esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In modern agriculture, reducing the internal moisture content of crop seeds is essential to enhance the activity and mobility of seed oil molecules, thereby increasing oil yield while minimizing the risk of mold and deterioration. However, traditional drying methods often result in uneven heating, leading to seed scorching and diminished drying efficiency and economic returns. To address these limitations, this study proposes a novel thin-layer seed drying system incorporating a redesigned drying tray structure. Specifically, the system places the seed-bearing tray beneath a vibration module operating at a predetermined frequency. The vibration mechanism induces the uniform motion of the seeds, thereby preventing localized overheating (scaling) and enabling automatic weighing for the real-time monitoring of moisture reduction during the drying process. The advancement of wireless sensor technologies in agriculture has enabled the deployment of more refined, large-scale monitoring networks. In this work, a commercial chip-based piezoelectric vibration detection device was integrated into the experimental setup to collect time-domain response signals resulting from interactions among seeds, impurities, and the drying tray. These signals were used to construct a comprehensive database of seed collision signatures. To mitigate discontinuities in signal transmission caused by vibration and potential equipment failure, the shortest routing protocol (SRP) was implemented. Additionally, the system outage probability (OP) and a refined closed-form solution for signal transmission reliability were derived under a Rayleigh fading channel model. To validate the proposed method, a series of experiments were conducted to determine the optimal vibration frequencies for various seed types. The results demonstrated a reduction in seed scaling rate to 1.5%, a decrease in seed loss rate to 0.4%, and an increase in moisture monitoring accuracy to 97.0%. Compared to traditional drying approaches, the vibrating drying tray substantially reduced seed loss and effectively distinguished between seeds and impurities. Furthermore, the approach shows strong potential for broader applications in seed classification and moisture detection across different crop types.  
crop seeds; vibration module; wireless sensors; vibratory piezoelectric detection device; time-domain response signals +
Footnote †: journal: Applied sciences
## 6 Results and Discussion  
The study area used was in Hangzhou City. The remote sensing image is a Worldview II image with 8 multispectral bands and a panchromatic band. To integrate the advantages of multispectral and panchromatic images, the images were fused first. Because of the high correlation among the various bands, bands 1, 4, and 8, which had a low correlation, were selected as the best band combination for use in this study. A correlation analysis was performed to determine this result. Then, the sample set was obtained using the method proposed in Section 3. The training set contained 2000 samples and the testing set included 500 samples, including roads, forests, green space, water bodies, and residences.  
To verify the validity and rationality of the CAE_CNN model, three experiments were carried out. The first experiment utilized the proposed CAE_CNN model to classify the remote sensing objects. The second experiment took the input, encoder, and feature maps module in the CAE of Figure 2 and connected the layer that produces feature maps to Conv1 of the CNN in Figure 2. We used the softmax classifier and trained the resulting network with supervision directly; this model is referred to as the CAE_SMX model. The third experiment analysed different pure CNN architectures to classify the original image objects and resulted in the selection of the best one; the resulting model contained nine layers with weights, including eight convolutional layers and a fully-connected layer. The structure is more complicated than the CAE_CNN model (as shown in Figure 7). This model is referred to as the Best_CNN model.  
Figure 7: Architecture of the Best_CNN model (the green layers represent the feature maps obtained by convolution operations, and the yellow layers represent the feature maps obtained by max pooling operations).  
The three experiments were evaluated based on the overall accuracy, temporal efficiency, and dependency of the labelled samples.
## Materials and Methods
### Devices and Analysis  
#### 2.2.1 Experimental Equipment Configuration and Analysis  
A rigid \"cantilever\" structure [35] was used to transmit the weight and impact forces to the torque sensor. The lever length increases the delivered torque, improving the reliability of the measurements. The same cantilever was used to transmit the shaking motions to the model. The cantilever structure also distances other equipment from the rain field. The structure was designed as a hollow shell to minimize its mass and eliminate the potential impact on the measurements; the material is stiff, and so it transmits torque and driven vibrations without significant effects. The surface of the cantilever was also made superhydrophobic by the coating method described above. Through this connection, the torque sensor can directly measure the multidrop impact load (equivalent to torque). The measurement range of the miniature torque sensor is from 0 to 2 Nm, the sampling rate is 200 Hz, and the sensitivity is 1.5 \(\pm\) 0.001 mV/V. It is connected to the computer through a signal amplifier, and the measured torque is calculated, converted, and written into the computer through the supporting software. The configuration of the cantilever in both its uses is shown in Figure 2.  
