esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 4 Experimental Results
### Baseline Performance Evaluation  
To rigorously assess the effectiveness of the proposed watermarking method, we conducted a sequence of experiments designed to isolate and quantify the contribution of each of its key components: adaptive quantization modulation, temporal redundancy, and structured multiframe insertion. All tests were conducted in the absence of attacks to capture baseline performance in ideal but realistic conditions. We report results in terms of BER, PSNR, SSIM, VMAF, and recovery accuracy.
###### Abstract  
Channel modeling is crucial for inter-satellite terahertz communication system design. The conventional method involves manually constructing a mathematical channel model, which is labor-intensive, and using a neural network directly as a channel model lacks interpretability. This paper introduces a channel modeling approach based on symbolic regression. It is the first time that using transformer neural networks as the implementation tool of symbolic regression to generate the mathematical channel model from the channel data directly. It can save manpower and avoid the interpretability issue of using neural networks as a channel model. The feasibility of the proposed method is verified by generating a free space path loss model from simulation data in the terahertz frequency band.  
symbolic regression; channel modeling; terahertz; artificial intelligence Article
## 4 Results and Discussion
### Effect of Indenter Size  
The 'pressure-area effect' or'scale effect' is a well-known concept in the ice research community and refers to the scale dependence of the material during failure [28; 29]. The effect was observed in this test series as the indenter sizes were varied keeping the temperature, structural compliance and ram speed constant. Although the effect was observed in all combinations of test parameters, it was most pronounced with compliant system 1 during slow tests. Figure 14 (Left) shows the nominal pressure vs. time for tests T1\(5\)19_C1\(2\)5, T1_10_18_C1\(2\)5, T1_15_18_C1\(2\)5 and T1_15_18_C1\(2\)5, where the ram speed was 2.5 mm/s and structural compliance was C1 for all tests. The ice temperature varied between \(-\)16.5 \({}^{\circ}\)C to \(-\)19.5 \({}^{\circ}\)C and the result shows a clear scale effect for three indenter sizes. The maximum failure pressure for the 5 cm indenter was above 60 MPa whereas the maximum failure pressure for the 15 cm indenter was just above 10 MPa. The frequency of failure was also observed to be somewhat dependent on the indenter size (Figure 13 (Right)); however, such dependency was not found to be very strong.  
Figure 13: Comparison of results for tests T1_10_6_C2\(2\)5 and T1_10_5_C2_16; (**Left**) total force plotted against time; (**Right**) average failure frequency plotted against indentation speed.
# Performance Analysis of Ocean Surface Topography
## 3 OST Altimetry Results and Analysis of InIRA  
InIRA acquired the first batch of data in September 2016; the intensity image shown in Figure 6 is located in the South China Sea, next to Vietnam. The South China Sea is famous for its abundant ocean internal waves [23], and the data has just recorded this phenomenon. Some intensity discontinuity, also referred to as scalloping [24], can be seen in the far range of the ocean along the azimuth direction; this is caused by the burst working mode of InIRA. Contrary to conventional synthetic aperture radar (SAR) images, the intensity of the ocean surface in Figure 6 is much brighter than the land. The reason for this unique feature is that the received signal of InIRA contains both backscattered energy and specular-reflected energy. Compared with rough land surfaces, the signals are much easier to be specular-reflected by the ocean surface, and the specular-reflected signal takes up a much bigger proportion than the backscattered signal. In this section, the processing procedures of the interferometric data are briefly introduced, then the altimetric precision on a 1-km grid is estimated and analyzed by the processed interferometric phase.  
Figure 5: (**a**) Signal-to-noise ratio (SNR) curve estimated from InIRA acquired data; (**b**) volumetric decorrelation coefficient; (**c**) coherence, and; (**d**) altimetric precision under a different significant wave height (SWH) setting; (**e**) altimetric precision on a different resolution grid, the SWH is set to be 2 m during the simulation.
