esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Topicality of the Problem and the Scope of Possible Solutions
These instruments were properly calibrated, which resulted in the relation to the gas flow meters used, which are given in Table 5.  
The analysis of the measurement error was also a subject of our consideration. It was found that with respect to the measured values, this analysis did not bring any significant improvement to the estimation of the measurement error. In this regard, the detailed description of the measurement error analysis was omitted. \begin{table}
\begin{tabular}{c c c c} \hline
**Flow Meter Type** & **Measurement Range** & **Scaling Equation—The Value of the Air** & **Accuracy of Scaling** \\  & & **Stream, m\({}^{3}\)-s\({}^{-1}\)** & **Accuracy of Scaling** \\ \hline RDN 06-03 & 0–48 & \(Q_{g}=1.6\cdot 10^{-3}\cdot((0.216\cdot\text{scala})+1.4112)\) & 0.99 \\ \hline \end{tabular}
\end{table} 
Table 5: Parameters of gas flow meters calibration [own elaboration]. By way of an example, the algorithm for calculating the error and the uncertainty of air volume flow measurement, which results from the analysis of measurement errors, is presented. The calculation results according to the algorithm are presented in Table 6.  
As can be seen from Table 6, the expected (average) value for the adopted measurement is 3.74 \(\times\) 10\({}^{-4}\) m\({}^{3}\cdot\)s\({}^{-1}\), and at the result (at the confidence level 0.99), it is 3.74 \(\times\) 10\({}^{-4}\)\(\pm\) 3.29 \(\times\) 10\({}^{-5}\) m\({}^{3}\cdot\)s\({}^{-1}\), which gives an average measurement error for the analyzed series of 8.7%. The average relative mistake for the entire scope of the flow of gas amounted to \(\pm\)5.3%. Also noteworthy are publications pointing to the thermodynamic model of the microbial hydrolysis process [41], proposed biological strategies that can be divided into three groups, i.e., \"upstream\", \"mainstream\" and \"downstream\" approaches [42], and techniques for biological removal of pollutants from biogas [43]. Certainly, future research will focus on sustainability features when assessing bioenergy and bioproduct systems [44].
# Influence of Pre-Radiation and Photo-Bleaching on the Yb-Doped Fiber Laser Radiated with Gamma-ray
## 2 Experiments  
### Laser Setup  
In this research, the commercial Yb-doped fiber (Nuferm-976-20/400) was used as the active fiber in the fiber laser system. Using gamma-ray radiation at a dose of 5 krad(Si),the pre-radiated Yb-doped fiber was processed and fabricated into a pre-radiated fiber laser, while the non-pre-radiated fiber laser was fabricated by the Yb-doped fiber without pre-radiation. Except for the active fiber, both fiber lasers had the same configuration, as shown in Figure 1. Output power of the seed laser from the homemade 1060 nm oscillating cavity was about 10 W, with a narrow 3 dB bandwidth of 0.1 nm. The seed laser was followed by a high-power optical isolator (ISO, HPI-1064-20W, provided by Advanced Fiber Resources, Inc.) to isolate backward light and protect the oscillating cavity seed. A mode-field adaptor (MFA, fabricated by Raycuslaser, Inc., Wuhan, China) connected the seed with the amplifier stage, matching the core/cladding size of 10/125 um in the oscillator stage and 20/250 um in the combiner's signal fiber. The (\(6+1\)) \(\times\) 1 combiner (fabricated by Raycuslaser, Inc., Wuhan, China) injected the seed laser into the active fiber together with the 976 nm amplifier pump light. The pump power of non-pre-radiated and pre-radiated fiber lasers was 1277 W and 1089 W, respectively. The length of both active fibers was 12 m, and a counter-pump (\(6+1\)) \(\times\) 1 combiner (fabricated by Raycuslaser, Inc., Wuhan, China) provided the potential ability to further expand the pump power in fiber lasers. A cladding light stripper (CLS, fabricated by Raycuslaser, Inc., Wuhan, China) was used to stripe the residual pump, and a quartz blockhead (QBH, fabricated by Raycuslaser, Inc., Wuhan, China) was spliced to deliver the output laser. The output power was measured with a 10 Kw power meter (10K-W-BB-45, Ophir Inc., Littleton, CO, USA).
