esa-sceva/satcom-chunk-collection huggingface dataset:
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### GPS Navigation  
An orbital filter has been implemented that integrates the GPS observations with an orbital forces model, by means of an adaptive Extended Kalman filter (EKF). The EKF integrates the least square single-epoch position and velocity solutions that are computed using the pseudoranges and the pseudorange rates output by the _WeakHEO_ GPS receiver, together with the position and velocity predicted by an orbital forces model. The resultant filtered navigation solution can efficiently mitigate the effect of the very high GDOP and large ranging errors; indeed, it is significantly more accurate than the unfiltered least square single-epoch solution, as shown in Section 3. Thus, it can be used to aid the GPS receiver to acquire and track weak GPS signals while experiencing high dynamics. A detailed description of the orbital filter and its aiding to the acquisition and tracking modules can be found in [26]. In a different configuration, less simple but potentially more efficient, the EKF can integrate directly pseudoranges and pseudorange rates provided by the tracking loops together with their prediction provided by the orbital forces model. This has been investigated in our previous study [25], which also describes the performance of our orbital filter when it uses signals transmitted by the GPS-Galileo combined constellation. A comparison of the two configurations is described in [38].  
As described in [24], during a MTO, once the receiver is above the GPS constellation, the GPS accuracy strongly decreases with the altitude. Both pseudorange error and GDOP worsen as the receiver goes further from the GPS constellation. For this reason, if the covariance matrix of the measurements \(R\) is composed of constant variance values over time, the filter cannot be tuned properly during the full MTO (Figures 6 and 7 of [38] shows the effect of modelling \(R\) as constant matrix).  
As described in [38], the covariance matrix \(R\) of the filter has to be adapted to the GPS measurements accuracy. Indeed, the \(R\) matrix is modelled as function of the GDOP multiplied by the estimated pseudorange error. The adaptive strategy is illustrated in Figure 15. From the estimated state computed at the previous time-step, the filter estimates the GDOP and the noise on both pseudorange and pseudorange rate measures to update the covariance matrix \(R\).  
Figure 14: _WeakHEO_ DLL jitter _versus_ C/No.
# Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning
## 1 Introduction  
Traffic signal control plays an essential role in city management because traffic congestion brings economic, environmental, and social disadvantages. Traffic signal control aims to minimize congestion by determining the optimal values of parameters such as the cycle length and phases duration [1, 2]. In many areas, the traffic signal control systems based on a fixed-time model are still in use [3, 4, 5]. While these systems are easy to implement, they cannot respond flexibly to dynamic traffic flows [6, 7].  
To quickly respond to variety in the traffic environment, signal control systems should be able to choose their own actions without waiting for instructions from a central computer [8]. Therefore, reinforcement learning models are being studied that allow the traffic signal controller to receive realtime data around the intersection, such as traffic volume and vehicle speed, and change signal appropriately for the given traffic situation [9, 10]. If the above sentence is expressed in reinforcement learning terms, the controller is the agent, the data input to the controller is the state, the controller's decision is the action, and the benefit provided to the agent according to the action is called reward. The goal of reinforcement learning is to maximize the future reward that an agent can obtain [11].
