esa-sceva/satcom-chunk-collection huggingface dataset:
# Production of Cellulosic Ethanol from Enzymatically Hydrolysed Wheat Straws
## 3 Results and Discussions
### Determination of Individual Phenolic Compounds  
The WS contain a wide range of individual phenolic compounds (caffeic, p-coumaric, 4-hydroxybenzoic, protocatechuic, chlorogenic, vanillic acid etc.,). The phenols are inhibitory compounds for fermentative microorganisms and cellulases in the process of obtaining bioethanol [59]. The polyphenols must be removed before the fermentation to improve the yield of bioethanol in WS and decrease the amount of acid lactic, thus inhibiting the activity of lactic bacteria [60]. During pretreatment, phenolic compounds are removed from the lignocellulosic material because of the action of acid which leads to the breaking of bonds between polysaccharides and polyphenols.  
Figure 2: Spectroscopic analysis. ATR-FTIR spectra of the liquid fraction after acid pretreatment with 1% H\({}_{2}\)SO\({}_{4}\) (_v/v_), 2% H\({}_{2}\)SO\({}_{4}\) (_v/v_), 3% H\({}_{2}\)SO\({}_{4}\) (_v/v_) (**A**) and of the solid fraction of enzymatically hydrolys straw (**B–D**), compared to the untreated sample and standards of cellulose, xylose and glucose respectively. Depending on the concentration of H\({}_{2}\)SO\({}_{4}\) used, the spectra were stacked to make their interpretation easier.  
[PERSON] et al. (2018) studied the chemical compounds of rice straws after pretreatment with 2% H\({}_{2}\)SO\({}_{4}\) (_v/v_) and obtained that p-coumaric acid has the highest concentration (0.40 mg/g) compared to vanmillic (0.09 mg/g), chlorogenic (0.02 mg/g) and caffe acid (0.04 mg/g) [61].  
In the studied samples it can be observed that increasing the concentration of sulphuric acid results in an enhancement of the phenolic compounds. Thus, the highest value for the protocatechic (34.72 \(\pm\) 0.06 \(\mu\)g/g), vanmillic (18.92 \(\pm\) 0.02 \(\mu\)g/g), 4-hydroxybenzoic (18.74 \(\pm\) 0.01 \(\mu\)g/g) were obtained at the 3% concentration of sulphuric acid and 1/30 S/L ratio, but p-coumaric (3.21 \(\pm\) 0.01 \(\mu\)g/g), chlorogenic (3.36 \(\pm\) 0.04 \(\mu\)g/g) and caffe acid (3.96 \(\pm\) 0.03 \(\mu\)g/g) were obtained at the 2% concentration of sulphuric acid and 1/30 S/L ratio. The values for individual phenolic compounds are shown in Table 4 (the chromatograms obtained for standards and one of the samples can be found in Supplementary Materials).
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 6 Validation of the Developed Application to Avoid DDoS
### When an Authenticated Node Sends Emergency Messages  
Figure 12 shows the emergency message transfer by the node Car1 to the emergency server. As Car1 is an authenticated node, the packet is sent to the controller. After that, it follows the steps described in Section 4.2 and sends the data to the primary servers. For validation purposes, a limit is set on the number of emergency messages that can be sent by the nodes: a maximum of ten messages per minute or six hundred messages per hour. The intervals between the sending of emergency messages are tracked by the controller. If this limit is exceeded by a node, this behavior is observed by the controller and the packets start being dropped. As shown in Figure 13, as the authenticated node sends the eleventh packet, exceeding the allowed limit, the SDN application tracks this abnormality of the node and starts dropping the packets.  
Figure 11: Considered scenario: shared access network and shared core; track perpendicular to road.  
Figure 12: Serving authenticated node: screenshot of developed SDN application.  
From Figure 12, it can be inferred that when Car1 sends ten emergency messages per minute, it is successfully sent to the primary emergency servers. However, as soon as the allowed limit is reached, the application begins to drop the data packets.
