esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The Context of the Water Energy Nexus in Abu Dhabi
Approximately 60% of the AD wastewater is planned to be returned to the sewer by 2030 to be recycled for reuse in applications such as irrigation [27] in [28]. This strategy forms part of the AD government's aims to not permit potable water to be utilized for irrigation purposes, and so alleviate stress on the existing water infrastructure [29] in [28]. As such, it will be mandatory to use treated sewage effluent (TSE) for irrigation (ibid.). To date (2019), to the authors' knowledge, there is no TSE infrastructure near the MFCS location, and the timing for this to happen is presently unknown to the community [30] in [28]. The International Energy Agency (IEA) [31] reported that in 2014, almost 10% of the Middle East's (including AD) electricity consumption was used to extract, distribute, and treat water and wastewater including the use of large quantities of oil equivalent of thermal energy for irrigation pumps and gas in desalination plants (see Figure 3). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **MSF** & **MED** & **RO** \\ \hline Water cost production (USD/m\({}^{3}\)) & 1.10–1.25 & 0.75–0.85 & 0.68–0.82 \\ Energy use (kWh/m\({}^{3}\)) & 15.40 & 7.5 & 4–7 \\ Volume of saline feed water per m\({}^{3}\) of fresh water & 4 & 3 & 2–2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Desalination techniques against financial and environmental impacts [20]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Abu Dhabi Water Supply** & **Desalination Technique Used** & **Water Production Share by Company** \\ \hline AMPC & MSF & 2\% \\ TAPCO & MSF & 18\% \\ GTTPC & MSF + MDF & 10\% \\ ECPC & MSF & 6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Desalination plants serving AD Island [18]. Figure 3: Electricity consumption in the water sector by process and region in 2014 [31]. Supply includes water extraction from groundwater and surface water as well as water treatment. Transfer refers to large-scale inter-basin transfer projects. Water systems can save energy by reducing the amount of water that must be withdrawn, treated, and distributed [32], especially in locations like AD, where the technique of desalination is highly energy intensive [20]. Figure 4 illustrates the energy consumption to treat and move water from the source to the end-user [29; 31]. Irrigation is the primary water consumption worldwide [33]. In arid regions where the mean annual precipitation (P) is substantially less than the characteristic potential evapotranspiration (ETo), the appropriate selection of plants and efficient irrigation systems can conserve a great amount of urban irrigation water [34]. In addition, wasting water in sectors such as landscaping in arid regions is further aggravating water scarcity and emphasizing the need to develop ways to improve irrigation efficiency (ibid). This improvement cannot solely depend on improving the efficiency of water pumps without changing the price of electricity or irrigation practices to avoid a water consumption increase and further depletion of aquifers [31].
## 1 Introduction
In this paper, we propose integrating the RAFT algorithm into the HBFT algorithm to address two issues: the performance reduction of the HBFT algorithm when facing a large influx of message traffic and the inability of the RAFT algorithm to be used in blockchains with lower security requirements. In our proposed algorithm, nodes not only exchange information within the group but also rapidly eliminate nodes experiencing BFT or CFT behavior, thereby improving the algorithm's performance. To ensure the security and efficiency of interactions between blockchain nodes, we also integrate the ECC (Elliptic Curve Cryptography) algorithm into our proposed algorithm. Experimental results demonstrate that our proposed algorithm outperforms the existing HBFT algorithm in comparative experiments, showing excellent performance. By integrating the strengths of the RAFT and HBFT algorithms, and incorporating ECC for secure and efficient interactions between nodes, our proposed algorithm offers an innovative and effective solution to the performance and security challenges faced by existing consensus algorithms. The experimental results validate the superiority of our approach, making it a promising candidate for practical implementation in various blockchain systems.
## 3 The Asymmetric Hyperchaotic Image Encryption Scheme
### The Formation of the Key and the Hyperchaotic Sequences  
Before the generation of the hyperchaotic sequences by using the LF-NCHM, the initial values were generated based on the hash value derived from the plaintext image. To ensure security, we used the SHA-256 algorithm to obtain the dynamic hash which is related to the plaintext image, which indicates that even the most trivial alteration would still result in a huge difference for the result. On the basis of the dynamic hash, the initial conditions with extremely strong sensitivity were then determined. After that, these conditions were treated as the input of the LF-NCHM for the construction of the hyperchaotic sequences used in further operations. The specific steps are as follows:  
1. Generate the hash value by using the plaintext image, then split the obtained hash into 32 pieces, like \(K_{1},K_{2}\ldots K_{32}\), and each of these pieces contains 8 bits of data related to the plaintext image.
