esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Identification of Phytochemical Compounds of Artichoke By-Products by HPLC-ESI-TOF-MS
#### 3.1.3 Saponins, Lipids, and Other Polar Compounds  
Peak 1, with _m/z_ at 191.0561, was proposed as quinic acid, a carboxylic acid commonly found in artichoke [21].  
Figure 2: Extracted ion chromatograms (EIC) and mass spectra of some phenolic compounds characterized by HPLC-time-of-flight mass spectrometry with an electrospray interface (ESI-TOF/MS) in artichoke by-products.  
On the other hand, compounds belonging to saponins family correspond to peaks 18 and 19. They displayed a _m/z_ 925.4802 and retention times of 26.62 and 26.92 min, respectively. Both were identified as isomers of cynarasaponin A or H [21].  
Lastly, several lipids were also found in these artichoke samples. Thence, hydroxy-octadecatrienoic acid (peak 21) is a lipid previously identified in artichoke [21]. Trihydroxy-octadecenoic acid (peak 15), hydroxy-octadecadienoic acid (peak 22), and linolenic acid (peak 23) were also detected in the present analysis. All these lipids have been previously identified in other vegetable PLE extracts, such as _Morus nigra_[23] and _Prunus avium_[24]. Similarly, dihydroxyhexadecanoic acid (peak 16) was identified in the present study as well as in _Symphytum officinal_ L. samples [25]. Although these plant matrices belong to different species, both molecular formula and _m/z_ generated by the software match with data reported in the bibliography.
## 2 Materials and Methods
### Auxiliary Datasets
#### 2.3.4 Copernicus Global Landcover  
The ESA Copernicus Global Land Cover Collection 3 [42], derived from ESA's Proba-V mission data, was acquired over the Amazon Basin and used to generate a binary mask representing areas considered to be suitable or unsuitable for extracting 'pure' inundation training data. Specifically, areas defined as (a) Urban, (b) Herbaceous Vegetation, (c) Cultivated/Managed Vegetation, and (d) Bare/Sparse Vegetation were all considered unsuitable. Conversely, landcover types referring to forest cover, shrubs, or water bodies were considered suitable areas for extracting open water and inundated forest training data.
# Physicochemical and Antioxidant Properties of Selected Polish and Slovak Honeys
## 2 Materials and Methods
### Methods
#### 2.2.9 Total Phenolic Compounds Content (TPC)  
Total phenolic compounds content was determined applying the Folin-Ciocalteu modified method [24]. A total of 200 \(\mu\)L of honey samples, 15.8 mL of distilled water, and 1 mL of Folin-Ciocalteu reagent were mixed. After 10 min, 3 mL of 20% sodium carbonate was added, and after 60 min, the absorbance of final solution was measured at 765 nm. Standard solutions of gallic acid were used for calibration curve construction and results were expressed as gallic acid equivalents (GAE, mg/kg).
## 3 Background
### Unitary Matrices  
A reversible quantum gate acting on \(n\) perfect qubits can be fully described as unitary matrix [10]. The set of unitary matrices of size \(2^{n}\) by \(2^{n}\) is written as \(U(2^{n})\) and has the following properties [6]:  
* \(U^{\dagger}=U^{-1}\);
* \(U\) is diagonalizable;
* \(U(2^{n})\) has a set of \(2^{n}\) orthogonal eigenvectors;
* For a \(2\times 2\) unitary matrix \(U=\begin{bmatrix}A&B\\ C&D\end{bmatrix}\), \(\sqrt{A^{2}+B^{2}}=1\).  
The Special Unitary group, \(SU\), is a subgroup of unitary matrices where
* \(\left|{det(U)}\right|=1\) for \(U\) in \(SU\)[11].  
When a measurement is performed, the global phase (\(\Phi\)) of the qubits does not influence the measurement probabilities. This means that all quantum gate operations can be represented by a matrix in \(SU(2^{n})\)[12]. These properties are used to decompose the matrix, using one of the algorithms described in Section 5.
