esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Inflammatory and Antioxidant Effects Induced by
## 3 Results and Discussion
### Phytochemical Analyses
#### 3.1.2 HS-SPME-GC-MS and DI-ESI-MS Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound** & **Class** & **Area\%** & **RI** & **RI\({}_{\text{L}}\)a** ** \\ \hline
2-Propenalb  & Aldehyde & 1.89 & & \\ Heptaneb  & Other & 0.33 & & \\ Diallyl sulfide & SCC & 0.43 & 864 & 857 \\ Methyl allyl disulfide & SCC & 1.47 & 927 & 919 \\ Methoxy phenyl oximeb  & Other & 5.72 & 943 & \\ p-cyclene & Monoterpene & 0.47 & 1041 & 1025 \\ Allyl disulfide & SCC & 9.52 & 1088 & 1090 \\
3-Allyl-thio-propionic acid & SCC & 1.70 & 1107 & 1093 \\ Linalyl anthranilate & Monoterpenoid & 0.95 & 1110 & 1104 \\ Nonanal & Aldehyde & 1.16 & 1113 & 1102 \\ Methyl 2-propenyl trisulfide & SCC & 2.51 & 1146 & 1142 \\ Isoborneol & Monoterpenoid & 0.44 & 1178 & 1165 \\ Terpinen-4-ol & Monoterpenoid & 0.55 & 1187 & 1184 \\
3-Vinyl-1,2-dithiacyclohex-4-ene & SCC & 3.18 & 1195 & 1191 \\ Alpha-terpineol & Monoterpenoid & 0.27 & 1203 & 1207 \\ Decanal & Aldehyde & 0.63 & 1214 & 1208 \\
3-Vinyl-1,2-dithiacyclohex-5-ene & SCC & 3.96 & 1222 & 1224 \\ Thymol & Monoterpenoid & 22.4 & 1310 & 1293 \\ Carvacrol & Monoterpenoid & 36.1 & 1319 & 1317 \\
7 unknown compounds & & 6.32 & & \\ \hline
**Class** & & & & \\ Monoterpene and derivatives & & 61.2 & & \\ SCC & & 22.8 & & \\ Aldehyde & & 3.68 & & \\ Other & & 6.05 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: HS–SPME–GC–MS analysis of dry residue of garlic water extract (GWE). Figure 2: (+) ESI–MS full scan of the dry residue of garlic water extract (GWE). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{\(mlz\)} & \multicolumn{2}{c}{**Compound**} & \multicolumn{2}{c}{**Charge**} \\ \hline \multicolumn{4}{c}{Positive mode} \\ \hline
163 & Allicin & & H\({}^{+}\) \\
175 & Arginine & & H\({}^{+}\) \\
214 & N-butylbenzene & & H\({}^{+}\) \\  & sulfonamide & & \\  & & **DP** & \\  
\({}^{+}\) \\ \hline \multicolumn{4}{c}{_Negative mode_} \\ \hline
191 & Citric acid & & [M-H]\({}^{-}\) \\
289 & Catechin & & [M-H]\({}^{-}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: DI–ESI–MS analysis of dry residue of garlic water extract (GWE). Figure 3: MS/MS spectrum of the \(m\)/\(z\) 1029 positively charged ion.
# Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access
## 5 Current Protection Optimization Enhancement Program
### Adaptive Full Line Current Protection Sensitivity Adjustment Logic  
The flow of the adaptive full line current protection method proposed in this paper is illustrated in Figure 4.  
The protection process is as follows:  
(1) The line current is monitored in real time. In the event of a change in the line current that does not exceed the threshold value of Criterion 1, the threshold value is adjusted in accordance with Equations (17) and (23).  
(2) In the event that the increased line current exceeds the threshold value of Criterion 1, the current difference between each section must be calculated in accordance with Equation (21). Thereafter, Criterion 2 must be satisfied, as defined by Equation (24).  
(3) In the event that Criterion 2 is not satisfied, the threshold value shall be adaptively adjusted in accordance with Equations (17) and (23). Conversely, if Criterion 2 is satisfied, the sensitivity shall be outputted in accordance with Equation (25).
## 3 Materials and Methods
### Method Validation
#### 3.4.3 Accuracy and Precision  
Within-run and between-run accuracy and precision were determined at four concentration levels, using the four quality control (QC) samples: LLOQ, low (L, three times LLOQ), medium (M) and high (H), each in five replicates in three separated runs. Accuracy was given as the percentage ratio of test results to the expected values and precision was defined as the coefficient of variation (CV%). The acceptance criteria for within- and between-run accuracy were 85-115% of nominal concentration (except LLOQ, with 80-120% of nominal concentration) and, for precision, \(\leq\)15% (except LLOQ, with \(\leq\)20%). For both, 75% of samples had to meet the above criteria.
