esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 3 Path-Tracker Design
### Proposed Path Tracker for 4 WS Vehicle
#### 3.2.2 Rear Wheel Controller Based on Curvature of Vehicle Turning Radius  
Vehicles that are 4 WS-based have both same-phase and counter-phase steering modes. Same-phase means the front and rear wheels turn in the same direction, while counter-phase means they steer in opposite directions. The rear steering gain, \(k_{r}\), is used not only to limit the rear wheel steering angle but also to determine the steering mode. If this gain value is set to be negative, the vehicle will be operated with counter-phase steering. In  
Figure 5: Schematic diagram of proposed 4 WS path tracker.  
evaluation, the rear steering angle, \(\delta_{r}\), is limited to 30% of the front steering angle, and \(k_{r}\) is set to a negative value to operate in counter-phase. The rear steering angle range was set with reference to the fact that the rear steering angle in production vehicles like the Mercedes-Benz S-Class can operate up to about 10 degrees, ensuring that passengers do not feel a sense of discomfort compared to a traditional 2 WS vehicle [30]. Considering this specification, \(k_{r}\) is set to 0.3 so that the steering angle of the rear wheel is approximately 10 degrees as the front wheel angle range of the experimental vehicle is limited to \(\pm\)30 [deg]. As a result, the rear steering angle, \(\delta_{r}\), is designed to handle the environment where the vehicle is driving at low speeds in a confined space, and it operates in a counter-phase steering mode to ensure superior path-tracking performance in sections with a small turning radius.  
The rear steering angle is calculated by taking the front steering angle, \(\delta_{f}\), which incorporates the curvature, \(C_{p}\), of the reference path as a feedforward term and adjusting it. To correct this value, the curvature, \(C_{t}\), of the vehicle turning radius, \(R\), derived from Ackermann steering geometry as shown in Equation (11), is used as a correction parameter for calculating the rear steering angle. As a result, the vehicle can follow the current turning path without significant deviation. In other words, the curvature of the path, reflected in the front steering angle, is used as a feedforward term for the calculation of the rear steering angle. This improves the response performance of the proposed path tracker, enabling excellent performance when driving in confined spaces.  
The rear steering angle of the proposed path tracker is presented in Equation (12).  
\[C_{t}=\frac{1}{R} \tag{11}\]  
\[\delta_{r}=k_{r}\Big{(}\delta_{f}+k_{t}C_{t}\Big{)}\qquad\begin{cases}\text{ if }k_{r}>0,\text{ then same-phase}\\ \text{if }k_{r}<0,\text{ then counter-phase}\end{cases} \tag{12}\]
## 1 Introduction
WBANs for monitoring physiological state collect data with very low-bandwidth. In typical scenarios of the proposed network architectures no more than three hops would be required to relay data from node to AP, and the expected latency would be marginal compared to the dynamics of the physiological parameters of interest. We evaluate the proposed global routing protocol for two network architectures. The first architecture comprises eight on-body nodes and an on-body AP. The second architecture comprises five on-body nodes and an off-body AP equipped with up to four antennas. NL and EpB are evaluated using simulation with recorded channel data and hardware experimental setups in typical indoor environments. In addition, network connectivity is evaluated as a function of the normalized transmission power providing insight on reliability of global routing for WBAN application. To provide accurate channel conditions, _Received Signal Strength Indicator_ (RSSI) measurements are used as input to simulations. To demonstrate the feasibility of the proposed global routing protocol, real-time hardware implementations are used in the experimental setups. Results demonstrate efficient balancing of energy consumption across the nodes in the network, an average increase of 40% in NL corresponding to an average increase of 0.4 dB in EpB for the on-body AP architecture, and an average 16%-20% increase in NL corresponding to an average increase of 0.025 dB in EpB for the off-body AP architectures. Increasing NL and balancing energy consumption across the nodes means that the WBAN needs to be serviced less frequently for recharging batteries. This outcome helps make WBANs a more attractive alternative for physiological monitoring of patients. Connectivity analysis demonstrates a cutoff effect, where exceeding a threshold transmission power level allows for practically full connectivity throughout the network's lifetime.
