esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 4 Strategy of Communication Path Selection  
The paper studies the routing under the SDMWN environment, in order to get transmission paths for several users at the same time to minimize the global energy consumption with the premise of satisfying the QoS. In addition, the routing algorithm requires each mobile device to provide only one user with transmission service. Hence, if each user directly chooses the delay constrained least cost path, it may not satisfy the condition that the node has no duplication. In this section, we firstly introduce the LARAC algorithm, which can solve the delay constrained least cost path problem in polynomial time. Then, by integrating LARAC and K-Dijkstra, we propose a K-LARAC algorithm to get the top \(K\) energy-minimum paths that satisfy the QoS as the alternative path set for each user. Afterward, an improved genetic algorithm is used to select the paths from each set to obtain the routing strategy. According to the solving process, the pseudocode of the method can be describe indetail in Algorithm 1:  
```
0: A graph \(G=(V,E)\), each edge \(e\) with energy consumption \(c(e)\) and delay \(d(e)\), a given delay constraint \(D_{i}\in Z^{+}\), \(i=1,2,\ldots,M\).
0: Global c-minimal paths between the nodes \(s_{i}\) and \(t_{i}\).
1:procedureKLG (\(s_{i}\), \(t_{i}\), \(c\), \(d\), \(\Delta_{delay}\))
2:repeat
3:\(S_{i}\) = K-LARAC (\(s_{i}\), \(t_{i}\), \(c\), \(d\), \(\Delta_{delay}\))
4:\(i=i+1\)
5:until\(i=M\)
6: result = GA (\(S_{1}\), \(S_{2}\),..., \(S_{M}\), \(c\))
7:endprocedure
```  
**Algorithm 1** K-LARAC and Genetic Algorithm (KLG)  
In the pseudocode of the method, \(M\) denotes the number of users, and \(S_{i}\) is the alternative path set for user \(i\). Therefore, the rest of this section introduces LARAC, K-LARAC, and the genetic algorithm in turn.
## 2 Model Assumption and Notation  
Assume that \(n\in\mathbb{N}\) homogeneous units are assigned to a lifetime test. For each unit, there are \(s\) failure causes. We suppose that these \(s\) latent failure times are mutually independent and conform to the Burr-XII distribution.  
Thus, define \(T_{i}\) as the exact lifetime of the \(i\)-th tested unit, and  
\[T_{i}=\min\{X_{i1},X_{i2},\ldots,X_{is}\},\]  
where \(X_{ij}\) represents the random variable corresponding to the \(j\)-th latent failure cause. Moreover, \(X_{i1},X_{i2},\ldots,X_{is}\) are independent of each other. We assume that \(X_{ij},j=1,2,\ldots,s\) follow the Burr-XII distributions with the same second parameter (\(b>0\)) and differ ent first parameters \((a_{1},a_{2},\ldots,a_{s}>0)\). The PDF and CDF of \(X_{ij}\) are presented as follows, respectively:  
\[f_{j}(t;a_{j},b)=a_{j}bt^{b-1}(1+t^{b})^{-(a_{j}+1)},t>0;\,a_{j},b>0, \tag{5}\]  
and  
\[F_{j}(t;a_{j},b)=1-(1+t^{b})^{-a_{j}},t>0, \tag{6}\]  
where \(a_{j}\) and \(b\) are unknown parameters to be estimated and they are all positive, \(j=1,2,\ldots,s\).  
As exhibited in Figure 1, the parameter \(b\) affects the overall shape of the density curve. For different failure causes of the same product, it is reasonable to assume that the distributions share the same parameter \(b\). The assumption that the potential failure distributions share a common parameter while differing in the other has been widely used in previous studies on competing risks data, such as [20,21].  
Additionally, we use \(D_{i}\) to denote the failure cause. \(D_{i}=j\) if the \(i\)-th unit has failed as a result of the \(j\)-th failure cause. We suppose that \(\{X_{i1},X_{i2},\ldots,X_{is}\}\) are independent and cannot be observed directly, while \(T_{i}\) and \(D_{i}\) can be observed if the data are not censored. Therefore, taking into account the competing risks data under a middle-censoring framework, we consider a corresponding random censoring interval \([L_{i},R_{i}]\) for the \(i\)-th tested unit, and the interval is independent of its actual lifetime \(T_{i}\). That is,  
\[(Y_{i},D_{i},\delta_{i})=\begin{cases}(T_{i},D_{i},1)&\text{if }T_{i}\
ot\in[L_{i},R_{i}],\\ ([L_{i},R_{i}],D_{i},0)&\text{if }T_{i}\in[L_{i},R_{i}].\end{cases}\]  
We suppose \(L_{i}\) and \(R_{i}-L_{i}\) are also independent and exponentially distributed with known parameters.  
