esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture
## 4 Grow Lights Monitoring System
### The Network  
In order to ensure optimal cultivation conditions, various microcontrollers equipped with sensors, actuators, and wireless communication modules are deployed in the indoor farming facility to monitor and control the system according to the user's requirements. The indoor farming facility is supposed to be divided into several compartments representing growth chambers and containing different plant species or plants at different growth stages. This means that each growth chamber requires particular environmental conditions and management procedures. The microcontrollers are organized in equidistant positions in a grid topology because the growth chambers are also supposed to be positioned next to each other in a plant factory. On the other hand, a control room is positioned on the side of the growth chambers where another microcontroller, a computer, or a server with the necessary computational power is used for the remote monitoring of the growth conditions. The communication between the microcontrollers is based on the IPv6 protocol, and the range of each microcontroller, including the server, only reaches the neighboring microcontrollers. Multi-hop communication is established to transfer the data between devices out of range.  
Figure 1: General architecture of a 6 LoWPAN network.  
The RPL (routing protocol for low-power and lossy networks) routing protocol works effectively in networks composed of constrained IoT devices used for data collection, generally called WSN (wireless sensor network). Overall, two types of devices exist in the proposed network: a master device and slave devices, also called server and clients. The master device is the root node of the network responsible for processing the data received from other nodes, whereas the slave devices are source nodes that can play two roles: sensing information and forwarding it periodically to the next nodes until reaching the root node, receiving control information from the root and forwarding it to the corresponding sensor node, or applying this information in the growth chamber. Figure 2 illustrates the proposed remote grow light control architecture for indoor farming, and Figure 3 shows the flowchart of the process.  
Figure 3: Flowchart of the grow light remote control system for indoor farming.  
Figure 2: Grow light remote control system for indoor farming. Colored lines represent wired communication, while dashed arrows represent wireless communication.
# Exploring CP Array Antenna Characteristics and Its Applications to a Deployable Structure
## 1 Introduction
The performance of the proposed transmitting antenna is then evaluated under three different mounting configurations. Figure 1: 27U CubeSat example configuration.
## 6 Simulation Results
### Experimental Results  
Figure 6 shows how routes are discovered differently by PLGPa, AODV-EHA and RCPED, receptively, in a particular network consisting of 30 nodes. Normal nodes are marked with dark circles, while red stars represent compromised nodes. PLGPa intends to find the shortest route, as encryption is utilized to tackle malicious behavior of any compromised node, and it does not have to bypass compromised nodes intentionally. AODV-EHA looks for the most energy-efficient route while assuming all nodes are honest, and the selected route may likely contain some malicious nodes. As previously mentioned in this paper, RCPED is designed initially to bypass nodes that are not \"clean\" while trying to minimize the energy cost by selecting the relatively energy-efficient routes simultaneously.
_Article_  
**Thermography of Asteroid and Future Applications**  
**in Space Missions**  
**[PERSON]**  
Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, 3-1-1 Yoshinodai, Chuo, Sagamihara 252-5210, Japan; [EMAIL_ADDRESS]; Tel.: +81-50-3362-2471  
Received: 31 January 2020; Accepted: 19 March 2020; Published: 22 March 2020  
**Featureued Application: Activities in lunar, planetary, and asteroid science and exploration.**  
**Abstract: The Near-Earth Asteroid 162173 Ryugu is a C-type asteroid which preserves information about the ancient Solar System and is considered enriched in volatiles such as water and organics associated with the building blocks of life, and it is a potentially hazardous object that might impact Earth. Hayabusa2 is the asteroid explorer organized by the Japan Aerospace Exploration Agency to rendezvous with the asteroid and collect surface materials to return them to Earth. Thermography has been carried out from Hayabusa2 during the asteroid proximity phase, to unveil the thermophysical properties of the primitive Solar System small body, which offered a new insight for understanding the origin and evolution of the Solar System, and demonstrated the technology for future applications in space missions. Global, local, and close-up thermal images taken from various distances from the asteroid strongly contributed to the mission success, including suitable landing site selection, safe assessment during descents into the thermal environments and hazardous boulder abundance, and the detection of deployable devices against the sunlit asteroid surface. Potential applications of thermography in future planetary missions are introduced.**  
**Keywords: thermography; uncooled micro-bolometer array; asteroid; planetary exploration; thermal inertia**
## 4 Analysis Results
### Prediction Model Comparison  
Table 6 summarizes an analysis of the linear and nonlinear algorithms reviewed to predict the amount of power generated by the floating PV system. The linear regression prediction model showed a percentage error of 9.61-11.35%, and the neural network prediction model with five hidden nodes showed an error range of 8.78-10.36%. As a result, it was confirmed that the neural network prediction model, which was a nonlinear algorithm, showed 2.59% higher accuracy than the linear regression analysis prediction model, on average. The numbers of hidden nodes were set to one to twenty in the neural network algorithm, and it was confirmed that overfitting happened in twenty nodes and ten hidden nodes produced the best accurate results among them. In conclusion, the power generation must be calculated in the non-linear model and the number of hidden nodes should be taken into consideration so as not to have overfitting, as below.