# Computation of Compact Distributions of Discrete Elements
## 3 Methods of This Paper  
### Voronoi Diagram Based on Several Distance Metrics  
In this paper, the calculation process of the centroidal Voronoi diagram is adopted for the Voronoi diagram generation with the following calculation process. First, generate the Voronoi diagram of N non-overlapping seed points in a plane, which partitions this plane into N regions with all the points in each region closer to the corresponding seed points than to other seed points, as shown in Figure 2a, Then, move the seed point of each region to the centroid of the corresponding region and generate the Voronoi diagram of the seed points again, and repeat the above process. According to the centroidal Voronoi diagram, the distribution of seed points will become increasingly balanced. Normally, the Voronoi region of a set of evenly distributed points will be similar to a relatively regular shape (as shown in Figure 2b).  
In the most classical Voronoi diagram, L\({}_{\text{P}}\)-metric is used to measure the distance between the p\({}_{0}(x_{0},y_{0})\) and p\({}_{1}(x_{1},y_{1})\) and among all the L\({}_{\text{P}}\)-metrics, L\({}_{2}\)-metric is the most commonly used.  
\[\text{d}(\text{p}_{0},\text{ p}_{1})\ =\ \sqrt{(x_{1}-x_{0})^{2}+(y_{1}-y_{0})^{2}}\]  
With this metric, the generated Voronoi region is close to a regular hexagon (as shown in Figure 3a). [PERSON] et al. [8] used the L\({}_{1}\)-metric (Manhattan distance).  
\[\text{d}\big{(}\text{p}_{0},\text{ p}_{1})\ =\ |x_{1}-x_{0}|+|y_{1}-y_{0}|\]  
Figure 3: Voronoi diagrams generated with different distance function: (**a**) L\({}_{2}\)-metric distance; (**b**) Manhattan distance; (**c**) Chebyshev distance; (**d**) Chebyshev distance with axial scale; (**e**) L\({}_{2}\)-metric with axial scale; and (**f**) equidistant line distance. The partitioned regions are presented in different colors.  
Figure 2: Voronoi diagrams: (**a**) Voronoi diagram of original seed points; (**b**) centroidal Voronoi diagram.  
The polygons of the generated Voronoi diagram were close to square. To obtain Voronoi regions with more shapes, we attempt to adopt more metrics. Four additional distance metrics are introduced, which are Chebyshev distance, Chebyshev distance with axial scale, L\({}_{2}\)-metric distance with axial scale, and equidistant line distance, and finally, distributions of squares, rectangles, ovals, and triangles are achieved, respectively. In the following part of this paper, the different metrics will be discussed in detail.
# Research on Monocular-Vision-Based Finger-Joint-Angle-Measurement System
## 4 Vision-Based Finger-Joint-Angle-and-Length-Detection Method
### Vision-Based Finger-Joint-Angle-and-Length-Detection Method  
When detecting the angle of each finger joint, firstly, the position of each finger bone in the image is identified and, secondly, the position and joint angle of each finger joint by the intersection point and the angle between each finger bone are identified. A finger-joint identifier for which it was easy to perform image segmentation was used for the identification of finger phalanges in the image. The finger-joint identifiers of different scales are shown in Figure 4a, and the most suitable finger-joint identifier was selected by comparing the accuracy of the angle detection of the identifiers at different scales. Figure 4b shows the method of attaching the finger-joint identifiers.  