# A Deeper Look at Sheet Music Composer Classification Using Self-Supervised Pretraining
## 3 Results
### Experimental Setup  
There are two sets of data that we use in our experiments: an extremely large unlabeled dataset and a smaller, carefully curated labeled dataset.  
The unlabeled dataset consists of all solo piano sheet music images in IMSLP. We used the instrument metadata to identify all solo piano pieces. Because popular pieces often have many different sheet music editions, we selected one sheet music version (i.e., PDF) per piece to avoid overrepresentation of a small number of popular pieces. We computed bootleg score features for all sheet music images and discarded any pages containing less than a threshold number of bootleg score features. This threshold was selected as a simple heuristic to remove non-music pages such as title page, forematerial, and other filler pages. The resulting set of data contains 29,310 PDFs, 255,539 pages, and 48.5 million bootleg score features. We will refer to this unlabeled dataset as the IMSLP data. 90% of the unlabeled data was used for language model training and 10% for validation.  
The labeled dataset is a carefully curated subset of the IMSLP data. We first identified nine composers who had a substantial amount of piano sheet music. (The limit of 9 was chosen to avoid extreme imbalance of data among composers.) We constructed an exhaustive list of all solo piano pieces composed by these nine composers, and then selected one sheet music version per piece. We then manually identified filler pages in the resulting set of PDFs to ensure that every labeled image contains actual sheet music. The resulting set of data contains 787 PDFs, 7151 pages, and 1.47 million bootleg score features. Table 1 shows a breakdown of the labeled dataset by composer. The labeled data was split by piece into training, validation, and test sets. In total, there are 4347 training, 1500 validation, and 1304 test images. This dataset will be referred to as the full-page data.  
The full-page data was further preprocessed into fragments as described in Section 2.2. In our experiments, we considered three different fragment sizes: 64, 128, and 256. We sampled the same number of fragments from each composer to ensure balanced classes. For fragment size of 64, we sampled a total of 32,400, 10,800, and 10,800 fragments across all composers for training, validation, and test, respectively. For fragment size of 128, we sampled 16,200, 5400, and 5400 fragments across all composers for training, validation, and test. For fragment size of 256, we sampled 8100, 2700, and 2700 fragments for training, validation, and test. This sampling strategy ensures the same \"coverage\" of the data regardless of the fragment size. These datasets will be referred to as the fragment data.
## 3 Results
### Physical Performance Data  
Concerning sprinting and jumping performance (Table 4), apart from CMJs (Pivots: 44.4 \(\pm\) 2.21 cm), Wings were the players with the highest performance level in three parameters (15 m and 30 m sprint and SJ). For the sprinting parameters, we detected a large position effect for all parameters (15 m sprint: \(\eta_{\text{p}}\)\({}^{2}\) = 0.71; 30 m sprint: \(\eta_{\text{p}}\)\({}^{2}\) = 0.72). The differences were mainly induced due to the lower performance of goalkeepers. Goalkeepers were involved in 70% (7 out of 10) of the partial position effects. ### Physical Performance Data  
Concerning sprinting and jumping performance (Table 4), apart from CMJs (Pivots: 44.4 \(\pm\) 2.21 cm), Wings were the players with the highest performance level in three parameters (15 m and 30 m sprint and SJ). For the sprinting parameters, we detected a large position effect for all parameters (15 m sprint: \(\eta_{\text{p}}\)\({}^{2}\) = 0.71; 30 m sprint: \(\eta_{\text{p}}\)\({}^{2}\) = 0.72). The differences were mainly induced due to the lower performance of goalkeepers. Goalkeepers were involved in 70% (7 out of 10) of the partial position effects. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Playing** & **15 m** **Sprint [s]** & **30 m** **Sprint [s]** & **CMJ [cm]** & **SJ [cm]** \\ \cline{2-5}
