esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Uncertainty
### Impact of Motion and Doppler Effect  
Doppler Effect is noticeable when either the transmitting or receiving device is in motion. In the LPS _LOSNUS_, only the receiver is in motion and the transmitters are fixed. If the chirp is centered at a carrier frequency \(f_{0}\) the Doppler shift can be approximated by [31] where \(\overrightarrow{v_{R}}\) is the direction and speed of motion of the receiver R. The direction of wave propagation between the transmitter and receiver is given by the unity vector \(\overrightarrow{e_{TR}}\).  
\[f_{d}=-f_{0}\frac{\overrightarrow{v_{R}}\cdot\overrightarrow{e_{TR}}}{c} \tag{30}\]  
The effect of Doppler for a linear frequency modulated chirp with the settings of Figure 3 has been evaluated using an analytical formula from ([30], p. 210) and by a simulation. The effect on the peak of the correlation amplitude is neglect able and the time shift due to the Doppler effect is shown in Figure 9.  
Using the result from Figure 9, the effect of Doppler shifts and motion on localization can be calculated. If a sound wave is emitted at time \(t=0\) and the receiver is moving in direction \(\overrightarrow{v}\) the solution of the Equation (31) determines the ToF.  
Figure 9: Time delays due to Doppler shifts for analytical function and simulation.  
\[\vec{\mathrm{R}}+\vec{\mathrm{v}}\vec{\mathrm{t}}=\vec{\mathrm{T}}+\frac{\vec{ \mathrm{R}}+\vec{\mathrm{v}}\mathrm{t}-\vec{\mathrm{T}}}{\left\|\vec{\mathrm{R }}+\vec{\mathrm{v}}\mathrm{t}-\vec{\mathrm{T}}\right\|}\mathrm{ct} \tag{31}\]  
As the node is moving and the distances to all transmitters are typically different, what is meant with by the value of the position of the receiver must be defined. In this article, the position of the receiver is defined as the position where the first transmitting signal within a locating sequence has been received. This definition was chosen for the following reasons: (a) without additional measures the receiver does not know in which direction it is moving. (b) It is logical to timestamp the position with the timestamp when the locating sequence starts. This position is denoted by \(\mathrm{R}_{0}\). The error for a located position R can therefore be defined according to Equation (32).  
\[\mathrm{err}=\vec{\mathrm{R}}_{0}-\mathrm{R} \tag{32}\]  
Figure 10a,b show locating errors for different receiver velocities and interframe spacings. The values have been calculated by simulation using uniformly spaced receivers within the room. For each receiver all six possible directions of movement have been evaluated. For a given velocity, the value of the curve represents the mean error over all receiver positions where the error bar represents the standard deviation. As a general result, the system works well in this configuration up to target velocities of 0.5 m/s where the effect of Doppler is existent but not significant. Increasing the inter frame spacing worsens the result although at receiver velocities high than 0.5 m/s both systems experience problems.
## 3 Results and Discussion
### Effect of Different Production Rates  
Gas production rate is a significant parameter in the development of gas reservoirs, which affects water coning and ultimate recovery [11; 13]. In order to study the effects of different production rates, the core sample 2 is used to simulate water invasion in carbonate gas reservoirs. The aquifer size and original saturation pressure are 7 times and 30 MPa, respectively. The effects of gas production rates from 500 mL/min to 4000 mL/min are selected to simulate water invasion. Figure 6a,b shows bottom-hole pressure and producing pressure drop versus recovery factor for different production rates, respectively. It can be seen that when the recovery factor is less than 30%, the gas production rate has less impact on bottom-hole pressure and pressure drop. In the middle and later period, the gas production rate has a large effect on bottom-hole pressure and pressure drop. At the same recovery  
Figure 5: Water invasion and recovery factor versus aquifer times.  
factor, the bigger the gas production rate is, the lower the bottom-hole pressure is and the larger the pressure drop is. This suggests that the bigger the gas production rate is, the more that the water invasion affects the recovery factor.  