Since the light-source intensity and light-irradiation angle have a significant impact on the segmentation and extraction of finger-joint markers, the height of the light source can be adjusted to alter the light-irradiation angle and the light-source-irradiation intensity of the markers, enhancing the difference between the markers and the background, making it easy to segment the markers from the background and simplifying the marker-segmentation process. The image-acquisition method based on the active multi-angle light-source detection method is shown in Figure 5: (a) represents high angle lighting; (b) represents medium angle lighting; (c) represents low angle lighting.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 4 Experiments and Results
### Detection Testing  
To test detection performance, we created another dataset from unseen videos. Next, we manually calculated the number of cars appearing in those videos. Then, we randomly selected videos taken at different time intervals. We chose various time intervals including morning, afternoon, dusk, and evening times. For evaluating detection precision, we decided to calculate the mean average precision (mAP or \(\upmu\)) with IOU threshold \(\upmu\geq 0.75\), where the mAP with threshold is found by,  
\[\upmu\ =\frac{\text{bounding box area of detected car}}{\text{bounding box area from manual annotation}}\geq 0.75 \tag{9}\]  
We manually counted the number of cars that appeared within the mentioned intervals and annotated them to determine the cars' positions in a frame. We counted only unique cars. The car was considered as correctly detected one only when the IOU rate was above 0.75. As seen in Table 1, the detection method identified cars with average mAP of 90.80% for both daytime and nighttime. The reason for the slight change in mAP is the different lighting conditions. Since our application is supposed to be used in real-world scenarios, we tested it for precision and speed. Therefore, along with precision, we also checked the processing time of our algorithm, which was similar to that of [PERSON] and [PERSON] [16]. Despite having only one class to detect, we achieved a higher mAP score than the authors of the original YOLOv3 (see Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time Interval** & **10.23–10.25** & **13.49–13.52** & **15.50–15.54** & **18.05–18.07** & **21.10–21.13** & **Results** \\ \hline Num. of cars & 30 & 44 & 85 & 50 & 41 & 250 \\ Detected cars & 27 & 41 & 80 & 44 & 35 & 227 \\ mAP \(>0.75\) (\%) & 90.00 & 93.18 & 94.12 & 88.00 & 85.36 & 90.80 \\ APT (ms) & 27 & 25 & 26 & 28 & 29 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection network evaluation table.
## 3 Results and Analysis
### Wear Depth Analysis of the DPRS  
#### 3.3.1 Influence of Ambient Temperature  
Figure 17 shows a graph of the total actual contact area versus wear depth for each contact ellipse as a function of the ambient temperature when the axial load is 30 kN, the speed is 3000 rpm, and the operating time is 38,000 h. The total actual area of the first six threaded teeth increases while that of the last six threaded teeth decreases with increasing temperature. This is because the contact area is influenced by the load distribution and shows the same pattern of variation as the load distribution at different ambient temperatures. Owing to the variation in the total contact area, the DPRS wear depth increases for the first six threaded teeth and decreases for the last six threaded teeth as the temperature increases.  
The results show that excessive ambient temperatures can exacerbate the unevenness of thread wear and that lowering the working ambient temperature helps to maintain the accuracy of the part to some extent.  
In [26], [PERSON] et al. use the martensitic stainless steel as the material of experimental samples. They describe the contact between the roller and the screw as an ellipsoid on flat contact. The maximum wear depth of the screw is measured to be 5 \(\times 10^{-4}\) mm in ten  
Figure 16: Influence of the roller pitch at different ambient temperatures. (**a**) Ideal state (without thermal deformation); (**b**) 20 \({}^{\circ}\)C; (**c**) 70 \({}^{\circ}\)C; (**d**) 120 \({}^{\circ}\)C.  
Figure 17: Wear depth of the DPRS threaded teeth at different temperatures.  
cycles, which, as well as the wear curves, is considered to correspond to the theoretical analysis in this paper, taking into account the different working hour settings.
## 2 Particle Swarm Optimization
### Background Information Based on Recent Research on the Parallelization of the PSO
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Main Strategy** & **References** & **Years** \\ \hline Accelerating PSO in CUDA/GPU & [1,2,17,18] & 2011, 2005, 2021,2022 \\ Adjustment of control parameters & [13] & 2002 \\ Hybrid mechanisms with PSO & [3,19,20] & 2019, 2015, 2020 \\ Big data and PSO & [23] & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected approaches for improvement in PSO algorithm implementation to date. Spark-based particle swarm optimization (PSO) and differential evolution (DE) algorithms (SparkPSODE) is proposed. SparkPSODE is a parallel algorithm that employs RDD and island models. The island model is used to divide the global population into several sub-populations, which are used to reduce the computational time corresponding to the RDD partitions. The efficiency of SparkPSODE is demonstrated through its application to a set of global large-scale optimization benchmark problems. The experimental results, which are presented in comparison with other algorithms, reveal that SparkPSODE achieves superior performance in terms of speedup, scalability and robustness. There is currently work using Spark to parallelize the genetic algorithm and tackle the problem with very good results. For instance, in reference [3], the GPU is employed to parallelise an evolutionary training algorithm. Additionally, one potential avenue for addressing the challenge of climate change and its ramifications is to examine the energy consumption patterns of buildings. The study of energy consumption can facilitate the acquisition of pertinent information, which may be employed to inform more efficacious decision-making processes and, consequently, to reduce costs and pollution. However, ANN training models based on evolutionary methods generally have a high computational cost in terms of time. This work exploits the high-performance computing capabilities of GPUs to parallelise the PSO evolutionary algorithm for the purpose of training the ANN. Another area of intense research is the parallel implementation of PSO for very large datasets. Conventional methodologies are inadequate for meeting the demands of big data environments for prediction. Consequently, a hybrid distributed computing framework has been developed in Apache Spark [23] for wind speed prediction using a distributed computing strategy. The framework is capable of dividing the speed data into RDD groups and operating them in parallel. The primary issue with these solutions is that they are constrained to the parallelisation of the algorithm's repetitive processes, based on a population size of the dataset.This is essential for accurately computing the population fitness function, but it does not extend to further advances in terms of parallel structuring of the algorithm's subtasks, which is one of the main objectives of our study.
