esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes a method to store excess or missing heat from thermal units during deep peaking in a molten salt thermal storage system by extracting or adding steam. Based on a MATLAB software simulation platform, a coordinated control simulation model of the thermal power unit of the coupled molten salt reservoir is established, and the coordination control strategy of the steam heating and heat depth modulation system of the fire power plant is developed. The influence of the control ability and the AGC is verified. The contributions and conclusions of this work are summarized by the following:  
1. The model can precisely analyze the coordinated control system of the heat depth peak regulation of the molten salt storage for steam extraction heating.
2. The coordinated control strategy of the steam-molten salt heat exchanger, molten salt and water heat exchanger, and steam turbine main control reduce the depth of the FPU by 10% THA.
3. The coordinated control approach of the thermal power units with a steam extraction system coupled with a molten salt thermal storage system could effectively improve the AGC response speed of the thermal power units, and the output power response speed of 302.55 s could be increased by 75.60%.  
By contrasting various thermal storage techniques for thermal units, we plan to study the corresponding potential for deep peaking of thermal units as well as primary frequency control in our upcoming work. We are also interested in examining whether developing molten salt heat storage technology will enable us to lower the fire power unit's deep peak-regulating capacity to 25% THA.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Response** & **Output Power** & & **No Storage** & **Percentage** \\
**Time/s** & **/MW** & & **Heat** & **Increase/\%** \\ \hline
73.32 & 237.6 & 0.7959 & 0.007175 & 99.10 \\
302.55 & 237.6 & 0.8101 & 0.1977 & 75.60 \\
500 & 237.6 & 0.8123 & 0.3658 & 54.97 \\
600 & 237.6 & 0.8114 & 0.4358 & 46.29 \\ \hline \end{tabular}
\end{table}
Table 1: The heat effect of the power units on the steam heating and melting salt storage heat.  
**Author Contributions:** Conceptualization, L.L. and W.L.; methodology, L.L. and W.L.; software, L.L.; validation, L.L., W.L. and J.M.; formal analysis, L.L.; investigation, L.L. and J.M.; resources, L.L.; data curation, J.M.; writing--original draft preparation, L.L.; writing--review and editing, L.L., W.L. and J.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the 2021 special purpose of the Inner Mongolia autonomous region: \"Key technical research on hot heat storage heat storage heat storage heat storage heat storage heat storage\" [No. 2021 ZD0036].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 The Receiving Process of the Composite Signal
### The Tracking Process of a BPSK Signal  
In the tracking system, we obtain the Doppler frequency and code tracking results of a BOC signal. A BOC signal and BPSK signal have the same carrier frequency and phase. Therefore, the carrier tracking results of Section 3.2 can be used by BPSK demodulation. For the bit synchronization of the BPSK signal, since the frequency of the PN code in the BOC signal at the transmitting end is the same as the chip rate of the BPSK signal, the chip lengths of the two received signals are equal without the Doppler frequency. However, the code frequency will change due to the influence of the Doppler frequency. The PN code frequency and Doppler frequency are shown in Equations (11) and (12), respectively.  
\[f_{\text{PNcode}}(t)=\lambda f_{\text{RF}}(t)=f_{\text{PNcode}}+\lambda f_{d}(t) \tag{11}\]  
\[f_{\text{RF}}(t)=f_{\text{RF}}+f_{d}(t) \tag{12}\]  
where \(f_{\text{PNcode}}(t)\) and \(f_{\text{RF}}(t)\) are the PN code frequency and radio frequency (RF) at time \(t\), respectively. \(f_{\text{PNcode}}\) and \(f_{\text{RF}}\) represent the PN code frequency and RF without the influence of the Doppler frequency, respectively. \(\lambda=\frac{f_{\text{PNcode}}}{f_{\text{RF}}}=\frac{f_{\text{PNcode}}(t)}{f_{ \text{RF}}(t)}\) is the ratio of the chip rate to RF output frequency.  