Figure 7a,b shows water invasion versus time for different production rates and recovery factor versus gas production rate, respectively. From the result, it can be known that water invasion changes linearly with time in the same aquifer size, and the rate of water invasion approximates to a certain value. This suggests that the water invasion of gas reservoirs with dissolved pores belongs to a uniform advancing process. The greater the gas production rate is, the faster the water invasion is [4]. Gas production rate increases from 500 mL/min to 4000 mL/min, and recovery factor decreases from 69.66% to 60.82%. The excessively high production gas rate goes against the recovery factor.
## 4 Results  
### The Analysis of Impact Factors  
The output result of Pearson correlation (Figure 8) is shown. When the threshold of correlation is lower than 0.7, it is considered that there is no obvious positive correlation between parameters, among which the highest correlation between elevation and NDVI was 0.466, indicating that the above parameters are effective for landslide susceptibility.  
According to the results in Table 2, there was no significant collinearity between the parameters because the TOL and VIF of all factors were within the threshold. Soil type had a maximum TOL of 0.955 and the VIF of elevation was 1.661. On the other hand, the IGR of the impact factors ranged from 0.016 to 0.301, and NDVI showed the highest value, followed by variables such as distance to road, land use, elevation, etc., while TWI, aspect, slope angle, and other variables had less IGR, but were all greater than 0.01. Therefore, the impact factors proposed in this study were considered as suitable for subsequent modeling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multirow{2}{*}{**Impact Factors**} & \multicolumn{2}{c}{**Collinearity Statistics**} & \multirow{2}{*}{**IGR**} \\ \cline{3-3} \cline{5-5}  & & **TOL** & **VIF** & \\ \hline
1 & Elevation & 0.602 & 1.661 & 0.146 \\
2 & Slope angle & 0.612 & 1.634 & 0.020 \\
3 & Aspect & 0.930 & 1.076 & 0.019 \\
4 & Curvature & 0.881 & 1.135 & 0.060 \\
5 & TWI & 0.729 & 1.372 & 0.016 \\
6 & NDVI & 0.578 & 1.730 & 0.301 \\
7 & Land use & 0.615 & 1.625 & 0.205 \\
8 & Soil type & 0.956 & 1.046 & 0.026 \\
9 & Lithology & 0.948 & 1.055 & 0.084 \\
10 & Distance to fault & 0.930 & 1.075 & 0.028 \\
11 & Distance to stream & 0.747 & 1.338 & 0.086 \\
12 & Distance to road & 0.650 & 1.539 & 0.263 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-collinearity and IGR among impact factors.
###### Abstract  
Due to the rapid expansion of urban areas, the aging of agricultural labor, and the loss of rural workforce, some regions in China have experienced farmland abandonment. The use of remote sensing technology allows for the rapid and accurate extraction of abandoned farmland, which is of great significance for research on land-using change, food security protection, and ecological and environmental conservation. This research focuses on Qiaotou Town in Chengmai County, Hainan Province, as the study area. Using four high-resolution satellite imagery scenes, digital elevation models, and other relevant data, the random forest classification method was applied to extract abandoned farmland and analyze its spatial distribution characteristics. The accuracy of the results was verified. Based on these findings, the study examines the influence of four factors--irrigation conditions, slope, accessibility, and proximity to residential areas--on farmland abandonment and proposes corresponding governance policies. The results indicate that the accuracy of abandoned farmland extraction using high-resolution satellite imagery is 93.29%. The phenomenon of seasonal farmland abandonment is more prevalent than perennial farmland abandonment in the study area. Among the influencing factors, the abandonment rate decreases with increasing distance from road buffer zones, increases with greater distance from water systems, and decreases with increasing distance from residential areas. Most of the abandoned farmland is located in areas with gentler slopes, which have a relatively smaller impact on farmland abandonment. This study provides valuable references for the extraction of abandoned farmland and for analyzing the abandonment mechanisms in the study area, which have a profound impact on agricultural economic development and help to support the implementation of rural revitalization strategies.  
