esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 9 Discussion
### Performance Comparison
#### 9.3.2 Antenna Options and Connectivity  
When discussing external antennas, Siemens does not specify the compatibility of its SCALANCE wireless modules with various external antennas and offers only a limited selection. The gains of their omnidirectional and sector antennas are relatively low, at 8 and 9 dBi, respectively. This necessitated the use of high-gain directional antennas, boasting 23 dBi, especially in the locations of the wells. Considering the use of different types of antennas, the link budget calculations were performed in both directions, from the base station to the wells and vice versa.  
In contrast, Phoenix Contact's Trusted Wireless technology explicitly supports the use of different external antennas. This flexibility enables the integration of high-gain omnidirectional antennas, significantly enhancing link connectivity. Such a feature is crucial, particularly as CHs and remote nodes must transmit signals in various directions to facilitate data transmission and explore alternate paths to the BS.
## 3 Results
### Estimated Demand for Road Transport  
The estimation of the alternative fuels demand for the road sector required, first, to distinguish between cars: light- (LDV) and heavy-duty (HDV) sub-sectors. This was needed as the energy demand, the type of fuel, and, most importantly, the available alternative fuels can significantly differ in the 2030 scenario.  
The study [14] presents a meta-analysis over 18 selected publications, resulting in 56 scenarios. The authors found that based on pre-COVID-19 estimations, the 2030 demand for alternative fuels ranged between 7.0 and 17.7 Mtoe for passenger cars and light-duty vehicles (LDV) and between 8.1 and 9.5 Mtoe for heavy-duty vehicles (HDV). The resulting total for the presented estimations varied between 15.1 and 27.2 Mtoe in 2030. These results are in line with other studies, as the Concawe 2021 report [15] defines an expected biofuel use in the road sector in 2030 of about 20-22 Mtoe.  
The type of fuels that can be used to cover these estimated demands was based on the current trends: according to [26], biodiesel and HVO represented 82% of the EU biofuels consumption in 2018, followed by bioethanol for 17%, and biogas derived fuels for less than 1%. These shares appropriately represent the pool of fuels used for the LDV subsector; while in the HDV sub-sector, the contribution of ethanol, so far, is negligible. For this reason, for the HDV sector, a consumption of 98% biodiesel and HVO is considered, complemented by 2% biomethane, either in the form of compressed biomethane (CBM) or liquified biomethane (LBM).
###### Abstract  
Wearable inertial measurement units (IMUs) are increasingly used in human motion analysis due to their ability to measure movement in real-world environments. However, with rapid technological advancement and a wide variety of models available, it is essential to evaluate their performance and suitability for analyzing specific body regions. This study aimed to assess the accuracy and precision of an IMU-based sensor in measuring trunk range of motion (ROM). Twenty-seven healthy adults (11 males, 16 females; mean age: 31.1 \(\pm\) 11.0 years) participated. Each performed trunk movements--flexion, extension, lateral bending, and rotation--while angular data were recorded simultaneously using a single IMU and a marker-based optoelectronic motion capture (MoCap) system. Analyses included accuracy indices, Root Mean Square Error (RMSE), Pearson's correlation coefficient (r), concordance correlation coefficient (CCC), and Bland-Altman limits of agreement. The IMU showed high accuracy in rotation (92.4%), with strong correlation (r = 0.944, \(p\) < 0.001) and excellent agreement [CCC = 0.927; (0.977-0.957)]. Flexion (72.1%), extension (64.1%), and lateral bending (61.4%) showed moderate accuracy and correlations (r = 0.703, 0.564, and 0.430, \(p\) < 0.05). The RMSE ranged from 1.09\({}^{\circ}\) (rotation) to 3.01\({}^{\circ}\) (flexion). While the IMU consistently underestimated ROM, its accuracy in rotation highlights its potential as a cost-effective MoCap alternative, warranting further study for broader clinical use.  
