esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Graph-Enhanced Structure for Event Fusion and Clustering
#### 3.4.2 DBN-Enhanced DBSCAN for Event Clustering  
Before clustering events, we first merge the same events, which are determined based on the event correlation \(\varphi_{e}\). We assume that when the correlation of two events is greater than a fixed threshold, i.e., 0.88, the two events describe the same life story. For the same events, we merge them to form a new event node \(E\) and represent its feature as \(E=(Em,Et,Ec)\), where \(Em\), \(Et\), and \(Ec\) represent the merged event element feature, event text feature, and event environmental feature, respectively.  
For the event element feature merging, we apply the majority voting rule to determine the final element value for each type of element. If two element values have the same appearance number, we choose the value with finer granularity as the final element value. Therefore, we can obtain the merged event element feature vector as  
\[Em=\left(\overline{m}_{ei},\overline{m}_{tm},\overline{m}_{loc},\overline{m}_ {ag},\overline{m}_{pa}\right) \tag{12}\]  
For event text feature merging, we find the event with the greatest number of elements appearing in \(Em\) to be the most reliable event. We then apply BERT to transfer this event text to the event text feature. For event environmental feature merging, we directly utilize the environmental feature of the most reliable event determined before as the merged feature.  
Most clustering algorithms have a weak ability to handle high-dimensional data [39; 40]. Moreover, graph-enhancement-based event feature optimization further increases the event feature dimension. This poses a challenge to the task of clustering related events. Therefore, after the merged event feature vector \(E=(Em,Et,Ec)\), we integrate the deep belief network (DBN) [41] and DBSCAN [42] to build a deep clustering model, D2E-EC. Figure 7 illustrates the framework. The feature vector is initially fed into the DBN to reduce its dimensionality [43]. Subsequently, the diminished feature vector is employed as input for the DBSCAN clustering algorithm.  
Figure 6: An example of the event map. Each node denotes an event, and they are connected according to \(\varphi_{e}(e_{i},e_{j})\). For simplification, we just show the edges connected with \(e_{i}\). In addition, \(e_{2}\), \(e_{3}\), \(e_{4}\), \(e_{5}\), \(e_{6}\) are the five most highly related events with \(e_{1}\); therefore, they are fused as the environmental feature of \(e_{1}\) to obtain the extended feature \(\overline{e}_{1}\).
## 4 Preprocessing of the Measured Data
### WT Analysis of the Measured Signal  
Wavelet transform (WT) is another time-frequency analysis tool. Since the width of the window function--which remains constant throughout the transformation in STFT--constantly changes in the WT, the resolution of both time and frequency domains increases [70]. The mother wavelet function is both scaled and shifted during the transformation process. Mathematically, the continuous wavelet transform (CWT) is expressed by the following equation:  
\[CWT_{f}(a,b)=\int_{-\infty}^{\infty}x(t)\frac{1}{\sqrt{a}}\psi\bigg{(}\frac{t- b}{a}\bigg{)}dt, \tag{2}\]  
where \(\psi\) symbolizes the wavelet function, \(a\) represents the scale parameter that controls the width of the wavelet, and \(b\) represents the position parameter that determines the position of the wavelet on the signal.  
Figure 6 presents CWT images of the signals that were analyzed by STFT in the previous subsection.  
STFT employs a predefined window, the properties of which remain constant throughout the analysis, resulting in a fixed time-frequency resolution. This characteristic allows STFT to consistently capture changes in respiratory signals that typically occur within specific frequency bands. In contrast, the wavelet transform provides variable resolution, which, in some cases, may lead to information loss or inconsistencies in feature extraction. Additionally, the wavelet transform applies longer time windows for low-frequency components and shorter time windows for high-frequency components. While this adaptability can be beneficial, it may also obscure certain critical respiratory features, making them less distinguishable.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 3 Methodology
### Performance Evaluation  
The models were evaluated based on three standard metrics:  
Mean Squared Error (MSE): Measures the average squared difference between predicted and actual yields.  
Mean Absolute Error (MAE): Captures average prediction error magnitude without emphasizing large deviations.  
R-squared (R\({}^{2}\)): Indicates the proportion of variance in the target variable explained by the model.  