2. With the application of the formulas shown in (2), calculate the intermediate values \(h_{1},h_{2},h_{3},h_{4}\), and the inputs of the formula are \(K_{1},K_{2}\ldots K_{32}\) from Step 1.  
\[\begin{cases}h_{1}=\dfrac{(k_{25}\bigoplus k_{26}\bigoplus\ldots\bigoplus k_{ 32})}{256}\\ h_{2}=\dfrac{(k_{1}\bigoplus k_{2}\bigoplus\ldots\bigoplus k_{8})}{256}\\ h_{3}=\dfrac{(k_{17}\bigoplus k_{18}\bigoplus\ldots\bigoplus k_{24})}{256}\\ h_{4}=\dfrac{(k_{9}\bigoplus k_{10}\bigoplus\ldots\bigoplus k_{16})}{256} \end{cases} \tag{2}\]  
Figure 4: The general flow chart of the algorithm.  
Calculate the initial conditions \(\mu\), \(\lambda\), \(x_{0}\), and \(y_{0}\) in accordance with the \(h_{1},h_{2},h_{3},h_{4}\), and the equations applied are displayed in (3).  
\[\begin{cases}\mu=mod(\mu_{0}^{{}^{\prime}}+\frac{(h_{1}+h_{2})}{256},4)\\ \lambda=mod(\lambda_{0}^{{}^{\prime}}+\frac{(h_{2}+h_{3})}{256},6)\\ x_{0}=mod(x_{0}^{{}^{\prime}}+\frac{(h_{3}+h_{4}){\times}10^{14}}{256},1)\\ y_{0}=mod(y_{0}^{{}^{\prime}}+\frac{(h_{4}+h_{1}){\times}10^{14}}{256},1)\\ \end{cases} \tag{3}\]  
In the formula, the \(\mu_{0}^{{}^{\prime}}\), \(\lambda_{0}^{{}^{\prime}}\), \(\lambda_{0}^{{}^{\prime}}\), and \(y_{0}^{{}^{\prime}}\) are the initial values given in advance. For the algorithm in this scheme, these conditions are designated here: \(\mu_{0}^{{}^{\prime}}=3.1\), \(\lambda_{0}^{{}^{\prime}}=4.4\), \(x_{0}^{{}^{\prime}}=0.3\), and \(y_{0}^{{}^{\prime}}=0.5\).
4. Take the obtained initial values as the input of LF-NCHM to generate the hyperchaotic sequences with the length needed.
# Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose
## 2 Materials and Methods
### Gas Chromatographic Mass Spectrometry  
For the gas chromatography-mass spectrometry test, an Agilent 7890A/5975 (Agilent Corporation, Palo Alto, CA, USA) and a HP-INNO Wax capillary column (Agilent Corporation, Palo Alto, CA, USA) were used. The gas sample was pretreated with a PDMS100 um extraction head (Supelco, Bellefonte, PA, USA) before use. The extraction head needed to be activated in the forward sample port at 260 \({}^{\circ}\)C for one hour before use in order to ensure a good working condition.  
Chromotographic working parameters: the inlet temperature was 250 \({}^{\circ}\)C, the desorption time was 15 s, and the gas to be measured was separated on the chromatographic column. Over heating program: the initial temperature was 60 \({}^{\circ}\)C, which was maintained for 4 min, increased to 80 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, and then increased to 240 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, which was maintained for 15 min; the total time of heating program is 34 min. The carrier gas was high-purity helium (99.999%), with a flow rate of 1 ml/min, and a splitless mode.  
MS working parameters: GC-MS interface temperature was 260 \({}^{\circ}\)C, electron bombardment ion source, ion source temperature was 230 \({}^{\circ}\)C, quadrupole temperature was 150 \({}^{\circ}\)C, and mass range was 20-500 mass units.  