## 2 Related Work
### NMPC Strategies for Greenhouse Climate Control  
The optimal control of greenhouse climate has a long history, with foundational work by [PERSON] framing greenhouse climate regulation as an open-loop optimal control problem aimed at maximizing net revenue [7]. Subsequent sensitivity analyses by [PERSON] also highlighted that uncertainties in humidity and radiation typically lead to the largest profit losses [8]. Time-scale decomposition of the optimal control problem has also been explored [9]. However, open-loop trajectories are not feasible for autonomous operation in dynamic environments, as they cannot correct for actual disturbances.  
NMPC has emerged as an advanced closed-loop solution, offering distinct advantages for handling the complex nonlinear dynamics and operational constraints of horticultural systems [1,2]. NMPC has been applied to diverse objectives, such as reducing energy and water consumption [10], improving temperature control and energy utilization [11], and enabling smart energy management in plant factories [12]. Other notable applications include NMPC based on Volterra series models for temperature regulation [13].  
To address system uncertainties, more advanced NMPC formulations have been proposed, including multi-scenario MPC considering market price uncertainty [3] ([PERSON] et al.), robust MPC [14], and chance-constrained stochastic MPC [15]. Concepts like the advanced-step NMPC controller have also been explored [16]. In parallel, the integration of data-driven and AI-based methods--such as reinforcement learning (RL) [17,18] and deep neural networks [19]--has received increasing attention, aiming to enhance adaptability and performance. A recent comprehensive comparison between RL and NMPC on lettuce greenhouse climate control found that while RL can offer adaptability and lower computational demands, NMPC retains superior constraint handling and economic performance under certain scenarios [20]. While NMPC provides a robust framework, its real-world performance still heavily relies on effective disturbance handling and the integration of predictive intelligence of future uncertainties.
## 2 Methodology
### Target Decomposition for FCP Data  
Among a substantial number of incoherent decomposition techniques, we chose Cloude-Pottier eigenvector-based \(H/\bar{\kappa}\) decomposition (hereafter referred to as \(H/\bar{\kappa}\) decomposition) [17] and four-component model-based decomposition for the FCP mode [18; 19]. Applying the \(H/\bar{\alpha}\) decomposition, the polarimetric entropy \(H\) and alpha angle \(\alpha\) can be deduced, where \(H\) represents scattering randomness, and the \(\alpha\) is associated to the corresponding scattering mechanism represented by each eigenvector [17]. The four-component decomposition technique yields four elementary scattering mechanisms, i.e., surface, double-bounce (or dihedral), volume (or multiple), and helix scattering. This methodology was first proposed by [PERSON] et al. [18], and we adopt herein the improved four-component decomposition proposed by [PERSON] et al. [19] which fully accounts for coherency matrix coefficients.  
