esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Analysis of the Structural Behavior of a Corroded Pipe Culvert
###### Abstract  
The stress analysis of buried pipe culverts is a complex task, and accurately characterizing the deterioration of mechanical properties caused by corrosion poses significant challenges. In this study, the finite element analysis software PLAXIS 3D was employed to construct a numerical simulation model of a pipe culvert. By varying the stiffness and thickness of either the entire structure or specific sections, different degrees of corrosion were simulated to investigate the influence of various cross-sectional shapes on corrosion effects. Multiple experimental controls were set to analyze both the bearing capacity and deformation characteristics under different conditions. The findings reveal that different levels of corrosion have distinct impacts on the deformation behavior of pipe culverts. Overall corrosion has the most significant effect on the overall deformation, while crown and middle corrosion show a similar effect on stiffness-related deformations. In contrast, invert corrosion has minimal impact on the stiffness-related deformation. Corrosion affects circular and elliptical pipe culverts similarly. However, the circular pipe culvert is evidently influenced by overall corrosion more significantly than the elliptical ones due to the combined effects from overall and local corrosion in their deformations. Through finite element numerical simulation, it can be used as a reference for practical engineering design and construction.  
pipe culvert; corrosion; finite element method; deformation; cross-sectional shape Article  
1  
[PERSON], [PERSON], [PERSON], [PERSON], X.
## 3 Results and Analysis
### Processes and Data Inputs in the Planning of Road Schedules
#### 3.1.4 Estimating Duration of Construction Activities  
This stage involves estimating the duration of construction activities by calculating the labor time needed for each activity defined in Stage 2. The schedule accuracy depends heavily on precise duration estimates for each activity. Therefore, it is crucial to analyze the requirements and constraints of each activity to make realistic estimates based on potential scenarios during road construction. It also involves defining the resources needed, including machinery, materials, and personnel. Estimating machinery and personnel performance is crucial due to the high degree of mechanization in road construction projects. This estimation should be conducted rigorously, using previous estimates and experiences to achieve high equipment and personnel productivity accuracy. Additionally, precise estimation of the quantities for each activity, based on the unit of measurement defined in earlier stages, is vital. In the absence of historical performance data, alternative techniques such as scenario-based estimation or breaking down activities into sub-activities and estimating the duration of each sub-activity can be employed. It is necessary to analyze the risks and obstacles that may impact the duration of activities and to account for the time needed for contingency activities to mitigate such risks. Site and weather conditions must also be considered, with particular attention to traffic conditions, which will influence the duration of construction activities based on the zoning established for the construction process. The availability of equipment, such as heavy machinery, should be considered, as the number of available units will affect the duration of activities depending on the contractor's resources.
# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 4 Instantiation of the Proposed Framework to SoC Estimation and RUL Prediction
### Configuration of Battery State Prediction Instantiated for SoC and RUL
#### 4.1.3 Features Selection  
Features selection is a crucial component as it directly affects the performance, efficiency, and interpretability of the model. By identifying and utilizing the most relevant features, this process reduces the dimensionality of the dataset, thereby minimizing computational complexity and the risk of overfitting [20]. It ensures that the model focuses on the variables that have the most significant influence on the prediction task, improving both accuracy and generalizability. Moreover, feature selection enhances the interpretability of the model by simplifying its structure, making it easier to understand the relationships between input variables and predictions.  
Within this framework, the following features selection procedures have been implemented, offering enhanced adjustability and flexibility to the applied pipelines:  
* The \"raw data\" procedure considers the features as several variables that describe the behavior of the battery during cycling and can be captured as timeseries measurements from sensors connected to the batteries.
* The \"statistics\" procedure extracts various statistical metrics from the raw data to be used as features, such as the mean, variance, skewness, standard deviation, etc.
* The \"features by resampling\" procedure resamples the raw data according to a specific frequency.
* The \"features by interpolation\" procedure determines a mathematical function that passes through a given set of data points, which then allows for the estimation of values at points not originally included in the dataset.
* The \"cycle-based features\" procedure derives features from various attributes associated with either specific, configurable cycles or every cycle within the dataset, thus capturing cycle-centric characteristics.
# Comparison of Deepfake Detection Techniques through Deep Learning
## 3 Approach
### Evaluation  
The performance of the described models is assessed with accuracy, precision, recall, F1-score, average precision (AP) and area under the ROC curve.  