# Effect of Unit-Cell Size on the Barely Visible Impact Damage in Woven Composites
When it comes to BVID detection and quantification, many researchers [9; 10; 17; 18; 19; 20; 21; 22] have been using various nondestructive testing (NDT) techniques such as: ultrasonic, acoustic emission (AE), X-ray and electromagnetics. These methods promote the ability to identify and inspect material for different types of damage such as: fibre breakage, matrix cracking and interlaminar delamination. Ultrasonic and X-ray computed tomography (CT) have been widely used as nondestructive testing techniques for composite materials [23; 24; 25; 26]. In the case of ultrasonic, [PERSON] et al. [18] used ultrasonic testing for UD carbon/epoxy symmetric stacking sequence [45\({}^{\circ}\)/0\({}^{\circ}\)/\(-\)45\({}^{\circ}\)/90\({}^{\circ}\)]\({}_{\text{S}\text{S}}\). The damaged area through the thickness has been identified, and the correlation between delamination area and impact energy was established. However, X-ray CT damage characterisation of 3D woven composites due to impact did not receive enough attention due to the complexity of its analysis and segmentation. Thus, this study is an attempt to provide a comprehensive benchmark comparison for the impact damage resistance of NCF, 2D and 3D woven composites utilising different NDT techniques: ultrasonic, digital image correlation (DIC) and X-ray CT. Moreover, the effect of changing the path of the z-binding yarn and consequently the unit-cell size for 3D woven composites represents a key design parameter in this research. The impact damage resistance is characterised in the light of the macroscopic behaviour, the energy absorption and the size of damage induced by low-velocity impact for four different composite architectures and accurately quantified by the proposed NDT techniques.
# Quality of Maize Silage After Using Meat Bone Meal as a Phosphorus Fertilizer in a Field Experiment
## 4 Discussion
### Macronutrient Content of Maize Silage  
The macronutrient content of silage plays an important role in animal nutrition, especially in high-yielding cows with high mineral requirements [39]. In the current study, P concentration in maize silage was close to the INRA norms for ruminants [40], whereas Ca concentration was more than twice lower than the reference value. Maize silage analyzed by [PERSON] et al. [3] contained around 30-50% more Ca, P, and Mg, and its K content was similar to that noted in this experiment.  
In the present study, an interaction between fertilization and the year of the experiment was found for the P content of maize silage. In successive years of the experiment, P concentration decreased to different extents at different fertilization levels. The greatest decrease in P content was noted in the 1.0 Mg MBM + N\(\gamma_{9}\) treatment where the rate of P fertilizer was lowest (45 kg\(\cdot\)ha\({}^{-1}\)) and the proportion of mineral N (50%) in the total constant N rate (158 kg\(\cdot\)ha\({}^{-1}\)) was highest. In the second and third years, the decrease in P concentration was at the level of the zero-fert treatment. The decrease in the P content of maize silage observed throughout the study accompanied the decrease in CP content. It should be noted that P affects N metabolism in plants by decreasing the content of low-molecular-weight N and increasing the content of protein N and exogenous amino acids. In turn, progressive P deficiency hinders the conversion of sugars to starch or cellulose,which leads to the synthesis of anthocyanins [10]. The comparison of the macronutrient composition (P, K, Ca, and Mg) of the maize herbage achieved in our experiment [22] to the silage made from it described in this study, revealed a decrease in the content of these components after ensiling. The greatest decrease in silage content (on average) was in Mg (12%), then P (6.6%), K (3.1%), and Ca (2.5%).  
