esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
## 3 European Electronic Authentication Regulation
### Weaknesses and Limits of the eID Scheme  
The use of biometric data in the context of eIDAS implies strict compliance with data protection regulations, including the General Data Protection Regulation (GDPR). The challenge lies in the fact that eIDAS acknowledges the presence of biometric data but does not impose mandatory requirements. Instead, it provides a legal framework for use, leaving the decision to use biometric data in eID services up to EU Member States and the organizations implementing these services. Consequently, the misuse of digital identities persists because biometric data in eIDs are not uniformly used, and there is no guarantee that the person accessing a digital identity is indeed its rightful owner.  
Although almost all countries use at least one eID scheme with a high level of security, it is not comprehensive enough to defend the user's identity. This is also confirmed by the analysis of levels of security of the different eID schemes in eIDAS provided by [23] leveraging the MuFASA tool by NIST. Concerning the use of biometrics, none of the eID schemes adopted today include biometric authentication, but some of them allow the use of biometric authenticators embedded in smartphones and provided by the operating system. This authentication modality allows bypassing the input of username and password, improving the usability of the application but also raising paramount security concerns. First, the biometric data registered in the device could belong to a different person than the cardholder, i.e., the keys can be associated with the biometric data of a different person who can use it to bypass the 1-factor authentication step. Furthermore, current mobile phones allow registering more than one biometric, which can be used to verify the identity of the user interchangeably; it is thus possible to register more fingerprints belonging to different fingers and to acquire facial information, even from different users. These biometric accesses are not associated with the specific user who generated them but with the mobile devices where they are stored. It is thus possible that two different persons can access the same device with two different biometric data (e.g., one with the fingerprint and the other with the face recognition). This is a realistic scenario for shared devices, for example with relatives, like wires with husbands, father/mother with son/daughter, etc.
## 2 Pattern-Matching Algorithms Review
### Probabilistic-Data-Structure-Based Technique  
The bloom filter (BF) [17] is a probabilistic data structure used to represent a set in the membership check. Improved BFs exist to support deletion, provide a better location, or reduce the cost of space [18; 19; 20]. The quotient filter (QF) [21] that supports deletion requires additional metadata to encode each entry. This requires 10-25% more space than a standard BF. Another probabilistic data structure is the cuckoo filter (CF) [22], in which elements can be dynamically added and removed in O(1) time. The biggest challenge that CF performance comes across is using three hash functions that cause extra computations. The quotient-based cuckoo filter (QCF) [23] performs fewer computations than the standard CF, using only two hash functions.  
In many DPI applications, probabilistic data structures have been used as a matching tool [23; 24; 25; 26; 27; 28]. While the studies numbered [24; 26] focus on increasing the system efficiency by reducing the storage space required, the study numbered [25] aims to reduce the overall power consumption of the BF. In another study numbered [27], the results obtained with the real dataset show that the QF achieves higher efficiency (30-75%) and improves the false-positive rate compared to the BF. The system developed in the study numbered [28], in which the CF is used as the DPI-matching tool, provides time savings of 93% compared to the BF and 87% compared to the QF. Another study numbered [23] tries to increase the CF performance with a new proposal called the QCF. Application results show that using the QCF as an identification tool in a DPI system results in time savings of up to 77% in the CF and up to 98% in the BF and QF.  
Figure 2: Pattern-matching-based DPI techniques.
## 2 Proposed Approach and Its Verification
### Mutual Similarity of Binary Images
Since no-reference quality metrics for binary images are not known and the binary image quality assessment is based on the comparison with ground-truth images, it is proposed to determine the mutual similarities assuming that one of the binary images is considered a pseudo-reference image. The average mutual similarity values obtained as the results of such comparisons with images obtained the other binarization methods are saved as the final metrics separately for each of the considered elementary metrics. The illustration of this idea is presented in the form of a simplified flowchart in Figure 5.  
The first stage of the experimental verification of the proposed idea has been related to the calculation of the correlation values for each of the above discussed metrics, applied for the computations of the aggregated mutual similarity and the Levenshtein Distances obtained for each of the 1760 images.
