esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
### Discussion for Signaling DDoS in 5G CN  
Failures involving CN functions caused by signaling DDoS attacks occur in a varied fashion. For instance, AMF- or SMF-involved failure makes user authentication fail and all services unavailable, except emergency call. UPF-involved failure causes user traffic processing to be made impossible and all services unavailable including emergency call.  
Signaling DDoS attacks can be targeted on RAN. Malicious actors may take advantage of the vulnerabilities of the RAN, designed for the efficient management of limited radio resources, which deprives resources from a mobile terminal unused for a specific period of time but allocates resources again to the terminal for allowing data reception/transmission over the network. It is also possible to deprive legitimate users of the service or resource they expected by depleting limited radio resources. If one base station is put into jeopardy or shut down caused by a DDoS attack, it brings about service outage within a geographic coverage of about 20 km\({}^{2}\) only. However, if a DDoS attack is successfully perpetrated into any CN-layer function, the resulting service outage overwhelms even one metropolitan city, meaning that the adverse ripple effect in the 5G service landscape is in a totally different sphere than ever before. It is, therefore, of paramount importance to respond to signaling DDoS attacks using 5G CN protocols.  
From this study, it was verified that the use of fundamental ML classifiers together with preprocessing with EBA and SBA applied enables us to proactively react against  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline  & **Abnormal Data** & & \multicolumn{2}{c|}{**Normal Data**} \\ \hline
**Time** & **IMSI** & **Class** & **Time** & **IMSI** & **Class** \\ \hline
16:11 & 450052005000001 & 1 & 16:11 & 45005200600001 & 0 \\ \hline
16:11  
signaling DDoS attacks. This research surely provides an insight in order for mobile network operators to reinforce their service security.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Dynamic Bandwidth
#### 5.2.3 Bandwidth Utilization  
We use total network throughput as an indicator to evaluate bandwidth utilization. Figure 18 shows the total throughput of congested Link 1. Compared with CCLF, dynamic bandwidth cannot be fully utilized when using the PCON method, as is evident from the 10 th to the 14 th second.  
Figure 16: Network throughput under dynamic bandwidth using the CCLF method.  
Figure 17: Network throughput under dynamic bandwidth using the PCON method.  
Specifically, when the available bandwidth of Link 1 is 14 Mbps, the CCLF method achieves a total throughput of 14 Mbps, resulting in nearly 100% bandwidth utilization. In contrast, the PCON method yields a total throughput of only 6 Mbps, corresponding to a 43% bandwidth utilization. Thus, the CCLF method increases bandwidth utilization by 57% compared with PCON.  
This discrepancy is attributed to the lack of data buffering and storage in PCON. Consequently, even if the bandwidth of Link 1 exceeds that of Link 2, the throughput of Link 1 is constrained by the bottleneck bandwidth of Link 2. By leveraging data storage and packet buffering in CS, CCLF ensures that the bandwidth of Link 1 remains fully utilized. As a result, it takes 17.91 s to complete all data flow transmissions using the CCLF method, compared with 23.19 s for the PCON method. Thus, the CCLF method reduces the total flow completion time by 23% compared with PCON.
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 3 Results
### Reference Measurements  
The characteristics of the ranging device without FOV redirection were first measured. Results of those measurements are shown in Figure 7. Figure 7a shows uncompensated and walk error compensated measurements with orange and blue lines, respectively. Figure 7b shows uncompensated errors plotted against distance, and Figure 7c shows TOT measurement in regard to distance to the target. Lastly, Figure 7d shows the measurement errors between expected and measured distance in blue, and calculated standard deviations in orange.  
Figure 7: **(a)** shows raw and compensated measurements, (b) depicts errors of uncompensated measurements, (c) reflected pulse width, and (d) errors and standard deviations of compensated measurements in blue and orange, respectively.
## 4 Experimental Results and Discussion
### Output Power of the SLM Nd:YVO\({}_{4}\) Laser
Consequently, the power disparity between free-running and SLM operation became prominent.
# Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays
## 5 Conclusions  
In this letter, we proposed a novel phase shift design to facilitate the estimation of a single AoA in a localized hybrid array. Based on the ratio of the number of antennas per subarray to the number of different phase shifts per symbol being even or odd, we presented two different AoA estimation schemes. Employing difference beam steering in each subarray, the proposed approach can effectively improve the phase offset estimate accuracy between adjacent subarrays, and thus the AoA estimate. Simulation results of MSEs showed that the proposed one achieved better AoA estimation performance over the state of the art when \(Q\) is designed to be an even number.  