The GC-MS volatile components were qualitatively determined by NIST and Wiley libraries. After the gas was detected, the retention time and peak area were obtained. The retention time was used for qualitative analysis of substances. The peak height was related to the perceived intensity of these gases. The peak area normalization method was used to calculate the relative content of each volatile compound.
# Reconfiguration Decision-Making of IoT based Reconfigurable Manufacturing Systems
## 1 Introduction  
With the development of information and communication technologies or services (Internet, social media, etc.) and with the increased social diversification, a long tail market in which various products are consumed in a small quantity according to diverse preferences has been popular [1]. However, it is hard for a mass-production system to meet small demands efficiently. For this reason, the need for a new manufacturing system capable of responding to the long tail market has emerged.  
An intelligent production system that highlights flexibility has been developed, and various relevant studies have been conducted [2]. As a result, various manufacturing systems such as flexible manufacturing systems (FMSs) and reconfigurable manufacturing systems (RMSs) have been suggested. In addition, various emerging technologies such as the Internet of Things (IoT), cloud system, and 3D printing have tried to be applied [3].  
This study focused on RMSs with the IoT sensor system. An RMS is a production system that implements flexible responses to external and internal needs through the reconfiguration of modules [4]. With the concept of reconfiguration, RMS can implement flexibility with less cost when compared to a full flexibility manufacturing system. However, reconfiguration requires a complex decision-making process including the status of modules, line conditions, and surrounding environments. To make a proper reconfiguration decision, it is necessary to obtain a variety of information including detailed information in modules.  
Recently, with the development of IoT technology, it is possible to collect the hidden data of a manufacturing system, which had never been collected previously. This study proposes a decision-making algorithm for reconfiguration that uses information obtained by the IoT sensors installed in RMS modules. The proposed system is expected to detect reconfiguration situations quickly to create appropriate reconfiguration planning and achieve fast stabilization after reconfiguration by using the IoT information.  
The structure of this study is as follows. Section 2 describes the related studies and their differences with this study. Section 3 presents the features of the RMS and the application of the proposed system. We explain the application of IoT technology to the RMS in Section 4 and present the proposed reconfiguration decision-making process in Section 5. Section 6 describes the evaluation of the proposed system in a simulation experiment. Section 7 presents the achievements of this study and the follow-up research.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 2 Problem Formulation
### Problem Setting  
The main objective of this research is to determine the damping ratio (\(\zeta_{j}\)) using various statistical methodologies based on the observation model from Equation (4). To achieve this, \(K\) samples are collected at a frequency of Fs. This requires an energy input into the system, such as with a hammer, as well as a sufficiently lengthy recording duration to allow for the complete dissipation of the energy. From a statistical standpoint, as discussed in Section 1, the optimal approach for averaging the extracted features remains unresolved [34]. This issue is further complicated by the varying averaging methods employed by different algorithms, which are predominantly due to their mathematical variations. As a result, this article will perform a comparative analysis of the results derived from these different approaches. An additional, yet often overlooked, query in this field relates to the impact of signal non-alignment, represented as \(\tau_{0}^{k}\) in Equation (4). This study extends its analysis to investigate the effects of non-alignment on the performance of well-established damping extraction methods, thereby contributing to a more comprehensive understanding of the underlying dynamics. It is essential to note that, in real-world scenarios, analysis is typically centered on the sampled (discrete) version of the measurement model, as shown in Equation (5).  
\[\text{Signal}^{k}[n]=\text{Signal}^{k}(n\Delta t). \tag{5}\]
## 2 The Symmetric Zero-Area Conversion Method
### The Limitation of SZAC  
Analyzing the implementation process of SZAC can find that the conversion results in specific scenes cannot accurately distinguish peak positions. For example, when \(m=1\), the conversion function is denoted as \(C=[-c,2c,-c]\), the original spectral data is \(y=[y_{1},y_{2},y_{3}]\), and the conversion result while \(i=2\) is shown as Expression (8).  
\[y_{2}^{*}=(-c)\cdot y_{1}+2c\cdot y_{2}+(-c)\cdot y_{3}=c(2y_{2}-y_{1}-y_{3}) \tag{8}\]  
If \(y_{2}\) is the peak, \(y_{2}>y_{1}\), \(y_{2}>y_{3}\), then \(y_{2}^{*}>0\). If \(y_{2}\) is not the peak but has the characteristics of \(2y_{2}-y_{1}-y_{3}>0\), the calculation result is still greater than 0. If the result of \(2y_{2}-y_{1}-y_{3}\) is large enough, the threshold processing result here will be greater than the given threshold, resulting in an error in the judgment result, which is one of the limitations of SZAC.  
