esa-sceva/satcom-chunk-collection huggingface dataset:
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 3 Proposed Speech Inpainting Methods and Model Architecture
### Hyperparameter Optimisation
#### 3.5.3 Location of the Dropout Layers  
According to the models proposed in this research, five strategies for the placement of the dropout layers were considered, as follows:  
* Location 1: dropout layers are placed after the first and last LSTM layers.
* Location 2: dropout layers are placed after the second and last LSTM layers.
* Location 3: dropout layers are placed after the third and last LSTM layers.
* Location 4: dropout layers are placed after the fourth and last LSTM layers.
* Location 5: dropout layers are placed after every LSTM layer.  
As shown in Table 4, overall, the best-performing dropout layer position was Location 1, which corresponds to placing the dropout layers after the first and last LSTM layers.  
To summarise, the final selection of the proposed hyperparameters was as follows: batch size = 1024, dropout rate = 0.4, and the dropout layers were placed after the first and last LSTM layers.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Dropout** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{**Bandwidth**} & \multicolumn{6}{c}{**Gap Length (ms)**} \\ \cline{3-8}  & & **20** & **40** & **50** & **100** & **200** & **500** & **1000** \\ \hline \multirow{2}{*}{0.1} & NB & 4.10 & 3.94 & 3.91 & 3.82 & 3.58 & 3.18 & 2.63 \\  & WB & 4.11 & 3.96 & 3.90 & 3.78 & 3.48 & 2.92 & 2.25 \\ \hline \multirow{2}{*}{0.2} & NB & 4.08 & 3.90 & 3.89 & 3.77 & 3.52 & 3.15 & 2.62 \\  & WB & 4.07 & 3.93 & 3.90 & 3.80 & 3.45 & 2.83 & 2.19 \\ \hline \multirow{2}{*}{0.3} & NB & 4.07 & 3.93 & 3.88 & 3.77 & 3.57 & 3.15 & 2.61 \\  & WB & 4.04 & 3.92 & 3.86 & 3.75 & 3.47 & 2.90 & 2.23 \\ \hline \multirow{2}{*}{0.4} & NB & 4.13 & 3.99 & 3.94 & 3.82 & 3.61 & 3.25 & 2.77 \\  & WB & 4.12 & 3.97 & 3.92 & 3.79 & 3.50 & 2.97 & 2.33 \\ \hline \multirow{2}{*}{0.5} & NB & 4.12 & 3.96 & 3.90 & 3.80 & 3.55 & 3.16 & 2.61 \\  & WB & 4.14 & 3.97 & 3.91 & 3.81 & 3.51 & 2.93 & 2.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average MOSs of the inpainted speech with different gap lengths under different dropout rates.
## 3 Results
### Electronic Trajectory  
Based on the experimental observations in Figures 6-8, the control of electron trajectories by laser parameters exhibits distinct nonlinear characteristics. Under low-intensity conditions (e.g., \(a_{\mathbf{0}}=1\)), a small beam waist radius (\(\omega_{\mathbf{0}}\)) causes drastic transverse-field-gradient variations. Electrons undergo asymmetric acceleration within a single oscillation cycle, resulting in irregular \"serrated\" trajectories. At this stage, the transverse displacement of electrons significantly exceeds longitudinal displacement, accompanied by large-amplitude energy fluctuations. As \(\omega_{\mathbf{0}}\) increases, the field gradient becomes smoother, allowing electrons to complete full oscillations over multiple laser cycles. The trajectories gradually evolve into symmetric \"zigzag\" patterns, with markedly improved stability in energy variations.  