The chip rate \(f_{\text{BPSKcode}}(t)\) of the BPSK signal at time \(t\) is shown in (13):  
\[f_{\text{BPSKcode}}(t)=\lambda_{B}f_{\text{RF}}(t)=f_{\text{BPSKcode}}+\lambda _{B}f_{d}(t) \tag{13}\]  
Here, \(f_{\text{BPSKcode}}\) is the chip rate of the BPSK signal without Doppler influence. \(\lambda_{B}=\frac{f_{\text{BPSKcode}}}{f_{\text{RF}}}=\frac{f_{\text{BPSK code}}(t)}{f_{\text{RF}}(t)}\) represents the ratio of the BPSK chip rate to the RF output frequency. According to (11-13), when \(f_{\text{BPSKcode}}=f_{\text{PNcode}}\), the PN code frequency is also equal to the BPSK chip rate when the Doppler frequency exists. Therefore, the chip offset of the BOC signal is the same as that of the BPSK signal. This means that we can simplify the bit synchronization process of the BPSK signal by using the bit synchronization clock of the BOC signal.  
The time flow is shown in Figure 5. The DLL of the BOC signal outputs the bit synchronization clock, which assists the demodulation of the BPSK signal. In addition, the signal is accumulated according to the synchronization clock and Equation (14), and the resulting symbol is the communication data in the BPSK signal.  
\[b(n)=\sum_{w=(n-1)T_{s}^{\prime}}^{nT_{s}^{\prime}}s_{r}(w) \tag{14}\]  
Here, \(s_{r}(\cdot)\) is the BPSK signal. \(T_{s}^{\prime}\) is the interval of the synchronization clock.
# Chord Recognition Based on Temporal Correlation Support Vector Machine
## 4 Automatic Chord Recognition  
Our chord recognition system entails two parts: support vector machine classification and the Viterbi algorithm. For SVM classification, we use LIBSVM (Library for Support Vector Machines) to obtain the chord probability estimates [46]. Then the Viterbi algorithm uses the probability estimates and trained state transition probability to estimate the chord of the music. Because of the temporal correlation of chords, we combine the SVM classification with the Viterbi algorithm and call the system TCSVM (Temporal Correlation Support Vector Machine).
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 6 Performance Evaluation  
In this section, we present simulation-based studies to evaluate the performance of the proposed protocol based on ns-2 simulator (version 2.29) and Matlab. We compare our protocol with four other well-known MAC protocols; IEEE 802.11b DCF [6] (dented by 802.11 DCF), LMAC [12], IEEE 802.11e EDCA [13] (denoted by 802.11 EDCA), DMAC [5] and CoopMAC [19]. In addition, the simulation setup, the results, and the analysis of different scenarios are presented.
## 6 Evaluation of the USN-Platform Semantic Extension
### Discussion
Much longer response times, of around 25 s, are obtained for Query 1 and repository sizes of 500,000 observations. These delays are inadequate for interactive applications in the context of smart devices. However, they can be perfectly acceptable for other non-real-time information systems such as applications for medium-term optimization of smart grid infrastructures (corresponding to queries which, like Query 1, wish to access all of the electric vehicles referenced in a repository). From these experiments we can conclude that the most suitable solution for the long-term evolution of the USN-Platform is to offer both traditional and semantic functions, the latter considered as an extension of the current offering. In this manner existing applications using traditional mechanisms would continue to work without having to be altered, while future applications may decide whether semantics is appropriate for them or not (considering the increased response times for queries requiring inferences).