# mGEODAR--A Mobile Radar System for Detection and Monitoring of Gravitational Mass-Movements
## 2 Materials and Methods  
The mGEODAR (mobile GEODAR) is an X-band FMCW-type radar with a centre carrier frequency of 10.4 GHz and 400 MHz bandwidth. The RF circuitry of mGEODAR is based on a previously developed radar EREDAR (Erebus radar), which is a purpose-built radar for monitoring the lava lake level in the Erebus volcano, Antarctica [10]. Although the system requirements to capture the variations of a lava lake in the range of centimetres per minute are quite different compared to the rapid movements of gravitational mass-movements, the digital nature of the signal generation enables the flexibility for various usage. Most of the radar hardware descriptions resemble EREDAR [10] and are only summarised here. First, we describe the radar hardware with settings of the current system featuring transmit frequencies from 10.2 GHz to 10.6 GHz. In the following subsections, we describe common FMCW design variables and focus on the flexibility of the radar hardware in respect to a variable signal generation and the baseband filter.
###### Abstract
When a C-RAN network is being sliced, the BBU pool's radio resources are divided up and allocated to each individual slice. It satisfies the Quality-of-Service (QoS) needs of all the slices, and it offers numerous other advantages in terms of resource utilization [5]. Network slicing in C-RAN has several advantages, but it is still difficult to implement in 5G networks. In order to dynamically assign PRBs from the BBU pool to each network slice depending on the various QoS factors required by the slices, C-RAN-based 5G networks need efficient slicing algorithms [10]. It also needs an admission control method to reduce the call blocking probability (BP) [11]. In addition, in a C-RAN, slice isolation is critical to safeguard the minimum QoS needs of all the slices by allocating a minimum resource portion in the BBU pool to each slice [12]. The proposed system has many advantages. It aims to increase resource utilization in the 5G CRAN's BBU pool of resources. In addition, it seeks to guarantee the minimum QoS requirements for each 5G network slice while maintaining isolation. The proposed system dynamically allocates the resources of the BBU pool among the three 5G slices, each with different traffic sources. Moreover, it assigns a resource portion for each 5G slice based on its QoS requirement in terms of bandwidth and delay. This paper's structure continues below. The relevant literature and research issue is presented in Section 2. The 5G C-RAN network model and detailed analysis of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Requirements** & **eMBB** & **mMTC** & **uRLLC** \\ \hline Average throughput & 0.1–1 Gbps & Up to 1 Mbps & 25 Mbps \\ \hline Latency & 10 ms & \textgreater{}1 h & 1 ms \\ \hline Availability & 0.97\% & 0.999\% & 0.99999\% \\ \hline Other & Mobility up to 500 km/h & Battery life up to 10 years & Reliability up to 0.99999\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance requirements of the 5G use cases [4]. suggested systems are presented in Section 3. The simulation setup and the implemented experiments to evaluate the suggested systems are shown and discussed in detail in Section 4. The paper is wrapped up in Section 5 with a summary and some suggestions for moving forward.
# Designing Sustainable Flexible Manufacturing Cells with Multi-Objective Optimization Models +
## 3 Material and Methods
### Developed Augmented \(\epsilon\)-Constraint Model (AUGMECON)  
The AUGMECON method is a novel version of the classical \(\epsilon\)-constraint method. This method suggests transforming the objective function constraints into equations by including the slack or surplus variables of the classical method [41].  
The AUGMECON method improves the classical epsilon constraint method by employing lexicographic optimization to structure the payoff table in alignment with the desired priorities, subsequently optimizing the objective functions in accordance with these priorities. The lexicographic optimization initially focuses on optimizing the first objective function \(f_{1}\), resulting in the optimal value \(z_{1}\)*. To maintain this optimality for the first objective function, a constraint is introduced, setting \(f_{1}=z_{1}\)*, in a model dedicated to optimizing the second objective function. This process is iterated until each individual objective function has been separately optimized. To address another weakness of the epsilon constraint method, the objective function constraints are converted into equalities by introducing slack or surplus variables. The value of \(\delta\) is a small number [43]. \(r_{i}\) is the range of the \(i\)-th objective function, which is determined based on the data in the payoff table [41].  
The AUGMECON model formulation stated in [PERSON] et al. [43] was considered. In this study, the below equations are presented. As in the \(\epsilon\)-constraint method, minimization of the total cost is considered as relatively important and modeled as a single objectivefunction as shown in Equation (35). The expression \(SNC_{2}\) represents the total cost and is shown in Equation (35). The minimization of the total amount of carbon emissions is represented with \(SNC_{1}\). The expression indicated by \(SLV_{1}\) is a slack or surplus variable in the model.  
\[Min(SNC_{2}-\delta*(SLV_{1}/r_{1})) \tag{38}\]  
\[s.t:\ SNC_{1}+SLV_{1}=\epsilon_{1} \tag{39}\]  
\[SLV_{1}\in R^{+} \tag{40}\]  
In this study, the value of \(\delta\) is assumed to be 0.0001 as shown in Equation (38). In addition, Equations (10)-(27) and Equations (30)-(33) are included in the AUGMECON model. Additionally, Equations (36) and (38)-(40) are included in the AUGMECON model in this study.  