Keywords:land use change; remote sensing; extraction; driving factors; Qiaotou town; random forest +
Footnote †: journal: _Article_
## 2 Proximate Composition of Sea Cucumber  
It has been reported that the proximate composition, such as moisture, protein, ash, and lipid content, in sea cucumbers varies greatly from species to species (Table 1). For instance, _Holothuria atra_ could be a good source of protein over 80% [30] while _Cucumaria fondosa_ represented a great source of lipids (26.12%) on a dry basis [1]. In addition, Table 2 shows that sea cucumbers also contain minerals, including mean calcium (3918.84 mg/g), sodium (5935.39 mg/100 g), magnesium (1377.85 mg/100 g), potassium (505.54 mg/100 g), and many more, which is a crucial part of the important enzymes, and they are involved in various roles as catalysts and antioxidants [31; 32; 33; 34; 35; 36; 37]. Mineral content also varies from species to species, and it was observed that _Holothuria tubulosa_ and _Apostichopus japonicus_ could be good sources of minerals [33; 37].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Species** & **Moisture (\%)** & **Ash (\%)** & **Protein (\%)** & **Lipid (\%)** & **References** \\ \hline _Bohadschia vitiensis_ (DB) & - & 30.78 & 17.69 & - & [9] \\ _Apostichopus japonicus [PERSON]_ (DB) & 5.10 & 34.90 & 52 & 5.11 & [37] \\ _Cucumaria fondosa_ & Viscera (DB) & - & 11.95 & 48.26 & 26.12 & [1] \\ _Stichopus japonicus_ (DB) & - & 34.15 & 52.4 & 12.4 & [38] \\ _Holothuria lessomi_ (DB) & 13.47 & 34.51 & 41.18 & 3.02 & [39] \\ _Holothuria atra_ (DB) & 4.5 & 7.3 & 82.9 & 2.4 & [30] \\ _Stichopus hermanni_ (DB) & 10.2 & 37.9 & 47.0 & 0.8 & [40] \\ _Thelenota ananas_ (DB) & 15.1 & 25.1 & 55.2 & 1.9 & [40] \\ _Thelenota anax_ (DB) & 1.2 & 39.2 & 40.7 & 9.9 & [40] \\ _Holdthuria fusogliva_ (DB) & 11.6 & 26.4 & 57.8 & 0.3 & [40] \\ _Holothuria fusogliva_ (DB) & 7.0 & 39.6 & 50.1 & 0.3 & [40] \\ _Actropaya anuritana_ (DB) & 11.6 & 15.4 & 63.3 & 1.4 & [40] \\ _Actropyoga caruelta_ (DB) & 0.81 & 28.4 & 56.9 & 10.1 & [40] \\ _Boladschia argus_ (DB) & 13.0 & 17.7 & 62.1 & 1.1 & [40] \\ _Actropyoga chimites_ (DB) & 9.30 & 29.25 & 60.20 & 1.25 & [41] \\ _Parastichopus californicus_ (DB) & 4.03 & 25.73 & 47.03 & 8.19 & [30] \\ _Stichopus chloronotus_ (DB) & - & 27.7 & 57.93 & 3.94 & [42] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate composition of different species of sea cucumbers.
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 2 Taxonomy of Blockchain-Based Multimedia Content Protection Systems
### Consensus Protocols  
A consensus mechanism comprises protocols and algorithms, which establish the rules that the nodes (devices on the blockchain that maintain the blockchain and sometimes process transactions) must follow to validate the blocks. This mechanism solves data synchronization between the nodes that do not trust each other in a distributed system. The consensus protocol is a fault-tolerant mechanism that is used to achieve the necessary agreement on a single data value or a single state of the network. It consists of the following objectives: reaching an agreement, collaboration, cooperation, equal rights to every node, and mandatory participation of each node. Most blockchains use one of the following commonly used consensus protocols:  
* PoW: The nodes (miners) involved in this process compete with each other to solve a complex mathematical puzzle. The first node to find a solution obtains the right to validate the block to create a new block that implements a transaction. Bitcoin and Ethereum 1.0 use the PoW [28]. However, PoW requires a lot of energy and computer power to reach a consensus, and therefore, it is a very expensive option.