## 2 The Principle of the Invariant Imbedding (IIM) T-Matrix Method
### T-Matrix Computation Based on the Invariant Embedding Technique  
In principle, Equation (29) is essentially a linear equation system, and can be solved by standard numerical methods, such as the Gaussian elimination method, the conjugate gradient method, etc., after \(\overline{\overline{\mathbf{F}}}(r_{i})\) is obtained, and then, the T-matrix can be obtained easily by substituting \(\overline{\overline{\mathbf{F}}}(r_{i})\) into Equation (30). However, because the matrices involved in this model are all super-matrices, the implementation of this scheme is very difficult, especially for large particles. Therefore, in order to improve the computational efficiency of the T-matrix, [PERSON] [33] first introduced the invariant embedding technique into the light scattering simulation (this technique was originally developed to solve quantum mechanical scattering problems). This method not only has the advantage of high computational efficiency, but can also calculate the T-matrix directly without calculating the matrix \(\overline{\overline{\mathbf{F}}}(r_{i})\), so it can greatly reduce computational time and memory consumption. According to Equations (29) and (30), the non-spherical particle should be discretized along the radial direction, which is equivalent to dividing the particle into multiple inhomogeneous spherical layers in the spherical coordinate system, as shown in Figure 1. In this way, the non-spherical particle can be viewed as an inhomogeneous sphere, i.e., a portion of the sphere has the dielectric properties of the scattering particle and the rest is regarded as a vacuum. Figure 1: The discretization of the non-spherical particle in the spherical coordinate system. Assuming \(n\) is an integer in \((1,N)\) (\(N\) is the number of the Gaussian integral points), then according to Equations (29) and (30), we define the matrix function \(\mathbf{F}(n|r_{i})\) and \(\overline{\overline{\mathbf{T}}}(r_{n})\), which satisfies the following equations:  
\[\overline{\overline{\mathbf{F}}}(n|r_{i})=\overline{\overline{\mathbf{U}}}(r_ {i})\overline{\overline{\mathbf{J}}}(r_{i})+\sum_{j=1}^{n}\omega_{j}\overline {\overline{\mathbf{J}}}(r_{i})\overline{\overline{\mathbf{g}}}(r_{i},r_{j}) \overline{\overline{\mathbf{F}}}(n|r_{j}) \tag{31}\]  
\[\overline{\overline{\mathbf{T}}}(r_{n})=ik\sum_{j=1}^{n}\omega_{j}\overline{ \overline{\mathbf{J}}}^{T}(r_{j})\overline{\overline{\mathbf{F}}}(n|r_{j}) \tag{32}\]  
where, \(\overline{\overline{\mathbf{F}}}(n|r_{i})\) and \(\overline{\overline{\mathbf{T}}}(r_{n})\) can be visually regarded as the vector amplitude density function and T-matrix of the sphere with \(n\)th spherical shell. From the Equations (31) and (32), it can be found that except for the fact that \(N\) is replaced by \(n\), these equations are identical to Equations (29) and (30). Therefore, if we let \(N=n\), we can obtain \(\overline{\overline{\mathbf{F}}}(r_{i})=\overline{\overline{\mathbf{F}}}(N|r_ {i})\) and \(\overline{\overline{\mathbf{T}}}=\overline{\overline{\overline{\mathbf{T}}}}( r_{N})\).
# RFID Technology for Management and Tracking: e-Health Applications
## 2 System Architecture
### Radio Frequency Identification (RFID) Hardware and Considerations about Signal Propagation
In that case, RFID antennas and readers which characteristic  
Figure 3: Experimental evaluation of UHF RFID antenna coverage, that was conducted for the research presented in [30]. (**a**) Measured received signal strength (RSS); (**b**) Estimated RSS considering an indoor propagation losses model and the UHF RFID antenna pattern; (**c**) Difference between measured and estimated RSS; (**d**) Picture of the scenario where antenna coverage was assessed. impedance of 75 ohm (instead of the most common 50 ohm) should be used. In Europe, the upper frequency band of radio and television broadcasting is more than 100 MHz below UHF RFID frequency band (865-868 MHz), so there would not be interferences between RFID and the signals of radios and televisions. Next, to connect the RFID readers and the computer that manages them, the easiest solution is to provide local area network (LAN) connectivity, since most of the hospitals have several public and protected LAN domains, depending on the targeted users (visitors and patients, doctors and physicians, etc.). It must be pointed out that most of current RFID readers existing in the market can be connected via Wi-Fi (e.g., by means of an Ethernet/Wi-Fi Access Point), thus reducing the need for deploying additional Ethernet cable infrastructure.
## 2 Materials and Methods
### Test Procedure  
The discrimination test was implemented with a Yes-No experiment design [24]. Figure 3 shows the graphic user interface (GUI) used in the test. In each trial, the reference signal and the test signal are both presented on the GUI, indicated by button A and button B, respectively. The subject's task was to compare the test signal with the reference signal and determine whether the test signal was the same as the reference signal. Listeners could freely switch between the two signals to compare until they made a choice.  
Figure 3: Graphical user interface used for collecting the test data. Button A represents the reference signal (original program) while button B, as the test signal, is randomly assigned as one of the six equalized versions or the unprocessed version of the program.  
