esa-sceva/satcom-chunk-collection huggingface dataset:
# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 2 Materials and Methods  
### Overview of the System  
The object-environment fusion visual system is schematically shown in Figure 1. The system consists of three modules: object perception, environmental perception, and picking pose estimation. Object perception includes instance segmentation and object localization; picking pose estimation involves the sampler and evaluator. Object perception is responsible for identifying and locating objects, which provides the mask of the object and background for environmental perception, as well as the 3D position of the object for picking pose estimation. Environmental perception plays a role in analyzing the 3D space of the objects and obstacles. It provides the feedback signal and the 3D space of the obstacles for object perception and picking pose estimation, respectively. Picking pose estimation combines the information of the objects and environment to generate a set of collision-free picking poses.  
In Figure 1, object localization includes first localization and second localization. In the stage of objects' first localization, it is responsible for obtaining the masks of the object and background based on the results of instance segmentation. The masks of the object and background are provided to the environmental perception module to distinguish the 3D space of the object and obstacles. However, the integrity of the depth data is compromised by the varying illumination, which may result in the depth being zero or infinite. As shown in Figure 2, when environmental perception fails to obtain the normal depth of the object, a request signal should be fed back to the object perception module. It requests the re-initiation of object first localization to replace the masks of other objects until the object's depth is successfully perceived. Afterwards, we proceed to calculate the distribution of the obstacles in the background. Concurrently, it feeds back the control signal to the object perception module, which means supplying the point cloud of the object for the completion of second localization.
# An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
## 2 Materials and Methods
### Methods  
The proposed method consists of two processes: positive terrain-constrained ridgeline extraction and terrain feature-point cluster extraction. According to this workflow, the digital elevation model data are used as input data to obtain the optimal flow accumulation threshold for ridgeline extraction based on morphological analysis, and ridgelines are extracted based on this optimal threshold. The peaks and their control areas are extracted using the spatial subdivision method [33], and the saddles and runoff nodes are obtained by spatial intersection. Finally, the integrated terrain feature-point clusters are obtained by merging these extracted point clusters.  
Among them, the positive terrain-constrained ridgeline extraction includes three steps: (1) optimal window size determination of the positive terrain analysis; (2) optimal threshold determination of the flow accumulation; and (3) ridgeline extraction based on the runoff simulation method. In contrast, the terrain feature-point cluster extraction includes two main processes: peak extraction based on the spatial subdivision method and feature-point cluster extraction and calibration. More details of these steps are described in the following subsections.
## 3 Power Performance Assessment Methodology
### Calculation Flowchart  
The assessment flowchart of the power performance based on the PAM is shown in Figure 4. First, the useful parameters are collected from the SCADA system, including the nacelle wind speed, power, hub rotational speed, and electricity limitation command. Next, the time window length \(T\) and slide step length \(\Delta T\) need to be determined, as shown in Figure 5. The window length \(T\) is related to the quality of data of the considered wind farm, and these data are of a size similar to the baseline data used in this study. \(\Delta T\) is determined according to the assessment needs. SCADA data of 10 min are taken as an example; \(T\) pertains to 20 days, and the wind speed and power data pertain to 2280 days. If we set \(\Delta T\) as the data obtained in a day, then 144 data points should be collected. However, due to data loss, downtime data and abnormal data, the actual number of \(T\) is less than 2280, and \(\Delta T\) is less than 144. Therefore, to avoid the impact of insufficient data, \(T\) and \(\Delta T\) are determined by referring to the preprocessed data. The corresponding assessment result \(\gamma_{\text{index}}\) is thus not the result of equal time intervals, as shown in Figure 5.  
