esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
## 7 Conclusions  
In this work, Convolutional Neural Networks (CNNs) have been proposed as a useful tool for predicting blocking events in EONs. Spectrum snapshots are labeled during EON network simulation, stored, and then used to train and test the CNN model. The proposed model efficiently predicts connection blocking with 92% accuracy.  
Even though we use previously recorded simulation results to train and evaluate the CNN models, the proposed solutions can easily be utilized in real networks. In real  
Figure 12: Confusion matrix of the CNN 2D model used.  
situations, typically, snapshots of link occupation are recorded continuously; thus, many data about blocking events are available. In the case reported here, the previously generated dataset is utilized to train the CNN model.  
The CNN models utilized here can be easily deployed in network control engines and can be used as a tool to predict blocking events during EON operation. This machine learning model can be continuously run during network operation and trigger the network engine to take the appropriate action when potential blocking situations are foreseen. In general, if blockings can be predicted earlier in a network, it is possible to react on the network management system to prevent them.  
The proposed model can reduce blocking events by preventively running the appropriate defragmentation mechanisms. Using such a forecasting tool can optimize the process of resource allocation and reduce the network's operating expenditure (OPEX).
## 4 Discussion
### Insights into the Proposed Model's Runtime Performance  
The proposed model, including its DBSCAN-based alternative, demonstrated strong computational performance, completing feature matching for the six pipeline segments in an average of 0.22 --approximately 170% faster than the Voronoi model. This efficiency is achieved by reducing the number of features requiring computationally intensive affine transformation and linear optimization. Many features are either identified as outliers through clustering, directly matched, or efficiently processed using fast distance-based filtering methods. These measurements were based on 100 iterations using the optimal configurations obtained from the analysis presented in Section 3. The experiments were conducted on a personal computer running Windows 10 with 16 GB of RAM and an Intel i7 CPU. By comparison, other methods, such as the annealing model, require significantly longer runtimes of 3.1 s per segment [13]. This runtime advantage underscores the model's suitability for large-scale applications, where both speed and accuracy are critical.  
While machine differences undoubtedly impact these metrics, variations can also arise from the model's configurations. For instance, higher proximity thresholds reduce cluster diversity, leading to more point matching problems and consequently longer runtimes. Additionally, runtime is significantly influenced by the choice of clustering and point matching techniques.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 4 Evaluation
### Sagittal Plane Localisation  
The effect of DFE on elevation localisation in the sagittal plane was assessed using a perceptual model [44], which compares two data sets of HRIRs (in this case Ambisonic and reference), and simulates human auditory processing to predict height localisation. It produces two psychoacoustic performance metrics: quadrant error (QE), a prediction of localisation confusion (presented as a percentage), and polar RMS error (PE), a prediction of precision and accuracy in degrees. The frequency range of the model's filterbank was set to 1.5-18 kHz due to the KU 100's lack of torso and therefore no elevation cues below 1.5 kHz [45], and the upper limit of human hearing.  
Table 4 shows the predicted QE and PE values for Ambisonic orders 1, 3 and 5 and Figure 7 illustrates predicted sagittal plane localisation. Results indicate improved localisation performance with the implementation of DFE for all three tested configurations.
## 3 Results
However, the tension of the ligaments changed also in the PCL and LCL components as it will be shown later. The quantification of the effects of muscles removal is reported in Table 2. The difference of ligaments tension between \"All muscles\" condition and the absence of one muscle group was averaged by keeping separately the phases in which the difference was negative (tension reduction) from the ones in which it was positive (tension increased). The opposite behavior of ACL and PCL is evident: the elimination of quadriceps produced a reduction of tension in the ACL and an increase in the PCL, while the elimination of hamstrings produced the increase of ACL tension and a decrease in the PCL. The NoGas condition produced an unclear situation in the ACL: positive and negative variations were almost equivalent, but a net prevalence of tension increase in the PCL was observed. Concerning the other ligaments, the NoQuad condition produced in both load acceptance and knee flexion phases a prevalence of tension reduction in all MCL and capsular ligaments. Interestingly, in the same NoQuad condition, the LCL tension increased in the load acceptance phase and decreased in the knee flexion phase. LCL tension decreased also in the NoHam condition and increased when the gastrocnemius muscles were removed. Other remarkable variations were the increase of MCL and anterior capsule tension when the hamstrings were removed, and the decrease of capsular ligaments tension with gastrocnemius muscles removal. No clear effect could be observed on the deep components of MCL, in which the negative and positive changes were almost equivalent in all muscle removal conditions. Figure 10: Changes of ligaments tensions obtained by removing separately the quadriceps group (NoQuad), the hamstrings group (NoHam) or the gastrocnemius group (NoGas). The black curve represents the reference condition in which all muscle forces were applied in the model.
