esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The studies allow determining the concentration and spread of radionuclides in the soil in the Poleski National Park. The spread of anthropogenic radionuclides concentration in the Poleski National Park was the highest. There is a large region which is characterized by low natural radionuclides concentration and two sites with high concentration of natural radionuclides. All things considered, regarding anthropogenic radionuclides, the southern regions are characterized by their lower concentration than the northern regions. Moreover, the study enables people to assess the radiation hazard by determining the absorbed doses and the annual effective dose. The absorbed dose for humans from each sampling site was lower than the average worldwide value. While the annual effective dose obtained for each sampling site was mainly higher than the worldwide average value. Also, the annual effective dose calculated for the Poleski National Park was over three times higher than the worldwide average value. Despite the fact that the annual effective dose is higher than average worldwide value, it does not pose a threat to human health and life. There are areas where the annual effective dose is several hundred times higher, e.g., Ramsar in Iran. The soils of the Poleski National Park are characterized by pollution with the tested heavy metals, especially cadmium. The content of heavy metals does not exceed the average content of heavy metals in the sandy soils in Europe and in the world. Nevertheless, the differences between the content of these metals in the surface layers were significant for all analyzed heavy metals. I\({}_{\text{geo}}\) values indicate cadmium contamination of the soils. The importance of this type of research for the Poleski National Park comes down to the movement and distribution of metals in the soil, which are the main problems that affect plants in the contaminated soil and their possible consumption by animals and, consequently, humans in the food chain process.  
Methodology, K.W. and M.D.-L.; Software, K.W.; Validation, K.W. and A.W.; Formal analysis, K.W.; Investigation, K.W.; Resources, M.W.; Data curation, K.W.; Writing--original draft, M.D.-L. and A.W.; Writing--review & editing, M.W. and M.D.-L.; Supervision, M.W.; Project administration, A.W.; Funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm
## 2 DIWPSO-SVM Algorithm
### DIWPSO Algorithm
The parameters \(\gamma_{1}\), \(\gamma_{2}\), and the inertia weight \(\omega\) in our dynamic inertia weight PSO (DIWPSO) algorithm were determined through extensive pre-experiments and theoretical analysis to align with the piecewise-function-based regulation logic of \(\omega\). Specifically, \(\gamma_{1}\) and \(\gamma_{2}\) serve as foundational scaling coefficients in the piecewise formula, ensuring that \(\omega\) dynamically balances exploration (global search) and exploitation (local refinement) across different iteration stages. Their values were not independently optimized but were instead selected to support the overall efficacy of the piecewise inertia weight structure. Further fine-tuning of \(\gamma_{1}\) and \(\gamma_{2}\) could be explored in future studies focused on parameter refinement, but their current settings were sufficient to demonstrate the formula's overall innovation. Regarding the learning factors \(c_{1}\) and \(c_{2}\), which control the cognitive and social components of particle movement, we adopted the classic setting \(c_{1}=c_{2}=2\), which is a widely accepted default in the PSO literature that balances exploration and exploitation. While dynamic adjustments of \(c_{1}\) and \(c_{2}\) have been proposed in some PSO variants, empirical evidence supports the robustness of fixed values in the base PSO framework, and our study does not innovate these factors. The swarm size (50 particles) and maximum iterations (100) were empirically selected to balance computational cost and convergence stability, with the latter ensuring sufficient optimization without excessive runtime. Together, these parameter choices prioritize the validation of our piecewise inertia weight mechanism over isolated parameter tuning, aligning with the study's focus on enhancing SVM parameter optimization for structural health monitoring.
