esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analysis
### Ensemble-2 Results  
As mentioned earlier in Section 3.5, the ensemble-2 is based on consolidating the feature subset acquired from different scoring functions in such a manner that the majority of the features are retained from the individual subsets while the rest of the features are discarded. Table 12 shows features subsets acquired from different scoring functions and the final consolidated solution based on ensemble-2. In this regard, Table 13 shows the evaluation metrics applied to the classification models constructed from the ensemble-2 feature subset. It is important to note that the ensemble-2 approach employs the majority vote among individual partial solutions. In the case of three scoring functions, the majority vote based selection heuristic can also be stated as a 2/3 rd rule i.e., a feature would be selected if it is present in at least two partial solutions. The alternative options available are a union case in which all the distinct features obtained in partial solutions are selected, and in intersection case, only those features present in all partial solutions are admissible. In this regard, the majority vote can be seen as a multi-point intersection as discussed in [17]. Comparative results of the aforementioned subset combining cases are provided in Table 14.  
In terms of comparative analysis between ensemble-1 and ensemble-2 it can be observed that for the CKD problem, the latter performs better in terms of accuracy, precision, specificity, F1-measure, the cardinality of selected features and the overall cost of the solution. It would be interesting to explore whether these results generalize to other cost-sensitive diagnosis problems or not? \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-measure** & **AUC** & **Cost** \\ \hline NB & 96.5 \(\pm\) 3.60\% & 100.0 \(\pm\) 0.0\% & 94.40 \(\pm\) 6.0\% & 100.0 \(\pm\) 0.0\% & 97.0 \(\pm\) 3.20\% & 1.0 \(\pm\) 0.0 \\ LG & 96.5 \(\pm\) 2.0\% & 100.0 \(\pm\) 0.0\% & 94.50 \(\pm\) 3.10\% & 100.0 \(\pm\) 0.0\% & 97.10 \(\pm\) 1.60\% & 0.997 \(\pm\) 0.007 \\ DL & 100.0 \(\pm\) 0.0\% & 100.0 \(\pm\) 0.0\% & 100.0 \(\pm\) 0.0\% & 100.0 \(\pm\) 0.0\% & 100.0 \(\pm\) 0.0\% & 1.0 \(\pm\) 0.0 \\ DT & 93.90 \(\pm\) 6.60\% & 100.0 \(\pm\) 0.0\% & 90.40 \(\pm\) 10.50\% & 100.0 \(\pm\) 0.0\% & 94.70 \(\pm\) 5.9\% & 0.952 \(\pm\) 0.052 \\ RF & 98.30 \(\pm\) 2.40\% & 97.40 \(\pm\) 3.50\% & 100.0 \(\pm\) 0.0\% & 95.30 \(\pm\) 1.80\% & 98.70 \(\pm\) 1.8\% & 0.998 \(\pm\) 0.004 \\ GBT & 100.0 \(\pm\) 0.0\% & 100.0 \(\pm\) 0.0\% & 100.0 \(\pm\) 0.0\% & 100.0 \(\pm\) 0.0\% & 100.0 \(\pm\) 0.0\% & 1.0 \(\pm\) 0.0 \\ SVM & 95.70 \(\pm\) 4.30\% & 98.70 \(\pm\) 3.0\% & 94.50 \(\pm\) 7.60\% & 97.80 \(\pm\) 5.0\% & 96.30 \(\pm\) 3.70\% & 0.961 \(\pm\) 0.057 \\ Average & 97.27 \(\pm\) 2.92\% & 99.44 \(\pm\) 0.92\% & 96.25 \(\pm\) 3.88\% & 99.01 \(\pm\) 1.64\% & 97.68 \(\pm\) 2.31\% & 0.986 \(\pm\) 0.017 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Ensemble-2 results based on selected features.