# Spatial Data Analysis for Deformation Monitoring of Bridge Structures
## 1 Introduction  
Bridge structures are integral parts of the transport infrastructure in the Slovak Republic, and their number in recent decades has increased. The modernization of the transport infrastructure has caused an increase in traffic intensity, which is also reflected in the increased operating load of the bridges. This causes changes in the spatial position and the shape of the structures, which affect their static and dynamic functions and reliability. Due to these facts, taking geodetic measurements is an integral part of bridge structure diagnosis; the geometry of the structures can be measured, along with their static and dynamic responses.  
Because terrestrial laser scanning (TLS) is used by the wider community of surveyors and structural and civil engineers, it is used for different surveying jobs, e.g., [1; 2; 3; 4; 5; 6; 7]. The benefit of TLS over the more usual surveying methods is the three-dimensional (3D) data collection's efficiency. TLS enables non-contact measurement of the 3D coordinates of points lying on the surface of the scanned object. The state-of-the-art instruments are able to measure with a scan rate of up to 2 million p/s, which results in an expressive reduction of the time necessary for the measurements and an increases in the amount of information about the structures being measured. Using suitable spatial data analysis approaches, the resulting point cloud can be used for deformation analysis [8].  
At present, knowledge of a bridge structure's dynamics and interpretation of its complex behavior is also increasingly important. They are especially affected by weather conditions (wind, sunlight, rain) and road, railway, and pedestrian traffic. These affect the dynamic behavior of the monitored structure, which results in deformations. The dynamic behavior can be explained by the modal properties of the structure's deformation (by vibration modes). To ensure safe and smooth operation, it is also necessary to perform dynamic monitoring of these structures [9; 10; 11; 12]. The technique of radar interferometry is an innovative and rapidly evolving technique for the analysis of engineering structure deformation. The technology allows for the long-term deformation monitoring (dynamic and static) of structures with high precision at a level of up to 10 \(\upmu\)m. It can be applicable to the monitoring of large engineering investments (dams, mining facilities, etc.) and landslides. Dynamic deformation analyses can be used for the deformation measurements of high-rise buildings (or any other high-rise engineering structure such as skyscrapers, towers, etc.) and bridge [13; 14].  
This paper describes experimental deformation monitoring of the Liberty Bridge (Bratislava, Slovak Republic) performed using terrestrial laser scanning and ground-based (G-B) radar interferometry. The basic principle of the used techniques, uses the data analysis procedures, and the results of the deformation analysis are described in the following chapters.