Through the independence of \(X_{i1},X_{i2},\ldots,X_{is}\), the joint PDF and CDF of lifetime with its corresponding cause \((T,D)\) are concluded as follows:  
\[f_{T,D}(t,j;\mathbf{a},b)=f_{j}(t;a_{j},b)\prod_{k=1,k\
eq j}^{s}(1-F_{k}(t;a_ {k},b))=a_{j}bt^{b-1}(1+t^{b})^{-(a^{*}+1)}, \tag{7}\]  
where \(\mathbf{a}=(a_{1},a_{2},\ldots,a_{s})\) and \(a^{*}=\sum_{j=1}^{s}a_{j}\).  
Through integration, we derive:  
\[F_{T,D}(t,j;\mathbf{a},b)=\frac{a_{j}}{a^{*}}[1-(1+t^{b})^{-a^{*}}]. \tag{8}\]  
To simplify the notation, we will replace \(F_{T,D}(t,j;\mathbf{a},b)\) and \(f_{T,D}(t,j;\mathbf{a},b)\) with \(F(t,j;\mathbf{a},b)\) and \(f(t,j;\mathbf{a},b)\), respectively, in the following text.
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 5 Discussion  
While the proposed CNN-based framework demonstrated promising results for malicious network traffic detection, several limitations should be addressed in future research.  
First, our evaluation is based exclusively on the NSL-KDD dataset. While this dataset provides a balanced representation of normal and attack traffic and remains a standard benchmark in IDS research, it does not capture contemporary attack vectors or modern network traffic characteristics. This limitation restricts the generalizability of our findings to current network environments, where threats have evolved significantly. Additionally, since our current approach groups all attack types in the NSL-KDD dataset into a single attack class for binary classification, our analysis lacks a detailed examination of which specific attack categories contribute to misclassification. Multi-class classification experiments are required to evaluate performance across individual attack types and identify patterns in false positives and negatives. While our model achieved an FNR of 1.25%, this rate could still result in missed attacks in high-throughput production environments, necessitating further FNR reduction. Future work should extend the evaluation to more recent datasets such as CICIDS2017, CSE-CIC-IDS2018, or CIC-DDoS2019, which better represent modern network infrastructures and contain more sophisticated attack patterns and include a comprehensive analysis of misclassified samples across different attack categories.  
Second, while our approach improves training and testing times through dimensionality reduction, the image conversion step incurs additional computational overhead. Although we observed a 17.47% reduction in training time and a 19.44% reduction in testing time, comprehensive benchmarking against optimized traditional machine learning methods (e.g., Random Forest, XGBoost, and SVM) on the same hardware platform was not conducted. Such comparisons are necessary to determine the conditions under which our approach outperforms conventional models.  
Third, our framework has not been validated on real-world network traffic. Experiments were conducted solely on benchmark datasets, which do not reflect the variability and unpredictability of production environments. Without such validation, its effectiveness under real-time conditions remains unverified. Future studies should evaluate the framework on diverse real-world traffic sources such as corporate networks, IoT systems, and industrial control environments.  
Addressing these limitations will significantly enhance the proposed framework's performance and practicality, ensuring robust intrusion detection capability in real-world network environments.
# Side-Channel Power Analysis Based on SA-SVM
## 6 Conclusions  
This paper presents an SA-SVM model for side-channel power analysis. The approach seeks continuous decision variables and optimizes SVM parameter values to achieve superior classification results. Experiments were conducted on DPAv4.1, testing the model under various POIs and kernel functions. Compared with the SVM grid search method, SA-SVM improved accuracy by approximately 0.25-3.25% while reducing the running time by up to 98.02%. We compared the linear, RBF, and three kinds of wavelet kernels, and it was revealed that the wavelet kernels had higher accuracy than RBF while requiring only three traces to recover the key. The combination of SA and SVM provides a new method for addressing the parameter selection challenge in side-channel power analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **MLSCA** & **No. of Traces** & **No. of POIs** & **Acc (\%)** \\ \hline
[14] & NB & 1000 & 50 & 37.6 \\ \hline
[14] & MLP & 1000 & 50 & 44.8 \\ \hline
[14] & XGBoost & 1000 & 50 & 52.0 \\ \hline
[14] & RF & 1000 & 50 & 49.2 \\ \hline
[14] & CNN & 1000 & 50 & 60.4 \\ \hline
[12] & RF\_SMOTE & 2000 & - & 93.0 \\ \hline This study & SA-SVM & 1000 & 53 & 94.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Machine learning, DPAv4.1.  
