esa-sceva/satcom-chunk-collection huggingface dataset:
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 4 Results and Discussion
### Analysis and Evaluation (Scenario 2)  
At this stage, the performance analysis of the proposed method focused on the expected goals after the harvesting phase, based on all stages. In this study, some of the most important parameters in smart and sustainable agriculture are assumed. One of them is the profit rate. It is preferable that the collected crops offer more profit. In our real world, not only the growth of the crops but also the logistics are important in the collection of agricultural products. Therefore, it is possible to collect the crops before they are fully grown. In addition, the collected products start to grow again as time progresses. These two issues have been taken into account in our performance analyses. The second parameter is the amount of power consumed by each autonomous robot. The third parameter is that the robots perform the product-picking tasks in as short a time as possible. These three parameters will lead us to the main goal. This aim will generate an efficient and optimum roadmap for each robot. According to this map, each robot will be assigned the task of collecting items. The path that provides the maximum profit and consumes the least energy in the shortest time is attempted to be selected by the algorithm. Here a route for the robot is revealed. At the same time, when the relevant route is selected by the algorithm, it is shown how much profit will be achieved, how long the robots will take to complete their tasks, and how much power they will use during this time. The results obtained from the simulation environment are presented. In this scenario, performances are considered from two dimensions. In Table 5, results including all phases are presented. Since the SCSO-based method gave the best results in the first phase, this approach was used for all the methods used. In the second, the results are obtained based on only the 2 nd and 3 rd phases, excluding the 1 st phase, and are presented in Table 6. In this model, the planting was carried out with an analytical and classical approach. All results were evaluated on five different parameters. When an analysis including all phases is made, improvements are seen in all evaluation parameters (except profit). In other words, phase 1 has an effect on other parameters apart from the profit rate. The profit value does not change. This is because it has nothing to do with the realization of the first phase. According to the results in Tables 5 and 6, it can be seen that the proposed method (RACO)  
Figure 4: The MSE convergence of each method. performs better than the others. Therefore, it was more realistic and efficient to continue the evaluation and analysis to include all phases.
## 2 Background
### Hyperledger Fabric  
IBM's Hyperledger Fabric is a primary private blockchain platform for creating and maintaining distributed systems using modular consensus to follow a customized trust model. Fabric applications are written in general purpose languages including Java, Go, and node.js, whereas the smart contract is written in a domain-specific language. Hyperledger Fabric provides greater flexibility and an entirely different blockchain design that deals efficiently with the exhaustion of resources, attacks, and non-determinism [38]. Hyperledger Fabric is written in the Go language, which consists of endorsers, committers, a ledger, a database, and gossip. Endorsers are the peers in favor of transaction or chaincode execution. Committers are the peers that validate the proper configuration and verify the transaction according to the endorsement policy. The ledger consists of a transaction manager and a block store, to verify other transactions and to update the ledger. Gossip checks for ledger failures and maintains the correctness and efficiency of the whole system [39].  
Hyperledger Fabric has two main components i.e., chaincode and the endorsement policy. Chaincode is a smart contract that lies at the heart of all the applications in Fabric and runs in the execution phase. It runs separately from Fabric code in a separate container called a Docker container. It stores data in CouchDB through a key-value that can be 'get' or 'put' to read or write transactions in the database. The endorsement policy runs validation and behaves as a protocol of the transaction validation, and cannot be altered by any non-trusted application. An endorsement policy enables chaincode to select the endorser of a transaction. Transactions are initiated by the clients using a chaincode function, who then digitally sign it and send it to the channel. All the peers check the authenticity, structure, and authorization of newly created transactions, via a number of checks that must be verified by the peers. If the peers verify all of the checks, the transaction is executed and the response value is stored in the key-value store.  
All endorsements sent by the peers are gathered by the client and matched with the endorsement policy requirements. After gathering the required endorsements, resources are provided in the case of a read request. In the case of a write request, then all of the endorsements are collected and forwarded to an ordering service for the addition of the transaction. The ordering service sends all of the transactions, with their endorsement, to all peers on the same channel, where each channel contains all of the nodes communicating with each other and sharing information with each other over a blockchain network. The peers on the channel verify each of the transactions according to the endorsement fulfillment policy, which contains the smart contract agreement between all the stakeholders. On successful verification, the block is added to the ledger, but all of the endorsing peers have to commit the transaction.