**Positions** & \multicolumn{4}{c}{**Mean \(\pm\) SD (95\% CI)**} \\ \hline Pivots & 2.54 \(\pm\) 0.21 & 4.39 \(\pm\) 0.26 & **44.4 \(\pm\) 2.21** & 40.3 \(\pm\) 4.71 \\ (n = 14) & (2.47–2.61) & (4.29–4.50) & (43.0–45.7) & (38.4–42.1) \\ Wings & **2.39 \(\pm\) 0.09** & **4.11 \(\pm\) 0.19** & 43.8 \(\pm\) 3.21 & **42.2 \(\pm\) 3.38** \\ (n = 14) & (2.31–2.46) & (4.00–4.22) & (42.4–45.2) & (40.3–44.0) \\ Backs & 2.39 \(\pm\) 0.10 & 4.22 \(\pm\) 0.19 & 42.7 \(\pm\) 3.06 & 39.9 \(\pm\) 2.77 \\ (n = 12) & (2.31–2.47) & (4.10–4.33) & (41.2–44.2) & (37.9–41.9) \\ Goalkeepers & 2.93 \(\pm\) 0.07 & 4.97 \(\pm\) 0.13 & 41.7 \(\pm\) 0.77 & 37.9 \(\pm\) 1.32 \\ (GK; n = 10) & (2.84–3.02) & (4.84–5.10) & (40.1–43.3) & (35.7–40.1) \\ \hline \hline \(p\) & **<0.001** & **<0.001** & 0.075 & 0.035 \\ \(\eta_{\text{p}}\)\({}^{2}\) & **0.71** & **0.72** & 0.14 & 0.17 \\ \hline \hline \end{tabular}
\begin{tabular}{l c c c c} \hline \hline  & GK vs. Wings: \textless{}0.001 & & & \\  & GK vs. Backs: \textless{}0.001 & & & \\  & GK vs. Pivots: \textless{}0.001 & & & \\  & Pivots vs. Backs: \textless{}0.001 & & & \\  & Pivots vs. Backs: 0.049 & & & \\  & Pivots vs. Wings: & & & \\  & & 0.030 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sprinting and jumping performance depending on playing positions. CI = confidence interval. Relevant mean differences (dependent variable: positions) and performance maxima are marked in bold.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 1 Introduction  
The computing environment has changed rapidly in the past two decades on account of technological advancements and innovations. The modern computing environment implements the automation of tasks and processes largely. Automatic identification and data capture are one of the most important components of process automation, and are used in a variety of applications. The technologies behind automatic identification and data capture include bar codes, chip cards, biometrics, voice recognition, optical character recognition, smart cards, magnetic stripes, and radio frequency identification (RFID). Among these technologies, smart cards are being used widely in many critical applications due to their advantages of longer life, larger memory, high security, and lesser cost of operations [1]. The applications of smart cards include secure electronic payments, secure identification, mobile applications, secure physical access, and healthcare systems, to name a few. Usually, a CPU (central processing unit), ROM (read-only memory), a RAM (random access memory), a co-processor, and an electrically erasable programmable read-only memory (EEPROM) are the major components ofa smart card. The representative diagram of a smart card has been demonstrated in Figure 1. ROM is used to store the operating system (OS) and the run-time environment. The data and the instructions to be executed by the CPU are kept in RAM. The application and its data are stored in EEPROM [2]. All data processing and instructions are executed by the CPU, while the co-processor is used to enhance the performance of security functions such as encryption and decryption.
## 3 Results
### Isolation of the 3-Carene Synthase in P. densiflora  
Full-length cDNA of 3-carene synthase gene (_Pd3-cars_) was isolated from _P. densiflora_. The open reading frame (ORF) of _Pd3-cars_ gene was 1881 bp and the deduced amino acid sequence (626 amino acids with a predicted molecular mass of 71.3 kDa) is 97% identical to that of 3-carene synthase in Lodgepole (_P. contorta_) and Jack pine (_P. banksiana_) (Supplementary Figure S2). A phylogenetic tree was constructed based on the deduced amino acid sequences with previously characterized carrier monotereprene synthase (Figure 5). The contier monotereprene synthases were largely classified into two groups in phylogenetic analysis. Major components of one group were 3-carene, (-)-betapinene, and (-)-alpha pinene, and another group included myrcene, (-)-beta phellandrene, and (+)-alpha pinene. In alpha pinene, the difference between (+)-alpha and (-)-alpha pinene was noticeable. 3-Carene synthase showed a closer relationship with (-)-alpha pinene than (+)-alpha pinene synthase. It is obvious that 3-carene synthase of _P. densiflora_ is closely related to the other conifer 3-carene synthase and has a higher relationship with _Pinus_ than _Pican_.
## 4 Simulation and Discussion
### Performance of the Proposed Initial Sparsity Level Estimation Approach  
Firstly, the proposed initial sparsity level estimation was evaluated in contrast to AStMP [20], which shows the state of art performance in initial sparsity estimation. In this simulation, real signals were extracted with the length \(N=128\). And the measurement length \(M\) and sparsity level \(K\) were fixed as \(M=64\) and \(K=32\), respectively.  