## 4 Experimental Result  
### Implementation  
The suggested system architecture is shown on Figure 5. The system for providing AI-based broadcast commentary is structured as follows. Users upload videos, which are stored in an S3 bucket called Input Video, and view the generated commentary through a web page. The system includes another S3 bucket named Model Results for saving the results of model executions. Events triggered by storing or updating videos in the Input Video bucket or results in the Model Results bucket are detected by Model Execute Triggers, an AWS Lambda trigger, which initiates the execution of the subsequent model. Model Endpoints leverage a GPU endpoint to run the R2+1D and YOLO models, implemented using FastAPI, and are provided through Nginx to manage access to each model's endpoint. The Model Weights component consists of an S3 bucket that stores the fine-tuned model weights after training. When the weights are updated, Model Weight Triggers, implemented as an AWS Lambda function, automatically update the.pt file of the corresponding model locally. The system operates as follows. When a user uploads a video, it is stored in the S3 bucket, triggering a Lambda function to execute the next step in the model pipeline. Once a model completes its process, the results are stored in the S3 bucket, and the process repeats. Ultimately, the commentary results are stored in the S3 bucket and retrieved by the frontend page to display them to the user.  
Figure 6 shows the screenshot of the system provided to the user. The broadcasting service has information about the teams playing in the current game on top along with the player and ball detection result on the right side of the screen. The commentaries generated with the detected team, players, ball, and player positions are located at the bottom part of the screen. The current performance indicates that processing a 20-s video, including the entire pipeline, takes approximately 1 min. To generate commentary, the system requires a minimum of 16 frames per second, which is sufficient to produce results.  
Figure 4: Prompt example for game commentary generation.  
Figure 5: Overall system architecture.
## 2 Samples and Methods
### Experimental Methods and Procedures
#### 2.6.1 Initial Parameters Setup
6.3 Permeability Test of Sandstones with Different Pore Structures under the Sole Influence of Water Pressure  
Because the influence of water pressure on sandstone seepage varies under different confining pressures according to previous research results [25], this test was carried out in three groups under three different confining pressures of 20 MPa, 30 MPa and 40 MPa to ensure the accuracy of the test results. In the first group of tests, first, the confining pressure was fixed at 20 MPa, the water pressure of the outlet (\(P_{4}\)) was fixed at 1 MPa, and the water pressure of the inlet (\(P_{3}\)) was set at 2 MPa; the sandstone permeability was measured at a water pressure difference of 1 MPa. Then, the water pressure of the inlet (\(P_{3}\)) was increased to 4, 6, 8, 10 and 12 MPa, and the permeability of the sandstone sample was measured under water pressure differences of 3, 5, 7, 9, and 11 MPa. After the first group of tests was completed, the confining pressure was increased to 30 MPa and 40 MPa, and the above steps were repeated to measure the variations in the permeability in the sandstone samples with different water pressures under the confining pressures of 30 MPa and 40 MPa respectively. After the test of the first sample was completed, the next sample was placed, and the above steps were repeated until all samples were tested. The flow chart of this test is shown in Figure 8.  
Figure 8: The flow chart of permeability testing under different water pressures. 6.4 Permeability Test of Sandstones with Different Pore Structures under the Sole Influence of Temperature  
The operation process was as follows: First, the confining pressure was fixed at 20 MPa, the water pressure of the inlet (\(P_{3}\)) was fixed at 6 MPa, and the water pressure of the outlet (\(P_{4}\)) was fixed at 1 MPa; the sandstone permeability was measured at 25 \({}^{\circ}\)C. Then, the temperature was increased to 35 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, 55 \({}^{\circ}\)C and 65 \({}^{\circ}\)C, and the permeability of the sandstone sample under each temperature gradient was measured in turn. After the test of the first sample was completed, the next sample was placed, and the above steps were repeated until all samples were tested. 6.5 Permeability Test of Sandstones with Different Pore Structures under the Coupled Action of High Confining Pressure, High Water Pressure and High Temperature  
There exists a very complex interaction between high confining pressure, high water pressure and high temperature [32; 33]. In this paper, as per the L9 (3 \(\times\) 3) orthogonal table, an orthogonal test with 3 factors and 3 levels (see Table 4) was designed to analyze the permeability evolution of I\({}_{2}\) and II\({}_{2}\) sandstones to further study the permeability evolution of sandstone with different pore structures under the coupled action of high confining pressures, high water pressures and high temperatures and to verify the conclusions of the above single-factor test.
