esa-sceva/satcom-chunk-collection huggingface dataset:
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 3 A Method of Speaker-Independent Bone-Conducted Speech Enhancement
### Spectrum Distance Constraint  
The proposed BSEGAN-SI method, under the confrontation training of the generator discriminator, enables the generator to continuously correct its output BC speech feature data to the data distribution similar to AC speech. In the experiment, we found that a single generator loss could not better establish a feature map closer to AC. To make the generated BC spectrum feature closer to the target AC spectrum data, we use the spectral distance constraint to narrow the gap. We choose to introduce the spectral distance constraint (L1 norm) in the BSEGAN-SI model due to the fact that it has been effectively applied in the field of image processing, and its formula is:  
\[\mathcal{L}_{sd}(G)=\mathbb{E}_{\mathbf{s}\sim\mathbf{S}}[\left\|G(\mathbf{s})-\mathbf{t} \right\|_{1}] \tag{10}\]  
In summary, the target loss functions \(\mathcal{L}\) of generator \(G\) and discriminator \(D\) of the proposed BSEGAN-SI are, respectively, as follows,  
\[\mathcal{L}_{total}(G)=\mathcal{L}_{gen}^{adv}+\lambda_{sd}\mathcal{L}_{sd} \tag{11}\]  
\[\mathcal{L}_{total}(D)=\mathcal{L}_{dis}^{adv} \tag{12}\]  
where \(\lambda_{sd}\) represents the trade-off coefficient of the loss function \(\mathcal{L}_{sd}\). When the feature data generated by the generator gradually approaches the target data, the two-loss function Formulae (11) and (12) are used together to control the stability of the training. The network is iteratively trained to achieve convergence by optimizing the following Formula (13),  
\[G^{*},D^{*}=\arg\min_{G}\max_{D}\mathcal{L}_{total}(G,D) \tag{13}\]  
Algorithm 1--the overall optimization process of the proposed BSEGAN-SI model in the training process is described in detail.  
```
0: source BC speech features: \(\mathbf{S}\); target AC speech; features: \(\mathbf{T}\); Number of iterations: \(N=2\times 10^{5}\).
0: Trained BSEGAN-SI model. forn = 1 : N do  Sample \(\mathbf{s}\in\mathbb{R}^{Q\times L_{BC}}\), \(\mathbf{t}\in\mathbb{R}^{Q\times L_{AC}}\) from \(\mathbf{S}\), \(\mathbf{T}\). \(G(\mathbf{s})\leftarrow\) Generate enhanced features from \(\mathbf{s}\) with \(G\). \(D(G(\mathbf{s}))\leftarrow\) Classify real/fake features with \(D\).  Compute adversarial losses \(\mathcal{L}_{gen}^{adv}\) and \(\mathcal{L}_{dis}^{adv}\).  Compute spectrum-distance loss \(\mathcal{L}_{sd}\).  Optimizing generator: \(G\leftarrow\
abla\Big{(}\mathcal{L}_{gen}^{adv}+\lambda_{sd}\mathcal{L}_{sd} \Big{)}\)  Optimizing discriminator: \(D\leftarrow\
abla\Big{(}\mathcal{L}_{dis}^{adv}\Big{)}\) endfor
```  
**Algorithm 1** BSEGAN-SI Model Training
## 3 Control Structures
### Implementation of DVC in RSCAD
#### 3.1.3 Current Limitation  
The current limitation is an important block that protects the PE components from damage due to over current. A new maximum current (\(i_{max\_ref}\)) value is computed whenever the maximum current (\(i_{max0\_ref}\)) of the PE converter is exceeded. The current limitation is incorporated in GSC based on Equations (11) and (12). The upper and lower limits for \(i_{q\_ref}\) and \(i_{d\_ref}\) are calculated based on the grid impedance (shown as Priority in Figure 5) and the new maximum currents (\(i_{d\_ref\_lim}\) and \(i_{q\_ref\_lim}\)) are computed. Once the reference limits are computed, the converter voltage limits are calculated by Equation (13) or by Equations (14) and (15) [9].  