# A Cooperative Communication Protocol for QoS Provisioning in IEEE 802.11p/Wave Vehicular Networks
## 4 Proposed Scheme
### Resource Allocation Scheme for Cooperative Communication
The OBU receiving the Coop Request message calculates the PMET with the RSU and transmits a Coop Response message including the calculated PMET and the data rate to the RSU. The RSU receiving the Coop Response message calculates the RDP based on the received link information and selects the relay node based on the calculated RDP. RSU also selects the SCH Interval for cooperative communication and broadcasts the WSA message including information for the relay path and the allocated SCH interval. The relay node receiving the WSA ascertains the relay path information, and sends the Coop Response message including the received information back to the Dest Dev. The Dest Dev receiving the Coop Response message checks the information for the cooperative communication and requests a service to the RSU in the allocated SCH interval. Figure 11 shows an example of exchanging data between the RSU and the destination node in the SCH interval. Figure 10: An example for the proposed cooperative communication scheme. Figure 9: The format of the proposed Coop Response message. In Figure 11, devices participating in WBSS perform communication in SCH 1 channel. During the Relay Interval allocated through the Coop Response message, the RSU and the relay node change their channels to the SCH 2 channel and exchange the WSA message and data frames with the destination device. During the Upper Link interval, the RSU and the Relay node communicate with each other, and during the Lower Link interval, the relay node and the destination node communicate with each other. After the Relay interval, the RSU and the relay node return to the SCH 1 channel and communicate with OBUs in the WBSS. Since all devices in the WBSS are aware of the information of the Relay Interval through the Coop Response message, they do not transmit data frames to the RSU and the relay node in the Relay interval.
## 3 Experimental Analysis
### Model Lightweight Analysis  
In machine learning, especially in the context of complex deep neural networks, ablation experiments can effectively verify the necessity of individual modules [26]. It can improve and optimize the neural network by adding or deleting different modules to enhance the model's performance.  
The ablation experiments of the GCB YOLOv7 model are shown in Table 1. As can be seen from Table 1, Models 1 to 3 are based on the YOLOv7 algorithm, and the lightweight Ghost convolution, the CBAM mechanism, and the information fusion Bi FPN mechanism are introduced sequentially based on the previous model.  
Figure 5: Loss function convergence curve.  
In the model lightweight analysis, the number of model network layers, parameters, and floating-point operations, as well as the training time and model volume, are the main parameters for judging the lightweight of the model, so the structural parameters of each ablation experimental model are shown in Table 2. In order to highlight the advantages of the GCB YOLOv7 algorithm proposed in this paper, the YOLOv5 algorithm is added to the comparative analysis experiments for comparison.  
Compared with the YOLOv5 algorithm, the YOLOv7 algorithm has fewer network layers, which is reduced by about 53 layers compared to YOLOv5. The number of parameters and floating-point operations are reduced by about 8.94 million and 3.1 G, respectively, and the model volume is compressed from 92.7 M to 74.8 M. It can be seen that, as analyzed from a number of performance metrics, YOLOv7 is lighter than YOLOv5 by using multi-branch stacking.  
Compared with the YOLOv7 algorithm, the improved GCB YOLOv7 shows superior performance with more obvious lightweight advantages. First, in terms of model structure, the number of network layers increases from 415 to 511 for the GCB YOLOv7 model due to the introduction of linear transformation, CBAM, and Bi FPN. However, the number of parameters and floating-point operations are reduced by about 5.46% and 6.96%, respectively. Secondly, the model training duration is reduced from 8.025 h to 7.903 h, which is about 1.52%. Finally, affected by the model structure, the model volume obtained from GCB YOLOv7 training is only 70.8 M, which is reduced by about 4 M.  
From the model lightweight analysis, it can be seen that, after the introduction of lightweight convolution, attention mechanism, and cross-stage information fusion in the original YOLOv7 algorithm, this simplifies the model structure, reduces the amount of model computation, and compresses the model training time and volume.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 2 Background and Methodology
### Estimation Method Development  
#### 2.3.1 Correlation of Hydrological Alteration and Reservoir Operation  
The cascade reservoirs in the upper reaches of the Yangtze River serve multiple functions, including flood prevention, water supply, electricity generation, ecology conservation, and ship navigation. Consequently, the operational principle of these reservoirs is focused on peak clipping during the main flood period and compensation during the dry period, as depicted in Figures 2b and 3b, considering the observed alterations in river runoff discussed in Sections 2.2.1 and 2.2.2. It is important to highlight that the magnitude of downstream river runoff alteration is highly dependent on the regulation capacity of the cascade reservoirs.  