m _Applied Sciences_  
_Article_
## 5 Methodology for Anomaly Detection and Mitigation
### Mitigation
#### 5.2.4 Mitigation against Adversarial Attack  
Preventing adversarial attacks is a problem that must be solved urgently. There are many studies addressing adversarial attacks, but almost none of them can be applied to the factory setting. The reason is that the factory environment is too complex with too many variables, making traditional defense methods almost impossible to achieve, such as adversarial learning. In addition, although there is a large amount of data in the factory, there are very few clearly labeled data, which greatly increases the difficulty of the used training data sets and makes most supervised-based defense methods unsuitable; thus, an unsupervised or semi-supervised method has to be used. In this project, effective and useful defense methods for FoF are provided. It is assumed that the FoF uses artificial intelligence methods or will use artificial intelligence methods, that there are real-time monitoring systems in the factory, and that the AGVs have sensitive vision sensors. First, the suggested method is used by restarting the AGV and looking for other suitable systems, such as charging stations. This is the first step of defense: try to initialize the parameters of the AGV again. This method is usually very effective for anomaly detection, and this method is very simple, so it is recommended as the first step of defense or the pre-step of the adversarial defense. Next, it is intended to offer two more specific defenses.
# Repair Performance of Self-Healing Microcapsule/Epoxy Resin Insulating Composite to Physical Damage
## 1 Introduction
Furthermore, the repair mechanism was analyzed by the capillary theory and molecular diffusion model on the defect surface. In addition, the change of alternating current (AC) breakdown strength of epoxy resin composite was studied innovatively. Combined with the reaction mechanism and the interface electric field model, the changes of composite electrical insulation performance were explained. The goal of this work is to explore the repair ability of insulating material to physical damage and prolong the service life of insulation material fundamentally. Furthermore, the work in this paper can lay an experimental foundation for the development of self-healing insulation material.
## 2 Optimal Trajectory Formulation
### Final Formulation
In fact, the problem of minimizing (4) subject to (2) can be formulated as:  
\[ \begin{split}\min_{\boldsymbol{\tau}}I(\boldsymbol{\tau})& =\min_{\boldsymbol{\tau}}\left\{\mathbf{b}^{\top}\mathbf{A}^{- \top}(\boldsymbol{\tau})\mathbf{Q}(\boldsymbol{\tau})\mathbf{A}^{-1}( \boldsymbol{\tau})\mathbf{b}\right\}\\ \text{s.t.}&\begin{cases}\mathbf{1}^{\top} \boldsymbol{\tau}=t_{f},\\ \tau_{i}>0,\text{ for }i=1,\ldots,N,\end{cases}\end{split}  \tag{29}\]  
where \(\mathbf{1}\in\mathbb{R}^{N}\) is the all-ones column vector. This formulation has a convex feasible domain, and due to the inversion of (28), its computational complexity is proportional to \(\mathcal{O}(n^{3}N^{3})\).
## 3 Control Strategy Design
### The Switching Mode
#### 3.3.1 Switching Mode for Extension  
In the NIMV system, the PE and HSRE modes are two extension modes typically employed for the boom excavator. To switch between these two modes, it is necessary to find out the limited velocity of cylinder and load capability of each mode. In this case, the limited cylinder's velocity is determined by the maximum oil which is supplied by the pump. Moreover, the load capability is the maximum load that the cylinder can lift at the maximum pressure supplied from the pump and at a specified velocity. The limitation of the cylinder's velocity is described as follows:  
\[\dot{x}_{PE}=\frac{Q_{max}}{A_{a}} \tag{16}\]  
\[\dot{x}_{HSRE}=\frac{Q_{max}}{(A_{a}-A_{b})} \tag{17}\]  
Substituting Equations (16) and (17) for Equation (2), respectively. The load capability of the PE and HSRE mode can be written as:  
\[m_{PE}=\frac{p_{s}A_{a}-p_{r}A_{b}-B_{v}\dot{x}_{PE}}{g+\ddot{x}_{PE}} \tag{18}\]  
\[m_{HSRE}=\frac{p_{s}(A_{a}-A_{b})-B_{v}\dot{x}_{HSRE}}{g+\ddot{x}_{HSRE}} \tag{19}\]  
Figure 7: Membership functions of FLC for valve control.3.3. Switching modes.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{\(\mathbf{R_{FCL}}\) for Pump} & \multicolumn{8}{c}{_e_} \\ \cline{2-9}  & **NB** & **NM** & **NS** & **Z** & **PS** & **PM** & **PB** \\ \hline \multirow{3}{*}{\(\
u_{r}\)} & NS & PL & PB & PM & PS & Z & Z & Z \\  & Z & Z & Z & Z & Z & Z & Z \\  & PS & Z & Z & Z & PS & PM & PB & PL \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rule table of FLC for pump.  