Figure 2: In-room response of the loudspeakers at the listening location.  
In this work, the original program was referred to as the reference signal, and the test signal was randomly assigned as the equalized or unprocessed version of the program. The equalizations could include one of the six peak or dip filters described in Section 2.2. To eliminate the error or bias caused by listeners choosing the \"different\" answer by default, an equal number of \"lures\"--test signals that were unprocessed were introduced. Therefore, each program was presented in 12 trials where six equalizations and six repeated original programs were separately used as the test signal.  
The test was double-blind, and the order of the trials and the order of the programs presented were randomized. Before the formal test, a training phase was conducted where listeners could get familiar with the GUI and the program material. At a time, only one listener took part in the test in the listening room. After completing the test, listeners were also asked to name the programs they thought difficult or easy to make judgments on during the listening process.  
To minimize the impact of test duration on listener fatigue and reliability [25], the whole experiment was composed of four listening sessions with repeated observations performed for each program. Listeners only completed one listening session per day. In total, the listening test was composed of 480 trials (20 programs \(\times\) 12 trials \(\times\) 2 observations) for each subject. On average the listening panel spent about 35 min finishing one session. All listeners completed four sessions within one week.
## 3 Results and Discussion
### The Influence of the Yielding Bol Length on Its Support Effect  
To investigate the influence of the length of yielding bolts on the support effectiveness, eight different lengths of bolts, namely 2 m, 3 m, 4 m, 5 m, 6 m, 7 m, 8 m, and 9 m, were selected. A plane strain model of the tunnel, as shown in Figure 7, was employed for excavation and support simulation. The proportion of the yielding component of the bolt remained constant, relative to the total length of the bolt. During the simulation process, monitoring was conducted at the 16 positions shown in Figure 9. After the simulation calculations, the deformation of the surrounding rock at the four key locations, such as the arch crown, sidewalls, and arch base, were recorded and are presented in Table 5, and the total deformation of the surrounding rock at the 16 monitoring points under different lengths of bolt support is illustrated in Figure 25.  
With increasing the yielding bolts' length, the deformation at the 16 key positions of the surrounding rock and the total deformation at all points exhibited a trend of initially decreasing, then stabilizing, and then slightly increasing. As the length of the yielding bolts increased from 2 m to 5 m, the deformation of the surrounding rock decreased rapidly. For every additional meter in bolt length, the total deformation at the arch crown, left and right sidewalls, and arch base decreased by 6.13 mm. During this stage, the impact of the bolt length on support effectiveness is significant. As the length of the yielding bolts increased from 5 m to 7 m, the deformation remained relatively stable with minimal changes. Further increasing the length from 7 m to 9 m resulted in a slight increase in the deformation of the surrounding rock, but the magnitude of increase was minimal. Therefore, it can be inferred that the yielding bolts with lengths of 5 m, 6 m, and 7 m exhibited better support effectiveness. Considering utilization and cost, a preliminary determination suggests that 5 m is the optimal length for yielding bolts under these conditions.
## 3 Results and Discussion
### Direct Carbon Emissions Assessed by Three Different Methods
#### 3.1.2 Comparison of CH\({}_{4}\) Emission Calculations Using Different Methods  
The annual CH\({}_{4}\) emissions from 49 WWTPs in Shandong Province in 2022 were calculated using three methods, with the results being 1,082,451.6 kg, 113,716.9 kg, and 1,050,117.8 kg, respectively (Figure 2a). It can be seen that the results of the IPCC 2019 and T/CUWA 50055-2023 methods are very close, and both are nearly 10 times the result of the T/CAEPI 49-2022 method.  
The differences in the CH\({}_{4}\) emission factor and correction factor values are the main reasons for the deviation of the results. In the IPCC method, the CH\({}_{4}\) emission factor is 0.6, and the correction factor is 0.03; in T/CAEPI 49-2022, the CH\({}_{4}\) correction factor is 0.003, and the CH\({}_{4}\) emission factor for the anaerobic process degrading unit COD is 0.25 kgCH\({}_{4}\)/kgCOD. Its low correction factor results in a low CH\({}_{4}\) emission. In contrast, the CH\({}_{4}\) correction factors in the IPCC 2019 method and T/CUWA 50055-2023 are higher. T/CUWA 50055-2023 recommends a CH\({}_{4}\) emission factor of 0.004 to 0.0075 kgCH\({}_{4}\)/kgCOD, and in this study, the value is taken as 0.00575 kgCH\({}_{4}\)/kgCOD. It also considers the CH\({}_{4}\) emissions from the pump station and sand settling tank, which improves the accuracy of the calculation. Therefore, the result of T/CUWA 50055-2023 is close to that of IPCC 2019 but slightly lower.  