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Modes Control
#### 3.2.7 State of Charge (SoC) Estimation  
Accurate estimation of the battery's SoC is crucial for efficient charging. Control strategies utilize algorithms and models to estimate the SoC based on various parameters such as voltage, current, and temperature measurements [136; 137; 138]. These methods consider the battery's discharge and charge characteristics to provide a reliable estimate of its state of charge [136]. SoC estimation helps regulate the charging process, ensuring the battery is neither undercharged nor overcharged [139].
# Influence of Suction on Capacity of Pile Foundation
## 4 Results
### Type 1 Soil  
4.4.1. Modified \(\beta\) Method [7]  
In order to calculate the shear strength of the soil, it is essential to consider the matric suction along with the degree of saturation. Equation (17) is used for the calculation of the shear strength.  
\[\tau=\left[c^{\prime}+(\sigma_{n}-u_{a})*tan\phi^{\prime}\right]+\left[(u_{a} -u_{w})*S^{k}*tan\phi^{\prime}\right] \tag{17}\]  
where \(c^{\prime}\)--effective cohesion; \((\sigma_{n}-u_{a})\)--net normal stress; \(\phi^{\prime}\)--effective internal friction angle; \((u_{a}-u_{w})\)--matric suction; \(S\)--degree of saturation; and \(k\)--fitting parameter [7].  
The fitting parameter used for shear strength can be found though the graph showing its relation between Plasticity Index (PI). However, in the original document, there were no exact values for PI, so in order to find its value, a graph of the friction angle vs. PI was used [27] before finding the value of \(k\). The fitting parameter can be calculated using Equation (18).  
\[k=-0.0016*PI^{2}+0.0975*PI+1 \tag{18}\]  
From the above methods, values of the fitting parameter were taken as shown in Table 8.  
Using all the given and obtained data, the shear strength can be calculated and results are shown in Table 9.  
Using Equation (19) of the modified beta method, the shaft capacity of a pile can be calculated.  
\[Q_{s}=\left[\beta\sigma_{v}^{\prime}+\left\{(u_{a}-u_{w})*S^{k}*tan\delta^{ \prime}\right\}\right]*\tau dL \tag{19}\]  
where \(\delta^{\prime}\)--effective angle of interface [7].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Depth (m)** & **Friction Angle, \(\phi^{\prime}\)** & **Plasticity Index, PI** & **Fitting Parameter, k** \\ \hline
0–3 & 31 & 16 & 2.1504 \\ \hline
3–4 & 25 & 50 & 1.875 \\ \hline
4–6 & 25 & 50 & 1.875 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fitting parameter values for different depths.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(c^{\prime}\) (**kPa)** & \(\sigma_{n}-u_{a}\) (**kPa)** & \(u_{a}-u_{w}\) (**kPa)** & **S** & **k** & \(tan\phi^{\prime}\) & \(\tau\) (**kPa)** \\ \hline
9.6 & 0 & 64 & 0.45 & 2.15 & 0.60086062 & 16.5081319 \\ \hline
9.6 & 18.3 & 54 & 0.52 & 2.15 & 0.60086062 & 28.5495498 \\ \hline
9.6 & 36.6 & 44 & 0.6 & 2.15 & 0.60086062 & 40.4070932 \\ \hline
6.6 & 50.1 & 34 & 0.68 & 1.875 & 0.46630766 & 37.6551898 \\ \hline
6.6 & 66.8 & 24 & 0.77 & 1.875 & 0.46630766 & 44.6050846 \\ \hline
4.2 & 82 & 14 & 0.86 & 1.875 & 0.46630766 & 47.3574554 \\ \hline
4.2 & 98.4 & 4 & 0.96 & 1.875 & 0.46630766 & 51.8124641 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Calculated values for shear strength.  