## 2 Generalized Longitudinal Equivalent Continuous Model
### Basic Assumptions  
Some assumptions are proposed in the generalized longitudinal equivalent continuous model:  
1. The initial cross-section is uniform circular and develops into ellipse under the pressure of surrounding rock.
2. The cross-section before and after deformation is always plane under the longitudinal bending.
3. The stress distribution along the tunnel axial keeps unchanged on the neutral axis and inside the lining ring. One side of the neutral axis is under tension while the other side is under compression. The variation of the stress of lining structure along the thickness is ignored.
4. The bolts discretely distributed along the ring are transformed into continuously distributed bolt-ring, which can connect the annular gap. The stiffness of bolt-ring is uniformly distributed along the longitudinal length and radial thickness of the joints. The equivalent stiffness of bolt rings \(k_{j}\) is as follows [30]. \[k_{j}=\frac{nk_{ji}}{2\pi Rt}=\frac{nE_{j}A_{j}}{2\pi Rt},\] (1) where \(k_{ji}\) is the elastic stiffness coefficient of a single bolt, \(n\) is the number of bolts inside the ring, \(R\) is the average of the internal and external diameter of the segment, \(t\) is the thickness of the segment, \(E_{j}\) is the elastic modulus of the bolt, \(A_{j}\) is the cross-section area of the bolt, and \(l\) is the interaction length of the bolt.
5. The influence coefficient of the annular gap \(\lambda\) is introduced, and the influence scope of the annular gap is \(\lambda l\). When \(\lambda<1\), the interaction length of ring direction joints within the influence scope of the annular gap is \(\lambda l\); when \(\lambda\geq 1\), the interaction length of ring direction joints within the influence scope of the annular gap is \(l\).
6. Within the influence scope of the annular gap, the lining ring undertakes all the compressive stress caused by bending, and the tensile stress is jointly undertaken by equivalent bolt-ring and segment lining ring. Outside the influence scope of the annular gap, the tensile and compressive stresses caused by bending are both undertaken by the segment lining ring.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods  
### Study Area  
Lizhou District is a municipal district under the jurisdiction of Guangzhou City, Sichuan Province, China. It is located on the northern edge of the Sichuan Basin, on the upper reaches of the Jialing River, at the intersection of Sichuan, Shaanxi, and Gansu provinces, in the middle of Guangzhou City. It lies between 105\({}^{\circ}\)27\({}^{\prime}\) and 106\({}^{\circ}\)04\({}^{\prime}\) east longitude and 32\({}^{\circ}\)19\({}^{\prime}\) and 32\({}^{\circ}\)37\({}^{\prime}\) north latitude, covering an area of 1538 square kilometers (Figure 1). Crops are interspersed in this region, with the main types being rice, corn, soybeans, peanuts, potatoes, winter wheat, and rape. The region is underdeveloped, and its per capita GDP is far below the national average. According to the Guangyuan Municipal Government's 2021 sample survey, migrant workers accounted for 33.6% of the total labor force in the first quarter and 36.5% in the second quarter (data available at: [[https://www.cngy.gov.cn/article/show/20210909102901371.html](https://www.cngy.gov.cn/article/show/20210909102901371.html)]([https://www.cngy.gov.cn/article/show/20210909102901371.html](https://www.cngy.gov.cn/article/show/20210909102901371.html)), accessed on 22 June 2022). A high proportion of migrant workers no longer engage in agriculture work, which may lead to unmanaged cropland, low activity, or even abandonment. Moreover, the area is dominated by hills and basins with a fragmented terrain and complex planting situation, labile climate, and frequent cloud interference, all of which are considerable barriers for remote sensing monitoring.
