esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The demand for efficient water use and automatic systems has been increasing due to the frequent drought damage to crops as a result of climate change, the shortage of water resources in rural areas, and the aging of farmers. The existing automatic irrigation systems reduce the amount of labor required for irrigation and maintain soil moisture. However, the irrigation threshold criteria are user-determined as opposed to being automated according to input objectives such as improving crop productivity and saving water. In this study, an algorithm that could automatically determine suitable soil moisture according to a database and an automatic irrigation system with intermittent irrigation for efficient water use were developed. An experiment was then conducted on the productivity of crops for protected cultivation according to the application of the system. As the frequency domain reflectometry (FDR) sensor used in this system measured the volumetric water content of the soil, the soil moisture tension corresponding with the set value was converted into the volumetric water content using a regression equation. The process of intermittent irrigation was defined by using the moisture movement modeling of Hydrus 2D to reduce water loss on the soil surface and allow moisture to penetrate the soil unobstructed. An experimental field of a tomato farm was divided into empirical manual and controlled automatic irrigation plots. A total of 97.3% of the soil moisture values in the \(-\)33 kPa-controlled automatic irrigation plot and 96.6% of the soil moisture values in the \(-\)25 kPa-controlled automatic irrigation plot were within each set range during the first cropping season. During the second cropping season, a total of 94.8% of the soil moisture values in the \(-\)33 kPa-controlled automatic irrigation plot was within the set range. Compared with the empirical manual irrigation plot, the water productivity in the first cropping season was 113.9% in the \(-\)33 kPa-controlled automatic irrigation plot and 106.3% in the \(-\)25 kPa-controlled automatic irrigation plot. In the second cropping season, the water productivity was 117.3% in the \(-\)33 kPa-controlled automatic irrigation plot. Therefore, an automatic irrigation system applied with intermittent irrigation could be critical to increasing agricultural production and improving water-use efficiency.  
Keywords:irrigation method; moisture movement modeling; soil conditions; threshold criteria algorithm; tomato; upload crops +
Footnote †: journal: Applied Sciences
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 3 Experiments
### Ablation Experiments
#### 3.3.4 Different Encoders  
In many related studies, most used Transformer-based encoders, which are effective for accuracy improvement but introduce a large computational overhead. In this study, in order to verify the effect of different encoders on the final segmentation accuracy, we replaced the ResNet18 encoder with different lightweight Transformer encoders (ViT-Tiny [50], Swin-Tiny [22], and CoaT-Mini [51]) to conduct ablation experiments on the UAVid dataset. Specifically, the use of a lightweight Transformer encoder only achieved a limited accuracy improvement, but it introduced a large computational cost that limited the model's real-time scene application potential, demonstrating that using the ResNet18 as the encoder is a reasonable choice. The details can be seen in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Size** & **Building** & **MovC.** & **StaC.** & **Human** & **mIoU** \\ \hline
512 \(\times\) 512 & 87.3 & 75.0 & 56.5 & 30.4 & 67.9 \\
512 \(\times\) 1024 & 86.9 & 74.8 & 56.3 & 30.6 & 67.4 \\
1024 \(\times\) 1024 & 87.6 & 75.3 & 56.8 & 31.1 & 68.2 \\
2048 \(\times\) 2048 & 87.1 & 75.2 & 55.9 & 29.8 & 67.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments for different input sizes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Encoder** & **Speed** & **Parameters (M)** & **Complexity (G)** & **mIoU** \\ \hline ViT-Tiny & 32 & 8.5 & 35.4 & 67.5 \\ CoaT-Mini & 14 & 10.5 & 159.8 & 68.7 \\ Swin-Tiny & 31 & 28.1 & 104.3 & 68.9 \\ ResNet18 & 114 & 11.7 & 47.1 & 68.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiments for different encoders on the UAVid dataset. The input size was 1024 \(\times\) 1024 and we used a single 3090 GPU.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention Mechanism** & **Speed** & **Parameters (M)** & **Complexity (G)** & **mIoU** \\ \hline LNA & 92 & 12.5 & 67.7 & 66.9 \\ MSA & 63 & 12.7 & 67.5 & 67.3 \\ SWA & 69 & 13.2 & 72.9 & 67.6 \\ GLIA & 114 & 11.7 & 47.1 & 68.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments for different attention mechanisms. The input size was 1024 \(\times\) 1024 and we used a single 3090 GPU. LNA: linear attention; MSA: multi-head self-attention; SWA: shifted window attention.