These metrics were computed using the test set to assess each model's out-of-sample forecasting ability [8].
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 6 Performance Analysis  
This section addresses a performance analysis of our protocol and a comparison with those developed by [PERSON] et al. [7] and [PERSON] et al. [6]. The analysis evaluated computational, communication and energy costs and was based on the data available in [6] and [7], in parallel with their own cost evaluation. The registration phase of the protocol was not included in the analysis because it is performed over a secure channel and the focus of the comparisons was on operations executed and parameters exchanged over an insecure channel. It is considered that \"n\" is the number of devices executing mutual authentication with the cloud server using a traditional 3 GPP network. It is also considered that \"m\" is the number of devices using D2D communication to perform mutual authentication with the cloud server.
## 5 Applications of Activated Carbon from Waste Tyres
### Energy Storage Devices  
Energy storage devices such as lithium-ion batteries, microbial fuel cells, and supercapacitors can benefit from the AC made from the waste tyres through pyrolysis and steam activation. It is well known that AC with high surface area and high porosity is the most common electrode material used in supercapacitors. [PERSON] et al. examined that AC displayed good electrochemical properties with a specific capacitance of about 200 F g\({}^{-1}\), long cycling life, low equivalent series resistance, and relaxation time which indicate that the AC is suitable for supercapacitors. Activation time and temperature (4 h and 800 \({}^{\circ}\)C respectively) have a significant role in achieving excellent electrochemical properties [67]. The impact of KOH activation and iron oxide doping on the physical, chemical, and electrochemical properties of waste tyre-derived carbon was investigated by [PERSON] et al., [68]. The activation was carried out at 900 \({}^{\circ}\)C for 3 h, followed by the incorporation of iron oxide. This iron-incorporated AC as electrode material recorded the highest electrochemical performance with a capacitance of 1400 F/g at 1 A/g. In microbial fuel cells, AC produced from waste tyres is used as anode materials. In the study by [PERSON] et al., carbonization that took place at 800 \({}^{\circ}\)C displayed a current density of 23.1 \(\pm\) 1.4 Am\({}^{-2}\) which is greater than that achieved with traditional graphite anodes (5.5 \(\pm\) 0.1 Am\({}^{-2}\)) [14]. Recently, waste tyre-derived AC was used as potential anode material for a Li-ion battery which exhibited a specific capacity of 350 mAhg\({}^{-1}\) with 80% capacity retention after 100 cycles [15]. In this study, AC was prepared through calcination in the CO\({}_{2}\) atmosphere. Hence, waste tyre-derived AC exhibited good electrochemical properties, making them cost-effective and eco-friendly electrode materials.
# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 3 Digital Twin-Based Security Assessment for CPPS
### Vulnerability Virtual Patch and Risk Analysis  
To further refine our vulnerability assessment, we gather detailed system configuration and component information, enabling us to query a localized vulnerability database introduced in our previous work [57]. Specifically, we integrated cybersecurity data from diverse open-source repositories, such as \(NVD\) and _Shodan_ into a localized database using _MongoDB_. This integration process also includes the correlation of vulnerability instances to standard enumerations and categorizations such as _CWE_ and _CAPEC_.  
The vulnerabilities are documented up to the investigation date and are analyzed with the average severity scores associated with each component calculated to reflect the vulnerable levels. The idea of a vulnerability score calculation considering different severity scales is inspired by [58]. We calculate the average score of vulnerabilities under different severity scales according to the Common Vulnerability Scoring System (CVSS) [59], including _none_ ([0]), _low_ ([0.1-3.9]), _medium_ ([4.0-6.9]), _high_ ([7.0-8.9]), and _critical_ ([9.0-10.0]).  
We define the component risk score as \(CRS\), considering multiple contributing factors, including \(CCS\), and the weighted average score of vulnerabilities across different severity scales for each component, such as \(CVS\), as shown in Equations (2) and (3).  
\[CRS=CCS\times CVS \tag{2}\]\[CVS=\left(\sum_{i\in\{C,H,M,L\}}w_{i}\cdot\text{Sum}(S_{i})\right)\cdot\frac{1}{ \sum_{i\in\{C,H,M,L\}}N_{i}} \tag{3}\]  
where  
* \(w_{i}\): Weighing factor for each severity level \(i\).