# Recent Progress in On-Chip Erbium-Based Light Sources
## 4 Discussion
However, the confinement factor in the gain medium is relatively low, which would reduce the gain of the waveguide amplifier. In addition, the coupler structure of the waveguide amplifier with the traditional Si waveguide needs to be considered and optimized. In addition, the optimal length of waveguide amplifier is associated with the Er3+ concentration and the maximum input on-chip pump power [42]. The signal gain would be attenuated if the pump power cannot achieve Er3+ population inversion over the entire length of the waveguide amplifier. Bi-directional pumping can also be considered to improve the net gain of the waveguide amplifier [76]. The resonant cavity structure of the on-chip waveguide laser should be improved. The pumping threshold of the waveguide amplifier can be reduced and the output power can be increased by adopting appropriate resonant cavity structures. At present, the microring resonator, DBR resonator and DFB resonator are the mainly used resonant cavity structures. However, most of the reported microring waveguide lasers operate under the condition \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Host Material**} & **Signal** & **Pump** & **Waveguide** & **Internal Net** & **Propagation** & \multirow{2}{*}{**Refs.**} \\  & **Wavelength** & **Wavelength** & **Length** & **Gain** & **Loss** & \\ \hline Al2O3 & 1533 nm & 977 nm & 5.4 cm & 2 dB/cm & 0.3 dB/cm & [44] \\ Al2O3 & 1533 nm & 1470 nm & 250 μm & 20.1 dB/cm & 0.62 dB/cm & [40] \\ Al2O3 & 1532 nm & 980 nm & 10 cm & 1.81 dB/cm & 0.64 dB/cm & [49] \\ Silicate & 1532 nm & 980 nm & 56.2 μm & 122 dB/cm & – & [39] \\ LN & 1532 nm & 980 nm & 10 cm & 2 dB/cm & 0.72 dB/cm & [74] \\ LN & 1531.6 nm & 1484 nm & 2.58 cm & 6.2 dB/cm & 2.5 dB/cm & [76] \\ Si3N4 & 1562 nm & 1480 nm & 0.5 m & 0.6 dB/cm & 0.05 dB/cm & [41] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison for representative on-chip Er-based waveguide amplifiers. of multi-longitudinal modes. Although some methods have been adopted, the side mode suppression effect is still not good enough. Furthermore, limited by the compact size of the microring waveguide laser, the output power is relatively low, which is hard to meet the requirements for on-chip integrated waveguide lasers. On the other hand, combined with Bragg gratings, the DBR and DFB resonant cavity structures provide a good technical solution for realizing on-chip small-sized, high-gain waveguide lasers. Compared to DBR waveguide lasers, DFB waveguide lasers have better single-mode stability so that they are more widely adopted. In addition, the resonant cavity structures of the DFB waveguide lasers can be future optimized to improve the output power and the linewidth characteristics [63; 64; 65].
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 4 Critical Analysis and Discussion
### Multi-Vector Approaches
Another concern for experimental application of DB schemes is the switching frequency. DB methods often require more frequent changes in the VSI configuration than single-vector approaches. For example, ref. [73] limits switching by selecting four VVs per sampling period from a lookup table that restricts the number of commutations, thereby reducing the Average Switching Frequency (AFS). Another aspect, related to the actual implementation, is that DB-FSMPC methods often rely on trigonometric functions to determine the VV combination. These functions can be computationally intensive and unreliable at times, and they may not be feasible on low-end hardware.
## 5 Experimental Results
### Formation Tracking Experiment Results  
In the experiment, two of spherical robots formed a line shape and tracked a desired circle path. The line formation shape was set as \(d_{12}=d_{21}=0.8\) m. The diameter of the circle path was 0.45 m. The sampling time was 0.05 s, which is consistent with that in the physical simulation. The formation desired reference point was obtained by uniformly sampling around the circumference at an angular velocity of \(0.02\pi/\)s with a sampling period of 0.02. The experiment results are illustrated in Figures 30-32. The black circular position in Figure 30 represents the initial position of the two robots, while the square box position represents the final position. '\(\ast\)' is the starting point of the reference trajectory for Robot 1, and '\(\diamond\)' is the starting point of the reference trajectory for Robot 2. The two solid lines in the figure represent the actual trajectory of the robot. It can be seen that two of the robots could basically form a linear formation and could track their respective reference trajectories. Figure 31 depicts the errors between the actual and desired positions. The maximum errors of Robot 1 in the \(x\) and \(y\) direction were 0.23 m and 0.28 m, respectively. The maximum errors of Robot 2 in the \(x\) and \(y\) direction were 0.21 m and 0.20 m, respectively. Figure 32 shows the formation error during the formation process. Initially, two of the robots were relatively close but they gradually converged to the expected distance. The oscillation occurred during the intermediate process, which was due to the influence of the experimental pool wall. The root mean square errors of the trajectory tracking and formation are listed in Table 6. The formation error was within 0.09 m (0.3 BL), which was 11% of the formation shape size and thus satisfied the formation error requirement.  
