esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 4 Experimental Results
### Evaluation Metrics for Performance  
Several performance indicators, including accuracy, sensitivity, specificity, precision, and the F1-score, are used to evaluate the efficacy of various approaches for identifying skin cancer. These measurements are computed using the true positive (TP), false positive (FP), false negative (FN), and true negative (TN) values. Performance metrics are critical for assessing the success of a model. After the model has been trained on the existing data, performance measurements are used to assess its ability to properly categorize  
Figure 2: Images of a skin lesion. (**a**) Melanoma image. (**b**) Benign image.  
fresh, unseen data that were not included in the training process. To evaluate a model's performance, numerous formulae are employed to determine key metrics including as sensitivity, accuracy, precision, recall, and F1 score.  
These formulae are as follows:  
1. Sensitivity: Also known as the true positive rate, is computed by dividing the number of true positive (TP) predictions by the total number of true positives and false negatives (FN). The following formula is used to calculate sensitivity: \[\text{Sensitivity }(\text{TP})=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (5)
2. (Specificity) true negative rate: The true negative rate is determined by dividing the number of true negative (TN) predictions by the sum of TN and FP. The formula for specificity is given below: \[\text{Sensitivity }(\text{TN})=\frac{\text{TN}}{\text{TN}+\text{FP}}\] (6)
3. Accuracy: is calculated by dividing the sum of true positives and true negatives by the total number of samples. The accuracy formula is as follows: \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\] (7)
4. Precision: The amount of genuine positive predictions is divided by the sum of true positives and false positives. Precision is calculated using the formula below: \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (8)
5. Recall (Sensitivity): To calculate recall, divide the number of true positive predictions by the sum of true positives and false negatives to compute recall. The recall formula is as follows: \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (9)
6. F1 Score: This metric combines precision and recall. It is calculated using the harmonic mean of accuracy and recall, assigning equal weight to both measures. The F1 score is calculated using the formula below: \[\text{F1 Score}=2\;(\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}})\] (10)  
These formulas provide a quantitative assessment of the model's performance by comparing its capacity to accurately classify data and discriminate between true positives, true negatives, false positives, and false negatives.
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
## 4 Discussion
### Relationship between Modifier Chemistry and Binder Rheology  
Modifier's chemical make-up contributed significantly to the long-term rheological response of MABs. Nitrogen-based compounds are known for their antioxidant properties [45]. The presence of higher nitrogen content was validated with elemental analyses and FTIR spectra and its impact was observed in the change of frequency sweep measurements as aging progressed. Modifier C, containing 3.5% nitrogen, shows similar or better crack resisting properties than the unmodified product (S5) at PAV, 2 PAV, and 3 PAV conditions. Superior rheological properties of G-modified binder can be attributed to the presence of high nitrogen content (9.0% in modifier G), which is composed of nitrogen-based compounds like amines. This validates the impact of antioxidants on resisting binder aging, and hence, reducing cracking susceptibility.  
On the other hand, sulfur presence in modifiers E (0.33%) and K (0.98%) is accompanied by lower expected performance based on the reported rheological parameters. Excessive sulfur content (>4%) in binders can cause increased oxidation due to the formation of additional sulfoxides causing embrittlement in binders [46].  
Lower molecular weight of modifiers could be promoting phase compatibility. Rheological test results and M\({}_{\text{w}}\) distinguish three groups: MABs containing A, D, and E have similar characteristics, MAB containing K has lower expected performance and highest M\({}_{\text{w}}\), and MABs containing C and G have higher expected performance and lower M\({}_{\text{w}}\).  
The rheological parameters: \(\Delta\text{T}_{\text{c}}\), GR, and phase angle from black space diagram have consistent trends among all modifiers and are able to distinguish MABs based on their expected cracking performance.
# Inferential Statistics Is an Unfit Tool for Interpreting Data
## 2 When Inferential Statistics Is Not Helpful  
Raw data are seldom straightforward. Researchers must collate, describe, and generally \"make sense\" of the sample data so that it relates to the research question in a meaningful way. For example, in a study evaluating a new measurement procedure, researchers must interpret the sample data to understand whether the instrument successfully measures the phenomena of interest or not. In a study investigating the relationship between background noise and obesity, researchers must interpret the sample data to understand if the relationship is something noteworthy or not. In all empirical fields and in all study designs, researchers must measure the phenomenon of interest in a way that allows them to apply their domain knowledge to understand the result. The main example here will be that in a study evaluating a clinical intervention, researchers must interpret the individual data of the patients to understand whether, and for whom, the intervention was effective, and to what degree [17, 18, 19].  