# Life-Cycle Cost Analysis on Application of Asphalt and Concrete Pavement Overlay
## 3 Analysis and Results  
### User Cost  
#### 3.1.1 Effects of Traffic Volume and Repair Length on User Cost  
The value per vehicle under traffic congestion caused by lane blocking was converted to user cost for LCC analysis. During asphalt overlay after blocking one lane, traffic volumes of 15,000, 25,000, and 35,000 annual average daily traffic (AADT) were considered. The repair length ranged from 0.3 km, which is the minimum length for the application of concrete pavement overlay by the Korea Expressway Corporation (EX), to 15.0 km, which is assumed to be the repair length from one interchange (IC) to another on expressways.  
The LCC analysis was conducted for the traffic volume and the repair length, as shown in Figure 1. The analysis results showed that the user cost tended to increase as the traffic volume and repair length increased. The increased rate of the user cost, however, was higher with respect to the increase in traffic volume than with that of the increase in repair length. Cost saving will be possible when smooth traffic flow is achieved by distributing the concentrated traffic caused by traffic blocking. Traffic distribution will be possible if the number of lanes is maintained using the lane shoulders.
## 4 Simulation Results and Discussions
### Stability Period  
Sensor node random deployment does not necessarily give the same number of dense and sparse regions. If the number of dense regions is maximal and the nodes interact with the same transmission power within the same dense regions, the stability duration is compromised.  
On the other hand, the regulated transmission power technique is implemented, as seen in Figures 4 and 5, as a result the stability period is increased. In addition to regulated transmission capacity, the clustering of denser regions used and the relocation of mobile sink nodes in sparse regions in the CPT-SEEC enhances the stability period relative to SEEC, EEDBR and DBR. In DBR, the greedy process forces immutable forwarder node selection when network nodes are partitioned and resources remain underused, allowing the protocol to have the lowest stability duration. It can be seen that the nodes in DBR without CTP are dead in round 2000 as shown in Figure 5. In EEDBR, because of excessive data forwarding and rapid energy reduction, nodes start to die quickly.  
Figure 4: Network Lifetime.  
Figure 5: Stability Period.
## 2 Study Area and Methods
### Methods
#### 2.3.3 Hurst Exponent  
Predicting future trends of drought in MUSL utilizing the Hurstexponent. A quantitative measure of time-series data over time, the autocorrelation that is useful for forecasting potential patterns is the Hurst exponent [31]. The Hurst value range is [0, 1], where Hurst = 0.5 means there is no process, as it is white noise; in this study, there are no pixels with Hurst = 0.5 in four seasons and the whole year round. Therefore, in order to draw  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\lambda\) & F & **Trending Traits** \\ \hline \multirow{3}{*}{\(\lambda>0\)} & 2.58 \textless{} F & Highly significant drying \\  & 1.96 \textless{} F \textless{} 2.58 & Significant drying \\  & F \textless{} 1.96 & Slightly drying \\ \hline \(\lambda=0\) & F & No change \\ \hline \multirow{3}{*}{\(\lambda<0\)} & 2.58 \textless{} F & Highly significant wetting \\  & 1.96 \textless{} F \textless{} 2.58 & Significant wetting \\  & F \textless{} 1.96 & Slightly wetting \\ \hline \hline \end{tabular}
\end{table}
Table 1: Division for drought change trend altering MUSL.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TVDI** & **0\textless{}0.46** & **0.46\textless{}0.57** & **0.57\textless{}0.76** & **0.76\textless{}0.86** & **0.86\textless{}1** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Drought \\ grade \\ \end{tabular} } & \multirow{2}{*}{Drought free} & \multirow{2}{*}{Mild drought} & Moderate & Severe & Extreme \\  & & & drought & drought & drought \\ \hline \hline \end{tabular}
\end{table}
Table 2: TVDI drought classification.  
a clear boundary between white noise and non-white noise, based on the actual distribution characteristics of the data, statistical fluctuations, and measurement errors, 0.5 is used as the center, expanded to 0.01 on both sides, respectively, making the prediction results more reliable and intuitive; 0 \(<\) Hurst \(<\) 0.5 suggests that the TVDI has an ant continuity series, meaning the projected pattern is the opposite from the past; and 0.5 \(<\) Hurst \(<\) 1 indicates that the later years pattern corresponds in past results.