The above analysis proves that the VS-ESO-MPC is valid for multi-small spherical robot formation tracking.  
Figure 28: Two of the spherical robots used in the experiment.  
Figure 29: Experimental environment setup.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 3 Security Framework Overview
### Intent-Driven Security Closed-Loop Workflow  
Before delving into the PoC definition, it is essential to understand the relationship between the entities that compose the ZSM framework. This section presents the concept of a closed loop as a set of recurrent steps that interconnect entities in a protocol-less manner. The closed loop was also defined in our previous work [10], but with a high-level approach. Here, we present a more detailed view of the closed-loop phases, both proactive and reactive. These processes are illustrated in Figure 5 and Figure 6, respectively. While the proactive process focuses on initial SSLA enforcement, the reactive process ensures the slice remains operational and secure through continuous monitoring and adjustment. To maintain clarity, we provide a brief description of each process.  
The process begins upon SSLA reception or E2E-level MSPL-OP generation when a slice is identified as necessary. This proactive scenario involves the Plan and Execute steps of a MAPE-K (Monitor, Analyze, Plan, Execute, and Knowledge) framework. This proactive scenario is detailed in Figure 5, where steps 3, 8, and 15 involve MSPL-OP refinement, while steps 1 and 9 highlight conflict detection with existing services or infrastructure capabilities. From this point, the Monitor, Analyze, and Knowledge-related steps of the closed loop run continuously until the service's end of life. A vulnerability might be detected during the Analyze step of the closed loop, as shown in steps 1 and 2 in Figure 6.  
Figure 4: Intent-driven life-cycle security orchestration.  
If an available countermeasure is identified, a new MSPL-OP message is generated and sent to the SMD's Security Orchestrator. This orchestrator then checks the resources for availability and adequacy (e.g., in terms of trust), which may lead to a reconfiguration of the already deployed slice. This reconfiguration might involve the removal of a compromised asset whose trust has been reduced as a result of step 2.  
Figure 5: Proactive ZSM-aligned security slice orchestration solution.  
Figure 6: ZSM 5G component anomalous behaviour reaction diagram.  
Although not depicted in the figure, resource unavailability or other conflicts may arise, requiring additional countermeasures or reconfigurations. These steps are omitted for simplicity; only the expected positive path is depicted. Locally employed alerts and countermeasures are reported to the E2E level, which may trigger cascading effects on other SMDs. This possibility is shown starting from step 10, where a neighboring affected SMD reconfigures the slice as a result of the MSPL-OP received in step 12. In this step, any involved asset might be replaced with an alternative that has higher trust or a better score in relation to the newly detected threat, as described in steps 1 and 2.
## 3 Results
### Effects on Serum Osteocalcin and bALP Levels  
Significant (\(p\) < 0.01) increases in serum osteocalcin levels (144.59%) and significant (\(p\) < 0.01) decreases in serum bALP levels (64.80%) were observed in the OVX control mice against the sham control. Conversely, significant (\(p\) < 0.01) decreases in serum osteocalcin and non-significant increases in bALP levels were demonstrated in 200 mg/kg of PCP (\(-\)45.33% and 54.23%) and 200 mg/kg (\(-\)45.51% and 57.91%) and 100 mg/kg of WC (\(-\)36.18% and 33.01%) administered OVX mice against the OVX control (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{5}{c}{**Serum Biochemical Values**} \\ \cline{2-7}  & **AST (U/L)** & **ALT (U/L)** & **TC (mg/dL)** & **LDL (mg/dL)** & **HDL (mg/dL)** & **TG (mg/dL)** \\ \hline Sham & 75.80 \(\pm\) 1.54 & 34.00 \(\pm\) 10.12 & 75.30 \(\pm\) 20.69 & 62.60 \(\pm\) 11.93 & 108.20 \(\pm\) 14.98 & 36.90 \(\pm\) 14.99 \\ \hline OVX & 169.80 \(\pm\) 20.45 \({}^{\mathrm{c}}\) & 94.70 \(\pm\) 19.41 \({}^{\mathrm{a}}\) & 210.50 \(\pm\) 30.95 \({}^{\mathrm{a}}\) & 189.20 \(\pm\) 23.04 \({}^{\mathrm{a}}\) & 39.50 \(\pm\) 10.22 \({}^{\mathrm{a}}\) & 189.80 \(\pm\) 23.60 \({}^{\mathrm{c}}\) \\ \hline PCP 200 & 113.00 \(\pm\) 11.18 \({}^{\mathrm{cd}}\) & 60.20 \(\pm\) 12.64 \({}^{\mathrm{ab}}\) & 138.90 \(\pm\) 15.72 \({}^{\mathrm{ab}}\) & 123.20 \(\pm\) 23.99 \({}^{\mathrm{ab}}\) & 79.20 \(\pm\) 13.71 \({}^{\mathrm{ab}}\) & 127.10 \(\pm\) 17.75 \({}^{\mathrm{cd}}\) \\ \hline WC 200 & 111.00 \(\pm\) 11.52 \({}^{\mathrm{cd}}\) & 58.30 \(\pm\) 11.53 \({}^{\mathrm{ab}}\) & 138.10 \(\pm\) 17.28 \({}^{\mathrm{ab}}\) & 121.20 \(\pm\) 18.14 \({}^{\mathrm{ab}}\) & 81.70 \(\pm\) 12.98 \({}^{\mathrm{ab}}\) & 123.50 \(\pm\) 22.29 \({}^{\mathrm{cd}}\) \\ \hline WC 100 & 131.40 \(\pm\) 8.11 \({}^{\mathrm{cd}}\) & 64.00 \(\pm\) 10.19 \({}^{\mathrm{ab}}\) & 158.00 \(\pm\) 16.28 \({}^{\mathrm{ab}}\) & 144.60 \(\pm\) 9.14 \({}^{\mathrm{ab}}\) & 69.90 \(\pm\) 11.57 \({}^{\mathrm{ab}}\) & 140.90 \(\pm\) 10.64 \({}^{\mathrm{cd}}\) \\ \hline WC 50 & 159.70 \(\pm\) 15.87 \({}^{\mathrm{c}}\) & 87.20 \(\pm\) 14.95 \({}^{\mathrm{a}}\) & 199.50 \(\pm\) 22.88 \({}^{\mathrm{a}}\) & 178.50 \(\pm\) 25.59 \({}^{\mathrm{ab}}\) & 41.40 \(\pm\) 13.07 \({}^{\mathrm{a}}\) & 179.70 \(\pm\) 32.94 \({}^{\mathrm{c}}\) \\ \hline \hline \end{tabular} Values are expressed mean \(\pm\) SD of 10 mice. Statistical significance: \(p\) < 0.01 vs. sham control and \({}^{\mathrm{b}}\)\(p\) < 0.01 vs. OVX control by THSD test, and \({}^{\mathrm{c}}\)\(p\) < 0.01 vs. sham control and \({}^{\mathrm{d}}\)\(p\) < 0.01 vs. OVX control by DT3 test.  
\end{table}
Table 1: Effect of WC on AST, ALT, TC, LDL, HDL, and TG levels in OVX mice.  
Figure 3: Effect of WC on serum estradiol levels in OVX mice. Statistical significance: a \(p\) < 0.01 vs. sham control and b \(p\) < 0.01 vs. OVX control by DT3 test. Values are expressed mean \(\pm\) SD of 10 mice. Different letters indicate significantly different.
## 4 Experimental Validation of the Geometrical Efficiency Calculation
### Final Comparison Experiment vs. Modeling  
Figure 14 finally shows an overview of all considered experimental efficiencies and the calculated theoretical efficiencies. It can be concluded that the proposed geometrical analysis is indeed able to predict at least the tendencies in efficiency regarding the different cage armour systems and taking into account the impact angles in both the horizontal and the vertical plane. As already stated before, the models can be improved by fitting the parameters of the projectile (effective diameters of the projectile and the piezoelectric element) to the experimental data. In this way, accurate efficiency estimations can be done for a given cage armour system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Impact** & **Number** & **Short-Circuited** & **Experimental** & **Theoretical** \\  & **Angle (\({}^{\circ}\))** & **of Shots** & **Fuzes** & **Probability (\%)** & **Probability (\%)** \\ \hline Benchmark & 0 & 30 & 17 & 57 & N/A \\ G & 45 & 28 & 11 & 39 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results for the two considered polymer fibre-based net armour configurations and a comparison with the predicted theoretical efficiency for the 45\({}^{\circ}\) impact angle based on the 0\({}^{\circ}\) impact angle results.