There are flaws in the IPCC 2019 method for calculating methane emissions. The calculation method of its methane emission factor relies on restricted literature data. It fails to comprehensively take into account the BOD\({}_{5}\) removed in the form of sludge. As a consequence, there is a lack of consistency between the CH\({}_{4}\) emission factor and the emission calculation formula.
## 4 Methodology
### Data Encryption: Pre-Processing
#### 4.2.2 Data Normalization  
Given that FHE operates on integers, data normalization was a critical step before applying encryption. We used min-max scaling to transform the feature values into a fixed range, typically [0,1], ensuring uniformity across features. This transformation is essential because FHE requires integer-based computations, and normalization helps maintain model performance during encryption and inference [41; 42].
# Reduction of Video Capsule Endoscopy Reading Times Using Deep Learning with Small Data
## 4 Discussion
Our CNN model was trained only on two videos and tested on one, which limits the scale of our study and the results achieved. We hope to improve this by increasing the data available in training our CNN model, with the goal of establishing an algorithm that can delineate between normal and abnormal frames instantly. Our goal is to expand our data and to continue to train our CNN model to detect abnormal frameworks, ultimately reducing physician reading time. Artificial intelligence (AI) is an emerging field in gastroenterology, particularly in VCE. Our study focused on training a CNN-based model to identify subtle inflammatory and vascular lesions, with the goal of developing an algorithm that can quickly differentiate between normal and abnormal frames. Our model demonstrated the ability to find all abnormal regions with significant reduction in physician reading time. Our study results demonstrate the benefit of using a CNN in processing VCE images. We believe our study lays an excellent foundation for further validation in large multi-center trials. Conceptualization, R.W., R.M., A.A.B., P.B., S.S., G.M., J.L. and G.V. ; Methodology, H.M., L.O.H. and D.G. ; Software, H.M., L.O.H. and D.G. ; Supervision, L.O.H., D.G. and G.V. ; Validation, H.M., L.O.H. and D.G. ; Writing--original draft, H.M., A.A., C.U. and G.V. ; Writing--review and editing, H.M., I.R., N.R., A.K., N.B., R.M., A.A.B., P.B., S.S., G.M., J.L., L.O.H., D.G. and G.V. All authors have read and agreed to the submitted version of the manuscript. This research received no external funding. The KID dataset is publicly available upon request [[https://mdss.uth.gr/datasets/endoscopy/kid/](https://mdss.uth.gr/datasets/endoscopy/kid/)]([https://mdss.uth.gr/datasets/endoscopy/kid/](https://mdss.uth.gr/datasets/endoscopy/kid/)), accessed on 2 April 2022. The code repository will be released upon acceptance for publication. We would like to thank the physicians at our local Veterans Affairs Hospital for their input in the development of this study. We would also like to thank [PERSON], for her extensive work in reviewing and editing this manuscript. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
VCE Video Capsule Endoscopy CNN Convolutional Neural Network
## 5 Experimental Results
### Synthetic Data in Native Network Form
#### 5.2.2 Scale-Free LFR Benchmark Networks of 1024 Nodes  
An important generalized class of synthetic networks is the LFR networks [61]. Note that the networks that may be generated by the LFR model include regular degree Girvan-Newman networks as well as scale-free networks of various parametric settings. A series of LFR benchmark networks was generated as described in [61]. Each of these graphs has 1024 nodes. In contrast to the regular degree structure of the Girvan-Newman graphs, these are scale-free networks, generated using power law distributions for degree, where the degree sequence exponent = \(-2\), and the community size distribution exponent = \(-1\). These are similar to real complex networks, which \"are known to be characterized by heterogeneous distributions of degree and community sizes\" [65]. Fifty different networks were generated, each containing between 4 and 14 clusters, with the cluster size ranging from 50 to 200 nodes. The degree distributions were highly irregular, with a typical degree distribution ranging from 7 to 64. The average degree was 16. The mixing parameter varied from 0.05 to 0.25, where a higher mixing parameter implies less tightly bound communities, and therefore a more difficult graph to cluster.  