# Research Progress on Router Devices for the OAM Optical Communication
## 6 OAM Channel Adding/Extracting
By leveraging the add and drop functions, the OADM can selectively upload and download OAM channels without affecting other transmitted OAM channels. In simulations, both the diffraction efficiency and mode purity exceeded 95%. Four OAM channels carrying 16-quadrature amplitude modulation signals were successfully downloaded and uploaded with only 1 dB loss in optical signal-to-noise ratio at a BER of \(3.8\times 10^{-3}\). This innovative approach overcomes the limitations of conventional OADMs, which have single functionalities and poor flexibility. It provides new possibilities for OAM multiplexing and all-optical interconnection. In general, the research on the OAM-RD that can implement channel addition/extraction is still in its infancy, and more in-depth related research is needed. These studies have developed from the extraction and addition of a single OAM beam to the extraction and addition of multiple OAM beams, which will improve the expansion ability of OAM channels step by step, and build the foundation for realizing multi-channel networks, in which intermediate-node users can access their selected channels without interfering with other channels. Table 6 summarizes the works reported to date for the channel adding/extracting routers OAM-based optical communication.
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 5 Accuracy Validation
### Comparison of Accuracy with Static PIM Prediction Methods  
In order to verify the advantages of the InSAR-3D-CTPIM method proposed in this paper over the traditional methods, vertical deformation prediction experiments are carried out here using the method of the literature [18], and the results were compared with the results of the method in this paper, respectively, and a total of 300 high-coherence points were extracted for the deformation prediction results of the four periods (from 31 December 2015 to 1 December 2016, 18 January 2017, 23 February 2017, and 7 March 2017, respectively). Due to the small number of external level points, in order to comprehensively analyze the comparison of the accuracy of the prediction results of the two methods, the SBAS-InSAR monitoring results were used as the real values, and compared with the  
Figure 12: Comparison of residual phases in the Jiansheng coal mine area based on two different models.  
deformation prediction results generated by the two methods, respectively, to measure the accuracy of the prediction results of the two methods. As shown in Figure 13e-h, the deviation distribution of InSAR-3D-CTPIM was more concentrated, and the deviation of the deformation prediction results was smaller than that of the static-PIM deformation prediction results in all four statistical periods; from the statistical data (e.g., Figure 13a-d), most of the deviation values of the deformation prediction results for InSAR-3D-CTPIM were distributed between \([-20,\,20]\) mm, while most of the deviation values of the deformation prediction results for Static-PIM were distributed between \([-30,\,30]\) mm. The total standard deviation (STD) of the InSAR-3D-CTPIM was calculated to be \(\pm 9.6\) mm, while the total STD of Static-PIM was calculated to be \(\pm 13.4\) mm. The total standard deviation of the deformation prediction results for the InSAR-3D-CTPIM model was calculated to be \(\pm 3.4\) mm, and the total standard deviation of the InSAR-3D-CTPIM model was \(\pm 4.4\) mm. The prediction accuracy of the CTPIM model deformation prediction results, compared to the traditional Static-PIM model deformation prediction results, increased by 28.5%, which indicates that InSAR-3D-CTPIM using the CT-PIM model more accurately reflects the mine in the vertical deformation time-sequence characteristics of the mine, the modeling accuracy is higher, and the vertical deformation prediction is more accurate.
# Identification of the Wear Margin of a Pipeline-Machine Subsystem
## 3 The Hydrodynamic Model of an Open Pipeline-Machine Subsystem
### Subsystem Energy Efficiency  
Energy efficiency is defined as  
\[\eta_{e}~{}=~{}\frac{E_{\text{out}}}{E_{\text{in}}} \tag{3}\]  
or  
\[\eta_{e}~{}=~{}\frac{E_{\text{out}}}{E_{\text{out}}+E_{\text{losses}}}~{}=~{} \frac{1}{1+\frac{E_{\text{loss}}}{E_{\text{out}}}} \tag{4}\]The energy efficiency \(\eta_{e}\) of pipeline-machine subsystem is given by  
\[\eta_{e}\ =\ \frac{E_{2}-E_{1}}{E_{\text{IN}}}\ =\ \frac{P_{2}-P_{1}}{P_{\text{IN}}} \tag{5}\]  
where \(E_{1}\) is fluid energy at the beginning of the subsystem, \(E_{2}\) is fluid energy at the end of the subsystem, \(E_{\text{IN}}\) is energy delivered from outside to the engine of the set, \(P_{2}\) is fluid power at the beginning of the subsystem, \(P_{1}\) is fluid power at the end of the subsystem and \(P_{\text{IN}}\) is external power delivered to the motor of the set.  