* PoS: The miners in this process are required to prove the ownership of a certain amount of currency tokens to establish their stake. Hence, the more tokens a node has, the more likely it will validate the block, and thus, will determine the authenticity of the block. Dash and Neo use PoS [29]. Ethereum 2.0, an upgrade to the Ethereum blockchain, has switched to PoS [28] from PoW, in order to provide increased scalability and throughput. One of the major drawbacks of PoS is that it promotes \"crypto-coin saving\", rather than spending.  
Delegated proof-of-stake (dPoS): As a variation of the PoS, dPoS requires all token owners to select a group of delegates, who they trust to participate in the validation process. The nodes with the highest votes then authenticate the transactions. Lisk and BitShares use dPoS [30]. Though dPoS does not require much computing power, it is vulnerable to centralization as the number of witnesses is strictly limited. Practical Byzantine Fault Tolerance (PBFT): Byzantine fault tolerance (BFT) refers to reaching a consensus between two nodes communicating safely across a distributed network in the presence of malicious nodes. One of the examples of BFT, PBFT, is designed to be a high performance consensus algorithm that can rely on a set of trusted nodes in the network. PBFT can only tolerate faulty or malicious nodes until the number of such nodes is less than one-third of all the nodes. Since greater number of honest nodes will agree on a correct decision than a faulty or malicious nodes agreeing on an incorrect decision, false information will be rejected by the majority. This mechanism is currently being leveraged by Hyperledger Fabric and Zilliqa [31].
## 4 New Classification of Methods for the Detection of Water Network Operating Conditions
### Hardware Methods (Direct)
#### 4.1.2 Non-Acoustic Methods  
Non-acoustic methods use changes in environmental properties that occur near leaks to locate them. Pipeline leaks can be detected by the tracer gas technique (TGT), that is, tracer gas injection. A non-toxic, lighter-than-air gas, most commonly helium and less commonly hydrogen, is added to the tap water. At the point of leakage from the water into the soil air and then into the atmosphere, the gas travels by diffusion. The presence of gas is detected using sensitive detectors.  
Water leakage sites can be located using thermal imaging cameras, which locate temperature differences between the pipeline and its surroundings. The ground in the vicinity of a damaged water pipe, due to increased moisture content, has a different thermal conductivity, which causes a different temperature distribution in the vicinity of the leak compared to the surrounding area. Thermal imaging scanning is performed by cameras installed on drones or helicopters [39].  
In water supply companies, ground-penetrating radar (GRP) is often used to detect water leaks. The working principle is based on sending electromagnetic waves with frequencies ranging from short to ultra-short radio waves to the ground medium and analyzing the spectrum of reflected waves using specialized software. This method can be classified as an instrumental-software method.  
In recent years, hybrid methods using both GPR and high-precision acoustic devices have also been applied for leak detection [40]. The operation of these devices must be synchronized over time. This solution improves leak localization, but in urban conditions, due to noise and the previously mentioned disadvantages of geophones, it has not found much use in water supply companies so far.
## 3 Results
### Classification Method Comparison
#### 3.1.1 Breast Cancer Metastasis  
Figure 2 shows the comparison for accuracy, recall, and precision of WoC-Bots with the other classification methods for a breast cancer dataset, predicting node-positive or node-negative disease in breast cancer patients. The results presented here were generated using the clinical features identified in Table 2, but not the additional cellular morphology features described in Section 2.2.1. The leftmost set of columns shows results for the WoC-Bots method, the rightmost set of columns shows results for the \"meta-swarm\" method described in Section 2.1.4, taking all of the methods listed on Figure 2 as input. The other sets of columns represent popular classification methods. Random Forest produced the best accuracy at 86.9% and the best precision results at 84.0%. The DNN implementation produced the best recall at 92.5%. WoC-Bots outperformed AdaBoost and Logistic Regression in accuracy and recall, but had the second lowest precision results at 72.0% (tied with AdaBoost). The meta-swarm implementation, while not producing the best results for any of the three metrics, improved consistency between the three metrics, produced the second highest accuracy at 84.8%, and the second highest precision at 82.0%.  