Writing--original draft, H.L.; Writing--review, editing, Z.C. 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 (Grant Nos. 62071163, 91938201 and 61871169), Natural Science Foundation of Zhejiang Province (Grant LZ20F010004) and Project of Ministry of Science and Technology (Grant D20011).  
Not applicable.  
The authors declare no conflict of interest.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 4 Self-Adaptive Algorithm  
The objective of the self-adaptive FE analysis of this paper is to find an optimal mesh \(\pi\), such that the linear FE solution obtained on \(\pi\) satisfies the user-specified tolerance \(T_{l}\) in the maximum norm of displacements on each element \(e\):  
\[\max_{e}\left|u-u^{h}\right|\leq T_{l} \tag{15}\]  
Since the exact solution \(u\) is generally unknown, the super-convergent \(\widetilde{u}^{*}\) is used to replace \(u\) for the error checking, and the stopping criterion in Equation (15) is replaced in practical implementation by the following  
\[\max_{e}\left|\widetilde{u}^{*}-u^{h}\right|\leq T_{l} \tag{16}\]  
Numerous numerical experiments show that satisfaction of Equation (16) would indeed also make Equation (15) satisfied. Note that the present paper employed the more stringent maximum norm rather than the more commonly used energy norm used in most adaptive analyses.  
Since the calculation of \(\widetilde{u}^{*}\) involves an additional process for nodal accuracy improvement, to save computational cost and to make the algorithm more efficient, the goal in Equation (16) is achieved by two phases in practical computation:  
1. Find a mesh \(\pi^{*}\) such that the linear FE solution \(u^{h}\) on \(\pi^{*}\) satisfies \[\max_{e}\left|u^{*}-u^{h}\right|\leq T_{l}\] (17)
2. Further refine the mesh \(\pi^{*}\) if necessary to find an optimal mesh \(\pi\), such that the linear FE solution \(u^{h}\) on \(\pi\) satisfies Equation (16).  
More often than not, the first phase, without any nodal accurancy improvement involved, is able to render an almost optimal mesh \(\pi^{*}\) (\(=\pi\)) with the obtained FE solution \(u^{h}\) on \(\pi^{*}\) not only already satisfying Equation (17) but also Equation (16), and the subsequent second phase mostly serves as a post checker and safeguard.  
In the above adaptive FE progress, any element for which Equation (17) or (16) is not satisfied is to be subdivided uniformly into two sub-elements for 1D problems and four sub-elements for 2D problems; for more details please refer to [PERSON]'s work [21] and [PERSON]'s work [22]. For 3D problems, a local adaptive algorithm based on the EEP technique remains to be developed and is not covered in this paper. Specific adaptive examples will be shown in the following section.
## 3 Results
### Comparison with Untreated OA Patients  
In the 44 knees that received a TKA in CHECK, 124 KIDA measurements were available in the years before the TKA, which were used to confirm a linear approach to osteophyte change over time could be assumed, as the variable 'years to TKA squared' did not contribute significantly to the linear regression model predicting osteophyte size (\(p=0.759\)). CHECK patients showed a significant decrease in total WOMAC (\(-6.3\); 95%CI \(-12.1\)--\(-0.5\); \(p=0.04\)) and MAC JSW (\(-0.67\); 95%CI \(-0.86\)--\(-0.47\); \(p<0.001\)) before undergoing TKA (Table 2).  
Before TKA, CHECK knees showed a small nonsignificant increase in osteophyte size (\(+2.1\) mm\({}^{2}\); 95%CI \(-1.2\)-\(5.5\); \(p=0.207\); Figure 3A). Correcting for baseline osteophyte size, all KJD patients together (\(p=0.027\)), KJDHTO patients (\(p=0.043\)), and HTO patients (\(p=0.027\)) showed a significantly greater osteophyte increase than CHECK patients prior to TKA. Taking the average of both knees in patients with a TKA in both knees, instead of using the knees separately, did not change significance. Figure 3B displays the 2-year changes in total osteophyte size for the different groups.  