## 3 Consideration of the Conversion Losses  
The losses in any power converting system depend on the losses caused by the filtering components, solid-state devices, and gate control circuits. Power losses produced by the filtering component can be ignorable as they have a low value of their internal resistances. The losses produced by the solid-state transistors and diodes are the sum of their switching (\(P_{sw}\)) and conduction losses (\(P_{con}\)).  
The switching losses depend on the switching frequency and the value of the voltages and currents at which they change their states from off to on and vice versa. These losses in transistors are a function of their current and voltage rise and fall characteristics and but in diode, they are related to their reverse recovery characteristics. The instantaneous voltage and currents in any switching period can be considered constant as switching periods are much less than that of the periods of the output or input voltages. The switching losses of a MOSFET and diode may be calculated by considering the procedure developed in [24].  
\[P_{sw(M)}=\frac{1}{6}V_{DS}I_{DS}f_{s}(t_{rise}+t_{fall}) \tag{29}\]  
\[P_{sw(D)}=\ Q_{RR}V_{RR}f_{s} \tag{30}\]  
Here \(V_{DS}\) and \(I_{DS}\) are the drain to source voltage and current, respectively. The \(t_{rise}\) and \(t_{fall}\) are the intervals in which the drain current increases to the final steady-state value and decreases to the minimum value, respectively. \(Q_{RR}\) is the amount of reverse recovery charge that flows across the junction and \(V_{RR}\) is the reverse voltage across the junction during the reverse recovery intervals. The value of the conduction losses for a MOSFET and diode is calculated as  
\[P_{con(M)}=\ I_{DS(rms)}^{2}R_{DS(on)} \tag{31}\]  
\[P_{con(D)}=\ I_{D(rms)}^{2}R_{D(on)}+V_{D}I_{D(avg)} \tag{32}\]  
The conduction losses of a transistor and diode depend on the value of their internal resistances and rms and average currents.  
The losses produced by the gate control circuits (\(P_{GC}\)) are the sum of isolated DC supplies and gate drivers. That may be considered constant as they depend on their number. There is a need for only six gate control circuits in the developed topology but the execution of the circuit in [20] requires the use of eight such circuits. There is the conduction of four low-frequency transistors and diodes along with the high-frequency switching operation of two transistors and diodes during one output interval. The total conversion losses of the developed circuit and circuit reported in [11,20] may be approximated as  
\[P_{Loss[20]}=\frac{V_{DS}I_{DS}f_{s}(t_{rise}+t_{fall})}{3}+2Q_{RR}V_{RR}f_{s}+ 6\Big{[}I_{DS(rms)}^{2}R_{DS(on)}+V_{D}I_{D(avg)}+I_{D(rms)}^{2}R_{D(on)} \Big{]}+8P_{GC} \tag{33}\]  
\[P_{Loss[proposed]}=\frac{V_{DS}I_{DS}f_{s}(t_{rise}+t_{fall})}{3}+2Q_{RR}V_{RR}f_{ s}+6\Big{[}I_{DS(rms)}^{2}R_{DS(on)}+V_{D}I_{D(avg)}+I_{D(rms)}^{2}R_{D( on)}\Big{]}+6P_{GC} \tag{34}\]  
The elimination of two gate control circuits in the developed circuit not only reduces the conversion losses but also drastically lowers the circuit's overall volume and cost.
## 3 Materials and Methods
### Testing Environment  
The primary experiment environment includes a warehouse, outdoor space, and buildings as the testing ground for the DSCT Site Level. Figure 1A shows the layout of the testing ground in scale, which is the facilities of the Industry 4.0 Implementation Center at the National Taiwan University of Science and Technology. The layout space uses computer-aided design (CAD) to represent the geometric location of the building and antenna. The warehouse space is about 10 \(\times\) 10 square meters, and the radio waves transmission range is about 30 \(\times\) 40 m around the warehouse, peripheral space, and building. Figure 1B shows the location of the antenna at the testing site. The antenna sits at the top of the Industry 4.0 Implementation Center building, with a relative height of 5.3 m from the ground.  