## 2 Problem Statement and Model Formulation
### Time-Space State Network Modeling Perspective
\begin{table}
\begin{tabular}{c c} \hline \hline \((i,s,w),\ (j,e,w^{\prime})\) & Time-space state index of nodes. \\ \((i,j,s,e,w,w^{\prime})\) & Link index, meaning that the current link can transmit a task with state \(w\) on node \(i\) at time \(s\) to node \(j\) at time \(e\) and change the task state to \(w^{\prime}\). \\ \(\{Pr_{d},Dur_{d},Blo_{d}\}\subseteq d\) & Task \(d\) contains attributes such as priority \(Pr_{d}\), maximum transmission time \(Dur_{d}\), maximum congestion time \(Blo_{d}\). \\ \(c(d,i,j,s,e,w,w^{\prime})\) & The transmission cost of task \(d\). \\ \(t(d,i,j,s,e,w,w^{\prime})\) & The transmission time of task \(d\). \\ \(y(d,i,j,s,e,w,w^{\prime})\) & \(y(d,i,j,s,e,w,w^{\prime})=1\) if link \((i,j,s,e,w,w^{\prime})\) is used to transmit task \(d\); \\ \((i,j,s,e,w,w^{\prime})\in D_{d}\) & The link set \(D_{d}\) of task \(d\). \\ \(\phi_{(j,e,w^{\prime})}\) & The set of forward links connecting nodes \((j,e,w^{\prime})\). \\ \(\Gamma_{d}\) denotes the set of links where a task is in the waiting state and, \(\Lambda_{d}\) denotes the set of links where a task is not in the waiting state, \\  & respectively. \(D_{d}=\Gamma_{d}\cup\Lambda_{d}\) \\ \(Cap_{(i,s,w)},Cap_{(i,j,s,e,w,w^{\prime})}\)\(Cap_{(i,j,s,e,w,w^{\prime})}\)\(\text{denotes node }\ (i,s,w)\) capacity and link \\ \(Cap_{(i,j,s,e,w,w^{\prime})}\)\((i,j,s,e,w,w^{\prime})\)\(\text{capacity, respectively.}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sets, indices, and parameters.
## 5 Filter Validation  
This section validates the developed filter using the test data collected during battery aging. As described in Section 2, a total of 30 datasets were provided for this validation. These datasets encompassed the three load profiles applied to ten aged cells. The lowest capacity among these cells was 2.254 Ah, which is equivalent to less than 70% SOH. The validation result refers to an error between the estimated and measured values. As described in Section 3, the ESC 1 RC model created by averaging the parameters derived from the three load profiles was used for this validation. Compared to the Thevenin model, the ESC model provided greater fidelity to the battery aging test data, primarily due to the inclusion of a hysteresis element that captured changes in the SOC-OCV curve with battery aging. This result implies that the key aging-related parameters include \(R_{0}\), which reflects an increase in internal resistance, and \(M\) and \(M_{0}\), which represent alterations in the SOC-OCV relationship within the hysteresis element. In this study, a mean value (RMSE) was used to evaluate SOC estimates, whereas a final value was used for capacity estimates. Their measurements were commonly based on Coulomb counting with current sensors enhanced with internal compensation.  
Based on the validation result, we developed three different schemes successively, as illustrated in Figure 6. The first scheme relies solely on the simultaneous estimator to concurrently update the SOC and capacity, leading to inaccuracy in capacity estimation. To address this issue in the DEKF, the second scheme substitutes its parameter filter for capacity estimation with the WLS. Despite having this separate capacity estimator, maintaining accuracy during battery aging is still challenging. As an effective alternative, the third scheme repeats the sequential estimator to intermittently update all the model parameters. In the first and second schemes, this parameter identification for cell modeling is performed only once with a fresh cell, as described in Section 3. Figure 7 compares the sequential and simultaneous estimators in detail.
## 5 Result and Discussions
### User Quality Of Experience  
We designed proposed system to facilitate patients at their site, therefore it was necessary to check proposed system significance for target users i.e., skin wound patients of different age. We did experiments on human to verify working of proposed system and user's satisfaction level. We considered four important parameters to verify quality of proposed system i.e., system response time, output accuracy, portability and complexity level [33]. We did experiments with users of different age and obtained results given in Table 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Users** & **Complexity** & **Time** & **Accuracy** & **Portable** \\ \hline Children & 80\% & 82\% & 85\% & 98\% \\ Adults & 90\% & 89\% & 92\% & 97\% \\ Old Age & 70\% & 75\% & 73\% & 89\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Users Experience Level.  