For the gap between flood month and dry month, i.e., May in the pre-flood period and October in the post-flood period, the alteration of monthly average runoff was relatively complex. The reservoir would be declined to maintain a relatively low water level in advance of the main flood, resulting in an increase of 764 m\({}^{3}\)/s in the Jinsha River in May. However, the virtual increment was close to the succedent flood climate prediction, and more obvious alteration always emerged in a large basin, e.g., a little attenuation of 220 m\({}^{3}\)/s in Min River, oppositely.  
During the post-flood period, reservoirs typically store water to ensure sufficient supply for water and electricity generation in the subsequent dry season. As a result, a relative attenuation of river runoff is observed in October. Before 2014, this attenuation was evident in Jinsha River (465 m\({}^{3}\)/s) and Min River (607 m\({}^{3}\)/s), with Min River experiencing a more pronounced decrease due to its smaller basin size. However, the significant decline in downstream river runoff has been mitigated due to efforts in ecology restoration. In recent years, the decrease in Min River's runoff has been reduced to only 157 m\({}^{3}\)/s.  
Although there is an obvious increase in river runoff during the dry season, the presence of dams also introduces a notable difference. The highest increments typically occur in February to March, which were the months with the lowest runoff before the construction of dams. For example, in the Jinsha River, the increment ranged from 817 to 1254 m\({}^{3}\)/s. However, after the implementation of compensation measures for the driest month, the variation of the monthly average runoff during the dry period becomes smaller. In the Jinsha River, the runoff was 849 m\({}^{3}\)/s in the absence of dams before 2000, and it reduced to 384 m\({}^{3}\)/s between 2014 and 2020 with the presence of multiple dams. Similarly, a similar change from 398 to 324 m\({}^{3}\)/s was observed in Min River.
### Application of TES Systems in Various Types of Greenhouses
#### 1.2.1 Performance of TES in Greenhouses  
Solar radiation is accessible throughout the day; however, greenhouse systems confront a significant challenge due to the absence of sunlight at night, mainly because these systems depend on solar energy to maintain the ideal temperature and light levels for plant growth. When temperatures fall sharply in the absence of sunlight, plants get stressed, and their growth is slowed, making energy-intensive heating systems necessary to keep them warm. Artificial lighting is also required to prolong photoperiods due to a lack of natural light, which raises energy expenses even further. In the absence of sun radiation's drying action, humidity levels can also rise, increasing the risk of fungal infections. Together, these elements increase the complexity and expense of managing greenhouses sustainably and effectively, particularly at night.  
Additionally, a considerable amount of heat is lost through the greenhouse walls at night because of the temperature difference between the inside and outside. Because of this, the greenhouse needs a lot of heat, and the heating system must spend a lot of energy to compensate for the heat that escapes through the walls.  
Some greenhouses use coverings on their transparent walls to reduce heat loss, but this strategy cannot significantly mitigate heat loss at night. During the day, when solar radiation is abundant, the system uses this heat to meet its demands, while the excess energy is either lost through the transparent walls or stored in the non-transparent wall. However, the amount of heat stored in this manner is minimal.  
Given these challenges, using TES systems inside the greenhouse is essential. These systems should absorb excess heat during the day and release it at night to maintain the proper temperature and humidity inside the greenhouse.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 5 Evaluation and Discussion  
### Experiment Configuration  
For all of experiments, 81 radars are distributed in the surveillance area, which consists of 9 \(\times\) 9 cells as described in Figure 3a. A cell is a hexagon which is covered by a circle with a radius of 10 km. A radar is assigned at each cell, and a radar is randomly located within 2 km from the center of its assigned cell. The detection range of a radar is 15 km, and some of the detection range of a radar overlaps with the detection range of its neighbors.  