The baseline data and actual data are both divided into MWPTR and RPOR. The DBSCAN method is used to preprocess the data, in which Eps_opt is calculated using Equation (1). Next, the wind speed and power data in the MWPTR and RPOR are evaluated using the methods proposed in Sections 3.3.1 and 3.3.2, respectively. The values of \(\gamma_{\text{MWPTR}}\) and \(\gamma_{\text{rated}}\) of the wind turbine can be computed by Equations (8) and (9). After assessing the performance of the \(\mathbf{R}\) region, the new assessment data can be obtained by sliding the windows with step \(\Delta T\). The update method of the evaluation data is shown in Figure 5. With the same calculation process, the HV of different time windows can be obtained. For SCADA data pertaining to a long duration, two time series can be acquired, which are defined as \(\left\{\gamma_{\text{MWPTR}}^{1},\gamma_{\text{MWPTR}}^{2},\cdots\cdots, \gamma_{\text{MWPTR}}^{n}\right\}\) and \(\left\{\gamma_{\text{rated}}^{1},\gamma_{\text{rated}}^{2},\cdots\cdots, \gamma_{\text{rated}}^{n}\right\}\). Later, through analysis of the time series of the HV, operations and maintenance activities, such as performance assessment, degradation analysis and early warning, fault detection, can be better performed.  
Figure 4: Process flow of the performance assessment method.  
Figure 5: Method to update time window.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### MRP  
MRP is a Multipath Routing Protocol for clustering based on Ant Colony Optimization for WSNs [67]. MRP adopts a dynamic clustering algorithm and is divided into three main phases: cluster formation, multipath construction, and data transmission. During the cluster formation, only a node with higher residual energy, more neighbors and stronger RSSI (more than a threshold) has more opportunity of becoming a CH. An improved version of ACO algorithm is used in order to establish multiple paths with minimal energy cost. Three types of ants are used in MRP: _search ants_ (_SANTs_) which have to find paths and gather information along these paths until the sink; _backward ants_ (_BANTs_) which are used to evaluate the cost of each path discovered by the _SANTs_; and the _abnormal ants_ (_AANTs_), which are used to avoid the failure of the protocol. When the residual energy of the current CH is 50% lower than the average energy of all nodes in the cluster, a new CH will be elected. The CH will initiate a new route discovery process when the number of multiple paths is less than two, which means the reliability of path decreased significantly. The evaluation of MRP is conducted by the used of comparisons metrics like average energy, energy consumption, the deviation of energy and the network lifetime on a network with a population varying from 100 to 500 nodes. Based on these parameters, MRP is compared to the clustering algorithm TEEN and the multipath algorithms MP [68] and MACS [69]. The results have shown that MRP has the best average energy and the lowest energy consumption. The standard deviation of energy in MRP is slightly lower than in TEEN and results have also revealed that the proposed ACO-based improves the network lifetime significantly more than the other three protocols.
# Predicting Human Location Using Correlated Movements
## 7 Evaluation Results and Discussion
Specifically, there is 1 hidden layer of 150 sigmoid activation units in the NN of the BSB approach. In the NN-based predictor, 500, 150, and 50 sigmoid hidden units are used in the first, second, and third hidden layers, respectively. Weight and bias values are initialized with normal distribution. The \(L^{2}\) regularization coefficient is set to 0.01. Learning rate in the gradient descent and the momentum coefficient are set to 0.4 and 0.1, respectively. Full batch learning with 1500 iterations is examined. Note that only the training set is used to select the \(r\) PCMs in the first phase of the prediction models. For example, in case of the Dartmouth dataset, \(r\) PCMs are determined by using the training set of 1866 samples which are randomly selected from the total 3776 instances. Moreover, three types of temporal information are considered. The number of selected PCMs varies from 1 to 5, while top-\(k\) accuracy is considered with \(k=\{1,2,3,4\}\). In addition, un-grouped and grouped time slot features are compared. The bold text in the second column of Table 5 denotes default values of the prediction model. In this work, performance metrics including average and standard deviation of prediction accuracy are obtained over 5 runs. The results are collected via averaging the entire people. The most noticeable results are summarized as follows. From the conducted experiments, the performance of designed PLP model outperforms that of the baseline method, MFL. With regard to the PCMs selection methods, the proposed BSB shows significantly better performance than other PCM extraction approaches. In addition, the generalization capability of the proposed framework increases as a larger number of PCMs are embedded into the model. In particular, this increase is more clearly shown in case of BSB.
