esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Parameterization and Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Grassland Type** & **Alpine Grasslands** & **Temperate Grasslands** \\ \hline \multirow{4}{*}{_Reref_} & \(p_{1}\) & \(-2.646\pm 0.456\) & \(-0.019\pm 0.023\) & \\  & \(p_{2}\) & \(15.100\pm 0.262\) & \(8.347\pm 1.068\) & \\  & \(p_{3}\) & \(0.173\pm 0.024\) & \(-0.029\pm 0.009\) & \\  & \(p_{4}\) & \(18.828\pm 0.939\) & \(3.952\pm 0.322\) & \\ \hline \multirow{4}{*}{_f(T,P,W)_} & \(p_{5}\) & \(1.787\pm 0.081\) & \((1.421\pm 0.136)\) * & \(0.658\pm 0.119\) & \((0.941\pm 0.147)\) * \\  & \(E_{0}\) & \(279.764\pm 53.87\) & \((164.507\pm 31.874)\) * & \(166.327\pm 12.402\) & \((154.305\pm 18.729)\) * \\ \cline{1-1}  & \(p_{6}\) & \(0.250\pm 0.024\) & \((0.193\pm 0.046)\) * & \(0.944\pm 0.039\) & \((0.085\pm 0.061)\) * \\ \cline{1-1}  & \(p_{7}\) & \(0.094\pm 0.039\) & \((0.108\pm 0.046)\) * & \(-0.363\pm 0.081\) & \((0.190\pm 0.050)\) * \\ \cline{1-1}  & \(k\) & \(0.021\pm 0.064\) & & \(0.207\pm 0.082\) & \\ \hline \hline \end{tabular}
* Among the parameters for _f(T,P,W)_, the values inside the parentheses are for the non-growing season, while the others are for the growing season.  
\end{table} 
Table 2: Parameter estimation of _Reref_ and _f(T,P,W)_ for two grassland types in two regions. desert steppe do not represent for the typical steppe well, the other vegetation types differ slightly in performances from the cross-validation and parameter estimation, which verifies the robustness of the parameterization results.
## 2 Materials and Methods
### Determination of In Vitro of Antioxidant Activity
#### 2.4.1 Determination of DPPH Radical Scavenging Activity (RSADPPH)  
The RSADPPH assay, based on both electron transfer and hydrogen atom transfer reactions, was determined according to the method of [PERSON] [22], with slight modifications [23]. The RSA of each aqueous extract, at various concentrations, was tested by measuring their ability to quench DPPH, a stable free radical, that was reduced in the presence of antioxidants, causing the change of purple color of the DPPH solution to bright yellow color; the intensity of this change can be monitored spectrophotometrically. An aliquot of 250 \(\upmu\)L of each extract (concentration range of 3.33-20 \(\upmu\)g/mL) or BHT was added to 500 \(\upmu\)L of 100 \(\upmu\)M DPPH solution. BHT was used as the reference sample and a mixture without sample, or BHT, was used as the control. The absorbance (Abs) was measured with a Shimadzu UV model 1800 at 517 nm after incubation (at room temperature in the dark) for a period of 30 min. The RSADPPH was calculated as a percentage of DPPH discoloration using the following equation: RSA (%) = (1 - Abssample/Abscontrol) \(\times\) 100. The results are expressed as an EC\({}_{50}\) value (\(\upmu\)g/mL), which is defined as the sample concentration that can quench 50% of the DPPH free radicals. A lower EC\({}_{50}\) value is indicative of higher antioxidant activity.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 4 QoS-Oriented Extended Functions
### Compression
In the example of Figure 11, the receiver reconstructs the CoAP version, type, token length, code, and content format using the values stored in the corresponding target value entries; it rebuilds the message id value concatenating the most significant bits stored in the target value entry and the received bits, and it builds the token value from the received value. We can notice that SCHC reduces the size of the packet, from 8 bytes to 12 bits; however, the overall packet-size reduction is limited because the largest component of the packet, i.e., the payload, is still sent uncompressed. We can also notice that the payload marker used to indicate the end of options and the start of the payload is not present in the compressed packet because the length of the compression residue of the header is known; moreover, even if the compression is bitwise, the final size of the compressed packet is byte aligned to be sent on the network. We refer the reader to [26; 27; 28] for further details on SCHC.
