esa-sceva/satcom-chunk-collection huggingface dataset:
# Modernising Receiver Operating Characteristic (ROC) Curves +
## 3 Materials and Methods  
As an illustration and case study of a modernised ROC methodology, we chose tests for pregnancy in order to exemplify the need for improved performance metrics and the establishment of performance goals for devices with binary responses (positive: hormone human choriogonadotropin (hCG) detected or negative: hCG not detected) in laboratory medicine. Pregnancy testing is chosen, not because it is particularly challenging, but rather because it is widely used and has many similarities with other binary classifications (see Introduction).  
Single use devices for detecting the presence of a drug, hormone, or infectious agent in a sample are extensively used in health care, operated by both health care professionals and by lay people for self-testing. The measurement principle is often lateral flow immunoassay (Gomez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopez-Lopezopez-Lopez-Lopez-Lopezopez-Lopez-Lopezopez-Lopez-Lopezopez-Lopezopez-Lopez-Lopezopez-Lopezopez-Lopezopez-Lopezopez-Lopezopez-  
The ROC curves are produced by registering the binary response of each device: ^hLCG detected = positive' or ^hLCG not detected = negative'. The original data covered distributions with assigned values in the concentration range 0 to 130 IU/L hCG, for five different device types in an external quality assessment scheme (proficiency testing) with four distributions per year for the period 2014-2021. We focused on the hormone concentration range from 6 IU/L hCG to 58 IU/L hCG, which covers the typical \(SL\), and for the five most commonly used device types. A total of almost 19,000 responses across the 5 types of devices and range of hormone concentrations were investigated in this study.  
A diagnosis of pregnancy is made, perhaps based on a typical \(SL\) of 25 IU/L hCG, which, of course, in the majority of cases can be confirmed by an actual pregnancy. Such observations can thus be used as 'gold standard' references for decision-making, for instance, in acting as diagnostic references for ROC curves when field devices such as those studied here are to be evaluated for performance. A limited product quality in pregnancy testing kits may lead to individual devices having cut-off values different from \(SL\) which, in turn, can lead to poorer measurement quality. A typical commercial device type often has a declared cut-off close in value to the diagnostic \(SL=25\) IU/L hCG, but with some uncertainty. Measurement uncertainty--as addressed further using MSA (Section 3.2) below--will also mean that risks of incorrect decisions are likely across the full range of concentration: false classifications of non-pregnancy (at concentrations above \(SL\)) and false classifications of pregnancy (at concentrations below \(SL\)).
# GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
## 2 Materials and Methods
### Procedures of the Network Real-Time Differential Positioning Using the Improved Algorithm  
Figure 1 gives the specific procedures of the network real-time differential positioning using the improved algorithm in the position domain. It contains two steps:  
1. Calculate the differential corrections at each reference station according to Equation (3). After the multiple reference consistency check [35], the correct differential corrections of reference stations are used to interpolate the user differential corrections according to the approximate user position.
2. After getting the user differential corrections, the corrected pseudorange observables for the user can be achieved to estimate the user position. If the Kalman filter is not initialized, the single point positioning with user corrections is applied to initialize. Otherwise, \(\mathbf{X}_{0_{k}}\) and the receiver's clock errors are computed to estimate the position variation information. Combined with the position variation information, the Kalman filter can then be carried out to obtain the position results of the current \(k\) epoch. If the position variation estimation is failed, the filter will be re-initialized by the single point positioning with user corrections.  
Figure 1: Flow chart of the network real-time differential positioning using the improved algorithm.
# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 3 Joint Simulation Analysis to Preliminarily Determine Location Features
### Actuator Multi-Body Dynamics Establishment  
Adams multibody dynamics simulation software is frequently used in engineering to develop and analyze complicated mechanical systems. Three crucial considerations to consider while utilizing Adams for loader model analysis:  
(1) Kinematic Constraints  
Kinematic constraints describe the restrictions between the various parts of the loader, including the steering system, lift arm, and bucket linkage, as well as their range of motion and interrelationships, as shown in Table 1. If redundant constraints are established, the simulation program may arbitrarily eliminate some constraints, which can lead to errors during motion simulation. Therefore, it is necessary to accurately describe the kinematic constraints between the various parts to ensure that the simulation is consistent with actual conditions.  
