esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Hydrophobicity for Treatment Samples  
Kenaf has higher hygroscopicity when compared with PSD and there is a general trend for the reduction of absorbed moisture content after torrefaction for both PSD and Kenaf (Figure 4). Both R. PSD  
Figure 3: SEM images of PSD samples (**a**) and kenaf samples (**b**).  
and R. Kenaf experienced the highest uptake of water during the two hours of immersion (41% and 81%, respectively) and uptake of water for R. Kenaf was higher than that of R. PSD, while A. PSD and A. Kenaf absorbed considerably lesser water than that of raw samples (36% and 67%, respectively). After torrefraction of raw samples, the uptake of water was much lower (2% and 9%, respectively) and torrefied; further, ashless samples absorbed the least amount of water (approximately 0%). These findings proved that torrefraction improved the hydrophobicity of both woody and herbaceous biomass. This type of torrefraction impact was in agreement with results of [PERSON] et al. [26]. Water is absorbed by the woody materials owing to the presence of hydroxyl groups (-OH) that attract and hold water molecules through hydrogen bonding. In wood materials, hemicelluloses are more hydrophilic than cellulose and lignin. Hemicelluloses and the noncrystalline region of cellulose chains can attract water easily, owing to the availability of hydroxyl groups [27]. The carboxylic acid groups (-COOH) in hemicelluloses are also active in absorbing water [28]. However, the degradation of hemicelluloses and amorphous cellulose by torrefraction results in the removal of -OH and -COOH groups and further decreases hydrogen bonding with wasteR. As a result, tar condenses inside the pores, thereby obstructing the passage of moist air through the solid, avoiding water vapor condensation. The polar character of condensed tar on the solid also prevents the condensation of water vapor inside the pores [29].  
Here, evidently, the uptake rate of water (9%) and reduction rate of water (88%) after torrefraction for kenaf is higher than that of PSD, which means that hydrophobicity of kenaf after torrefraction improved. Most importantly, it was observed that hydrophobicity of torrefied ashless samples was markedly improved in both T. A PSD and T. A Kenaf, which indicated that ashless process with torrefraction lead to further hydrophobic characteristics. These improvements can efficiently enhance the storage and transport of biomass samples, particularly different types of herbaceous. Alternatively, the prolonged storage life of biomass implies the enhancement of carbon sequestration ability.
## 3 Experimental Methods
### Calculation of Digital Resistance, Photosensitive Resistance and Reference Voltage  
As shown in Figure 6[22], the ambient light sensor circuit is a low-pass filter composed of two sets of operational amplifiers (IC TL072), a 1 uF capacitance, a 4.7 uF capacitance and an 8.2 k\(\Omega\) resistor. This circuit only allowed signals with frequency less than 100 Hz to pass and its input is connected to the A1 analog pin of LinkIt ONE.  
Figure 5: MCP41010 (**a**) pin configuration; (**b**) two-terminal of the rheostat mode.  
Figure 6: Ambient Light Sensor, where GND is the ground \(V=0\).  
The following Equations (1)-(3) are the equations for ambient light illuminance calculation used in our work:  
\[R_{L}=\frac{500}{Lux}(\Omega) \tag{1}\]  
where \(R_{L}\) in Equation (1) represents the photosensitive resistance.  
\[V_{\text{out}}=\frac{3.3\times R_{L}}{3.3+R_{L}}\ (\text{V}) \tag{2}\]  
where \(V_{\text{out}}\) in Equation (2), as demonstrated in Figure 6, indicates the output voltage between the photoresistor and the fixed 3.3 k\(\Omega\) resistor.  
\[Lux=\frac{\left(\frac{1650}{V_{\text{out}}}-500\right)}{3.3} \tag{3}\]  
Finally, after \(Lux\) is determined, we are able to get the digital resistance (\(R_{v}\)) of MCP41010 from Equation (4).  