**Author Contributions:** Data curation, P.F.; methodology, Y.Z.; supervision, H.Z.; validation, P.H.; writing--original draft preparation, Y.Z.; writing--review and editing, H.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation Program of China (62201491, 62071057) and the Yantai City 2021 School-Land Integration Development Project Fund (1521001-WL21 JY01).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments  
### Experimental Setup  
We conducted experiments on two SRL tasks: the Korean PropBank (KPB) [26] and CoNLL-2009 English. For the Korean dataset, we constructed the same training and evaluation data as in previous studies. A total of 19,302 sentences were used for training, and 3733 sentences were used for evaluation. In English, we followed standard splits for training, development, and test sets. In order to compose a simple semantic feature for  
Figure 5: Architecture of our semantic role labeling model.  
Korean SRL, 2835 frameset files in KPB were used. There were 1217 sentences in KPB where a simple semantic feature was not available. In this case, the \"<None>\" token was used. In addition, 10,687 frameset files in PropBank were used to construct simple English semantic features. In this study, we used a pre-trained KorBERT model for Korean and a pre-trained BERT model for English.  
The BiLSTM RNN model used for fine-tuning the Korean SRL model comprised four 1200 dimensional hidden layers. The KorBERT representation comprised 768 dimensions, and the feature embedding had 2000 dimensions. The BiLSTM RNN model used for fine-tuning the English SRL model used one hidden layer of 1600 dimensions. The BERT representation was 768 dimensions for the base models, 1024 dimensions for large models, and 800 dimensions for feature embedding. The dropout value was 0.1, which was the same for both models. Adam was used as the learning algorithm, and CRF was used as the output layer of the BiLSTM RNN for dependency modeling. All the experimental environment variables described above were marked with optimized values.
## 3 TDLAS Tomographic Reconstruction Model (Prototype, Tokushima University, Tokushima, Japan, 2016)  
In order to get a two-dimensional temperature distribution via CT calculations, multiple laser beams produced in a form of mesh shown in Figure 3 were constructed by a semiconductor laser, and then all laser intensity information at the grid points of the mesh was reconstructed theoretically using the experimental absorption spectra.  
Figure 2: Theoretical temperature dependence H\({}_{2}\)O absorption spectra.  
The signal intensity of the absorption lines of the laser pass \(j\) is can be expressed by the following Equation (2).  
\[A_{\lambda,j}=-\sum_{i}n_{i}\cdot L_{i,j}\cdot\alpha_{\lambda,i} \tag{2}\]  
Here, \(A_{\lambda,j}\) is the integrated absorbance of wavelength \(\lambda\) in the path \(j\), \(\alpha_{\lambda,i}\) is the absorption coefficient of wavelength \(\lambda\) inside a grid \(i\) on the path and is dependent upon the temperature and the density of species. \(L_{ij}\) is the path length inside the grid \(i\).  
Figure 4 shows how the temperatures and the concentrations at the grids of the mesh in Figure 3 are obtained in CT calculations.  
In the CT process, it is important to adopt the initial values for the temperatures and the concentrations at all grids. In this study, CSLOS (corrective summation of line of sight) method has been adopted. In this method, all absorption signals passing at a grid are added and they are used as the experimental spectra at all grids. The initial values of temperature and concentration at the grids have been installed considering the length and the location of the laser lines. At the initial stages of  
Figure 4: Calculation process of temperature and concentration at all grids in CT-TDLAS.  
Figure 3: Calculation grids for tomographic reconstructions.  
CT calculation, the initial values obtained by the CSLOS method are used for the calculation of the SMART algorithm, in which Equation (3) is used for the calculation of the absorption coefficients \(\alpha_{\lambda,i}\).  
\[\alpha_{\lambda,i}{}^{k+1}=\alpha_{\lambda,i}{}^{k}\cdot exp\left(\sum_{j=1}^{J} \frac{L_{ij}}{\sum\limits_{i=1}^{I}L_{ij}}\cdot log\frac{A_{\lambda,j}}{\sum \limits_{i=1}^{I}\alpha_{\lambda,i}\cdot L_{ij}}\right) \tag{3}\]  
The SMART algorithm is based on a combination product of algebraic reconstruction. This is an iterative calculation approach that allows a cross-entropy minimization. It has been mainly used to reduce convergence time in iterative calculations.