### Contribution  
In this paper, we investigate using ultrasonic sensors to detect the location of the walls (obstacle's distance) and guide the visually impaired person to move freely (forward, right, or left). Because it is built from off-the-shelf parts, our solution is inexpensive and convenient to use because it does not need any specialized hardware. Additionally, our methodology took advantage of the wavelet form of ultrasonic waves. Ultrasonic sensors obtain range data associated with the obstacles in the surrounding environment, but these readings are inherently imprecise. This is the data sent into the neutrosophic logic model for locating and identifying items in a scene, regardless of their size, shape, or position.  
Multi-criteria decision-making issues and other technical applications in uncertain settings benefit greatly from the usage of neutrosophic sets [16]. Our major goal in this article is to investigate the application of the concept of neutrosophic logic to the problem of obstacle identification in a dynamic, ambiguous indoor environment through (1) using trapezoidal neutrosophic numbers to represent input parameters (antecedents), (2) by modeling qualitative aspects of human comprehension through the AND operator, we may construct neutrosophic IF-THEN rules to estimate the location of the walls, and (3) analyzing the accuracy rate in light of the de-neutrosophication of the consequents. To the best of our knowledge and from the latest research, neutrosophic logic is not yet used to detect and recognize indoor obstacles for VIPs.  
This article's remaining sections are organized as follows: The Section 2 performs a literature review on indoor object identification using machine learning approaches. The recommended strategy is discussed in Section 3, which is based on a review of existing approaches. The suggested model is validated in Section 4 via a series of experiments, followed by a discussion of the results. The concluding portion, Section 5, provides a summary of the work and proposes prospective future research topics.
## 3 Results  
The descriptive statistics for all variables are presented in Table 1. Good-to-excellent inter-repetition reliability was shown for the sprint for all vertical (ICC = 0.788-0.978; CV = 2.79-6.22), horizontal and lateral jump parameters (ICC = 0.969-0.979; CV = 1.60-2.36). On the other hand, the 505 test showed good to excellent inter-repetition reliability (ICC = 0.831-0.963; CV = 0.96-2.95), while the CoDD showed good inter-repetition reliability (ICC = 0.825-0.857) but poor absolute reliability (CV = 13.08-14.66), highlighting potential variability in the performance or measurement error. Nevertheless, the best repetition from the 10 m sprint and 505 test was used to calculate CoDD, which was taken for further analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Outcome Variables** & **Mean \(\pm\) SD** & **Range (Min–Max)** & **ICC** & **CV** \\ \hline DSI & 0.91 \(\pm\) 0.13 & 0.60–1.22 & 0.967 (0.942–0.982) & 4.00 \\ IMTP & 2486 \(\pm\) 433 & 1734–3679 & 0.989 (0.982–0.994) & 2.60 \\ Bilateral horizontal jump & 247 \(\pm\) 17 & 209–282 & 0.977 (0.953–0.989) & 1.60 \\ Horizontal jump—L & 196 \(\pm\) 15 & 170–231 & 0.971 (0.949–0.983) & 1.76 \\ Horizontal jump—R & 193 \(\pm\) 14 & 160–223 & 0.967 (0.939–0.982) & 1.86 \\ Lateral jump—L & 179 \(\pm\) 17 & 140–219 & 0.970 (0.941–0.985) & 2.24 \\ Lateral jump—R & 175 \(\pm\) 15 & 138–208 & 0.969 (0.949–0.982) & 2.36 \\ Specific jump—L & 183 \(\pm\) 17 & 146–214 & 0.979 (0.965–0.988) & 1.68 \\ Specific jump—L & 182 \(\pm\) 17 & 138–220 & 0.975 (0.952–0.986) & 2.11 \\ Linear sprint 10 m & 1.75 \(\pm\) 0.08 & 1.61–1.97 & 0.909 (0.848–0.949) & 1.53 \\ Linear sprint 20 m & 3.06 \(\pm\) 0.11 & 2.86–3.34 & 0.963 (0.937–0.979) & 0.96 \\
3 pt line sprint L & 3.28 \(\pm\) 0.16 & 2.91–3.68 & 0.936 (0.877–0.967) & 1.65 \\
3 pt line sprint R & 3.32 \(\pm\) 0.17 & 2.96–3.72 & 0.933 (0.886–0.962) & 1.77 \\
505—L & 2.24 \(\pm\) 0.11 & 2.02–2.49 & 0.831 (0.705–0.908) & 2.95 \\
505—R & 2.23 \(\pm\) 0.10 & 2.01–2.39 & 0.838 (0.716–0.914) & 2.55 \\ CoDD—L & 0.50 \(\pm\) 0.11 & 0.23–0.72 & 0.825 (0.696–0.906) & 14.66 \\ CoDD—R & 0.49 \(\pm\) 0.11 & 0.26–0.65 & 0.857 (0.748–0.932) & 13.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of descriptive statistics and reliability for all outcome measures.