## 4 Simulation and Water Tank Experiment Results  
### Simulation Results  
In order to verify the feasibility of the method we proposed, we conducted simulation experiments on the photon-counting UOWC system based on deep learning and gave some simulation results. In the simulation experiment, we selected two typical water qualities,  
Figure 8: Production process of training data.  
Jerlov IB and Jerlov II; their attenuation coefficients are given in Table 1. In order to simulate real deep-sea communication, we did not consider the background light in the simulation process, but only considered the non-standard Poisson noise and dark counting noise in the process of SPAD detecting photons. The detailed parameters of the simulation are given in Table 2.  
The results of the performance evaluation of two network models are shown in Figure 9. The training data sets used for training and testing are different. It can be seen from the figure that, whether it is in Jerlov IB or Jerlov II water quality, as the transmission distance increases, the probability of CPD-Net recognizing the correct time slot synchronous clock phase value decreases. For CPR-Net, with the increase of the transmission distance, the error between the predicted phase value output by the network and the ideal phase will increase.  
As shown in Figure 10, we compared the pulse counting method and the deep learning method. It can be seen from the figure that under the same conditions, the system communication BER using the deep learning method will be lower. We can also see from the figure that the BER is lower when using regression-based CPR-Net to reconstruct the time slot synchronous clock phase value. The reason for this result is that the time slot synchronous  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Jerlov IB** & **Jerlov II** \\ \hline a(\(\lambda\))(m\({}^{-1}\)) & 0.064 & 0.087 \\ b(\(\lambda\))(m\({}^{-1}\)) & 0.08 & 0.216 \\ c(\(\lambda\))(m\({}^{-1}\)) & 0.144 & 0.303 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Jerlov IB, II water quality attenuation parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Modulation & On-Off Keying (OOK) \\ Baud rate (\(b_{r}\)) & 10 Mbps \\ Length of synchronization header (\(N\)) & 10 \\ Phase type of synchronization clock (\(M\)) & 20 \\ Sampling frequency (\(f_{s}\)) & 200 MHz \\ Wavelength & 450 nm \\ Power of LED & 1 w \\ Max efficiency & 35\% \\ Dark count rate & 25 Hz \\ Pulse width & 5 ns \\ Dead-time & 8 ns \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters of photon-counting UOWC system based on deep learning.  
Figure 9: Performance evaluation of CPD-Net and CPR-Net under different water quality.  
clock phase predicted by CPR-Net is more accurate, so the system communication BER using CPR-Net recovery time slot synchronous clock is lower.
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
## 4 Discussion
Dew point cooling is recommended mainly for air conditioning in the building sector, due to the fact that the parameters of air subjected to the dew point cooling process are suitable for ensuring thermal comfort conditions. A standard Dew Point Cooling (DPC), and therefore, the DPC-WR system, is characterized by an almost 10 times higher COP than other conventional cooling methods like Vapor Mechanical Compression (VMC), Absorption (AB) cooling, Adsorption (AD) cooling, or Thermoelectric Cooling (TEC) (Table 6). DPC, and thus the DPC-WR system, has many advantages over other conventional cooling methods i.e., low energy consumption, utilization of renewable psychrometric energy, high durability, or low failure rate due to the limited number of moving parts. However, DPC is not without some limitations, i.e., it is highly dependent on ambient air parameters, it has low effectiveness in humid climates, and has larger dimensions than other cooling methods.