Energy \(E_{x}\) measured in a selected cross-section \(x\) of the subsystem is a sum of potential energy, kinetic energy and pressure [27]:  
\[E_{x}\ =\ m\frac{w_{x}^{2}}{2}+mgh_{x}+m\frac{p_{x}}{\rho} \tag{6}\]  
Without the change in height above reference plane and without change in flow velocity, the difference of energies \(E_{2}-E_{1}\) results from the difference in pressures \(p_{2}-p_{1}\). If the energy supplied to the subsystem \(E_{\text{IN}}\) is electric energy \(E_{el}\), then the efficiency \(\eta_{e}\) of the subsystem is given by  
\[\eta_{e}\ =\ \frac{m\cdot\frac{(p_{2}-p_{1})}{\rho}}{E_{el}}\ =\ \frac{\dot{V} \cdot(p_{2}-p_{1})}{P_{el}} \tag{7}\]  
Electric energy delivered to the machine set is converted into hydraulic energy, proportional to the difference \(p_{d}-p_{s}\) of discharge and suction pressures. Without including fluid mass losses, changes in conduit cross-section and height above reference plane, the energy efficiency of the machine set \(\eta_{a}\) equals  
\[\eta_{a}\ =\ \frac{\frac{\underline{w}}{\rho}(p_{d}-p_{s})}{E_{el}}\ =\ \frac{\frac{ \underline{w}}{\rho}(p_{d}-p_{s})}{P_{el}}\ =\ \frac{\dot{V}\cdot(p_{d}-p_{s})}{P_{el}} \tag{8}\]  
The energy efficiency of the pipeline-machine subsystem is a product of hydraulic efficiency of subsystem components (the suction pipe, machine and discharge pipe); mechanical efficiency \(\eta_{ma}\) of the machine set; and electric efficiency \(\eta_{el}\) of the motor.  
\[\eta_{e}\ =\ \eta_{Hsp}\cdot\eta_{Hm}\cdot\eta_{a}\ =\ \eta_{Hsp}\cdot\eta_{Hm} \cdot\eta_{Hdp}\cdot\eta_{ma}\cdot\eta_{el} \tag{9}\]  
where \(\eta_{Hsp}\) is the hydraulic efficiency of the suction pipe, \(\eta_{Hdp}\) is the hydraulic efficiency of the discharge pipe and \(\eta_{Hm}\) is the hydraulic efficiency of the machine.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 1 Introduction
### Channel Estimation in NB-IoT Downlink  
Similar to LTE, the NB-IoT downlink physical layer is based on the OFDM transmission scheme [8]. A reference signal is transmitted in resource elements at specific subcarriersand symbols. The NB-IoT physical layer, designed to allow for coexistence with LTE transmissions, uses a single physical resource block (PRB) of 180 kHz [9]. For downlink transmission, 12 OFDM subcarriers are available, separated by 15 kHz. In the time domain, the downlink is organized into 10 ms frames. Each frame consists of 10 subframes divided into 2 slots, each carrying 7 OFDM symbols [10; 11]. A dedicated reference signal, called Narrowband Reference Signal (NRS), is introduced in the NB-IoT downlink. NRS is a pseudorandom sequence of complex numbers mapped onto QPSK constellation points. Real and imaginary parts of these numbers correspond to even and odd elements of a \(2^{31}-1\)-bit long Gold sequence. An NRS is assigned four resource elements located in the last two symbols of each NB-IoT subframe slot. If the eNodeB (eNB) base station uses two antenna ports for NB-IoT transmit diversity, each of these ports is assigned an NRS with a different resource allocation. The four NRS subcarrier indices are dependent on the NB-IoT cell identifier (NB cell ID) according to [12]. An NRS is not transmitted in subframes reserved for synchronization signals (NPSS and NSSS). Figure 1 presents the possible allocation of NRS resources in a single subframe.  
