esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### Bit-Summation Detection (BSD)  
We begin with the \(3\times 1\) LLR vector obtained from the 1D SOVA detectors, \([\lambda_{k,-1},\lambda_{k,0},\lambda_{k,1}]^{\mathbf{T}}\). As mentioned earlier, all three readback signals convey some information about the \(k\)-th user bit on the desired track, \(u_{k,0}\). In the BSD technique, we propose to improve the LLR of \(u_{k,0}\) by using  
\[L\hat{L}R=\lambda_{k,-1}+\lambda_{k,0}+\lambda_{k,1}, \tag{7}\]  
where the derivation of (7) is given in Appendix A. Then, the improved LLR, \(L\hat{L}R\), in Equation (7) is sent to the threshold detector to determine the estimated user bits, \(\hat{u}_{k,0}\).  
An example of how the BSD technique operates is demonstrated in Figure 9. First, the LLRs produced from the 1D SOVA detectors are arranged in a \(3\times 1\) column vector, i.e., \([\lambda_{k,-1},\lambda_{k,0},\lambda_{k,1}]^{\mathbf{T}}\), as in Figure 9a. Then, the elements in each column are summed according to (7) to obtain the improved LLRs, as shown in Figure 9b. Finally, the estimated user bits are obtained by passing the improved LLRs through a threshold detector, giving the results shown in Figure 9c.  
Figure 8: Example of MSI technique operation (**a**) LLRs obtained from SOVA detector and (**b**) the estimated user bits that produced from MSI technique.
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 3 Results and Discussion
### The Mechanical Behavior of Stabilized and Unstabilized Soil
#### 3.3.4 UPV Impacts  
The results of UPV tests performed on stabilized and unstabilized samples using different combinations of WMP and water contents during the curing period are presented in Figure 9. The results show that as the WMP increases, the UCS of X1, X2, X3, X4, and X5 increase and then decrease slightly at X6, while the maximum UPV was obtained at 28 days of curing and with the addition of X5 marble powder, with calculated DMC values of 412 m/s, OMC values of 849 m/s, and WMC values of 561 m/s. Additionally, the results showed that the UPV rose to its maximum value at the OMC. After that, the UPV fell as the moisture content rose. The results also showed that the UPV rose to its maximum value at the OMC. After that, the UPV fell as the moisture content rose. The UPV enhancement directly correlates with the measured gains in density and strength, validating UPV testing to characterize soil stabilization effectiveness.  
Importantly, the UPV values were lower than that observed in experiments using additives other than fly ash and lime [74]. This reveals the uniquely low performance of WMP, attributed to its fine particle size and chemical reactivity.  
[PERSON] and [PERSON] vindicate the results of this study [47] by examining marble powder for soil improvement after seven days of curing. This work systematically optimized WMP dosage, moisture content, and curing time to maximize UPV. The broader analysis of factors influencing UPV adds novel insights.  
Moreover, this is the first known study to combine UPV measurements with advanced artificial neural network modeling to predict the optimal stabilization process. The validated model provides engineers with a new design tool for WMP-modified soil mixtures.
## 7 Conclusions  
In this paper, a flexible DTRN-based physical layer authentication mechanism is proposed, known as FPLA, to address security vulnerabilities in wireless access of QKD networks. The scheme utilizes the SRS signal transmitted by UEs for authentication. A D-block module is designed to handle multiple morphological features, enabling DTRN to authenticate multi-antenna devices in 5G MU-MIMO systems. A communication model is constructed on the CDL time-varying channel model, operating in the 5G FR1 n78 (3.5 GHz) frequency band and TDD duplex mode. Various evaluation metrics are employed to assess the performance of the authenticator. In multiple experiments, where the training dataset consisted of 300 samples per malicious UE, FPLA demonstrated an average certified accuracy of 96.8% in a test environment with SNR = 3 dB. Simulation results demonstrate that the authenticator performs well in terms of FAR, MDR, and ACC on small datasets, and it is robust to multi-antenna, multi-morphology channel features and UL SNR.  
Conceptualization, Y.L. and J.H.; methodology, Y.L., J.H. and G.L.; software, Y.L., J.H. and T.L.; validation, Y.L., Y.Z. and T.L.; formal analysis, Y.L.; investigation, Y.L., J.H. and Y.Z.; resources, Y.Z. and T.L.; data curation, G.L. and T.L.; writing--original draft preparation, Y.L., J.H. and G.L.; writing--review and editing, J.H. and G.L.; visualization, Y.L. and J.H.; supervision, Y.Z.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Innovation Network Research Program of China Telecom with grant number T-2023-26, and by the Atomic Capabilities Research Program of China Telecom with grant number T-2023-7.  