From Equations (18) and (19), the load-velocity curve of cylinder for PE and HSRE mode are drawn at the maximum pressure of 100 bar and maximum flow rate at 12.45 L/min as shown in Figure 8 where the blue and black regions are selected for the PE mode, the red region represents for the HSRE mode. Based on the Figure 8, the maximum load that the cylinder can lift, and the maximum velocity are determined. In detail, the PE mode can push the maximum load of 1267 kg and obtain a limited velocity of 0.165 m/s. Meanwhile, the HSRE mode can only lift the capability load of 320 kg, but the maximum velocity which can achieve at 0.66 m/s is much larger compared with the velocity of the PE mode. Based on the velocity-load condition, and combined with the energy saving element, the PE and HSRE modes are flexibly switched to ensure the system working in the suitable mode.
## 3 Results  
### Interpretation of VIs and SIF for Different Vegetation Groups  
From the TOC reflectance data of HyPlant DUAL, we calculated several VIs maps as presented in Figure 4. The SR (Figure 4a) and NDVI (Figure 4b), which act as a proxy for vegetation greenness content, showed wide range of values between 0 to 56 and 0 to 0.96, respectively, over the study area [49; 50] (Figure 4a,b). Grasslands and deciduous forest covers showed the highest values of SR and NDVI. Sparsely vegetated areas like post-agricultural land, pioneer vegetation of sandy and shallow soils, as well as herbaceous vegetation of forest clearings, showed significantly smaller values in both of the indices mentioned above.  
EVI acts as a proxy of vegetation biomass, and also helps in the minimization of background signal and the atmospheric impact [51]. The derived EVI map (Figure 4c) showed comparatively higher values inside the peatland area due to the high density and biomass of the vegetation, whereas the smallest EVI values were recorded in forested areas surrounding the peatland. EVI ranged from 0 to 2.25, and the highest values of EVI were observed in extensive grassland areas located nearby the peatland. Xanthophyll cycle indicator PRI [52] was observed between a \(-\)0.43 to 0.30 range over the study area (Figure 4d). Higher values of PRI were observed from the peatland, whereas outer forest areas showed lower PRI values.  
The maps provided in Figure 4e,f show the first SIF maps from a fan peatland. Maps of SIF\({}_{760}\) and SIF\({}_{687}\) from the Rzecin peatland represent novel and complementary information on the plant structure-function relationship. SIF\({}_{760}\) and SIF\({}_{687}\) for the heterogeneous peatland and its surroundings ranged between 0 to 3.21 mW\(\cdot\)m\({}^{-2}\)sr\({}^{-1}\) nm\({}^{-1}\) and 0 to 2.93 mW\(\cdot\)m\({}^{-2}\)sr\({}^{-1}\) nm\({}^{-1}\), respectively. Highest SIF emissions in both oxygen absorption bands (O\({}_{2}\)A and O\({}_{2}\)B) were obtained from grassland areas and deciduous forests, whereas non-fluorescent or very low-fluorescent targets like mowed grasslands,post-agricultural land, and herbaceous vegetation of forest clearings showed SIF values close to 0. A mixture of SIF signals was observed from both inside and outside of the peatland area.