Initial estimations of sparsity level in different experiments are shown in Figure 3(a). Performance of FRSVssMP and AStMP are compared with different \(\delta_{k}\). The blue dotted line indicates the actual sparsity level (\(K=32\)). According to Figure 3(a), FRSVssMP achieves superior initial estimation of \(K\) over AStMP as the estimated level is close to the actual level with the same \(\delta_{k}\). In addition, by choosing an appropriate \(\delta_{k}\), the initial estimation of \(K\) will approximate to actual level obviously.  
Figure 3: Examples of one-dimensional sparse data. (**a**) Synthetic signals; (**b**) Real signals.  
As illustrated in Section 3, with the estimated sparsity level \(k_{0}\), we set the initial step size to \(\lfloor k_{0}/2\rfloor\) for subsequent process. Further, to demonstrate the superiority of this setting, Figure 4b demonstrates the estimated sparsity level obtained by different algorithms during iterations, with a focus on the selection of initial step size. Step size adaptively changes in different iterations, and finally, the sparsity level is accurately approximated. Taking advantage of the initial step size setting \(\lfloor k_{0}/2\rfloor\), in contrast to SAMP, SASP and AStMP, FRSVssMP approximates the actual sparsity level more efficiently as the number of iterations required can be significantly reduced.
# The Environmental Impact of Inland Empty Container Movements Within Two-Depot Systems
## 5 Results and Discussion
Conversely, without an inland depot, waiting times remain stable. This means that direct routing ensures a more efficient and responsive supply chain, with containers reaching their destinations in a timely manner. However, it may lead to higher unmet demand if trucks are not available when needed. Meanwhile, the result shows  
Figure 4: GHG emissions under different scenarios. Figure 5: The availability of empty containers. that while the inland depot system may be beneficial in terms of environmental impact and overall container efficiency, it comes at the cost of severely reduced service levels for customers. The sensitivity analysis using Behavior Space revealed that customer demand frequency and depot routing significantly influence emissions and waiting time. The proposed ABM consistently outperformed the baseline in all tested configurations, indicating robust performance under varying operational conditions. Hence, the statistical evidence highlights that the depot system significantly reduces emissions and optimizes truck utilization, but it increases the average waiting times due to additional storage processes. By comparison, the direct routing scenario is advantageous in minimizing delivery delays but results in higher emissions and operational costs. Hence, this result highlights a major trade-off between cost, time, and sustainability. Ultimately, the model suggests that while an inland depot enhances sustainability and logistical efficiency, it does so at the expense of customer satisfaction. Decision-makers must balance these factors based on operational priorities, and whether they prioritize environmental benefits and cost reductions or faster service and system flexibility.
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
## 1 Introduction
Thus, an entire city or several farms can be covered by a single base station [17]. In addition, LPWA networks are highly energy efficient and inexpensive, with a radio chipset cost less than 2 \(\upcels\) and an operating cost of 1 \(\upcels\) per device [18]. Hereafter, it is expected that the 5 th generation (5G) of wireless mobile communications will provide the means to allow an all-connected world of humans and devices, which would lead to a global LPWAN solution for IoT applications [17]. Precision livestock farming (PLF) aims to manage individual animals by continuous and real-time monitoring of their health, welfare, production/reproduction, and/or environmental impact [19]. Despite the advances in GPS-based technologies for animal tracking in recent years, several constraints limit their use as PLF tools in commercial farms, especially in the case of extensive production systems. First, wireless data transmission is challenging in most rural areas. As described in the previous paragraph, IoT solutions are an opportunity to overcome that problem, but there are still few IoT developments devoted to animal tracking [20; 21]. On the other hand, the financial cost of GPS tracking systems (generally several hundreds of euros per unit) remains a problem for widespread use of these technologies [22]. This is especially relevant in small ruminants, as their individual cost is low when compared to the tracking device cost. At best, some animals within the herd are monitored by GPS devices, which could be sufficient for some utilities, e.g., the monitorization of the percentage of time devoted to rest, graze, and travel [23; 24]. Nevertheless, most authors argue that few animals are not representative of an entire herd [3; 25; 26]. Additionally, there are some utilitiesthat would require knowing the location of all the animals in a herd, e.g., theft control, heat detection, or calving monitoring. The main objective of this paper is to develop a low-cost solution that enables the monitoring of the location of all the animals in a herd and the continuous updating of location data, which is needed for farmer decision support. The secondary objective is to test the real-world performance of the solution in commercial farms with different production conditions. The rest of the paper is organized as follows: Section 2 presents the hardware components of the solution and describes the methodology of system validation. Section 3 presents the results obtained regarding system functioning, configuration and validation in commercial farms. In Section 4, the results are discussed, and new utilities of the solution are proposed. Conclusions and further research needs are presented in Section 5.