If  
\[(|i_{d}+ji_{q}|-i_{max0\_ref})>0\longrightarrow i_{max\_ref}=i_{max0\_ref}-k_{ red}.(|i_{d}+ji_{d}|-i_{max0\_ref}) \tag{11}\]  
else  
\[i_{max\_ref}=i_{max0\_ref}. \tag{12}\]  
As long as the reference current is not limited,  
\[V_{GSC\_{q\_max}}=V_{GSC\_{d\_max}}=x.i_{max\_ref}, \tag{13}\]  
else  
\[V_{GSC\_{q\_max}}=x.i_{d\_ref\_lim} \tag{14}\]  
\[V_{GSC\_{d\_max}}=x.i_{q\_ref\_lim}. \tag{15}\]  
The current limitation parameter for DVC is provided in Table A3.
# A Ship Trajectory Prediction Method Based on an Optuna-BILSTM Model
## 3 BILSTM neural network
### Setting of Parameters  
Once the model has been built and trained, Optuna performs a Bayesian optimization based on the sample information to find the set of hyperparameters that are likely to result in the highest accuracy of trajectory prediction. This is followed by a nearby search. One of the main features it applies to the model is trial and error, budgeting the possible final results based on the learning curve and eliminating them early if the prediction is bad. Its efficiency is shown more clearly when the model is complex and training is slow due to a large amount of data.  
In the designed experiments, Optuna ran for a total duration of 3 h, 16 min and 16 s, exploring 30 combinations of hyperparameters. During the optimization process in Optuna, the Objective Value represents the objective function value for each trial, while the Best Value signifies the best objective function value found so far. The Objective Value is evaluated based on the mean absolute percentage error (MAPE) in the validation set, calculated as the model's loss value for each trial on the validation set. The Best Value corresponds to the minimum MAPE achieved on the validation set during the optimization process, indicating the optimal model performance obtained thus far. Despite the relatively small number of combinations attempted, the subsequent results exhibited an accuracy exceeding 98%, with the initial few returns demonstrating an accuracy of 93%. The process of parameter tuning, along with the optimization history, is illustrated in Figure 6. The optimal model structure is shown in Table 1:  
Here are the definitions of the relevant parameters to be optimized in the model:  
Activation function: Activation functions introduce non-linearity to neural networks. Common choices include ReLU, Sigmoid, and Tanh.  
Batch size: The number of training samples used in each iteration to update the model parameters.  
Epochs: The number of complete passes of the entire training dataset through the neural network during training.  
Optimizer: Algorithms that adjust the neural network parameters to minimize the loss function. Common optimizers include Adam, RMSprop, and SGD.  
Learning rate: Governs the size of parameter updates during optimization. Affects the convergence speed and model performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Hidden Layers** & **1** \\ \hline BILSTM layer: number of nodes, dropouts, activation function & 350, 0.25, ReLU \\ Output layer: number of nodes, activation function & 2, sigmoid \\ Batch size & 32 \\ Epochs & 250 \\ Learning Rate & 0.27 \\ Loss & 0.002271022451067804 \\ Optimizer & adam \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal model structure.
## 3 Integrity Monitoring Method of Space-Ground Integration
### LEO Satellites as Navigation Satellites  
LEO satellites can act as navigation satellites that transmit navigation signals to users in a similar way to GNSS satellites. This will increase the number of visible satellites for users, thereby improving positioning accuracy and reducing the protection levels. Additionally, at the user end, the signal power of LEO satellites will be higher than that of MEO/IGSO/GEO satellites, which provides advantages in multipath resistance and interference mitigation. Moreover, LEO satellites offer high data transmission rates, allowing them to timely broadcast integrity information of LEO satellites and GNSS satellites within their navigation messages. The principle of this scheme is illustrated in Figure 7 and described as follows:  
First, LEO satellites receive GNSS signals based on their onboard GNSS receivers. Second, LEO satellites determine their own orbits and clock offsets based on GNSS observations and the data from inter-satellite links. Optionally, together with the second step, LEO satellites can fulfill integrity monitoring for GNSS satellites based on a consistency check. Third, LEO satellites transmit navigation signals and broadcast ephemeris (including the integrity information for LEO and GNSS satellites) to users. Finally, the user receiver realizes positioning, navigation, and timing based on LEO signals and GNSS signals. The user receiver can also realize receiver autonomous integrity monitoring (RAIM) to detect faulted satellites and compute the protection levels.