Figure 3: Two year changes in total joint osteophyte size in mm\({}^{2}\). (**A**) Osteophyte size after treatment for all patients treated with knee joint distraction (KJD, \(n=58\)), high tibial osteotomy (HTO, \(n=36\)), HTO-indicated KJD patients (KJDHTO, \(n=20\)), and for untreated knee osteoarthritis patients before receiving a total knee arthroplasty (CHECK, \(n=44\)). Mean and standard error of the mean (SEM) are shown, * indicates significant changes (\(p<0.05\)) compared to baseline using paired \(t\)-tests. (**B**) Two-year osteophyte size changes for individual KJD, HTO, KJDHTO and CHECK patients. Mean and 95% confidence interval are shown, \(p\)-values above groups indicate significance of two-year changes using paired \(t\)-tests (bold values indicating statistical significance).
## 3 Status and Application
### Repeat-Pass Interference
#### 3.1.1 D-InSAR
The accuracy of the ground surface deformation rate in the Chinese region was 4.82 mm/year. In December 2020, the Aerospace Information Research Institute of the Chinese Academy of Sciences released a national ground subsidy monitoring map based on a supercomputing InSAR system [39; 40]. This map is the world's first of its kind, with extensive coverage and a large amount of processed data. In 2022, [PERSON] et al. [41] used InSAR time-series technology to systematically detect earthquake-accelerated landslides triggered by the 2016-2017 Central Italy earthquake sequence. This marked the first large-scale detection of earthquake-accelerated landslides, revealing their long-term response to seismic effects.
## 3 Results and Discussion
### Hydraulic Conductivity of Soil--Semi-Technical Scale Test
Particular areas of concern, as mentioned in the introduction of this paper, are high chloride concentrations [22; 23]. Domestic wastewater has a high nutrient content in organic matter and macro- and micro-nutrients, especially nitrogen and phosphorus, which could favour crop production. Wastewater reuse has been practised for centuries in drylands and semi-arid countries such as Israel, Saudi Arabia, Libya, and Egypt [45]. With the growth of the closed-loop economy and environmental pressures, the disposal and management of wastewater are becoming an increasingly important research topic [46]. However, the reuse of raw wastewater in a soil application may endanger the environment through increased salinity, heavy metal contamination, densification, and the introduction of pathogens, viruses, and others [47]. The source literature describes a growing number of technologies that address the above-mentioned issues. For example, [PERSON] et al. developed a short-term column experiment on simulating the infiltration process of wastewater in desert soil [25]. Wastewater irrigation and plant growth decreased the soil zeta potential while increasing the formation of aggregates and bacterial abundance and diversity in soil. This course of action is promising. Figure 14: Hydraulic conductivity for: (**a**) column group 1, supplied with treated wastewater, (**b**) column group 2, supplied with treated wastewater and brine solution periodically, and (**c**) column group 3, supplied with tap water; terms from 1–8 represent the measurement periods after 1–8 regenerations.
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 5 Simulation Results
### Stop Iteration Parameter \(\beta\)  
As the SNR continues to decrease, the effect of noise on the observed value of the signal \(Y\) will increase, so \(\beta\) will change accordingly at different SNRs. In this section, from the perspective of comparative analysis, the exact LS algorithm is used to obtain the values of \(\beta\) under different SNRs.  
The channel estimation algorithm can generally be divided into two stages. In the first stage, the estimated position of the tap is reconstructed. In the second stage, the value of the position tap is estimated. The exact LS algorithm directly gives the exact position of the tap and only needs to estimate the value of the tap at that position. Thus, the exact LS algorithm represents the performance benchmark of the CS estimation. Therefore, in this experiment, the SSA-CoSaMP algorithm with a gradual change of \(\beta\) is compared with the exact LS algorithm for NMSE, where \(\beta\), which just makes the two NMSEs equal, is the value of \(\beta\) under the SNR.  