Figure 21: NN prediction Evaluation Chart.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 2 Related Work
### Semantic-Guided Methods
#### 2.2.1 Loss-Level Semantic Guidance  
This methodology integrates semantic awareness by designing or adapting loss functions, enabling networks to adjust parameters based on semantic constraints during training. [PERSON] et al. [29] introduced high-level vision tasks (e.g., image segmentation) into image denoising through a joint loss function combining Reconstruction Loss (\(L_{D}\)) and High-Level Vision Task Loss (\(L_{H}\)). The latter employs semantic information via cross-entropy loss to measure discrepancies between segmentation predictions and ground-truth labels, ensuring enhanced images retain accuracy for downstream tasks like object classification. In super-resolution tasks, [PERSON] et al. [30] proposed a guided loss function that jointly optimizes semantic segmentation and super-resolution by aligning segmentation outputs with ground-truth labels. For LLIE, [PERSON] et al. [7] designed a brightness consistency loss, enforcing luminance uniformity across identical semantic regions (e.g., clustered objects) to prevent local over-/under-exposure, which proved critical for natural enhancement.
## 2 Description of the Study Area  
The study area is located between the Alenda Golf urbanization and the \"Sierra de las Aguilas y San Pascual\" Municipal Natural Park (UTM 30N ETRS89 coordinates: 30S7030004248100; 30S7030004249750; 30S7054004248100; 30S7054004249750) in the municipality of Monforte Cid in the inland of the province of Alicante, Valencian Community, southeast of Spain (Figure 1).  
Figure 1: The site of the study area in the province of Alicante, southeast of Spain.  
The study area comprises 43 cadastral plots (Figure 2) divided into 121 cadastral subplots. Those are situated in the municipality of Monforte del Cid (Alicante, Spain) and are under the ownership of one company. The area encompasses 1,389,088 m\({}^{2}\), with 52.9% classified as agricultural use and 47.1% as non-agricultural use (basically scrubs and pastures) [26].  
The geographical location of the study area and the preparation of thematic maps were based on a collection of orthophotos and a digital terrain model (DTM) from the Spanish National Plan of Aerial Orthophotography (PNOA 2014-2018 CC-BY since.es).
# Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station
## 7 Conclusions  
A novel horizontally polarized omnidirectional antenna that is built on the slot structures is introduced in this work. By positioning four vertical slots along the antenna circumference and energizing them with a robust central feeding mechanism, steady gain with improved polarization stability is realized in the antenna's azimuth axis. The internal axis-symmetric feed network itself radiates no power, hence it does not interfere with the radiating structure. Low ripples appear among the antenna azimuth gain. Steady yaw  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Polarization Purity** & **Gain Ripple (dB)** & **DC Ground** & **Feed Type** \\ \hline
[17] & 11 & \(\pm\)5.5 & No & Exposed \\
[18] & 20 & NA & No & Exposed \\
[19] & 20 & 1.3 & No & Exposed \\
[20] & 25 & 1.0 & No & Exposed \\
[21] & 18 & 1.5 & No & Exposed \\
[22] & 20 & 1.5 & No & Exposed \\
[23] & 15 & 1.5 & No & Exposed \\
[24] & 15 & NA & No & Exposed \\
[25] & 20 & 2.0 & No & Exposed \\
[26] & 20 & 2.1 & No & Exposed \\ Proposed work & 40 & \(\pm\)0.5 & Yes & Enclosed \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the proposed antenna with the existing literature.  
Figure 9: (a) Measured and simulated azimuth gain ripple vs. frequency. (b) Measured and simulated antenna gain vs. frequency.  
plane gain with low gain fluctuations enhances coverage area or increases the link efficiency. So it is desirable in numerous ground station-based applications, such as UAV communication and direction finding, to have a low azimuth gain ripple antenna. This antenna also possesses requisite mechanical features, which are crucial for its smooth operation. It has a sturdy, DC-grounded construction that does not need any exterior framework or support. Its conformal and aerodynamic shape minimizes air drag and any corresponding degradation attributed to military operations' environmental and terrain conditions. Altogether, this may make this antenna a favorable candidate for ground stations.  
M.S.S., in collaboration with H.N., proposed the antenna's configuration and simulated it in CST. M.S.S. executed the fabrication and tuning of the antenna prototype. H.N. did the measurements. M.S.S. and S.U. did the plotting and manuscript preparation. [PERSON] and [PERSON] provided step-by-step supervision, reviewed the work, and did manuscript refinement. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (Grant No. 61831001) and the High-Level Talent Introduction Project of Beihang and the Youth-Top-Talent University (Grant No. ZG216S1878) and Support Project of Beihang University (Grant No. KG12060401)).  
Not applicable.