## 3 Results and Discussion
### Results of the Second Tests and Analysis of the Air Blast Wave Due to Bombs  
Table 8 shows the parameters and results related to the ten explosions with a large amount of TNT explosive. Detonation number 91 was canceled because the microphone did not work properly.  
In the case of detonation of TNT charges, two different behaviors can be seen. There is one test in which the air blast wave is moderate, and the shape of the overpressure pulse is similar to that described above. It is rather symmetrical, and the positive and negative parts are approximately of the same magnitude as can be seen in the overpressure record measured in test no. 92 (\(S_{b}\) = 16.0 kPa), Figure 7 (left).  
Nevertheless, when the air blast wave is high, the shape of the pulse is equal to the ideal blast wave pressure with the positive part much higher than the negative one. On the other hand, the duration of the positive phase in these tests is significantly higher than in the others. For example, the overpressure measured in test no. 95 (\(S_{b}\) = 63.8 kPa) is shown in Figure 7 (right).  
These overpressure results can be drawn together with the results obtained with the TNT explosive equivalent to Riodin dynamite. Then the graph of Figure 8 was obtained.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**N**} & **Distance** & **TNT Charge** & **Scaled Distance** & **Overpressure** \\  & **(m)** & **(kg)** & **(m/kg\({}^{1/3}\))** & **(kPa)** \\ \hline
**91** & 25 & 84 & 5.71 & - \\ \hline
**92** & 50 & 84 & 11.42 & 16.00 \\ \hline
**93** & 50 & 84 & 11.42 & 21.90 \\ \hline
**94** & 30 & 84 & 6.85 & 45.30 \\ \hline
**95** & 25 & 84 & 5.71 & 63.80 \\ \hline
**96** & 25 & 84 & 5.71 & 57.60 \\ \hline
**97** & 25 & 42 & 7.19 & 36.30 \\ \hline
**98** & 25 & 25 & 8.55 & 33.00 \\ \hline
**99** & 25 & 42 & 7.19 & 57.00 \\ \hline
**100** & 25 & 84 & 5.71 & 54.10 \\ \hline \end{tabular}
\end{table}
Table 8: Values of scaled distances and air overpressure for each detonated TNT charge.  
Figure 7: Air overpressure measured in tests no. 92 (**left**) and no. 95 (**right**).  
The expression derived from the data set is:  
\[S_{b}=396.27\cdot Z^{-1.280} \tag{8}\]  
With a correlation coefficient r\({}^{2}\) = 95.9%.  
By using the safety coefficient 1.35, the predicted air overpressure will be higher than the actual one in more than 90% of the cases, and the attenuation law of the air wave in the case of TNT explosive is:  
\[S_{b}=534.96\cdot Z^{-1.280} \tag{9}\]  
Formula (9), or alternatively the graphic of Figure 6, is useful to predict air blast wave overpressure near the explosion even in the case of detonation of a large amount of explosive.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 2 Designing DaaG
### Structure of DaaG
#### 2.2.2 Drone Flight Control Modules  
There are two functionalities in the drone flight control module: flight path-planning and drone coordination. A flight path-planning function is used to plan the flight course to reduce the main service delay. The details of this function are described in Section 2.3.3. The DaaG flight maneuver requires the ability to change the path and destination during the flight, but some commercial drone controllers do not have this feature, called the off-board controller. A drone coordination function is important for the case where mission areas may overlap [19; 20; 21]. The module basically controls the deployment drones and prevents drone collisions, which is important in using the multi-hop network method. This components guarantees an automated system with respect to the features.  
To acquire the position of the drone, the control module utilizes inertial sensors, wireless ranging sensors and/or the Global Positioning System (GPS). There are many research works utilizing these devices for positioning and navigation [22; 23]. We use inertial sensors and GPS for positioning and navigation, since other methods require additional devices or infrastructure.