The testing environment layout allows radio coverage simulation using an electromagnetic radio simulator, an initial approach for radio coverage analysis and network deployment. The generated radio coverage simulation data can compare with practical signal measurements in a further plan. Acquiring dedicated hardware devices such as industrial boards with 5G capabilities or software-defined radio (SDR) modules is required to obtain practical measurements. The acquisition of dedicated hardware is currently under consideration and included in our future research development plan.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Band/MNO** & **N1** & **N3** & **N28** & **N41** & **N78** \\ \hline Chunghwa Telecom & \(\bullet\) & \(\bullet\) & & & & \(\bullet\) \\ Taiwan Mobile & \(\bullet\) & \(\bullet\) & \(\bullet\) & & & \(\bullet\) \\ Far East Tone & \(\bullet\) & \(\bullet\) & & & \(\bullet\) & \(\bullet\) \\ T-Star Telecom & \(\bullet\) & \(\bullet\) & \(\bullet\) & & & \(\bullet\) \\ Asia Pacific Telecom & & & \(\bullet\) & & \(\bullet\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of frequency bands available in Taiwan by MNO.
## 1 Introduction
To address these questions, we utilized synthesized texture images as research stimuli. We generated synthesized textures through the primitive, grain, and point configuration (PGPC) texture model, a mathematical morphology-based method [25; 26]. This model enables the independent characterization of global and local features. We synthesized textures by separately controlling global and local textural features. We then employed an eye-tracker to acquire eye-movement data when the subjects were viewing the synthesized texture scenes with the task of selecting textures similar to a standard texture (the texture being compared). We statistically analyzed the eye-movement data in terms of three eye-fixation metrics: total fixation duration (TFD) in each region of interest (ROI), fixation-point variance (FPV) in each ROI, and fixation-transfer counts (FTCs) between different ROIs. We assumed that global and local features affected eye-movement patterns. We hypothesized that, if the global features of a texture were similar to those of the standard texture, then the fixation points in the texture would be highly dispersed. By contrast, if the local features of a texture were similar to those of the standard texture, then fixation points in the texture would be highly concentrated. We also verified that the final similarity choice was closely related to FTCs between ROIs of different textures viewed in the experiment. Our research differs from conventional studies on visual perception. First, instead of artificial images (e.g., patterns or fabric images), the utilized textures in the eye-tracking experiment were synthesized by a mathematical morphology-based texture model. We artificially synthesized textures with the required features instead of simply analyzing and extracting features from the existing textures. Second, it was identified that viewing distance influences the visual perception of global and local texture features. Viewers are attracted to local features when viewing images from a short distance and to global features when viewing images from a long distance. In [17], a logistic-regression model was built to construct a statistical relationship between viewing distance and visual perception of global/local features. In this research, viewing distances in the visual-similarity experiment were determined according to the distance equation proposed in [17]. We then set different viewing distances to ensure the equal probability of similarity selection in the experiment.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Data and Processing  
#### 2.3.1 Data Sets  
In this work, the hourly foF2 time series from 10 ionosonde stations both in China and in Australia are utilized. Figure 4 displays the geographical distribution of the 10 stations. The URSI (Union of Radio Science International) code is labeled as the name of stations and the red stars are marked as the location of the selected stations, of which MH453, BP440, WU430, SH427, and SA418 are located in the China region, DW41K, BR52P, CN53L, CB53N, and HO54K are distributed in Australia region. Table 1 listed the URSI code, name, country, time range, and geographic coordinates of the selected ionosonde stations. These stations are mainly concentrated in the middle and low latitudes of the northern and southern hemispheres. The hourly ionospheric observed data foF2 from 2006 to 2019 and the relevant parameters are utilized, and training sample sets (2015 and 2019 are excluded) are formed to train the neural network models and the deep learning model. The data of 2015 and 2019 are