Accuracy, simply put, indicates how close the model prediction is to the target or actual value (fake vs. real), meaning how many times the model was able to make a correct predication among all the predictions it has made. Equation (1) indicates the overall formula used to calculate prediction, where \(TPR\) stands for true prediction and \(TOPR\) stands for total predictions made by the model.  
\[Accuracy=\frac{TPR}{TOPR} \tag{1}\]  
Precision, on the other hand, refers to how consistent results are regardless of how close to the true value they are using the target label. Equation (2) demonstrates the ratio that indicates the proportion of positive identifications by model that were actually correct. \(TP\) in Equation (2) stands for the number of true positives and \(FP\) stands for the number of false positives.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
The recall is the proportion of actual positives that were identified by the model that were correct. Equation (3) demonstrates this ratio where \(TP\) is the number of true positives and \(FN\) the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
The F1-score, by taking into account both precision and recall, balances the precision and recall and indicates model ability to accurately predict both true-positive and true-negative classes. The F1 score can be interpreted as a harmonic mean of the precision and recall. For the task of deepfake classification, F1-score is a better measure to assess model performance, since both classes are of importance and the relative contribution of precision and recall to the F1 score are better than equal. Equation (4) demonstrates how F1-score is calculated.  
\[\text{F1}=\frac{2*(Precision*Recall)}{Precision+Recall} \tag{4}\]  
Average Precision (AP) was used as an aggregation function for the task of object detection to summarize the precision-recall curve as the weighted mean of precision achieved at each threshold, with the increase in recall from the previous threshold used as the weight based on Equation (5), where \(R_{n}\) and \(P_{n}\) are the precision and recall at the nth threshold [58].  
\[AP=\sum_{n}(R_{n}-R_{n-1})P_{n} \tag{5}\]  
Finally, as shown in Figure 3, the output vectors of the final hidden layer of the analyzed architectures were extracted and treated as a representation of the images. Dimensions of the vectors for the Custom CNN architecture, VGG-19 and DenseNet architectures were 512, 2048 and 1024, respectively. Principal Component Analysis (PCA) was performed to keep the most dominant variable vector points and preserved 50 principal components. The resulting vectors from the PCA were fed into a support vector machine (SVM) to classify them into the two classes of real and fake.
## 3 Model Parameter Estimation
### Propulsion Noise
#### 3.2.5 Backpropagation  
As the interface to the propagation model, the emission signals are defined at a (virtual) reference distance of \(r_{0}=1\) meter from the source position. For the measured levels \(L_{\text{lab}}\), the following inverse sound propagation model is used:  
\[L_{\text{Em,1 m}}=L_{\text{lab}}+20\log\left(\frac{r_{\text{Ac}}}{r_{0}}\right) +A_{\text{room}}+A_{\text{gr}} \tag{45}\]  
with the ground effect \(A_{\text{gr}}=-6\) dB, as all microphones were mounted on the ground. For the microphones placed close to the room edge (emission angles \(\varphi=60^{\circ}\) and \(120^{\circ}\)), the room correction \(A_{\text{room}}\) was set to \(-6\) dB for frequency bands below \(1\) kHz and to \(-3\) dB otherwise. As for the microphones placed in front and at the back of the car, the distance to the closest wall was about three meters; \(A_{\text{room}}\) was set to \(0\) dB for these signals. \(r_{\text{Ac}}\) is the distance to the acoustical center, which, by assuming geometrical spreading of a point source, is obtained by simultaneous free field measurements at two points at distances \(r\) and \(r^{\prime}\)\((r^{\prime}>r)\) with:  
\[r_{\text{Ac}}=\frac{r^{\prime}-r}{1-10^{\left(L_{\text{dr},r^{\prime}}-L_{ \text{dr}}\right)/20}} \tag{46}\]  
Parameter \(r_{\text{Ac}}\) is evaluated separately for each emission angle, engine speed and frequency band (or engine order, respectively).