## Results and Discussion
### HS-SPME Optimization and Validation
The limits of detection (LODs) and limits of quantification (LOQs) were calculated as the lowest concentration giving an average signal-to-noise (S/N) ratio above 3 and 10, respectively, by evaluating the background noise in blank samples. LOD values ranged from 0.42 \(\mu\)g mL\({}^{-1}\) (for geraniol) to 3.53 \(\mu\)g mL\({}^{-1}\) (for cis-nerolidol), whereas LOQs ranged from 1.41 \(\mu\)g mL\({}^{-1}\) (for geraniol) to 11.78 \(\mu\)g mL\({}^{-1}\) (for cis-nerolidol), expressed as IS equivalents. The analysis of standard solutions and spiked strawberry samples demonstrated the absence of significant degradation processes during the extraction (i.e., the signal intensity for standards dissolved in solvent that were analyzed without performing HS-SPME was comparable with that obtained after the extraction of the same standard solutions and spiked strawberry samples).
[PERSON] et al., in 2014, developed a natural-language tool kit using annotation, which can be utilized for sentence separation and other natural-language processing tasks [11]. The annotators include conditional-random-field taggers trained on various corpora and two rule-based systems to recognize money and numbers. This library also provides means to add custom annotations to the existing one to help improve the accuracy of the task. Another NLTK library, developed by Loper & Bird [12], is a computational linguistic library designed to compute most natural-language problems, including sentence splitting. All these methods of splitting sentences require a well-punctuated document, but the main problem from social media reviews is that they are not properly punctuated. In this research, we apply punctuation restoration to the sentiment analysis process to extract all the possible information from the sentences. A properly punctuated statement is necessary to extract more variety of sentiment polarity, so a review statement must be divided into simple sentences. Therefore, this paper focuses on creating a method of restoring punctuation in review statements extracted from social media. We separate the sentences by positioning full stops, exclamation marks, and question marks. This enables us to classify each sentence and provide sentiment value. However, restoring punctuation requires us to consider all the other punctuation, like commas, semicolons, etc., in our model. This study is organized as follows: Section 2 discusses the related work associated with punctuation restoration. Section 3 is about the methodology employed in developing the punctuation-restoration model. Section 4 focuses on the experimental result, and Section 5 concludes the paper.
## 2 Problem of Robotic Welding under Constraint  
The analyzed problem of planning the robot's path of passage concerns the process of robotized welding of a thin-walled sheet metal structure--a metal electrical box used to protect the switching station (Figure 1). The subject of a thin-walled structure is frequently analyzed in the literature [44]. The research mainly includes inter alia, strength analysis [45], stability assessment [46] and the very important aspect of structure joining [47; 48].  
The considered detail is a product manufactured by the facility from the range of small and medium-sized enterprises. Robotized welding of metal boxes with overall dimensions of 500 \(\times\) 500 \(\times\) 190 mm is subjected to analysis. The operations performed prior to robotized welding are the laser cutting of the box section and bending of the edges of the sidewalls and the flange on a press brake. The welding process is performed on a workstation equipped with a welding robot, as well as a turntable that allows one to perform all connections of the construction in two positions (two corners in each position).  
In this case, the welding robot will perform two types of movements [8]:  
* Effective movements (effective tasks)--during which the robot performs the target work--welding.
* Supporting movements (supporting tasks)--during which the robot moves between the effective tasks (between performing successive welds).  
An additional aspect of the process is the assumptions resulting from the structure of the workpiece:  
1. The box is placed on a turntable so that one of the walls is parallel to the base of the robot, and the operation is performed in two positions--in one position, the overall joints of the two corners are made (Figure 2).
2. In order to stiffen the structure, the wall joints must be manufactured first (section \(AB\) and \(DE\) in Figure 2), and the direction is arbitrary.
3. The flange connections are to be made last in the given position (sections \(BC\) and \(EF\) in Figure 2), and due to the possibility of corner burns, they must be made in the \"top-down\" direction--e.g., from point \(C\) to point \(B\).
4. After completing the connections, the table with the part is rotated by 180\({}^{\circ}\) in order to make opposite connections in the second position.  
The main limitation to this process is the sequence of the effective tasks and the supporting tasks of the welding robot (Figure 2).  
