esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Applications
### Analysis of the Results  
As exposed previously, Figure 10 shows the results of the full-scale crowd pedestrian tests performed on the Pedro e Ines footbridge, together with the numerical values obtained by applying the proposed method and by following the Hivoss guideline [12]. It can be observed that in the prelock-in stage, a good agreement was achieved between the experimental data and the proposed method that, in addition, predicted very well the lock-in point (75 pedestrians). Furthermore, the proposed method permits the response after HSI develops to be estimated. In the postlock-in stage the analytical line follows and constitutes an upper bound of the experimental data.  
Although the Hivoss guideline represented adequately the prelock-in stage, particularly below 30 pedestrians, and [PERSON]'s formula estimated accurately the critical number of pedestrians (73 pedestrians), it was not possible to evaluate the footbridge response in the postlock-in stage, as the Hivoss load did not include the auto-induced component of the pedestrian action. Moreover, the guide criterion for checking the lock-in based on the acceleration (0.1-0.15 m/s\({}^{2}\)) gave a value of 167 pedestrians, which was far from the experimental result.  
Similarly, Figure 11 shows the results of the full-scale crowd pedestrian tests that were performed on the Lardal footbridge, together with the numerical values obtained by applying the proposed method and by following the Hivoss guideline [12]. It can be observed that in the prelock-in stage the proposed method estimated the footbridge response less accurately than the Hivoss guideline, particularly for low pedestrian traffic (less than five pedestrians). However, it accurately predicted the lock-in point (around 13 pedestrians), while the two criteria of the Hivoss guideline gave poor results (31 and four pedestrians).  
In addition, the proposed method predicted accurately the response in the postlock-in stage, as the analytical line was in good agreement with the experiments, while it was not possible to evaluate the footbridge response in this stage by using the Hivoss guideline.  
Hence, it can be concluded that the proposed method provided a more complete and accurate procedure for estimating the lateral acceleration response of both footbridges than the Hivoss guideline, as it predicted successfully the lock-in point and the footbridge response with HSI. However, the Hivoss guideline gave a better approach in the prelock-in stage with light traffic.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Footbridge** & \(\left|\mathbf{H(f_{b})}\right|\) **(mN\({}^{-1}\))** & \(F_{0}\)* **(N)** & \(a_{ma}\) **(m/s\({}^{2}\))** & \(N_{crit}\) **(Arup’s) (-)** & \(N_{crit}\) **(acc.) (-)** \\ \hline Pedro e Inés & \(1.59\times 10^{-5}\) & 11.20 & \(1.20\times 10^{-2}\) & 73 & 167 \\ Lardal & \(4.09\times 10^{-5}\) & 33.45 & \(7.40\times 10^{-2}\) & 31 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical response simulation. Results obtained by applying Hivoss guideline.
## 3 Method
### Human Evaluation  
The human evaluation was conducted by seventeen native Greek-speaking individuals (11 men, 6 women). Their demographics spanned various age groups (23.5% 18-24, 29.4% 25-34, 41.2% 35-44, 5.9% 45+). Regarding educational background, 52.9% were at the PhD level, 35.3% had postgraduate (Master's level) education, and 11.8% had an undergraduate degree. These individuals were selected for their extensive practical experience assisting TCNs with asylum processes, residency, and integration challenges, granting them deep domain expertise and familiarity with the specific linguistic needs and common issues faced by migrants. Choosing Greek speakers for this stage was essential, as their command of the language allowed for a detailed assessment of the LLMs' handling of grammar, syntax, and reading comprehension. Their expertise secured an accurate evaluation of the models to produce well-structured and meaningful responses, which is critical for ensuring that the chatbot works as expected before being used by migrants.  
This evaluation phase was conducted with domain experts rather than members of the target migrant population due to practical considerations. The chatbot is still undergoing iterative refinement, and expert users, such as professionals working in migrant support services, were better positioned to provide structured, actionable feedback. Their familiarity with typical user needs and interaction patterns enabled a more focused assessment of the system's functionality, linguistic clarity, and coverage of relevant topics. Engaging experts allowed for a more efficient and technically informed evaluation process, helping to identify and address early design issues before involving end users in subsequent testing rounds.  