When laser intensity is significantly increased (e.g., \(a_{\mathbf{0}}=5\) or \(9\)), electrons are accelerated to higher energies, and radiation reaction forces increasingly influence their motion. If \(\omega_{0}\) is insufficient (e.g., \(\omega_{\mathbf{0}}=1\)), cumulative directional shifts in velocity vectors after each oscillation cause gradual tilting of the trajectory midline, forming asymmetric \"tilted zigzag\" patterns. This tilting originates from the nonuniform energy release during intense acceleration: radiation energy loss after each oscillation induces systematic directional shifts in velocity vectors, ultimately manifesting as angular deflection of the entire trajectory. Only when \(\omega_{0}\) is proportionally increased (e.g., \(\omega_{0}=5\) or \(9\)) does the expanded spatial scale of the laser field provide sufficient interaction length for electrons to maintain directional symmetry over multiple oscillations, leading to linearized trajectories.  
Figure 5: Radiation power variation diagram with \(a_{\mathbf{0}}\) ranging from 1 to 7 under the condition that \(\omega_{\mathbf{0}}=7\). Among them, \(a_{\mathbf{0}}\) was normalized, so there is no unit.  
A further comparison of trajectory symmetry and energy evolution reveals that symmetric \"zigzag\" trajectories correspond to more uniform energy fluctuations. When electron motion exhibits high symmetry, balanced forward/backward accelerations produce periodic energy oscillations. In contrast, asymmetric trajectories enhance randomness in energy release, characterized by significant differences in fluctuation amplitudes.  
Figure 6: Under the condition when \(\mathbf{\omega_{0}}=1\). Comparison and translation of the electronic trajectory diagrams, where \(\mathbf{a_{0}}\) is 1, 5, and 9, respectively.  
Figure 7: Under the condition when \(\mathbf{\omega_{0}}=5\). Comparison and translation of the electronic trajectory diagrams, where \(\mathbf{a_{0}}\) is 1, 5, and 9, respectively.
## 3 Experimental Results and Analysis
### Ablation Experiment  
To assess the effectiveness of each module in the BDiT model, a series of ablation experiments were conducted on the Tuyere-Data dataset. These experiments involved progressively incorporating various improvement modules into the ViT architecture. Specifically, the impact of the improved self-attention mechanism, the introduction of the spatial attention module, and the distillation loss were evaluated.
### Scope  
A significant amount of progress has been made on the methodology of STST, which involves, among others, the integration algorithm, loading control method, and delay compensation. It is well known that the effectiveness of STST depends largely on the simulation accuracy of the boundary conditions between the two substructures. However, the boundary condition problem is not well resolved in STST. On the other hand, most integration algorithms are explicit only for displacements and implicit for both velocities and accelerations. Thus, the existing STST method normally focuses solely on the displacement loading target regardless of the velocity and acceleration boundary conditions. The generation of explicit loading targets at each integration time step in terms of displacement, velocity, and acceleration is a key problem that needs to be investigated. However, from the viewpoint of loading control, the realization of high stability and accuracy control on the desired displacement, velocity, and acceleration targets is another key problem for STST. Many shaking tables were originally designed only with a displacement control mode, and as such, the target velocity and acceleration may not be accurately achieved in such tables. Various newly developed shaking tables are designed with three-variable control methods (TVCMs). However, in STST, the loading target at each integration time step is dependent on the information obtained from previous steps. To address the aforementioned problems, this study proposes a novel STST based on a TVCM with velocity positive feedback (TVCM-VPF). The proposed novel STST provides reliable and economical testing support for the investigation on dynamic behavior of engineering structures or dynamic systems, and hence, provides necessary guidance for the design of structures or dynamic systems.  
The remainder of this paper is organized as follows. The explicit central difference method (CDM) for STST is presented in Section 2. The methodology of the TVCM-VPF for the STST is proposed in Section 3. To verify the effectiveness of the proposed methods, experimental tests are conducted and discussed in detail in Section 4. Finally, the conclusions are presented in Section 5.
