esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 3 Methodology
### Evaluation Criteria
#### 3.2.1 Trustworthiness and Continuity  
To evaluate the structural preservation of each technique, the metrics _trustworthiness_ and _continuity_[23] are applied to the dataset. Here, the distance of point \(i\) in high-dimensional space is measured against its \(k\) closest neighbours using rank order, and the extent to which each rank changes in low-dimensional space is measured. For \(n\) samples, let \(r(i,j)\) be the rank in distance of  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \(n\) & **Assignment** & **Range** & \(n\) & **Assignment** & **Range** \\ \hline
0 & LS gain & \(-\)12–12 dB & 7 & \(Pf_{1}\) Q & 0.1–10 Hz \\
1 & LS Freq & 22–1000 Hz & 8 & \(Pf_{2}\) Gain & \(-\)12–12 dB \\
2 & \(Pf_{0}\) Gain & \(-\)12–12 dB & 9 & \(Pf_{2}\) Freq & 220–10,000 Hz \\
3 & \(Pf_{0}\) Freq & 82–3900 Hz & 10 & \(Pf_{2}\) Q & 0.1–10 Hz \\
4 & \(Pf_{0}\) Q & 0.1–10 Hz & 11 & HS Gain & \(-\)12–12 dB \\
5 & \(Pf_{1}\) Gain & \(-\)12–12 dB & 12 & HS Freq & 580–20,000 Hz \\
6 & \(Pf_{1}\) Freq & 180–4700 Hz & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of the parameter space variables and their ranges of possible values, taken from the Semantic Audio Feature Extraction (SAFE) parametric equaliser interface.  
sample \(i\) to sample \(j\) in the high-dimensional space \(U_{i}^{k}\). Similarly, let \(\hat{r}(i,j)\) be the rank of the distance between sample \(i\) and sample \(j\) in low-dimensional space \(V_{i}^{k}\). Using the \(k\)-nearest neighbours, the map is considered _trustworthy_ if these \(k\) neighbours are also placed close to point \(i\) in the low-dimensional space, as shown in Equation (1).  
\[T(k)=1-\frac{2}{nk(2n-3k-1)}\sum_{i=1}^{n}\sum_{j\in U_{i}^{(k)}}\left(r(i,j)-k\right) \tag{1}\]  
Similarly, _continuity_ (shown in Equation 2) measures the extent to which original clusters of datapoints are preserved, and can be considered the inverse to _trustworthiness_, finding sample points that are close to point \(i\) in low-dimensional space, but not in the high-dimensional plane.  
\[C(k)=1-\frac{2}{nk(2n-3k-1)}\sum_{i=1}^{n}\sum_{j\in V_{i}^{(k)}}\left(\hat{r }(i,j)-k\right) \tag{2}\]  
In both of these equations, a normalising factor is used to bound the _trustworthiness_ and _continuity_ scores between 0 and 1. These measures evaluate the extent to which the local structure of the original dataset is preserved in a low-dimensional map; this is described in [24], where it is shown that the local structure of the dataset needs to be retained for a successful map of the datapoints.
# Mental Health and Safety Assessment Methods of Bus Drivers
## 4 Identification of Risk Factors for Bus Drivers Prone to Accidents
### Socio-Demographic Characteristics Analysis  
Demographic information includes drivers' age, driving experience (driving experience refers to the number of years for a motor vehicle driver to obtain the driving qualification, which starts from the date when the driver obtained their driving qualification), marital status (married/unmarried), and education (below junior high school, high school, and technical secondary school, junior college, or above). Drivers' ages were divided into five groups: \(\leq\)25, 26-35, 36-45, 46-55 and \(>\)55 years old; driving experience was divided into six groups: \(\leq\)2, 3-5, 6-10, 11-15, 16-20 and \(>\)21 years [26].  