## 4 Datasets and Signals
### Signals
#### 4.2.1 Accelerometer Data
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Author** & **Year** & **Data Used for the Road Anomaly Detection and Classification** \\ \hline [PERSON] et al. [14] & 2018 & \(Z\)-axis of the accelerometer sensor. \\ \hline [PERSON] et al. [5] & 2018 & X and Y axes gyroscope data. \\  & & \(Y\)-axis accelerometer data. \\ \hline [PERSON] et al. [22] & 2019 & \(Z\)-axis of the accelerometer sensor. \\ \hline [PERSON] et al. [42] & 2019 & Three-axes of the accelerometer data. \\ \hline [PERSON] et al. [36] & 2019 & \(Z\)-axis and \(X\)-axis of acceleration data. \\ \hline [PERSON] et al. [21] & 2020 & \(Z\)-axis acceleration. \\ \hline [PERSON] et al. [3] & 2020 & Three-axes acceleration and gyroscope data. \\ \hline [PERSON] et al. [1] & 2020 & Three-axes accelerometer sensor. \\ \hline [PERSON] et al. [40] & 2020 & \(Z\)-axis of the accelerometer sensor. \\  & & Y-axis of the gyroscope sensor. \\ \hline [PERSON] et al. [26] & 2020 & Three-axes of the accelerometer sensor. \\ \hline [PERSON] et al. [40] & 2020 & Three-axes acceleration and gyroscope data. \\ \hline \multirow{4}{*}{[PERSON] et al. [11]} & \multirow{4}{*}{2021} & \(X\), \(Y\), and \(Z\)-axes linear acceleration \\  & & (Calculated from gyroscope and magnetometer data) \\ \cline{1-1}  & & Gyroscope data used for reorientation \\ \cline{1-1}  & & of linear acceleration. \\ \hline [PERSON] et al. [43] & 2021 & Three-axes acceleration and gyroscope data \\ \hline [PERSON] et al. [38] & 2022 &
\begin{tabular}{l} Z and Y-axes linear acceleration \\ Roll and pitch angles gyroscope data \\ \end{tabular} \\ \hline [PERSON] et al. [37] & 2022 & Three-axes of accelerometer and gyroscope sensors. \\ \hline [PERSON] et al. [35] & 2022 & Three-axes accelerometer data. \\ \hline [PERSON] et al. [18] & 2022 & Three-axes accelerometer data. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the type of signals used for road surface anomaly detection and classification through vibration-based techniques. Equation (1). Euler angles are the three angles that provide the three rotation matrices [52] established in Equation (1). \[A=BCD \tag{1}\]  
One component of accelerometers commonly analyzed for road anomaly detection is the \(z\)-axis, which is related to the vehicle's vertical acceleration. However, other authors have also proposed to work with the other two axes to improve the performance of detection systems as proposed by [PERSON] et al. [36]. Table 6 shows a detailed overview of the accelerometer and gyroscope axes analyzed in the literature for road surface anomaly detection and classification.
# E-Eye-Based Approach for Traceability and Annuality Compliance of Lentils
## 1 Introduction
These studies confirmed the existence of a genetic metapopulation of the Abruzzo landraces, which will be named in this study as the L'Aquila class, that is differentiated from the most known Italian lentil variety, i.e., the PGI lentil of Castelluccio di Norcia. These studies are of enormous importance in monitoring the diversity or similarity of autochthonous varieties, and, over time, they can provide information about crop resilience against climate change. Focusing on this last reported study, it is interesting to note the agreement of the image analysis performed by the authors with the experimental evidence of the morphological and genetic characterization, demonstrating the power of this tool as a preliminary approach for the authentication and characterization of local varieties belonging to the same species. To date, image analysis has been widely used to study and characterize lentils [24]. This has been accomplished using different methods, depending on the end purpose. [PERSON] et al. employed image analysis to obtain lentil seed size and shape, demonstrating that seed diameter, thickness, plumpness, and degree of edge roundness, coupled with multivariate linear regression can predict dehulling efficiency [25]. A flatbed scanner and an image processing program were used to obtain seed morphological characteristics. The method was also combined with the mean color information and a Linear Discriminant Analysis to identify five Sicilian landraces and three common Canadian accessions [26]. However, a mere colorimetric analysis has been shown to be efficient for quality control [27], allowing for distinguishing between the Abruzzo and Castelluccio di Norcia ecotypes [23], recognizing deteriorated lentils [28], and identifying adulterated lentil flour [29]. Thus, based on the evidence reported in the literature, Multivariate Image Analysis (MIA), which is a rapid non-destructive, and objective method, was applied as a preliminary stage of landraces authentication. In detail, we have applied MIA to distinguish the typical genetic traits of the studied ecotypes, namely L'Aquila, Castelluccio di Norcia, and Colfiorito. Due to the way the dataset was constructed (more details will be given in Section 2.1), the possibility of distinguishing expired lentils from those that are edible was also considered. The most advanced chemometric methods were applied to the colorgramsobtained from MIA. In addition, ANOVA-Simultaneous Component Analysis (ASCA) was employed as an exploratory analysis to evaluate the significant factors that actually characterize the dataset. Sequential Preprocessing through ORThogonalization (SPORT) was the supervised pattern recognition method employed to discriminate between the Italian landraces and among expired and edible samples. Therefore, although several image analysis methods have been applied to lentil characterization, no examples are reported in the literature regarding the use of MIA coupled with such chemometric approaches.