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection  
Because of the limitations of the GPU's memory, the original large-scale SAR remote sensing images should be broken into image patches before target detection. Because of the large-size of the original image, the number of image patches will be relatively large, which will not only take up the limited storage, but it will also result in the model processing many empty image blocks. This is why this paper proposed an on-board ship detection scheme based on the CFAR algorithm and deep learning. Using the CFAR method before deep learning, target recognition can significantly reduce the number of image patches submitted to deep learning detection. Because of the high complexity of the deep learning algorithm, the processing time will increase with an increased number of target patches. Therefore, the first extracting targets using CFAR will greatly reduce the computations that are required by deep learning. Our experimental results showed that using the CFAR method before deep learning reduced the computational time by more than half when compared to using only the deep learning method. First, the image is categorized as either sea or land, and if it is includes the coast, it will be divided by sea and land. This is done to prevent land features from affecting the detection of ships. Subsequently, the CFAR algorithm is used for an initial quick and rough ship detection, and any detected targets are stored as 256 \(\times\) 256 pixels image patches. The resulting image patches are input into the trained and lightweight deep learning model for further detection to obtain a more accurate final ship detection. Finally, the targets are mapped on the original image to obtain its pixel coordinates, the latitude and longitude coordinates of the four corners of the original image are used to determine the targets actual location using the bilinear interpolation method, and then this effective information is transmitted through the satellite-earth data transmission system. Figure 1 shows the process of on-board ship detection.
# Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network
## 3 Application of Recurrent Network
### Estimation of Moving State and Offset Voltage  
Assigning \(t=0\) in Equation (9), \(v(0)\) is determined with the initial value of mean curvature \(\bar{k}(0)\). This means that, if \(\bar{k}(0)\) is nonzero, there is a voltage offset. Ideally, the mean curvature is zero at the original position. However, it is difficult to achieve zero mean curvature in a real case. In addition, given that a finger moves continuously in real-time experiment, the initial mean value can have smallvariation when it comes back to the original position. The difference between original position and varied position also affects the offset value.  
As shown in Figure 4, calculation of joint angle values using improper offsets results in incorrect results. If \(v(t_{m})\) is the same as the \(O(t_{m})\), as shown in Figure 4a, the joint angle is calculated normally. However, if there is a difference between the estimated offset and real offset of the raw data, as shown in Figure 4b,c, the error \((v(t_{m})-O(t_{m}))\) is integrated as the time \(\Delta t_{m}\). Therefore, the selection of an appropriate offset voltage value is an important issue. In [18], we defined the offset value \(O_{a}\) as the average of 3000 data, such that:  
\[O_{a}(t_{n})=\frac{\sum_{m=n-2999}^{n}v(t_{m})}{3000}. \tag{11}\]  
Using the average value is a reasonable choice for following the tendency of the signal. However, the average value can oscillate when data having a large difference are entered, and a slight offset change is applied slowly. To solve this problem, we determined the offset voltage with voltages for non-movement to update \(\bar{k}(0)\) in [19]. Assuming only curvature change of the sensor results in significant change of piezoelectric voltage sensor, the heuristic moving state \(m_{h}\) is estimated as follows:  
\[m_{h}(t_{n})=\begin{cases}0,&|\Delta v(t_{n})|<v_{th}\\ 1,&|\Delta v(t_{n})|\geq v_{th}\end{cases} \tag{12}\]  
\[\Delta v(t_{n})=v(t_{n})-v(t_{n-1}). \tag{13}\]  
where \(v_{th}\) is the threshold voltage for deciding the moving state. We consider the finger as moving when \(m_{h}(t_{n})=1\) and stopped when \(m_{h}(t_{n})=0\). This method can continuously renew offset change that occurs via variation of \(\bar{k}(0)\) faster than previous one. However, heuristic moving state estimation of Equation (12) confronts following problems in a real-time system:  
* If noise is added to \(v\), it is hard to distinguish noise from curvature change.
* Input noise can be random value in case that the system is operated in different environment.
* When noise is random, \(v_{th}\) is hard to determine.  
For these reasons, it is difficult to estimate \(m_{h}\) in noisy environment. Therefore, we propose a network to train a moving state using recurrent neural networks as alternative.  
Figure 4: Joint angle calculation using various offsets: (**a**) proper offset cases; (**b**) low offset; and (**c**) high offset.
