esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Experiments
### Evaluation Criteria  
In this paper, the performance of the different versions of the algorithms was measured through five metrics:1. \(|F_{0}|\): The number of solutions in the first front \(F_{0}\).
2. \(SP\): The spacing measure proposed by [PERSON] [67], which estimates the relative distance between two consecutive solutions obtained in the non-dominated set, as follows: \[SP=\sqrt{\frac{1}{|F_{0}|}\sum_{i=1}^{|F_{0}|}(d_{i}-\bar{d})^{2}}\] (6) where \(d_{i}=\min_{j\in F_{0}:j\
eq i}|[Z_{1}(i)-Z_{1}(j)]+[Z_{2}(i)-Z_{2}(j)]|\) is the minimum value of the sum of the absolute difference in the objective function values between the \(i^{\text{th}}\) solution and any other solution in the obtained non-dominated set. \(\bar{d}=\frac{\sum_{i=1}^{F_{0}}d_{i}}{|F_{0}|}\) is the mean value of the above distance measure. When solutions are near uniformly spaced, the corresponding distance measure will be small. Thus, the method with smaller spacing \(SP\) is better.
3. \(HV\): The hyper-volume introduced by [PERSON] et al. [68], which calculates the volume covered by members of \(F_{0}\). Mathematically, for each solution \(i\in F_{0}\), a hyper-rectangle \(v_{i}\) is constructed with a reference point. The reference point is defined by the maximal value of \(Z_{1}\) and \(Z_{2}\) obtained by all tested versions. Thereafter, a union of all hyper-rectangle is found, and the hyper-volume \(HV\) is calculated (see Figure 5). In this case, one method with a large value of \(HV\) is desirable. \[HV=volume\bigcup_{i=1}^{|F_{0}|}v_{i}\] (7) The spacing and the hyper-volume were not free from arbitrary scaling objectives, and then, the above metrics were evaluated by using normalized objective functions in the interval \([0,1]\). For the hyper-volume measure, the selected reference point corresponded to the worst solution provided by all the tested versions.
4. \(DO\): The deviation from the optimal solution found by [PERSON], which was used to solve the single-objective model in [PERSON] et al. [34], where \(Z_{1}+Z_{2}\) was minimized. First, the best solution (called _p_) is identified by calculating the Euclidean distance of each solution in the first front to the optimal solution (called _q_). \(p\) corresponds to the solution with the smallest sum of the Euclidean distance. Then, the percentage of the deviation is calculated as follows: \[DO_{1}(p,q) =((Z_{1}^{p}-Z_{1}^{q})/|Z_{1}^{q}|)*100\] (8) \[DO_{2}(p,q) =((Z_{2}^{p}-Z_{2}^{q})/|Z_{2}^{q}|)*100\] (9)
5. \(CPU\): The computational execution time reported in seconds.  
Figure 5: Hyper-volume for \(Z_{1}\) and \(Z_{2}\) of the Vehicle Routing Problem with Time Windows, Synchronization, and Precedence constraints (VRPTW-SP).
## 4 Conclusions  
Many studies have been carried out on the inverse problem and different solutions have been proposed, including the third-order theory [8], the inverse solution [4], the two-step method [9; 10; 11], and the iterative method [7]. In this paper, a geometric method is proposed to solve the rotation angles for a given target trajectory in less computing time and with high accuracy.  
Two approximations were made for the geometric method, and the accuracy and time obtained by both were analyzed. The error of the circumference approximation is approximately twice that of the ellipse approximation. However, the ellipse approximation has a longer computation time than the circumference approximation.  
The geometric method was also compared with the two-step method [9], with the results showing that the geometric method is faster and more accurate.  
Until now, the most accurate method was the iterative method [7]. Therefore, the geometric method was implemented in an iterative process to determine whether it reduces computation time and improves accuracy. It was found that [PERSON] et al. iterative method [7] obtains good time and accuracy results for small \(D2\) distances, while the iterative process with the geometric method obtains good results for both small and large \(D2\) distances. In addition, the iterative method [7] takes more iterations to reach the established error threshold, and, therefore, the computation time is longer than the iterative process with the geometric method. The method developed in this work achieves an accuracy of 1 pm in only three iterations.  
