esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics
## 4 Experiments
### Scenario 1  
An environment with straight walls is defined in order to assess the movement of the agent. The environment exposes the agent to the cases listed above to assess its behavior.  
The left side of Figure 12 shows the traveled path with a dotted line. Circles are points visited by the agent. It can be observed that the path forms some loops. This experiment shows that, by storing the visited points, these are considered in the heuristics and avoid generating target points in the same positions, leading the agent effectively through the openings in the walls.  
Avoiding visited points helps the agent to move to unexplored regions as long as the space is sufficient. Otherwise, the agent will again pass over visited points, ignoring any avoiding strategy for the benefit of the ability to move. That is, the agent will avoid becoming stuck.  
When the agent detects an interesting point, it will prioritize that direction over others. However, this also happens when the agent crosses the gap between the walls. Thus, it can go back. To avoid this behavior, visited points are used again. If the agent sees an  
Figure 11: Force and speed of agent number 2. (**a**) Forces, (**b**) Speed.  
Figure 12: Scenario 1: one passage.  
interesting point and there is a visited point in that direction, it is then ignored and the next target will be positioned in an area with fewer obstacles. Notice that this overwrites the priority of avoiding visited points, given that the new target will ignore any of them.  
The above shows that, according to the desired exploration strategy and the objective of the task to be performed, different priorities or rules can be established.  
As mentioned in the previous section, random angles and distances are used when there is no further information. This is shown at the right side of Figure 12. The scenario and initial conditions are the same. However, the agent takes a different path.  
It is important to notice that the agent has a limited field of view; thus, the heuristics receive as input what the agent can see and the information stored in memory. Figure 13 shows the explored portion of the environment; gray regions are not known by the agent. When the agent finds the object, the set of visited points then forms a suitable route for the object and also contains a subset of points from which a better route can be built. Namely, the route can be optimized with the acquired knowledge, but this does not mean that a global optimum path can be found.
## 4 Methods
### Test Results  
#### 4.3.1 Analysis of the Influence of SMC Content on Fatigue Performance  
For the sake of comparison, the loading frequency 10 Hz, fatigue life of different SMC content, and different asphalt aggregate ratio were selected, as shown in Figure 2. It can be seen from Figure 2a that under the same stress ratio conditions, the fatigue life of the mixture with a 10% SMCSBS asphalt aggregate ratio of 4.3 was the largest, followed by the fatigue life of the mixture with an 8% SMCSBS asphalt aggregate ratio of 4.8, which is consistent with [PERSON] et al.'s research on the properties of SMCSBS composite modified asphalts [28]. As shown in Figure 2b, under the same stress amplitude, it is obvious that the mixture with the 10% SMCSBS asphalt aggregate ratio of 4.3 had the maximum fatigue life, the mixture with the 12% asphalt aggregate ratio of 4.3 had the minimum fatigue life, and the others were close. The results show that the effect of SMC content on fatigue performance is that the fatigue life of the mixture was the largest when the SMC content was 10%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Levels** \\ \hline SMC content & 8\%, 10\%, 12\% \\ Asphal aggregate ratio & 4.3\%, 4.8\%, 5.3\% \\ Stress ratio & 0.2, 0.3, 0.4, 0.5 \\ Loading frequency & 10 Hz, 15 Hz \\ Test temperature & 15 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fatigue test scheme of SMCSBS composite modified asphalt mixtures.
## 6 Conclusions  
This study compares six satellite-based vegetation photosynthesis models for simulating the GPP of deciduous broadleaf forest. We find that the simulated multiyear AAGPPs from differentsatellite-based models have mismatched spatial patterns with varied changing trends along both latitude and longitude. Model evaluation using flux tower data indicates that all tested models, despite performing well on the 8-day basis, fail to capture the interannual variation of GPP across sites and years. Given that modeling annual GPP greatly influences our ability to predict the carbon exchanges between the land surface and the atmosphere, especially under the scenario of a changing climate, there is a need to develop robust models that are capable of capturing the interannual variation, in addition to intra-annual variation, of vegetation productivity.  