utilized as test sample sets to ensure the correctness and rationality of the test results. Figure 4: Spatial distribution map of the ionosonde stations. The URSI code in red font and the red stars indicate the name and the location of the selected ionosonde stations, respectively. Table 2 shows the data availability of 10 ionosonde stations from 2006 to 2019. Most data are concentrated in 2013-2019 with a one-hour resolution. The diagonals indicate the missing measured data. As can be seen in Table 2, the data availability of each station is different. To make sample datasets as big as possible, all the observed data are utilized as sample datasets in the training BPNN model and GABP model. While in the training LSTM model, unwanted interruptions might be caused due to low data availability in the time series training practice, so we only utilize the data observed in stations that data availability more than 70% during the period of 2014-2018. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **URSI** & **Station/Abbreviation** & **Country** & **Time Zone** & **Lat** & **Lon** \\ \hline
01 & DW41K & Darwin (DAR) & Australia & UTC + 10 & \(-\)12.45 & 130.95 \\
02 & SA418 & Sanya (SAY) & China & UTC + 8 & 18.34 & 109.62 \\
03 & SH427 & Shaoyang (SHY) & China & UTC + 8 & 26.9 & 111.5 \\
04 & BR52P & Brisbane (BRI) & Australia & UTC + 10 & \(-\)27.06 & 153.06 \\
05 & WU430 & Wuhan (WUH) & China & UTC + 8 & 30.54 & 114.34 \\
06 & CN53L & Camden (CAM) & Australia & UTC + 10 & \(-\)34.05 & 150.67 \\
07 & CB53N & Canberra (CAN) & Australia & UTC + 10 & \(-\)35.32 & 149 \\
08 & BP440 & Beijing (BEI) & China & UTC + 8 & 39.98 & 116.37 \\
09 & HO54K & Hobart (HOB) & Australia & UTC + 10 & \(-\)42.92 & 147.32 \\
10 & MH453 & Mohe (MOH) & China & UTC + 8 & 53.49 & 122.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geographic coordinates and details of the ionosonde stations.
## 3 Proposed Method
### Figures, Tables, and Schemes  
We used the above analysis to design our rain streaks removal network. As shown in Figure 1, it used a bilateral filter to decompose the high-frequency part of the input image and used it as input. Moreover, the network first generated a residual image to learn the residual and better remove the rain streaks. The rainy image was eventually subtracted from the residual image to obtain the image without streaks.  
The good performance of U-Net has been applied in image segmentation [33]. In recent years, some studies have applied it in the task of image rain streaks removal. It can be divided into two parts, a contracting path for feature extraction by downsampling and an expansive path for gradual recovery of image scale by upsampling. The skip connection structure connects the downsampling layer to the upsampling layer, so that the features extracted from the downsampling layer can be directly passed to the upsampling layer. Inspired by this, in this paper, we designed the rain streaks removal network based on U-Net. The structure of the rain streaks removal network is shown in Table 1. It contained 12 convolutional layers and three deconvolutional layers. The first layer used a convolutional kernel of size \(5\times 5\), and the other layers use one of size \(3\times 3\). The larger convolutional kernel could afford a larger perceptual field to ensure the extraction of more features of the original image. The strides of the third, fifth, and seventh convolutional layers were two, and thus the size of the feature map changed from I to I/4, I/16, and I/64. Correspondingly, after three iterations of deconvolutional operations on the 9 th, 11 th, and 13 th layers, the size of the feature map changed back to the original. The input to layer 10 was the concatenation of the outputs of layers 9 and 6; layer 12 was the concatenation of the outputs of layers 11 and 4; and the input to layer 14 was the concatenation of the outputs of layers 13 and 3. This preserved the image original information and improved the correlation between contexts. Layers 1 to 14 used the leaky rectified linear units (LReLU) activation function, and the last layer used the Tanh function to map the result to the interval \([-1,1]\).  
Figure 3: There is an image after the rainy image is subjected to bilateral filter: (**a**) Rainy image; (**b**) Low-frequency layer; (**c**) High-frequency layer.  
The high-frequency part of the rainy image is used as input to the CNN to generate the residual image:  
\[\overline{S}=F_{\text{G}}^{\theta}\left(\widetilde{X}\right), \tag{8}\]  
where \(F_{\text{G}}^{\theta}\) represents the part of the convolutional network in the rain streaks removal network.  
Finally, we can obtain the de-rained image \(\overline{Y}\) according to (2):  
\[\overline{Y}=X-\overline{S}. \tag{9}\]