In order to optimize the presented process of robotized welding of thin-walled structures, it is necessary to thoroughly analyze the process while taking into account the limitations mentioned above.  
Due to the symmetrical nature of the structure, the problem can be limited to scheduling the robot to work in one position of the turntable. The most beneficial path of the robot in one position will allow it to be repeated during the execution of subsequent joints.  
Figure 1: Photos of the welded detail.
# Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs
## 2 Problem Formulation
### EMP of Multiple Energy Hubs
 \tag{3}\]  
subject to  
\[\sum\limits_{i\ =\ 1}^{n}\ (\eta_{i}^{\mathcal{S}}E_{i}^{\mathcal{S}}+\eta_{i}^{ e,clip}E_{i}^{\mathcal{S},\rho})\ =\ \sum\limits_{i\ =\ 1}^{n}\ I_{i}^{\mathcal{S}} \tag{4a}\] \[\sum\limits_{i\ =\ 1}^{n}\ (\eta_{i}^{h,clip}E_{i}^{\mathcal{S},\rho}+ \eta_{i}^{\text{b}oil}E_{i}^{\mathcal{S},1-\rho})\ =\ \sum\limits_{i\ =\ 1}^{n}\ I_{i}^{h} \tag{4b}\]  
and  
\[E_{i,in}^{e,min}\leq E_{i}^{e}\leq E_{i,in}^{e,max} \tag{5a}\] \[E_{i,in}^{\mathcal{S},min}\leq E_{i}^{\mathcal{S},\rho}+E_{i}^{\mathcal{S},1- \rho}\leq E_{i,in}^{\mathcal{S},max}\] (5b) \[0\leq E_{i}^{\mathcal{S},\rho},0\leq E_{i}^{\mathcal{S},1-\rho} \tag{5c}\]  
where \(\alpha_{i}^{\mathcal{S}}\ =\ a_{i}^{\mathcal{S}}+\omega_{i}^{e}(\eta_{i}^{e,clip} )^{2}+\omega_{i}^{h}(\eta_{i}^{h,clip})^{2}\), \(\beta_{i}^{\mathcal{S}}\ =\ a_{i}^{\mathcal{S}}+\omega_{i}^{h}(\eta_{i}^{\text{b}oil})^{2}\) and \(\gamma_{i}^{\mathcal{S}}\ =\ 2\alpha_{i}^{\mathcal{S}}+2\omega_{i}^{h}\eta_{i}^{h,clip}\eta_{i}^{ \text{b}oil}\). For convenience, Equations (5b) and (5c) can equally be expressed in the following form  
\[\widetilde{d}_{i,m}E_{i}^{\mathcal{S},\rho}+\widetilde{e}_{i,m,t}E_{i}^{ \mathcal{S},1-\rho}+\widetilde{f}_{i,m}\geq 0,\ (m\ =\ 1,2,3,4) \tag{6}\]  
**Remark 1**. : _By means of the coordinate transformation in Equation (2), the coupled product form between variables, i.e., \(\rho_{i}E_{i,in}^{\mathcal{S}}\) and \((1-\rho_{i})E_{i,in}^{\mathcal{S}}\) for the global equality constraints, can be transformed into one, i.e., \(\gamma_{i}^{\mathcal{S}}E_{i}^{\mathcal{S},\rho}E_{i}^{\mathcal{S},1-\rho}\) for the local objective function. As a result, the non-affine constraints (1a and 1b) are converged to affine ones, i.e., Equations (4a) and (4b)._  
Figure 1: Structure of a multiple energy hubs system. Next, the Lagrange multiplier theory can be used to analyze the problem in Equations (3)-(6). Inspired by the Lagrange multiplier theory, a fully distributed algorithm will be introduced in Section 3. The Lagrange function and the corresponding KKT conditions are shown in Appendix A-(d).
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 6 Discussion  
With the proliferation of IoT devices in all kinds of scenarios, we may foresee the ever-increasing needs of CTC in wireless systems. Our design has shown its good performance as a software implementation on USRP, it will have tremendous potential on future hardware implementation of CTC WiFi devices. Note that in our design, most of the standard WiFi modules are reused. According to the scheme of our design, WiFi devices obtain the new ability of CTC, with limited modification, while all the properties and functions of a WiFi device are completely preserved.  