While WoC-Bots were outperformed by the XGBoost, Random Forest, and DNN methods, WoC-Bots can provide associated confidence values with each prediction. Table 5 shows how many predictions fall into each confidence interval and the intervals associated  
Figure 2: Comparison of five classification methods with two versions of the swarm (first and last columns) for the breast cancer dataset.  
accuracy. The combined \"Very High\", \"High\", and \"Medium\" confidence interval captured 78.9% of patients with a combined accuracy of 86.8%. The \"High\" confidence interval captured 31.9% of patients with an accuracy of 93.1%. WoC-Bots are able to stratify patients into confidence intervals, allowing for improved accuracy within a subset of samples.  
Figure 3 presents computation times, in milliseconds, needed to perform a single classification for this dataset using WoC-Bots and other methods. The timing results for all methods were run on the same hardware--AMD Threadripper 1950x CPU, 64 GB of RAM, and 2x Nvidia 2070 GPUs. WoC-Bots and the deep neural network were implemented in Java. The other methods were implemented in Python. As expected, methods which require additional iterative steps take longer to complete, with the WoC-Bots system having the most steps, and taking the most time.
# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
## 3 Results
### Algorithm Implementations and Setup
#### Bag of Words (BOW)  
The CountVectorizer class from the scikit-learn Python library [55] is used to implement the bag of words representation. The minimum and maximum document frequency for a word to be included in the vocabulary were set to common-sense values 0.01 and 0.95, respectively, resulting in a moderate vocabulary size (typically of several hundred words). Basic text preprocessing was performed with tokenization, non-word, and stop-word removal using the spaCy Python library [56]. Using TF-IDF instead of raw term frequencies or performing lemmatization was found not to have much effect in prior work with the same data [13], and therefore, these alternative variants of the bag of words representation are not investigated here.
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion
### Parametric Analysis and Optimization of the Results of the 60-GHz Metasurface  
In order to optimize the performance of the designed metasurface structure, it is important to examine the effect of variation of the design parameters of the unit-cell geometry on the magnitude and phase of the S-parameters. The effect of some main parameters such as dimensions of the dual loop metasurface and the angle of the incidence wave on the transmission, reflection coefficient and their phases are investigated in this analysis.
# Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot
## 4 Experiments and Results
### Application in Citrus Picking Robot  
To further verify the effectiveness and feasibility of IAPF, in this section, the motion planning experiment was carried out on the ICPR and experimental results were shown in Table 8. The target citrus tree and its simplified model are shown in Figure 14. Firstly, the target citrus tree was modeled by the partial spherical envelope (PSE) method which was like a bounding sphere algorithm [36; 37]. PSE method only includes sphere and line segment models, which can greatly reduce the amount of calculation in the process of collision detection, to further ensure that IAPF can meet the real-time requirements of ICPR. PSE envelops all non-current picking parts of citrus trees through spheres and models branches in the current picking part through straight segments. The PSE model of picking three oranges on the top of the target citrus tree is presented in Figure 15, the picking simulation of the ICPR and the trajectory curves are shown in Figure 16, the experiment results of IAPF applied to the ICPR are shown in Figure 17.  
Figure 14: Target citrus tree and its PSE model. (**a**) Target citrus tree; (**b**) PSE model of the target citrus tree.  
Figure 15: Picking part of target citrus tree and its PSE model. (**a**) Amplification 1 of picking part; (**b**) Amplification 2 of picking part; (**c**) Amplification 3 of picking part; (**d**) Picking part; (**e**) Fitting the branches; (**f**) PSE model of picking part.  
IAPF took 0.40 s, 1.12 s, and 1.04 s respectively in the process of path planning for ICPR picking citrus 2 on the top of the target citrus tree, and the total joint errors were 1.33, 1.10, and 1.13 degrees respectively in the stages of picking, releasing, and initialization. ICPR took 2.56 s to plan a path to pick single citrus. Since the time spent on the EC63M mechanical arm's movement is much longer than the time spent in path planning, the IAPF algorithm can fully plan the next citrus picking path during the ICPR picking process. From the experimental results, it can be concluded that the IAPF algorithm has fast solving speed and accuracy, and can meet the real-time requirements of the ICPR.