## 3 Results and Discussions  
We first calculate Equations (1)-(10) using the fourth-order Runge-Kutta method, employing the parameters listed in Table 1. Since the PPLN cannot generate chaotic polarized light, we investigate the influence of the applied electric field \(E_{\rm A}\) and the optical injection amplitude \(E_{\rm inj}\) on the dynamics of \(\rm x_{S}\)-PC and \(\rm y_{S}\)-PC outputs from the S-VCSEL, illustrated in Figure 2a,b. The figures demonstrate that within the parameter space defined by \(E_{\rm A}\) and \(E_{\rm inj}\), \(\rm x_{S}\)-PC and \(\rm y_{S}\)-PC exhibit various typical dynamic states such as single-period oscillation (P1), two-period oscillation (P2), quasi-periodic oscillation (QP) and chaotic behavior (CO). Our focus here is specifically on the evolution of the chaotic state. From Figure 2a, it can be observed that \(\rm x_{S}\)-PC remains in a chaotic state within the regions corresponding to 0-42 \(\rm kV/mm\) (\(E_{\rm A}\)) and 0.1-10 (\(E_{\rm inj}\)). As the \(E_{\rm A}\) varies from 42 \(\rm kV/mm\) to 100 \(\rm kV/mm\), the chaotic state of \(\rm x_{S}\)-PC shows quasi-periodic variations with \(E_{\rm inj}\). Specifically, \(\rm x_{S}\)-PC exhibits chaotic states when \(E_{\rm inj}\) falls within the ranges such as 0.18-0.23, 0.89-0.93, 1.59-1.64, 2.3-2.35 and 3.01-3.06, with a period of approximately 0.7. Figure 2b indicates that \(\rm y_{S}\)-PC is in a chaotic state across most regions corresponding to 0-42 \(\rm kV/mm\) (\(E_{\rm A}\)) and 0.07-10 (\(E_{\rm inj}\)). As the \(E_{\rm A}\) varies from 42 \(\rm kV/mm\) to 58 \(\rm kV/mm\), the chaotic state of \(\rm y_{S}\)-PC also exhibits quasi-periodic variations with \(E_{\rm inj}\). Based on the theory of generalized chaos synchronization, when the S-VCSEL experiences intense optical injection from the PPLN, significant correlations may arise between \(\rm x_{P}\)-PC and \(\rm x_{S}\)-PC, as well as between \(\rm y_{P}\)-PC and \(\rm y_{S}\)-PC. Here, the correlation coefficient \(\rho_{\rm x,y}\) is introduced to quantify the magnitude of the correlation between these pairs:  
\[\rho_{\rm x,y}=\frac{\big{\langle}\big{|}I_{P_{\rm x},Py}(t-\tau)-\big{\langle} I_{P_{\rm x},Py}(t-\tau)\big{\rangle}\big{|}\big{[}I_{S_{\rm x},Sy}(t)-\big{\langle} I_{S_{\rm x},Sy}(t)\big{\rangle}\big{]}\big{\rangle}}{\Big{\{}\Big{\langle} \big{[}I_{P_{\rm x},Py}(t-\tau)-\big{\langle}I_{P_{\rm x},Py}(t-\tau)\big{\rangle} \big{]}^{2}\Big{\rangle}\Big{\rangle}\Big{\rangle}\Big{\}}^{1/2}} \tag{11}\]  
where \(I_{P_{\rm x},Py}(t-\tau)=\mid E_{P_{\rm x},Py}(t-\tau)\rangle\mid^{2}\), \(I_{S_{\rm x},Sy}(t)=\mid E_{S_{\rm x},Sy}(t)\mid^{2}\) and the symbol \(<\)\(>\) denotes the time average. The correlation coefficient \(\rho_{\rm x,y}\) ranges from 0 to 1, with higher values indicating a stronger correlation between the respective pairs.
# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 4 Performance and Experimental Results
### Analysis of the Trained YOLO Model Based on Real-Time Experiments
#### 4.2.4 Experiment 4: Lighting  
The objective of this experiment was to assess the resilience of the proposed model under varying lighting conditions. The model underwent testing under three different lighting conditions: clear lighting (A), dark lighting (B), and high-light-intensity conditions. In order to simulate different lighting conditions, the vehicle was operated in shaded areas to represent low-lighting conditions, in areas with direct sunlight to simulate high light intensity, and in areas with moderate sunlight to represent good lighting conditions for approaching vehicle occupants. No detections were observed under conditions of high light intensity, which can be attributed to the distortion of safety belt features caused by excessive sunlight exposure on the camera. Figure 17 illustrates the detection of safety belts under different lighting conditions, specifically in clear lighting (A) and dark lighting (B).
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 7 Conclusions  
This paper has proposed an efficient imaging method that can finely focus static scenes and coarsely focus the moving targets of no Doppler ambiguity at the same time. Based on the fact that, in the 2-D frequency domain, the couplings between the range and azimuth of static targets and the moving targets without Doppler ambiguity are approximately identical, the proposed method efficiently corrects the RCMs of these targets via a phase multiplication simultaneously. In addition, moving targets' characteristics, including the range and azimuth displacements and ATI phases, in the SAR image focused by the proposed method have been analyzed. Furthermore, the dependence of these characteristics on the parameters of moving targets have been revealed. Experiments have been conducted to verify the proposed imaging method and theoretical analyses.  