## 3 Analysis of SCSRR-Based DGS MIMO Antennas with Different Feed Networks
### Microstrip Line Feed  
The schematic view of the microstrip line feed MIMO antenna structure and its design parameters are illustrated in Figure 14. The SCSRR was implemented on the same MIMO antenna, as shown in Figure 15. The simulated S-parameters of microstrip line feed MIMO antennas with and without an SCSRR-based DGS simulation are shown in Figure 16. The implemented SCSRR demonstrated insignificant improvement in the isolation response compared to the coaxial probe feed MIMO antenna. Since each feeding network has a different current distribution on the ground plane that leads to the change in the impedance of the surface thus directly influencing the impedance matching.  
The 3D gain patterns of the microstrip line feed MIMO antennas with SCSRR-based DGS is illustrated in Figure 17. The current distribution of microstrip line feed MIMO antennas with and without CSRR-based DGS at the 5.8 GHz resonance frequency is displayed in Figure 18. The surface current was not highly distributed on the microstrip line feed MIMO antenna with CSRR-based DGS (see Figure 18b) than without it because the capacitance and inductance effect had not been adequately increased. A comparison of microstrip line  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Without SCSRR** & **With SCSRR** \\ \hline \(f_{c}\) [GHz] & 5.76 & 5.8 \\ \hline Peak return loss [dB] & 19.15 & 17.67 \\ \hline BW [MHz] & 170 & 180 \\ \hline Peak gain [dBi] & 6.8 & 6 \\ \hline Peak isolation [dB] & 16.5 & 58 \\ \hline RE [\%] & 95.9 & 95.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of coaxial probe feed MIMO antennas with and without SCSRR-based DGS.  
Figure 14: Microstrip line feed MIMO antenna model without SCSRR-based DGS. Dimensions of antenna: L\({}_{sub}\) = 36 mm, W\({}_{sub}\) = 58 mm, L\({}_{p}\) = 16 mm, W\({}_{p}\) = 13 mm, d\({}_{1}\) = 26 mm, w\({}_{f}\) = 3 mm, w\({}_{l}\) = 0.8 mm.  
Figure 15: The microstrip line feed MIMO antenna model with SCSRR-based DGS. The dimensions of antenna are as follows: L\({}_{sub}\) = 36 mm, W\({}_{sub}\) = 58 mm, L\({}_{p}\) = 16 mm, W\({}_{p}\) = 13 mm, d\({}_{1}\) = 26 mm, w\({}_{f}\) = 3 mm, w\({}_{t}\) = 0.8 mm. Note: Dimensions of SCSRR are listed in Table 3.
## 2 Related Work
Considering this uncertainty in available energy, the main goal of this paper is to study how transmission power control can be effective in a clustered or structured network in EH-WSN. In our earlier work, we proposed a transmission power control for an unstructured/flat network, where each node sends their data to the base-station that is located in the center of the network using multihop forwarding. We have concluded that the power control can help the load balancing among the sensors in directional communication from sensors to base-station, which results in the better end-to-end performance. However, in this paper, we take a step further to study how power control can affect the performance of the network where there is more dynamicity in the transmission among the nodes. Therefore, we considered a network without any central entity, where sensors communicate together in multihop fashion bi-directionally. Thus, at one round, a node can be a forwarder and at another, it can be the receiver of the packet. We study this dynamicity and randomness in an unstructured network, and then we compare it with a structured network. The main contributions of this paper can be summarized as follows:  
* Studying the effect of power control in an uninstructed network where nodes communicating together in a random multi-hop fashion. * Studying the adaptive transmission power control effect in the clustered network. * Comparing the proposed method with some existing work in terms of network lifetime.
## 3 DRL-Based Joint User Selection and Blocklength Allocation Design
### MDP Formulation
* Reward Function: To minimize the average task AuD, the reward function we have designed is as follows: \[r_{t}=-\alpha\Delta_{d,t}+C\] (19) where \(\Delta_{d,t}\) is the task AuD at frame \(t\), \(\alpha\) is a scaling factor that adjusts the sensitivity to changes in AuD, ensuring that the reward is neither too large nor too small, and \(C\) is a constant term that provides a fixed uplift to the reward, potentially aiding in learning stability and convergence of the learning process.