## 4 Methodology
### Data Description, Collection, and Preparation
#### 4.2.2 Fitness Function  
In this phase, the assessment of each solution provided by the WOA algorithm is measured using the fitness function. Therefore, identifying the quality of every individual can be accomplished through the fitness function performed by the SVM classifier. In our case, the selected fitness function is the classification accuracy that is calculated by Equation (13). Further, the WOA tries to maximize the value of the accuracy during the whole process, thus achieving the best possible outcome.  
Figure 2: A simple example of the weighting mechanism.  
\[fitness(I_{i}^{t})=\frac{1}{K}\sum_{k=1}^{K}\frac{1}{N}\sum_{j=1}^{N}\delta(c(x_{j}),y_{j}) \tag{13}\]  
where \(c(x_{j})\) denotes the accuracy of the \(j\)th instance, while \(y_{j}\) indicates the label (actual class) of the \(j\)th instance. Further, \(\delta\) is the relation between \(y_{j}\) and \(c(x_{j})\), where if \(c(x_{j})=y_{j}\), then \(\delta=1\), else \(\delta=0\). The number of folds denoted by \(K\) and \(N\) is the number of instances in the testing subset.
# Establishment and Numerical Analysis of Rolling Force Model Based on Dynamic Roll Gap
## 4 Numerical Analysis of the Rolling Force Model
Therefore, designing lubrication technologies is an effective approach to reducing the rolling force, saving energy, and improving strip production quality. The relationship between stress and rolling force is depicted in Figure 5. It can be observed that as the stress increases from 50 to 150, the rolling force exhibits a linear growth. Thus, to enhance the stability of the rolling force, it is advisable to utilize high-quality strip and blank materials. Figure 4: Relationship between friction coefficient and rolling force. Figure 5: Relationship between pressure stress and rolling force. The relationship between tension and rolling force is depicted in Figure 6, which shows that as the front and rear tension increases, the rolling force decreases. This can be attributed to the reduction of three-way compressive stress in the plastic deformation zone of the strip due to the increase in tension. Consequently, there is a transformation of compressive stress into tensile stress. As a result, the rolling force decreases, and the thickness of the rolled piece becomes thinner than the standard. Therefore, effective control of the tension between stands can enhance rolling stability. By iteratively applying the rolling force calculation formula and the roll flattening radius calculation formula to decouple them, as depicted in Figure 7, the rolling force values gradually stabilize and converge after four iterations. To investigate the impact of the roll flattening radius on the rolling force, data from the five passes mentioned above are selected for analysis. The comparison between the rolling force calculation results obtained without considering the roll flattening radius and those obtained by considering the roll flattening radius is depicted in Figure 8. The calculated results obtained without considering the roll-flattening radius deviate significantly from the measured values of the rolling force. This observation emphasizes the crucial role of the roll-flattening radius in determining the accuracy of the rolling force calculation. Figure 6: Relationship between tension stress and rolling force. Figure 7: Variation rolling force with the iteration process.
## 4 Results and Discussion
### WM-OA-ICOS Combined with White Noise Perturbation  
In WM-OA-ICOS, the choice of an appropriate modulation amplitude was critical to optimize system performance [33]. The measured amplitude of the 2f signal was recorded for the 1000 ppm CH\({}_{4}\) sample with different modulation depth. An optimal modulation depth of 0.3 V was chosen, resulting in a modulation factor of 2.1 for the best signal response.  
The white noise perturbation of 30% modulation depth was chosen for system optimization. The gas mixing system was equipped with 1000 ppm CH\({}_{4}\) and the absorption spectroscopy of the WM-OA-ICOS was measured with the white noise perturbation of different modulation depths (0%/30%). The calculated SNRs were 31.2 and 113.9, respectively. And the SNR was significantly increased 3.65 times. By adding white noise perturbation, the cavity mode noise level of the system was effectively reduced and the SNR was significantly improved.