Figure 2: Loader execution device model, among them: 1. Bucket 2. Linkage 3. Boom 4. Rocker 5. Bucket hydraulic rod 6. Bucket hydraulic cylinder 7. Frame 8. Boom hydraulic cylinder 9. Boom hydraulic rod.  
Model Accuracy and Precision  
This mainly refers to the parameters of the geometry, mass distribution, and inertia characteristics of the loader model accurately reflecting the actual situation to ensure the accuracy and reliability of the simulation results. The material of the working device is set to steel in Adams, with a density of 7801 kg/m\({}^{3}\), [PERSON]'s modulus of 2.07 \(\times\) 1011 N/m\({}^{2}\), and Poisson's ratio of 0.29.  
Load the excavation trajectory  
Loaders can work in a variety of ways, but their efficiency is often determined by the worker's experience. Common activities include single and multiple filling, excavating, and compound filling. Among them, the compound shovel excavation method considers the flexibility of multiple fillings in its operation and can adapt to varied loading conditions while boosting loading efficiency. As a result, this paper selects the compound filling trajectory for simulation. In Adams, the driving mechanism of the frame movement and the configuration of each hydraulic cylinder are shown in Table 2, and Figure 3 depicts the trajectory curve of the loader bucket tip.
## 6 Future Directions
### New Antennas  
One of the basic cornerstones of wireless systems is antenna and antenna technologies. In order for 6G technology to fulfill its promises, breakthroughs in antenna technologies will be needed. 6G networks will be technologies that will provide services at high frequencies and wide bandwidths. High-frequency bands and the mmWave wave frequencies are affected by gases in the atmosphere and undergo further attenuation and path loss [136]. To compensate for this situation, antennas must have high gain. High power supplies and highly sensitive receivers are needed. It is necessary to study antennas and antenna architectures for tolerating path losses and achieving high gains and wide coverage. MIMO technology was introduced, which was developed as a way to increase the distance of the THz spectrum in 6G networks [137; 138; 139; 140].  
6G networks have a broad range of uses and scenarios. According to all these usage scenarios, antenna needs will be different. The general antenna specifications needed in 6G networks are as follows:  
* Cost-effective [38];
* Antenna types suitable for different 6G use cases, including omnidirectional antennas for broad-area coverage, highly directional antennas for focused, point-to-point communication, and high-gain antennas to compensate for signal losses at THz frequencies [38];
* The communication spectrum is the THz frequency band (0.1-10 THz);
* Ultra-wide band and multi-band antennas [38];
* For communication at 6G frequencies, a circular, polarized antenna is preferred, which receives signals in a horizontal and vertical plane [141].  
However, developments in silicon technology, decreasing costs, and the development of materials in a more compact form also make it possible to obtain more integrated circuits in antenna designs [37]. In 6G, new antennas and new material technologies that will facilitate their deployment will continue to develop.
# An FCM-GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template
## 3 Methodology
### Nonlinear Aggregation with Another BPN and Transformation  
For aggregating the predicted results from the component GABPNs into a single value representing the predicted scrap rate of the template, another BPN was employed in this study to conduct nonlinear aggregation, and the configuration is set as follows:  
1. Input: \(2K\) parameters consisted of the predicted results of each component GABPNs for the template and the membership values of the template belonging to each category.
2. Architecture: Single hidden layer and the number of nodes in the hidden layer were set to the same as that in the input layer, \(2K\).
3. Output: normalized scrap rate forecast of the template.
4. Learning rule: Delta rule.
5. Propagation function: sigmoid activation function.  
Learning rate: 0.05.
* Number of iterations: 10000.  
The BPN also underwent training and testing. Then, the network output (i.e., the aggregation result) determined the normalized scrap-rate prediction of the template. Finally, the transformation of scrap rate to surplus rate and supplemental feeding rate were carried out. The reverse normalization was conducted for the output of the aggregation BPN and taking it as the predicted scrap rate (_Scrar_Pd_). Thereafter, the transformation for predicted feeding panel (_Fedp_Pd_) was conducted by \(\textit{Fedq\_Pd}=100\times\text{Re}qq/(100-\textit{Scrar\_Pd})\) and \(\textit{Fedp\_Pd}=\left[\textit{Fedq\_Pd}/\textit{Duap}\right]\), where _Reqq_ is the required quantity, _Duap_ is the delivery unit in a panel, and _Fedp_Pd_ is the predicted panel.