On the other hand, three parameters, \(m\), \(H_{G}\), and \(T_{0}\), need to be set during peak searching with SZAC. One study found that when the peak-like function is similar to the original spectral data, the variation results are optimal. Therefore, the peak type of the original spectral data can be obtained in advance, and \(H_{G}\) can be determined according to its FWHM. The selection of \(T_{0}\) is appropriate to distinguish the peak position effectively, which has strong dependence on the data. \(m\) directly affects the change results. Wide windows can suppress noise, while narrow windows can improve the recognition ability of overlapping peaks. The setting of this parameter depends on the performance requirements of the algorithm. The dependence of parameter selection on spectral data characteristics and experience is another limitation of SZAC.  
The above two aspects limit the application scope of SZAC. To reduce or eliminate the influence of limitations and improve the applicability of SZAC is the focus of this paper.
## 3 TCM Protocol
### The Topology Maintenance Phase
#### 3.3.2 Nodes Moving between Clusters  
When the nodes move between different clusters, the communication mechanism of soft handover was adopted to keep the continuity of data transmission. When the node moves to cross the boundary of the clusters, this mechanism can keep the node communicate with the new and the old cluster heads at the same time, and when it establishes a stable link with the new cluster, it will cut off the link with the old cluster head. As shown in Figure 4, the node \(S_{i}\) moves from cluster \(C_{i}\) to \(C_{j}\), and from position A to B. In the time slot \(T_{i}\) of \(S_{i}\), \(C_{i}\) broadcasts the request \(Data\_req\). Since \(S_{i}\) has already  
Figure 4: A schematic description of nodes’ mobility in a sensor network.  
moved to \(C_{j}\), \(C_{i}\) cannot receive the data uploaded by \(S_{i}\). In this case, \(C_{i}\) signs \(S_{i}\) as a pseudo-mobile node, and cluster head \(C_{i}\) increases the communication range adaptively to keep the link with \(S_{i}\). On the other hand, \(S_{i}\) also cannot receive _Data_req_ in time slot \(T_{i}\), it signs itself as a pseudo-mobile node, and broadcasts the _Join_req_: \(S_{i}\)\(\rightarrow\)*_Join_req\(||D_{S_{i}}||P_{S_{i}}||(W_{S_{i}}\)_vs\({}_{i}\)) to request joining the cluster \(C_{j}\). The cluster \(C_{j}\) makes an identity authentication to \(S_{i}\) by using the digital signature (\(W_{S_{i}}\),\(w_{S_{i}}\)), and then it gives the response information \(ACK\): \(C_{i}\)\(\rightarrow\)\(S_{i}\):\(ACK||D_{C_{i}}||P_{C_{i}}||(W_{C_{i}}\)_v\({}_{C_{i}}\)). Then the cluster \(C_{j}\) updates the member list of cluster, assigns a new time plot to \(S_{i}\), and broadcasts this within the cluster. When the node \(S_{i}\) receives the new TDMA frame, confirms itself as a mobile node, and sends a quit request _Exit_req_ to the original cluster \(C_{i}\). After cluster head \(C_{i}\) receives the quit request _Exit_req_, it will sign \(S_{i}\) as a mobile node, and delete the time plot of \(S_{i}\) and other relative key information.
# Development of Potato Mass Estimation System Based on Deep Learning
## 3 Results and Discussion
### Mass Estimation Equation  
#### 3.2.1 Physical Properties of Potatoes  
Table 2 shows the result of examining the major axis length, minor axis length, volume, mass, and density of the potatoes of the three varieties of Superior, Atlantic, and Jopung. As a result of measuring the major axis length and minor axis length of potatoes from the front and the side, the minor axis length showed differences ranging from 10 to 15 mm depending on the direction of the examination. Accordingly, it is judged that the direction of the potato in the acquired image will substantially affect the estimation of the potato mass, and that there will be a need for a volume estimation model that takes this into account. The densities of the three varieties of Superior, Atlantic, and Jopung were found to be 1085.3 kg m\({}^{-3}\), 1080.7 kg m\({}^{-3}\), and 1043.9 kg m\({}^{-3}\), respectively, showing no substantial differences between the varieties.