The results for the scale-free LFR networks are shown in Table 3. The average percentage accuracy is presented over 10 separate randomly generated graphs. For graphs with the best-defined community structures (a mixing factor of \(\mu=5\%\)), all measures except scattering number correctly classify more than 95% of the nodes. Even where \(\mu=20\%\), toughness and tenacity correctly classify over 85% of the nodes. For this class of graphs, all of the NBR measures, except for scattering, clustered the graphs in one step.
Different implementations of BOC signaling (e.g., multiplexed BOC or MBOC) are also considered as the common baseline for Galileo and modernized GPS. In this context, analysis of the basic BPSK(1) and BOC(1,1) signals is of interest [18]. SQM metric variation profiles are proposed in Section 4 as a function of multipath relative delay and power. SQM metric effectiveness and sensitivity are then defined based on multipath range error envelopes and the proposed SQM profiles. These results are accompanied with an analytical discussion to evaluate SQM performance under different multipath scenarios. Two different correlator strategies, namely \"Narrow Correlator\" (NC) [19] and \"High Resolution Correlator\" (HRC) [20], are considered. These methods are commonly used in many receivers to mitigate multipath. Real data analysis is performed in Section 5 to validate the proposed SQM variation profiles and examine SQM detection performance for real static and kinematic multipath scenarios.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 5 Feasibility and Safety Analysis  
We have introduced the general model of the NFEM. In this section, we analyze the safety and feasibility of our proposed method, and the results show that the NFEM is suitable for vehicular networking applications.
# Path Planning of Multi-Type Robot Systems with Time Windows Based on Timed Colored Petri Nets
## 7 Conclusions  
This paper addressed the path planning of multi-type robot systems with time windows using TCPNs. To deal with complex tasks that need specific types of robots to complete, the tasks of the systems were classified into three different types, i.e., common, exclusive, and collaborative. Combining the state equations of TCPNs, some approaches to convert the logic constraints and time windows imposed on the different types of tasks into a set of linear constraints was developed. Then, an efficient path planning method was  
Figure 5: Gantt chart of the obtained paths.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Nb. of Regions** & **Nb. of Task Regions** & **Nb. of Robots** & **CPU Time [s]** \\ \hline Case 1 & 74 & 12 & 3 & 23 \\ Case 2 & 144 & 16 & 5 & 243 \\ Case 3 & 180 & 23 & 8 & 5408 \\ Case 4 & 230 & 31 & 14 & 84256 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation results for different cases.  
proposed so that regions containing different types of tasks were visited by a specified type of robot in a predefined time window. Simulation studies illustrated the effectiveness of this approach. TCPN can effectively avoid the state explosion problem as the number of robots increases. The developed approach can be used in application areas where tasks may need specific types of robots to complete. It should be noticed that the collision avoidance problem is not addressed in this paper. Our future research aims to study the path planning of MTRSs with high-level specifications such as Boolean specifications or linear temporal logic, which can be used for patrolling path planning and path planning with multi-task selections.  
Conceptualization, Z.H.; Data curation, R.Z.; Formal analysis, Z.H.; Funding acquisition, C.G.; Methodology, C.G.; Project administration, N.R.; Software, R.Z.; Supervision, Z.H.; Validation, N.R.; Visualization, N.R.; Writing--original draft, R.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61803246, 61903119, and 62003201, the China Postdoctoral Science Foundation under Grant No. 2019M663608, the Young Talent fund of University Association for Science and Technology in Shaanxi China under Grant No. 20210117, the Natural Science Foundation of Hebei Province under Grant A2020201021, the Hebei Province Foundation for Returned Overseas Chinese Scholars under Grant C20190319, and the Foundation of Hebei Education Department under Grant BJ2021008.  
Not applicable.  
The authors declare no conflict of interest.