## 3 Materials and Methods
### Modeling and Statistical Analysis  
As described in the Introduction section, the main hypothesis of this study was that an RRI-based deep learning model would offer similar or superior performance in age prediction compared with a conventional HRV feature-based model. To test our hypothesis, we developed three different types of models for age group prediction: an HRV feature-based model (Figure 4a), an RRI-based model (Figure 3), and a combined model that used both HRV and RRI data (Figure 4b). The HRV model used only HRV features as input data and consisted of three fully connected layers (number of neurons: 200, 100, and 50), where each layer consisted of batch normalization and a dropout layer. The RRI model used only RRIs as input data with the modified 1D-ResNet34 as described in Section 3.6. The combined model used both HRV and RRI as input data, but the two types of data were separately fed into the model. The RRIs were processed by the same modified ResNet34 until the average pool layer extracted the representation vectors from the RRIs. The processed RRIs were then concatenated with HRV features and fed into three fully connected layers (number of neurons: 1000).  
Figure 4: Overall architecture of (**a**) HRV model; (**b**) combined model.  
We used stratified 5-fold subject-wise cross-validation for the model evaluation. Age group was used as a stratification option. Four folds were used for training and the remaining fold was used for evaluation. Five models were developed and evaluated for each fold. We gathered all evaluation results and bootstrapped them to estimate the model's mean performance with confidence intervals.  
One-way analysis of variance (ANOVA) was used to investigate the effect of age on mean HRV features. In addition, we used an independent t-test for two samples to compare the evaluation metrics between the RRI and HRV models, the combined and RRI models, and the combined and HRV models.
## 2 Materials and Methods
### Quality Control and Quality Assurance  
A method blank and a matrix-spiked sample were analyzed with each batch of 10 samples. Method blanks contained the target analyte at a concentration of less than the limit of detection (LOD) for PAHs, or between the LOD and 0.08 ng/mL for OH-PAHs. The recovery rates for the spiked samples were 85%-110% (PAHs) and 81%-100% (OH-PAHs). The instrumental LODs for the analytes were 9.1-83 pg/mL (PAHs) and 0.05-1.00 ng/mL (OH-PAHs). PAHs with two to three rings were excluded as target analytes because they had high vapor pressures and were difficult to quantify. Because of the difficulty of peak separation and identification by LC/MS, the following appeared as overlapping peaks: 2 OH- and 3 OH-Phe, 1 OH- and 9 OH-Phe, 2 OH- and 3 OH-Chr, and 6 OH-Chr and 3 OH-BaA.
## 4 Analysis Results
### Cycle Life Prediction  
The cycle life prediction of the proposed model is evaluated by using an absolute percentage error (APE), which is given by  
\[\text{APE}(\%)=\frac{\left|\text{ACL}-\text{PCL}\right|}{\text{ACL}}*100 \tag{7}\]  
where ACL represents actual cycle life and PCL represents the predicted cycle life. The lowest APE value indicates that the model has better performance. Table 6 provides the performance of the different models by the cycle life of a single cell experiment. The actual cycle life values for seven cells are given in the second column of Table 6. In addition, Table 7 shows the estimated life cycle of six cells under one cell or two cells as training data with the actual cycle life for four test cells. For cells #2_26 and #2_27, The results show that by averaging the experimental results, the model has better performance than the single model.  
Figure 6: The prediction performance of the proposed model using one cell or two cells as training data for cell 2_27: (**a**) predicted capacity and (**b**) absolute prediction error.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Data**} & \multirow{2}{*}{**Test Data**} & \multicolumn{2}{c}{**GBR Model**} & \multicolumn{2}{c}{**LSTM with Attention**} & \multicolumn{2}{c}{**Proposed Model**} \\ \cline{3-8}  & & **MAPE** & **RMSE** & **MAPE** & **RMSE** & **MAPE** & **RMSE** \\ \hline
1 & 2 & 2.0698 & 0.0257 & 1.9664 & 0.0241 & 1.7802 & 0.0219 \\  & 3 & 2.0254 & 0.0254 & 1.9340 & 0.0239 & 1.7519 & 0.0217 \\ \hline
1 \& 2 & 3 & 0.1297 & 0.0022 & 0.0406 & 0.0017 & 0.0330 & 0.0019 \\ \hline
2\_25 & 2\_26 & 1.1099 & 0.0194 & 0.5025 & 0.0188 & 0.2467 & 0.0118 \\  & 2\_27 & 1.4351 & 0.0220 & 0.5599 & 0.0194 & 0.3058 & 0.0123 \\ \hline
2\_25 \& 2\_26 & 2\_27 & 0.8704 & 0.0113 & 0.4139 & 0.0144 & 0.2585 & 0.0128 \\ \hline  & Average & 1.2734 & 0.0177 & 0.9029 & 0.0171 & 0.7294 & 0.0137 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The prediction performance of different models using one cell or two cells as training data.