[PERSON] performed the analysis and wrote the paper; [PERSON] and [PERSON] interpreted the results; [PERSON] designed the experiments and wrote the paper. All authors commented on and approved the final manuscript.  
This research was funded by National Key R&D Program of China grant number 2017 YFA0604302 and 2017 YFA0604402, Key Projects for Young Teachers at Sun Yat-sen University grant number 17 lgzd02, and the Science and Technology Program of Guangzhou Province grant number 201803030034 and 201802030008.  
We thank the researchers and investigators that are involved in the data collection and analysis at the AmeriFlux sites. We also thank anonymous reviewers for their constructive comments.  
The authors declare no conflict of interest.
# Quantum Leap from Gold and Silver to Aluminum Nanoplasmonics for Enhanced Biomedical Applications
## 2 Aluminum Plasmonics
### Treatment Mechanisms
#### 2.2.2 Ultraviolet Therapy (UVT)  
Ultraviolet radiation has found applications in photodynamic therapy (PDT), sterilization, and medical diagnostics [61]. UV radiation has been widely used for antibacterial [62] and tissue treatments [63]. UV light has immunomodulatory effects on neutrophils, lymphocytes, monocytes, macrophages, and dendritic cells [63]. UV irradiation of blood has been used for the treatment of septicemia [64; 65]. Antibiotic-resistant bacteria pose a large threat to the global population [66], and UV radiation therapy needs to be further explored as a potential treatment.  
The optical response of plasmonic NPs is governed by scattering and absorption of incident radiation. These two effects provide the main mechanisms of the Al plasmonic UV radiation treatment. The scattering mechanism leads to near-field enhancement with the corresponding increase of the localized surface treatment. On the other hand, absorption of UV radiation may lead to the UV shielding effect and may reduce treatment efficiency [29].
# Transformative Technology for FLASH Radiation Therapy
## 2 Beam Conditions for the FLASH Effect  
Preclinical electron studies and other studies with non-electron radiation modalities and in different laboratories support the general impression that FLASH effects are observed for mean dose rates above 40 Gy/s, but for a full effect, dose rates equal to or higher than 100 Gy/s may be required [10; 12; 13; 14; 15]. The FLASH effect has been observed for a wide range of instantaneous dose rates (IDR), both for single and multiple pulses and CW delivery, as seen in Figure 1.  
The single proton outlier in Figure 1 could reflect a difference between the beam structure of the quasi-continuous wave (quasi-CW) proton therapy beam and the 100-300 Hz pulsed electron beam, which has an RF bunch microstructure. The RF GHz bunch microstructure of the electron beam is approximately two orders of magnitude shorter than the MHz RF bunch structure of the proton beam. However, the explanation that the absence of FLASH protection is due to the several-orders-of-magnitude-lower dose rate in the CW proton bunches compared to electron pulses (\(10^{3}\) vs. \(10^{6}\) Gy/s) is not supported by later data from Oncoray in Dresden [16] as well as the growing body of literature evidence showing that isochronous proton beams do produce a FLASH effect in various biological systems despite the lower IDR of the proton bunches compared to the IDR of electron pulses used for FLASH RT.  
Figure 1: Summary of preclinical studies at different accelerator facilities with different radiation types (right panel). Note the irradiation time for delivering 10 Gy on the vertical axis and the IDR of linac pulses or CW bunches on the horizontal axis. The FLASH effect has been observed for a wide IDR range of repeated linac pulses and different types of quasi-CW bunch delivery with iso-cyclotrons and synchrotron radiation light sources. FLASH effects were also seen with single electron pulses with IDR in the range of \(10^{6}\)-\(10^{7}\) Gy/s and \(10^{9}\)-\(10^{10}\) Gy/s, respectively. Modified from [PERSON] et al. [15]; for references of the individual data points, see that publication. We added the data point from [PERSON] et al. [16] and grouped the data according to delivery method.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 5 Discussion
### Potential Applications per Class
#### 5.2.3 Target Device  
The identification of the target device in PDoS attacks is a technical detail that aids in deploying a tailored and effective response by channeling resources toward the most critical and vulnerable systems. In incidents where multiple device types are affected, understanding the specific target devices helps in prioritizing actions based on their role and importance within the organization's infrastructure.  