###### Abstract  
In recent years, the rapid development of autonomous vehicles (AVs) has brought new challenges in terms of data security, privacy, and communication integrity. Our research investigates the potential of blockchain technology to improve the security of AVs by securing vehicle communication systems. By integrating blockchain with AI-based predictive algorithms, this approach aims to secure vehicle peer-to-peer communication, reduce traffic congestion, and improve safety for drivers and pedestrians. Blockchain's decentralized ledger ensures the integrity of data exchange between vehicles and smart city infrastructure and mitigates the risks of cyberattacks such as data manipulation and identity forgery. This paper also examines recent advances in vehicular ad hoc networks (VANETs) and vehicular social networks (VSNs), and it demonstrates how the immutability and cryptographic security of the blockchain can strengthen AV systems. The proposed architecture not only protects user privacy but also decentralizes access to critical data needed for AI-driven decisions, ultimately promoting a safer and more reliable environment for autonomous vehicles.  
blockchain; artificial intelligence; vehicular communication systems; autonomous vehicles; smart city; security; internet of things Article
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 4 Problem Statement and Requirements  
The problem this paper addressed was how to watermark tiny NNs so that IP was protected at the highest level possible. Furthermore, it was about guaranteeing its deployability on tiny Edge devices such as MCUs without affecting the behavior of the model and the resources required to run the model's inference. In terms of requirements, the solution to the problem shall provide support for the following:  
* Uniqueness, Secrecy, and Invisibility. NN watermarking uses a specific, distinct key that is unique while remaining secret and confidential, which prevents unauthorized users from detecting, altering, or removing it. A watermark should be invisible; therefore, it has to be imperceptible and undetectable. Additionally, it should not visibly alter the original host model, ensuring the legitimacy of the content. Importantly, any increase in inference time due to watermarking must be minimal to avoid negatively impacting user experience;
* Requirements for the MCU. Since the network should fit in the MCU, it should be tiny enough to be processed by the device with low latency, and the application of the watermark should use the least possible RAM and FLASH;
* Performance requirements and time of execution after the application of watermarks. The watermark needs to be applied without any impact on memory footprint and inference time. Since watermarking could actually affect the performance of the system, computational overhead and execution times for embedding extraction and detecting watermarks are crucial factors to be considered. It is important to optimize processes to minimize the impact on overall performance and ensure balance between watermarking robustness and invisibility, explained in the following previous points: security and robustness of watermarks in NNs and uniqueness, secrecy and invisibility.
* Security and robustness of watermarks in NNs. NNW techniques should be designed with security and robustness to withstand various attacks. Adversarial attacks, such as model fine-tuning or parameter pruning, can attempt to remove or tamper with the embedded watermark. Robust watermarking techniques aim to withstand such attacks and ensure the watermark remains intact. Moreover, the technique should be efficient in terms of memory usage to facilitate deployment on resource-constrained platforms while maintaining robustness.
# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 4 Experimental Results and Analysis
### Analysis of Results  
The experimental results of the baseline models and our model on the two datasets are shown in Table 5. We report the accuracy, precision, recall and F1-score of our model. The experimental results show that the accuracy of our model is better than the baseline models on the Weibo dataset and the Twitter dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Reality**} & \multicolumn{2}{c}{**Predict Result**} \\ \cline{2-3}  & **Positive Example** & **Negative Example** \\ \hline Positive example & _TP_ & _FN_ \\ Negative example & _FP_ & _TN_ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Meaning of _TP_, _FP_, _TN_, _FN_.  
By comparing the experimental results of the models using different late fusion methods, it can be found that the model using the proportional addition method for late fusion has higher accuracy, which shows that the effect of proportional addition is better than the two fusion strategies of voting and 'one-vote veto', so this paper uses OUR1 as the final false information detection model.  
From the perspective of single modality and multimodality, the accuracy of the multimodal model is higher than that of the single-modality model, indicating that multimodal models can obtain more informative features and improve the accuracy of model detection through complementary learning between modalities.  
The accuracy of our model on the Twitter dataset is 1.2% higher than that of AMFB, 15% higher than that of MVAE and 15.4% higher than that of EANN. On the Weibo dataset, the accuracy of our model is 4.3% higher than that of AMFB, 5.1% higher than that of MVAE and 8.4% higher than that of EANN.