## Nomenclature  
\begin{tabular}{l l}
**A, B, C** & Position vectors of the ball hinge center (A, B and C) without installation error \\ \(\mathbf{N_{0}}\) & Axis vector of the mirror facet without installation error \\ Q & Vertex position vector of the mirror facet without installation error \\ \(\mathbf{N_{t}}\) & Axis vector of the mirror facet with installation error \\ \(\mathbf{Q_{t}}\) & Vertex position vector of the mirror facet with installation error \\ \(\mathbf{A_{t}}\), \(\mathbf{B_{t}}\), \(\mathbf{C_{t}}\) & Position vector of the ball hinge center with installation error \\ \(\mathbf{T_{1}}\),\(\mathbf{T_{2}}\), \(\mathbf{T_{3}}\) & Position vector of the feature points (\(\text{T}_{1}\), \(\text{T}_{2}\) and \(\text{T}_{3}\)) without installation error \\ \(\mathbf{T_{1t}}\),\(\mathbf{T_{2t}}\), \(\mathbf{T_{3t}}\) & Position vector of the feature point with installation error \\ \(\mathbf{t_{1}}\), \(\mathbf{t_{2}}\), \(\mathbf{t_{3}}\) & Position error vector of the feature point \\ \(\mathbf{n_{1}}\), \(\mathbf{n_{2}}\) & Vector of rotation axis of the mirror facet \\ \(\varphi_{2}\), \(\varphi_{3}\) & Rotation angle of the mirror facet in OneT-TwoRmethod, mrad \\ \(\theta_{A}\), \(\theta_{B}\), \(\theta_{C}\) & Rotation angle of the mirror facet in ThreeR-OneTmethod, mrad \\ \(\mathbf{M_{1}}\) & Translational vector of the mirror facet, mm \\ \(\mathbf{N_{AL}}\) & Axis vector of the mirror facet after adjustments \\ \(\mathbf{Q_{AL}}\) & Vertex position vector of the mirror facet after adjustments \\ \(\mathbf{A_{AL}}\), \(\mathbf{B_{AL}}\), \(\mathbf{C_{AL}}\) & Position vectors of the ball hinge center after adjustments \\ \(\theta\) & Error angle, mrad \\ L & Error distance, mm \\ \(d_{1}\) & Diameter of the connection hole, mm \\ \(d_{2}\) & Diameter of the bolt, mm \\ \(L_{1}\) & Thickness of connection plate, mm \\ \(d_{\text{A}}\), \(d_{\text{B}}\), \(d_{\text{C}}\) & Adjustment values of the bolt 1, 2 and 3 respectively, mm \\ DS-CSP & Dish-Stirling concentrated solar power system \\ OneT-TwoR & One translational and two rotational rigid body movement \\ ThreeR-OneT & Three rotation and one translational rigid body movement \\ \end{tabular}
# GPS/BDS Medium/Long-Range RTK Constrained with Topospheric Delay Parameters from NWP Model
## 2 Materials and Methods  
### Data Collection  
In this study, the pressure, temperature, and specific humidity fields of the medium-range NWP model from the SMB are utilized to retrieve the tropospheric delay parameters. This model is established based on the Weather Research and Forecasting Model (WRF). It covers the East China area, and it includes three types of spatial resolutions: 3 km, 9 km and 15 km. The horizontal resolution of 9 km with 35 vertical levels is used for this study. The exponential model is used for the tropospheric correction up to the 50 hPa. The temporal resolution of the NWP in this study is 3 h. In addition, one year's NWP data over the East China area and GNSS observations of eight meteorological stations (BYFH, BTLU, BTUZ, BXTC, ZJHZ, SDHM, SDJM, SHBS) in eastern China were collected to validate the ZTD derived from NWP. The eight stations shown in Figure 1 are located between Latitude 28\({}^{\circ}\)N and 38\({}^{\circ}\)N, Longitude 116\({}^{\circ}\)E and 122\({}^{\circ}\)E.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 5 Application Analysis of Passenger Flow Prediction
### Data Processing  
The method described in Section 4.3 is used to preprocess the raw data and encode date attributes and weather factors. The time period 6:00-23:00 is selected as the target time period for passenger flow research, and 1 h as the time slice, i.e., passenger flow is counted every 1 h. The experimental running environment of this paper is python3.10, and the short-term passenger flow prediction model is built by the third-party libraries of Scikit-learn, Keras, and TensorFlow in python.  