One of the biggest drawbacks in our work is that the WiFi module completes the most calculations, which consume the resources. The decoding process of WiFi packets includes short preamble detection, long preamble detection, FFT, QAM demapping, and deinterleaving. The decoding process of our design is relatively simple compared with the decoding process of WiFi packets. Moreover, our design does not interrupt the ongoing ZigBee communication. It means that cross-technology communication can parallel with standard ongoing communications.  
Figure 15: CDF of Hamming distance.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Additives in Edible Films
#### 3.6.1 Plasticizers  
Most edible films made from biopolymers usually have poor mechanical properties; they are fragile and prone to cracking when drying. This is because of the cohesive forces in polymer films. These issues can be resolved by incorporating plasticizers into the film's composition [75]. Plasticizers are hydrophilic agents with a low molecular weight that are incorporated into film-forming preparations and aim to improve the film's mechanical properties [93]. The International Union of Pure and Applied Chemistry (IUPAC) defines a plasticizer as a substance or material that is added to a another material to enhance its workability, flexibility, or distensibility [94]. Films made from a variety of polysaccharides are often brittle in the absence of a plasticizer because of the interactions between polymer chains. Plasticizers reduce the intermolecular interactions between adjacent polymer chains within a film. By doing so, plasticizers decrease cohesion within the film network. Plasticizers alter or enhance the mechanical properties in this way [35; 44; 57]. Sucrose, glycerol, sorbitol, mannitol, propylene glycol, and polyethylene glycol are some of the plasticizers used in edible packaging [9; 10]. Glycerol has been frequently used to maintain moisture in starch-based products [95]. In a study conducted by [PERSON] et al. and [PERSON] et al. [35; 57], glycerol was used as a plasticizer to enhance the mechanical properties of their polysaccharide-based film.
## 2 Materials and Methods
### Single-Crystal X-Ray Diffraction Studies  
Single crystal X-ray diffraction data were collected for a Guanine2-Mg(6H\({}_{2}\)O) crystal with a Bruker D8 QUEST single crystal diffractometer with graphite-monochromated radiation (Mo K\(\alpha\), \(\lambda\) = 0.071073 A) at 293 K. Crystals were mounted with Fomblin(r) in a cryoloop. The X-ray generator was operated at 50 kV and 30 mA. The X-ray data collection was monitored by APEX3. The programs SAINT [28] and SADABS [29] were used to correct the obtained data to Lorentzian distribution, polarization, and absorption effects. The program SHELXT was used for structure solution [30], while SHELXL-97 was used for full-matrix least-squares refinement on F2 [31]. All programs are included in WINGX-Version 2021.3 [32]. Non-hydrogen atoms were refined anisotropically. A full-matrix least-squares refinement was used for the non-hydrogen atoms with anisotropic thermal parameters. All the hydrogen atoms bound to carbon atoms were inserted in idealized positions and refined in the parent carbon atom. Hydrogen atoms bound to nitrogen atoms were located from electron density map and the position was fixed for the NH moiety, and the distance was fixed for the NH. Despite the bad quality of the single crystal used, it was not possible to grow better-quality crystals. Nevertheless, despite the very high Rint, it was possible to reach a stable final refinement. Crystallographic data are deposited with the Cambridge Crystallographic Data Centre (CCDC 2408800).
## 3 Proposed Stereoscopic Image SR Method with View Incorporation and CNN
### Estimation of View Difference Image with Modified CNN
#### 3.1.3 Modified CNN and Training  
In this paper, the modified CNN is created to estimate the high-precision view difference image. Our objective is to train network \(f\) with three layers when given a training dataset, \(\left\{Z^{(i)},X^{(i)},Y_{B}{}^{(i)}\right\}_{i=1}^{N}\), so that the high precision view difference image, \(I_{d3}{}^{(i)}=f(Z^{(i)}-Y_{B}{}^{(i)})\), is estimated, where \(Z^{(i)}\) and \(X^{(i)}\) denote the ground-truth left view image and ground-truth right view image, respectively. Furthermore, \(Y_{B}{}^{(i)}\) is the interpolated right view image, and \(N\) is the number of training samples. Then, a labeled ground-truth difference image in the used CNN is defined as \(I_{d-label}{}^{(i)}=Z^{(i)}-X^{(i)}\). To produce the high-precision view difference image, we use the sum of the absolute difference (SAD) as the distortion function:  
\[L(\theta)=\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}\Big{|} f(Z^{(i)}-Y_{B}{}^{(i)})-I_{d-label}{}^{(i)}\Big{|} \tag{9}\]  
According to Equation (9), the distortion between the estimated high-precision view difference image \(I_{d3}\) and ground-truth difference image \(I_{d-label}\) is minimized. The parameters \(\theta=\left\{W_{1},W_{2},W_{3},B_{1},B_{2},B_{3}\right\}\) are hence obtained.  