An understanding of research methods and an application of _descriptive_ statistics can help researchers decode data and openly communicate it to others. For example, boxplots convey the inter-individual dispersion in a sample, graphs of individual treatment trajectories illustrate treatment outcomes on an individual level, and measures of test-retest reliability can explicate the robustness of the individual treatment outcomes [20] and the precision of the measures [21]. Clearly, an insight into research methods and _descriptive_ statistics is practical when interpreting data.  
However, after looking at the graphical presentations and various summary statistics, the researcher must still use their intellect to _interpret_ what they see in order to meaningfully relate the results in the sample to the research question. The problem I want to highlight is that at this juncture in the analysis, _inferential_ statistics have sometimes been used as a tool for _interpreting_ the sample data by mechanically labeling the study result as providing a positive or negative answer to the research question. In general, statistically significant results have often been confused with a difference being important, a relationship being interesting, a treatment outcome being beneficial, and similarly [7, 8, 14, 22]. However, what a result means and whether it is important or interesting is context sensitive and can only be evaluated in the light of domain knowledge. Abstract statistical procedures are blind to such things.
# A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area
## 2 A New Mapping Scheme of Rmap
### Path Planning Scheme Using Rmap  
In general, the SLAM algorithms and mapping algorithms do not take into account path planning algorithms. This is because the path planning is thought to be another problem. However, the robot's path planning has an important influence on how quickly the robot completes the map for the entire area in collecting information for the actual drawing of a map.  
Most of the path planning algorithms based on the grid map are completed by calculating pixels. Therefore, they require a considerable amount of operations because every pixel on the map needs to be considered every time a movement event occurs. Therefore, the path planning operation on the grid map causes overheads in the SLAM.  
However, this study's mapping scheme efficiently addresses this calculation overhead for path planning using the graph structure of the _Rmap_, as shown in Figure 5c. By using the _ANode_ and the _VNode_ information of the _Rmap_, the shortest path between two points can be calculated. For example, when the robot has located someplace in rectangle 1 in Figure 5b, if the robot needs to go to some position in rectangle 3, the path can be calculated as (1- > 4- > 3) using [PERSON]'s shortest path algorithm [PERSON] (1996). After calculating the path, the robot can move to the target using the _ACP_ information of the selected path. As previously described, the _ACP_ is a center point between two adjacent rectangles. Thus, for this case, the robot can start from its current place to the _ACP_ point of rectangle 1 and 4. Then, it can move to the _ACP_ point of rectangles 4 and 3. Finally, the mobile robot can move to the target position in rectangle 3. As seen in this example, if the _ACP_ information of the _Rmap_ is being used, the robot can move to any place in the map very quickly and safely.  
The _ACP_ is created and maintained when the _Rmap_ is updated. There are two cases of _ACP_ creation. One is the horizontal case, and the other is the vertical overlapping case as shown in Figure 6. The Equation for creating the _ACP_ is as follows:  
\[CoordinateR(A) :A\Big{(}A_{x1},A_{y1},A_{x2},A_{y2}\Big{)} \tag{5}\] \[CoordinateR(B) :B\Big{(}B_{x1},B_{y1},B_{x2},B_{y2}\Big{)}\] (6) \[ACP(x,y) =\Big{(}\frac{A_{x1}+B_{x2}}{2},A_{y2}\Big{)}\] (7) \[ACP(x,y) =\Big{(}A_{x2},\frac{B_{y1}+B_{y2}}{2}\Big{)} \tag{8}\]  
The Equations (5) and (6) represent the coordinates of the two points that make up the rectangle in the _VNode_ of the two adjacent maps \(A\) and \(B\). Equation (7) can calculate the _ACP_ in the case of  
Figure 5: The data structure for the _Rmap_. (**a**) the data structure composed of the _VNode_ and the _ANode_, the blue box is the _VNode_, and the green box is connected _ANode_; (**b**) The visualization of the structure; (**c**) The graph representation.  
a horizontal overlapping case, and Equation (8) is for calculating the \(ACP\) in the case of vertical overlapping. The \(ACP\) is stored in the \(ANode\) information.