A high-speed-camera system was designed to quantitatively capture images of the simulated precipitation as it interacted with the models. The small water pump with the adjustable flow has a lift of 5 m and a water-output range from 0 to 10 L/min. The water is filtered by a reverse-osmosis device, and the water outlet of the pump is connected to a transient flowmeter. The working range of the transient flowmeter is from 1 to 10 L/min, and the measurement accuracy is \(\pm\)1%. By controlling the flowmeter to read 1.10 L/min, the water level in the sprinkler can be kept constant. The model was placed in the simulated-precipitation area; images were captured by the high-speed camera (Phantom v711, Vision Research, Inc., Wayne, NJ, USA), and the physical parameters (speed, vibration status, etc.) were determined by customized open-source image-processing software (Image). The resolution of the images was 1280 \(\times\) 800 pixels, the sampling rate was 3000 frames per second (fps), and the exposure time was 330 \(\upmu\)s. All droplets were released from the same height. The diameter (D\({}_{0}\)) of the droplets was 4.2 mm, and the critical velocity (v\({}_{0}\)) of the impact was kept at 1.9 m/s.  
Three experimental modes were selected: The first mode was to drive the aircraft with a stepping motor to keep it moving in a straight line at a constant speed of 0.3 m/s (Figure 2b). The second mode was to use resonant speakers to induce the vibration of the aircraft model, simulating the vibration generated by aircraft during flight (Figure 2c); the vibration in this mode was controlled by a computer and analyzed by the Fourier transform method [36] (see \"S2. Vibration Control\" in the Supplementary Material). The third mode had the model stationary in the rain field to obtain data for comparing the responses of the hydrophilic and superhydrophobic models.
# Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication
## 2 System Model
### Transmission Parameters and Achievable Rates
The total bandwidth for D2D OMA communication is equally allocated to the total number of pairs and is equal to \(N/2\). Thus, the channel bandwidth that allocated to a pair of users with regard to the corresponding D2D link is equal to \(B_{c}^{D2D}=2B_{d}/N\). Also, it is noteworthy that the achievable rate for user UE\({}_{j}\) is equal to the achievable rate for user UE\({}_{i}\) in case of a D2D link \(R_{i}^{D2D_{(i,j)}}=R_{j}^{D2D_{(i,j)}}\). Finally, concerning the CSI reporting procedure, pilot symbols are sent from the satellite, prompting the UEs to estimate their satellite link CSI. Simultaneously, UEs transmit pilot symbols, enabling UEs within their coverage to estimate their D2D channel conditions. In the next step, the satellite receives the CSI of the satellite and D2D links, and while acting as a central node, executes SANOCO-D2D. It should be noted that the UEs are able to identify whether the D2D link is profitable compared to standalone optimal NOMA user pairing, by comparing the corresponding achievable rates. If a D2D link between a pair of users is not profitable, it is an outage, so UEs notify the satellite. After that, the satellite notifies the UEs about the formed pairs and whether or not the D2D link will be used. Moreover, retransmissions rely on an Acknowledgment/Negative-Acknowledgment (ACK/NACK) mechanism, where the UEs broadcast short-length error-free packets via a separate narrow-band link, informing the network on whether or not the packet transmission was successful.
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 2 Underwater Photon Propagation Model
### Monte Carlo Algorithm
#### 2.1.3 Validation of the Algorithm  
The photon propagation algorithm validity was confirmed by comparing it with experimental results for on-axis laser beam propagation through a closed 3.6 m water tank taken by [PERSON] and [PERSON] [PERSON] and [PERSON] (2018).  