## 3 Multi-Sensor Fusion
### Fusion of Horizontal Lidar and Vertical Lidar  
To accurately fuse the horizontal and vertical lidar data, their individual coordinate systems must be transformed into a unified coordinate system, which is termed \"frame coordinate system\". The horizontal lidar coordinate system is used as the frame coordinate system; the vertical lidar coordinate system is registered in this frame coordinate system through external parameter calibration, with the external parameters obtained by the CAD model of the scanning arm. This paper adopts the adaptive time synchronization algorithm to ensure that the timestamps of the horizontal and vertical lidars can be simultaneously output. The fusion process of dual lidar is divided into two parts: (1) external parameter calibration of the horizontal and vertical lidars and (2) the adaptive time synchronization algorithm. Both parts are described as follows.
## 2 Materials and Methods
### Transcriptome Analysis  
#### 2.5.1 RNA Extraction, Library Construction, and Sequencing  
Samples of embryo rice in different storage periods were taken, and TRIzol(r) reagent was used to extract total RNA from rice tissues in accordance with the instructions. DNase I was used to remove genomic DNA. The concentration and purity of the RNA were determined by Nanodrop2000, and the integrity of the RNA was detected by 1% agarose gel electrophoresis. High-quality RNA samples (OD260/280 \(\geq\) 1.8; OD260/230 \(\geq\) 1.0; 235:168 \(\geq\) 1.0; concentration \(\geq\) 50 ng/L; total amount \(\geq\) 1 ug) were screened for subsequent library construction. After enrichment, Phusion DNA polymerase was used for PCR amplification, and 15 cycles were amplified. After quantification of TB5380, Illumina Novaseq was used for RNA-seq double-ended sequencing. The data generated by the Illumina platform were used for bioinformatics analysis. All analyses were carried out using the cloud platform of Shanghai Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China).
# Effect of Variable Conditions of Exposure on the Physical and Mechanical Properties of Blockboards
###### Abstract  
The growing demand for wood and the continued increase of the raw material price have resulted in companies using more efficient wood resources in wood-based products such as blockboard. These boards are used in the field of interior design, especially. The aim of this research was to evaluate the effect of variable environmental conditions on the physical and mechanical properties of blockboard (BK). Two types of commercial BK were exposed in variable environmental conditions (kitchen, bathroom and climatic chamber) for three months. The BK structures were composed of veneer (V) and high-density fibreboards (HDF) for the face sheets and glued, solid wood, resinous strips for the core. The temperature and relative humidity of the air (RH) ranged from 18 \({}^{\circ}\)C to 25 \({}^{\circ}\)C and from 25% to 90%, respectively. In the climatic chamber (CC), the dynamics of the parameter variations were different than in the other two exposure climates, being determined by the programmed cyclic changes in RH. The changes observed on physical and mechanical properties were more extreme for the blockboards tested in the CC than in the kitchen (K) and bathroom (Ba). After 3 months of exposure in the CC, the thickness and weight of the boards increased by 1.8 and 1.1 times, respectively, for veneerd BK, and by 4.4 and 0.4 times, for BK with HDF faces, compared to values recorded in the kitchen. The panels exposed in the CC exhibited the highest increase in moisture content (MC): 41% for veneerd BK and 82% for BK with HDF faces after three months of exposure, followed by those exposed in the bathroom and kitchen. Increases in MC determined decreases of flexural properties and soundness surfaces, more evident for HDF face structures compared to V ones.  
blockboard; environmental conditions; moisture content; physical-mechanical properties; dimensional stability; warpage +
Footnote †: journal: Applied Sciences
# In Silico SwissADME Analysis of Antibacterial NHC-Silver Acetates and Halides Complexes
## 4 Conclusions  
We have demonstrated that antibacterial NHC-silver acetates and halides complexes represent an extremely chemically diverse group of compounds. However, the easily accessible pharmacokinetic parameters provided by the SwissADME platform cannot be directly used to explain the biological activity of a given silver complex. Since the biological activity of Ag-NHC complexes is primarily determined by the presence of silver ions, the theory proposed by some authors that lipophilicity enhances antibacterial activity is not supported by our findings. In our view, bulky and lipophilic \(N\)-substituents within any NHC system influence the strength of the carbon-silver bond, its susceptibility to hydrolysis, and the electronic structure of the carbon atom [74]. Consequently, lipophilicity and solubility parameters for entire complexes provided by theoretical models like SwissADME are secondary factors and should not be used in isolation to compare different NHC systems. Stability and susceptibility to hydrolysis of NHC-silver complexes can be linked to the LUMO absolute values mapped on the electron isodensity surface as evidenced by DFT calculations. This finding may suggest a direction for future research. Finally, the optimal lipophilicity value for antibacterial Ag-NHC complexes should be considered unknown.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14198865/s1](https://www.mdpi.com/article/10.3390/app14198865/s1)]([https://www.mdpi.com/article/10.3390/app14198865/s1](https://www.mdpi.com/article/10.3390/app14198865/s1)), Table S1: Full SwissAdme data for complexes 1-194.  