# Phase Compensation Technique for Effective Heat Focusing in Microwave Hyperthermia Systems
###### Abstract  
In this paper, effective electromagnetic (EM) focusing achieved with a phase compensation technique for microwave hyperthermia systems is proposed. To treat tumor cells positioned deep inside a human female breast, EM energy must be properly focused on the target area. A circular antenna array for microwave hyperthermia allows EM energy to concentrate on a specific target inside the breast tumor. Depending on the cancerous cell conditions in the breast, the input phases of each antenna are calculated for single and multiple tumor cell locations. In the case of multifocal breast cancer, sub-array beam focusing via the phase compensation technique is presented to enhance the ability of EM energy to concentrate on multiple targets while minimizing damage to normal cells. To demonstrate the thermal treatment effects on single and multiple tumor locations, the accumulation of the specific absorption rate (SAR) parameter and temperature changes were verified using both simulated and experimental results.  
microwave hyperthermia; circular array antenna; phase compensation technique; tumor treatment; electromagnetic beam focusing; female breast phantom +
Footnote †: journal: Applied Sciences  
1
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 2 Small Satellites for VIS, TIR, and MW Remote Sensing
### DORA: Deployable Optics for Remote Sensing Applications
The heterogeneity of the topics and areas examined in these applications, which are used as guidelines to define the instrument requirements, will ensure that the DORA prototype has suitable performances not only for these specific study cases, but also for a broader spectrum of future applications.
## 5 Results
### Ideal Scenario (No Interference)
Similarly, using Equations (10) and (11) and Table 3, \(\left[\frac{\text{C}}{\text{N}_{0}}\right]_{\text{down}}\) can be calculated using (22) and (23):  
\[\text{FSL}_{\text{down}}=32.4+20\text{log}\ (12,000)+20\ \text{log}\ (40,973)=206.2\ \text{dB} \tag{22}\]  
\[\left[\frac{\text{C}}{\text{N}_{0}}\right]_{\text{down}}=25.6-206.2+24+228.6=72 \ \text{dB-Hz} \tag{23}\]  
Now, Equation (24) calculates the end-to-end carrier-to-noise density ratio \(\left[\frac{\text{C}}{\text{N}_{0}}\right]_{\text{Total}}\) in the considered ideal case:  
\[\left[\frac{\text{C}}{\text{N}_{0}}\right]_{\text{Total}}^{-1}=\left[\frac{ \text{C}}{\text{N}_{0}}\right]_{\text{up}}^{-1}+\left[\frac{\text{C}}{\text{ N}_{0}}\right]_{\text{Down}}^{-1}=\ 71.2\ \text{dB-Hz} \tag{24}\]  
Using Equation (8), \(\left[\frac{\text{C}}{\text{N}}\right]\) can be calculated using Equation (25) as:  
\[\left[\frac{\text{C}}{\text{N}}\right]=\left[\frac{\text{C}}{\text{N}_{0}} \right]_{\text{Total}}-10\text{log}_{10}(3\times 10^{6})=6.5\ \text{dB} \tag{25}\]  
Using Equation (25), the energy per bit-to-noise power spectral density \(\left[\frac{\text{F}_{\text{b}}}{\text{N}_{0}}\right]\) can be calculated using Equation (26):  
\[\left[\frac{\text{C}}{\text{N}}\right]=10\text{log}\left[\frac{\text{E}_{ \text{b}}}{\text{N}_{0}}\right]+10\text{log}\left(\frac{\text{r}}{\text{B}} \right)\Rightarrow\left[\frac{\text{E}_{\text{b}}}{\text{N}_{0}}\right]=4.9 \ \text{dB-Hz} \tag{26}\]  
Table 4 summarizes the calculated values for the considered 3 MHz bandwidth QPSK signal under ideal conditions. In a later section, we will study the effect of varying the bandwidth of the signal. In the following section, we will start considering more real-life scenarios by including the ASI.