That is, the geometric method offers a far better accuracy solution for the inverse problem of Risley systems to prevent laser beam misalignment. Solving the inverse problem enables applications that involve the control of dual-prism rotary motions, including multimode beam tracking in the field of airborne laser communications, the monitoring of containers in harbor operations, and target tracking in the military domain. Moreover, the geometric method could be used in applications that require high degrees of accuracy, such as lithography, stereolithography, or 3D printing.  
Future research will use the geometric method in various real applications.  
Funding acquisition, J.D.S.; Investigation, K.D.; Methodology, F.d.l.P.; Project administration, M.M.; Resources, J.D.S.; Software, K.D.; Supervision, D.F.; Validation, D.F.; Visualization, R.E.-C.; Writing--original draft, K.D.; Writing--review & editing, D.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by 'PROGRAMA INVESTIGO 2021--MRR', grant number \"2022-5070-G/241H/4480200-40M5007-00020093\".  
The authors declare no conflict of interest.
# Effects of Night Ventilation on Indoor Air Quality in Educational Buildings--A Field Study
## 1 Introduction
PM2.5 and PM10 concentration levels have been usually discovered, that is the pollutants of a diameter of less than 2.5 and 10 \(\upmu\)m, respectively. It has been estimated that PM2.5 constitutes 50-70% of outdoor PM10 in many European regions [29]. In indoor air, occupants are exposed daily to different microbes. Therefore, it is relevant to assess their concentrations and temporal growth in indoor environments. In many studies, so-called surrogate methods have been used while investigating indoor microbial exposure [30; 31; 32; 33]. [PERSON] et al. [34] proposed that a settled dust method can be a reasonable method, reflecting well the microbial compositions and environmental determinants. The authors compared different methods and showed sampling strategies for the quantitative assessment. The general characteristics of microbial flora can be estimated by identifying large groups of microbial species. The commonly explored groups measured in this study are total fungal content (Unifung) [35], the group of _Penicillium_, _Apergillus_, and _Paecilomyces variotiti_ (Pen/Asp) [36], as well as the Gram-positive (Grampos) and the Gram-negative (Gramneg) bacteria groups [37]. [PERSON] et al. [36] showed that generic assay corresponds rather well the sum of individual assays. [PERSON] et al. [37] compared qPCR methods in determining a bacterial load in dust samples. The authors concluded that different methods represent different aspects of bacterial exposure and, as a consequence, those results may slightly have different characteristics. [PERSON] et al. [33] conducted the settled airborne dust as a surrogate for indoor exposure. The authors used plastic petri dishes and analyzed the samples with the qPCR method in quantifying a total bacterial and fungal biomass. Their finding was that petri dishes are an inexpensive, simple, and feasible method, meeting the requirements of passive sampling approaches. In this study, the knowledge cap is related to how different unoccupied ventilation strategies affect PM, TVOC, and microbial concentrations immediately in the morning and also during the occupied period. Consequently, the pre-started, continuous, and intermittent night ventilation methods were compared during the unoccupied hours. The study objects were selected among educational buildings without any recognized indoor air quality or thermal problems. The spaces were used and ventilated normally during occupied hours. The objective of the study was to provide insight into the night ventilation usage for building owners in educational buildings, and thus to optimize night ventilation usage without increasing unnecessary energy consumption. The novelty of the study is the analysis of the effects of night ventilation methods on indoor air quality at the beginning of the occupied hours. In addition, the microbial levels were measured during each test period to assess long-term cumulative exposure to microbes using the night ventilation methods.
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
## 5 QKD Authentication Model Setup and Details
### Quantum Communication Metrics: QBER, Maximum Corrected Errors, and Overhead
Our approach involved periodic authentication of the exchanged bits within a predefined timeframe, guided by a specified key rate, rather than a static request for a specific number of bits at the initiation of each round.
# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 4 Materials and Methods  
In order to determine whether the proposal is adequate, the following experiments were considered: (1) comparison of isolated YOLOv5 models with ensemble of multiple YOLOv5, and (2) comparison of the proposal with the state-of-the-art method DCN+ [4].  
Figure 3: Examples of bounding box generation. In column (**a**), we visualize the actual damage for each image, while in column (**b**), we show the predictions generated by all models and the selected clusters (C1, C2). Column (**c**) shows the final results after applying the box fusion using a voting number of 6 for this example.  
The models were trained on Ubuntu System 20.04 with Python 3.7.0, PyTorch 1.12.1, and CUDA 11.6 on an NVIDIA RTX 2080 GPU (installed in a server based at Grupo Valora, Sevilla, Spain). The inference process was carried out using the same system but with an Intel Core i5 processor, given that it is cheaper in a production environment than GPUs. We used the protocol currently used by insurance companies. First, the photographs were analyzed by a computer vision system. If no damage was found, the system moved on to contracting or renewing the insurance. However, if the system detected any damage, it moved on to an appraiser. In this way, we evaluated the detection of damage without distinguishing the category of damage.  
For each experiment, the following metrics were considered:  
* Precision: Defined as \(\frac{TP}{TP+TP}\), precision allows us to identify which models tend to generate false positives, since this value will be affected by them. In the production environment, the presence of false positives implies increases in operational costs. For this reason, false positives should be kept to a minimum.
* False positive rate: Defined as \(\frac{FP}{FP+TN}\), the false positive rate allows us to identify which models tend to generate false positives, which are negative results falsely labeled as positive. This is a very important metric for insurance companies, given that when a positive result appears, an appraiser will check it.
* Recall: Defined as \(\frac{TP}{TP+FN}\), recall allows us to identify models that can detect the greatest number of damaged areas.
* AUC: This metric allows us to summarize the overall performance of a model from the precision vs. recall curve.
* Inference time: An important aspect is to measure the inference speed of the model, as it allows us to see the scalability of the model if it is to be deployed in a production environment, as well as the computational cost. This is measured in milliseconds.  
Below, we define the datasets that have been used to both train and evaluate the voting system.
## 4 Performance Evaluation and Analysis
### Efficiency Evaluation Result
#### 4.2.2 QR Code Recognition Testing of the Proposed Orchestration System
When three databases were used, the speed was increased by three times and when four databases were used, the time was increased by four times compared to when one database was used. When five databases were used, the time required for processing was reduced to 20%. This shows that the required time decreased as 1/N (number of databases used). Through this performance test, the proposed QR code identification method supports wider scanning angles and shows faster QR code identification when the environment is set for the distributed image process. Likewise, the orchestration system proposed in this paper was tested in terms of virtual network function as well as QR code identification service in order to verify the distributed process method. The proposed orchestration system can set different network device connections based on different environments and we were able to verify that this system can provide better service for users. Figure 17: QR code similarity graph applied for the proposed recognition method. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance** & **APPD** & **Naver** & **Barcode Scanner** \\ \hline
15 cm & 35\({}^{\circ}\) & 40\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum angles for the Nexus 4’s QR code identification using existing applications.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 2 Related Work
### Opportunistic Sensor Data Collection Using Bluetooth Low Energy  
The authors of [19] defined the OSDC problem in a general fashion and mentioned BLE as a candidate technology, but not evaluated, in a scenario where the mobile entities are smartphones. The feasibility of using BLE for OSDC has been demonstrated through a proof of concept, also with smartphones as mobile entities [20]. The work included a limited study with fixed BLE parameters and mode configuration. Results showed the low impact of BLE activity on the power consumption of the mobile entity (that was able collect data for 3.5 days, with a maximum throughput of 83.33 kbit/s, a round-trip time between 30 ms and 50 ms, and a coverage range up to 16 m). As will be explained in the following subsection, BLE performance can be much better.  