The following Section 4 consists of the results and discussion, where the solution of the sample problem, obtained results, and corresponding sensitivity analyses are presented.
# Extending Partial Domain Adaptation Algorithms to the Open-Set Setting
## 6 Results and Discussion
### Effectiveness of DIWAN  
The results for our second experiment indicated that full DA would often fail in the OSDA setting. This was observed across all tasks. The PDA model performed better than ADDA, but still performed worse than DIWAN. The results are summarized in Tables 1 and 2. We further noted that in the partial DA setting (when the target labels were all common labels), DIWAN performed comparably well with respect to IWPDA, while ADDA could completely fail if there were too many outlier source labels.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & \multicolumn{6}{c}{**Obstruction**} & \multicolumn{6}{c}{**Displacement**} \\ \hline
**T** & **C** & **A** & **I** & **D** & **S** & **A** & **I** & **D** & **S** \\ \hline
3 & 3 & 90.1 & **93.2** & 91.4 & 34.8 & 59.8 & **85.7** & 84.6 & 80.6 \\
4 & 4 & 80.1 & **83.0** & 78.6 & 53.2 & 74.0 & **85.3** & 85.1 & 66.1 \\
4 & 2 & 57.3 & 85.6 & **87.8** & 57.4 & 60.2 & 60.0 & **65.2** & 56.4 \\
5 & 2 & 3.5 & 70.7 & **88.4** & 7.8 & 55.3 & 60.1 & **78.9** & 72.2 \\
6 & 2 & 60.9 & 78.5 & **80.1** & 49.2 & 79.9 & 79.2 & **90.5** & 59.2 \\
4 & 3 & 33.6 & 63.9 & **64.5** & 29.3 & 83.6 & 88.3 & **92.7** & 69.9 \\
5 & 3 & 38.9 & **74.9** & **74.8** & 41.5 & 34.6 & 35.2 & **55.8** & 35.3 \\ \end{tabular}
\end{table}
Table 1: The results of Experiment 2 for the obstruction and displacement cases. “T” is the number of target labels and “C” is the number of target labels common to the source. “A” is the performance of ADDA [9], “I” is the performance of IWPDA [8], “D” is the performance of DIWAN, and “S” is the performance of the source model without adaptations. The results are the average accuracy percentages over 10 trials. Best accuracy indicated in bold.  
Figure 4: From top to bottom: Plots of accuracy vs. number of iterations for the obstruction, displacement, rotation, and re-scaling tasks. The mean over 50 trials is drawn with the shaded region, representing \(\pm 2\sigma\) calculated empirically. The source domain included five random classes from the source domain, which were different for each trial. The red-shaded region illustrates the mean accuracy for the experiment where no target outliers were present. The blue-shaded region illustrates the mean accuracy when five outlier labels were added to the target domain. The figure is best viewed in color.
## 3 Proposed Methodology
### Self-Attention Layer  
In a self-attention layer, each word or token in a sequence is assigned a specified weight or importance score based on its significance to the other words in the sequence. These weights are then utilized to calculate a weighted sum of the input sequence, which is then used as the output of the layer. The impact of selecting only the important words instead of all the words depends on the dataset being applied and used. The majority of the information may be concentrated in only a few key words, so selecting only those important words may be sufficient to achieve high accuracy. In other cases, there may be important information distributed across multiple words, so selecting only a subset of words may lead to a loss of accuracy.  
For each word in the phrase, self-attention enables it to see the words around it, allowing it to choose which words are most important to the present word's meaning. Self-attention refers to the sentence's tendency to focus on itself while deciding how to describe each of its tokens. To comprehend a single word's meaning, one must know in what context that word is used, and that word is taken care of by the self-attention layer. The weights, which are matrices derived during model training, are multiplied by the input to produce the key, value, and query vectors for each input. Equation (1) represents how the input vectors are transformed when passed through the linear layers to get the key, value, and query vectors. Figure 2 shows the process for obtaining the key, value, and query vectors. The Y represents the input, each row of the Y matrix is the embedding vectors, and WK, WQ, and WV are the weight matrices of key, query, and value.  