## 4 Experiments and Results
### Results in Online Stage
In contrast, Deep-BPR+ requires the opponent's local information to achieve a high detection accuracy of 72%. BPR+ only achieves 65% detection accuracy by relying solely on reward signals. Bayes-Tomop has the lowest detection accuracy as it is hard to deal with opponents using random policies. Figure 10: The detection accuracy against non-stationary opponents. Figure 9: The accumulative rewards for 100 episodes of interactions. Figure 11 shows the switching interval's effect on the algorithms' performance. We design four switching intervals of 4, 8, 10, and 12, and we observe that the larger the switching interval, the higher the detection accuracy, with Bayes-Lab (A) and Bayes-Lab outperforming the other algorithms at all switching intervals. Specifically, when the switching interval is 12, the detection accuracy of all algorithms is relatively high, with Deep-BPR+ and BPR+ exceeding 70% and Bayes-Tomop exceeding 60%. When the switching interval changes from 12 to 8, there is a slight decrease in performance for all algorithms, and the degradation is relatively small for Bayes-Lab (A) and Bayes-Lab because they detect the opponent's type most quickly. Most algorithms suffer a dramatic drop in performance when the switching interval changes from 8 to 4 because the opponent's behaviors change rapidly. In this case, the gap between Bayes-Lab (A) and other algorithms increases obviously. In summary, the switching interval of the opponent policy has a crucial influence on detection accuracy. In other words, frequent switching strategies by opponents will lead to significant challenges to detection, while Bayes-Lab and Bayes-Lab (A) exhibit better performance in dealing with this problem due to \(\Omega\) and \(\Theta\). Table 1 shows the effect of the switching interval on the accumulative rewards. We observe that only Bayes-Lab (A) and Bayes-Lab have positive rewards when the switching interval is 4, and the other algorithms all have negative rewards, demonstrating that \(\Omega\) and \(\Theta\) effectively improve the performance against the opponent who switches policies frequently. Deep-BPR+ performs better than BPR+ and Bayes-Tomop, proving that it is essential for the agent to consider the opponent's behaviors. The rewards improve significantly as the switching interval increases, showing that the longer the opponent's policy remains fixed, the more accessible the agent can track the opponent. Bayes-Lab (A) and Bayes-Lab have similar performance, and their rewards far exceed that of BPR+ and Bayes-Tomop when the switching interval becomes 12. In summary, Bayes-Lab (A) and Bayes-Lab can adapt to a wide range of policy switching intervals without the opponent's information. Figure 11: The effect of the switching interval on the detection accuracy.
# Software-Defined Visible Light Communication for Internet of Things: A Low-Complexity Approach
## 5 Conclusions  
This work has introduced an innovative SD-VLC system that combines CAP modulation and adaptive SDLMS equalization to overcome the inherent limitations of LED-based VLC systems, including limited modulation bandwidth, ISI, and nonlinear distortions. By implementing the system on the PYNQ platform, we have demonstrated a flexible and scalable solution suitable for diverse IoT applications. Simulation results show that, in multipath environments, the proposed equalization approach improves BER performance by nearly two orders of magnitude compared to the uncompensated case, where multi-path effects render the received signal almost entirely unrecognizable. These promising results validate the effectiveness of our approach and highlight its potential for achieving energy-efficient and robust VLC in dynamic channel conditions.  
Furthermore, addressing challenges such as ambient light interference and ensuring interoperability with established IoT protocols is essential for translating SD-VLC from simulations to real-world applications, representing a valuable direction for continued exploration. In terms of practical deployment, hardware cost and energy consumption are critical factors for commercialization. In the proposed system, CAP modulation eliminates the need for digital carrier generation and IQ mixing, while the SDLMS equalizer simplifies adaptive processing by using only sign operations for weight updates. Specifically, these choices reduce the use of multipliers and logic resources in FPGA or embedded platforms, thereby lowering power consumption and hardware complexity. As a result, the system is particularly appropriate for VLC-based IoT applications, where low cost and energy efficiency are often prioritized. Building on the current signal processing framework, future extensions of this work will aim to address practical deployment issues, including ambient light interference, nonlinear behavior of LEDs, and the complexity of hardware implementation, which are critical for realizing robust SD-VLC systems in real-world IoT applications.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data supporting the findings of this study are already included within this article.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 5 Conclusions  
Acknowledging the influence of vertical pressure on the heat transfer in grain storage environment of large-scale storage structures, a self-made grain pile test chamber and multi-field coupling software platform, COMSOL, were used in the study. Further, an analysis was conducted based on the distribution law of the micro-airflow velocity field. The research results can predict and analyze the changes in the temperature field of the grain piles, facilitating the formulation of effective control measures to reduce grain spoilage during storage, maintain the quality of grain, and ensure the safety of grain storage. The main conclusions are as follows:  
* With the increase in vertical pressure, the heat conduction between the grains of the bulk grain pile is intensified, the convection heat transfer of micro-air flow between the pores is gradually reduced, and the heat transfer rate is accelerated.
