluating the fitness function. Numerical results in the DC monopolar versions of the IEEE 33- and IEEE 69-bus grids demonstrate that the GNDO approach allowed reaching better results than the DCVSA with reductions of USD/year 53.73, and USD/year 527.05, respectively, which turns the proposed master-slave approach into the best current reference regarding the studied problem.  
As for the processing times, the proposed GNDO approach took less than 3 and 6.5 min to solve the MINLP model (1)-(12) in test feeders with 33 and 69 nodes, respectively. These results implied discrete solution spaces of 4960 and 50,116 nodal combinations and infinite possibilities regarding continuous variables, yielding the best numerical results in connection with the objective function when compared to those of the CBGA and the DCVSA.  
The main limitation of the proposed optimization approach corresponds to the design of the PV systems, taking into account that these operate by tracking the maximum power point. However, it is possible to obtain better objective function values if these PV sources are freely operated, i.e., under a multi-period, optimal power flow design.  
As future works, the following work could be carried out: (i) extending the application of the GNDO approach in order to solve the problem of the optimal selection, location, and operation of batteries in AC and DC distribution grids; and (ii) transforming the exact MINLP model (1)-(12) into a mixed-integer conic formulation that allows determining the best set of nodes and their optimal sizes via convex programming.  
Conceptualization, methodology, software, and writing (review and editing): O.D.M., W.G.-G. and L.F.G.-N. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data used in this study are reported in the paper's figures and tables.  
The authors thank the Proyecto Curricular de Ingenieria Electrica at Universidad Distrial Francisco Jose de Caldas.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Experiment and Discussion
The RVM-KF method proposed in references [15] is based on the empirical model, RVM and Kalman filter for lithium-ion battery prognostic without considering the influence of the regeneration phenomenon, although they did not give the experimental results of battery #18, which was heavily affected by the regeneration phenomenon. The proposed hybrid method not only accurately predicts the local regeneration phenomenon but also has higher accuracy in predicting the battery RUL for short-term and long-term. Table 2 shows the comparison of prediction results of the hybrid method with the RVM-KF method. It should be noted that the RVM-KF algorithm has better prediction performance than the proposed hybrid method started prediction at cycle 80 of battery #6. One possible reason is that the hybrid method does not accurately predict the abnormal regeneration, and the end-of-life is in this inaccurate prediction. The rest time-based prognostic method proposed in reference [30] uses the data-driven method of the SVM and the Gaussian process model for lithium-ion battery prognostic.However, they only gave short-term prediction results for started prediction at the start cycle 100. The present experiment is started at different stages of battery life, and the prediction performance is better in both the short- and long-term prognostic. Figure 7: Prediction results at different started prediction cycles of different batteries. (**a**) Started prediction at cycle 100 of battery #5, (**b**) Started prediction at cycle 100 of battery #6, (**c**) Started prediction at cycle 100 of battery #7, (**d**) Started prediction at cycle 100 of battery #18, (**e**) Started prediction at cycle 80 of battery #5, (**f**) Started prediction at cycle 80 of battery #6, (**g**) Started prediction at cycle 80 of battery #7, (**h**) Started prediction at cycle 80 of battery #18, (**i**) Started prediction at cycle 60 of battery #5, (**j**) Started prediction at cycle 60 of battery #6, (**k**) Started prediction at cycle 60 of battery #7, (**l**) Started prediction at cycle 60 of battery #18. Moreover, as compared with the electrochemical model [2] and the equivalent circuit model [8; 25; 34] methods that provide high-precision short-term SOH prediction, the proposed hybrid method not only provides very closed short-term SOH prediction accuracy, but also provides effective long-term RUL prediction results.
## 2 Materials and Methods
### Accuracy Tests of the Light-Scattering Sensor and Processing of the Results  
The test bench is a typical Herring-bone milking system, reduced to the laboratory size of 1 \(\times\) 3 places. The technical characteristics of the test bench fully correspond to the milking systems used in animal farms (ISO 5707-83) [25]. Instead of an animal, the test bench uses an anatomically accurate udder model, to which the teat cups are connected.  
Images of the test bench with the installation of elements and their description are presented in Figure 2. The test bench is shown schematically in Figure 2a; a cross-sectional side view (perspective) of the light-scattering sensor is depicted in Figure 2b.  
The operation principle of the inline light-scattering sensor was described in the study [26]. Figure 2c shows the installation scheme of the light-scattering sensor in an operating milking unit.  
The study of the accuracy of the light-scattering sensor consists of 2 stages (measurements with clean and contaminated milk hoses), repeated twice with milk of different fat content to ensure the high accuracy of the results.  
First, 30 measurements of the percentage fat content of the milk were taken with clean milk hoses, and before and after the measurements these parameters were checked and compared using a reference method of milk quality analysis. Milk hoses and milking equipment were rinsed with warm water only. Then, after 4 h, 30 measurements of the milk fat content were taken again. The cycle was repeated for milk of 3.83% and 4.31% fat content. The somatic cell count is lower than 100 thousand/mL.  
When assessing the statistical significance of differences in measurements, the hypothesis H\({}_{0}\) assumes that the excessive contamination of milk hoses has no significant effect on the accuracy of the light-scattering sensor. The alternative hypothesis H\({}_{1}\) suggests that contamination factors have an effect on the accuracy of the light-scattering sensor.  
The data are tested for normal distribution according to the Shapiro-Wilk test. \(p\) < 0.05 is chosen as the level of significance. Comparison of these repetitions with the evaluation of significance by Student's t-criterion should prove or disprove the influence of contamination in milk hoses of the milking unit on the effect of light scattering in milk and, accordingly, on the accuracy of the light-scattering sensor operation.  
Figure 2: (a) Diagram of the milking line, (b) schematic diagram of the light-scattering sensor, (c) manufactured test bench: (1) teat cups; (2) milk collector; (3) PVC hose; (4) light-scattering sensor (4.1) case; (4.2) controller board; (4.3) silicone photodiode; (4.4) semiconductor laser; (4.5) measuring chamber; (4.6) laser scattered beam; (4.7) milk; (5) tray milk counter; (6) milk pipe.
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 6 Numerical Simulation Results Analysis
### Impact of Snapshot Number on Estimation Results  
The proposed radiated noise power estimation method necessitates the use of the ambient noise covariance matrix, which can be estimated through prolonged monitoring of ambient noise. However, when the number of snapshots \(K\) in Equation (17) is limited, discrepancies may arise between the actual noise covariance matrix and the sampling covariance matrix, potentially affecting the estimation accuracy. Figure 4 presents the estimation results and variances of radiated noise for different snapshot numbers.  
It can be observed that, except for the RPCA method, the estimation results of the other methods remain constant regardless of the number of snapshots, while their estimation variances decrease as the number of snapshots increases. Among these methods, the CBF and NFBF methods exhibit the largest estimation errors and variances. The estimation variance of the MFP method achieves CRLB, but its estimation error remains relatively large. The denoising performance of the RPCA method improves with the increasing number of snapshots [32], thereby enhancing the estimation accuracy, although its estimation variance remains relatively large. In contrast, the MLE method achieves optimal performance in both estimation result and variance, reaching the CRLB and demonstrating the best estimation performance.  
Figure 3: Radiated noise estimation results under different radiated noise power, (**a**) estimation results, (**b**) estimation variances.