\[R_{v}=\frac{R_{AB}\times D_{n}}{256}\ (\Omega) \tag{4}\]  
where \(R_{AB}\) denotes the upper limit of the variable resistor and it is 10 k\(\Omega\) for MCP41010. \(D_{n}\) obtained by the fuzzy controller, represents the 8-bit digital variable resistor of MCP41010 with value ranging from 0 to 225. For instance, when \(D_{n}\) = 50 H, \(R_{v}\) = 3.125 k\(\Omega\). Then, as shown in Figure 4, the output voltage of our optical Morse-code decoder, or the input voltage (\(V_{B}\)) into the transistor 9013 could be obtain by Equation (5):  
\[V_{B}=\frac{V_{\text{CC}}\times R_{v}}{R_{L}+R_{v}}\ (\text{V}) \tag{5}\]  
The CHD-1688 transformer specifically for computers (Input: AC 100-240 V, 50/60 Hz, 2 A, Output: DC 12 V, 2 A) is used in our system. Apart from the electronic lock, the power was also supplied to the voltage stabilizer IC (LM7805K) to feed DC 5 V to LinkIt ONE, which was connected to the ambient light sensor and the photosensitive resistor circuit.  
The complete hardware was shown in Figure 7. There were five main components in our work, including (A) system power supply circuit; (B) photosensitive resistor circuit; (C) development board (LinkIt ONE); (D) ambient light sensor circuit; (E) electronic lock.  
Figure 7: The complete hardware in our work.
# Compact Photonic-Crystals Based Isolator Using Ni-Zn Gyromagnetic Ferrite Posts
###### Abstract  
A Faraday rotation isolator is conventionally achieved by connecting a matched load to a three-port circulator. It obtains superior performance (isolation > 20 dB) at the inevitable cost of non-ideal size. In order to adapt to the miniaturizations and integrations required for future 5G communication systems, it is particularly important to reduce the size of the devices. This work demonstrates a photonic crystal-based isolator design, comprising a unique reflecting cavity and a built-in fan-shaped coupler, where four Ni-Zn ferrite posts achieve the rotations. The design with the compact size of about \(46.6\times 41.6\times 4.32\) mm\({}^{3}\) obtains excellent forward transmission efficiency and reverse isolation of 0.50 dB and 44.20 dB, respectively.  
photonic crystals; photonic crystals waveguides; isolator; photonic band gap; 5G communication +
Footnote †: journal: Applied sciences  
requires lower external magnetic field and provides greater tolerance. In addition, using Faraday rotation for isolation instead of chiral edge states, a more reliable device based on more mature mechanisms is envisaged to save the size of the isolator without two built-in antennas. Our isolator's input and output ports are designed according to the standard WR34 flanged interface in the 5G millimeter waveband.  
In this work, we propose and numerically investigate a compact PCs isolator based on Faraday rotation effect by inserting Ni-Zn gyromagnetic ferrite posts into a T-typed square lattice photonic crystals (SLPCs) defect structure. In the T-typed structure, the forward electromagnetic wave achieved \(90^{\circ}\) Faraday rotation by coupling two gyromagnetic ferrite posts. Aided with the unique reflecting cavity and built-in fan-shaped coupler, the function of the isolator is realized perfectly in the K waveband. The collinearity of the two isolator ports makes the structure more compact and easier for integration. Therefore, this design provides a convenient solution for using the circulator as an isolator without external matching load.  
The TE band gaps of the SLPCs are calculated by plane wave expansion method (PWEM). There are three PBGs observed in the different ratios between the dielectric rods' radius and the lattice constant. The external characteristics of the designed isolator are numerically calculated by finite element method (FEM) in the frequency range from 25 GHz to 27 GHz. The optimal forward insertion loss and reverse isolation of the isolator reach 0.50 dB and 44.20 dB, respectively. The high isolation, low insertion loss and easy integration indicate that our designed PCs isolator has significant advantage in the future communication systems.