The role of NRS is to provide reference elements (pilots) whose original values are known at the receiver side. The channel estimation starts by estimating channel coefficients for pilots. Next, the channel response is populated for the entire time-frequency resource grid within a designated time window. The following subsection briefly reviews the channel estimation methods proposed in the literature.
## 3 Experimental Results and Discussion
### Controlled Curvature (\(\kappa_{c}\)/\(\kappa_{\rm o}\))-N\({}_{f}\) Relationships
Based on this observation, they proposed the form of \(\alpha\) to be  
\[\alpha^{1/3}=\alpha_{1}\phi+\alpha_{2},0\leq\phi\leq\pi/2, \tag{5}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are material parameters. However, the \(\kappa_{\text{c}}/\kappa_{\text{o}}\)-\(N_{\text{f}}\) relationship on double logarithmic coordinates showed five non-parallel straight lines in this study. Therefore, this study attempts to establish the relationship between \(\alpha\) and \(d/t\). From experimental data in Figure 10a-d, the relationship between \(\alpha\) and \(d/t\) for different \(\phi\) can be obtained, as shown in Figure 12a. Thus, the following proposal is made:  
\[\text{log}\alpha=-\gamma(d/t)+\alpha_{\text{o}} \tag{6}\]  
where \(\gamma\) and \(\alpha_{\text{o}}\) are material parameters. The straight lines in Figure 12a are obtained by the least squares method. According to the corresponding \(\gamma\) and \(\alpha_{\text{o}}\) from the straight lines in Figure 12a, the relationships between these values and \(\phi\) are plotted in Figure 12b,c, respectively. Since they are all linear relationships, this study proposes the following:  
\[\gamma=g_{1}\phi+g_{2},0\leq\phi\leq\pi/2, \tag{7}\]  
and  
\[\alpha_{\text{o}}=a_{1}\phi+a_{2},0\leq\phi\leq\pi/2, \tag{8}\]  
Figure 11: (a) Relationships between log\(C\) and \(d/t\) for different \(\phi\), (b) relationship between \(\beta\) and \(\phi\), and (c) relationship between \(C_{\text{o}}\) and \(\phi\). where \(g_{1}\), \(g_{2}\), \(a_{1}\), and \(a_{2}\) are material constants and can be obtained as \(-0.0255\), \(-0.4554\), \(-0.0001\) and \(0.00108\), respectively, according to Figure 12b,c. Figure 13a-d illustrate the experimental and theoretical \(\kappa_{\rm c}/\kappa_{\rm o}\)-\(N_{\rm f}\) relationships for EMT carbon steel RHTs under cyclic bending on double logarithmic coordinates with \(\phi=0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), \(90^{\circ}\) and different \(d\). The comparison results demonstrate that the theoretical predictions reasonably describe the experimental results.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 5 Conclusions and Future Works  
The selection of the cluster head (\(CH\)) plays a crucial role in IoT devices by gathering and aggregating data from the cluster members and forwarding the data to the sink node. Inefficient \(CH\) selection causes packet failures during the data transfer and early battery depletion nearer to the sink. In this paper, a cluster tree-based routing protocol (CT-RPL) was proposed for addressing this issue. The purpose was served by the simulation of three processes. First, the virtual cluster was created according to the Euclidean distance. Later, the game theoretic approach was used to pick the \(CH\) node. Finally, the metrics QU and \(ETX\) were used to pick the optimum \(CH\) parent for effective data transfer. CT-RPL's efficiency was compared to RPL and E2 HRC-RPL. The CT-RPL showed an enhancement of the lifetime of the network by 30-40% and the packet delivery ratio by 5-10%.  
In future research, the sky note will be deployed in real time, and the performance of the CT-RPL protocol will be carried out in different scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{3}{c}{**Node Energy Consumption (mW)**} \\ \cline{2-4}  & **RPL** & **E2 HRC-RPL** & **CT-RPL** \\ \hline
10 & 0.35 & 0.28 & 0.20 \\
20 & 0.37 & 0.30 & 0.22 \\
30 & 0.40 & 0.35 & 0.25 \\
40 & 0.45 & 0.40 & 0.30 \\
50 & 0.52 & 0.45 & 0.35 \\
60 & 0.55 & 0.47 & 0.37 \\
70 & 0.7 & 0.6 & 0.45 \\
80 & 0.8 & 0.65 & 0.5 \\
90 & 0.85 & 0.7 & 0.6 \\
100 & 0.95 & 0.75 & 0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Node energy consumption with respect to different nodes.  