Maize requires large amounts of nutrients (NPK), and their availability can significantly affect the chemical composition of plants. Interactions between chemical compounds can be observed in the soil, leading to their mobilization (synergism) or fixation in unavailable forms (antagonism). Phosphorus uptake by plants is determined by the presence of nitrate ions in the soil. Nitrogen fertilization enhances Ca and Mg uptake, and limits K uptake. The uptake of most nutrients continues until the beginning of grain filling [41]. However, maize should be supplied with adequate amounts of P during the first weeks of growth (critical period). At the beginning of the growing season, the response to P deficiency is stronger under high N fertilization levels, acidic soil pH, and low air temperature. [PERSON] et al. [42] demonstrated that adequate N supply is one of the factors contributing to leaf area growth, and a high photosynthetic rate can improve other growth parameters and, consequently, crop yield and quality.
# Adaptive Packet Coding for Reliable Underwater Acoustic Communications
## 3 Problem Formulation  
Assume that the receiver has successfully demodulated \(q_{i-1}^{(k)}\) packets from the \(k\)-th block in the previous \(i-1\) transmissions according to the feedback messages. Then, \(q_{i}^{(k)}\) is given by  
\[q_{i}^{(k)}=\sum_{j=1}^{i}s_{j}^{(k)}=q_{i-1}^{(k)}+s_{i}^{(k)} \tag{8}\]  
According to the characteristics of RLPC, the information packets can be recovered correctly if [15; 16]  
\[q_{i}^{(k)}\geq W \tag{9}\]  
Then, according to (8), we have  
\[s_{i}^{(k)}\geq W-q_{i-1}^{(k)} \tag{10}\]  
Let \(u_{i}^{(k)}=W-q_{i-1}^{(k)}\) denote the minimum number of packets needed to guarantee correct decoding. Thus, the probability of \(W\) information packets can be received correctly after the \(i\)-th transmission is given by  
\[P_{S}^{(i)}=\sum_{s_{i}^{(k)}=u_{i}^{(k)}}^{N_{i}^{(k)}}P_{S}(s_{i}^{(k)},h_{ i}^{(k)},N_{i}^{(k)}) \tag{11}\]  
It is obvious that the number of transmitted packets during the \(i\)-th transmission, \(N_{i}^{(k)}\), cannot exceed a maximal level, denoted by \(B_{i}^{(k)}\). Then,  
\[0\leq N_{i}^{(k)}\leq B_{i}^{(k)} \tag{12}\]  
Our objective is to minimize the total transmission time of data blocks for delay-constrained applications by adjusting the packet-coding rate. Since the packet error is a stochastic process, there are certainly cases where the information cannot be received correctly after many transmissions, especially for error-prone UWA channels. However, considering the significant propagation delay of UWA channels, too many retransmissions is unacceptable in a practical communications system. Thus, we should set a maximal number of retransmissions, \(M\).  
To make sure that \(W\) information packets can be received correctly after \(M\) transmissions, we add a penalty term for the \(M\)-th transmission. In this way, the transmission time of the \(M\)-th transmission is given by  
\[T_{M}^{(k)}=T_{p}N_{M}^{(k)}+T_{\tau}+\pi(q_{M}^{(k)}) \tag{13}\]  
where  
\[\pi(q_{M}^{(k)})= \left\{\begin{array}{ll}0,&q_{M}^{(k)}\geq W\\ C,&q_{M}^{(k)}<W\end{array}\right. \tag{14}\]  
\(C\) is a large value. This setting implies that if the information cannot be recovered correctly after \(M\) transmissions, it will incur a large cost.  
Then, the objective function is given by  
\[J_{T}=\min_{N_{i}^{(k)}}\sum_{i=1}^{M}\gamma^{i}T_{i}^{(k)} \tag{15}\]  
where \(\gamma\) is discount factor. Consider the cost of storage space and number overhead, \(\gamma>1\).  
Naturally, there is a trade-off between the coding rate and the transmission time. Generally speaking, sending more coded packets will improve the reliability of each transmission. Moreover, it will reduce the probability of retransmission and then reduce the total transmission time. However, if we send too many packets in each transmission, the sending time also increases, which also decreases efficiency. Thus, there is an optimal coding rate for each transmission to minimize the total transmission time.