Knowing the target device is instrumental in shaping the response teams' approach, especially in environments with diverse technological ecosystems. For instance, an attack on an ICS would necessitate a different response strategy compared to an attack on general-purpose PCs or IoT devices. ICS attacks might require specialized knowledge and tools for mitigation and recovery, underlining the importance of having a team with the right expertise.
# Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things
## 5 Conclusions  
This paper presents DCO to guarantee the timeliness and reliability of the IIoT system. The DCO is designed to provide high-priority channel access and reliable burst transmission. For the former, three types of MAC configurations are considered: \"CSMA/CA starting with SIFS\", \"freezing of the backoff period\", and \"reduced CW\({}_{\text{min}}\).\" For the latter, we consider the SIFS between consecutive packets instead of DIFS. The simulation results show that the DCO can guarantee the dominant-channel occupancy of Wi-Fi backscatter devices regardless of the interference from other legacy Wi-Fi devices in the vicinity. The CSMA/CA starting with SIFS and the freezing of the backoff period can improve the channel occupancy by 85% and 29%, respectively. The reduced CW\({}_{\text{min}}\) is the most effective MAC configuration for the initial channel access in separate cases. When CW\({}_{\text{min}}\) = 1, most of the shared wireless channel is occupied by Wi-Fi backscatter devices. Moreover, the Wi-Fi backscatter devices achieve much higher channel occupancy than the separate cases when multiple MAC configurations are used compositely. If all of the MAC configurations with CW\({}_{\text{min}}\) = 1 are employed for the Wi-Fi backscatter devices, they always have the highest priority for channel access. In the case where the Wi-Fi backscatter devices use the SIFS between consecutive packets, they transmit the packets successively regardless of the transmissions of the legacy Wi-Fi devices in the vicinity. This paper focuses on the design of DCO and performance evaluation via experimental simulation. Future work will be directed towards implementing DCO using available Wi-Fi open source device drivers, since this requires the additional low MAC development of a Wi-Fi helper. In particular, we will evaluate and verify the feasibility of DCO in real IIoT environments through such an implementation.  
This work was supported by NRF (National Research Foundation of Korea) Grant funded by the Korean Government (NRF-2016-Fostering Core Leaders of the Future Basic Science Program/Global Ph.D. Fellowship Program) (2016H1A2A1908620). This research was also supported by the Leading Human Resource Training Program of Regional Neo Industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2016H1D5A1910427).
# Two-Dimensional Constellation Shaping in Fiber-Optic Communications
## 3 Performance Comparison in Gaussian-Noise-Limited Channels
It is reasonable to find that PAS-64 QAM always has the best post-FEC performance over shaped 32 QAM. While in a realistic communication system, we believe that the performance of HPGS-32 QAM (opti-32 QAM) should be similar to that of PAS-64 QAM, due to the higher implementation penalties that PAS-64 QAM may suffer. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(C\) [b/s] & **H/R** & **R-32 QAM** & **Opti-32 QAM** & **PS-32 QAM** & **PAS-64 QAM** \\ \hline \multirow{2}{*}{3.33} & \(H(p)\) & 5 & 5 & 4.33 & 4.53 \\  & \(R\) & 2/3 & 2/3 & 4/5 & 4/5 \\ \hline \multirow{2}{*}{4} & \(H(p)\) & 5 & 5 & 4.55 & 5.2 \\  & \(R\) & 4/5 & 4/5 & 8/9 & 4/5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters used in the post-forward error correction (FEC) bit error rate (BER) analysis under the same capacity. Figure 7: Mutual information (MI) versus signal-to-noise ratio (SNR) performance in Gaussian-noise-limited channels for (**a**) PS/GS/R-8 QAM, (**b**) PS/GS/R-16 QAM, (**c**) PS/GS/R-32 QAM. Insets (a1,a2) The constellation diagrams of PS-8 QAM and GS-8 QAM. Insets (b1,b2) The constellation diagrams of PS-16 QAM and GS-16 QAM. Insets (c1,c2) The constellation diagrams of PS-32 QAM and GS-32 QAM.