## 3 Results and Discussion
### Country Analysis Based on Coauthorship  
The dispersed nature of the field of traffic signal control methods and experiments based on CAs and FCD does not make it easy for a single paper or author to establish a definitive result. For this reason, in this section, we investigate potential collaborations among researchers of different countries by using country coauthorship analysis. An assessment of research exchanges and development between countries and the single-country contributions was conducted by setting a minimum number of considered documents from a given country to 10. Among 79 countries, 18 countries meet the threshold; among these 18 countries, 17 countries are connected and shown in Figure 8, where the dimension of the circles represents the number of documents for each given country. In Table 5, the 17 more active countries are listed in order of citations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Author** & **Weight (Total Link Strength)** \\ \hline
1 & [PERSON] & 5284 \\
2 & [PERSON] & 4701 \\
3 & [PERSON] & 4089 \\
4 & [PERSON] & 3255 \\
5 & [PERSON] & 3219 \\
6 & [PERSON] & 3123 \\
7 & [PERSON] & 3018 \\
8 & [PERSON] & 2648 \\
9 & [PERSON] & 2461 \\
10 & [PERSON] & 2457 \\
11 & [PERSON] & 2411 \\
12 & [PERSON] & 2221 \\
13 & [PERSON] & 2187 \\
14 & [PERSON] & 2001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Most influential authors after manually resolving name duplications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **Country** & **Documents** & **Citations** \\ \hline
1 & United States & 151 & 2639 \\
2 & China & 123 & 845 \\
3 & Germany & 56 & 513 \\
4 & Japan & 34 & 468 \\
5 & Spain & 22 & 316 \\
6 & Taiwan & 16 & 278 \\
7 & Italy & 29 & 268 \\
8 & United Kingdom & 19 & 260 \\
9 & India & 85 & 255 \\
10 & France & 20 & 251 \\
11 & South Korea & 19 & 131 \\
12 & Canada & 17 & 119 \\
13 & Malaysia & 20 & 77 \\
14 & Iran & 13 & 62 \\
15 & Australia & 11 & 54 \\
16 & Portugal & 10 & 45 \\
17 & Netherlands & 11 & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Most influential countries.
# A Wideband Dielectric Waveguide-Based 160-GHz Radar Target Generator
## 1 Introduction  
The progress in SiGe technology enabled the mass production of radar sensors in the frequency range above 60 GHz and initiated the development of radar sensors beyond 100 GHz [1; 2; 3; 4]. The advantage of radar systems in this frequency range is their large absolute bandwidth, which allows novel applications due to the high resolution in range and angle [5; 6]. Economical test systems to perform system and quality tests in series production are going along with the interest in these new millimeter wave radar sensors.  
Evaluating radar measurements from defined quasi-point targets at several distances can verify the functionality of the radar system. Especially for high resolution radars these targets are difficult to realize. Furthermore, an expensive anechoic chamber is required to avoid additional targets or clutter from the environment.  
Another approach to evaluate the sensor performance is a radar target generator (RTG). By processing the received radar signal under test, an RTG can simulate multiple targets with different radar cross sections and Doppler behavior. Several techniques, like direct sampling and digital signal processing [7; 8] or adjustable delay lines [9; 10] are used. These systems are commercially available at automotive frequencies (24 GHz and 77 GHz) but have narrow bandwidths and are cost intensive. Additionally, the minimum emulated target range is above 1 m due to a finite signal processing time.  
Consequently, these concepts are not applicable to RTGs for short range radars with large bandwidths above 100 GHz.  
Instead of using adjustable delay lines, a concept based on a dielectric waveguide is used in [11] for frequencies around 80 GHz. By manipulating the field distribution in the dielectric waveguide, different targets at several distances can be generated to evaluate the radar performance. Since no signal processing unit is included, the minimum target distance is below 50 cm and the RTG bandwidth is comparable to the radar bandwidth.  
In this paper, a 160-GHz RTG based on a dielectric waveguide with two different targets for tank level probing radars is presented. A rectangular waveguide interface is used to connect the radar under test and the RTG. Further, the influence of the waveguide dispersion on a frequency-modulated continuous wave (FMCW) radar is shown. First, the requirements for the RTG and the system are introduced followed by the presentation of the RTG components and the coupling concept with link budgets. The verification of the RTG with a 160-GHz radar is shown in the last section.