Figure 7: Node energy consumption vs. the number of nodes for a network size of 100; energy consumption of RPL, E2 HRC-RPL, and CT-RPL is 0.95, 0.75, and 0.65 mW, respectively.  
**Author Contributions:** Conceptualization, S.S. and S.R.; methodology, S.S. and S.R.; software, S.S, S.R. and A.N.; validation, A.N. and A.K.L.; formal analysis, S.R., A.N., S.S and A.K.L.; investigation, A.N. and B.Q.; resources, S.S. and S.R.; data curation, S.S. and S.R.; writing--original draft preparation, S.S. and S.R.; writing--review and editing, S.S, S.R. and A.N.; visualization, S.S., S.R. and A.N.; supervision, A.K.L. and A.N.; project administration, A.N. and B.Q.; funding acquisition, B.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Coaxial Dielectric Barrier Discharge Reactor for Treatment of Winter Wheat Seeds
## 2 Materials and Methods
### Plasma Treatment of Wheat Seeds  
Winter wheat seeds (variety reform and seed moisture around 12%) obtained from Ceravis AG (Rendsburg, Germany) were treated with plasma using the coaxial DBD working with two different gases: argon and helium. First, 15 cm\({}^{3}\) of seeds were filled inside the reactor for each treatment condition. All treatments were performed with the vortex mixer at medium speed, allowing a constant mixing of the seeds. To ensure treatment reproducibility, the reactor was always operated with the same amount of seeds, and the shaking speed was kept constant for all treatments. The air humidity was kept constant at around 30%. The maximum treatment time was set to 5 min due to an increase in temperature. Thus, the seeds were treated from 3 to 30 min where, for long treatment times (above 5 min), breaks of 5 min in between treatments were taken. Two control groups were considered here: the control (c) with untreated seeds and the gas control (c\({}_{\mathrm{g}}\)), where seeds were placed inside the reactor for 5 min with the gas and the vortex mixer on.
# On the Optimal Calculation of the Rice Coding Parameter
## 4 Performance on Different Datasets Using Single-Parameter Rice Coding
### Datasets and Preprocessing  
The datasets used for our numerical analysis are the sets of measurements from the weather stations located at beaches along Chicago's Lake Michigan lakefront. The weather stations have measured air temperature, wet bulb temperature, humidity, rain intensity, interval rain, total rain,precipitation type, wind direction, wind speed, maximum wind speed, barometric pressure, solar radiation, heading, and the station battery life from three different locations once per hour since the 22 nd of June 2015. By considering different sensor types and the different station locations, there are in total 37 datasets. Due to the occasional failure of some sensors or stations, the number of measurements in each dataset ranges from 27,058 to 35,774.  
We believe that the way data are preprocessed has an affect on the compression factor of the Rice coding. In this article, we consider three simple methods for data preprocessing.  
In the first preprocessing method--referred to as \"normalize_dataset_scale\"--all measurements are scaled by a constant factor so as to avoid having fractional values. This is needed since Rice coding does not have the possibility to encode non-integer (fractional) values. The scaling factor was calculated for each dataset as the minimum difference between any pair of measurements. All values are then divided by this scaling factor.  
In the second preprocessing method--referred to as \"normalize_dataset_scale_diff\"--all measurements are scaled as previously mentioned, and then, the difference between consecutive scaled measurements is taken. Since we expect that some observed phenomena will change slowly over time, the author believes that the optimal Rice parameter for the difference of consecutive scaled measurements can be smaller, yielding shorter codes on average. This reflects the estimator methods used in some IoT data compression algorithms, such as in [36].  
In the third preprocessing method--referred to as \"normalize_dataset_shift_mean\"--all measurements are scaled as mentioned in the first preprocessing model, and then, the difference between the scaled measurements and the mean of all scaled values in the batch is taken. This reflects the estimator methods used in some IoT data compression algorithms, such as in [36].