The communication system was composed of an emitter, a 532 nm diode-pumped solid-state laser with a 2 mm beam diameter and divergence of 1.5 mrad and a receiver composed of a 25.4 mm achromatic biconvex lens at a 75 mm focal distance of a photomultiplier tube (PMT) unit with an 8 mm active area. The laser was considered collimated in the simulation, and the different levels of water attenuation coefficients (water types) were obtained by adding Maalox as a scattering agent.  
This simulated scenario is the same one in which [PERSON]'s code was validated experimentally. The average power was measured as a function of the attenuation coefficient. The normalized intensity power was plotted versus the attenuation coefficient in Figure 4. Good agreement can be seen for all the points at which the simulation was performed.
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 2 Literature Review
### Capacity Outage Probability Table Method  
The capacity outage probability table (COPT) method was used to obtain the LOLP by estimating the outage capacity distribution according to the failure of the generator [10]. The general formulation of COPT is expressed as follows:  
\[\text{COPT}_{k}(x)=(1-FOR_{k})\text{COPT}_{k-1}(x-C_{k})+FOR_{k}\text{COPT}_{k- 1}(x), \tag{2}\]  
\[\text{COPT}_{0}(x)=\begin{cases}0,&x>0\\ 1,&x\leq 0\end{cases}, \tag{3}\]  
where \(\text{COPT}_{k}(x)\) denotes the probability of \(x\) outage capacity in generators \(1\) to \(k\), while \(FOR_{k}\) and \(C_{k}\) represent the failure probability and capacity of the generator \(k\), respectively. The COPT is updated through a recurrent process. In (2), the initial term (\(1-FOR_{k}\) term) corresponds to the contribution of generator \(k\) to the system, whereas the subsequent term (\(FOR_{k}\) term) shows the impact of the outage of generator \(k\) on the system. Employing COPT, the LOLP can be expressed as follows:  
\[\text{LOLP}=\int_{ICP-d}^{ICP}\text{COPT}_{N}(x)dx, \tag{4}\]  
where \(\text{COPT}_{N}\) is the capacity outage probability table by all generators within the system; \(ICP\) signifies the total installed capacity; \(d\) is the system demand. A loss of load occurs when the system availability falls below \(d\), i.e., \(ICP-x<d\). Consequently, LOLP equals the integral of \(\text{COPT}_{N}\) over the interval \([ICP-d,\ ICP]\).
## 4 Verification Experiment Results and Analysis
### HiLExperiment Results  
The actual trajectory is shown as the black dashed line in Figure 6, for a vehicle tracking the target trajectory at a speed of 30 km/h. The tracking lateral deviation mean square error is \(8.7814\times 10^{-4}\) m\({}^{2}\), which shows that the vehicle can track the target trajectory well. The mean calculation time is 6.7 ms. It is obvious from Figure 7 that the computing time for each step was within 100 ms, which illustrates that a real-time solution of the non-linear programming problem was realized under conditions of ensuring accuracy.  
The lateral deviations at speeds of 10, 20, 30, and 60 km/h are shown in Figure 8. As vehicle speeds increase, the amplitude of the lateral deviation fluctuates gradually, and remains in a small range, which illustrates the stability of the algorithm at different speeds. The AB and CD segments had large curvatures relative to the overall path. When the vehicle entered these seg  
Figure 6: Experiment schematic diagram.  
Figure 7: Computing time for each step at 30 km/h.  
increased slightly, but still fell within a low range, verifying the accuracy of the algorithm. Throughout the experiment, a total of nine speeds between 10-90 km/h were selected to complete trajectory tracking. The mean square errors for tests at different speeds were used as the evaluation index for trajectory tracking, as indicated by the solid line in Figure 9. As vehicle speeds increased, the steady-state mean square error values slowly increased. At low speeds between 0 and 30 km/h, the mean squared error values remained at approximately \(10^{-4}\) m\({}^{2}\). At medium and high speeds, the mean squared error values remained at approximately \(10^{-3}\) m\({}^{2}\). The steady-state mean square error values for different vehicle speeds reflect the algorithm's good stability and trajectory tracking ability. During actual automatic driving, large deviations may cause accidents. The mean square error cannot be used to effectively evaluate special situations, so we take the max deviation as another evaluation index. It can be seen from Figure 9 that the maximum deviation points are less than 0.6 m which guarantees the driving safety. The maximum deviation points can be divided into two trend lines. This is referred to as an overlap for a nonlinear system, and is a potential subject for future research.  