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 3 Experiment Strategy  
To explicitly investigate and analyze the performance of the time and frequency transfer solutions with the proposed DPT method, eight baselines composed of IGS stations were selected. The detailed information of the eight baselines is presented in Table 1. Using the GPS calibration results provided by BIPM as a reference, the time transfer results for the USN7-USN8 baseline verify the accuracy of DPT time transfer. For the baseline closure consisting of the three short baselines NYA1-NYA2, NYA2-NYAL, and NYAL-NYA1, it verifies the self-consistency of DPT time transfer. In evaluating the long-term time and frequency performance of the baselines, four sets of baselines with external atomic clocks, namely, KOKV-KOVB, CEBR-VILL, KOUR-KOUG, and ONSA-SPT0, are used to verify the DPT time and frequency stability. Table 2 summarizes the information about the data and settings in the DPT data processing.
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## 5 Conclusions  
This paper focuses on the problem of moving target tracking for fixed-wing UAV in a planar environment. A modified NFTSM guidance law is proposed to solve this problem. For the sake of solving the singularity problem, which can be occurred in the traditional TSM, a modified saturation function is used through a small range around the null point. Based on Lyapunov-based approach, theoretical analysis shows that the proposed method can guarantee the reaching phase to converge in finite-time as well as the sliding phase. Compared to the traditional SMC method, the proposed guidance law can steer the UAV to reach the desired distance in a short time. Meanwhile, the body constraints can be satisfied. Adopting three kinds of the target's motion state demonstrates that the proposed guidance law can be suitable for the problem of tracking the high maneuvering target. The proposed method is proven to converge to zero in a shorter time. The simulation results also confirm that the coefficients \(\alpha_{2}\) and \(\beta_{2}\) can reduce the convergence time but bring the shock near the equilibrium point. In our future research, we will extend the method on the problem of multi-target tracking by multi-UAVs and will attempt to perform the NFTSM guidance law on the real UAV platform.  
The authors would like to express their acknowledgement for the support from the Aviation Science Foundation of China (No. 20135851043).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] wrote the paper; and [PERSON] and [PERSON] provided some suggestions and revised the paper.  
The authors declare no conflict of interest.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
###### Abstract  
The use of global navigation satellite system reflectometry (GNSS-R) measurements for classification of inundated wetlands is presented. With the launch of NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission, space-borne GNSS-R measurements have become available over ocean and land. CYGNSS covers latitudes between \(\pm\)38 deg, providing measurements over tropical ecosystems and benefiting new studies of wetland inundation dynamics. The GNSS-R signal over inundated wetlands is driven mainly by coherent scattering associated with the presence of surface water, producing strong forward scattering and a distinctive bistatic scattering signature. This paper presents a methodology used to classify inundation in tropical wetlands using observables derived from GNSS-R measurements and ancillary data. The methodology employs a multiple decision tree randomized (MDTR) algorithm for classification and wetland inundation maps derived from the phased-array L-band synthetic aperture radar (PALSAR-2) as reference for training and validation. The development of an innovative GNSS-R wetland classification methodology is aimed to advance mapping of global wetland distribution and dynamics, which is critical for improved estimates of natural methane production. The results obtained in this manuscript demonstrate the ability of GNSS-R signals to detect inundation under dense vegetation over the Pacaya-Samiria Natural Reserve, a tropical wetland complex located in the Peruvian Amazon. Classification results report an accuracy of 69% for regions of inundated vegetation, 87% for open water regions, and 99% for non-inundated areas. Misclassification of inundated vegetation, primarily as non-inundated area, is likely related to the combination of two factors: partial inundation within the GNSS-R scattering area, and signal attenuation from dense overstory vegetation, resulting in a low signal.  
GNSS bistatic radar; wetlands; inundation; monitoring; classification Article