## 1 Introduction
The event-based control law and disturbance law are updated at the triggering instant, and the computation in the control process is effectively reduced. 3. For the zero-sum game problem, a single-critic network structure based on the event-triggered ADP is proposed to approximate the solution of the HJI equation. It not only greatly reduces the update frequency of the controller and reduces the computational cost, but also the reliance on known dynamic information is relaxed. The rest of this article is organized as follows. Section 2 gives the description and transformation of the problem. Section 3 introduces the event-triggered HJI equation. Section 4 describes the implementation of the event-based ADP algorithm, and gives an analysis of system stability. Section 5 demonstrates the simulation of the continuous-time linear system and the continuous-time (CT) nonlinear system, and Section 6 presents the conclusion of this paper. **Notation. ** Some parameters are defined in this article. \(\mathbb{R}\), \(\mathbb{R}^{m}\), \(\mathbb{R}^{m\times n}\) are all denoted the set of real matrices, and \(m\), \(m\times n\) are represented as the corresponding dimension matrix. \(\mathbb{N}^{+}\) is the set of positive real numbers. \(\tanh^{-\mathsf{T}}(\cdot)\) is expressed as an inverse hyperbolic function, \(\underline{\lambda}(\cdot)\) and \(\bar{\lambda}(\cdot)\) are defined as the minimum and maximum eigenvalue of the matrix. \(\|\cdot\|\) express as the 2-norm. \(\
abla V\equiv\partial V/\partial x\) is denoted as the partial derivative of the function \(V\) with respect to the variable \(x\), \(\rightarrow\) denotes as numerical infinite approximation.
## 2 Basic Concepts  
### The Multi-Objective Optimization Problems  
In general, a multi-objective optimization problem can be defined as a vector function f that maps a tuple of n decision variables to tuple of m objectives.  
\[N_{i}(t)=\left\{j:d_{i,j}(t)<r_{d}^{i};l_{i}(t)<l_{j}(t)\right\} \tag{4}\]  
where, \(j\in N_{i}(t)\), and \(N_{i}(t)\) represents the neighborhood set of firefly \(i\) in the \(t\)th iteration; \(r_{d}^{i}(t)\) represents the decision radius of firefly \(i\) in the \(t\)th iteration; \(d_{i,j}(t)\) represents the space distance between firefly \(i\) and \(j\) in the \(t\)th iteration; \(p_{ij}(t)\) represent sthe probability of firefly \(i\) to firefly \(j\) in the \(t\)th iteration.  
When the neighborhood firefly \(j\) of firefly \(i\) is selected, firefly \(i\) will update its position as the following update equation.  
\[x_{i}(t+1)=x_{i}(t)+s*(\frac{x_{j}(t)-x_{i}(t)}{\|x_{j}(t)-x_{i}(t)\|}) \tag{5}\]  
where, \(s\) represents the moving step size of firefly; \(\|x_{j}(t)-x_{i}(t)\|\) represents the Euclidean space distance between firefly \(i\) and firefly \(j\).  
(4) Neighborhood range update rule  
After the position of the firefly is updated, the range of perception will be dynamically adjusted. The size of the perceived radius is determined by the number of firefly individuals within the perceived radius. The specific equation of the updating perceptual range is as follows.  
\[r_{d}^{i}(t+1)=\min\Bigl{\{}r_{s},\max\Bigl{\{}0,r_{d}^{i}(t)+\beta*(n_{t}-|N_ {i}(t)|)\Bigr{\}}\Bigr{\}} \tag{6}\]  
where, \(r_{s}\) represents the perception radius; \(n_{t}\) represents the threshold for firefly neighborhood set; \(\beta\) is the parameter to adjust the size of firefly's dynamic perception range.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 7 Summary  
In the field of model-based fault diagnosis, structural analysis is a simple and efficient method. Structural analysis takes the structural model of the fault diagnosis system as the research object and searches for the analytic redundancy part by analyzing the topology of the system's model equation set. Structural analysis identifies the diagnosable characteristics of faults, designs the system residuals, and deals with the optimal configuration of the sensors to provide guiding opinions for the diagnosis of faults, the design of the diagnosable system, and the control of the fault tolerance of faults.  
Based on the structural analysis, this paper first designs a structural model of the control system's diagnosability and a flow chart of the diagnosability design. Secondly, it analyzes and summarizes the current development status of diagnosability based on the structural analysis in recent years related to diagnosability evaluation and design. It also designs a diagnosability index system according to the results of the quantitative and qualitative evaluation of diagnosability based on structural analysis, laying the foundation for diagnosability development. Lastly, based on current structural analysis and the future development focus, it discusses in detail the problems of diagnosability development.  