Figure 8: Lateral deviations.  
In order to compare the effects with other algorithms and prove the superiority of the MPC algorithm, we use the LQR algorithm to follow a similar target trajectory, and the effect is shown in Figure 10. The speed is set to be 30 km/h. It is clear that the vehicle cannot follow the target trajectory very well while using LQR algorithm. It is because LQR does not have a predictive model so that there will be a lag during the following process which causes a larger lateral deviation than MPC, as can be seen from Figure 11. Comparing the effects of MPC and LQR, it is convinced that MPC can guarantee the safety to some extent.
# Review of Anomaly Detection Algorithms for Data Streams
## 6 Algorithm Complexity and Scalability Analysis
### Semi-Online-Learning-Based Data Stream Anomaly Detection Algorithms
This is because incremental update algorithms exhibit better adaptability to unknown anomalies. * In the category of algorithms using offline training combined with incremental updates:
* The algorithm based on subspace partitioning utilizes subspace data structures, providing a unique advantage in feature extraction. Consequently, it exhibits the best scalability among the other three types. * The algorithm based on the similarity of data distribution has complexity that correlates with the statistical characteristics of the data, resulting in relatively good scalability compared to the other three types. * In the category of algorithms using offline training combined with batch updates:
* The algorithm based on the similarity of data distribution has complexity that is related to the statistical characteristics of the data, leading to relatively poorer scalability compared to the other three types. * The algorithm based on the classification principle typically requires more computational resources and time for model training, resulting in the worst scalability among the other three types.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Privacy
#### 3.2.4 Usage Privacy  
Although each BC user has a public pseudonymous address, all transactional data such as receiver and sender are accessible by all participants. Users' activity can be monitored by examining the data saved on the BC. Real users' identities might be discovered by merging the information analyzed from the BC with specific external data. When a user's identity is disclosed, all of the user's actions will be tracked, and their personal data, including financial secrets, will be revealed [26].  
As for location privacy, the BC segregation technique can help in maintaining usage privacy [10]. Litecoin announced that MW would be included to improve privacy. Cryptographic commitment methods, such as the Pedersen commitment, provide another approach to obfuscate transaction information. By using these methods, a sender can commit transaction data and communicate the commitment rather than the data [26]. The sender cannot later mislead about a fictitious value of the original data (which remains hidden). Third parties may be able to verify that a transaction's input and output numbers are similar without disclosing them in financial transactions if such guarantees are made (e.g., cryptocurrencies). Other cryptographic promises, such as the Ouroboros Crypsinous BC, are also utilized [26].
## 5 Discussion
### System Usability  
G-HI scores the lowest for system usability, as shown in Figure 7, while TI scores significantly higher. There is no significant difference in usability between G-HI and VI. However, VI is familiar to most participants because of technologies like Google Assistant and Siri. The SUS assesses each input's usability subjectively. Most participants have no experience using G-HI, unlike voice and touch inputs, which we believe contributed to its preference and usability rating; as drivers become more accustomed to it, we expect G-HI preference and usability ratings to improve. Moreover, participants noted the advantage of G-HI in open-ended questionnaires: \"_Gaze-head is fast and promising, but it can be improved (e.g., showing a cursor for eye movement)_\" Participant 16; \"_During a call or [when] my hands are busy, gaze-head will be better_\" Participant 1; \"_Gaze-head interaction was easy to use_\" Participant 11.  