Table 1 compares the demographic information of the accident group and the non-accident group of bus drivers. The average age of drivers in the accident group was 37.73 \(\pm\) 9.371 years old, and that of drivers in the non-accident group was 37.1 \(\pm\) 8.102 years old. The average driving experience in the accident group was 11.91 \(\pm\) 11.007 years, and that in the non-accident group was 7.43 \(\pm\) 9.177 years. The Chi-square test was used to analyze drivers' ages and driving experience; the results showed that the drivers' age and driving age were different and statistically significant (\(p\) < 0.05 or \(p\) < 0.01). The non-parametric, rank-sum test was used to analyze marriage and education, and the results indicated that marital status and education were not statistically significant (\(p\) > 0.05). Therefore, driving age and age may be influencing factors in bus-driver accidents.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Demographic Information**}} & \multicolumn{2}{c}{**Accident Group (\(n\) = 40)**} & \multicolumn{2}{c}{**Non-Accident Group (\(n\) = 80)**} & \multicolumn{1}{c}{\multirow{2}{*}{\(p\)}} \\ \cline{3-3} \cline{5-6}  & **Number of** & **Proportion** & **Number of** & **Proportion** & **(\%)** & \\ \hline \multirow{8}{*}{Age} & \(\leq\)25 years old & 12 & 30 & 7 & 8.8 & \\  & 26–35 years old & 9 & 22.5 & 21 & 26.3 & \\  & 36–45 years old & 6 & 15 & 34 & 42.5 & 0.005 * \\  & 46–55 years old & 6 & 15 & 9 & 11.3 & \\  & \(>\)55 years old & 7 & 17.5 & 9 & 11.3 & \\  & \(\leq\)2 years & 13 & 32.5 & 8 & 10 & \\  & 3–5 years & 7 & 17.5 & 10 & 12.5 & \\ Driving experience & 6–10 years & 6 & 15 & 22 & 27.5 & \\  & 11–15 years & 4 & 10 & 13 & 16.3 & \\  & 16–20 years & 6 & 15 & 10 & 12.5 & \\  & \(>\)21 years & 4 & 10 & 17 & 21.3 & \\  & Married & 30 & 75 & 68 & 85 & \\  & Unmarried & 10 & 25 & 12 & 15 & 0.184 \\ Education & Junior high school and below & 12 & 30 & 14 & 17.5 & \\  & High school and technical & 16 & 40 & 54 & 67.5 & 0.860 \\ background & secondary school & 12 & 30 & 12 & 15 & \\  & Junior college and above & 12 & 30 & 12 & 15 & \\ \hline \hline \end{tabular} Note: * \(p\) < 0.05.  
\end{table}
Table 1: Comparison of demographic information between the accident group and non-accident group of bus drivers.
## 3 Results and Discussion
### Effect of Additives on Viscosity  
As mentioned above, the two factors that have the greatest influence on the quality of the fibers during melt electrospinningning are the electrical conductivity and the viscosity of the polymer [40]. The viscosity of a polymer is a major property that must be controlled when optimizing the spinning voltage to obtain the desired fiber size and shape. In general, the lower the viscosity, the smaller the fiber diameter and the easier the processing. Appropriate adjustment of the melt viscosity not only speeds up the formation of fibers, but also enables the formation of stable Taylor cones without the jet sputtering on the entire collector [41; 42]. Therefore, in this study, the viscosity improvement was also analyzed to evaluate the suitability of using non-toxic natural additives for melt electrospinningning.  
The effects of various additives on the shear viscosity of PCL are shown in Figure 6. The viscosity of the pure PCL polymer was approximately 25,580 mPa-s when measured at the applied temperature (200 \({}^{\circ}\)C). When additives are introduced into the solution, the entanglement between the polymer chains is reduced and the viscosity gradually decreases [43]. The viscosity of the PCL solution with additives was measured up to the solubility limit of the additives (Tables S2-S6).  
As shown in Figure 7, the viscosity of the PCL solution increased to 9338 mPa-s with the addition of 1 wt% AZ, 4508 mPa-s for 10 wt% CH, mPa-s for 4 wt% QC, 975.9 mPa-s for 10 wt% OA, and 3 mPa-s for 7 wt% GA (note that the Y-axis is a logarithmic scale). GA produced the highest viscosity and conductance improvement, where the viscosity improvement rate was as much as 1/8526 times. Therefore, GA is expected to be the most effective additive for controlling melt electrospinningning of PCL, leading to the best improvement in its electrical and rheological properties.  
Figure 5: Conductance of PCL with various additives. The error bars indicate the standard deviation of three independent measurements.
