esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 2 Controller Design
### The Framework for Multilayer MPC  
In our previous work [5] we found that most path tracking controllers can only adapt to different reference velocities and cannot regulate the operation velocity automatically. The reason for this phenomenon is that in many path tracking controllers, the state of the path tracking systems is partly determined by the reference velocity.  
The performance of the controller varies greatly at different reference velocities. We guess that it is possible to get control inputs at different reference velocities through several controllers. These controllers are the first layer of the multilayer MPC, and we name them as path tracking controllers with a fixed velocity. As shown in Figure 1, we set up three path tracking controllers with fixed velocities in the multilayer MPC framework.  
In these three controllers:  
\[v_{ref}(t)=\left\{\begin{array}{rl}v_{ref}(t-1|t),&\text{in controller A}\\ v_{ref}(t-1|t)+\delta,&\text{in controller B}\\ v_{ref}(t-1|t)-\delta,&\text{in controller C}\end{array}\right. \tag{1}\]  
where \(v_{ref}(t|t)\) is the reference velocity in the current control period, \(v_{ref}(t-1|t)\) is the reference velocity in the last control period, and \(\delta\) is the acceleration amplitude or deceleration amplitude based on constraints and control period. In addition:  
\[\delta=\dot{v}_{lim}T \tag{2}\]  
where \(\dot{v}_{lim}\) is the acceleration limit and \(T\) is the control period.  
These three controllers can generate three sets of control inputs. Due to constraints such as the upper limit of the articulated angle speed, sometimes these inputs cannot completely eliminate errors between the articulated vehicle and the reference path. By comparing these residual errors it is possible to decide which set of control inputs can make the path tracking control system perform better. Therefore, the second layer controller is a velocity decision controller.
# Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges
## 2 Review of Probabilistic Model of Extreme Response Scenarios in Long-Span Bridges
### Heavy Vehicle Positions  
Stochastic process models have been widely used for modeling vehicle positions, such as compound Weibull processes and compound Poisson processes. In the study of extreme response scenarios, a suitable probability distribution needs to be chosen to simulate the occupancy state of cells occupied by heavy vehicles on specific lanes. The Poisson distribution, commonly used in statistics, describes the number of expected events occurring within a specific period or area and is used to simulate the number of occurrences of a specific event within a specific area [33].  
Based on the statistical results of the number of cells occupied by heavy vehicles on each lane, a simple and reasonable single-lane heavy vehicle spatial distribution model was established using the Poisson point process. In assuming that the number of cells occupied by heavy vehicles within the loading length \(L\), the probability of \(t\) can be determined as follows:  
\[P(T_{L}=t)=\frac{(\lambda L)^{t}exp(-\lambda L)}{t!},\ \ \ \ \ t=0,\ 1,\ 2,\ldots \tag{2}\]  
where \(P(\cdot)\) represents the probability of the event occurring, and \(\lambda\) denotes the average occurrence rate of vehicle occupancy in the cells. For a stationary heavy vehicle distribution process, \(\lambda\) remains constant over the entire loading length.  
In addition to the stationary process, the position distribution of heavy vehicles can also be considered a non-stationary process, where the occurrence rate \(\lambda\) varies along the loading length. For a given load effect, the occurrence rate of heavy vehicles occupying a cell can be represented by the Poisson parameter \(\lambda(l)\). This parameter is assumed to vary continuously with the loading position, and this variation is directly related to the shape of the influence line for the effect being considered (loading vehicles near the region of the influence line's maximum value tends to yield extreme effects, thus the probability of a cell being occupied by a heavy vehicle in extreme scenarios is correlated with the shape of the influence line). Since the occurrence probability is determined by the position of the cells along the loading length, \(\lambda(l)\) can be calculated using the following equation:  
\[\lambda(l)=\lambda_{0}(l)\cdot\theta \tag{3}\]  
where \(\lambda_{0}(l)\) represents the average occurrence rate of a cell being occupied by a heavy vehicle, and \(\theta\) reflects the variation in this parameter along the loading position. The observation of maximum load effects in long-span bridges has shown that the positions of the heavy vehicles are better reflected with the non-stationary Poisson process. Therefore, this process is considered in the probabilistic model of extreme response scenarios.