To apply the aforementioned decomposition theories to FCP data, linear to circular polarization basis transformation is used to obtain first the three-dimensional (3D) Pauli scattering vector in circular basis, given as:  
\[\mathbf{k_{p}}=\frac{1}{\sqrt{2}}\left[\begin{array}{c}-j2S_{LR}\\ (S_{LL}-S_{RR})\\ -j\left(S_{LL}-S_{RR}\right)\end{array}\right], \tag{1}\]  
where \(S_{xy}\) are the elements of the scattering matrix with \(x\) and \(y\) denoting the received and transmitted waves respectively, and subscripts L and R represent left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP) respectively. The averaged coherency matrix can then be obtained via the Pauli scattering vector in (1) as:  
\[\left\langle\left[\mathbf{T_{FCP}}\right]\right\rangle=\left\langle\mathbf{k_{ p}}\cdot\mathbf{k_{p}^{\dagger}}\right\rangle, \tag{2}\]  
where the superscript \(\dagger\) indicates conjugate transpose, and \(<\cdot>\) indicates the ensemble averaging operation. This tailored coherency matrix for circular polarization basis makes it possible to adopt decomposition techniques in a similar fashion to linear polarization basis. Under ideal conditionswhere there are no adverse effects, the information content of FCP and FLP data are identical because each can be transformed to the other via the unitary matrix.
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 1 Introduction
In both cases (2D and 3D models), the obtained data can be managed for calculating several parameters, such as water demand, detection of water deficit and indicators about solute transport of fertilizers in the plant [18]. Moreover, the images of the soil can be processed to obtain several parameters related to soil water management (such as soil moisture and detection of fertilizers) [19]. Image surveillance systems can be based on static cameras or aerial capture using drones or satellites. In any case, regular crop sampling is necessary to obtain robust and reliable systems. Remote sensing has the advantage of providing information of bigger areas in longer time periods [20]. These facts permit the farmers to analyze the change in crop growth over time. Moreover, remote sensing information combined with measurements obtained in situ is essential to make more timely and accurate decisions [20]. More recently, the technology related to big data and Internet of Things (IoT) has also been incorporated into the agricultural engineering domain; an interesting review of these applications can be found in [21].
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
## 3 System Design
### Network Diagram  
Our system used a virtual private network (VPN) to build link communication in our underlying network provided by the vendor. One of the advantages of IPFS is it supports for connection behind the firewall. IPFS was applied to all servers (sites and central), which used an IPFS-Cluster to connect them as members in one cluster. Each server had a unique identifier called a node ID and used a multiaddr-formatted byte string to communicate among nodes in the overlay network. Additionally, the IPFS-Cluster was used to give the limitation of file distribution flow only in our environment.  
All peers in our cluster only had two kinds of roles- peer and leader peer. Each site became a standard peer while the central server took a role as the leader. When nodes needed to exchange files, a peer-to-peer connection was built between them, so a node could connect to another directly (see Figure 5). Bandung was essential as the main site of our data center and had a significant number of users who need the data, so Bandung must collect all files by replication, each time new files were created. Currently, the total amount of data produced by all instruments is 724 MB/day, and they are collected centrally in the Bandung data center.  
As we described before, we replicated all files as fast as possible to reach real-time availability. However, the proposed system did not replicate files to all peers in consideration of network and storage costs. Each node had distinct characteristics in bandwidth capacity, link connectivity, and the total amount of data passed daily. According to Table 1, we considered choosing other nodes for  
Figure 5: Network Diagram.  
storing the data based on the same geographical location with Bandung in Java Island. This would have an impact on reducing the time when Bandung needed to get the file from other nodes, which were indicated as offline in their status. Moreover, we considered selecting the other nodes, which had the lowest total size in collecting data for daily, nodes with higher bandwidth capacity than others and preferred to choose nodes that were connected by fiber-optic links. Thus, based on these considerations, the nodes in Garut and Pasuruan were selected as the second and third center of data replication.  
Currently, node Garut has 5 Mbps of bandwidth capacity with a fiber optic connection and is collecting 21 MB of data daily. Meanwhile, node Pasuruan has 7 Mbps for the bandwidth capacity by fiber-optic link and is collecting 39 MB of data daily. Our proposed scheme added the second server in Garut and Pasuruan that had the role of only receiving the data replication from others together with Bandung. This approach was inspired by the hosting role in the Sia project.