## 7 Discussion of the Results  
This study demonstrated, by means of a very simple model, some of the limitations of the IFC protocol. Amongst these was the issue of data exchange, where, in many cases, the model exchanged via IFC did not keep all of the initially inherited data. In some cases, these reported issues could be easily solved by the receiving-end software, whereas in other cases more effort was required, and in some cases the model was completely useless. As described in this study, IFC 2x3 CV V2.0 objects could be represented in multiple ways. For example, the column object was exported as 'SweptSolid' geometry (the same as for the beam) from Archical, whereas it was exported as 'MappedRepresentation' from Revit (while the beam was exported as 'SweptSolid'). Moreover, some software, such as RFEM, imported elements only as 'SweptSolid' geometry. For example, 'MappedRepresentation' allowed for reusing the geometry definition of the column type for all occurrences of the same type. Therefore, in some of the possible scenarios of CAD-to-CAE data exchange, it is suggested to export the architectural model directly. Specifically, the model must be exported as an analytical model to be imported into the CAE software. Following this process, the mapping procedure is straightforward, and several mapping issues are overcome. According to the BIM methodology, each object should be classified to be able to exchange it with the correct interpretation. Classical objects are columns, beams, walls, stairs, slabs, etc., and the software applications offered a library of standard parametric components to speed up the design phase. Additional objects could be modelled, even in a parametric way, with any shape and eventually classified as standard objects (i.e., a 'SweptSolid' with a custom section classified as a column). When the customised object was exported into the CAD software, its geometrical shape was usually correctly recreated (though not necessarily its original classification), whereas, if exported into a CAE software, the reference to its \"natural\" classification was often lost, and that object was not recognised or even not imported in the receiving system. The IFC mapping procedures adopted by many software houses rely only on standard classifications or on a limited set of possible variants provided by the standard, and the end user is asked to edit the geometries and mapping assignments, so the data exchange process is not automatic at all. In this context, there is a useful solution available in a few software tools that provides the possibility to customise the property sets of the translation task for exchanging the original model into a known mapping schema of the receiving software. A common problem that was observed in all of the tests we performed was related to the material information attributed to the object.
## 3 Methodology
### Control Design
Furthermore, the boundaries for the supercapacitor, Figure 7, are extended to ensure its activation during nearly all short-time ripples. This deliberate extension is designed to prevent the EV battery from being active during such ripples, ultimately contributing to the extension of the EV battery's lifespan. Figure 8 illustrates the membership functions representing the reference currents for the battery, ultracapacitor, and over voltage discharge (OVD). The current is normalized and constrained between \(-1\) and \(1\). A positive value indicates that the current is injected into the grid (energy storage discharges into the DC microgrid), while a negative value  
Figure 6: The membership function for the bus voltage error and its integral [35]. Figure 7: The membership functions for state of charge of battery and supercapacitor [35]. represents the opposite. The battery's output current is characterized by five Gaussian membership functions, while the ultracapacitor features four membership functions. It is noteworthy that a zero-membership function is specified for the EV battery, reflecting its heightened sensitivity to activation. Fuzzy Rules  
The input and output specifications for the fuzzy inference system are listed in Table 1. It is noteworthy that the voltage error in Table 1 is defined as follows:  