In Figure 3(a), when \(\beta=0.058\), the NMSEs of the two algorithms are exactly equal. When \(\beta\) is greater than \(0.058\), the two NMSEs are inconsistent. When \(\beta\) is less than \(0.058\), although the two NMSEs are equal, they do not reach an equal critical value. Similarly, according to the experimental simulation results of Figure 3(b),c, the range of \(\beta\) values can be summarized, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of base station antennas (\(M\)) & 128 \\ Number of users (UEs) & 8 \\ Number of subcarriers & 1024 \\ Number of multipath channels & 6 \\ Number of consecutive OFDM symbols (\(R\)) & 5 \\ Channel characteristics & Independently distributed Rayleigh fading \\ Pilot allocation scheme & Non-orthogonal \\ Channel model & Rayleigh fading \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: _Cont._
# Development Status and Service Performance Preliminary Analysis for BDSBAS
## 3 Message User Algorithms and Service Performance Evaluation Methods  
SBAS can be used in many non-aviation applications and the SARPs proposed by ICAO for the SBAS system can be also extend beyond aviation to other SBAS users. This section describes the user algorithm referred to ICAO SARPs for determination of user position as well as the protection levels with BDSBAS message type (MT) information. The navigation and protection level results of monitoring receivers are used to evaluate the BDSBAS service performance and the evaluation methods are detailed discussed in the following paragraphs.
## 2 Modeling
### Operating Principle  
The push-pull converter, like all other power converters, owes its operation to the controlled flow of current through its circuit. This is accomplished by the controlled switching of its electronic switches and energy storage in its inductors and capacitors. The ideal circuit model is shown in Figure 1.  
Based on the ideal circuit model (Figure 1), its operating principle will be explained on the basis of three operating modes, depending on whether the electronic switches (Mosfets) are in conduction or cut-off (case 1: \(Q_{1}\) ON and \(Q_{2}\) OFF, case 2: \(Q_{1}\) OFF and \(Q_{2}\)\(ON\), ON and Case 3: \(Q_{1}\) and \(Q_{2}\) OFF). In cases 1 or 2, current flows through the corresponding transformer primary winding (see Figure 1), magnetizing its transformer core. The voltage induced in the secondary (the polarity correspondence between the primary and the secondary is given by the dots of the respective windings; see Figure 1) will drive diode  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{4}{c}{**Non-Idealities Considered**} & \multirow{2}{*}{**Formulation**} & \multirow{2}{*}{**Design Objective**} \\ \cline{2-2} \cline{5-6}  & **HF Transformer** & **Electronic Switches** & **Rectifier Diodes** & & **Output LC Filter** & \\ \hline
[11; 27] & - & - & - & - & Average state-space model & Control-oriented design \\ \hline
[10; 17] & - & - & - & - & Impedance model & Control-oriented design, sizing \\ \hline
[7] & - & - & - & Int. resistance & Transfer function & Control-oriented design \\ \hline
[19] & Int. resistance of windings & & & & & \\ Leakage inductance & - & - & Int. resistance & Average state-space model & Control-oriented design \\ of windings & & & & & & \\ \hline
[20; 21] & Leakage inductance & - & - & - & Transfer function & Control-oriented design, advanced analytics \\ \hline
[22] & Int. resistance of windings & & & & & \\ Leakage inductance & - & - & - & - &
\begin{tabular}{} \end{tabular} & Control-oriented design, sizing \\ of windings & & & & & state-space model & sizing \\ \hline
[23] & Leakage inductance & & & & & \\ \hline
[24] & Int. resistance of windings & & & & & \\ Core magnetization & & & & & \\ inductance & & & & & \\ \hline  & Int. resistance of windings & & & & \\ Leakage inductance & & & & & \\ \hline Authors & Parasitic capacitances & & & & \\ of windings & & & & & \\ \hline Core magnetization & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of push–pull converter modelling solutions found in the literature compared to the authors’ proposal.  
\(D_{1}\) or \(D_{2}\), with one of the branches always being active (see Figure 1). The current of each active branch will flow through the \(L_{F}\)-\(C_{F}\) circuit and supply the load \(R\). In case 3, there is no current through the primary windings and, therefore, no voltage induction in the secondary. Since the inductor current is continuous, it follows that the energy stored in \(L_{F}\), like that stored in \(C_{F}\), is also continuous, so \(D_{1}\) and \(D_{2}\) conduct.
# Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis
###### Abstract
Inspired by the above work, we will use the cuckoo search algorithm to optimize SVM and adapt it to the complexity of semantic mining in sentiment analysis tasks. However, when faced with large-scale complex tasks such as infrared small target monitoring [18], autonomous driving [19], and natural language understanding [20], strong classifiers are unable to obtain reasonable and reliable results during validation. A promising strategy to improve the performance of a standard classifier is to consider a pool of classification algorithms and to properly _integrate_ these algorithms to generate the final prediction. [PERSON] et al. [21] applied several heterogeneous integration strategies to explore the configuration of sentiment analysis data sets from different industries. The experimental comparison results found that the integrated model had stronger text sentiment classification ability than the optimal single strong classifier, and the median of several integrated models was 5.53% higher than that of the strong classifier. [PERSON] et al. [21] claimed that the integrated approach avoids the expensive computational consumption of a single strong classifier while integrating a weak classifier for retraining can effectively avoid the semantic mining ability of a single model. [PERSON] et al. [22] proposed a new spam mailbox detection method, HELPHED, which employed Stacking and soft voting. Integrated approaches can process features in parallel, which yields significant computational advantages. Moreover, the joint application of multiple classification algorithms (known in the literature as ensemble learning) leads to better classification accuracy and improves the generalization ability of an ML approach. However, ensemble learning methods have non-negligible weaknesses: for example, the performance of some classifiers depends crucially on some hyperparameters, and therefore, effective methods are mandatory to determine the best hyperparameter configuration for each classifier before proceeding to their integration. This paper introduces a new computing framework AdaECELM, which uses the optimal ML model as the base classifier for sentiment analysis of short texts. The model integrates three modules: TF-IDF feature selection optimization processing, CS-SVM adaptive optimization subclassifies and soft voting comprehensive learning, and finally obtains an ensemble learning model with effective performance, stable effect, and strong generalization ability.
# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 5 Robust Conformal Prognostic for Reliable Predictive Maintenance
### Experimental Setting
#### 5.3.2 Performance Metrics and Evaluation Procedure  
Model performances have been properly evaluated using several metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (\(R^{2}\)). In addition, a custom S-score, given by [PERSON] et al. [21], is employed to evaluate prognostic performance by asymmetrically penalizing over-estimations and under-estimations. More precisely, the S-score, defined as in Equation (21), is chosen as the principal metric for model selection since its straightforward impact in PdM practices is due to its weighted approach near EoL.  
\[S=\sum_{i}\begin{cases}\exp\Bigl{(}-\frac{\operatorname{rul}_{\text{pred},i}- \operatorname{rul}_{\text{true},i}}{13}\Bigr{)}-1,&\text{if }\operatorname{rul}_{\text{pred},i}< \operatorname{rul}_{\text{true},i},\\ \exp\Bigl{(}\frac{\operatorname{rul}_{\text{pred},i}-\operatorname{rul}_{ \text{true},i}}{10}\Bigr{)}-1,&\text{otherwise}.\end{cases} \tag{21}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Hyperparameters** \\ \hline \multirow{4}{*}{LSTM} & 3 LSTM layers with 300, 250, and 200 units (tanh activation) \\  & Dropout rate: 0.5 after each LSTM layer \\  & Batch Normalization after each LSTM layer \\  & Dense layer with 100 neurons (ReLU) and final single output neuron \\  & Optimizer: [PERSON], Learning rate: 0.0005 \\  & Batch size: 32, Epochs: configurable (50–100) \\ \hline BiLSTM & Same hyperparameters of LSTM but with BiLSTM layers \\ \hline \multirow{4}{*}{NBEATS} & 2 Fully connected dense blocks \\  & Each block: 2 dense layers with 256 neurons (ReLU activation) \\  & Optimizer: [PERSON], Learning rate: 0.001 \\ \hline \multirow{4}{*}{CNN-GRU} & 1D Convolutional layer: 64 filters, kernel size 3 (ReLU) \\  & GRU layer with 100 units \\  & Dense layers: 50 neurons followed by a single-neuron output \\  & Optimizer: [PERSON], Learning rate: 0.001 \\ \hline \multirow{4}{*}{XGBoost} & Maximum tree depth: 60 \\  & Learning rate: 0.01, Number of estimators: 200 \\ \cline{1-1}  & Subsample ratio: 0.8, Colsample ratio: 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter setting of the implemented models.
## 3 Results
The positioners from Standa, which we used in this work, with a displacement of 30 mm (the characteristic scan length of the interferograms in our experiments) can give an error of 10 \(\upmu\)m [61], at which the measurement error of the refractive index of the SPPs reaches up to 0.0003, which does not satisfy the required accuracy. Applying translation stages with encoders or more precise positioners can significantly increase the accuracy of the measurements.