**Author Contributions:** Conceptualization, X.G. and X.S.; formal analysis, X.G. and X.S.; investigation, X.G. and X.S.; writing--original draft preparation, X.G. and X.S.; writing--review and editing, X.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Results and Analysis
### Evaluation Indicators  
In this paper, root mean square error (RMSE) and mean absolute error (MAE) were chosen as the indicators to evaluate the accuracy of trajectory prediction. Among them, RMSE measures deviations between predictions and real values and is more sensitive to outliers in data. MAE represents the average of absolute errors between predictions and observations, denoted as:  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Parameter** \\ \hline Length (mm) & 6605 \\ Width (mm) & 2320 \\ Height (mm) & 2870 \\ Weight (kg) & 5800 \\ Maximum Speed (km/h) & 69 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of electric bus parameters configuration.  
\[RMSE(X,p)=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(p(x_{i})-T_{i})^{2}} \tag{24}\] \[MAE(X,p)=\frac{1}{m}\sum_{i=1}^{m}|p(x_{i})-T_{i}| \tag{25}\]  
where m is the number of samples, \(p(x_{i})\) is the predicted result, and \(T_{i}\) is the real operating data of the vehicle.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 2 Related Work
### Beam Recovery Schemes Based on Network Infrastructure  
Some efforts deploy devices to network infrastructure to overcome the beam blockage problem, such as vision-assisted cameras and intelligent reflecting surface (IRS). First, a vision-assisted scheme is proposed in [23] to tackle the problem of beam blockage by installing cameras at the mmWave BSs. Then, it uses deep learning at sub-6 GHz to extract spatial information. The scheme develops an image classification task by mapping each beam index in the beamforming codebook to an image based on a user's location. However, the scheme cannot work without sub-6 GHz channels, since detecting blockage in still images can be challenging as the instances of no user and blocked user are visually the same. Hence, images are paired with sub-6 GHz channels to identify blocked users. Overall, this solution can be impractical due to the large number of installed cameras in a dense network's deployment. Another infrastructure approach for beam recovery is to use IRS solutions. Here, the programmable electromagnetic elements in IRS manipulate the propagation channel into a more favorable condition that is free of blockage via judicious joint base station (BS)-IRS transmission design. Note that the coexistence of IRSs and mmWave BSs adds complexity to the architecture, and thus numerous challenges for the beam management problem. Hence, the authors in [24] propose a machine learning framework for IRS-assisted networks to solve the beam blockage problem at reduced system overhead, handover success rate and spectral efficiency. Moreover, a geometric position information assisted beamforming design is proposed for mobility awareness.
# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
## 2 Numerical Model  
### Configuration  
Figure 1 shows a schematic view of the entire bottom-set-gillnet system and the net assembly utilized in this study. The numerical models of the gillnet were based on an actual design used in Korea and the design parameters and material properties used in this study, given in Table 1, were obtained from previous research [21,22]. As mentioned before, the entire bottom-set gillnet consisted of the net assembly, mooring lines, and location buoys. The net assembly, which was made of nylon monofilament, had a length and height of 300 m and 2 m, respectively. The hanging ratio, which is defined as the actual gillnet length divided by the fully stretched length, is considered as an essential factor in catching fish, and a widely used value of 0.5 [23] was adopted in the present study. In the net assembly, many floats and weights were fixed to head and ground ropes at equal intervals of 0.5 m. Wood bars were positioned at fixed intervals of 50 m along the net-assembly length to maximize the stretching performance of the net assembly. The mooring lines were made of polypropylene and secured by strong anchors. They were installed at intervals of 50 m to maintain the location of the net assembly. The original purpose of the location buoy was to check the location of the underwater portion of the gillnet visually since they float on the sea surface. Each location buoy was connected to the net assembly through a polypropylene rope, and three location buoys were installed at 150 m intervals. The water depth was set as 25 m. The net bottom almost touched the seabed.
## 4 Experimental Set Up, Test and Results  
To verify the behavior of the system some tests have been executed both in laboratory and in a real scenario. The latter is the church of \"Santa Maria del Soccorso\" located in Corchiano about 70 km, from Rome, Italy (latitude 42.345043, longitude 12.345546) where a sixteen-century wall painting is located. The church is viewable on the Internet at the site [[http://www.prolocorchiano.com/il-nostro-paese/](http://www.prolocorchiano.com/il-nostro-paese/)]([http://www.prolocorchiano.com/il-nostro-paese/](http://www.prolocorchiano.com/il-nostro-paese/))le-chiese/madonna-del-soccorso/, while the picture viewable in Figure 4 is a fresco by the [PERSON] brothers placed in a lateral chapel called \"Paradise Chapel\" (in Italian \"Cappella del Paradiso\"). Two sensor cards equipped with a temperature sensor and a humidity one make up the first layer as indicated in Section 3. The cards have been programmed to collect data each 1 h. One time every day the Raspberry-Pi section is switched on, it acquires data from its ARDUINO card, and then it opens a connection with the remote server and uploads the data inside. The ancient church is far from any wired telephone lines; luckily, both GPRS and WiMAX signals are present in the area, while we do not use the mains to test the supply section realized by ourselves. The PV panel has been placed on the roof of the church as is the WiMAX antenna.