## 2 Materials and Methods
### Build a Monitoring Model  
The dataset was divided into training (70%), validation (20%), and testing (10%) subsets. The training set was utilized for _kNDVI_-PT-Former model parameter learning, the validation set served as a reference for hyperparameter optimization and early stopping, and the testing set provided an unbiased assessment of the final model performance. A time-series stratified sampling strategy was employed to ensure that each subset contained data from all study periods (2019-2024). This approach maintained the temporal representativeness of the data and prevented overfitting to specific seasons or years. Prior to cropping, radiometric calibration and atmospheric correction of the two-period remote sensing images were performed using ENVI 5.3. After splitting, data augmentation techniques including random cropping (size: 256 \(\times\) 256 pixels), rotation (\(\pm\)90\({}^{\circ}\)), and horizontal flipping were applied to the training and validation sets to enhance the model's generalization capability.  
Next, the optimization parameter settings will be carried out. The Adam optimizer was selected due to its adaptability in handling sparse gradients and non-stationary objectives. Its default \(\beta\)1 and \(\beta\)2 values (0.9 and 0.999, respectively) were retained, and the weight decay was set to 1 \(\times\) 10\({}^{-5}\) to prevent overfitting. The initial learning rate was set to 1 \(\times\) 10\({}^{-4}\), and a step decay strategy was applied, reducing the learning rate by a factor of 0.9 every 10 epochs. This approach balanced the speed of convergence and the risk of overshooting the optimal solution. A batch size of 32 was chosen to optimize the utilization of GPU memory while ensuring sufficient statistical diversity within each batch update. The model was trained for a maximum of 100 epochs, with early stopping triggered when the validation loss did not decrease for 5 consecutive epochs. This strategy avoided overfitting and ensured efficient training.  
The hyperparameter tuning was conducted using random search, an efficient strategy for exploring the hyperparameter space. This approach randomly sampled 50 combinations from predefined search ranges, substantially reducing computational costs compared to grid search. The key hyperparameters and their ranges are listed in Table 2:The mean cross-entropy loss on the validation set was employed as the primary metric to evaluate hyperparameter combinations. The configuration yielding the minimum validation loss was selected as the optimal setup for the final model, ensuring robust generalization to unseen data. (Note: These settings were implemented using Python's scikit-learn library with 5-fold cross-validation on the validation subset to ensure robustness).
## 3 Testing Results
### Shear Modulus Analysis  
The shear modulus of the rock shows a corresponding trend with the accumulation of cyclic loading and unloading times, attributed primarily to the gradual expansion and coalescence of microcracks and fissures within the rock during the loading process. This results in internal damage that progressively diminishes the rock's strength and stiffness, ultimately leading to its destruction [31; 32; 33]. The changing trend of the shear modulus of sillimanite in cyclic loading and unloading tests primarily exhibits characteristics of initial decrease, subsequent stabilization, and eventual attenuation, influenced by numerous factors such as the rock's microstructure, loading frequency, and stress level. Understanding this change pattern deeply is crucial for predicting the engineering behavior of the rock.  
Figure 7 displays the shear modulus of the four specimens versus the number of cycles. Analysis of the four graphs reveals that both the loading and unloading shear moduli increased equally as the amplitude interval was incremented. During the loading phase, the average shear modulus was 28.22 GPa for X43, 34.78 GPa for X54, 40.91 GPa for X48, and 45.04 GPa for X53. The growth rates relative to the X43 specimen were 23.3%, 45%, and 59.5%, respectively. In the unloading stage, the average shear modulus was 27.58 GPa for X43, 34.01 GPa for X54, 40.07 GPa for X48, and 44.04 GPa for X53. The growth rates relative to the X43 specimen were 23.3%, 45.3%, and 59.7%, respectively. In the growth rate test, the loading and unloading shear moduli at the same cycle level decrease as the number of cycles increases, and the transverse value test indicates a general decline in both shear moduli throughout the stage.  