* \(\text{Sum}(S_{i})\): Sum of vulnerability scores across different severity scales \(i\).
* \(N_{i}\): The number of vulnerabilities under each scale.  
Using \(CRS\), we define the patch prioritization rule in Definition 2.  
**Definition 2** (Patch Prioritization Rule).: _Let \(C_{i}\) denote a component with a set of vulnerability instances, \(V\), existing within it. For each vulnerability instance, \(v_{j}\in V\), the application of a patch influences the component risk score, \(CRS_{j}\). The prioritization of patches is determined by ranking the component risk scores, \(CRS_{j}\), in ascending order, from the lowest to the highest._  
Our DT-centered framework also includes pre-defined rules such as the cascading failures rule in Definition 3 to support the attack simulation. Such capability will enable us to further integrate attack simulation-based virtual patching in future works.  
**Definition 3** (Propagation Rule for Cascading Failure).: _If there exists a failure or a component, \(C_{i}\), where \(C_{j}\) is functionally dependent on (\(FD_{(i,j)}=1\)), then the failure is likely to propagate to \(C_{j}\) with a probability, \(p_{ij}\). The propagation probability, \(p_{ij}\), is influenced by system configurations, network structures, and security compliance measures._
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.4 Ablation Study  
To verify the effectiveness of each component of the proposed algorithm in improving the performance of iris segmentation, four different networks were designed for the ablation study. The baseline network was the network structure without a parallel dual attention mechanism. Two different networks were designed by adding the position attention module and the channel attention module, respectively, to the baseline network. The evaluation indexes of the four networks were compared on four heterogeneous iris datasets, and the results are shown in Table 7. Figure 12 shows the test results of the baseline network and the PFSegIris on the four datasets.  
Conclusions can be obtained as follows after the analysis of Table 7 and Figure 12:  
1. After adding the attention mechanism to the baseline network, the average time to predict heterogeneous iris images was greatly reduced.
2. With the attention mechanism, the model had higher accuracy than baseline segmentation, and both mIoU and F1-score were improved. The channel attention module had a stronger boosting performance than the position attention module.
3. It can be seen from the subjective vision that the baseline did not work well for iris segmentation with more noises and small targets, while our algorithm performed well and accurately segmented small iris regions on heterogeneous iris images, avoiding the interference of noise such as lighting. Thus, this experiment illustrates the effectiveness of the parallel dual attention mechanism combination to improve the segmentation precision of multi-source heterogeneous iris images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Dataset** & **mIoU/\%** & **F1-Score/\%** & **Average Time/s** \\ \hline \multirow{4}{*}{Baseline} & JLU-6.0 & 96.42 & 97.78 & 0.24 \\  & CASIA-Lamp & 96.27 & 96.83 & 0.21 \\  & UBIRIS.V2 & 95.80 & 96.47 & 0.19 \\  & MICHE-I & 95.41 & 96.39 & 0.11 \\ \hline \multirow{4}{*}{Baseline + PAM} & JLU-6.0 & 96.85 & 98.03 & 0.16 \\  & CASIA-Lamp & 96.63 & 97.14 & 0.14 \\  & UBIRIS.V2 & 96.29 & 96.90 & 0.12 \\  & MICHE-I & 95.85 & 96.72 & 0.08 \\ \hline \multirow{4}{*}{Baseline + CAM} & JLU-6.0 & 97.03 & 98.32 & 0.14 \\  & CASIA-Lamp & 96.85 & 97.55 & 0.13 \\ \cline{1-1}  & UBIRIS.V2 & 96.41 & 97.01 & 0.11 \\ \cline{1-1}  & MICHE-I & 96.03 & 96.95 & 0.07 \\ \hline \multirow{4}{*}{**PFSegIris**} & JLU-6.0 & **97.38** & **98.68** & **0.12** \\  & CASIA-Lamp & **97.15** & **97.91** & **0.10** \\ \cline{1-1}  & UBIRIS.V2 & **96.69** & **97.29** & **0.09** \\ \cline{1-1}  & MICHE-I & **96.24** & **97.13** & **0.06** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The evaluation indexes of different networks on four iris datasets.