According to this and the values found before, Table 10 shows the results of the calculated ultimate shaft capacity. Values for \(\beta\) were considered to be the same as calculated in the previous conventional method.
# Performance of Parallel K-Means Algorithms in Java
## 2 Background on K-Means  
In the following, the basic concepts of K-Means are briefly reviewed. A dataset \(X=\{x_{1},x_{2},\ldots,x_{N}\}\) is considered with \(N\) data points (records). Each data point has \(D\) coordinates (number of features or dimensions). Data points must be partitioned into \(K\) clusters in such a way to ensure similarity among the points of a same cluster and dissimilarity among points belonging to different clusters. Every cluster \(C_{k}\), \(1\leq k\leq K\), is represented by its _centroid_ point \(c_{k}\), e.g., its mean or central point. The goal of K-Means is to assign points to clusters in such a way to minimize the _sum of squared distances_ (\(SSD\)) objective function:  
\[SSD=\sum_{k=1}^{K}\sum_{\begin{array}{c}i=1\\ x_{i}\in C_{k}\end{array}}^{n_{k}}d(x_{i},c_{k})^{2}\]  
where \(n_{k}\) is the number of points of \(X\) assigned to cluster \(C_{k}\) and \(d(x_{i},c_{k})\) denotes the Euclidean distance between \(x_{i}\) and \(c_{k}\). The problem is NP-hard and practically is approximated by heuristic algorithms. The standard method of Lloyd's K-means [14] is articulated in the iterative steps shown in Figure 1.  
Several initialization methods for the step 1 in Figure 1, either stochastic or deterministic (see also later in this section), have been considered and studied in the literature, see e.g., [3; 15; 16; 17; 18], where each one influences the evolution and the accuracy of K-Means. Often, also considering the complexity of some initialization algorithms, the _random_ method is adopted which initializes the centroids as random selected points in the dataset, although it can imply K-Means evolves toward a local minima.  
The convergence at step 4 can be checked in different ways. In this work, the distance \(d\left(c_{k}^{\prime},c_{k}\right)\) is evaluated and when it, for all the centroids, is found to be less than an assumed threshold, convergence is supposed to be reached. A maximum number of iterations can be established to terminate the algorithm in any case.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 4 Experiments
### Experimental Setting
#### 4.1.2 Evaluation Metrics  
To assess the performance of SQ-DETR, we employed the COCO evaluation protocol, which provides a comprehensive set of metrics for object detection. The primary metric used in our evaluation is average precision (AP), which quantifies the area under the precision-recall curve. COCO AP is computed by averaging precision values over multiple Intersection over Union (IoU) thresholds, ranging from 0.5 to 0.95 in 0.05 increments, offering a holistic measure of model performance.  
In addition to overall \(AP\), we report \(AP_{50}\) and \(AP_{75}\), which represent precision at fixed IoU thresholds of 0.5 and 0.75, respectively. These metrics highlight model robustness across varying localization requirements. Furthermore, we evaluate performance across object scales using \(AP_{S}\), \(AP_{M}\), and \(AP_{L}\), which correspond to small (\(area<32^{2}\) pixels), medium (\(32^{2}\leq area<96^{2}\) pixels), and large (\(area\geq 96^{2}\) pixels) objects.  
Through these various evaluation metrics, we can comprehensively analyze the performance of the proposed model and determine the performance improvement by comparing it with existing models.
## 3 Overview of 3 GPP AKMA
### TLS as an AKMA Ua\({}^{*}\) Protocol  
The objective of the Ua* protocol within AKMA is not only to enable the UE to transmit its A-KID to the AF but also to ensure secure communication between these two entities based on their application-specific key \(K_{AF}\). For secure communication, it should facilitate mutual authentication, negotiation of session keys, and secure data exchange through encryption and authentication, thereby protecting against various security threats. Specifically, the Ua* protocol is tailored to specific applications, i.e., 5G cApps, allowing different applications to securely interact with the UE using their dedicated \(K_{AF}\) derived from the AKMA anchor key \(K_{AKMA}\). The protocol can handle key expiration, including mechanisms for refreshing keys and re-establishing secure communication sessions without disrupting the application's functionality. As its major candidate, TLS 1.2 or 1.3 is recommended. Particularly, since the Ua* protocol should be protected with \(K_{AF}\), we can consider shared key-based approaches such as the session resumption option of TLS 1.2 and the PSK option of TLS 1.3. Here, we focus on the TLS 1.3 PSK option because TLS 1.3 is the latest TLS version, and perfect forward secrecy is important.