Perceived Workload Measurement (National Aeronautics and Space Administration Task Load Index, NASA-TLX)  
Workload measurement reflects no significant differences in using the three input interactions, however using G-HI scores the highest, as shown in Figure 9. Looking closer at the six factors in the NASA-TLX calculation as shown in Figure 10, G-HI creates a significantly higher cognitive load and frustration, but its physical load is significantly lower than the other input interactions. For example, using TI interrupts user engagement in the NDRT \"using smart phone\" and thus causes an extra physical burden. This indicates that hands-free input interactions (G-HI and VI) are more effective while engaged in NDRTs, which confirms findings that drivers are likely to use both hands to engage in NDRTs in AVs [3]. Participant behaviour when performing G-HI shows clear confusion because of the lack of familiarity compared to VI and TI. When we asked participants to nod to trigger the selection, some participants rolled their heads instead of pitching them even though we trained them how to perform the action. This may be due to cultural differences, as in some societies rolling the head rather than nodding is an expression of assent. Consequently, G-HI needs more time before it can be a universal input interaction.
## 3 Results and Discussion  
### Compressive Strength and Flexural Strength of HSC in Different RH  
Figure 1 illustrates the strength evolution patterns of various concrete types under different RHs. As depicted in the figure, both compressive and flexural strengths of all HSC variants exhibit an increase with rising RH. Optimal mechanical properties are observed for HSC under standard curing conditions (RH > 95%). Specifically, concerning the compressive strength of HSC, HSC-5 demonstrates a compressive strength 1.19 times higher than that at 30% RH and 1.02 times higher than that at 50% RH under standard curing conditions. This implies that dry environments negatively impact the development of HSC strength.  
Among high-strength concrete with various mix ratios, HSC-5 displays the highest compressive and flexural strengths. Taking standard curing conditions as an example, the compressive strength of HSC-5 is 1.87 times that of the base concrete, and the flexural strength is 2.84 times that of the base concrete. This indicates a positive influence on HSC strength due to the addition of expansion agents and steel fiber.  
Figure 1: The Law of Concrete Strength Variation with Respect to Relative Humidity.  
For HSC with varying mix ratios, the compressive and flexural strengths follow the order: HSC-5 > HSC-3 > HSC-6 > HSC-2 > HSC-4 > HSC-1. This suggests that both the expansion agent and fiber contribute to enhancing the strength of HSC. Notably, the composite of expansive agent and steel fiber results in the highest HSC strength, and the strength of HSC with steel fiber exhibits the least variation in dry environments.
## 5 Conclusions  
State detection and RUL prediction of an HSV applied to a water hydraulic manipulator present a complex challenge, and there is a non-linear relationship between states and manifestations. The present study proposed a hybrid model to detect the state and predict the RUL of water hydraulic HSVs used for water hydraulic manipulators. The major conclusions of the current study are outlined below:  
1. An HSV current acquisition system was constructed. This system was used to analyze the current characteristics of the HSV under degrading performance. A health index was defined, and the entire life of the HSV can be defined as three states.
2. The current study proposed a state detection method for an HSV based on the FNN algorithm. A total of 300 groups of current signals were selected for neural network training and 60 groups of current signals were used for verification of the accuracy of the neural network. The state detection results showed the high accuracy of 93.3% based on the FNN algorithm.
3. A life prediction model of the HSV based on the ARIMA-LSTM model was constructed to predict the RUL of the HSV. The results showed that the ARIMA-LSTM model can accurately predict and track the degradation trend of an HSV, with a relative error of 1.6% for the predicted RUL of the HSV.  
The developed method can provide guidance for operation and maintenance personnel to reasonably plan maintenance of water hydraulic HSVs used for water hydraulic manipulators.  
**Author Contributions:** S.N. ([PERSON]): methodology, supervision. Q.L.: investigation, data curation, writing--original draft preparation. H.J.: conceptualization, software. R.H.: software, data curation. S.N. ([PERSON]): software, writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant Nos. 51905011, 51975010, 52005013, 52075007). The authors are very grateful to the editors and the anonymous reviewers for their insightful comments and suggestions.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 13: Prediction curve after filtered.  
Data Availability Statement: All data included in this study are available upon request by contact with the corresponding author.