Not applicable.  
The authors declare no conflict of interest.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 5 Case Studies
### Case Study III
#### 5.4.4 Results Analysis  
The average speeds and standard deviations of the collected speeds in the field and simulation are presented in Figure 10. For absolute validity, the hypothesis posited that the simulator system cannot fully reflect real-world conditions.  
In Tables 3 and 4, the statistical test results are presented for the segments from Forbes Avenue. Absolute validity was established for 7 out of 10 segments, demonstrating highly accurate performance. Bayesian analysis provided additional evidence of similarity. Relative validity was established for Forbes Avenue (\(r=0.76\)). It is worth noting that all the applied tests produced consistent results. This shows that the simulation system provides unbiased and reliable estimates of speed rates compared to real-world data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Segment** & \multicolumn{2}{c}{**ANOVA F-Stat**} & \multicolumn{2}{c}{**\(p\)-Value**} & **Result** & **Bayesian Diff** & **Prob Sim \textgreater{} Field** & **Result** \\ \hline
1 & 0.34 & 0.5642 & R & 0.16 & 0.60 & R \\
2 & 0.03 & 0.8620 & R & \(-\)0.04 & 0.45 & R \\
3 & 0.15 & 0.7046 & R & 0.29 & 0.55 & R \\
4 & 0.03 & 0.8639 & R & 0.35 & 0.70 & R \\
5 & 2.24 & 0.1430 & R & 1.01 & 0.90 & R \\
6 & 3.54 & 0.0674 & R & 1.44 & 1.00 & F \\
7 & 1.06 & 0.3103 & R & 0.52 & 0.85 & R \\
8 & 14.43 & 0.0005 & F & 1.86 & 1.00 & F \\
9 & 18.20 & 0.0001 & F & 2.42 & 1.00 & F \\
10 & 29.03 & \(3.93\times 10^{-6}\) & F & 4.21 & 1.00 & F \\ \hline \hline \end{tabular} Note: R = Reject the null hypothesis, F = Fail to reject the null hypothesis.  
\end{table}
Table 4: Statistical results (ANOVA and Bayesian) for Forbes Avenue segments speed.  
Figure 10: Average speed in co-simulation and real-world.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Segment**} & \multicolumn{2}{c}{**\(t\)-Test**} & \multicolumn{2}{c}{**K-S Test**} & \multicolumn{2}{c}{**Wilcoxon Test**} \\ \cline{2-9}  & **Stat** & \(p\)**-Value** & **Result** & **Stat** & \(p\)**-Value** & **Result** & **Stat** & \(p\)**-Value** & **Result** \\ \hline
1 & 0.58 & 0.5642 & R & 0.20 & 0.8319 & R & 16 & 0.2411 & R \\
2 & 0.18 & 0.8620 & R & 0.20 & 0.8319 & R & 22 & 0.5751 & R \\
3 & 0.38 & 0.7046 & R & 0.20 & 0.8319 & R & 21 & 0.5076 & R \\
4 & 0.17 & 0.8639 & R & 0.25 & 0.5713 & R & 23 & 0.6465 & R \\
5 & 1.50 & 0.1430 & R & 0.25 & 0.5713 & R & 9 & 0.0593 & R \\
6 & 1.88 & 0.0674 & R & 0.30 & 0.3356 & R & 6 & 0.0284 & F \\
7 & 1.03 & 0.3103 & R & 0.15 & 0.9831 & R & 24 & 0.7213 & R \\
8 & 3.80 & 0.0005 & F & 0.40 & 0.0811 & R & 0 & 0.0051 & F \\
9 & 4.27 & 0.0001 & F & 0.50 & 0.0123 & F & 0 & 0.0051 & F \\
10 & 5.39 & \(3.93\times 10^{-6}\) & F & 0.50 & 0.0123 & F & 0 & 0.0051 & F \\ \hline \hline \end{tabular} Note: R = Reject the null hypothesis, F = Fail to reject the null hypothesis.  
\end{table}
Table 3: Statistical results (\(t\)-test, KS, and Wilcoxon) for Forbes Avenue segments speed.
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 4 Discussion
### Conclusion 1: The LoE of the Available Studies Comparing SPN vs. IPN Is Very Low (LoE1- and LoE 2-)
#### 4.3.2 Ready to Use SPN  
The available data is very limited. SPN vs. historic control IPN without pharmaceutical individualization was associated with improved weight gain and head growth until 36 weeks in the observational study by [PERSON] et al. (Figure 5) [18]. SPN reduced the value of maximum weight loss [17; 21] and increased mean cumulative weight gain during the first three weeks [17] in two further studies.  