# A Comparative Study on Violent Sloshing with Complex Baffles Using the ISPH Method
## 5 Analyses and Discussion
### T-Shaped Baffle  
Finally, a T-shaped baffle is investigated based on the configuration as shown in Figure 5f. By considering the different heights of T-shape baffle in Table 2, the maximum impact pressures at measuring point P2 with different excitation frequencies are provided in Figure 10. It shows that when the height of the baffle is lower, such as \(h=0.083\) and \(0.117\), the impact pressure of the sloshing liquid is higher than the corresponding one with only a normal vertical baffle. However, when the height of the T-shaped baffle increases to \(h=0.167\), its performance is similar to that of a simple vertical one. Furthermore, with the increase of the height to \(h=0.217\), the reduction of impact pressures through the T-shaped baffle becomes much more pronounced.  
By further normalizing the designed flow depth of \(0.3\) m, it is found that when the ratio of the baffle height over water depth is smaller than \(60\%\), the normal vertical baffle is better in view of reducing the impact pressure of the sloshing liquids. In between \(60\%\) and \(70\%\) of the two types of baffle have a similar performance. The T-shaped baffle demonstrates its superiority in reducing the sloshing loads when the height-to-depth ratio is greater than \(70\%\).  
Figure 10: Comparisons of impact pressure under different heights of the T-shaped baffle.  
Figure 9: Comparisons of impact pressure with two horizontal and one vertical baffle, one horizontal and one vertical baffle, and two horizontal baffles only.
## 4 Numerical Examples
### Non-Uniform and Non-Circular Formation  
This simulation considers a multi-robot system composed of nine robots and the center point position (0, 0) m with the velocity (0, 0) m/s. The initial positions of the nine robots are generated randomly. At the same time, the predetermined distribution formation is derived from archimedean spiral antennas, as shown in Figure 5.  
The simulation results are shown in Figures 6 and 7. As shown in Figure 6, the simulation results indicate that the group of mobile robots move asymptotically to target points so as to form the prescribed non-uniform and non-circular formation under the proposed control law (12). We can observe from Figure 7, the system has the characteristics of order preservation and collision free under the proposed control law.  
Figure 5: Desired non-uniform and non-circular formation on a plane.
## 2 Materials and Methods
### N-Way Wilkinson Power Dividers Based on Slow-Wave Structures
#### 2.2.1 WPDs with Narrow-Slit-Loaded Transmission Lines  
A 2-way WPD was constructed by loading narrow slits as shown in Figure 6 [28]. An RT/Duroid substrate with a relative dielectric constant of 10.2 and a thickness of 1.27 mm was used in all design processes. The number of slits and the location of the isolation resistor affect the frequency response as depicted in Figure 7a,b, respectively. It is clear that the center frequency decreases as the number of narrow slits increases. Therefore, a very compact size can be achieved by using the maximum number of slits. Since 50-ohm port impedances were utilized, the isolation resistor was chosen as 100 ohm.  
The proposed slit-loaded transmission lines in [28] were also used to design 4- and 8-way WPDs. Figure 8 illustrates the layouts of the 4- and 8-way WPDs in a cascaded model. It is clear that 2 more paths have been added instead of output ports for the 4- and 8-way power dividers.  
Figure 5: Slow-wave structures. (**a**) Slit-loaded. (**b**) Meandered. (**c**) Interdigital.  
Figure 6: (**a**) Conventional 2-way WPD-loading with narrow slits, and (**b**) location of the isolation resistor.
# Lightweight and Privacy-Preserving Multi-Keyword Search over Outsourced Data
## 1 Introduction
### Our Contributions  
This paper aims to provide a solution to protect the privacy of outsourced data, which supports privacy-preserving keyword search and does not require resource-intensive computations. Specifically, this paper proposes lightweight privacy-preserving ranked multi-keyword intelligent search (PRMS) schemes over outsourced documents, where the search results are determined by the similarity score between the search query and the keyword index of each document. The searched documents can be sorted by the cloud server according to their similarity scores. Only the top-ranked documents whose similarity scores exceed the target threshold should be returned.  
With the basic PRMS scheme, which was previously presented in the preliminary version [15], each outsourced document can be processed with a group of keywords so that it can be searched according to multiple keywords. Only when the number of search keywords contained in the outsourced document exceeds the given search threshold, the outsourced document will be returned. With the extended PRMS scheme, each outsourced document can have multiple different types of keywords, so that the document can be searched and retrieved based on any type of keywords.  