# Static Factors in Sitting Comfort: Seat Foam Properties, Temperature, and Contact Pressure
## 3 Experimental Campaign
### Experiment 2: The Influence of Seat-Passenger Interface Pressure and Temperature
#### 3.3.1 Influence of Anthropometric Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sub.**} & \multicolumn{4}{c}{**\(P_{max}\) (mmHg)**} & \multicolumn{4}{c}{\(P_{avg}\) (mmHg)} & \multirow{2}{*}{**Contact Area**} \\ \cline{2-2} \cline{5-8}  & **R1** & **R2** & **L1** & **L2** & **R1** & **R2** & **L1** & **L2** & **(cm2)** \\ \hline S1 & 38 & 21 & 35 & 24 & 28 & 16 & 28 & 19 & 1100 \\ \hline S2 & 51 & 15 & 46 & 34 & 41 & 13 & 38 & 25 & 1035 \\ \hline S3 & 50 & 49 & 46 & 35 & 37 & 38 & 40 & 29 & 1350 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Maximum and mean pressure and contact area regarding the current standard AP seat. Figure 10: Thermographic illustrations where (**a**) is on the course of the experiment and (**b**) is the defined sitting area and profile line. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme.**} & \multicolumn{4}{c}{**Sitting Area**} & \multicolumn{4}{c}{**Sitting Profile Line**} \\ \cline{2-9}  & **Min. (\({}^{\circ}\)C)** & **Max. (\({}^{\circ}\)C)** & **Max-Min (\({}^{\circ}\)C)** & **Avg. (\({}^{\circ}\)C)** & **Min. (\({}^{\circ}\)C)** & **Max. (\({}^{\circ}\)C)** & **Max-Min (\({}^{\circ}\)C)** & **Avg. (\({}^{\circ}\)C)** \\ \hline S1 & 28 & 34.1 & 6.1 & 31.8 & 25.2 & 32.9 & 7.7 & 31.8 \\ \hline S2 & 27.1 & 33.1 & 6.0 & 31.3 & 25.0 & 31.2 & 6.1 & 28.5 \\ \hline S3 & 24.4 & 30.2 & 5.8 & 27.7 & 23 & 28.2 & 5.3 & 26.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Temperature records regarding the comfort seat. of subjects, the results only reveal a hint. However, to produce further conclusions, more experiments need to be performed. Regarding the standard seat, the results are exhibited in Table 10.  
The results of standard seats revealed a similar tendency to that of comfort. Both demonstrated equivalent maximum and average temperatures in the sitting area and profile line. Subjectively, three subjects unanimously ranked the ischial tuberosity areas (R1 and L1) as \"Slightly uncomfortable\", which, in fact, exhibited the highest pressure peaks. Therefore, complying with the findings of [PERSON] et al. [28] and [PERSON] et al. [29], the ischial tuberosity area seems to be the one most influencing the perception of sitting comfort. Additionally, based on the mentioned assumption, it is also possible to conclude that pressure profiles were strongly linked to discomfort rankings [9; 13; 14; 27]. Regarding temperature, the similarities between experiment results did not allow for conclusions to be made about its impact on sitting impressions. That influence will be further analysed in the following sub-section. It should be highlighted that the small number of volunteers is the main limitation of this experiment.
## 2 Materials and Methods
### Statistical Analysis  
Method error: to standardize the measurements, the data were collected by the main operator (S.I.) and revised by an operator with experience with the used software (A.U.). To determine the method error, the measurements and the reference points were repeated on ten cases selected randomly. The method error according to Dahlberg formula (inter and intra operator) varied from 0.21 to 0.55 mm (no significant value). Besides, interclass correlation coefficient was calculated, and all the values resulted higher than 0.95. Overall, the error in the methodology can be considered negligible.  
Shapiro-Wilks test demonstrated that the data were normally distributed (W = 0.93) so a parametric statistic was utilized. The sample size was calculated with a study's statistic power higher than 0.85 based on the variable \"maximally molar expansion\" (average values and standard deviations) found by [PERSON] et al. (2018) with alfa = 0.5 and beta = 2. Based on these parameters, the requested sample size resulted in a minimum 30 patients for each group.  
The descriptive statistics (average and standard deviation) were calculated for the considered variables. All the analysis were conducted on the distinct differences at T0 and T1 identified in each patient. The difference between the two devices were tested using the parametric statistics using the T-Student test on all the found measurements. The significance of the values lower than 0.05 were accepted as significant in all the statistics.  
Figure 1: Measurements on Maestro 3D Ortho Studio’s digital impressions before and after the expansion; occlusal view (**A**), dental widths (**B**), gingival widths (**C**), arch perimeter (**D**).  
Figure 2: Measurements of first permanent upper molar dental width (bis) (**A**) and first permanent lower molar dental width (**B**).