However, the currently available commercial SPN solutions may not be the optimum approach. Commercial SPN may need an additional amino acid supply to achieve recommended target intakes and consequently adequate growth defined as intrauterine growth velocity [5].
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 3 Implementing Physics-Informed Surrogate Model
### Understanding Physics
#### 3.3.2 Identifying Physical Constraints  
Before incorporating physical constraints into a model, they must first be systematically identified. Fundamental conservation laws--mass, energy, and momentum--are universally applicable and serve as the foundation for numerous physical principles. There  
Figure 8: Series, parallel, and coupled model configuration.  
fore, an initial step in defining constraints is to assess whether conservation laws can effectively regulate the model. Other physical constraints can be identified from governing laws, such as the fundamental equations within heat transfer (conduction, convection and radiation), fluid mechanics considerations (e.g., Navier-Stokes equations), and structural engineering concepts such as thermal expansion.  
Although such physical laws do not always provide direct parametric correlations, they can provide constraints for the data-driven models. For instance, when predicting temperature distribution in a concrete wall exposed to fire, enforcing the conservation of energy through a heat conduction PDE-based loss function ensures that the predicted temperature field adheres to fundamental thermodynamic laws [102].  
Beyond fundamental laws, domain-specific constraints can also play a critical role. These arise from expert knowledge and physical expectations of system behaviour. For example, in a fire protection scenario, the maximum temperature of a protected steel element must not exceed the maximum temperature of the fire itself. Such constraints introduce explicit parameter relationships that can be integrated into machine learning models through different approaches. They may be enforced via penalty terms in the loss function (LCT) as in [99], incorporated as a verification step in offline model validation (OCT) as in [97], or directly embedded within the model architecture to enforce predefined correlations (ACT) as in [101].
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 4 Toward an MPC-Based Solution for the CFDP  
A main obstacle for the adoption of collaborative approaches in competitive sectors is confidentiality: collaboration often demands sharing sensitive operational data with business competitors, a requirement that would often be unacceptable. This is in particular the case for the logistics sector, where hailiers would most likely be highly reluctant to share such data with any party, let alone with business competitors, cf. [PERSON] et al. [4].  
However, outputting to each haulier only the orders assigned to it within a valid solution of the CFDP does not disclose any other sensitive information like order situation or availability. Hence, the root of the problem lies in the sensitivity of the _input_ data, not the _output_.  
This duality of sensitive input vs. non-sensitive (to some extent) output is addressed by so-called privacy-enhancing technologies, or PETs. In particular secure multi-party computation is a canonical candidate technology to address the logistic challenge we are interested in.  
In the following we describe our solution, argue its security, discuss practical deployment considerations, and finally provide efficiency and effectiveness benchmarks.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 6 Experimental Results
### Scenario 1 (WiMAX to UMTS)  
Figures 11-13 depict the results of handover_delay, delivery ratio, and bandwidth utilization for the service request 50, 100, 150, 200, and 250 in FVHDA and VHDA protocols. The performance of the two protocols is compared. From that comparison, we may infer that FVHDA executes better from VHDA by 14.2% in handover_delay, 18% in delivery ratio, and 17% bandwidth utilization.
## 4 Areas that Require Further Research  
### Use of Radar data  
To form the basis for developing sustainable water management practices requires knowledge of the current extent, area, and the status of irrigated agricultural landscapes. Advances in remote sensing based mapping technologies have partially contributed in documenting and monitoring irrigated land use types. There are, however, several unresolved problems associated with mapping irrigated areas at regional to continental scales in regions with frequent cloud cover. As described in previous sections, optical remote sensing has been successfully used for classification of irrigated areas from local to global scales using visual interpretation and classification as their primary approach for extracting thematic information.  
During the past decade, several radar sensors have been deployed in space. Though none were designed specifically for irrigation mapping, several investigations have demonstrated that the data provide unique information about the characteristics of irrigated landscapes [85-87]. First, the radar data can be acquired as frequently as possible without atmospheric interference and solar angle variations. Second, depending on the wavelength, the radar backscatter signal carries information about the moisture status of vegetated landscapes. For example, [85] discovered that the radar backscatter coefficient of rice fields, appears to have a significant temporal variation and that this variation can be used to identify paddy rice fields. Similarly, [86] report a strong relationship between L-band backscatter and plant growth in manually planted rice areas although the radar response is strongly dependent on parameters related to the spatial distribution of the plants. Despite these artifacts, however, a correlation between plant growth and backscatter has been observed for all fields, independent of plant spacing and field orientation [86]. While these investigations established the physical foundation for understanding the relationship between radar backscatter in paddy rice fields, another study [87] suggests that rice production estimates require at least three radar acquisitions taken at three crop growth and development stages. However, if multi-parameter radar data are available, only two acquisitions may be sufficient.  