# A Case Study on Integrating an Eco-Design Tool into the Construction Decision-Making Process
## 3 Results and Discussion
### Case Study: Comparison of Window Frame Solutions
In this analysis, equal factors were assigned to environmental impacts (25%) and non-renewable resource consumption (25%), with their total equaling the factor attributed to the sum of acquisition and maintenance cost (50%). Figure 4: Economic assessment of different window frame solutions. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameters** & \multicolumn{3}{c}{**Case: Window Frame Material**} \\ \hline Code & A11 & A22 & A33 \\ Material/solution & PVC & Aluminium & Wood \\ Units & 263 & 263 & 263 \\ Unit cost (EUR) & 193.98 & 264.62 & 302.49 \\ Total acquisition cost (EUR) & 51,016.74 & 69,595.06 & 79,554.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Acquisition cost of different window frame solutions. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameters** & \multicolumn{3}{c}{**Window Frame Material**} \\ \hline Code & A11 & A22 & A33 \\ Material/solution & PVC & Aluminium & Wood \\ Material/solution & General & General & General \\ Action & maintenance & maintenance & maintenance \\ Service life (years) & 30 & 30 & 30 \\ Maintenance frequency (years) & 10 & 10 & 10 \\ Cost per maintenance action (EUR) & 4591.98 & 7655.93 & 19,888.06 \\ Life cycle maintenance cost (EUR) & 13,775.94 & 22,967.79 & 59,664.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Maintenance cost of different window frame solutions. The results shown in Table 10 suggest PVC window frames as the best solution among those considered for the CTA building renovation project due to their low associated costs and environmental performance that is comparable to wood frame windows. It should be noted that such an outcome could be modified if the user assigns considerably different weighting factors to the examined economic or environments aspects.
# Optimization of Multi-Day Flexible EMU Routing Plan for High-Speed Rail Networks
## 2 Model Description  
### Problem Description  
#### 2.1.1 Problem Analysis  
The EMU routing plan connects all train services in the timetable through feasible service connections, generating an ordered sequence that reflects the logical continuity of train operations. This sequence serves as the operational framework for daily EMU operation. To efficiently fulfill the scheduled train services, EMUs typically depart from their assigned depots, perform a sequence of connected trips, and then return to the depot for scheduled inspection and the completion of the circulation cycle.  
To illustrate the operational logic of EMU routing under a multi-day planning horizon, we consider the representative multi-day train timetable shown in Figure 1. In this example, stations A, M, and B are intermediate or terminal, and two EMU depots are connected to stations A and C, respectively. Days 1 to 4 represent weekday service patterns, consisting of eight short-formation and two long-formation train services (depicted by thin and thick red slanted lines, respectively). Due to increased passenger demand on weekends (Days 5 to 7), two additional short-train services are added (shown by green slanted lines).  
To illustrate the characteristics of the EMU routing problem under a multi-day timetable, two operation strategies are compared in Figures 2 and 3. Figure 2 shows a fixed operation scheme, while Figure 3 presents a flexible operation scheme, both designed to fulfill the service requirements in Figure 1.  
In the fixed operation scheme (Figure 2), five EMU routing plans are required, involving five short-formation EMUs. EMUs are not allowed to couple or decouple and can only connect with services of the same formation type. In addition, each EMU must return to its assigned depot for inspection at the end of each day, leading to an average of two empty trips per day.  
In contrast, the flexible operation scheme (Figure 3) requires only four EMU routing plans. Under this approach, EMUs are permitted to couple and decouple at designated stations, and maintenance can be performed at any available depot according to operational conditions, rather than being limited to the originally assigned depot. As a result, only  
Figure 1: Multi-day train timetable.  
one empty trip is needed to initiate the first-day service, and the total number of required EMUs and the empty-running mileage are significantly reduced.  
Based on this, the problem addressed in this study is formulated as follows: Given a set of stations, EMU depots, and a multi-day train timetable, the goal is to construct feasible EMU routing plans that incorporate in-station coupling and decoupling, along with flexible depot selection for inspection. The objective is to satisfy all maintenance constraints while minimizing total empty-running mileage and overall operational costs.
## 3 Interworking of Two Different QKD Networks  
The main objective of the key management system in a QKD network is to provide QKD keys to cryptographic applications in which cryptographic key-consuming applications at each node are referred to as secure application entities (SAEs), and the key management software they connect to are referred to as key management entities (KMEs). The key management system of the network is responsible for securely distributing keys between the KMEs, allowing key requests from the initiator and target SAEs to be fulfilled. APIs have been defined in ETSI GS QKD 014 [12] and ETSI GS QKD 004 [13] to specify methods and data formats for delivering keys from a KME to an SAE. However, if different parts of a network use incompatible or separately managed key management systems, a standardized interface is needed for KMEs to transfer keys horizontally between different parts of the network. This standard interface has been addressed in ETSI GS QKD 020 [14].  
The objective of this paper is to demonstrate the practical implementation of an SDN orchestrator that efficiently manages SDN controllers governing each QKD network, with the aim of enabling the transmission of QKD keys between two networks that employ distinct vendors.