## 3 Experiments and Analysis of Results
### Experiment Conditions and Procedure
#### 3.1.2 Parameters Setting  
Based on the DDPG algorithm, the training process of indoor thermal environment comfort and energy-saving is shown in Figure 7.  
The DDPG controller achieved comfort and energy-saving regulation by interacting with the building's thermal environment. At the initial moment, the indoor thermal environment state is \(S_{t}\). The state is input to the strategy network, which outputs the control action \(A_{t}\). The value network evaluated the goodness of the action by calculating the output action value of the strategy network. The pseudo-code of the training process based on the DDPG algorithm is shown in Algorithm 1.  
Figure 6: Distribution of subjects’ thermal discomfort body language expression.  
During the experimental training, the controller explored the state space. The Ornstein-Uhlenbeck procedure was used to avoid getting stuck in a local optimum solution. Each exploration centered on the mean value to improve the thermal comfort control, with the expression shown in Equation (14).  
\[A_{t}=\mu(S_{t}|\theta^{\mu})+N(t), \tag{14}\]  
where \(N(t)\) denotes the exploration noise, \(A_{t}\) describes the control action with added noise, and \(\mu\) represents a deterministic strategy.  
Figure 7: Training process based on the DDPG algorithm. (Note: The blue color in the picture indicates the Indoor Thermal environment. Gray indicates OU-noise. Light orange indicates Actor Network. Green indicates Critic Network. Orange indicates Replay buffer B. N* is used to store state parameters).  
For each learned thermal environmental state (\(s_{t},a_{t},r_{t},s_{t+1}\)) \(\in\) N, the reward value expression is shown in Equation (15).  
\[R_{i}^{\prime}=R_{i}+\gamma Q^{\prime}(S_{i+1},\mu^{\prime}(S_{i+1}|\theta^{ \mu^{\prime}})|+\theta^{Q^{\prime}}). \tag{15}\]  
On the sampled small batch data, the value network was updated by minimizing the estimated reward, i.e., Equation (15), and the average absolute error of the value network prediction. The Target network was updated with the expression shown in Equation (16).  
\[\left\{\begin{aligned} \theta^{Q^{\prime}}& \leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}\\ \theta^{\mu^{\prime}}&\leftarrow\tau\theta^{\mu^{ \prime}}+(1-\tau)\theta^{\mu^{\prime}}\end{aligned}\right., \tag{16}\]  
where \(\tau\) is the discount factor of the model. After training, only the policy network is applied to control the actions.  
The DDPG controller was used for the intelligent setting of indoor environmental parameters. The value function was estimated approximately using a neural network. The input features to the network were the building environment state. The Actor-Critic network had two hidden layers with 128 neurons per layer. The tanh activation function was used as a batch normalization, gradient-based optimization using Adam, a learning rate of 0.001, a discount factor of 0.001 for model updates, a batch size of 128, a duration of 30 min for each period, and iterative training every 48 s. Parameters in the DDPG algorithm are set as shown in Table 4.