We define the simulation environment with three parameters. The first parameter, \(N_{tgt}\), is the number of deployed targets. The second parameter, \(\Delta T\), is the varied start time of radars. A radar starts the NU-FTG algorithm at a randomly-selected time between 0 and \(\Delta T\). The third parameter, \(P_{r_{\#}}\), is the percentage that targets are deployed in the region \(r_{\#}\). As shown in Figure 3b, the surveillance area is divided into four regions. Some of the targets are called regional targets which belong to one of the regions. Regional targets are uniformly distributed and move as random waypoint models in their region. The other targets are non-regional targets that do not belong to one of the regions. Non-regional targets are uniformly distributed and move as random waypoint models in the entire surveillance area.
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 3 Methodology
### Variable Importance Measure and Classification  
A supervised machine learning RF classification algorithm [44] was used to classify the bi-temporal RapidEye image data. RF is considered a robust and efficient classification approach for crop mapping using high spatial resolution satellite data like RapidEye, especially within heterogeneous landscape [10]. It has the potential to handle noisy and highly correlated predictor variables, which commonly occur in remotely sensed data [45]. In particular, RF is an ensemble modeling technique, developed by [PERSON] and [PERSON] [46], to improve the classification and regression trees (CART) by combining a large set of decision trees. Each tree in the RF ensemble is built from a bootstrapped random sample containing approximately two-thirds of the training data drawn at random with replacement. The remaining one-third of the data that is not included in the bootstrapped training sample, i.e., the out-of-bag (OOB) samples, is used to internally evaluate the classification performance. The RF classifier uses two user-defined parameters (_ntree_ and _mtry_). To improve the classification accuracy, the number of trees (_ntree_) grown and variables used at each tree split (_mtry_) were optimized based on the OOB error rate with a grid search and a tenfold cross validation method [47]. The number of optimal trees (_ntree_) was searched between 500 to 2500 using a 500 interval, while the optimal _mtry_ was searched on the _mtry_ vector of a multiplicative factor with the default _mtry_ being the square root of the total number of spectral variables (indices and/or bands) [48]. The ensemble measures the importance of each spectral variable used in the classification by utilizing the permutation of variables which calculates variable importance as the mean decrease in classification accuracy using the OOB samples.  
To select the optimal combination of spectral variables that achieved significant accuracies from the important variables returned by the RF classification model using the OOB error rate, we used the RF backward feature elimination method using the \"varSelRF\" package [49] in the R statistical software [50] for level 2 of distinguishing the two maize cropping systems. To select the most relevant spectral variables without any over-fitting, a.632+ bootstrap method with a leave-one-out cross-validation procedure and replacement from samples that are not part of the RF classification was applied [51]. The optimum numbers of spectral variables selected were employed to produce the final cropping systems map. Finally, a 3 \(\times\) 3 post-classification majority filter was applied to spatially smooth the classified images' dominant classes so as to reduce salt-and-pepper effects in the classification output map.
# On Noise Modeling of Capacitive Feedback Transimpedance Amplifiers in CMOS
## 3 Mathematical and Computer Simulation Results
While this effect gives the 10 dB/decade drop, it is mainly responsible for the noise behavior between 10 kHz and 1 MHz, while for very low frequencies, the noise with a slope around 20 dB/decade can be seen, which cannot be explained by the flicker noise \(M_{1}\). This was a major motivation to incorporate the impact of \(M_{5}\) flicker noise into the noise model. \[\frac{\overline{i_{\text{n}t}^{2}}}{\Delta f}+\frac{\overline{i_{\text{n}t}^{ 2}}}{\Delta f}\approx\frac{1}{Z_{\text{IN,eff}}^{2}}\frac{8}{3}\frac{\text{k}_ {\text{B}}T}{g_{\text{m},1}}+\frac{4\text{k}_{\text{B}}T}{R_{\text{bias,1}}}. \tag{19}\]2 Analytically calculated complete input-referred noise performance for programmable-gain configuration (example case for TSMC 0.18 \(\upmu\)m CMOS).
# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 5 Conclusions  
In this paper, we proposed a real-time Chinese traffic sign detection algorithm based on modified YOLOv2, and we also constructed the CCTSDB. We employed the end-to-end learning model to achieve the fast detection. Specifically, we used multiple \(1\times 1\) convolutional layers in the intermediate layers and fewer convolutional layers in the top layers to construct a single convolutional network. We also used a fine grid to divide the images, aiming to detect the small sizes of traffic signs. The results evaluated on CCTSDB and GTSDB demonstrated that our method was the fastest and more robust in real-world environments. For future work, we plan to improve the CCTSDB for further research. Moreover, we will incorporate more high resolution images and videos with the development of high  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Prohibitory** & **Mandatory** & **Danger** & **Time (s)** \\ \hline Ours & 96.81\% & 94.02\% & 96.12\% & **0.017** \\ [6] & 99.29\% & 96.74\% & 97.13\% & 0.162 \\ [9] & 97.46\% & 93.45\% & 91.12\% & 0.3 \\ [11] & **100\%** & 92\% & 98.85\% & 0.4–1.0 \\ [12] & **100\%** & **100\%** & **99.91\%** & 3.533 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The AUC values and processing times.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Threshold (\(t\))** & **0.1** & **0.2** & **0.4** & **0.5** & **0.6** & **0.65** & **0.7** \\ \hline Precision & 0.7544 & 0.8729 & 0.9531 & 0.9669 & 0.9779 & 0.9950 & 1 \\ Recall & 0.9556 & 0.9407 & 0.9037 & 0.8667 & 0.8222 & 0.7407 & 0.6185 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The numerical values of precision and recall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall** & **Precision** & **Time (s)** \\ \hline Ours (\(t=0.4\)) & 90.37\% & 95.31\% & **0.017** \\ Ours (\(t=0.5\)) & 86.67\% & 96.69\% & **0.017** \\ HOG-BCNN [13] & 92.15\% & 89.17\% & - \\ CNN(Zhu) [13; 22] & 94\% & 91\% & - \\ ConvNet [20] & **96.49\%** & **99.89\%** & 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The rates of precision and recall for all traffic signs and processing time.  
definition devices. We plan to change the network structure appropriately to solve the problem of small traffic sign detection in high resolution images.  
The research work was supported by National Natural Science Foundation of China (61402053), the Scientific Research Fund of Hunan Provincial Education Department (16A008), the Scientific Research Fund of Hunan Provincial Transportation Department (201446), the Postgraduate Scientific Research Innovation Fund of Hunan Province (CX2017B486), and the Postgraduate Course Construction Fund of Changsha University of Science and Technology (KC201611).  
[PERSON] and [PERSON] conceived and designed the algorithm; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed experiment tools; [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
## 4 Solving FJSPLSP-LTCC Using DRL
### MDP Formulation  
A Markov Decision Process (MDP) is typically defined by a five-tuple: \(<S,\mathcal{A},\mathcal{P},\mathcal{C},\gamma>\), where \(S\) is the state space, including machine states, job statuses, and AGV states. \(\mathcal{A}\) is the action space, covering job selection, machine assignment, AGV selection, charging decisions, and charging station assignment. \(\mathcal{P}\) is the state transition probability, denoting the likelihood of transitioning from state \(s_{t}\) to \(s_{t+1}\) upon taking action \(a_{t}\). \(\mathcal{C}\) is the immediate reward function, representing the feedback received from the environment based on state-action pairs. \(\gamma\) is the discount factor that determines the impact of future rewards on current decision-making. The objective of reinforcement learning is to maximize the expected total discounted reward: \(C_{t}^{\gamma}=\sum_{t=0}^{\infty}\gamma^{t}c_{t}(s_{t},a_{t})\). To evaluate the effectiveness of a given policy \(\pi\), the following value functions are defined: State Value Function (\(V^{\pi}(s)=\mathbb{E}\big{[}C_{t}^{\gamma}\big{|}s_{t}=s;\pi\big{]}\)), Actu Value Function (\(Q^{\pi}(s,a)=\mathbb{E}\big{[}C_{t}^{\gamma}\big{|}s_{t}=s,a_{t}=a;\pi\big{]}\)), Advantage Function (measuring the benefit of an action over the average, \(A^{\pi}(s,a)=\mathbb{E}\big{[}C_{t}^{\gamma}\big{|}s_{t}=s,a_{t}=a;\pi\big{]}- \mathbb{E}\big{[}C_{t}^{\gamma}\big{|}s_{t}=s;\pi\big{]}\)). The formulation above lays the foundation for the DRL-based solver using the PPO algorithm presented in the subsequent section.