Security analysis shows that the proposed PRMS schemes can guarantee the privacy of searchable index of outsourced documents and queries. Specifically, the cloud server cannot deduce any private information of outsourced documents from the encrypted index and queries. Furthermore, the proposed PRMS schemes offer the unlinkability of search trapdoors--that is, the cloud server is unable to identify whether two search trapdoors are generated for the same query.  
In both PRMS schemes, the data processing and query generation phases only take lightweight computing operations at user side. Performance analysis demonstrates that our PRMS schemes are much more efficient than existing technologies; thus, they can be deployed in an IoT setting to support resource-constrained devices.  
Compared with the preliminary version [15], this paper provides an extended PRMS scheme to support multiple types of keywords for each outsourced document, in which security and performance are also analyzed. Moreover, the experiments on the basic PRMS scheme are tested on a new platform as in evaluating the extension scheme.
# AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features
## 4 Experimental Results
### User Study  
A user study involving 21 adult participants was conducted to compare the quality of StageMix produced by AutoStageMix against those edited by professional editors. Participants rated the videos based on editing naturalness and transition smoothness, excluding factors such as music or video content. A 5-point Likert scale was used, where 1 indicated \"very unsatisfied\" and 5 indicated \"very satisfied.\" Three songs were included in the study: IVE's \"HEYA\" [37], NewJeans \"Hype Boy\" [38], and asepa's \"Supernova\" [39]. For each song, one video was generated using AutoStageMix and two were edited by professional editors, yielding a total of nine videos. The three AutoStageMix were grouped as Group 1, and the six professionally edited videos as Group 2.  
Table 7 summarizes the overall satisfaction scores. Group 1 achieved an average score of 4.349 (SD = 0.716), while Group 2 received 4.143 (SD = 0.774). To assess variance homogeneity between the two groups, an F-test was performed due to the unequal sample sizes. The F-test result (F = 0.362, \(p\) = 0.548) indicated no significant difference in variance, validating the use of a \(t\)-test.  
The null hypothesis of the \(t\)-test assumed no significant difference in satisfaction scores between the two groups. The \(t\)-test produced a t-statistic of 1.762 and a \(p\)-value of 0.080. Since the \(p\)-value exceeds 0.05, the null hypothesis could not be rejected, suggesting no statistically significant difference between AutoStageMix and professional editing in terms of viewer satisfaction.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Song**} & \multirow{2}{*}{\begin{tabular}{c} **Pre-** \\ **Processing** \\ \end{tabular} } & \multicolumn{2}{c}{**Feature Extraction**} & \multirow{2}{*}{\begin{tabular}{c} **Identifying a** \\ **RetinaFace** [9]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Identifying a** \\ **SPIGA** [12]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Identifying a** \\ **Transition Point** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Editing Path** \\ **Determination** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **StageMix** \\ **Generation** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Total** \\ **Time** \\ **(s)** \\ \end{tabular} } \\ \hline \hline Darl+ing [28] & 258.71 & 1350.37 & 960.08 & 260.25 & 73.35 & 379.15 & 3281.91 \\ \hline Ready to love [29] & 326.22 & 1268.96 & 571.15 & 218.72 & 6.13 & 417.47 & 2808.65 \\ \hline God of Music [33] & 438.65 & 2769.19 & 1013.43 & 378.02 & 30.29 & 502.31 & 5131.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Processing time per pipeline stage (in seconds).  
These results indicate that AutoStageMix performs comparably to professional editors, with no significant differences in satisfaction or variability. This supports the potential of AutoStageMix as a tool for producing high-quality video edits.