## 4 Experimental Verification  
### Simulation Verification  
A set of bearing periodic impact fault simulation signals is designed to verify the effectiveness of the proposed method in the feature extraction of motor bearing fault signals. The specific expression is as follows:  
\[\left\{\begin{array}{l}y(t)=s(t)+n(t)\\ s(t)=\sum\limits_{i}A_{i}h(t-iT)\\ A_{i}=1+A_{o}\sin(2\pi f_{r}t)\\ h(t)=\exp(-Ct)\sin(2\pi f_{n}t)\end{array}\right. \tag{15}\]  
where \(s(t)\) is the impact fault signal, \(n(t)\) is the Gaussian white noise, and the signal-to-noise ratio is \(-13\) db, simulating real-world noise conditions. The amplitude \(A_{o}\) is 0.3, the frequency \(f_{r}\) is 30 Hz, the attenuation coefficient C is 700, the sampling point number is 4096, the sampling frequency is 16 kHz, the period \(T\) is 0.00833, and the fault frequency is 120 Hz. The time domain diagram and spectrum diagram of the generated simulation signal are shown in Figure 2.  
Figure 1: Flowchart for determining the optimal number of modes \(K\) and \(\alpha\) from SDE values.  
The simulation signal is decomposed by VMD, and the VMD parameters are optimized by SDE. The optimization iteration process of mode number \(K\) and penalty factor \(\alpha\) is shown in Figure 3.  
Table 1 is the correlation coefficient of IMF components obtained by VMD decomposition. IMF6 and IMF7 with a larger correlation coefficient are selected for signal reconstruction, and the reconstructed signal is input into the NLMS adaptive filter. From Table 2, it can be seen that, compared to no VMD-NLMS, the signal-noise reduction and reconstruction is decomposed, and the time domain waveform amplitude of the signal is reconstructed and significantly reduced, comparing the root mean square error and signal noise before and after reconstruction. It can be found that the root mean square error is reduced by quantitative analysis 60%, and the signal-to-noise ratio increased by about 119.87%. After VMD-NLMS decomposition and reconstruction, the root mean square error is greatly reduced, and the signal-to-noise ratio is significantly improved, indicating that the method has a significant noise reduction effect.  
Figure 3: SDE optimizes VMD parameters. (**a**) SDE optimizes \(K\); (**b**) SDE optimizes \(\alpha\).  
Figure 2: Simulation signal time domain, spectrum diagram. (**a**) Time domain waveform; (**b**) spectrum diagram.
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.2.4 Advantages and Disadvantages  
On the whole, there are many existing artificial interference features that can accurately reflect the physical characteristics of specific interference signals owing to long-term research on artificial feature extraction. The interference feature has clear mathematical definition and physical meaning. The structure of the classifier has good transparency and readability. Once there is an abnormal result, it is easy to locate quickly.  
The disadvantage of the traditional artificial feature extraction method is that it requires manual selection of the appropriate feature parameters for specific interference types. When adding new interference, it needs to re-select the parameters and train the classifier. In addition, when there are many interference types, the process of manual feature selection will be very  
The time spectrum image of the input signal can be obtained by short-time Fourier transform (STFT). STFT Fourier transforms the signal by selecting a sliding window function, and then takes the square of the modulus of the transformed signal to obtain the energy distribution map in time and frequency domain, which is called spectrum. The short-time Fourier transform provides a general method for the analysis of non-stationary signals in the field of signal analysis. Through the window function, the spectrum analysis of the signal in any time period can be realized. The perfect combination of time domain analysis and frequency domain analysis of the signal is helpful to extract the time-domain characteristics and frequency domain characteristics of the signal at the same time.  
Target detection technology based on deep learning has developed rapidly, and has exceeded the human level. In this scheme, the target detection algorithm is used to retrain the model through the self-made data set to make it have the ability to detect interference signals.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 4 Conclusions  
Soluble cellulose acetoacetate (CAA) was synthesized via the acetoacetylation of modified cellulose, utilizing cellulose and chitosan as raw materials. Chitin (CS) was subsequently cross-linked within a three-dimensional network. By optimizing the concentration ratio of CAA and CS to determine the appropriate gelation time, an injectable drug-loaded polysaccharide hydrogel with covalent enamine bonds (CAA-CS) was prepared under mild conditions. The physicochemical properties of the drug-loaded polysaccharide hydrogel (CAA-CS) were systematically investigated, including the analysis of microscopic morphology, stability, and rheological properties. The results indicated that the CAA-CS  
Figure 8: (**a**) The morphological characteristics and growth status of L929 murine fibroblasts cultured in a medium supplemented with IL-2 loaded gel extract for 3 days were examined using an optical microscope. (**b**) The fluorescence test images of L929 cells under different culture media (**c**). The cell survival rate of different extracts. (**d**) The cell survival rate at different times.  