# An Intelligent TCP Congestion Control Method Based on Deep Q Network
## 4 Framework
### State Space  
Designing a reasonable state space \(s_{t}\) is the key to achieve an efficient reinforcement learning goal. Only by observing enough information, the reinforcement learning algorithm can choose good actions. However, too much state information will increase the computation cost and slow down the learning speed. Therefore, we design the state space \(s_{t}\) including the following parameters:  
1. The relative time \(t\). The time that has been elapsed since the TCP connection was established. In cubic algorithm, the window size is designed as a cubic function of time \(t\). Therefore, we consider \(t\) as an important parameter to determine the congestion window.
2. Congestion window size. Congestion control algorithm needs to adjust the new window size based on the current one. If the current congestion window is small, the agent will increase the window faster; else if the window is large, the agent will stop increasing the window size or slowly increasing the window size.
3. Number of unacknowledged bytes. The number of bytes sent but not confirmed by the receiver. If the network link is compared to a water pipe, the number of unacknowledged bytes can be understood as the water stored in the pipeline. If there is enough water in the pipeline, the water injected into the pipeline should be stopped or reduced, else the water should be injected more. Therefore, the water injection rate (congestion window size) should be determined according to the water volume (number of unacknowledged bytes) in the pipeline.
4. Number of ACK packets. This parameter can indirectly reflect the congestion situation. If the number of received ACK packets is normal, it indicates that the network is in a good condition and congestion has not occurred. The size of the congestion window can be increased. Otherwise, it indicates that the network is congested and the congestion window should be maintained or reduced.
5. Average RTT (round trip time) value. The average RTT value in the observation period. RTT refers to the total time spent from sending to receiving ACK for a data packet. The RTT value is closely related to the network congestion. If the network congestion is serious, the RTT value will increase significantly. Therefore, RTT value can reflect the network congestion. The congestion control algorithm should adjust the congestion window according to RTT value.  
6. Throughput. The throughput during the observation period. Throughput is defined as the number of data bytes confirmed by the receiver per second. This parameter directly reflects the network status. High throughput indicates that enough packets have been sent into the network link. Otherwise, it indicates that the current network bandwidth is more redundant, and more packets can be sent on the link.
7. Number of lost packets. The number of lost packets indicates the congestion situation of the current network. If the number of lost packets is small, the agent should increase the congestion window. Otherwise, the agent should decrease the congestion window.
# A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones
## 3 Background
### Few-Shot Learning Method  
Humans can learn from just a few examples, whereas deep learning models typically require hundreds of data points for effective training [22]. The concept of enabling deep learning models to learn new concepts like humans is called meta-learning, and learning with minimal examples is referred to as few-shot learning [23; 24; 25; 26; 27; 28]. Few-shot learning aims to train classifiers to recognize new classes with only a small number of examples. When training involves K data points for N classes, the process is described as N-way K-shot learning. Figure 1 illustrates the three-way three-shot learning method in few-shot learning. Few-shot learning operates in task units, consisting of a support set and a query set, to achieve meta-learning. The dataset used for training is called the meta-training set, while the dataset used for evaluation is referred to as the meta-test set. For validation, the dataset is known as the meta-validation set. One notable few-shot learning model, Matching Networks, facilitates rapid learning. Deep learning models often require large datasets due to the need for iterative weight updates using Stochastic Gradient Descent (SGD). Matching Networks, however, leverages ideas from non-parametric models, such as K-Nearest Neighbors (KNN), which dynamically adjust to the data instead of predefining parameters. This allows the design of models that achieve excellent generalization performance with limited data. Matching Networks utilizes an attention mechanism to predict unlabeled query sets using embeddings learned from labeled support sets. It also employs mini-batches, called episodes, to achieve superior generalization performance.  
Prototypical Networks (ProtoNet) [28] are another popular model designed for few-shot and zero-shot learning. Their primary focus is on addressing the overfitting problem. This model assumes that a highly simplified classification is necessary for learning with limited data. ProtoNet defines a single prototype for each class as a cluster in the embedding space. The support set is mapped into the embedding space through nonlinear transformations via neural networks, and the meaning of these mappings is used as the class prototype. Classification is then performed by identifying the prototype closest to a given query point.