Conceptualization, J.S.; methodology, J.S.; investigation, J.S. and L.P.; resources, J.S. and L.P.; data curation, J.S. and J.F.; writing--original draft preparation, J.S.
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 3 Ophthalmological Examination: A Key Tool in the Diagnosis and Management of Sphingolipidoses
### Fundus Autofluorescence (FAF)  
FAF is a noninvasive technique that visualizes intrinsic fluorescence within the ocular fundus, primarily attributed to lipofuscin accumulation in the retinal pigment epithelium (RPE). By detecting abnormal autofluorescence patterns, FAF imaging provides valuable insight into RPE integrity and disease progression, making it an essential tool for diagnosing and monitoring retinal disorders [39; 40]. The cherry-red spot or macular halos can be identified through FAF, as they correspond to areas of hyperautofluorescence or hypoautofluorescence in the parafoveal macular region, respectively [37].
# Improving Data Augmentation for YOLOv5 Using Enhanced Segment Anything Model
## 2 Materials and Methods  
### YOLOv5  
YOLOv5 [6] is a one-stage object detection model with four versions: YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The network structures of these versions are identical, with differences in network depth and width achieved through the parameters of depth_multiple and width_multiple. The overall framework structure of the YOLOv5 model is shown in Figure 1.  
The structural characteristics of YOLOv5 include the adoption of Cross-Stage Partial (CSP) [7] and Spatial Pyramid Pooling Fast (SPPF) [6; 8] methods in the backbone network, as well as the utilization of Feature Pyramid Network (FPN) [9] and Path Aggregation Network (PAN) [10] methods in the neck network. Additionally, techniques such as Mosaic data augmentation [11], adaptive anchor box calculation, and adaptive image scaling are employed to enhance training effectiveness.  
Figure 1: YOLOv5 Overall Frame Structure.  
Since its release, YOLOv5 has gained widespread recognition in both academia and industry. Similar to one-stage algorithms such as SSD [12], YOLO series [13; 14; 15; 16; 17], Centernet [18], Efficient Det [19], YOLOv5 treats object detection as a regression task, directly regressing bounding boxes and predicting the categories at multiple positions in the entire image for comprehensive information. As one of the most advanced target detection algorithms, the performance of YOLOv5 depends heavily on the dataset used for training, and there is still room for improvement in license plate detection in the face of abnormal weather scenarios.  
The quality of the training dataset directly affects the model's generalization ability and detection accuracy [20]. YOLOv5 requires a large amount and high quality of labeled data to learn the features and contextual information of the target. If the training dataset is of poor quality, the model may produce misleading learning, leading to performance degradation. Second, the diversity of training datasets is crucial to improve the robustness of the model. Factors such as different scenes, lighting conditions, weather conditions, and target poses can affect the performance of the detector. Therefore, the training dataset needs to cover a variety of possible real-world scenarios so that the model can perform effective detection in different environments. In addition, the size of the training dataset is also an important factor that affects the performance of the model. Larger datasets usually provide more samples and richer contextual information, which helps the model learn a more complex and accurate representation of the target. In contrast, if the size of the training dataset is small, the model may face the problems of overfitting and insufficient generalization ability. Therefore, in this paper, the improved Segment Anything Model is used for data augmentation as a way to build the dataset so that the detection target is more accurate and the detection performance of YOLOv5 is improved.