## 2 Color Analysis
### Region Based Retrieval  
The simple approach of adding XY to feature vectors is encouraging. However, the introduction of XY into the VQ training is somewhat ad-hoc, and does introduce some added complexity (although still less complexity than the GMVQ approach to which we have made comparisons). We have performed a second set of experiments to implicitly incorporate positional information. The system computes similarity between corresponding image regions, where separate VQ codebooks trained on color feature vectors are associated with each region. To compare a query image with a database image, the total similarity score is the sum of the scores for each region, where the score for a region is  
Figure 6: Color _vs._ color and position.  
the MSE when the query image features for that region are encoded using a database VQ codebook for that region. The size of each region's codebook is varied by query and region by setting the _query threshold_. This parameter will determine how many codewords to use when encoding a query. Since the database codebook size needs to be varied based on the query image statistics, we use tree-structured codebooks (TSVQ) for the database images. After partitioning the image into regions, we train relatively large TSVQ codebooks by successively splitting nodes with the largest distortion until we arrive at the desired number of leaf codewords. That is, before codebooks of a given database image are used to encode the regions of the query image (to test similarity with that database image), each codebook is pruned to the same size as the codebook for that region associated with the query image. Figure 7 shows two images from the database and the number of codebook entries for each region using a threshold of 1500.  
Experiments with threshold value have shown that best performance is achieved when average codebook size is 5.41 (Figure 8), but nearly identical (within 3%) and still better performance to previous methods can be achieved with average codebook size of only 1.08, allowing for significant increase in speed over our second phased system (and an even bigger improvement over other systems  
Figure 8: Region VQ compared to previous methods.  
Figure 7: Sample region codebook sizes.  
based on more complex methods like GMVQ). As an example, consider the number of multiplications for an elementary step that searches for the best codeword in a codebook. A color-position codebook with 8 colors and 8 positions per color, uses \(8\times(6+2\times 8)=176\) multiplications. The best performing region VQ (average codebook size 5.41) uses 5.41 \(\times\) 6 = 32.46 multiplications, and using region VQ with an average codebook size of 1.08 reduces the number of multiplications to only 1.08 \(\times\) 6 = 6.48 (while still within 3% of the best performance). In contrast, MDIR of Jeong and [PERSON] [4] with 8 components per Gaussian mixture and full covariances uses a matrix multiplication and an inner product for each of the 8 Gaussians for a total of \(8\times(8\times 8+8)=576\) multiplications, nearly two orders of magnitude greater complexity.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 5 Fibers with Anti-Resonant Guidance
### Origins of Fiber Loss
* SSL, which imposes a low limit of fiber loss at short and medium wavelengths, up to 1800-1900 nm, where the associated loss can fall down to 0.1-0.2 dB/km [26] in optimized fibers, but is much higher, \(\approx\)2 dB/km in current commercial HC-PBGFs. * Bulk absorption in fused silica, multiplied by the optical overlap coefficient \(\eta\). Due to low \(\eta\), bulk absorption is important only at wavelengths above 1800 nm [26]. In a state-of-the-art NANF/DNANF, the largest components of attenuation are:
* CL, with minima at anti-resonant wavelengths and rising closer to resonant wavelengths [48; 52]. This splits the low-loss spectrum into multiple \"windows\" (Figure 9). CL can be very low, down to 10\({}^{-5}\) dB/km [48; 50; 52], but other loss components are larger. * SSL, \(\approx\)0.03-0.05 dB/km at wavelengths in the 1500-1800 nm range [61], less than in HC-PBGFs due to lower power density at surfaces of cladding tubes. * Absorption by contaminants of filler gas (Section 6.3). * Microbending loss. * Loss resulting from structural imperfections of cladding like variations in capillary diameters (Section 9) or their excessive fusion with fiber jacket. Loss due to bulk absorption in fused silica, multiplied by \(\eta\), is below 0.01 dB/km in typical NANF/DNANF at wavelengths up to 2200 nm of interest in telecom networks [61]. Microbending loss in a typical NANF:
* Falls with an increase in wavelength [38; 61]--in SMF, the situation is opposite;
* Rises with an increase in core diameter and MFD--as in SMF. It is the rising microbending loss that imposes a practical limit on the NANF/DNANF core diameter, approx. 40 \(\upmu\)m. The minimum predicted total attenuation of optimized and flawless DNANF of current design is around 0.055 dB/km at 1550 nm [61].