## 5 Security Analysis
### Heuristic Analysis
#### 5.2.8 Temporary Private Key Disclosure Attacks  
Within this protocol, secure communication between the user (\(U_{i}\)) and the aggregation server (\(S_{j}\)) is facilitated through designated secret parameters, such as the user's and server's random numbers, \(a\) and \(r_{i}\), respectively. These numbers are pivotal for generating temporary private keys and other authentication parameters throughout the session. Nonetheless, acquisition of these keys by \(\mathcal{A}\) does not compromise security due to the insurmountable challenge of producing valid authentication values like \(V_{i}\). The creation of these values, along with session key negotiation, depends not just on temporary keys but also on a hashed private secret key (\(x\)) shared between the user and server. This key remains confidential, thwarting any attempts by adversaries to undermine the authentication mechanism.
## 3 Results
### Weight Coefficient and Error Calculation Results  
#### 3.2.1 Coefficient and Static Weighing Error  
To calculate the coefficient of each scale and verify the static weighing performance, we used the feeder's weight scale, feed scale, and drop weighing scale to weigh at certain intervals of known weight, and obtained each scale. The results are shown in Figures 9-11. The results show that the digital signals of the weight scale, the feed scale, and the drop weight scale have a linear relationship with the weight; the weight coefficient of the scale is 0.3187, the average deviation is 0.827 g, and the standard deviation is 0.943. The weight coefficient of the feed scale is 0.188, the average deviation is 0.108 g, and the standard deviation is 0.124. The weight coefficient of the drop weighing scale is 0.1877, the average deviation is 0.259 g, and the standard deviation is 0.27.  
Figure 8: Filtering by median mean: solid line, dynamic weighing raw signal acquired at 5 Hz; dotted line, weight signals are sorted from smallest to largest.  
Figure 10: Static errors and scale coefficients for feed scale.  
Figure 9: Static errors and scale coefficients for weight scale.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
###### Abstract  
Basemap and Planet Fusion--derived from PlanetScope imagery--represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)--small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (\(\textbf{r}^{2}\geq 0.87\)), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had \(\textbf{r}^{2}\geq 0.55\), and 70% of the OFRs had MAPE <5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs.  
analysis ready datasets; PlanetScope; Basemap; Planet Fusion; on-farm reservoirs; water management 2021
## 2 Materials and Methods
### Data Analysis and Statistics
#### 2.4.2 Data Analysis  
The classification was performed using a Support Vector Machine-based classifier. This approach seeks one or more hyperplanes that define a boundary (maximum margin) that separates classes. Our implementation was based on the Python library scikit-learn (1.0.2), with a regularisation parameter of 20 and the default radial basis function kernel. A 5-fold cross-validation paradigm was used to validate the SVM by randomly selecting a validation fold five times, training the SVM on the remaining data, and then validating the data on the validation fold (that the SVM had not been trained on). The classification was then performed by assigning a class based on the location and distance to the hyperplane.  
As shown in Figure 1, the raw Raman data were smoothed by fitting a cubic B-spline to identify peaks. A continuous wavelet transform (CWT), well suited to finding sharp peaks in noisy data, was then used to identify peaks using a window width of 12.5 (empirically determined). Whilst this method gave satisfactory results for visually inspecting the spectra, it was sensitive to the selected parameters. These peaks are shown in Figure 1 as blue dots.