Finally, Unmanned Aerial Vehicles (UAVs) have been proposed to collect data from a WSN using BLE [21]. The work includes an analytical model of the power consumption of sensor nodes, based on published information of commercial Systems on Chip (SoCs). To our best knowledge, this is the only published study that evaluates the performance for a specific OSDC variable: the contact time. However, the model only considers the maximum and minimum values of discovery and connection times for one of the two BLE modes of operation, leaves out important performance parameters like the amount of collected information per contact interval, and does not evaluate the impact of the main BLE parameters on performance.
# Simulation Model and Method for Active Torsional Vibration Control of an HEV
## 1 Introduction  
### Motivations and Technical Challenges  
Hybrid electric vehicles (HEV) have been widely adopted by the automotive industry as a practical solution to increasing fuel efficiency and extending driving range [1; 2; 3; 4; 5; 6; 7; 8]. However, because the configuration is more complicated, if the design is not properly executed torsional vibration problems are more likely to occur, such as torsional damper damage and broken shafts, but it also provides a new means for torsional vibration control [9; 10; 11]. To develop a hybrid system with excellent performance and improve the powertrain configuration quickly and at low cost, simulation analysis is indispensable. Therefore, accurate kinetic parameters and control performance parameters for critical components are required in a closed drivetrain. Another research hotspot is concerned with how to add the torsional vibration active control module under the original driving and energy management control strategy.
## 4 Simulation of Non-Stationary Turbulent Wind Fields for the RSB  
### The Hypotheses  
Turbulent wind field simulation for long-span bridges is complicated due to the huge number of simulated points. According to the structural features of the suspension bridge, some hypotheses can be construed to enhance the simulation. In this work, non-stationary winds are regarded as a time-varying mean wind plus non-stationary turbulence:  
\[\left\{\begin{array}{l}U=\overline{U}(Z,t)+u(x,Z,t)\\ v=v(x,Z,t)\\ w=w(x,Z,t)\end{array}\right. \tag{41}\]  
where \(u\) is the turbulence in the across-bridge direction, \(v\) is the turbulence in the along-bridge direction \(x\) and \(w\) is the turbulence in vertical direction \(Z\), \(t\) is the time. \(U(Z,t)\) represents the time-varying mean wind. This is assuming that the EPSD matrices are uniform for all simulated points [34]. To simplify the calculation, the wind fields should be simulated in finite simulated points. Usually, only the correlation in spatial distributions of turbulent winds is considered in practice. Hence, a 3D-\(n\)V stochastic vector process can be simplified as three independent 1D-\(n\)V stochastic processes [35]. Specifically, turbulence \(v\) is not considered for the main girder since it is a linear component. As along-bridge vibrations of the towers are determined by the main cables, turbulence \(v\) for towers is not simulated. Meanwhile, turbulence \(w\) for towers is also not simulated [35]. In general, four independent 1D-\(n\)V stochastic processes can be assumed for the main girder and towers of the RSB based on the above hypotheses, listed in Table 1.  
The simulated points in the RSB is shown in Figure 4. There are 19 simulated points (from G1 to G19) uniformly distributed along the main girder (69.3 m in elevation), with the interval being 80.5 m. Similarly, there are 7 simulated points distributed uniformly along each tower. Specifically, T1 to T7 points in the left tower and T8 to T14 points in the right tower, with the interval being 30 m.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 8 Discussion
### Research Challenges
#### 8.2.4 Resources Consumed by Complex Models  
As shown in this study in Section 6, most IDSs are very complex and require a lot of computing resources and time. This requirement may affect the efficiency of IDS in a real environment. The use of multi-core GPUs can be a solution to reduce the time required, however, this solution is costly. Therefore, the algorithms developed need to use feature extraction to select the most important features to monitor to speed up the processing. However, as shown in Figure 5, 40% of the solutions reviewed were not using feature selection. Future solutions should try to find new feature extraction methods to reduce the processing of their algorithm.
## 4 Case Study and Results
### DG Curtailment  
In scenarios with low demand and high generation, the power curtailment of DG takes place. Figures 9 and 10 show the total DG power curtailment for the analysed three configurations. The highest total DG power curtailment occurred in the base case (5.03 MW), as shown in Figure 9. With P2P configuration, DG power curtailment was reduced to 4.29 MW (a reduction of 15%) and DG power curtailment with the proposed MVDC interface was reduced to 2.05 MW (a substantial reduction of 59% was achieved). Furthermore, the DG curtailment was at a minimum when using the proposed MVDC configuration, as illustrated in Figure 10. Results clearly proved that the application of the proposed MVDC interface maximised the usage of renewable DG resources in the distribution network.  
Figure 8: The distribution of maximum demand shift within the study period.  
Figure 7: The comparison of maximum demand shift at different system configurations.
# Product Image Generation Method Based on Morphological Optimization and Image Style Transfer
## 5 Case Study
### Automotive Front-Face Style Transfer Experiment Design
#### 5.2.2 Product Image Generation  
In this process, the optimized car front-face solution depicted in Figure 7 serves as the input content image. The target style image is chosen as the 2022 Mercedes-Benz C-Class model's car front face. The encoder is utilized to extract both content and style features from these images. Subsequently, these extracted features are input into the generator to produce style-transferred images, which aim to blend the content of the input image with the style of the target image. Ultimately, the discriminator assesses whether the style of the generated image matches the target image style. Through iterative optimization, a product form scheme that conforms to the desired target style is ultimately obtained.  
The experiment with the GAN entails the generation of car front faces through adversarial learning between the generator and the discriminator, utilizing a predefined set of test samples. The training process, depicted in Figure 9, showcases the progress at various epochs: 100, 200, 300, 400, 500, 600, 700, and 800.  
The figure illustrates the progression of generating car front-face images during the training process. Initially, at the early stages, the generated images exhibit rough outlines of car contours but still deviate significantly from real images. After 200 epochs, the car front-face images begin to exhibit noticeable features, such as the overall form. By 500 epochs, the generated images show more distinct features, including headlights and grille structures, though further refinement of details is required. At 700 epochs, the primary design of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample ID** & **Evaluation Value** & **Ranking** & **Sample ID** & **Evaluation Value** & **Ranking** \\ \hline
1 & 0.600 & 6 & 11 & 0.592 & 9 \\
2 & 0.568 & 16 & 12 & 0.580 & 11 \\
3 & 0.578 & 12 & 13 & 0.573 & 15 \\
4 & 0.612 & 5 & 14 & 0.548 & 18 \\
5 & 0.595 & 7 & 15 & 0.568 & 16 \\
6 & 0.614 & 4 & 16 & 0.526 & 19 \\
7 &car front face has undergone significant enhancement, with improved details compared to earlier stages. Ultimately, after 800 epochs of training, the generated images achieve a more pronounced and realistic representation of car front-face features.  
After training the network for 800 epochs, the definitive experimental result is presented in Figure 10. It is visually apparent that the generated scheme falls slightly short of achieving the authentic effect. While the generated scheme is relatively clear and displays distinct characteristics, capable of serving as a guide for the design of visual representations, it still exhibits notable disparities when compared to the work of professional designers, particularly in the realm of visual details. However, it remains advantageous in aiding designers to swiftly procure a car front-face effect scheme during the initial phases of the design process.
# Mode Profile Shaping in Wire Media: Towards An Experimental Verification
## 4 Frequency Isolation  
When attempting to experimentally demonstrate profile shaping it is necessary to not merely excite the appropriate mode in the structure, but also excite _only_ that mode. In particular, we need to be able to avoid exciting the nearby modes which may not be longitudinal, nor be correctly shaped. To assist with this selection, in Table 1 we show an eigenmode analysis for the cavity in the vicinity of the index 90 (12.430 GHz) mode we wish to excite. At first sight this does not look promising, with nearby modes being within 0.1% (i.e. about 0.04 GHz). However, we can also see that the nearest modes are transverse (as in Figure 14) rather than longitudinal, so that they should not be strongly excited as long as we only attempt to excite the desired mode with a well designed source. The nearest unwanted longitudinal mode is further away in frequency (about 0.9% or 0.1 GHz), so it need not present too much of a challenge if we can constrain the bandwidth of the mode and source sufficiently.