## 3 Results
### Accuracy and Repeatability
This lack of symmetry of the tolerance limits is more noticeable for the 33 D lens, as it has greater power. Regarding random errors, the magnitude was similar for all three lenses. For a 10.00 mm diameter aperture for the +10.00 and +20.00 D lenses, and a 7 mm diameter aperture for the +33.33 D (due to magnification), errors were lower than 0.003 D (0.02% in relative terms), indicating excellent repeatability of the measurements across the whole lens diameter. The central zone had a higher random error. For a 0.50 mm diameter lens, the random errors for all three lenses were lower than 0.02 D, with a maximum relative error of 0.13% for the +10.00 D lens. Again, compare this relative error with the 1% manufacturing error claimed by the manufacturer of the calibration lenses. To stress this point, a monofocal standard intraocular lens (RayOne) with a power of +10.00 was measured 10 times, as well as a multifocal one (ReZoom) with a base power of +20.00 D. The accuracy of these measurements could not be estimated using the same method as was employed with the calibration lenses because the corresponding theoretical profiles had not been published by the manufacturers. However, we could estimate the repeatability from the random errors. The mean profile and the corresponding confidence interval are presented in Figures 2 and 3. The power profile of both intraocular lenses shows good repeatability, particularly at the lens periphery, except for the lens center, where the random error increases. However, it should be noted that the area for which these errors are higher than 0.2% is delimited by a zone with a radius of 0.25 mm. Outside this zone, the confidence intervals closely follow the average power profile for both lenses. Figure 2: Average power profile of the RayOne RAO600C +10.00 D lens. (**A**) shows the profile along the entire optic size while (**B**) shows the central area. The solid blue line is the average of 10 measurements and the dashed lines are the intervals of confidence computed from the standard deviation. The measurement exhibits good repeatability but for the central zone of the lens marked by the black dashed vertical lines.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 8 Service Composition for WSNs
### Service Composition with Persistent Queries (SCPQ)  
The service composition can reduce the total number of solutions over the lifetime of persistent queries. Reduction in this number can decrease the total cost of service composition [92]. Routing in WSNs is used only to find a path from the source sensors to the receiver node. Thus, Service-Oriented query routing protocols are applied in order to guarantee a path from the source sensors to the sink and should also include service providers [92]. Two algorithms are applied to minimize energy consumption, which can provide service composition solutions for a persistent query. These algorithms are called Greedy and Dynamic Programming. The Greedy algorithm is applied to minimize the total number of service composition solutions during the lifetime of a persistent query. The Dynamic Programming algorithm uses the results of the Greedy algorithm to find a shorter path and reduce the total cost of service composition solutions. The time complexity of the Dynamic Programming algorithm is defined as O ((D/T) ~3) [92]. Similarly, another study uses the Greedy algorithm to select the best nodes. The middleware system service-based approach for WSNs provides QoS and context-awareness [93].
## 2 Mathematical Model
### Best Scheduling Constraints Formulation: Comparison  
We study three different versions of the mathematical model. Each version has the same Objective Function (24), and resource allocation constraints (21)-(23) and different type of scheduling constraints implementation:  
1. job on on-off variables, presented in Section 2.2.1;job pulse start-end variables, presented in Section 2.2.2;
3. job step start-end variables, presented in Section 2.2.3.  
In order to study the impact of these scheduling constraints on the performance of the model, we make numerical experiments on two datasets. Each dataset includes 100 instances. Parameters of these datasets are presented in Table 3. The instances were generated using a continuous uniform distribution of parameters. Precedence graphs were created with the given total number of directed edges under the condition of its acyclicity.  