# Psychoacoustic Approaches for Harmonic Music Mixing +
## 2 Consonance Models  
In this section, we present the theoretical approaches for the computational estimation of consonance that will form the core of the overall implementation described in Section 3 for estimating the most consonant combination of two tracks. To avoid misunderstandings due to ambiguous terminology, we define consonance by means of [PERSON]'s psychoacoustic model [5; 6], which is divided into two categories: The first, sensory consonance, combines roughness (and fluctuations, standing for slow beatings and therefore equated with roughness throughout), sharpness (referring to high energy in high registers of a sound's timbre) and tonalness (the degree of tonal components a sound holds). The second, harmony, is mostly built upon [PERSON]'s virtual pitch theory, which describes the effect of perceiving an imaginary root pitch of a sonority's harmonic pattern. This, in terms of musical consonance, he calls the root relationship, whereas he describes pitch commonality as the degree of how similar the harmonic patterns of two sonorities are. We take these categories as the basis for our approach. To estimate the degree of sensory consonance, we use a modified version of [PERSON]'s [8] roughness model. For calculating the pitch commonality  
Figure 1: An overview of the proposed approach for consonance-based mixing. Each input track is analyzed by a sinusoidal model with 90-ms frames (with a 6-ms hop size). These are median-averaged into sixteenth note temporal frames. The frequencies of Track 2 are scaled over a single octave range and the sensory roughness calculated between the two tracks per frequency shift. The frequency shifts leading to the lowest roughness are used to determine the harmonic consonance via a model of pitch commonality.  
of a combination of sonorities, we propose a model that combines [PERSON]'s [9] pitch categorization procedure with [PERSON]'s [10] virtual pitch and chord similarity model. Both models take a sequence of sinusoids, expressed as frequencies, \(f_{i}\) in Hz, and amplitudes, \(M_{i}\) in dBSPL (sound pressure level), as input.
## 3 Constraint-Handling Techniques in Metaheuristic Algorithms
### Constraint-Handling Techniques
#### 3.2.3 \(\epsilon\)-Constraint Method  
The \(e\)C method was proposed by [PERSON] et al. [54]. This method relaxes the constraints to select solutions close to the feasible region. The selection process is described by (21).  
\[(\bar{J}(x_{1}),\phi(x_{1}))<_{\epsilon}(\bar{J}(x_{2}),\phi(x_{2}))\left\{ \begin{aligned} \bar{J}(x_{1})<\bar{J}(x_{2}),&\text{ if }\phi(x_{1}),\phi(x_{2})\leq \epsilon\\ \bar{J}(x_{1})<\bar{J}(x_{2}),&\text{ if }\phi(x_{1})==\phi(x_{2})\\ \phi(x_{1})<\phi(x_{2}),&\text{ otherwise}\end{aligned}\right. \tag{21}\]  
where \(\epsilon\) is a relaxation factor defined by (22) and (23), \(cp\) is a parameter to control the speed of reducing relaxation of constraints, \(T_{c}\) is the maximum number of iterations (generation or time) to relax such constraints, \(\phi(x_{\theta})\) is \(\theta\)-th best value of the objective function in the initial solution vector (population, swarm, or chromosomes).  
\[\epsilon(0) = \phi(x_{\theta}) \tag{22}\] \[\epsilon(G) \left\{\begin{aligned} \epsilon(0)\Big{(}1-\frac{1}{T_{c}} \Big{)}^{cp},& 0<G<Tc\\ 0,& Tc\leq G\end{aligned}\right. \tag{23}\]  
The \(\epsilon\)-constraint method finds feasible regions using the gradient of constraints at an infeasible region. Algorithm 6 describes the operation of the \(e\)C method. This constraint-handling technique requires three sets of solutions (three input arguments). The gradient-based mutation process can improve an infeasible solution to obtain a new solution \(u_{j}^{new}\). The new solution \(u_{j}^{new}\) and old solution are compared using (21), and the best solution is stored in the current solution \(b_{j}\). The set of current solutions are compared according to (21), and the best solutions are stored in the output arguments.  
```
1:Function \(fncCHT(\bar{a},\bar{b},\bar{c})\)

Input: \(\bar{a}\in X_{G}\) and \(\bar{b}\in U_{G}\)

Output: \(X_{G+1}\) and \(x^{best}\)

\(X_{G+1}=\bar{a}\) and \(x^{best}=\bar{c}\)

for\(j=1\) to \(NP\)do

\(rand(0,1)<Pg\)then

\(k=0\)

while\(k<Rg\) and \(\phi(\tilde{b}_{j})>\epsilon\)do

Obtain \(\Delta\tilde{b}_{j}=-\
abla C(\tilde{b}_{j})^{-1}\Delta C(\tilde{b}_{j})\)

\(\Delta C(\tilde{b}_{j})\) is the constraint vector, \(\Delta C(\tilde{b}_{j})^{-1}\) is the pseudo-inverse of the constraint's derivative obtained by Moore-Penrose pseudo-inverse using the singular value decomposition and \(Rg\) is the number of attempts to improve the solution.