## 3 Procedure for Omnidirectionality Analysis
### Bidirectionality Error  
The bidirectionality error is computed from the execution of a specific motion command. It is defined as the difference between the motion command applied to the mobile platform and the motion estimated from its kinematics. The bidirectionality error is computed in two steps. Firstly, the target motion command applied to the mobile platform \((v,\alpha,\omega)_{\{b\}}\), expressed as \(\big{(}v_{x},v_{y},\omega\big{)}_{\{b\}}\), is used to compute the angular velocities of the wheels using inverse kinematics (10). Secondly, the angular velocities of the wheels obtained in the first step are used to compute the motion of the mobile platform \((v^{\prime}_{x},v^{\prime}_{y},\omega^{\prime})_{\{b\}}\) using direct kinematics (11). Thirdly, the computed motion of the mobile platform, expressed as \((v^{\prime},\alpha^{\prime},\omega^{\prime})_{\{b\}}\), is compared with the target motion command to obtain the bidirectionality errors \((ev,ex,e\omega)\) using  
\[ev = |v-v^{\prime}|, \tag{12}\] \[e\alpha = |\alpha-\alpha^{\prime}|,\] (13) \[e\omega = |\omega-\omega^{\prime}|. \tag{14}\]  
These errors are zero or numerical noise if the computed motion is equal to the original target motion command applied to the mobile platform, which means that the bidirectionality compliance is satisfied for this motion command.  
Figure 4 shows the flowchart of the procedure used to estimate the bidirectionality error associated with one specific target motion command for a mobile platform with three omni wheels. The inputs of the procedure are the platform model (see Figure 2, Table 1), and a target motion command expressed in the platform base \((v,\alpha,\omega)_{\{b\}}\). The outputs of the process are the bidirectionality error for each variable of the motion command, \(ev,e\alpha,e\omega\). For the sake of comparison, this procedure is executed in 0.522 ms on a testbench computer.
## 3 Results and Discussion
### FTIR Measurements  
FTIR was used in order to explore the coordination of the organic material to the ZVI in ZVI-PHB complexes (Figure 2). FTIR analysis of the uncoated ZVI displays a wide infrared undefined spectrum between 750 and 1800 cm\({}^{-1}\). The PHB displays a peak at 1720 cm\({}^{-1}\) that can be assigned to the carbonyl group in the polymer. FTIR spectra of the ZVI-PHB complexes present the same peaks found in the PHB spectrum, yet with a smaller intensity of the peaks. [PERSON] and [PERSON] [33] reported similar observations, in which they evidenced the disappearance of the -COOH and -OH stretch (close to 3030 cm\({}^{-1}\)), and C=O strong stretch modes (around 1720 cm\({}^{-1}\)) after mercaptoacetic acid coating of Ag surfaces. In particular, in our data, the deficiency of a band representing carbonyl (close to 1720 cm\({}^{-1}\)) group indicates that a monolayer of PHB is adsorbed chiefly via carbonyl group on the metallic Fe. Therefore, FTIR analysis clearly indicated the efficiency of the procedure employed for the preparation of ZVI-PHB composites.  
Figure 1: Scanning electron microscopy (SEM) micrographs of: (**A**) Sieved zero-valent iron (ZVI) sample; (**B**) Polyhydroxybutyrate (PHB)-ZVI complex with PHB/ZVI ratio 2:1 (\(w\)/\(w\)); (**C**) PHB-ZVI complex with PHB/ZVI ratio 1:1 (\(w\)/\(w\)); (**D**) PHB-ZVI complex with PHB/ZVI ratio 1:2 (\(w\)/\(w\)). Scale bars represent 200 nm.  
Figure 2: Fourier transform infrared spectroscopy (FTIR) measurements of bare ZVI, thermally treated PHB, and ZVI–PHB composite (ZVI/PHB ratio 1:2 \(w\)/\(w\)).