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
###### Abstract  
Carrier-phase measurements are essential in precise Global Navigation Satellite System (GNSS) positioning applications. The quality of those observations, as well as the final positioning result, is influenced by an extensive list of GNSS error sources, one of which is the receiver antenna phase center (PC) model. It has been well established that the antenna PC exhibits variability depending on the frequency, direction, and intensity of the incoming GNSS signal. To mitigate the corresponding range errors, phase center corrections (PCCs) are determined through a specialized procedure known as receiver antenna calibration and subsequently applied in data processing. In 2023, the Laboratory for Measurements and Measuring Technique (LMMT) of the Faculty of Geodesy, University of Zagreb, Croatia, initiated the development of a new robotic GNSS receiver antenna calibration system. The system implements absolute field calibration and PCC modeling through triple-difference (TD) carrier-phase observations and spherical harmonics (SH) expansion. This study presents and documents dual-frequency (L1 and L2) Global Positioning System (GPS) calibration results for several distinct receiver antennas. Furthermore, the main goals of this contribution are to evaluate the accuracy of dual-frequency GPS calibration results on the pattern level with respect to independent calibrations obtained from Geo++ GmbH and to extensively experimentally validate LMMT calibration results in the spatial (coordinate) domain, i.e., to investigate how the application of LMMT PPC models reflects on geodetic-grade GNSS positioning. Our experimental research results showed a submillimeter calibration accuracy, i.e., 0.36 mm for GPS L1 and 0.54 mm for the GPS L2 frequency. Furthermore, our field results confirmed that the application of LMMT PCC models significantly increases baseline accuracy and GNSS network solution accuracy when compared to type-mean PCC models of the International GNSS Service (IGS).  
GNSS; receiver antenna; absolute calibration; robotics; phase center correction (PCC); dual-frequency; GPS L1; GPS L2 2020
## 3 Methods and Materials
### Routing Problems
The aim is always to touch the more interesting waypoints with the highest reward, respecting energy constraints driven by the \(D_{max}\), while simultaneously lowering the overall travelled distance. If no energy constraints are needed, \(D_{max}\) can be set to a high value. For example, in the code developed for the framework presented in this paper, when no distance constraint is imposed, \(D_{max}\) is evaluated by solving a travelling salesman problem. If the robotic system can recharge when it reaches a waypoint, then the first constraints' (Equation (6)) can be dropped. A reachability analysis is used when defining the distance matrix. If the distance between two waypoints is more than the \(D_{max}\), then the related \(x_{ij}\) is set to be infinitely distant. Equations (7) and (8) impose that if a node is selected, then there must be one incoming and one outcoming edge. The index \(i\) represent the rows, while index \(j\) represents the columns. Equation (9) is imposed to avoid subtours. It avoids that the number of selected arcs within S (a subset of V) to be equal to or larger than the number of nodes in S [58]. Equation (10) defines the allowed values for \(y_{i}\) and \(x_{ij}\). The distance between the waypoints can be evaluated as a simple Euclidean distance or using more refined path planning algorithms such as A*. If the environment the system explores is not particularly texturized, the Euclidean distance may provide a good enough estimate of the travelled distance. A simple example of a routing plan considering the Euclidean distance and the \"open vehicle routing problem with reward with a distance constraint\" is shown in Figure 5. Figure 6 shows the same simulation considering an A* algorithm to assess the path between the different waypoints. The green areas in Figure 6 represent clusters of obstacles the system should avoid. The distance evaluated with the A\({}^{\star}\) algorithm and the one estimated as Euclidean distance differ by about 10%.
# First Observations of Cirrus Clouds Using the UZ Mie Lidar over uMhalthuze City, South Africa
## 1 Introduction
More importantly is that the instrument is used to measure emissions during the harvest season when harvesters first burn sugar cane before curing it, which greatly tempers the air quality of the area and its surrounding. Thus, apart from the Highveld area of South Africa, which is known for its air quality issues, the study area where the lidar is situated, is an area that requires attention when it comes to accurate measurements of atmospheric chemistry and composition. However, due to the lack of funding for projects that can accurately probe the atmosphere in this study area, little or no studies have been performed in order to investigate the air quality of this study area. Thus, this lidar is strategically placed in this area for this reason. The other plan is to use the lidar data to calibrate the performance of satellite measurements in this area and also submit the data for use in reanalysis products and models. This case study, though, came about because of the capabilities of this lidar to detect even activities in the upper troposphere and lower stratosphere. In terms of the use of the UZ lidar for cirrus clouds observations over South Africa, this is very important research because, in terms of the meteorology of South Africa, cirrus clouds are known to form from the ascent of dry air, which makes the small quantity of water vapor in the air undergo deposition into ice, which means it solidifies directly. The South African Weather Services (SAWS) use the sighting of the cirrus clouds as an indicator of the arrival of a warm front as the air masses meet at high levels, which indicates that a change in the weather is on the way. For many years, pictures have been used to profile these cirrus clouds. However, an instrument such as lidar is always a better option for the purpose of profiling these cirrus clouds because it can provide detailed characteristics of these clouds very accurately. Thus, cirrus clouds are important for the context of South Africa because the meteorology of this part of the world is heavily influenced by pressure systems. Apart from using the UZ lidar for air quality and cloud studies, another plan is to modify and use the UZ lidar for investigating the thermal structure over this region. Other similar have been conducted by [PERSON] et al. [13], [PERSON] et al. [14], and [PERSON] and [PERSON] [15]. The study presents for the first time the observation of cirrus clouds based on two successive nights (10 and 11 April 2019) by the UZ lidar system. The UZ lidar results are further compared with CALIPSO observations. In particular, the study aims at determining the cloud properties such as cloud optical depth (COD), cloud position, occurrence height, backscatter coefficient and extinction coefficients.
# Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4
## 4 Experimental Results and Analysis
### Robustness and Generalization Ability Verification  
Due to the drastic changes in time, environment, angle, and other factors during the shooting process, the images were prone to uneven illumination and blurring; thus, images under special conditions like exposure, multi-target, and motion blur were used to verify the robustness and generalization ability of the improved YOLOv4 lightweight detection model, which was used to detect images under the above three special conditions. The detection results were compared with the YOLOv4, YOLOv3, Faster RCNN, and SSD models, and the results are shown in Figure 11.  
Figure 11: Detection results under special conditions.  
Figure 10: The mAP and AP of each class under gamma correction.  
With respect to multi-target detection, it can be seen that both SSD and Faster RCNN miss a person, and SSD incorrectly detects the operating bar as the power checker, while YOLOv4 and YOLOv3 both miss the operating bar. With respect to detection under exposure, it can be seen that SSD and Faster RCNN missed too many targets, YOLOv3 cannot detect the gloves, and YOLOv4 missed one glove. Finally, with respect to detection under motion blur, it can be seen that Faster RCNN cannot detect the gloves or badge, while YOLOv4, YOLOv3, and SSD all incorrectly detect the hand as a glove. However, the improved YOLOv4 lightweight detection model can accurately detect the targets in the above special cases, verifying that the proposed method has good detection accuracy in complex and diverse substation environments.  
Meanwhile, on the basis of the improved YOLOv4 lightweight detection model with the usage of gamma correction preprocessing method, exposed images caused by uneven lighting or improper shooting can be adjusted and image contrast can be enhanced. As shown in Figure 12, when the image was preprocessed by gamma correction with \(\gamma=1.2\), the targets in the image can be clearly highlighted, and the person in Figure 12a and the power checker in Figure 12c are accurately detected. These results effectively prove that the proposed method is helpful for monitoring irregular behavior of substation personnel.
###### Abstract
Next, based on state-of-the-art works, the generic use-case groups that cover the majority of the applications in each of the sectors of smart agriculture, forestry, biodiversity monitoring, and water management are identified and described (Section 3). Using the above-mentioned groupings, as well as the reviewed research achievements and described solutions' properties, the relevant communication services' requirements are identified in Section 4. Next, Section 5 presents a high-level review of the applications for the sectors in focus that demand capabilities provided by mobile networks and the status of the relevant research advancements, which address the identified needs for communication services. Section 6 is devoted to the presentation of the mobile networks' systems, 5G System (5 GS) and 6G System (6 GS), including details regarding standardization, supporting technologies, envisioned future capabilities, current technological advancements, and the status of carrier-grade 5 GS, as well as already deployed 5G-based solutions in the context of the needs of the sectors in focus. Section 7 describes the applicability of 5 GS and envisioned 6 GS to the use cases identified in Section 3 using the network feature analysis conducted in the previous sections of the paper. Based on the thorough investigation of standardization and the sectors in focus, multiple gaps and open issues are identified and discussed in Section 8. Finally, Section 9 presents a summary and concludes the paper.
## 2 Materials and Methods
### Methodology
#### 2.6.1 The General Framework of the MSAMT Method  
The main algorithm design framework of the MSAMT is given in Figure 1. During the surrogate construction process, all of the samples in the database are first divided into \(n\) clusters using k-means clustering to prevent SAEAs from entering local optima. Then, the entire space is divided into \(n\) disjoint regions, and each of which is associated with a different cluster. Finally, a corresponding RBF model is constructed in each region to assist the optimizer in finding the promising point for the global search, and a dynamic ensemble of surrogates is constructed using the best part of the sample selected from the database to search for the promising point within the local region. The former is concerned with exploring the entire decision space, while the latter tends to exploit the subregion near the optimal individual. The promising points identified by the surrogate-assisted G-MFEA are accurately evaluated and added to the database (DB). Algorithm 1 provides the pseudocode of the MSAMT. Firstly, \(N\) initial samples are sampled by Latin hypercube sampling (LHS). Each sample is evaluated by the real objective function and added to the DB. Secondly, all the exactly evaluated samples are clustered into \(n\) groups utilizing k-means clustering, and the entire space is divided into \(n\) disjoint areas based on the clusters. Then, the RBF model for each region is built. Thirdly, \(ND\) samples having the optimal fitness values are chosen to construct a dynamic ensemble of surrogates proposed in Algorithm 2. Finally, \(n+1\) surrogates are subsequently applied to help the G-MFEA in the population evolution of \(k_{max}\) generations as shown in Algorithm 3. Algorithm 4 is applied to produce an initial evolutionary population. Individuals with superior fitness values predicted by the surrogates are exactly evaluated and archived in the DB. The global and local searches are conducted simultaneously, and each iteration  
Figure 1: The flow chart of the proposed MSAMT. locates \(n\) promising points distributed in disjoint regions and one promising point in the region around the optimal individual. The entire process is repeated until the maximum number of FEs is exhausted.
# A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System
## 3 Two Proposed OFDM-Based QG-MIMO Schemes  
In this section, the fundamental principles of two novel OFDM-based QG-MIMO transmission schemes, including OFDM-based TD-QGSM and TD-QGSMP, are proposed first. Then, the theoretical SE performance of the two proposed schemes is analyzed accordingly.
# Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
###### Abstract  
Thin-walled structures, when used for high-speed railways, can effectively mitigate the irreversible destruction when a malfunction occurs. Nested thin-walled tubes, as energy-absorbing structures, possess excellent specific energy absorption (\(SEA\)) and crushing force efficiency (\(CFE\)). This paper conducts multi-objective optimization by focusing on a square corrugation nested structure with a double octagon inner wall, namely SCOD, to ameliorate the crashworthiness of the nested structure. The finite element model of the SCOD is constructed and validated by test data. A set of experimental design points with good spatial distribution are obtained using the optimal Latin hypercube (LHC) method. The polynomial response surface (PRS) method was applied to establish the fitting relationship between design variables and optimization objectives, and validation is accomplished. The DCNSGA-III algorithm is employed for optimization, resulting in a Pareto alternative solution set with good population diversity and convergence. In addition, to observe the optimized performance, a set of optimal solutions considering a single objective value is derived, and a comprehensive optimal solution is obtained by applying the minimum distance selection method (TMDSM). Finally, the proposed optimized system is analyzed and validated. According to the alternative reference solutions, the initial peak force (\(IPCF\)) reduces by 53.75% and \(CFE\) increases by 8.7%. This paper provides some reference for the optimization design in practical engineering.  
optimization; parameter reduction; MABAC; multi-criteria decision-making; energy absorbing devices; train +
Footnote †: journal: Applied Sciences