## 3 Results and Discussion
### Dispersity Sand Column Test
have conducted numerous statistical analyses on the data of flow paths exceeding 100 m and obtained the empirical formula of field longitudinal dispersion and flow distance as follows [44]:  
\[a_{l}=0.0169L_{s}^{1.53} \tag{19}\]  
The flow path cannot be expressed by this formula when it exceeds 100 m, and other formulas are needed to determine the longitudinal dispersion [44]. To overcome this problem, [PERSON] and [PERSON] classified the field data reliability of flow paths of different scale into three categories: high, medium, and low [45]. The empirical formula of longitudinal dispersion was obtained through the regression analysis of field data, which was not limited by the size of the flow path. \[a_{l}=0.83(\log L_{s})^{2.414} \tag{20}\]  
In the range of the No. 4 well foundation pit, the solute flow path was less than 20 m, and the longitudinal dispersion was calculated as 1.56 m and 1.38 m by using empirical Equations (19) and (20) considering the scale effect. In the context of laboratory-scale tests, the results of vertical dispersion of the sand column obtained by some scholars were approximately 0.2-1.0 cm. In accordance with the results of the sand column test conducted in the current study, the dispersion of fine sand from Shanghai was 11.5 cm. Therefore, the longitudinal dispersion of MICP bacteria and cementation fluid in layer 4 silty fine sand ranged from 0.2 cm to 1000 cm. The solute migrates with the water flow, and its migration rate was mainly determined by the flow velocity. Recharge wells were used to form the seepage field to drive the bacterial liquid. Due to the large quantity of water injected into the recharge well, the flow velocity in the aquifer was large, and the influence of convection was far greater than that of other effects. At this time, it can be considered that only convection occurred in the sand, and the proportion of dispersion was very small. Therefore, it is necessary to determine the influence of longitudinal dispersion ranging from 0.2 cm to 1000 cm on solute migration under field working conditions, where the further sensitivity analysis of longitudinal dispersion is necessary in a future study.
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## 4 Efficient Symmetric-Key Encryption Scheme  
The above public-key scheme expands the message space due to the noise scaling, resulting in low efficiency. In this section, we will introduce a KDM secure symmetric-key scheme without scaling the noise. For the symmetric-key scheme, we can generate the following ciphertext (\(c_{1}=a,\ c_{2}=b+m\)) by the RLWE\({}^{*}\) problem, where \(b=a\cdot s+e\). If secret key \(s\) replaces message \(m\), consider the ciphertext (\(c_{1}=a,\ c_{2}=b+s\)), we will have that \(c=(a,\ (a+1)\cdot s+e)\). If we define \(a^{\prime}=a+1\), then \((a^{\prime},\ a^{\prime}\cdot s+e)\) is an instance of the RLWE problem, so the challenge ciphertext (\(c_{1}=a^{\prime},\ c_{2}=a^{\prime}\cdot s+e\)) is pseudorandom, and it is easy to prove the KDM security. By way of the above, we can easily extend to any linear function about \(s\), just like \(f(sk)=k\cdot s+w\), where \(k\in R_{q}\) and \(w\leftarrow\chi\). Then, we will obtain a challenge ciphertext (\(a,(a+k)+w+e\)), therefore, for the sake of convenience, we might wish to define the following problem.
## 4 Conclusions  
This study demonstrated the capability of hyperspectral transmittance imaging for detecting black heart in white radiishes. The combination of SNV and mean center was determined as the best spectral preprocessing method. Optimal wavelengths were selected by a successive projection algorithm (SPA) and Pearson linear correlation coefficients (PLCC). Ten wavelengths selected by SPA and four wavelengths further selected by PLCC, as well as the full spectrum (420 wavebands), were used to build classification models. The prediction performance in the four models of PLS-DA, SVM, ANN, and FLDA were compared, and all had good results with overall discriminating accuracy of 95.6%-100%. With the full-spectrum and best 10 wavelengths as inputs, ANN presented the highest overall accuracy of 100%. On the basis of the reduced four wavelengths, FLDA obtained better results than the other models for both calibration and prediction. Overall, these results demonstrated that hyperspectral transmittance imaging was suitable for detecting black heart in white radiishes when combined with the PLS-DA, SVM, ANN, and FLDA models. A simple FLDA model using four wavelengths shows promise for fast, real-time monitoring of internal defects, namely black heart in white radiishes.  
The research was financially supported by National Key Technology R & D Program of China (2015 BAD19B03). The authors acknowledge the support received.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed materials and analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.