After moving targets have been efficiently and coarsely focused by the proposed method, their SCNRs will be improved. Therefore, the proposed method is very suitable for MEO SAR, which suffers from limited energy and computation resources due to the high orbital altitude. Moreover, the analysis of the target's displacements and ATI phase in the SAR image will benefit the design of the GMTI method for MEO multichannel SAR.  
[PERSON] wrote the manuscript, conducted the theoretical analyses, and verify the proposed method. [PERSON] conducted the theoretical derivations and experiments, and wrote the manuscript. [PERSON] proofed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This paper was funded by the National Natural Science Foundation of China under Grant 62271398, and by the Postdoctoral Science Foundation of China under Grants BX20180261 and 2019M653739.  
The authors declare no conflict of interest.
## 5 NB-IoT Cell Search
### NSSS Detection and Cell ID Identification  
According to the discussion in Section 1, the non-coherent partial correlation may be a preferable solution for NB-IoT NSSS detection. However, the 504 cell IDs are differentiated by four complementary sequences, and this means that any partial correlation applied to the NSSS will increase the probability of error detection. What is worse is that NB-IoT actually needs to deal with 2016 different candidate sequences because NSSS is constructed by both cell IDs and frame numbers. To identify the transmitted NSSS sequence, the number of matched filters (MF) should theoretically be equal to the candidate sequences. By using the complementary property, [PERSON] and [PERSON] reduce the scale of the MF bank to one-quarter of direct implementation [8]. However, the power and hardware resources occupied by a massive amount of complex multipliers are still intolerable for an NB-IoT terminal. In our design, we combine the four complementary sequences and frame number terms of Equation (2) into reconstructed complementary sequences, and then the frame number and cell ID are obtained by maximum likelihood estimation.  
We define the frame number term \(e^{-2\pi\theta/k}\) as \(T_{r_{f}}\). By inspecting Table 2, it can be observed that \(T_{r_{f}}\) is a cyclic variable of \(r_{f}\) and \(k\). Figure 6 gives the detailed illustration.  
Because the four cyclic sequences represented by Figure 6a-d contain only real units and imaginary units, we combine the four cyclic sequences and complementary sequences to form 16 new complementary sequences which only contain \(\pm 1\) and \(\pm i\). In this way, three-quarters of multiplications used by [8] are saved. On the other hand, as NSSS only locates in even radio frames and the frame number is still unknown, two detection statistics should be calculated simultaneously. The detection window of each statistic is \((2\text{W}-1)\) radio frames, so the entire detection window is 2W.  
\[B_{(1)}(u) =\sum_{w=1}^{W}\sum_{m=3}^{M}\sum_{k=1}^{12}\widetilde{R}_{9_{f} +2(w-1)}^{(m)}(k)\widetilde{b}(m,k,u), \tag{33}\] \[B_{(2)}(u) =\sum_{w=1}^{W}\sum_{m=3}^{M}\sum_{k=1}^{12}\widetilde{R}_{9_{f} +2w-1}^{(m)}(k)\widetilde{b}(m,k,u), \tag{34}\]  
where \(\widetilde{R}\) is the results after applying the 16 complementary sequences as code cover of the received signal \(R\). It is important to note that during the cross correlation process, different complementary sequences are used with the variation of radio frame number. \(\widetilde{b}(m,k,u)\) represents the part of \(b(n)\)  
Figure 6: Diagram of generation of the four complementary sequences based on frame number.  
without reconstructed complementary sequences, and \(n^{\prime}=k+12(m-3)-1\). Then, the cell ID and frame number can be jointly estimated by  
\[\left\{\hat{N}_{ID}^{Ncell},\hat{p}_{f}\right\}=\operatorname*{arg\,max}_{u} \left\{\left|B_{(1)}(u)\right|,\left|B_{(2)}(u)\right|\right\}. \tag{35}\]  
The number of multiplications of this method is 16.6K, which is one-quarter of [8] and one-sixteenth of [33].
# Federated Learning for Secure In-Vehicle Communication
## 4 Experimental Setup
### Data Splitting  
In our federated learning setup, we utilize three distinct datasets in each training round. These datasets correspond to different vehicle manufacturers: Dataset 1 (Hyundai), Dataset 2 (Chevrolet), and Dataset 3 (Kia). The Hyundai dataset size is 41 MB, and the Chevrolet is 27.9 MB and the Kia is 17.8 MB, which indicates variations in data quantities for different vehicles. We employ a total of 40 federated learning rounds. To ensure balanced participation of all datasets throughout training, we divide each dataset into 40 equal subsets. In each round, we use 1/40 th of each dataset, ensuring a fair and consistent distribution of data across rounds.  