We define three Mixed-Integer Linear Programming models. These models are implemented using the IBM ILOG CPLEX 12.8 mathematical programming solver with Java code on a workstation with 4 thread 2.70 GHz processor and 8 Gb RAM.  
Figure 2 presents solution times obtained for these two datasets with three different model versions.  
We can conclude that the best performance in terms of solution time is obtained for generalized model with step formulation of scheduling constraints. This type of constraints has been also used in models of [PERSON] et al. (2018) and [PERSON] et al. (2019). In next sections, we use the generalized model with step start and end variables (presented in Section 2.2.3) to compare it with an aggregated fraction approach.
## 2 Object Extraction Method of X-ray Image  
In the field of image detection, the region of concern is called the region of interest (ROI). In the detection and tracking algorithm, the region where the target is detected is the region of interest, and other regions are the background. As shown in Figure 1, the car in the red box is the region of interest. In contrast to the background, the area of interest is the object that needs to be extracted. The area where the luggage package is located in the X-ray security check image is the object, and the area outside the package is the background. The first step of detection tracking algorithm is object extraction. The deep learning model is used to extract a specific object in complex scenes, such as in the detection of vehicles and pedestrians on the road as shown in Figure 2. The advantages of this method are that it has high detection accuracy for relatively homogeneous targets (such as people and vehicles), has good robustness, and is not susceptible to interference from complex environments. However, it has high requirements on hardware and has a poor detection effect on targets with unfixed features.  
The specific method of X-ray package object extraction is shown in Figure 3. Binarization is carried out on the input image; then, the contour detection method is used to extract the object area; luggage package target tracking and key frame judgment are carried out; and finally package target extraction is carried out [17]. As shown in Figure 4, in the X-ray security inspection image, the shape features of the luggage package are different, while the background is often a pure color. The interference mainly comes from the noise in the image and the image of the small stains on the conveyor belt of the security inspection machine. The deep learning detection algorithm is not applicable to this scene. Due to the relatively simple background and interference in the X-ray image, this paper uses the image processing method to extract the X-ray image of the luggage package. The area where the package is located can be determined.  
Figure 1: Region of Interest.  
Figure 3: Package extraction process in an X-ray image.  
Figure 2: Vehicle and pedestrian object detection.
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 1 Introduction  
With the significant increase in the urban population, the demand for transportation services has also risen, posing new challenges for the development and management of urban public transportation systems. As a vital part of urban public transportation, taxis play an important role in the city's transportation network, which includes public transport, private cars, electric vehicles, motorcycles, and bicycles. Taxis have notable advantages in terms of shared use and travel distance, providing flexible and fast transportation services that meet the spatial and temporal needs of urban travel to a high degree, making them a key choice for medium- and short-distance travel for city residents [1]. However, with the continuous increase in the number and variety of vehicles and the diversified growth of travel demands, taxi operations have encountered difficulties. According to surveys, the current taxi vacancy rate remains high at 30-40%, while passengers in many locations still face the issue of \"difficulty hailing a taxi\". Taxis exhibit an imbalanced supply and demand in terms of spatial and temporal distribution, with either \"clustering\" or \"absence\", resulting in two problems: \"drivers having difficulty finding passengers\" and \"passengers having difficulty finding taxis\". This imbalance leads to a higher taxi vacancy rate, a decline in service levels, and challenges for drivers in maintaining profitability.  
To resolve the issues of \"clustering\" and \"absence\", smarter vehicle dispatching and route guidance are needed to achieve a dynamic balance between supply and demand, improve taxi operational efficiency, and enhance the passenger experience, thereby creating a positive cycle and further promoting the development of the taxi industry. The GPS devices installed in taxis record important information such as the taxi's latitude and longitude, time, speed, direction, and passenger status. By mining and analyzing taxi GPS trajectory data, the operational patterns of taxis and the travel patterns of residents can be determined, facilitating the recommendation of passenger-seeking routes and destinations, which improves passenger-seeking efficiency and reduces vacancy rates.