* The temperature of the bulk grain pile decreases by approximately 0.6-2.7 \({}^{\circ}\)C, compared to the initial storage temperature, and the rate of change of the temperature gradient reaches 7.4%.
* The maximum temperature difference between the middle plane of the bulk grain pile and the low-temperature boundary wall is 9.4 \({}^{\circ}\)C, when the vertical pressure is 50 kPa; the corresponding minimum value is 7.7 \({}^{\circ}\)C, when the vertical pressure is 200 kPa.
* Under different vertical pressures, the simulated and experimental values of temperature of the grain pile are consistent. With the increase in vertical pressure, the convective heat transfer is hindered, and the temperature distribution gradually becomes more uniform.
* Under different pressures, the proportion of high-temperature area decreases linearly with the storage duration, and the decline coefficient increases with increase in the vertical pressure.
* The micro-airflow affects the temperature transfer in the grain pile. The high-temperature region exhibits a wide top and narrow bottom, while the low-temperature region exhibits a narrow top and wide bottom in the temperature distribution.  
Conceptualization, M.G., G.C. and W.L.; methodology, M.G. and D.Z.; software, M.G.; validation, M.G. and C.L.; formal analysis, M.G. and W.L.; investigation, M.G. and C.L.; resources, G.C. and D.Z.; data curation, M.G. and W.L.; writing--original draft preparation, M.G.; writing review and editing, G.C. and D.Z.; supervision, G.C. and D.Z.; project administration, D.Z.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Grain Public Welfare Research Project of China, grant number 201513001-01; the Innovative Funds Plan of the Henan University of Technology, grant number 2020 ZKCJ05; Henan Province Science and Technology R&D Program Joint Fund, grant number 222103810073; and the Open Project of Henan Key Laboratory of Grain and Oil Storage Facility & Safety, grant number 2020 KF-A02.  
Not applicable.
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Coupled CC  
Coupled CC is a common approach to solve the TCP-friendliness issue of uncoupled CC. MPTCP coordinates _cvond_ changes across subflows to avoid sending traffic through congested bottlenecks. In the case of shared bottlenecks, MPTCP adjusts the _cvond_ at different rates to ensure fairness to cross-traffic protocols.  
While detecting common bottlenecks can be challenging, the more important decision is how much bandwidth should an MPTCP connection get, even if no bottleneck exists[6]. The major proposed coupled CC algorithms are Linked Increased Algorithm (LIA) [6], Opportunistic Linked Increase Algorithm (OLIA) [7], Balanced Linked Adaption Congestion Control Algorithm (BALIA) [8] and Weighted Vegas (wVegas) [9].
## 3 Research Methods
### Comprehensive PS-InSAR and SBAS-InSAR Methods  
PS-InSAR simulates linear deformation, while SBAS-InSAR simulates nonlinear deformation. It has been contended that a small baseline approach with comprehensive resolution is comparable to a persistent scatter approach, and represents a unit primarily influenced by a single scatter [22]. However, it should be noted that the PS method offers two distinct benefits. Firstly, by generating all interferograms with respect to a single reference image, we can effectively minimize the noise contribution from the primary image before unfolding the phase; this is because such noise is consistently present in all interferograms. Secondly, by implementing spatial filtering, the amplification of noise caused by the non-dominant scatter has effectively been mitigated through resolution coarsening. Hence, a dataset comprises a sequence of pixels exhibiting scattering characteristics; it can be inferred that these two approaches possess complementary attributes. Through the integration of both approaches, more scattered signals can be extracted in general; thus, the spatial sampling error can be mitigated, which leads to an enhancement in the signal-to-noise ratio for the backscattering coefficients.