series gels exhibited adjustable porous structures and favorable mechanical properties. Interleukin-2 (IL-2) was employed as a model drug to evaluate the encapsulation efficiency and drug release kinetics of the polysaccharide hydrogel. The findings revealed that the encapsulation efficiency of the CAA-CS hydrogel could reach up to 83.3%, demonstrating excellent sustained-release capability and drug encapsulation efficiency. Consequently, the polysaccharide hydrogels (CAA-CS) derived from CAA and CS hold significant potential as drug-loading materials with promising applications. Furthermore, CCK-8 assays demonstrated that the CAA-CS hydrogel exhibited excellent biocompatibility with L929 mouse fibroblasts, maintaining cell viability above 85%. The injectable CAA-CS polysaccharide hydrogel shows substantial potential for use in biomedical materials, such as drug delivery systems and tissue engineering.  
Design of the concept and study, Y.Z. and Z.M.; acquisition, analysis, and interpretation of data, Y.Z., X.J. and H.X.; drafting of the article, Y.Z. and X.J.; data curation and writing--review and editing, Y.Z. and X.J.; funding acquisition, Y.Z.; final approval of the version to be submitted, Z.M., Y.Z., H.X. and X.F. All authors have read and agreed to the published version of the manuscript.  
The Science and Technology Plan Project of XPCC (Grant No. 2024 AB060).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors are thankful to the Science and Technology Plan Project of XPCC (Grant No. 2024 AB060). This research received support from Shanxi Medical University.  
The authors declare no conflicts of interest.
## 4 Channel Selection Algorithm Based on Multi-Armed-Bandit Algorithm
### Learning Rules of the Multi-Armed-Bandit Algorithms
#### 4.2.2 Upper Confidence Bounds1-Tuned Based Algorithm  
The UCB1-tuned algorithm is the best MAB algorithm with no parameter. It is also widely used in various applications. The characteristic of this algorithm is that it considers the mean value of the reward and the variance value \(V_{k}\) of the number of selections for each channel, as shown in Equation (3). After selecting all channels and transmitting once, to select channel \(k^{*}\) in \(t+1\) trial is with the highest UCB value \(X_{k}\), as represented by (4). Algorithm 2 describes the UCB1-tuned based channel selection process.  
\[X_{k}(t) =\frac{R_{k}(t)}{N_{k}(t)}+\sqrt{\frac{\ln t}{N_{k}(t)}\min(1/4,V_{k} )}, \tag{3}\] \[k^{*} =\arg\max_{k\in K}X_{k}(t). \tag{4}\]  
```
1: Initialize \(Q_{k}(0),R_{k}(0),N_{k}(0)\)
2:while wake time \(t=1,2,\ldots\), is not expired do
3:if\(t<K\)then
4: Select channel \(k^{*}=t\)
5:else
6: Select channel \(k^{*}=\arg\max X_{k}(t)(\forall k\in K)\)
7:endif
8: Transmit the data frame
9: Update \(N_{k}(t)+1\)
10:if the data frame is transmitted and the corresponding ACK frame is received then
11: Data transmission succeed
12: Update \(R_{k}(t)+1\)
13:else
14: Data transmission failed
15:endif
16: Calculate variance \(V_{k}\)
17: Update \(X_{k}(t)\) given by Equation (3)
18:\(t=t+1\)
19:endwhile
```  
**Algorithm 2** UCB1-tuned based channel selection algorithm.