Gr is defined as the ratio of the loaded cutline modulus to the unloaded cutline modulus, as illustrated in Figure 7a. Initially, Gr rapidly increases with the number of cycles, and within the same cycle group, the ratio increases as the loading rate increases. Between two adjacent cycle groups, Gr suddenly drops when the loading rate changes from 6 kN/s to 2 kN/s, highlighting that the loading rate significantly impacts the cutline modulus. This phenomenon is consistent across the three specimens undergoing transverse value loading. In the increased load test, Gr's trend at the same cycle level mirrors the transverse amplitude load's performance, initially growing and then stabilizing after a certain value is reached. At different cycle levels, Gr initially shows a marked increase, which gradually diminishes as the cycle level increases.  
Figure 6: Hysteresis loop of axial stress \(\sigma_{d}-\epsilon_{d}\).
# A Lightweight Image Cryptosystem for Cloud-Assisted Internet of Things
### Contribution  
In this paper, a lightweight image cryptosystem named Hash XOR Permutation (HXP) has been proposed. The use of an S-box is eliminated in HXP by applying a hash algorithm, exclusive-or (XOR) operation, and bit permutation. Through this approach, non-linearity, confusion, and diffusion properties were built into the HXP. Specifically, the following have been contributed to the existing knowledge:  
1. The use of an S-box and bit permutation techniques to achieve the confusion and diffusion properties of the cryptography algorithm was replaced with the use of the XOR function and bit permutation.
2. A hash algorithm was introduced to make the cryptosystem sensitive to both changes in the key and plain image bits.
3. The innovations (i) and (ii) were applied to design a new lightweight image cryptosystem.  
The rest of this paper is prepared as follows. Section 2 presents a literature review. Section 3 explains the materials and methods employed in this study, while Section 4 presents the results and discussion of the results. Section 5 justifies the suitability of the proposed lightweight cryptography algorithm on resource-starved devices. Finally, the conclusion and suggestions for future work are presented in Section 6.
# Model Retraining upon Concept Drift Detection in Network Traffic Big Data
## 5 Experimentation Including the Results and Discussion
### Retraining the Model
#### 5.6.2 Incremental Retraining or Full Retraining?
Precision remains consistently high across all batches in both scenarios, holding steady at 100% except for minor dips in a couple batches, indicating the model continues to effectively minimize false positives regardless of retraining. Recall, however, declined steadily before retraining, from 91.85% to 84.64%, but after retraining, the recall increased steadily from 89.76% to 95.45%. Although retraining does not prevent this downward trend, it provides a slight improvement in the earlier batches. There was a similar pattern in the F1 score, with a steady decline before retraining, but a steady increase after retraining. This strongly suggests the evidence of anomalies, and the enhancement of results once the anomalies are detected and the model is retrained. The results of the full retraining, Table 16, are even more impressive than the incremental retraining. Full retraining results are higher than 99%, while incremental testing results are 95.45%. The \(Z\)-test gives a value of 56.7054, which shows us that there is a significant difference between these two percentages. However, full retraining demands significant computational resources and time, which could be problematic in real-time implementations. In our experimentation, full retraining took 1293.41 s while incremental \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Total** \\ **Records** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** & **F1-Score** & \
\begin{tabular}{c} **Total** \\ **Times (s)** \\ \end{tabular} \\ \hline Training & 557,924 & 99.09\% & 99.10\% & 99.09\% & 99.09\% & \\ \hline Testing & 139,348 & 99.03\% & 99.03\% & 99.03\% & \\ \hline \hline \end{tabular}
\begin{tabular}{c} 1293.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Model performance metrics: training vs. testing data—after full retraining. retraining took 191.72 s; that is, it took 6.74 times longer with full retraining. Hence, in real-time scenarios, the relative pros and cons have to be weighed before a decision is made on incremental versus full retraining.