Figure 2: Architecture of ARABERT4 TWC.  
The calculation of attention weights as \(SOFT_{Max}\)-normalized dot products for each pair of words is shown in Equation (1), where \(\beta_{ij}\) is the attention weight, \(q_{i}\) is the query vector, \(k_{i}\) is the key vector, and \(v_{i}\) is the value vector.  
\[\beta=\frac{exp\big{(}q_{i}^{T}k_{j}\big{)}}{\sum_{i=1}^{n}exp\big{(}q_{i}^{T}k _{j}\big{)}} \tag{1}\]
## 3 Results and Discussion
### Frequency-Resolved Optical Gating  
The chirp of the pulse can be more accurately evaluated using a FROG system. The efficiency of THG induced in air was similar to that obtained using argon. Because of this, air was used as a nonlinear optical medium in this study for the sake of simplicity. Before recording the FROG trace, the THG beam pattern was measured to properly extract the signal of \(E_{\mathrm{sig}}(t,\,\tau)=E(t)^{2}E(t-\tau)\)[6]. The THG beams corresponding to \(E(t)^{3}\), \(E(t)^{2}E(t-\tau)\), \(E(t)E^{2}(t-\tau)\), and \(E^{3}(t-\tau)\) could be clearly observed by rotating the angle of the mirror (see the arrow in Figure 2). Therefore, the intensity of the THG beam for \(E_{\mathrm{sig}}(t,\,\tau)=E(t)^{2}E(t-\tau)\) was monitored for measuring the FROG trace. The observed and retrieved data obtained for negatively-chirped, FTL, and positively-chirped pulses are shown in Figure 5. The pulse width measured for an FTL pulse was 32 fs, which appears to be similar to or slightly shorter than the values obtained using IAC. On the other hand, the chirped pulse provided a slightly longer pulse width (positive 42 fs, negative 44 fs) due to the GDD value being calculated to be 300 fs\({}^{2}\).  
The minimal value of the pulse width measured using conventional SHG-FROG or SD-FROG is determined by the spectral region that is usable as a nonlinear optical medium: although a broad bandwidth spanning a multi-octave frequency domain can be covered using a thin optical crystal, the spectral region is practically limited to the VIS-NIR region [17]. On the other hand, SHG, which would restrict the spectral region of THG, does not occur in an isotropic gas, and a noble gas such as argon is transparent and has a small dispersion in the VUV to IR region. As a result, the present approach using a gas in conjunction with THG-FROG can be utilized to measure pulse widths in a wide frequency domain extending twice one octave. Because of this, this technique would be applied to the measurement of an ultrashort optical pulse less than 1 fs especially in the VUV-DUV region: it should be noted that a higher carrier frequency is desirable for generating a shorter optical pulse. However, in order to avoid the dispersion arising from the optics such as cell windows, it would be  
Figure 4: Intensity autocorrelation trace observed for the Ti:sapphire laser.  
necessary to use a nozzle for introduction of a rare gas (not air) into a vacuum. Efficient generation of THG (even HHG) reported to date suggests sufficient sensitivity of this THG-based method using a gas for the measurement of an ultrashort pulse width.
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 2 Technical Framework and Analysis
### Transport Layer  
The transport layer mainly pushes the structured packets encapsulated by the sensing layer from the operation site to the cloud server, and it consists of two services, namely, data pushing, and data receiving. The data-pushing service is located on the PC console of the operation site, and the data-receiving service is located on the remote cloud server. The data-pushing service consists  
Figure 3: Model of IoT sensing layer.  
Figure 2: Tensioning system structure.  
of four sub-services, namely, remote pushing request, remote response identification, data-pushing control, and exception handling. The data-receiving service consists of three parts, namely, monitoring service, data-receiving control, and job data identification. Packet transport mechanisms and rules are established to ensure its integrity and security during transport. The transport layer follows three rules. First, the encapsulated structured packets are taken as transport units. Second, pushing request, answer response, data-pushing and data-receiving control, exception handling, and other services are required between data-pushing and data-receiving services. Third, the receiving service performs unpacking inspection on packets in accordance with the data encapsulation and pushing rules and discards noncompliant packets to avoid junk data or malicious attacks.