Prior to the evaluation, participants attended a one-hour briefing session. This session outlined SALLY's objectives (delivering accurate, empathetic support in Greek) and provided detailed guidelines on interpreting and consistently applying the 1-5 Likert scale across the four criteria. Sample responses illustrating different quality levels (e.g., a score of 2 for a Reading Comprehension answer missing key details vs. a score of 5 for a fully accurate one) were reviewed to calibrate judgment.  
The evaluation itself spanned two weeks. Participants worked independently in a controlled setting, assessing the LLM-generated responses in a blind setup--they were unaware of which LLM produced each output they reviewed. For each response, they compared it against the original prompt and source text (where applicable) and assigned a score from 1 to 5 based on the predefined criteria: checking factual accuracy and completeness (Reading Comprehension), evaluating conciseness and preservation of key information (Summarization), verifying grammatical correctness and coherence (Grammar & Syntax), and judging the appropriateness and sensitivity of tone (Sentiment Analysis). This rigorous, expert-driven process ensured the ratings reflected not only technical accuracy but also practical utility and linguistic appropriateness within the migrant support context.
# Characterization of the Intelligibility of
## 1 Introduction
### Study Rationale and Specific Aims  
The aim of this study is to introduce a methodology for characterizing the intelligibility of a corpus of spoken VSVs in different languages in order to identify the most suitable subset of stimuli for application in listeners of an unknown language. The proposed approach is based on the integration of objective, computational estimates of VCV intelligibility derived from the Short-Time Objective Intelligibility (STOI) measure [33] and subjective, experimental estimates of VCV intelligibility derived from listening tests [21,22].  
The rationale is to estimate the psychometric curves of VCV recorded from speakers of different languages and compare them based on the following criteria, designed to maximize the ability of the speech material to compensate for the possible additional effort of non-native listeners:  
1. High average intelligibility, especially at low signal-to-noise ratios (SNR). This criterion is justified by the fact that the higher the theoretical intelligibility of a stimulus, the lower the effort associated with the recognition task, particularly in non-native listeners (e.g., [34]);
2. Low variability of intelligibility as a function of noise, especially at low SNR. This criterion is justified by the fact that the lower the variability of intelligibility with varying realizations of noise, the lower the aleatoric uncertainty introduced by noise on the measured speech recognition performance. Stated differently, if the variability of intelligibility is low, differences in measured speech recognition are more likely to reflect intra-individual differences in performance rather than random fluctuations of performance due to noise; and
3. Shallow slope of the psychometric function. This criterion is justified by the fact that the shallower the slope of the intelligibility functions, the wider the dynamic range of SNRs that can be used by the speech-in-noise test, therefore enabling higher flexibility to adapt the test to the individual performance of non-native listeners.
## 4 SME-SCCM: Security Culture Certificate Management
* Normalization: The development process is based on the use of reusable patterns. The use cases were created with patterns regarding the ISO27001. * Certification: The process developed permits the users to obtain a security points card, which enables them to access the system and provides an indication of the level they have. * Measurement: The system makes it possible to measure the company's level of security culture, at user, department and company levels. * Culture: The process developed takes multicultural aspects into account, which merely have to be added to the corresponding pattern. * Group 2. Characteristics oriented towards the users' human aspect. Figure 9: Managing the security culture certificates. Progressive adaptation: The process permits the users' security culture to be adapted in a progressive manner because it lets them know their real level of security culture at all times. This is achieved through the use of the points card. It is possible for them to take the test again to obtain higher marks. * Theoretical Approach: The process is based on the theoretical approach and appropriate regulations. In this case, the pattern used is based on the ISO27001 international standard. * Practical Approach: The process is oriented towards being applied by users in a practical manner because it ensures that they are continuously conscious of their limits and can compare themselves with their colleagues. * Critical Aspect: The human aspect has been considered critical when developing the process and is at its core. * Psychological Factors: The points card works as a psychological measure that pressurises the users to improve and to be careful not to incur sanctions, since these are also seen by their superiors. Various kinds of disciplinary action had been taken before the implementation of the points card, none of which worked. * Group 3. Other desirable aspects. * Oriented towards SMEs: Its validity for SMEs was borne in mind, and it has principally been validated at this type of companies. * Low cost resources: The implementation and maintenance costs are very low because of the simplicity of the process. More complex systems were tested but they did not work. * Core of the ISMS: SME-SCCM has not been considered to be the core of the MARISMA methodology, although it is a very important process. * Dynamic Knowledge Base: This process is able to learn from security incidents because the event module generates knowledge for the question base of the pattern selected. The tests are therefore generated in an intelligent manner, with priority being given to those questions that relate to greater vulnerabilities in the system. It is, therefore, possible to conclude that this activity allows users to obtain a security culture certificate in a simple manner, whilst making it easy for them to increase their knowledge of the ISMS and evaluating their level of knowledge of that ISMS. The security culture certificate is simultaneously updated in a dynamic manner via the application of sanctions for security violations, which allows its updating to take place dynamically and at no cost.
# Color CCD High-Temperature Measurement Method Based on Matrix Searching
## 2 Method
### CCD Temperature Measurement Theory
If the coordinate values in R and G space are divided into small enough intervals, there are the following:  
\[\Delta R=\frac{R_{\rm max}-R_{\rm min}}{p}\quad\Delta G=\frac{G_{\rm max}-G_{ \rm min}}{q} \tag{6}\]  
where \(p\) and \(q\) values determine the sensitivity of temperature measurement is called the sensitivity factor. According to the above relationship, the R channel signal value is used as the column vector index and the G channel signal value is used as the row vector index. According to the temperature mapping relationship with R and G space, the \(p\times q\) dimensional temperature mapping matrix can be established as follows:  
\[T_{map}= \begin{pmatrix}T_{11}&T_{11}&\cdots&T_{1j}&\cdots&T_{1q}\\ T_{21}&T_{22}&\cdots&T_{2j}&\cdots&T_{2q}\\ \vdots&\vdots&\cdots&\vdots&\cdots&\vdots\\ T_{i1}&T_{i2}&\cdots&T_{ij}&\cdots&T_{iq}\\ \vdots&\vdots&\cdots&\vdots&\cdots&\vdots\\ T_{p1}&T_{p2}&\cdots&T_{pj}&\cdots&T_{pq}\end{pmatrix}  \tag{7}\]  
Then the normalized R channel value and the normalized G channel value collected during the temperature measurement process can satisfy the following relations through the quotient operation of \(\Delta\)R\(\alpha\)G in the R-G spatial coordinate interval. \[ \begin{cases}R\in(R_{min}+(i-1)\Delta R,R_{min}+i\Delta R)\\ G\in(G_{min}+(j-1)\Delta G,G_{min}+\mathrm{j}\Delta G)\end{cases}  \tag{8}\]  
where \(i\) and \(j\) are the search addresses of the temperature mapping matrix, and the temperature value of the corresponding point of the pixel position is obtained by searching the \(i\) and \(j\) position elements in Equation (7). Therefore, the search address can be obtained by analyzing the chromaticity information of each pixel, and the temperature of the corresponding position of the pixel can be further obtained.
## 5 Conclusions  
In this paper, the effect of nanomaterials including nanofibers and nanoparticles on impact response of composite laminates is considered. The following conclusions can be drawn from the reviewed papers:  
* Electrospun nanofibers are suitable choices for toughening thermoset based laminates. Various types of polymers have been applied for interleaving composite laminates including NY6,66,69, PVDF, PCL, Carbon.
* Each nanofiber type has its specific mechanism for toughening laminates; for instance, NY activates bridging mechanism while PCL utilizes two different mechanisms depend on curing temperature. If PCL melts during the curing process, the phase separation mechanism predominant; if not, the bridging between the composite layers is the main mechanism of toughening.
* According to the published results, Carbon, NY66, and non-melted PCL are the best choices for toughening the laminates.
* A positive effect of nanoparticles to enhance interlaminar fracture toughness, impact performance and CAI strength of composite laminates is reported, especially for brittle resin systems.
* There is a higher improvement in interlaminar shear values (G\({}_{\text{IC}}\) and G\({}_{\text{IIC}}\)) compared with the impact and CAI behavior. On the other hand, some authors reported a negative effect of the nanoparticles on impact and CAI, which was mainly related to insufficient solvent of the nanoparticles in the resin that led to agglomeration of the nanoparticles.
* Manufacturing methods, reinforcement content and type, material property and many other parameters are affecting the performance of nanoparticle modified composites. Therefore, careful consideration must be done when choosing these parameters to target desired properties.
# Boosting Targeted Black-Box Attacks via Ensemble Substitute Training and Linear Augmentation
## 4 Approaches
### Ensemble Training  
During the studying of the white-box attacking method C&W [11], we found that the ensemble attack [19; 20; 21] is highly efficient and effective for increasing the transferability of adversarial examples. Thus, we try to train several substitute models \(F_{1},F_{2},...,F_{N}\) to generate our ensemble substitute models' attacks. However, despite the increase of attacking success rate, there's a serious problem that the query times of training substitute models are linearly increasing with the number of substitute models, which is not what we expect.  
Inspired by the thought of ensemble attack that combines the outputs of several models' logits layers [19], we try to combine the gradient information of several substitute models during augmentation:  
\[S_{\rho+1}=\{x+\lambda\cdot\text{sgn}(\sum_{i=1}^{N}\omega_{i}\
abla_{x}[ \tilde{F}(x,\theta_{\rho})]):x\in S_{0}\}, \tag{8}\]  
where \(\omega_{i}(i=1,2,...,N)\) is the weight of \(F_{i}\)'s gradient. In addition, in this way, we elevate the success rate of the gradient-based linear augmentation a lot. The detail results will be presented in Section 6.  
Combining these two ways above, we have our final process in Algorithm 1.  
Figure 2: Improvement to Jacobian-based Augmentation. On the left is the original Jacobian-based Augmentation flowchart, and the number of images is increasing exponentially with the epoch growing and the query times will be unacceptable after a few epochs. The right pattern is our improvement that all the augmenting images are from the initial data, and the query times increase _linearly_.  
```
0: The target black-box model \(O\); The maximum of iterative epoch \(\rho_{max}\); Initial dataset \(S_{0}\) for augmentation; Step size parameter \(\lambda\); Number of substitute models \(N\); Weights of substitute models' gradients \(\omega_{1},\omega_{2},...,\omega_{N}\).
0: Parameter of trained substitute models \(\theta_{F_{1}},\theta_{F_{2}},...,\theta_{F_{N}}\)
1: Define Architecture \(F_{1},F_{2},...,F_{N}\)
2:\(\rho\gets 0,S_{\rho}\gets S_{0}\)
3:for\(\rho\in 0,1,...,\rho_{\max}\)do
4:\(D\leftarrow\{(x,O(x)),x\in S_{\rho}\}\)
5:for\(i\in 1,2,...,N\)do
6:\(\theta_{F_{1}}\leftarrow\mathit{train}(F_{i},D)\)
7:endfor
8:if\(\rho<\rho_{\max}\)then
9: // Ensure all augmented data used to train substitute models
10:\(S_{\rho+1}=\{x+\lambda\cdot\text{sgn}(\sum\limits_{i=1}^{N}\omega_{i}\
abla_ {x}[\tilde{F}(x,\theta_{\rho})]):x\in S_{0}\}\)
11:endif
12:endfor
13:return\(\theta_{F_{1}},\theta_{F_{2}},...,\theta_{F_{N}}\)
```  
**Algorithm 1** A gradient-based linear dataset augmentation method for ensemble training.
## 3 Results and Discussion
### Most Relevant Organizations  
Twenty organizations with the highest number of research articles in this field are listed in Table 3. From the table, Univ Konstanz published the highest number of research documents (12.8%) while Univ Cincinnati produced the least (9 documents: 2.8%). Likewise, 15 of the top 20 organizations are in the United States of America, contributing 225 (70.1%) of the 321 obtained from WoS.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 3 Simulation Results and Discussion
### Measurement Domain Analysis  
This section analyzes pseudorange and carrier phase measurement errors after beamforming. The measurement domain results in the presence of chirp interference are provided here. The measurement differencing technique described in the methodology section is used to obtain pseudorange and carrier phase measurement errors.  