## 2 Molecular Mechanisms of Antimicrobial Agents Recalcitrance in Clinically Important Biofilms
### Biofilm Matrix
#### 2.1.4 The Viscous Environment of the EPS Matrix  
[PERSON] et al. revealed an interesting mechanosensing mechanism that uses a positive feedback control loop coming into play during biofilm development in _Bacillus subtilis_ chosen as the model Gram-positive bacterium [32; 33]. These authors showed that the formation of the _B. subtilis_ biofilm matrix is regulated by the bacterial flagellum acting as a mechanosensor that activates the DegS-DegU two-component signaling pathway. The secretion of exopolsaccharides during biofilm development renders the biofilm matrix more viscous. Increased viscosity restricts the rotation of the flagellum generating, in turn, an increased torque force on the flagellar motor. This mechanosensing mechanism is presumed to alter gene expression during the initial attachment step in biofilm development. In _B. subtilis_ hampered flagellum, rotation triggers the biosynthesis of \(\gamma\)-poly glutamic acid (\(\gamma\)-PGA) in a DegS-DegU-mediated signal transduction process. Phosphorylated DegU activates the pgs operon, which is responsible for \(\gamma\)-PGA production, the main component of the EPS matrix of _B. subtilis_. So, increased biofilm matrix viscosity stops flagellar rotation, which in turn reinforces EPS matrix production and further raises viscosity. Thereby, sensing the EPS matrix viscosity by bacterial flagellum allows for a positive feedback loop regulation of EPS matrix production [33].  
Last but not least, restricted bacterial motion due to biofilm matrix viscosity holds bacteria together and creates an optimal environment for horizontal gene transfer, including those DNA segments that encode virulence factors and antibiotic resistance genes [10].
# Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)
## 2 Image Preprocessing
### Image Enhancement Processing  
To focus on the useful information and weaken or exclude interference information, the gray images need to be processed using image enhancement technology. Histogram equalization is applied to process the gray image, as it can increase the dynamic range of the pixels and lessen the computation load. After histogram equalization, the dynamic range of pixels becomes wider, and the difference between the lane line information and other interference information becomes more obvious. The effect of the histogram equalization is shown in the Figure 1.
## 3 The Proposed Approach
### The Training and Prediction Phase  
The PEE method aims at the correlations between the pixels to derive accurate predictions where the prediction-errors are modified separately. However, the traditional PEE method uses the same algorithm to predict pixels for all images. This results in poor prediction accuracy and the prediction error increases as the image is relatively complex. Therefore, our proposed method, which leverages the power of trained MLP model, can predict the pixels of the cover image and significantly reduces the prediction-error so that the embedding capacity can be hopefully increased.  
In the MLP training stage, except for pixels located in borders, the pixels are scanned from left to right and top to bottom to derive the cover sequence \((y_{1},\ldots,y_{n})\). Consider the four-neighbor tuple \((x_{\text{top}},x_{\text{bottom}},x_{\text{left}},x_{\text{right}})\) of a given pixel \(y_{i}\), shown in the left part of the Figure 3. The four-neighbor tuple is used as input data of the neural network, and the desired output value is \(y_{i}\).  
The structure of an MLP neural network has one input layer, two hidden layers, and one output layer, as shown in Figure 3. The input of four-neighbor tuples \((x_{\text{top}},x_{\text{bottom}},x_{\text{left}},x_{\text{right}})\) from the cover image is fed into the input layer of the MLP. Between the input and output  
Figure 2: The flowchart of our method.  
layers, there have 100 and 200 neurons in two hidden layers, respectively. After the information income is processed by the network, the output layer of the neural network provides one output \(\tilde{y}_{i}\) as the predicted value by the MLP, and the corresponding \(y_{i}\) in the cover image is used as reference data. We use the mean squared error (MSE) as the loss function which is calculated by taking the average squared difference between the predicted pixel value and the reference pixel value. The MSE function is defined as the Equation (1). Apparently, there is no prediction errors if and only if the MSE value is 0.  
\[\text{MSE}=\frac{1}{N}\cdot\sum_{i=1}^{N}(\tilde{y}_{i}-y_{i})^{2}. \tag{1}\]  
Here, \(N\) is the number of data points, \(\tilde{y}_{i}\) is the value returned by the model, and \(y_{i}\) is the actual value for data point. Based on those input and reference data, the MLP network is then trained with the loss function such that the edge weights of the MLP are optimized to best associate given neighborhoods with the reference pixel values.