## 4 Proposed Bayesian Hybrid EWMA (HEWMA) Control Chart
### Posterior-Based Control Limits under Normal Prior Distribution
#### 4.1.1 Control Limits under SELF Using RSS Schemes  
\[\hat{\theta}_{(SELF)}=\frac{n\overline{x}_{(RSS_{1})}\delta_{0}^{2}+\delta^{2 }\theta_{0}}{\delta^{2}+n\delta_{0}^{2}} \tag{30}\]  
Here, the Bayes estimator based on SELF using RSS schemes for normal distribution with informative prior is derived as follows:  
\[E\Big{(}\hat{\theta}_{(SELF)}\Big{)}=\frac{n\theta_{1}\delta_{0}^{2}+\delta^ {2}\theta_{0}}{\delta^{2}+n\delta_{0}^{2}} \tag{31}\]The mean and variance of the Bayes estimator \(\hat{\theta}_{(SELF)}\) are given as and  
\[\text{var}\Big{(}\hat{\theta}_{(SELF)}\Big{)}=\frac{n\delta_{(RSS_{i})}^{2} \delta_{0}^{4}}{\delta^{2}+n\delta_{0}^{2}} \tag{32}\]  
The asymptotic control limits of the suggested HEWMA control chart under the Bayesian approach utilizing RSS schemes with SELF are given below:  
\[LCL_{RSS_{i}}=E\Big{(}\hat{\theta}_{(SELF)}\Big{)}-L\eta\sqrt{\text{var}\Big{(} \hat{\theta}_{(SELF)}\Big{)}} \tag{33}\]  
\[CL_{RSS_{i}}=E\Big{(}\hat{\theta}_{(SEL  
\[p(y/x)=\frac{1}{\sqrt{2\pi\delta_{1}^{2}}}\exp\left\{-\frac{1}{2\delta_{1}^{2}}(Y- \theta_{n})^{2}\right\} \tag{42}\]  
Control Limits under LLF Using RSS Schemes  
The LLF is utilized to construct the control limits for the Bayesian HEWMA control chart under RSS schemes, and the using LLF is derived as  
\[\hat{\theta}_{LLF}=\frac{n\overline{x}_{(RSS_{i})}\delta_{0}^{2}+\delta^{2} \theta_{0}}{\delta^{2}+n\delta_{0}^{2}}-\frac{C^{\prime}}{2}\overset{\sim}{ \delta}_{1}^{2} \tag{43}\]  
where \(\overset{\sim}{\delta}_{1}^{2}=\frac{\delta^{2}}{K}+\frac{\delta^{2}\delta_{ 0}^{2}}{\delta^{2}+n\delta_{0}^{2}}\).  
The mean and variance of the Bayes estimator are given below:  
\[E\big{(}\hat{\theta}_{LLF}\big{)}=\frac{n\theta_{1}\delta_{0}^{2}+\delta^{2} \theta_{0}}{\delta^{2}+n\delta_{0}^{2}}-\frac{C^{\prime}}{2}\overset{\sim}{ \delta}_{1}^{2} \tag{44}\]  
and  
\[\text{var}\big{(}\hat{\theta}_{LLF}\big{)}=\frac{n\delta_{(RSS_{i})}^{2} \delta_{0}^{4}}{\big{(}\delta^{2}+n\delta_{0}^{2}\big{)}^{2}} \tag{45}\]  
The control limits for the Bayesian HEWMA control chart with LLF using RSS schemes are given below:  
\[LCL_{RSS_{i}}=E\Big{(}\hat{\theta}_{(LLF)}\Big{)}-L\eta\sqrt{\text{var}\Big{(} \hat{\theta}_{(LLF)}\Big{)}} \tag{46}\]  
\[CL_{RSS_{i}}=E\Big{(}\hat{\theta}_{(LLF)}\Big{)} \tag{47}\]  
\[UCL_{RSS_{i}}=E\Big{(}\hat{\theta}_{(LLF)}\Big{)}+L\eta\sqrt{\text{var}\Big{(} \hat{\theta}_{(LLF)}\Big{)}} \tag{48}\]