For each round, the extracted data subset is split into training and testing sets. We follow an 80-20% split, where 80% of the data is used for model training and 20% is reserved for evaluation. Initially, the datasets exhibit significant class imbalances, as shown in Table 3. To address this, we apply the SMOTE to balance class distributions before training. Table 4 presents the new class distributions after applying SMOTE.  
Since each dataset is split into 40 subsets, and we apply an 80-20% training-testing split in each round, the number of samples used per round is shown in Table 5.  
By implementing this data partitioning, we ensure fair training conditions across federated learning rounds while mitigating the impact of class imbalance through oversampling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Normal** & **Flooding** & **Fuzzy** & **Malfunction** \\ \hline Hyundai & 189,297 & 189,297 & 189,297 & 189,297 \\ Chevrolet & 123,987 & 123,987 & 123,987 & 123,987 \\ Kia & 267,616 & 267,616 & 267,616 & 267,616 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Class Distribution After Oversampling (SMOTE).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Normal** & **Flooding** & **Fuzzy** & **Malfunction** \\ \hline Hyundai & 236,607 & 17,093 & 9095 & 8202 \\ Chevrolet & 154,960 & 14,999 & 3043 & 3995 \\ Kia & 334,542 & 16,072 & 21,613 & 4770 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Class Distribution Before Oversampling.
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 3 Preliminaries
### Elliptic Curve Cryptosystem  
The Elliptic Curve Cryptosystem (ECC) is a public key cryptosystem. Let \(p\) and \(q\) be two large prime numbers. An elliptic curve \(E_{p}(a,b)\) is defined over the finite field \(F_{p}\) by the equation \(y^{2}=x^{3}+ax+b\), where \(4a^{3}+27b^{2}\
eq 0,(a,b)\in F_{p}\). Let \(\mathbb{G}\) denote an additive cyclic group of order \(q\), and \(P\) be a generator of \(\mathbb{G}\).  
Elliptic curve discrete logarithm problem (ECDLP): Given two points, \(P\in\mathbb{G}\) and \(k\cdot P\in\mathbb{G}\), the advantages of calculating \(k\in Z_{p}^{*}\) in probabilistic polynomial time are negligible.  
Elliptic curve computing Diffle-Hellman problem (ECCDHP): For a given \(P_{1}=a\cdot G\), \(P_{2}=b\cdot G\), it is extremely difficult to calculate \(k=ab\cdot G\).  
Elliptic curve determination Diffle-Hellman problem (ECDDH): For a given four points \(P,A=a\cdot P,B=b\cdot P,C=c\cdot P(a,b,c\in Z_{q}^{*})\) on \(\mathbb{G}\), it is difficult to determine \(C=ab\cdot P\).  
The latter two problems are considered mathematically insurmountable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(ID_{V_{i}}\) & The identity of \(V_{i}\) \\ \(ID_{RSU_{j}}\) & The identity of \(RSU_{j}\) \\ \(PID_{i}\) & Temporary pseudonym of \(V_{i}\) \\ \(s,P_{pub}\) & Master secret key and public key of \(TA\) \\ \(d_{V_{i}},P_{V_{i}}\) & Secret key and public key of \(V_{j}\) \\ \(d_{RSU_{j}},P_{RSU_{j}}\) & Secret key and public key of \(RSU_{j}\) \\ \(\mathbb{G}\) & A cyclic additive group of an order \(q\) \\ \(P\) & A generator of \(\mathbb{G}\) \\ \(p,q\) & Prime numbers of \(Z_{p}^{*}\) \\ \(T_{i}\) & Timestamp \(i\in[1,4]\) \\ \(h(\cdot)\) & A one-way hash algorithm: \\ \(\{0,1\}^{*}\rightarrow\{0,1\}^{l}\) \\ \(l\) & The output length of \(h(\cdot)\) \\ \(||\) & Concatenation operation \\ \(ID_{V_{i}}\) & Bitwise XOR operation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations.