esa-sceva/satcom-chunk-collection huggingface dataset:
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 6 Results and Discussions
### Recommendations-\(R_{Q}\), \(R_{l}\) and \(R_{i}\)  
\(R_{Q}\), \(R_{l}\), and \(R_{i}\) are the recommendations given to the client from the server. \(R_{Q}\) indicates quantity skew, \(R_{l}\) refers to label distribution skew, \(R_{i}\) represents image skew. Based on this inference, to reduce the amount of data heterogeneity, the client performs data augmentation or clustering. The recommendations mentioned for each client are shown in Table 13. Based on \(JSD_{d}\), \(D_{l}S,0\), \(D_{l}S,1\) and SSIM values, similarity in the distributions can be identified. The distributions of client 1 and client 3 are similar. Likewise, client 2 and client 4 exhibit a similarity in their distributions. Thus, clustering is recommended for client 1 and client 3, client 2 and client 4. As the \(JSD_{d}\) for client 2 with other clients is 0.5 to 0.7, which indicates higher divergence, the data augmentation [28] is recommended for client 2.  
Data augmentation can be achieved through various strategies, including the use of synthetic data or basic transformations. However, when a synthetic data generator is used to address data heterogeneity, there is no statistically significant evidence that it outperforms standard baselines in correcting class imbalance [41]. Nonetheless, some studies [42] suggest sharing synthetic data generated by clients with other participating clients. Additionally, other research explores a method where a client utilizes a foundation model [43] to generate synthetic data based on its local dataset. A subset of this synthetic data is then transmitted to a central server, which aggregates it into a global synthetic dataset. The server subsequently redistributes this dataset to clients, allowing them to enhance their local data with more diverse and high-quality synthetic samples. While careful generation and distribution of synthetic data can help mitigate under-representation in augmented datasets, it may also raise privacy concerns.  
The results after data augmentation with basic augmentation operations show a 10% improvement in accuracy for clients with limited data. The experiment should be extended to include more communication rounds in order to assess whether there is a further increase in accuracy. This could be explored as part of future work. The clustering of clients based on structural similarities in their images was proposed as a direction for future research.
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
###### Abstract  
Stresses in structures are usually converted by measured strains based on corresponding constitutive relations of materials. However, it is difficult to determine the constitutive relation of the material accurately, which leads to significant uncertainty of the converted stresses. This paper proposed a method to estimate the stresses in concrete by using the coda wave interference technique and established a model to describe the relation between the deformation of microcracks and the sound velocity. Based on the experiments, the development law of the coda waves with different frequencies of sound signal propagating in concrete was verified and discussed, and the change of the coda wave velocity during the loading and unloading of the specimen was tested. Parameters involved in the established model were regressed according to the experimental data. The analysis results show that the method can be used to test concrete stress and has a desirable accuracy.  
coda wave interferometry; concrete; stress; microcracks Article
## 4 Power Control Techniques
### Range-Based Power Control Technique (RBPCT)  
The Femtocell can adjust its downlink transmission power by considering the coverage area and the radius of both the macrocell and the femtocell; it also helps to maintain co tier-interference for inadequate deployment and dense femtocell networks [36]. The femtocell can dynamically tune its downlink transmission power by dividing the macrocell transmission power (\(P_{M}\)) over its coverage area (\(X_{M}=log_{10}(R_{M})\)) and femtocell coverage area(\(x_{F_{i}}=log_{10}(r_{F_{i}})\)), we obtained \(Y\) as follows:  
\[Y=\frac{P_{M}}{X_{M}} \tag{7}\]  
In (7), the relationship between eNB downlink transmission power and its coverage area represented by \(Y\). Consequently, it can be utilized to derive HeNB downlink power (\(P_{i}\)) as follows:  
\[P_{i}=Y\times x_{F_{i}} \tag{8}\]  
Using Equation (8), Femtocell can adjust its downlink transmission power.
## 2 Preliminaries  
In the theory of compressed sensing, if a signal is sparsely represented under an orthogonal basis \((\psi_{1},\psi_{2},\ldots,\psi_{N})\), the original signal \(s\in R^{N}\) with length \(N(N>>M)\) can be reconstructed through the sensing matrix \(A\in R^{M+N}\) and the observation vector \(y\in R^{M}\) obtained from \(m\) instances of observation.  
Suppose \(s\in R^{N}\) is a real signal and \((\psi_{1},\psi_{2},\ldots,\psi_{N})\) is a set of orthogonal vectors, then the signal \(s\) can be expressed as \(s=\sum\limits_{i=1}^{N}\psi_{i}x_{i}\), where \(x_{i}\) is the representation coefficient of the signal \(s\) under the corresponding orthogonal basis (i.e., \(x_{i}=<s,\psi_{i}>\), \(<,>\) denotes the inner product operation). If the \(\psi_{i}\) and \(x_{i}\) in the above formulas are expressed in matrix form, respectively, the orthogonal basis matrix \(\Psi\in R^{N*N}\) and the representation coefficients vector \(x\in R^{N},s=\sum\limits_{i=1}^{N}\psi_{i}x_{i}\) can be written in the following matrix form,  
\[s=\Psi x \tag{1}\]  
when the coefficients indicate that there are few non-zero elements in vector \(x\); that is, \(x\) is sparse. According to the CS theory described above, we can use the observation matrix \(\Phi\) to observe \(m\) times to get \(y=\Phi s\), where y is the observed M-dimensional column vector. According to (1), we can further get \(y=\Phi\Psi x\). Let \(A=\Phi\Psi\) and \(y=\Phi\Psi x\) be rewritten as \(y=Ax\), which is the general expression of compressed sensing, where matrix \(A\) is called the sensing matrix. After that, \(x\) can be reconstructed from \(y\) by sensing matrix \(A\) and the observation vectors \(y\). However, in most cases, the observed signal in reality will be contaminated by noise, so a more general form is given here.  
\[y=Ax+w \tag{2}\]  
in which \(w\) is usually Gauss white noise. Besides, the matrix \(A\) needs to satisfy the restricted isometry property (RIP) [3] described below:  
\[(1-\delta_{k})\big{\|}x\big{\|}_{2}^{2}\leq\big{\|}Ax\big{\|}_{2}^{2}\leq(1+ \delta_{k})\big{\|}x\big{\|}_{2}^{2} \tag{3}\]  
Although Equation (2) is an underdetermined equation problem, the signal can be recovered because of the sparsity of the signal by solving an \(l_{0}\) -minimization problem, as below:  
\[\min\big{\|}x\big{\|}_{0}\quad s.t.\quad\big{\|}y-Ax\big{\|}_{2}\leq\varepsilon \tag{4}\]
# Forensic Analysis on Damage of a Cable-Stayed Bridge without Backstays Using BIM Information
## 3 Time-Dependent FE Modeling
### FE Model
#### 3.2.3 Concrete Shrinkage Simulation  
To involve the effect of concrete shrinkage in the FE model, the shrinkage development curves were obtained based on the construction information of each pylon segment from the BIM model, e.g., casting volume, time, and humidity. The shrinkage of concrete is related to the age and thickness of the casting segment, the relative ambient humidity, etc. In the following equations, the effect of shrinkage was determined based on the equations defined in the Chinese bridge design standard (JTG 3362-2018) [33].  
\[\epsilon_{\text{cs}}(t,t_{\text{s}})=\epsilon_{\text{cs}0}\cdot\beta_{\text{s} }(t-t_{\text{s}}) \tag{1}\]  
\[\epsilon_{\text{cs}0}=\epsilon_{\text{s}}(f_{\text{cm}})\cdot\beta_{\text{RH}} \tag{2}\]  
\[\epsilon_{\text{s}}(f_{\text{cm}})=\bigg{[}160+10\beta_{\text{sc}}\bigg{(}9- \frac{f_{\text{cm}}}{f_{\text{cm}0}}\bigg{)}\bigg{]}\cdot 10^{-6} \tag{3}\]  
\[\beta_{RH}=1.55\bigg{[}1-\bigg{(}\frac{RH}{RH_{0}}\bigg{)}^{3}\bigg{]} \tag{4}\]  
\[\beta_{\text{s}}(t-t_{\text{s}})=\bigg{[}\frac{(t-t_{\text{s}})/t_{1}}{350(h/ h_{0})^{2}+(t-t_{\text{s}})/t_{1}}\bigg{]}^{0.5} \tag{5}\]  
where \(t_{\text{s}}\) is the age of concrete when the shrinkage starts; \(t\) is the age of concrete at the time of calculation, which is determined by the casting time of each concrete segment; \(\epsilon_{\text{cs}}(t,t_{\text{s}})\) is the shrinkage strain at the time of \(t\); \(\epsilon_{\text{cs}0}\) is the nominal shrinkage coefficient of concrete; \(f_{\text{cm}}\) is the average compressive strength of cubic concrete with curing time of 28 days; \(\beta_{\text{RH}}\) is a coefficient related to the annual average relative humidity; \(RH_{0}\) is the annual average ambient relative humidity; \(RH_{0}\) = 100%; \(\beta_{\text{sc}}\) is a coefficient related to the cement type; \(\beta_{\text{sc}}=5.0\); \(h\) is the theoretical thickness of the concrete segment; \(h_{0}\) = 100 mm; \(t_{1}\) = 1 d.  
Based on Equations (1)-(5), the shrinkage development curves for each concrete segment were determined, as shown in Figure 12. These curves were used to apply the effect of the concrete shrinkage to the FE model.  
Figure 11: Modeling of construction process in FE model.
# A Methodological Approach to the Study of Retroreflective Pavements
## 3 Results and Discussions
### Stage 1: Total and Directional Reflectance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Bituminous**} & \multicolumn{2}{c}{**Global Reflectance (\%)**} & \multicolumn{2}{c}{**Reflectance at 45\({}^{\circ}\) (\%)**} & \multirow{2}{*}{**Reflectance Ratio**} \\ \cline{2-2} \cline{4-5}
**Mixture** & **Mean** & **Std. Dev.** & **Mean** & **Std. Dev.** & **45\({}^{\circ}\)/Global** \\ \hline AUTL-LW & 79.7 & 4.6 & 67.8 & 2.5 & 0.9 \\ \hline AUTL-GW & 63.2 & 6.6 & 58.8 & 2.4 & 0.9 \\ \hline AUTL-PW & 65.4 & 14.3 & 40.3 & 8.3 & 0.6 \\ \hline AUTL-LO & 38.7 & 1.7 & 23.6 & 1.1 & 0.6 \\ \hline AUTL-GO & 31.9 & 3.8 & 19.5 & 1.4 & 0.6 \\ \hline AUTL-PO & 35.0 & 5.2 & 19.0 & 1.0 & 0.5 \\ \hline AUTL-LBR & 32.8 & 7.2 & 19.2 & 1.6 & 0.6 \\ \hline AUTL-GBR & 26.0 & 3.9 & 15.3 & 1.8 & 0.6 \\ \hline AUTL-PBR & 26.4 & 3.3 & 14.5 & 1.1 & 0.6 \\ \hline AUTL-LR & 32.1 & 2.4 & 15.2 & 1.1 & 0.5 \\ \hline AUTL-GR & 26.1 & 3.3 & 13.3 & 1.0 & 0.5 \\ \hline AUTL-PR & 22.5 & 2.3 & 12.0 & 1.0 & 0.5 \\ \hline AUTL-LBL & 23.2 & 1.9 & 10.6 & 0.6 & 0.5 \\ \hline AUTL-GBL & 25.5 & 3.8 & 10.3 & 1.6 & 0.4 \\ \hline AUTL-PBL & 23.3 & 1.8 & 9.7 & 2.5 & 0.4 \\ \hline AUTL-LB & 20.0 & 1.0 & 5.1 & 0.8 & 0.3 \\ \hline AUTL-GB & 17.9 & 1.0 & 5.1 & 0.6 & 0.3 \\ \hline AUTL-PB & 20.9 & 3.8 & 4.9 & 1.1 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Reflectance values in the visible range obtained in the first work stage. Finally, the effect of the type of aggregate can be seen. With the limestone aggregate (lighter), the spectral reflectance values are higher than those of the other two aggregates, with more pronounced differences for the lighter shades of the mixture (white, ochre, or brown). For darker shades (red, blue, or black), the differences between mixtures with different aggregates are minimal. For the granite and porphyry aggregates, the total reflectance values are very similar, whatever the shade of the mixture. For example, when measurements are carried out with the integrating sphere, the reflectance of the limestone aggregate mixture with a lighter shade (AUTL-LW) is 79.7%, while for the darker-shaded mixture (AUTL-LB), it is 20%. When using the spectroradiometer, the reflectance values were 67.8% and 5.1%, respectively. For the granite aggregate, the reflectance of the lighter-shaded mixture (AUTL-GW) measured with the integrating sphere was 63.2%, while that of the darker mixture (AUTL-GB) was 17.9%. The same trend is observed with the spectroradiometer, where the reflectance values for the lighter and darker-shaded mixtures are 58% and 5.1%, respectively. Similar differences are also observed for the mixture manufactured with porphyry aggregate, where the reflectance measured with the integrating sphere for extreme shade mixtures is 65.4% (AUTL-PW) and 20.9% (AUTL-PB), and with the spectroradiometer, it is 40.3% and 4.9%, respectively.
## 4 Tools for WebAssembly Development
### Build Tools/Frameworks with WebAssembly Support
It enables efficient communication between Rust and JavaScript code, allowing developers to leverage Rust's performance and safety features in web applications [45]. Wasm-bindgen simplifies the interaction between Rust and JavaScript by automatically generating JavaScript bindings for Rust functions and data structures. However, using wasm-bindgen requires knowledge of both Rust and JavaScript, and while it offers high performance, out-of-the-box support for IoT-specific development may be limited. * Provides seamless integration between Rust and JavaScript/Wasm
* Offers efficient communication between Rust and JavaScript code
* Allows leveraging Rust's performance and safety features in web applications
* Requires knowledge of both Rust and JavaScript for effective use
* Limited out-of-the-box IoT-specific support
## 4 Construction Monitoring
### Stage III--Analysis of the Results  
The results were analyzed in the dedicated TRITOP Professional software. Built-in inspection tools were used for this purpose. First, a common coordinate system for all measurements was introduced. Due to the fact that the gondola was able to rotate relative to the vertical axis, compensation for this rotation was introduced in each subsequent measurement. An example screenshot showing a cloud of measurement and reference points is shown in Figure 11. Example displacements of measurement points are shown in Figure 12.  
Figure 10: Location of coded reference points (**a**) and example of the arrangement of reference and measurement points (**b**).
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 4 Experimental Results and Discussions
### Decoding on Out-of-Sync Data  
Figure 9 displays the WERs on out-of-sync data for clean and noisy reverberant data. Since audio and video were synchronized in the datasets, we synthetically shifted the video frames to get out-of-sync data as in [20]. Although the transformer model with the CTC loss only showed worse performance than that with the cross-entropy loss in [20], our model with the hybrid CTC/attention loss provided comparable WERs with the cross-entropy loss. Even with the CTC loss, our model still had robustness against out-of-sync data because our model was based on independent encoder-decoder and cross-modality attention mechanisms. The results demonstrated that our model might use the hybrid CTC/attention loss to force monotonic alignments required for AVSR without concerning relative performance degradation for out-of-sync data.
## 6 Lab-Built Scenario and Benchmarking
### Security Testing journey
#### 6.2.1 Security Testing Scenario: Factory Default Configuration  
This initial assessment targeted the Jetson Nano in its unconfigured, out-of-the-box state, mirroring the conditions an attacker would encounter on a newly deployed, unhardened device.  
**Outsider Perspective:**  
Our reconnaissance began with passive network observation, where we identified the Jetson Nano's MAC and IP address, and a simple ping sweep confirmed it was active. We then used specialized search queries to discover publicly available information, which often revealed the common default login credentials. In the scanning phase, we used Nmap to perform a comprehensive port scan (mmap -sV -p- <Jetson_Nano_IP>), which consistently revealed open ports for SSH (22) and VNC (5900) on default JetPack configurations. The service version detection (-sV) provided exact software versions, which were then fed into vulnerability scanners like Nessus and OpenVAS. These scans highlighted numerous unpatched CVEs in the kernel and NVIDIA's drivers, representing concrete pathways for exploitation.  
For gaining access, the most successful method was brute-forcing the SSH service with tools like Hydra, targeting the default nvidia:nvidia credentials. The lack of an account lockout mechanism on the default SSH daemon made this approach highly effective. Once initial access was gained as the nvidia user, the default sudo privileges allowed for immediate privilege escalation to root by executing sudo su. This single vulnerability granted full administrative control. To maintain access, we established persistence by modifying system initialization files (like /.bashrc' or '/etc/rc.local') to launch a reverse shell on boot. We also created hidden user accounts and used SSH tunneling to encrypt our command-and-control traffic. Finally, to clear tracks, we systematically wiped or altered logs in /var/log/ (specifically auth.log and syslog) and cleared shell command histories to hinder any forensic investigation.  
**Insider Perspective:**  
From the perspective of an authenticated user with default credentials, the system exhibited a catastrophic failure of ZT principles. The existence of a weak, default password meant the initial trust decision was already compromised. Once logged in, the user had unconstrained sudo privileges, which is a direct violation of the principle of least privilege. A ZT approach would mandate strong, unique credentials and granular sudo policies from the outset. We found that the default user could read sensitive system configuration files in /etc/ and view mutable, local log files in (/var/log/ without restriction. With root access being trivially achievable, we could modify any system file, install malware, create hidden accounts, and alter network configurations, completely bypassing any intended security policies and making a mockery of device integrity verification. The ability to erase logs as the root user further undermined the core ZT tenet of auditable, immutable logging.
# Method for Accuracy Assessment of the Length Measurement Unit of Laser Tracking Systems
###### Abstract  
Laser tracking systems are widely used in large-scale metrology of geometric quantities. Their importance is confirmed by the fact that one of the parts of the ISO 10360 series of standards has been devoted to the issue of assessing their accuracy (ISO 10360-10). A laser tracker is a device whose final measurement result is calculated using indications from various subsystems included in it, such as devices for measuring length and angle. The analysis of these individual impacts can be useful in creating simulation models of accuracy which, in regard to the Industry 4.0 concept, seem to be the most justified in terms of speed of operation and ease of use. For this reason, it may be particularly important to undertake research on the accuracy of this component in isolation from other factors affecting the measurement of the coordinates of the point. The article describes a method that allows separation of the length measurement error from the other components. The method uses a high-accuracy interferometer which is treated as a reference system that allows for the comparison of indications obtained using the tested distance measurement system. Thanks to the proposed method, it is possible to minimize errors from the optical system and other measuring systems. The use of a precise linear guide allows the reduction of errors related to the implementation of linear motion. The article presents the test method and the results obtained from performed experiments, as well as formulates conclusions and the directions of further development.  
laser tracking systems; laser tracker; accuracy; interferometer; virtual model +
Footnote †: journal: Applied Sciences  
Article
## 2 Materials and Methods: Transducers Design and Fabrication
### Methods for Transducer Characterization  
#### 2.3.1 Preliminary Transducer Verification: Pulse-Echo in Air  
A first verification of the proper FE film behavior in the transducer was performed by measuring the transducer response in pulse-eco in air. As it is well known, the FE film glued to a backing block and operating in air is quite a resonant device and the resonant mode is the quarter wavelength thickness resonance of the FE film. Sensitivity (\(SNS\)) in pulse echo is measured as:  
\[SNS=\ 20\ log\left(\frac{|FFT(s_{Rx})|}{|FFT(s_{Tx})|}\right) \tag{1}\]  
where \(FFT\) denotes the Fast Fourier Transform, \(s_{Tx}\) is the signal applied to transducer terminals and \(s_{Rx}\) is the electrical signal generated in the transducer when the echo is received. In order to obtain these signals a temporal square gate is applied. In the first case the gate is located at t = 0 s, while in the second case the gate is located at the time of arrival of the echo. We used an Olympus 4077 pulser-receiver PR in pulse-echo mode to drive the transducer with a half cycle square pulse, 400 V amplitude, 1.0 MHz center frequency, and to receive and amplify (40 dB) the received echo. Signals where digitized and stored using a Tektronix DPO oscilloscope.  
The advantage of this way of verifying transducer fabrication is that it is simple and fast. Any lack of resonant response or a response more damped than expected or a significant deviation of the resonant frequency away from the expected value (\(\lambda\)/4 thickness resonance) can easily be interpreted as defective design that can be attributed to a damaged FE film, or electrode, or to an imperfect binding  
Figure 2: Picture of transducer prototypes, from left to right: HS03 focused (radius of curvature: 22 mm), HS03 focused (radius of curvature: 35 mm), HS06 flat, HS03 flat.  
of the FE film to the backing block.Table 1 shows the value of the \(\lambda\)/2 thickness resonance measured in [10] in free standing films, so the \(\lambda\)/4 frequency is simply \(\frac{1}{2}\) of the \(\lambda\)/2 resonant frequency shown in Table 1.