\[\text{Voltage Error}=V_{\text{nominal}}-V_{\text{MG}}, \tag{3}\]  
where \(V_{\text{nominal}}\) denotes the reference voltage level in the microgrid, and \(V_{\text{MG}}\) denotes the actual measured voltage. In this equation, when the voltage of the DC microgrid exceeds the reference voltage, the error is negative; conversely, when the DC microgrid falls below the reference voltage, the error is positive. This distinction is pivotal in delineating the operational state of the microgrid and will inform the formulation of fuzzy rules. A total of 20 rules have been defined to map the inputs to the outputs, as shown in Table 2. Rules 1 through 6 establish relationships between the battery voltage and the bus voltage. For instance, Rule 1 indicates that when \(v_{b}\) is \"not high\", \(v_{u}\) is \"high\", and both \(e\) and \(\int e\,dt\) are \"negative\", then \(i_{b}\) should be \"very neg\", suggesting that the bus voltage is higher than the nominal value and the battery is not fully charged, allowing it to store excess energy. Rules 7 to 10 define the relationships between the ultracapacitor and the bus voltage. Rules 11 to 16 represent the overvoltage discharge (OVD) phase, and rules 17 to 20 determine the energy transfer between the battery and the ultracapacitor. \begin{table}
\begin{tabular}{c c} \hline \hline
**Input and Output** & **Term** \\ \hline Bus voltage error & \(e\) \\ Integrated bus voltage error & \(\int e\,\text{d}t\) \\ Battery voltage & \(v_{b}\) \\ Ultracapacitor voltage & \(v_{u}\) \\ Battery current & \(i_{b}\) \\ Ultracapacitor current & \(i_{u}\) \\ Over-voltage discharge current & \(i_{o}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input and outputs of the fuzzy interface system. Figure 8: Membership functions for the EV’s battery, ultracapacitor, and OVD.
## 3 Influence Metrics
### Quality of Service Metrics Analysis
#### 4.3.7 Discussion of the Results  
In the realm of networking protocols and QoS optimization, effective data transmission and low-latency routing are of utmost importance. This study deeply delves into the evaluation and refinement of routing algorithms to address these critical aspects, specifically comparing the performance of our proposed algorithms against well-established ones such as Spray and Wait, Epidemic, Prophet v1, and Prophet v2. The comparative analysis encompasses essential metrics, including packet latency, the number of hops, and overhead. It sheds light on the superior efficiency and effectiveness achieved by our meticulously designed algorithms. Subsequently, the discussion encapsulates noteworthy findings and implications of our research, illuminating the promising advancements in network optimization and reliability.  
Concerning packet latency, our algorithms significantly reduce average packet delay compared to the original Spray and Wait protocol, with an average reduction of approximately 2% compared to Spray and Wait, and more than 10% compared to Epidemic or Prophets v1 and v2 protocols. Additionally, our algorithms demonstrate a reduction in the number of hops, surpassing Spray and Wait by more than 3%, and significantly outperforming the Epidemic or Prophet v1 and v2 routing protocols. This reduction in hops is achieved through our meticulous node selection process and the principles we employ to determine packet forwarding. Regarding overhead, our algorithms adopt a more restricted approach to packet transmission compared to Spray and Wait, Epidemic, and Prophet v1 and v2 protocols, resulting in a substantial reduction in overhead (more than 32% less).  
In summary, our study highlights the remarkable performance enhancements achieved by our proposed algorithms. The reductions in packet latency, number of hops, and overhead represent significant advancements over established protocols like Spray and Wait, Epidemic, Prophet v1, and Prophet v2. These improvements are attributed to our meticulous node selection process and refined packet forwarding principles. The results underscore the potential impact of our algorithms in optimizing QoS for routing in various network scenarios, emphasizing their significance in advancing network efficiency and reliability.