Stochastic gradient descent [28] is used to minimize the distortion by standard back-propagation. Then, the parameters' matrices are updated as  
\[\Delta_{i+1}=M\cdot\Delta_{i}+r\cdot\frac{\partial L}{\partial\theta_{i}^{l}} \theta_{i+1}^{l}=\theta_{i}^{l}+\Delta_{i+1} \tag{10}\]  
where \(l\in\left\{1,2,3\right\}\) represents the indices of layers, \(i\) denotes iterations, \(M\) is the momentum parameter with a value \(0.9\), \(r\) is the learning rate, and \(\frac{\partial L}{\partial\theta_{i}^{l}}\) is the derivative of the network parameters. Initially, all filters are initialized with a random Gaussian distribution with a zero mean and standard deviation of \(0.001\). Meanwhile, the biases of each layer are initialized by zero. The learning rate is \(2.5\times 10^{-4}\) for the first two layers and \(2.5\times 10^{-5}\) for the last layer.
## 4 Proposed Architecture
### Hashing, Merkle Tree Signature Scheme, and One-Time Signatures
The signature of the MSS \(sig=(S||s_{2}||s_{3}||s_{n-1})\) comprises the one-time signature \(S\) of the message M, plus the nodes. The recipient knows the signature, \(sig=(S||s_{2}||s_{3}||s_{n-1})\), the message M, and the public key \(pub\_key\). Firstly, the one-time signature \(S\) of message M is verified by the recipient. \(P_{1}=H(Y_{i})\) is computed by the recipient, who hashes the public key of the one-time signature. For \(k=1,\ \ldots,\ n-1\), the nodes of \(P_{k}\) of path \(P\) are calculated with \(P_{k}=H(y_{k-1}||s_{k-1})\) if the sibling index is odd, \(P_{k}=H(s_{k-1}||y_{k-1})\) if it is even. The signature is valid if \(P_{n}=pub\_key\) of the MSS.
## 4 Results  
### Experimental Framework  
We implemented a real-time software tool for testing the ideas presented in previous sections. It was developed under the object-oriented paradigm with two programming languages: C++ and Java. The heuristic algorithms were implemented using the jMetal 4.0(r) [19] library. jMetal is an object-oriented framework for developing, experimenting, and studying metaheuristics for solving multi-objective optimization problems. Metatrader 4(r) software is utilized for data acquisition and real-time operation. MetaTrader is an online trading platform that provides brokerage services for real-time market investment. In addition, MetaTrader can be used to perform technical analysis on specific temporary intervals. It also allows the use of different time windows for investment, ranging from minutes to months. Our Software architecture permits the application of the heuristic algorithms of jMetal on the whole set of assets available at Metatrader. Our System architecture, which is represented in Figure 4, consists mainly of six modules (two of them are database structures):  
* The data collector obtains values of assets in real time and stores the information. This module is always running.
* The Information Manager is responsible for managing the system's responses from data provided by the experiment manager and the active experts. It determines the experts who should be engaged in real time.
* The manager of experiments: the Experiments Manager handles the parameters, such as the number of evaluations, the temporary period to analyze, the Evolutionary algorithm used, its probability values of mutation and crossover, etc. This can be used for conducting experiments or for operation in real time. The only thing that changes is the dataset used.
* The expert: the Expert (business solution) is responsible for buying and selling and works directly on the trading platform. The data needed for operation are generated in real time and stored in the operational database. It can operate concurrently with all solutions along the Pareto frontier.
* A database storing historical data.
* A database updated in real time containing the most recent data and the non-dominated solutions.