The above data is input into the TCN-LSTM model, and the convolution kernel size is \(k=3\). The upper and lower limits of the hidden layers of the TCN network are set to 8 and 64, respectively, and that of the LSTM network is set to 64 and 128, respectively. The training times are 100, and the learning rate is 0.01. Other parameters remain unchanged.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 5 Conclusions
In Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan, 18-21 September 2017; pp. 1-7. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] GNSS User Technology: State-of-the-Art and Future Trends. _IEEE Access_**2022**, _10_, 39939-39968. [CrossRef]
* _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Chapter 20. Applications of Array Signal Processing. _Acad. Press Libr. Signal Process._**2014**, \(3\), 859-953. [CrossRef]
* [PERSON] (1989) [PERSON]; [PERSON] ESPRIT-estimation of signal parameters via rotational invariance techniques. _IEEE Trans. Acoust. Speech Signal Process._**1989**, _37_, 984-995. [CrossRef]
* [PERSON] (1990) [PERSON]; [PERSON] Maximum likelihood methods for direction-of-arrival estimation. _IEEE Trans. Acoust. Speech Signal Process._**1990**, _38_, 1132-1143. [CrossRef]
* [PERSON] (1979) [PERSON] The Power-Inversion Adaptive Array: Concept and Performance. _IEEE Trans. Aerosp. Electron. Syst._**1979**, _AES-15_, 803-814. [CrossRef]
* _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Blind Adaptive Beamformer Based on Orthogonal Projections for GNSS. In Proceedings of the 25 th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2012), Nashville, TN, USA, 17-21 September 2012. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]. (Eds.) _GNSS Software Receivers_; Cambridge University Press: Cambridge, UK, 2022. [CrossRef]  
**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
## 3 System Model and Design Objectives
### Attacker Model  
Due to privacy issues such as packet drop and errors of some control packets, signature mismatch may occur. Signature mismatch may lead to the false detection and isolation of inauthentic nodes by the BS (base station).  
Therefore, we use the probability analysis attacker model for detection of any cyberattack such as sinkhole nodes and sybil attacks in the network. Thus, the probability analysis attacker model detects any packet drops or error messages for any malicious node attacks or sensor nodes compromised by cyberattacks. Therefore, the probability analysis attacker model investigates network reliability and reduces any packet drop or errors in the network. This probability analysis attacker model is deployed in our proposed SDAAA protocol. The probability analysis attacker model is not found in any of the contending secure data aggregation protocols. Therefore, we model the packet drop and errors in the network as below.  
As proposed in our previous work [41], which is extended by our proposed SDAAA protocol in this research, we optimize the probability of false detection/error by the BS by doubling isolation of any cyberattacks in the network system architecture, which is necessary and is modeled as follows:  
Let  
\(P_{r}\) = probability of error detection or packet drop in the network  
\(P_{D}\)P = probability of packet drops regarding any privacy concern in the network application  
\(RP\) = packet generation rate within the network  
\(t\) = time interval of the BS detecting attack or error in the network  
\(ASM_{i}\) = number of authorized signature mismatches of node \(N_{i}\) among its neighbors  
Then, the probability of \(ASM\) exceeding the maximum threshold \(ASM_{th}\) is as in Equation (1):  
\[P_{r}(ASM>ASM_{th})=1-2{\sum\limits_{\begin{subarray}{c}RPt\\ i\end{subarray}}^{t}}P_{D}^{i}\big{(}1-P_{Dp}\big{)}^{Rt-i} \tag{1}\]  
where  
\(W\) = number of warnings received by node \(Ni\) when any of its neighbors turns out to be malicious or there are vulnerable nodes in the network.  
Figure 2: Proposed schematic process for secure data aggregation in WSNs.  
The probability of \(W\) exceeding the maximum threshold \(W_{th}\) is given in Equation (2) as:  
\[P_{r}(W\big{(}N_{j}\big{)}>W_{th}=1-\sum_{i=1}^{W_{th}}\binom{NH}{i}P_{FD}\big{(} N_{j}\big{)}^{i}(1-P_{FD}(N_{j}))^{NH-i} \tag{2}\]  
where  
\(NH\) is the number of neighbors of node \(N_{i}\)  
\(P_{FD}\) (\(N_{j}\)) is the probability of false detection of node \(N_{j}\), which is given in Equation (3):  
\[P_{FD}\big{(}N_{j}\big{)}=1-exp\left(-2\cdot\frac{(R.t.P_{D}-ASM_{th})^{2}}{R.t.}\right) \tag{3}\]  
Then, the probability of doubling detection and Isolating any cyberattacks in the network is \(P_{FI}\) and is given by Equation (4) as:  
\[P_{FI}\big{(}N_{j}\big{)}=1-exp\left(-2\cdot\frac{\big{(}NH.P_{FD}\big{(}N_{j} \big{)}-W_{th}\big{)}^{2}}{NH}\right) \tag{4}\]