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 6 Experiments and Analysis
### Qualitative Analysis
#### 6.5.1 Impact of Interpolation Weight
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Test Seen**} & \multicolumn{8}{c}{**Test Unseen**} \\ \cline{2-13}  & \multicolumn{4}{c}{**Gold Knowledge**} & \multicolumn{4}{c}{**Predicted Knowledge**} & \multicolumn{4}{c}{**Gold Knowledge**} & \multicolumn{4}{c}{**Predicted Knowledge**} \\  & **C** & **F** & **KC** & **KD** & **C** & **F** & **KC** & **KD** & **C** & **F** & **KC** & **KD** & **C** & **F** & **KC** & **KD** \\ \hline Gold & 4.08 & 4.12 & 3.60 & 3.05 & 4.08 & 4.12 & 3.60 & 3.05 & 4.19 & 4.26 & 3.73 & 3.02 & 4.19 & 4.26 & 3.73 & 3.02 \\ \hline HRED & 3.39 & 3.52 & 2.14 & 2.45 & 3.15 & 3.17 & 1.71 & 2.26 & 3.17 & 3.29 & 1.92 & 2.38 & 3.09 & 2.95 & 1.69 & 1.84 \\ CVAE & 3.14 & 3.08 & 2.62 & 2.58 & 3.00 & 2.87 & 2.06 & 2.47 & 3.08 & 2.99 & 2.51 & 2.31 & 3.07 & 2.82 & 2.63 & 2.19 \\ GTTP & 3.31 & 3.47 & 3.26 & 1.94 & 3.28 & **3.28** & **3.19** & 1.88 & 3.09 & 3.30 & 3.15 & 2.01 & 3.05 & 3.29 & 3.03 & 1.97 \\ TMem & 3.32 & 3.37 & 3.01 & 2.46 & 3.18 & 3.22 & 2.86 & 2.29 & 2.91 & 3.04 & 2.76 & 2.24 & 2.75 & 2.83 & 2.65 & 2.10 \\ SKT & 3.12 & 3.09 & 3.21 & **2.89** & 3.10 & 3.02 & 2.61 & 2.13 & 2.12 & 3.13 & 3.24 & **3.15** & 2.23 & 2.31 & 3.10 & **2.45** \\ \hline KIV\({}_{\text{c}}\) & 3.39 & 3.14 & 3.19 & 2.52 & 3.15 & 3.22 & 2.96 & 2.46 & 2.94 & 2.82 & 2.79 & 2.35 & 2.87 & 2.72 & 2.58 & 2.22 \\ KIV\({}_{\text{d}}\) & **3.53** & **3.67** & 3.21 & 2.58 & **3.36** & 3.18 & 3.16 & 2.37 & **3.19** & **3.43** & 3.16 & 2.36 & 3.18 & **3.14** & 3.08 & 2.27 \\ KIV\({}_{\text{P}}\) & 3.49 & 3.62 & **3.31** & 2.87 & 3.35 & 3.27 & 3.14 & **2.59** & 3.13 & 3.31 & **3.21** & 2.48 & **3.19** & 3.09 & **3.16** & 2.39 \\ \hline \hline \end{tabular} The numbers in **Bold** represent the best results for the corresponding measure.  
\end{table} 
Table 3: Human Evaluation results on WoW Dataset with respect to _Coherence_ (C), _Fluency_ (F), _Knowledge Correctness_ (KC)/_Diversity_ (KD). The numbers in **Bold** represent the best results for the corresponding measure.
## 3 Stratum-Foundation Pit-Station-Track Interaction Model
### Model Calculation Parameters and Construction Simulation Process  
3.2.1 Soil Parameters  
According to the actual engineering geological survey report, the soil parameters of the numerical simulation are obtained, and the difficulty of model construction and calculation is simplified by combining the soil layers with similar physical properties. The parameters of the soil layer are shown in Table 5.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
## 6 Challenges of Adoption of Blockchain in 6G Network
### Storage Capacity  
Blockchain offers decentralized architecture with an immutable ledger that securely stores the data transaction that cannot be tampered or altered. Blockchain permanently keeps data in immutable blocks and is not allowed to be altered or deleted by anyone; therefore, it keeps increasing data volume and raising data storage capacity issues. During peak time, an increasing number of users and data transactions flood the network, leading to transaction delays and network overloading issues. Moreover, due to a growing demand for data storage capacity and bandwidth, nodes cannot synchronize with each other to join the network [64]. Various cloud platforms exist, such as IPFS, Merkle tree-based file systems (MTFS), StorJ, swarm, etc., that resolve the data storage issue in the blockchain-based 6G network. The research community has adopted these data storage platforms with blockchain and proposed solutions in supply chain management, smart agriculture, smart city, etc., to resolve storage issues. For example, [PERSON] et al. [65] proposed an IPFS-enabled blockchain repository for supply chain management to minimize file upload and download time. [PERSON] et al. [66] introduced a secured privacy-preserving scheme to maintain scalability using IPFS-based off-chain storage for smart agricultural UAVs data. However, these solutions face reliability, privacy, and security issues, such as that IPFS does not provide private file storage; it only offers public file access. Likewise, MTFS has access speed and scalability problems with public file systems. From that, it has been noticed that the limited data storage capacity of blockchain is a notable challenge in the current and future networks.