## 3 Materials and Methods  
### Overall Architecture  
As shown in Figure 1, the HPANet comprises an encoder, a backward multi-scale feature fusion module (BMFM), a forward noise reduction module (FNRM) and a boundary feature refinement module (BFRM).  
The encoder adopts PVTv2 as the backbone to capture multi-scale features and details of polyps. Through the PVTv2, the encoder outputs four layers of feature maps with different scales, which are represented as \(\{M_{1},\ M_{2},\ M_{3},\ M_{4}\}\). The BMFM receives \(M_{i}\) (\(i\in\{1,\ 2,\ 3,\ 4\}\)) as input and uses three spatial attention map(SAM) to aggregate the adjacent low-level feature map \(M_{i}\) and high-level feature map \(M_{i+1}\) to generate \(D_{i}\)(\(i\in\{1,\ 2,\ 3,\ 4\}\)). SAM is based on a multi-scale fusion strategy, which can effectively capture the morphological information of different sizes of polyps in the image. To better learn the texture features of polyps and reduce noise interference, the FNRM uses three boundary enhance blocks (BEBs) to aggregate each pair of adjacent \(D_{i}\) and \(D_{i+1}\), and progressively generates feature maps \(N_{i}\)(\(i\in\{1,\ 2,\ 3,\ 4\}\)). Based on \(N_{i}\), the BFRM incorporates the Convolutional Block Attention Module (CBAM) [26], which makes it easier for the network to focus on important areas such as boundaries, and then adjusts the number of channels in the feature map through a convolutional head. Finally, the four feature maps are upsampled to the original image size and aggregated by addition to produce the final segmentation result.
## 3 Contextual Received Signal Strength Approach
### CRSS Approach
#### 3.4.3 Probabilistic Approach in Interpreting RSS
Based on the log-distance path loss model, the value of the estimated PLE in Equation (18), and the received power value obtained at neighbor node \(k\), \(Pr_{ik}\) is the received power at node \(k\), and each node \(i=1,\cdots,N\), estimates its distances separating its connected neighbor nodes, \(k\
eq i\in\kappa=\{i_{1},\cdots,\hat{n}_{i}\}\), as \[\hat{d}_{ik}=10\frac{PL(d_{0})-Pr_{d_{ik}}}{10\hat{\gamma}}.\] (19)
* Localization of nodes To estimate the positions of nodes, we exploit estimated inter-node distances, RSS values, and the sink position. Our approach employs a gradient-based optimization method with weighted residuals, utilizing the RSS values as a key factor in the objective function. Subsequently, we assess the accuracy of the estimated positions through the calculation of the NRMSE.
## 3 Results and Discussion
### Analysis of Image Data Preprocessing  
As previously stated, the region of interest in this study was the leaf surface, and thus required proper isolation from background and/or other miscellaneous objects from the foreground. The foundational Segment Anything Model (SAM) was implemented during the image data preprocessing step to enable a bounding box for image isolation and the generation of segmentation masks (Figure 5A,B). Figure 5 shows a visual representation of the preprocessing segmentation process that the proposed software undergoes. The SAM generated up to 17 segmentation masks per image; however, 3 were utilized in further analysis. This model was evaluated with two separate datasets: digital images of leaves sprayed with pesticide under controlled laboratory conditions and another group with digital images of one-year-old citrus trees under field conditions.  
To assess the accuracy of the segmentation model with the datasets from both groups, average segmentation accuracy (IoU) is summarized in Table 1. The reported IoU values from the dataset in the leaves in the laboratory conditions group ranged from 97.1% to 99.5%. Conversely, the IoU values from the leaves in the field conditions group ranged from 80.8% to 98.2%. However, the segmentation accuracy for the field conditions datasets was significantly lower than the desired. An average segmentation accuracy of 0.92 was deemed to be effective at isolating the leaf surface as the RoI, isolating it from the unnecessary objects present in the image. Furthermore, this accuracy level is to be expected under field conditions considering the multitude of interfering objects in a grove and untrained growers collecting the image data.  
Figure 5: Overview of the image preprocessing method developed using a leaf spray with OTC under laboratory conditions. (**A**) Digital image used as a source after bounding box region of interests is determined.(**B**) collection of segmentation masks generated with the implemented SAM. SAM generates every possible segmentation mask based on the number of objects it can detect in the image. Only the most prominent masks featuring the desired object in focus are selected, in this case the first three masks generated as ranked by SAM.
# Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming
## 3 The Proposed Method
### Fitness Function Design  
The fitness function is the guideline for the evolution of the GEP algorithm. For feed-forward neural networks, the topology selection and training of weights can be regarded as an optimization process. The purpose of optimization is to design the network so that the fitness function value reaches the maximum value. The performance of the current network for a given training data set is described by a least squares error function.  
We use category-based multilabel evaluation indicators. We first measure the classifier's corresponding two-class classification performance on a single class, and then calculate the average performance of the classifier on all classes as the evaluation index value of the classifier. Suppose we have a multilabeled test set with \(p\)-many sample data \(S=\left\{(x_{i},Y_{i})\middle|1\leq i\leq p\right\}\) for the \(j\)th category \(y_{j}\) (\(1\leq j\leq q\)). In terms of the multilabel classifier, the two-class classification performance of \(h(\cdot)\) in this category can be described by the four statistics given by Equations (9)-(12).  
1. \(TP_{j}\) (#true positive instances)  
\[TP_{j}=|\left\{x_{i}\mid y_{j}\in Y_{i}\wedge y_{j}\in h(x_{i}),(x_{i},Y_{i}) \in S\right\}| \tag{9}\]  
2. \(FP_{j}\) (#false positive instances)  
\[FP_{j}=|\left\{x_{i}\mid y_{j}\
otin Y_{i}\wedge y_{j}\in h(x_{i}),(x_{i},Y_{i}) \in S\right\}| \tag{10}\]  
3. \(TP_{j}\) (#true negative instances)  
\[TP_{j}=|\left\{x_{i}\mid y_{j}\
otin Y_{i}\wedge y_{j}\
otin h(x_{i}),(x_{i},Y_{i })\in S\right\}| \tag{11}\]  
4. \(FP_{j}\) (#false negative instances)  
\[FP_{j}=|\left\{x_{i}\mid y_{j}\in Y_{i}\wedge y_{j}\
otin h(x_{i}),(x_{i},Y_{i })\in S\right\}| \tag{12}\]  
From Equations (9)-(12), \(TP_{j}+FP_{j}+TN_{j}+FN_{j}=p\) is established. Most classification performance indicators, such as accuracy, precision, and recall, can be derived from the above four statistics--see Equations (13)-(15).  
\[\text{Accuracy}=B\Big{(}TP_{j},FP_{j},TN_{j},FN_{j}\Big{)}=\ \frac{TP_{j}+TN_{j}}{TP_{j}+FP_{j}+TN_{j}+FN_{j}} \tag{13}\]  
\[\text{Precision}=B\Big{(}TP_{j},FP_{j},TN_{j},FN_{j}\Big{)}=\ \frac{TP_{j}}{TP_{j}+FP_{j}} \tag{14}\]  
\[\text{Recall}=B\Big{(}TP_{j},FP_{j},TN_{j},FN_{j}\Big{)}=\ \frac{TP_{j}}{TP_{j}+FN_{j}} \tag{15}\]  
Therefore, combined with category-based multilabel classification evaluation index, we design a fitness function for the MGEP classification algorithm.  
1. Design of fitness function based on macro-averaging:  
\[Fit_{i}=\left|R-100\times\frac{1}{q}\sum_{j=1}^{q}B\Big{(}TP_{j},FP_{j},TN_{j },FN_{j}\Big{)}\right| \tag{16}\]  
2. Design of fitness function based on micro-averaging:  
\[Fit_{i}=\left|R-B\left(\sum_{j=1}^{q}TP_{j},\sum_{j=1}^{q}FP_{j},\sum_{j=1}^{ q}TN_{j},\sum_{j=1}^{q}FN_{j}\right)\right| \tag{17}\]  
In Equations (16) and (17), \(Fit_{i}\) is the fitness value of the \(i\)th individual to the environment, and \(R\) is the selected bandwidth.
## 2 Data and Methods
### Data
#### 2.1.2 Remote Sensing and Water Quality Data  
Taihu Lake is one of the key lakes of China, and national water quality monitoring stations have already been established for this basin. Therefore, the water quality data utilized in this study are sourced from the National Surface Water Assessment Monitoring sections of Taihu Lake, spanning from 2021 to 2023.  