Note that the combination of radar data together with optical data has not yet been exploited to its fullest extent. Radar is useful because of its sensitivity to soil moisture status, even in complex environments. Furthermore, radar data can be collected in almost all weather conditions, a characteristic that is especially important in areas with frequent cloud cover.
## 2 Verification of Calculation Models and Methods
### Numerical Method Validation  
To validate the accuracy of the numerical calculation method, the double-hole parallel tunnel model introduced in reference [25] is employed for analyzing the seepage field after tunnel excavation. The resultant cloud map of pore water pressure distribution is illustrated in Figure 3. The water inflow for the double-hole tunnel can be computed as \(Q_{1}=Q_{2}=16.52\) m\({}^{3}\)/(m\(\cdot\)d). In reference [25], \(Q_{1}=Q_{2}=16.21\) m\({}^{3}\)/(m\(\cdot\)d); thus, it is evident that the two datasets exhibit fundamental consistency. In conclusion, the numerical calculation method employed in this study is well-founded.  
Figure 1: Three-hole parallel subsea tunnel.  
Figure 3: Distribution of pore water pressure during steady seepage (unit: kPa).  
Figure 2: Computational model for seepage field in three-hole parallel subsea tunnels.
## 3 Development History of Navigation Signal Authentication Technology
### Security
The TESLA protocol implements identity authentication based on CA digital certificates and implements message authentication based on a symmetric cryptographic algorithm combined with delayed key transmission. It requires that certain time synchronization requirements must be met between the satellite and the terminal. Attacks against the TESLA protocol include attacks on the keychain (such as keychain pre-computation attacks, keychain brute force attacks, and keychain replay attacks), message authentication code brute force attacks, and time synchronization attacks on transceivers. The security of TESLA protocol consists of TESLA key and MAC truncation length, TESLA keychain length (the replacement keychain period), and TESLA time synchronization requirements. Table 7 shows the security design of the TESLA protocol of a typical satellite navigation system. Therefore, considering the security of the BDS signal authentication protocol; balancing TESLA key; MAC truncation length; TESLA keychain length (replacement of the keychain period) and building a time synchronization, trusted mechanism will be one of the important directions of future research. In addition, the security of providing two-way communication authentication based on low-orbit navigation satellites also needs to be studied.
# Solving Optimal Electric Vehicle Charger Deployment Problem
## 6 Case Study 2: Region with High EV Density
### Case-Specific Refinement of Solving Method
#### 6.1.1 Data Collection and Preprocessing  
This case study features the use of QGIS [65], an open-source _geographic information_ platform, in data preprocessing and visualization during the research.  
We collect data on factors that are anticipated to influence the demand for charging stations and the data on currently installed EV charging stations. The collected data types include point, line, polygon, and grid data, totaling four types. For subsequent data merging, the line data will be divided into multiple point data with uniform values based on the point data as the reference. The polygon and grid data will be converted into point data with values at the center points of the grid based on a 250 m grid. Furthermore, considering that different types of buildings have varying impacts on charging station demand, we will group the building data based on the charging station utilization by building type data provided by the Korea Power Exchange [63]. EV charging stations can be broadly classified into fast-charging stations and regular charging stations, and this study focuses on public fast-charging stations for location selection.  
Figure 5 illustrates the data collected in Jeju Island visualized in QGIS. The points in the figure have the same attribute values based on their colors, and it is assumed that a single EV charging station data point (shown in yellow) is influenced by nearby data points. Accordingly, the study will proceed with training based on this assumption.
# OTFS-Based Handover Triggering in UAV Networks
## 5 Proposed UAV-Ground Handover Triggering Based on DD Channel Estimation
### FT-Channel-Based Handover Triggering  
The FT domain using OFDM modulation may be an alternative to the RSS for triggering handover decisions in time varying environments. This can be achieved by comparing the FT channel gains of the serving and the target cells for multiple OFDM symbols, i.e., TTT interval. If the FT channel gain of the serving cell falls below that of the target cell in dB, then the handover decision will be taken, as follows:  
\[\begin{array}{rl}\text{HO}(Ser\to Tar)=\bigg{\{}1,P_{\rm Tx}\bigg{(}\sum_{m= 0}^{M-1}\sum_{n=0}^{N-1}\left|\hat{f}_{FT}^{Tqr}[m,n]\right|^{2}-\sum_{m=0}^{ M-1}\sum_{n=0}^{N-1}\left|\hat{f}_{FT}^{Sqr}[m,n]\right|^{2}\bigg{)}>P_{th} \bigg{\}},\\ \text{For }NSym_{OFDM}\geq NSym_{th},\end{array} \tag{29}\]  
The \"1\" in (29) indicates that a handover should be executed if the condition \(\bigg{(}P_{\rm Tx}\bigg{(}\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}\left|\hat{f}_{FT}^ {Tqr}[m,n]\right|^{2}-\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}\left|\hat{f}_{FT}^{Sqr} [m,n]\right|^{2}\bigg{)}>P_{th}\bigg{)}\)For \(NSym_{OFDM}\geq NSym_{th}\) is satisfied.  