Obtain \(u^{new}=\tilde{b}_{j}+\Delta b_{j}\)

if\((\bar{\int}(u^{new}),\phi(u^{new}))<_{\epsilon}(\bar{\int}(\tilde{b}_{j}), \phi(\tilde{b}_{j}))\)then

\(\tilde{b}_{j}\gets u^{new}\)

else

\(break\)

\(k=k+1\)

endwhile

\(\bar{\int}(\bar{\int}(\tilde{b}_{j}),\phi(\tilde{b}_{j}))<_{\epsilon}(\bar{ \int}(\tilde{a}_{j}),\phi(\tilde{a}_{j}))\)then

\(X_{G+1}\gets b_{j}\)

if\((\bar{\int}(\tilde{b}_{j}),\phi(\tilde{b}_{j}))<_{\epsilon}(\bar{\int}(x^{ best}),\phi(x^{best}))\)then

\(x^{best}\gets b_{j}\)

endif

endif

endfor
```  
**Algorithm 6**\(\epsilon\)-constraint method pseudocode.
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 1 Introduction
SemKoRe helps to avoid this semantic mismatch, and captures all the machine, failure and maintenance data as a knowledge graph, allowing several actors to benefit (Sections 4 and 5). For example, Operators and Technicians can benefit from the knowledge provided by other operators at different sites to address their issues. This will also reduce the risk of mismanipulation of machines by incompetent operators, which is a considerable industrial threat in reality [4]. OEM Machine Builders can improve the next generations of the machines they build, thanks to the knowledge captured in SemKoRe that helps them to know why certain machines have higher failure rates. Analytic teams can improve their work, as SemKoRe will provide a global view of machine failures and the background of a variety of contexts. We implement our SemKoRe system using a triplestore called \"GraphDB by Ontotext\" (as cloud/gateway triplestore), IBM Node-Red (flow-based development tool), Microsoft Azure (for cloud service), Azure IoT Hub (for IoT connectivity), and Docker (to package SemKoRe services) (Section 6). To support the needs of different actors, SemKoRe is developed using the semantic web and ontologies [5]. This approach helps to accommodate future requirements and to provide a clear separation of concerns between the application needs and the domain knowledge which in this case is machine domain and failure domain knowledge. We adopted a distributed architecture in which knowledge collection is performed on the edge layer. The collected knowledge is shared with other actors and machines through a cloud-based instance. We elaborate the lessons learned in Section 7.1, and offer future research directions in Section 7.2 and conclude in Section 7.3.
## 3 Results and Discussion
### Characterization of Collagen Hydrolysate before and after Sterilization by DSC  
The examination of the thermal properties of collagen hydrolysate using DSC, as depicted in Figure 7, revealed the distinct characteristics of its heat absorption profile. The DSC thermogram displayed a heat absorption peak within the 30-90 \({}^{\circ}\)C range. Notably, the heat absorption peak for collagen hydrolysate remains at 40 \({}^{\circ}\)C both before and after sterilization with ethylene oxide. However, when collagen hydrolysate is sterilized using beta and gamma radiation, a peak is shifted to approximately 50 \({}^{\circ}\)C. This temperature shift corresponds to the findings in prior studies [31], suggesting that the highest peak temperature indicates the material's melting point. In the temperature range 175-200 \({}^{\circ}\)C, an endothermic peak was observed after sterilization, which resulted in the peak shifting to higher temperatures. The group sterilized with ethylene oxide had a peak at 185 \({}^{\circ}\)C, the group sterilized with beta radiation had a peak at 205 \({}^{\circ}\)C, and finally, the group sterilized with gamma radiation had a peak at 196 \({}^{\circ}\)C. These peaks are caused by the degradation of polypeptide chains and the evaporation of residual water and/or tightly bound water. The different sterilization methods may cause changes in the peak position compared to the original. Both beta and gamma irradiation can lead to the formation of radicals, which can cause the oxidation and aggregation of proteins. Ethylene oxide sterilization, which uses high temperatures, may result in the melting of collagen hydrolysate and, upon cooling, the formation of densely packed crystals with a higher melting point. These factors contribute to the requirement of higher temperatures for the degradation of polypeptide chains. Additionally, these peaks suggest a correlation with the material's water solubility.