# YOLOv5 DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs
###### Abstract  
Accurate posture detection is the foundation for analyzing animal behavior, which can promote animal welfare. With the development of computer vision, such technology has been widely used in analyzing animal behavior without physical contact. However, computer vision technology for pig posture detection often suffers from problems of missed or false detection due to complex scenarios. To solve the problem, this study proposed a novel object detection model YOLOv5 DA, which was based on YOLOv5s and designed for pig posture detection from 2D camera video. Firstly, we established the annotated dataset (7220 images) including the training set (5776 images), validation set (722 images), and test set (722 images). Secondly, an object detection model YOLOv5 DA based on YOLOv5s was proposed to recognize pig postures (standing, prone lying, and side lying), which incorporated Mosaic9 data augmentation, deformable convolution, and adaptive spatial feature fusion. The comparative and ablation experiments were conducted to verify the model's effectiveness and reliability. Finally, we used YOLOv5 DA to detect the posture distribution of pigs. The results revealed that the standing posture was more frequent in the morning and afternoon and the side-lying posture was most common at noon. This observation demonstrated that the posture of pigs is influenced by temperature variations. The study demonstrated that YOLOv5 DA could accurately identify three postures of standing, prone lying, and side lying with an average precision (AP) of 99.4%, 99.1%, and 99.1%, respectively. Compared with YOLOv5s, YOLOv5 DA could effectively handle occlusion while increasing the mean precision (mAP) by 1.7%. Overall, our work provided a highly accurate, effective, low-cost, and non-contact strategy of posture detection in grouped pigs, which can be used to monitor pig behavior and assist in the early prevention of disease.  
p ig posture detection; deep learning; machine vision; object detection; agricultural automation +
Footnote †: journal: applied sciences  
1
Footnote 1: [[https://doi.org/10.3390/appl42210104](https://doi.org/10.3390/appl42210104)]([https://doi.org/10.3390/appl42210104](https://doi.org/10.3390/appl42210104))
## 5 Future Trends and Perspectives
### Integration and Standardization
Seamless communication can thus be problematic in a multi-entity business model such as supply chain logistics if there is no one standard which is agreed upon and if one or more of the partners in the chain do not have the infrastructure in place to interrogate these sensing units [11]. This standardization is a must go area in the integration of RFIDs with WSNs, or developing a WSNs [167]. Besides, narrow-band IoT (NB-IoT) is a new radio technology standard that has been developed to enable a wide range of devices and things to be connected using long-term evolution (LTE) system [168]. The standardization of release 13 has been completed on June 2016 by the 3 rd Generation Partnership Project (3 GPP), which is one of a range of Mobile IoT (MIoT) technologies [169]. The transmitted power of the reader is limited to 23 dBm for communicating with cellular base-station [170]. Therefore, the design challenge is both the hardware, software, and firmware of the reader. More specifically, the antenna gain and bandwidth as well as the data collection, processing, and transmission latency play a strategic role for the integration and real-time monitoring.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Tool Radius Compensation**} & \multicolumn{2}{c}{**Mirror Compensation**} \\ \hline
**Surface Design** & **Before** & **After** & **Before** & **After** \\ \hline Surface one & 14.1 (6.2) & 2.0 (6.1) & 15.1 (6.2) & 0.1 (2.4) \\ Surface two & 17.2 (3.8) & 0.9 (3.7) & 17.2 (3.8) & 0.7 (1.5) \\ Surface three & 16.5 (3.2) & \(-\)0.3 (3.1) & 16.8 (3.1) & 0.3 (2.1) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean (the values without the parentheses) and the standard deviation (the values within the parentheses) of the errors for the three surfaces by using tool radius compensation or the mirror compensation, unit: \(\upmu\)m.  
Figure 12: (**a**) The error distribution of replica one before (blue circles) and after (red plus signs) tool radius compensation for Surface three; (**b**) the error distribution of replica one before (blue circles) and after (red plus signs) mirror compensation for Surface three.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 3 Case Study
### Data Description  
The meteorological data came from Fuyun Meteorological Station (N 46.59\({}^{\circ}\), E 89.31\({}^{\circ}\)) located at Xinjiang province in China [33]. The time span was one month (from 00:00 on the 15 th of July, 2018 to 23:00 on the 14 th of August). The total length of the sample was 744 with an hourly resolution. The basic information of meteorological factors is listed in Table 1. Hourly wind speed data shown in Figure 4 were divided into a training set (the first 624 points) and a test set (the last 120 points).