For remote sensing, Sentinel-2 data were chosen, obtained from the European Space Agency (ESA) website ([[https://dataspace.copernicus.eu/](https://dataspace.copernicus.eu/)]([https://dataspace.copernicus.eu/](https://dataspace.copernicus.eu/)) (accessed on 24 October 2024)). These data boast a high spatial resolution of up to 10 m, a temporal resolution of 5 days, and a short revisit period. Sentinel-2 images with minimal cloud cover were selected from 2021 to 2023 for the inversion of total phosphorus in Taihu Lake. A total of 32 scenes of Sentinel-2 remote sensing data were chosen for total phosphorus inversion. The band information of the acquired Sentinel-2 data is detailed in Table 1. All the bands in the Table 1 participate in the subsequent feature selection process, and all bands with a resolution other than 10 m are resampled to 10 m. The L1C level remote sensing data underwent atmospheric correction using the Sen2 Cor 2.10 software [41] to obtain surface reflectance. Subsequently, The GDAL (Geospatial Data Abstraction Library, [[https://gdal.org/](https://gdal.org/)]([https://gdal.org/](https://gdal.org/)) (accessed on 24 October 2024)) library in Python 3.10 was utilized for band synthesis and clipping of the Sentinel-2 remote sensing data, ultimately yielding usable remote sensing data.  
Figure 1: Geographical location of Taihu Lake.
## 2 Reaction Pathways
### Mechanism of HER  
The understanding of the HER mechanism is a key factor for designing the next generation HER catalysts. Depending on the pH values of the electrolyte solution, HER proceeds through the reduction of protons (H\({}^{+}\)) or water (H\({}_{2}\)O) molecules accompanied by the subsequent evolution of gaseous hydrogen (Figure 1).  
Figure 1: The hydrogen evolution reaction (HER) pathways on the catalyst surface.  
In acidic media (i.e., pH \(<\) 7.0), the HER involves two successive steps on the surface of the catalyst. The discharge or Volmer reaction takes place in the first step of HER. In this step, a proton (H+) is adsorbed on the empty active site of the NC surface to yield an adsorbed hydrogen atom (H\({}^{\text{ads}}\)) (Equation (9)). The hydronium cation (i.e., H\({}_{3}\)O\({}^{+}\)) is the proton source in the acidic medium. In the subsequent step, a second proton (i.e., H\({}^{+}\)) combines with an adsorbed hydrogen atom (H\({}^{\text{ads}}\), from step 1) and an electron (e\({}^{-}\)) to evolve H\({}_{2}\) (Equation (10)). This step is known as the Heyrovsky step or electrochemical desorption step or ion + atom step. Herein, H\({}_{2}\) formation may also occur via an alternative pathway, which is known as Tafel or chemical desorption step. In this pathway, H\({}_{2}\) is produced via a combination of two H\({}^{\text{ads}}\) on the NC surface (Equation (11)).  
\[\text{H}_{3}\text{O}^{+}+\text{e}^{-}\rightarrow\text{H}^{\text{ads}}+\text{ H}_{2}\text{O} \tag{9}\]  
\[\text{H}^{\text{ads}}+\text{H}_{3}\text{O}^{+}+\text{e}^{-}\rightarrow\text{ H}_{2}+\text{H}_{2}\text{O} \tag{10}\]  
\[2\text{H}^{\text{ads}}\rightarrow\text{H}_{2} \tag{11}\]  
The HER is relatively slower in an alkaline medium (i.e., pH \(>\) 7.0) due to the lack of protons. In an alkaline medium, the HER starts by dissociating water (i.e., H\({}_{2}\)O) molecules to generate protons. The dissociation of H\({}_{2}\)O is involved in both the Volmer (Equation (12)) and Heyrovsky (Equation (13)) steps during HER in an alkaline medium. On the other hand, the Tafel step is the same in an alkaline HER as that in an acidic HER. The overall HER in an alkaline medium is described in Equation (14).  
\[\text{H}_{2}\text{O}+\text{e}^{-}\rightarrow\text{H}^{\text{ads}}+\text{OH}^{-} \tag{12}\]  
\[\text{H}^{\text{ads}}+\text{H}_{2}\text{O}+\text{e}^{-}\rightarrow\text{OH}^{ -}+\text{H}_{2} \tag{13}\]  
\[2\text{H}_{2}\text{O}+2\text{e}^{-}\rightarrow\text{2 OH}^{-}+\text{H}_{2} \tag{14}\]  
It is evident from the aforementioned discussion that additional energy is required to generate protons in alkaline media, and the HER drives slowly in an alkaline medium. Previous studies reported that HER performance in an alkaline medium is significantly controlled by a fine balance between \(\Delta G_{\text{H}}\)\({}^{\circ}\) and the energy required to dissociate H\({}_{2}\)O.