A time series plot of the pseudorange and carrier phase measurement errors for both PM and MPDR are shown in Figure 8. The mean and standard deviations of the pseudorange errors for all the PRNs are provided in Table 3. First consider the results of PM. It can be seen from Table 3 that PM induces pseudorange biases of the order of 30 cm to 50 cm for different PRNs. It can also be observed that the biases observed are different for different PRNs. It can be seen from Figure 8 that PRN 10 distortions are higher than those of other PRNs. This is because the interference source AOA is close to that of PRN 10 as shown in the sky plot of Figure 4. Now consider the MPDR beamformer; it induces up to 4 cm of pseudorange bias, which is about the code phase measurement noise. For PRN 10, which is near the interference source, standard deviations of the measurement errors have increased compared to other PRNs.  
Figure 8: Time series plot of the pseudorange and carrier phase measurement errors using (**a**) PM; (**b**) MPDR.  
It can be observed that mean pseudorange bias values are higher for PM than MPDR. The mean and standard deviations of the carrier phase measurement errors for different PRNs are listed in Table 4. It can be observed from Figure 8 that PM induces different biases for different PRNs. The bias for a particular PRN remains the same for the entire test duration. This is because the interference source location and the array orientation were fixed and as such the beamformer weights were constant during the test. However, if the interference direction is changed or in the case of a dynamic user, the beamformer weights also change, different biases might be observed for the same PRN. A maximum bias value of 30 mm was observed in PRN 10 bias in the case of the PM beamformer. The carrier phase measurement biases for the MPDR beamformer are minimal as listed in Table 4.  
Similar observations were made with CW and BWGN jammers for code and carrier phase measurement distortions. The results corresponding to these jammers are provided in Tables 11-14.
## 7 Results and Discussion  
In the case of operating as a pure solar station, the lowest value for the cost of produced electricity was LCOE = 0.1781 EUR/kWh, which was archived at SM = 2 and FLHs = 5, and the annual electricity production at that point was 25.21 GWh. However, the maximum annual electricity production was 48.66 GWh at FLHs = 10 h and SM = 8, and the annual LCOE = 0.2896 EUR/kWh. This means that to achieve the maximum annual electricity production, the LCOE increases by approximately 63% compared to the lowest LCOE. On the other hand, the annual electrical energy produced is approximately two-times higher than the production at the minimum LCOE. It can obtain annual electricity energy of about 80% approximately with an LCOE of less than 0.2 EUR/kWh at points SM \(\geq\) 3.5 and FLHs \(\geq\) 5. For example, the annual energy produced of 39.7 GWh at FLHs = 10 h and SM = 3.5, which is about 82% of the maximum possible annual production, is at an LCOE = 0.1864 EUR/kWh, which is less than 5% higher than the lowest possible LCOE.  
When the system is a hybrid power plant, it can be found that the lowest LCOE produced was for case 3, amounting to LCOE = 0.1283 EUR/kWh, which meets FLHs = 0 h and SM = 1. When working at high temperatures (case 6), the minimum LCOE is 0.0824 EUR/kWh at FLHs = 0 h and SM = 1. The expected cost of the produced electricity  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case Number** & **6** \\ \hline The factor for doubling biogas production & 9.32 \\ \hline The generator’s corresponding capacity (kWel) & 8165 \\ \hline The total corresponding annual electrical & 75.56 \\ energy produced (GWhel/year) & 0.0293 \\ \hline LCOEbiogas (EUR/kWh) & 0.0293 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Expected annual production and the cost of the generated electrical energy for case 6.  
will decrease to about 64% of case 3, which is the lowest. However, this option is unsuitable because methanogens are unstable at this temperature range. Table 8 highlights the key findings of the hybrid power plant in consideration of the following operation cases: biogas net, solar net, and hybrid. The generator capacity is the same in all cases. In the solar net instance, the power plant could create two values of yearly energy output, which naturally resulted in two values for the power factor, annual efficiency, and LCOE. Increasing yearly energy output by about 57.5% results in a 4.6 percent increase in the LCOE. Table 9 compares LCOE values for various hybrid solar-biogas power plants worldwide. The table shows that the LCOE ranged between 0.065 and 0.27 EUR/kWh. Of course, this variation is due to the difference in climatic conditions, solar and gas energy percentage in the systems, and the nominal and technical capacity used.
## 6 Extension with Side-Lobes/Back-Lobes as Well as the Path Loss Effect  
In this section, we take side-lobes and back-lobes of a directional antenna as well as the signal path loss effect into our analysis. We next derive the results on the upper bounds of the number of channels with consideration of the above factors.