# Bridging Macro-Microscopic Parameters: Correspondence and Calibration Approaches
## 4 Bridging Macro-Micro Parameters
### Approaches Selection Comparison  
In the fields of architectural research, soil layer studies, freeze-thaw of slopes, and railway ballast research, many articles use the \"trial and error method\" for parameter calibration in simulations or directly refer to the simulation parameters of others. [PERSON] et al. (2023) observed the significant impact of soil layer properties on the settlement of historically important buildings [34]. Therefore, they conducted simulations of Cone Penetration Test (CPT) in saturated sandy soils to investigate the mechanical behavior of sand under cone penetration, establishing a link between a 2D DEM model and actual experimental data, overcoming the limitations of 2D simulations, and better studying settlement effects. They used fine particles with a diameter of 10 mm or less and applied the trial and error method by comparing real triaxial tests and simulated triaxial tests (DEM biaxial tests) to calibrate the discrete material parameters, aiming to match the results of laboratory tests on soil. This approach requires a large number of real triaxial tests and simulated triaxial tests, which leads to high repetition and low time efficiency, with continuous reliance on real cone penetration tests for validation, demonstrating the drawbacks of the trial and error method.  
[PERSON] et al. (2014) studied the effects of freeze-thaw cycles on the macro-mechanical properties and micro-pore structure evolution of soil-rock mixtures through laboratory experiments and numerical simulations [35]. Their samples consisted of particles smaller than 5 mm, and they used the \"trial and error method\" to calibrate the particle parameters in PFC2D. This method is time-consuming, making the simulation time uncontrollable. Moreover, the parameters are selected entirely based on the final simulation results, which reduces the reliability of the results. If similar studies could adopt the systematic calibration method outlined in this paper, the calibration process could be scientifically simplified, and the calibration parameters would be more accurately reconstructed.  
[PERSON] et al. (2016) used PFC2D to model the behavior of railroad ballast under cyclic loading [36]. They innovatively assisted in particle construction through a MATLAB (R2024b) program, but their simulation parameters were entirely based on previous experience, which significantly undermines the authenticity of the results. If the calibration method presented in this paper were adopted, the reliability of their simulation parameters would clearly be improved.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
###### Abstract
The existing research is mainly focused on a specific transportation problem, and the lack of an overall intelligent system solution that can unify each transportation spot makes the actual landing application slow. 2. For the problem of passing a crossroad, the personalized needs of vehicles have not been considered. For example, time-sensitive vehicles are eager to pass the crossroads quickly, and credit-sensitive vehicles can be supplemented with time in exchange for traffic tokens. 3. The need to protect the privacy of intelligent vehicles has not yet been addressed. 4. The fixed-bidding strategy is not flexible with limited trade success probability. To solve the above problems, we propose a blockchain-based solution for time-sensitive convoys to reduce waiting time and pass a crossroad earlier. The main contributions of this paper include the following:  
1. An intelligent traffic credit system is proposed to record all the vehicles' behaviors such as driving too slow, traffic accidents, and negotiating the sequence of crossing an intersection, punish bad behaviors such as vehicle violations by deducting traffic tokens, and reward traffic tokens to vehicles for good behaviors, thus creating a good intelligent autonomous traffic environment that is fair, efficient, and safe. 2. We propose a blockchain-based smart contract for convoys passing crossroads order negotiation, and the vehicle nodes automatically invoke the relevant smart contractsto perform V2V crossroad passing orders. It allows time-sensitive convoys to apply and negotiate with convoys ahead of them to pass earlier and pay for it according to the game's optimal bid. 3. A time-cost optimization problem is formulated under the premise of protecting the privacy of the convoy ahead. Based on the incomplete information static game, two optimal bidding solutions for right-of-way trading are given. They effectively increase the probability of making a deal and realizing Pareto improvement. The rest of the paper is organized as follows. Section 2 proposes an intelligent transportation credit system based on traffic tokens and describes its roles and applied scenarios in the IoV. Section 3 mathematically models a crossroad passing problem and describes its smart contract process. Section 4 proposes a game-based right-of-way trade strategy and two optimal bidding solutions. Section 5 simulates the two proposed optimal bidding solutions and gives a performance comparison. The Monte Carlo simulation results agree with their theoretical derivation. Section 6 summarizes the entire paper.