## 4 Experiments
### Qualitative Evaluation  
#### 4.5.1 Qualitative Evaluation of AnyLogic Simulation Datasets  
With the goal to evaluate the predicted accuracy of the proposed method, we selected five particular scenarios and conducted a study of the visual prediction results of the model, as shown in Figure 8. These scenarios involve intricate interactions, such as two pedestrians strolling close together, two pedestrians trailing behind one another, individuals avoiding collisions while walking, a group gathering, and another moving in opposite directions.  
Specifically, scenario (a) describes a situation where a pedestrian is followed by two other pedestrians. The outcomes predicted by our model align with the actual values, indicating that the algorithm accurately identifies scenarios where pedestrians have a shared destination. Since pedestrians tend to follow others when their origin and destination are similar, the travel trajectories of pedestrians with the same travel destination have certain similarities. In scenario (b), when a pedestrian walks toward a group of pedestrians, a potential conflict arises. Our model has the capability to detect the interactions among pedestrians, effectively capturing the phenomenon of simultaneous pedestrian gathering. Thus, the model's trajectory predictions align well with the real-world conditions.  
In scenario (c), our model effectively forecasts the future paths of three individuals walking in parallel by detecting both the social interactions within the group and their interactions with nearby barriers. In scenario (d), a cluster of pedestrians appears going in opposing directions and at risk of collision. Our model exhibits strong interactive capabilities and is able to provide accurate trajectory predictions under challenging conditions. Scenario (e) describes a scenario where multiple pedestrians gather from the same direction. By identifying situations where pedestrians from the same direction gather simultaneously, our model accurately captures the gathering dynamics and achieves accurate trajectory prediction. While our model performs well in most cases, there may be underperformance in some specific scenarios. In scenario (f), four pedestrians walk side by side, but our model only makes accurate predictions for two of them. The other two pedestrians do not follow the regular path to the gate, but instead walk towards the exit. The model fails to  
Figure 7: Results for various models on reduced training data.  
adequately respond to this unexpected situation, resulting in inaccurate predictions. Our model has some limitations in dealing with unexpected behavior.
## 3 Methodology
### Validation of Land Surface Water Mapping  
The essence of [PERSON]'s and [PERSON]'s NDWIs is to produce water-body-enhanced images. Then, an LSW map can be obtained from these images using threshold image segmentation. Hence, the validity of [PERSON]'s and [PERSON]'s NDWIs can be evaluated from an image segmentation perspective. To do this, the between-class variance (BCV), which is \(\sigma\) in Equation (5), and the contrast value were selected to evaluate the efficiency of water-body-information enhancement. The contrast value (CV) between water bodies and background features can be obtained by  
\[CV=\left|M_{w}-M_{ww}\right| \tag{7}\]  
On the other hand, a quantitative assessment of the accuracy was conducted for the entire area. The overall accuracy (OA) and the kappa coefficient (Kappa) on the basis of the error matrix [45] were employed to evaluate the accuracy of the LSW map. The kappa coefficient can be obtained by  
\[K\text{appa}=\frac{P\sum\limits_{i=1}^{n}p_{ii}-\sum\limits_{i=1}^{n}(p_{i+} \times p_{+i})}{P^{2}-\sum\limits_{i=1}^{n}(p_{i+}\times p_{+i})} \tag{8}\]  
where \(p\) is the total number of pixels in the reference data, \(p_{ii}\) is the total number of correct pixels of the \(i\)th category, \(p_{i+}\) is the total number of pixels for the \(i\)th category derived from the classified data, \(p_{+i}\) is the total number of pixels for the \(i\)th category derived from the reference data, and \(N\) is the total number of categories.  
In this study, we first converted the digital number (DN) values of the selected ALI, TM, and ETM+ images to at-sensor radiance (\(L_{i}\)) using Equation (2). We then converted the at-sensor spectral radiance (\(L_{i}\)) to the TOA reflectance (\(\rho_{i}\)) using Equation (1). Next, we used the TOA reflectance (\(\rho_{i}\)) of the ALI, TM, and ETM+ data in three regions to form twenty-four water-body-enhanced images based on Equations (3) and (4). We then used Equations (5) and (6) to define different image segmentation thresholds and used them to obtain twenty-four LSW maps. Finally, we used the CV, BCV, OA, and Kappa to assess the performance of eleven NDWIs for the LSW maps.