Using (1), \(H_{\rm FT}[m,n]\) can be written as follows [13; 14]:  
\[H_{\rm FT}[m,n]=\sum_{q=1}^{Q}\sum_{i_{q}=1}^{P_{q}}h_{i_{q}}e^{2\pi\beta_{i_{ q}}(nT-a_{i_{q}})}e^{-2\pi a_{i_{q}}m\Delta f}, \tag{30}\]  
Thus  
\[\begin{array}{rl}|H_{\rm FT}[m,n]|^{2}=\bigg{(}\sum_{q=1}^{Q}\sum_{i_{q}=1}^ {P_{q}}h_{i_{q}}e^{2\pi\beta_{i_{q}}(nT-a_{i_{q}})}e^{-2\pi a_{i_{q}}m\Delta f} \bigg{)}\\ \times\bigg{(}\sum_{q^{\prime}=1}^{Q}\sum_{i_{q}=1}^{P_{q}}h_{i_{q}}^{*}e^{-2 \pi\beta_{i_{q}}(nT-a_{i_{q^{\prime}}})}e^{2\pi a_{i_{q}}m\Delta f}\bigg{)}, \end{array} \tag{31}\]  
\[|H_{\rm FT}[m,n]|^{2}=\sum_{q=1}^{Q}\sum_{i_{q}=1}^{P_{q}}\sum_{q^{\prime}=1}^ {Q}\sum_{i_{q}^{\prime}=1}^{P_{q}}h_{i_{q}}h_{i_{q}}^{*}e^{2\pi(nT(\beta_{i_{ q}}-\beta_{i_{q}})+\beta_{i_{q}}\alpha_{q^{\prime}}-\beta_{i_{q}}\alpha_{i_{q}})}e^{2 \pi(\alpha_{q}-\alpha_{i_{q}})m\Delta f}, \tag{32}\]  
Similarly to RSS, the FT channel gain is time-dependent, as it varies with time \(n\), but unlike RSS, it changes at intervals of \(T\) s, i.e., OFDM symbol duration, rather than instantaneously, i.e., every chip duration. Additionally, unlike \(RSS[z]\) defined in (28), \(|H_{\rm FT}[m,n]|^{2}\) in (32) includes an extra phase shift based on the subcarrier frequency, introducing not only time dependency but also frequency dependency results in ICI. This ICI arises due to Doppler spread, which destroys the orthogonality between subcarriers, especially in high-mobility scenarios, where the rapid variations in velocity cause significant frequency shifts. Thus, in an FT domain framework, channel gains fluctuate rapidly due to Doppler-induced frequency dispersion, leading to erratic variations in signal quality. The presence of ICI reduces the accuracy of FT channel gains-based handover triggers, which causes incorrect handover decisions, leading to increased handover failures, and ping-pong effect.
# Quaternion Signal Analysis for Detection of Broken Rotor Fault Degrees in Induction Motors
## 2 Methodology
### Fitting and Evaluation  
The values of \(\mu\) are derived from test files corresponding to a healthy motor, one BRB, two BRBs, and three BRBs to model the function \(f(\mu)\) using polynomial regression and Fourier series. This function quantitatively assesses the BRB damage level in the induction motor. Polynomial regressions and the Fourier series were calculated in the second to ninth order, which is the range supported by Matlab's function capabilities. Results from the second to sixth order are presented, as higher-order fits produce similar algebraic expressions and accuracy, making them unnecessary for further consideration.  
The remaining files are evaluated in \(f(\mu)\) by calculating \(\mu\) to determine the extent of BRB damage. Figure 3 displays the fitted functions generated through polynomial and Fourier series methods, comparing these fitted functions with the actual data samples. The boxplot graphs present position results from one file for each fault state, analyzed using a fitted function. They distinctly showcase the minimum and maximum values, as well as the mean, providing clear insights into the data. As anticipated, the estimatedcurves closely align with the observed data, achieving the accuracy required for reliable damage assessment.