## 6 Keystroke Recognition
### Preliminary Experimental Setup for Keystroke Recognition  
The experiment was conducted with the same setup as in Figure 7. Two human participants performed 150 keystrokes per key (100 for training, 50 for testing in different trials), totalling 1500 samples for each person and 3000 across both. Each press was separated by at least 1 s to ensure signal clarity. Data were sampled at 1300 Hz on the 5.216 GHz band, yielding over 3.9 million CSI samples. This process was repeated for 2 unique experimental subjects. Previous research has demonstrated that 5 GHz surpasses 2.4 GHz in terms of performance due to its greater multitude of subcarriers [36]. Considering the involvement of motion in keystroke detection and later on name detection, it became imperative for us to employ a more robust hardware device to guarantee enhanced precision, which could be achieved by expanding the radio frequency range for WiFi. Hence, from this experiment and following ones, we replaced our 2.4 GHz channel with a 5.216 GHz channel, whichcontained 234 subcarriers and hence could give us more accurate data. Again, the CSI was sampled at 1300 Hz by the Rx.
# Direct Numerical Simulation of Turbulent Boundary Layer over Cubical Roughness Elements
###### Abstract  
The present study explores turbulence statistics in turbulent flow over urban-like terrain using direct numerical simulation (DNS). DNS is performed in a turbulent boundary layer (TBL) over 3D cubic roughness elements. The turbulence statistics at \(Re_{\tau}\) = 816 are compared with those of experimental and numerical studies for validation, where \(Re_{\tau}\) is the friction Reynolds number. The flow exhibits wake interference characteristics similar to k-type roughness. Logarithmic variations in streamwise and spanwise Reynolds stresses and a plateau in Reynolds shear stress are observed, reminiscent of [PERSON]'s attached-eddy hypothesis. The energy at long wavelengths near the top of elements extends to smaller scales, indicating a two-scale behavior and a potential link to amplitude modulation. The quadrant analysis of Reynolds shear stress is employed, revealing significant changes in the contributions of ejection and sweep events near the top of elements. The results of quadrant analysis in the outer region closely resemble those of a TBL over a smooth wall, aligning with [PERSON]'s outer-layer similarity. The analysis of the transport equation of turbulent kinetic energy highlights the role of the roughness elements in energy transfer, especially pressure transport. Streamwise energy is mainly reduced near upstream elements and redirected in other directions.  
direct numerical simulation; turbulent boundary layer; cubical roughness; turbulence statistics; urban boundary layer +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions  
As the behavior of the structure depends on the load profile, it is important to identify how the load profile of this study relates to the actual sloshing load. Thus, the statistical analysis results of the sloshing profiles in the LNG cargo hold [25] were compared to the reaction force time history performed in this study. The load profile can be defined primarily in terms of pressure magnitude, duration, and rise time. To identify the relationship between the actual sloshing load and the result of this study, a comparative analysis of the temporal patterns such as duration and rise time is required, which are important factors in determining the sloshing pressure profile. For all the scenarios considered in this study, the mean and standard deviation of the duration and rise time from the reaction force time history were compared with the reference literature. The results are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Peak Pressure (MPa)** & **Energy (J)** & **Drop Velocity (mm/s)** & **Drop Height (mm)** \\ \hline
1 & 0.88 & 123.3 & 851.5 & 38 \\
2 & 1.52 & 230.3 & 1164.0 & 71 \\
3 & 2.36 & 282.2 & 1128.5 & 87 \\
4 & 2.78 & 412.0 & 1556.7 & 127 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Drop height of impactor with drop mass using energy equivalence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario Name** & **Specimen Type** & **Impact Energy (J)** & **The Number of Repeated Loading** \\ \hline L123 & & 123.3 & 10 \\ L230 & Large corrugation & 230.3 & 10 \\ L282 & & 282.2 & 10 \\ L412 & & 412.0 & 5 \\ \hline S123 & & 123.3 & 10 \\ S230 & Small corrugation & 230.3 & 4 \\ S282 & & 282.2 & 3 \\ S412 & & 412.0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental scenario for primary barrier under the repeated impact loading.  
The results of this study show that the measured duration and rise time are longer than those reported in literature. The ratios of the rise time and duration, which determine the triangular shape of the pressure time history, are similar. Thus, the rationality of the lateral impact methodology was secured through similarity with the sloshing pressure profile.