## 3 Crash Prediction Testing Methodology
### Safety and Effectiveness Assessment  
The results of the validation tests were analyzed as a part of the safety and effectiveness assessment argumentation. Figure 10 shows the confusion matrix for PCS activation. True positive predicted cases define the area of efficiency of the system under test, which is usually smaller than the originally defined area of action. The degree of efficiency (DoE) of the system under test is defined by [32] as the quotient of the area of efficiency and the area of action. In addition to the simulative determination of the DoE, a retrospective determination on the basis of the accident data collection is possible. However, DoE shows the efficiency of the system but does not provide proof of safety. The acceptable probabilities for FP and FN cases are defined based on the ASIL classification of the concrete system and must be proven during the testing. For example, when considering FN classification, the main consideration is whether a fallback level is present or not. However, there is a trade-off between the advantage of TP and the disadvantage of the FN and FP cases. Thus, a positive risk balance proof is necessary. TN cases have neither a negative nor a positive effect, while the FP and FN cases lead to potential hazard. For the positive risk balance, the sum of the injury reduction in all the TP cases has to be higher than the sum of injuries potentially caused by all the FP and FN cases.
## 4 Results
### Experiment Results
#### 4.2.2 Classification Experiment Results for the B2 and B3 Types  
The JES classification was used to classify the B2 and B3 types, based on the morphological criterion that the thickness of the B3 type microvessel is more than three times that of the B2 type microvessels. The JES classification was applied based on the average measured thickness value of the microvessels. The largest thickness value among the measurements was not used, as this might lead to inaccurate results in the presence of an outlier. Thus, the average of the five largest measured thickness values was set as the representative thickness of a thick microvessel. Next, the B2 type microvessels were extracted to facilitate comparisons with the representative microvessel. The B2 type microvessels have long and slender morphological features. Therefore, five microvessels with the smallest area to perimeter ratio were selected as the B2 type among 20 microvessels with an object size ranking between 11 and 30, excluding the representative microvessels. The ratio of the thickness of the representative thick microvessels to the average thickness of the five extracted B2 type microvessels was compared and analyzed. The optimal cut-off value was 3.694. The microvessel classification performance of the B2 and B3 types is shown in Figure 26.  
The classification results for the B2 and B3 types with the optimal cut-off value of 3.694 are shown in Table 4. Substituting the TP, TN, FP, and FN values from Table 4 into Equation (30) resulted in an accuracy of 71.6% with a sensitivity and specificity of 73.7% and 70.8%, respectively.  
The clinical classification results for the B2 and B3 types with a cut-off-value of 3.7 instead of 3.694 are shown in Table 5. The TP, TN, FP, and FN values from Table 5 resulted in an accuracy of 73.1% with a sensitivity and specificity of 73.7% and 72.9%, respectively.  
\begin{table}
\begin{tabular}{l c} \hline True positive & 14 \\ True negative & 34 \\ False positive & 14 \\ False negative & 5 \\ \hline \end{tabular}
\end{table}
Table 4: Classification results of the B2 and B3 types with the optimal cut-off value.  
Figure 26: The ROC curve corresponding to the classification results of the B2 and B3 types.
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 3 The Proposed Method
### Problem Definition and Formulation  
The clothes pattern recognition problem is concerned with identifying the pattern of a textile captured under different constraints. An image of a fabric is given, and its pattern should be determined automatically according to predefined patterns. It is a multi-class, single-label classification problem.  
Let \(X\subset\mathbb{R}^{m\times n}\) be a set of clothes pattern images, each represented as a matrix of size \(m\times n\), and \(Y=\{0,1,...,c\}\) be the set of predefined labels (patterns). The problem of identifying clothes patterns is to design a mapping \(f:X\longrightarrow Y\) that predicts the label \(y\in Y\) for an unknown pattern input (image) \(x\in X\), i.e.,  
\[f(x;\theta)=y \tag{1}\]  
where \(\theta\) are the learnable parameters. We validate the proposed method in two different scenarios. In the first scenario, the number of labels (patterns) is six: checkered, patternless, striped, zigzag, floral, and dotted (i.e., \(y=1,2,...,6\)). The other scenario consists of four labels: checkered, patternless, irregular, and striped (i.e., \(y=1,2,3,4\)).