esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Design and Diet Formulation
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Experimental Diets**} \\ \hline  & HFM 0\% & HFM 1.5\% & HFM 3.0\% & HFM 4.5\% & HFM 6.0\% \\ \hline Samples, n & 2 & 2 & 2 & 2 \\ Alanine & 2110 \(\pm\) 7 & 2246 \(\pm\) 128 & 2309 \(\pm\) 160 & 2080 \(\pm\) 26 & 2296 \(\pm\) 437 \\ Arginine & 2181 \(\pm\) 25 & 2316 \(\pm\) 129 & 2335 \(\pm\) 66 & 2179 \(\pm\) 31 & 2339 \(\pm\) 251 \\ Aspartic & 3398 \(\pm\) 29 & 3568 \(\pm\) 139 & 3756 \(\pm\) 199 & 3374 \(\pm\) 126 & 3714 \(\pm\) 783 \\ Acid & 394 \(\pm\) 3 & 449 \(\pm\) 25 & 480 \(\pm\) 22 & 498 \(\pm\) 15 & 577 \(\pm\) 54 \\ Glutamic & 5907 \(\pm\) 70 & 6249 \(\pm\) 271 & 6556 \(\pm\) 336 & 5930 \(\pm\) 209 & 6544 \(\pm\) 1285 \\ Glycine & 2734 \(\pm\) 39 & 2925 \(\pm\) 262 & 2869 \(\pm\) 181 & 2649 \(\pm\) 84 & 2900 \(\pm\) 356 \\ Histidine & 1095 \(\pm\) 23 & 1119 \(\pm\) 17 & 1127 \(\pm\) 16 & 1051 \(\pm\) 29 & 1121 \(\pm\) 81 \\ Isoleucine & 971 \(\pm\) 26 & 1183 \(\pm\) 52 & 1220 \(\pm\) 24 & 1106 \(\pm\) 44 & 1188 \(\pm\) 188 \\ Leucine & 2445 \(\pm\) 28 & 2653 \(\pm\) 119 & 2786 \(\pm\) 90 & 2552 \(\pm\) 84 & 2791 \(\pm\) 425 \\ Lysine & 2317 \(\pm\) 23 & 2494 \(\pm\) 76 & 2659 \(\pm\) 172 & 2381 \(\pm\) 101 & 2723 \(\pm\) 673 \\ Methionine & 610 \(\pm\) 50 & 585 \(\pm\) 45 & 551 \(\pm\) 31 & 568 \(\pm\) 44 & 590 \(\pm\) 52 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ingredients (%) and proximate composition (% DM; mean \(\pm\) SD) of the experimental diets for _Macrobrachium rosenergii_ post-larvae containing different levels of hydrolyzed feather meal. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Experimental Diets**} \\ \hline  & HFM 0\% & HFM 1.5\% & HFM 3.0\% & HFM 4.5\% & HFM 6.0\% \\ \hline Phenylalanine & 1581 \(\pm\) 4 & 1679 \(\pm\) 103 & 1711 \(\pm\) 11 & 1603 \(\pm\) 31 & 1724 \(\pm\) 100 \\ Proline & 2049 \(\pm\) 4 & 2204 \(\pm\) 162 & 2268 \(\pm\) 144 & 2123 \(\pm\) 5 & 2386 \(\pm\) 362 \\ Serine & 1643 \(\pm\) 7 & 1743 \(\pm\) 98 & 1888 \(\pm\) 55 & 1847 \(\pm\) 22 & 2104 \(\pm\) 240 \\ Threonine & 1318 \(\pm\) 25 & 1396 \(\pm\) 67 & 1463  
For diet preparation, all ingredients were individually milled using a knife mill fitted with a 0.5-mm sieve to achieve uniform particle size. The ground ingredients were then mixed thoroughly according to the specific formulations. Pelleting was performed through cold extrusion to prevent nutrient degradation. The resulting pellets were stored at 4 \({}^{\circ}\)C under refrigeration to preserve quality until use.
## 4 AM Process  
Using AM technology to produce a component typically follows a sequence of seven essential steps, as shown in Figure 5, which apply universally across different AM techniques [30]. After the conceptual development of the idea, the AM process starts with creating a 3D digital model for the structure, which can be generated using 3D CAD software. The accuracy of this model is crucial because it has a direct impact on the finished product's quality. Several factors, such as geometric limitations and the need for support material, must be considered at this stage. Once the 3D CAD file is ready, it is converted into a Standard Tessellation Language (STL) file format, which simplifies the design by representing the object's surface with triangles. The 3D digital model is then transformed into G-Code, a programming language used to operate 3D printers, by importing the STL file into slicer software. The slicer software divides the model into layers and defines key printing parameters, including layer height, printing speed, and the layout of support structures. Once the file is prepared, the object is manufactured using the chosen 3D printing technology. After the printing process is complete, any support structures must be removed. For certain technologies, removing supports might only require separating the printed component from the build plate; for others, more intricate procedures would be needed. Finally, post-processing is often necessary to enhance the mechanical properties and surface quality of the printed part. Common techniques include post-heat treatment, which is employed to relieve residual stresses induced during the printing process and to refine the microstructure. Additionally, surface polishing or chemical smoothing is frequently used to reduce surface roughness and improve the overall finish of the component.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 1 Introduction
### Overview and Contributions  
The contribution of this paper is twofold. First, the numerical efficiency of the RNA methods applied to the SK algorithm to solve the regularized transport problem is shown. Second, a new extrapolation and relaxation technique for accelerating the Sinkhorn-Knopp (SK) algorithm while ensuring convergence is given. The numerical efficiency of this new algorithm is demonstrated, and a heuristic rule is also proposed to improve the rate of the algorithm.  
Section 2 is devoted to the Sinkhorn-Knopp algorithm. In Section 3, we propose to apply regularized nonlinear acceleration (RNA) schemes to the SK algorithm. We experimentally show that such methods lead to impressive accelerations for low values of the entropic regularization parameter. In order to have a globally converging method, we then propose a new overrelaxed algorithm: Sinkhorn-Knopp with successive overrelaxation (SK-SOR). In Section 4, we show the global convergence of this algorithm and analyze its local convergence rate to justify the acceleration. We finally demonstrate numerically in Section 5 the interest of our method. Larger accelerations are indeed observed for decreasing values of the entropic regularization parameter.  
**Remark 1**: _This paper is an updated version of an unpublished work [27] presented at the NIPS 2017 Workshop on Optimal Transport & Machine Learning. In the meantime, complementary results on the global convergence of our method presented in Section 4 were provided in [28]. The authors showed the existence of a parameter \(\theta_{0}\) such that both global convergence and local acceleration were ensured for overrelaxation parameters \(\omega\in(1,\theta_{0})\). This result was nevertheless theoretical, and the numerical estimation of \(\theta_{0}\) is still an open question. With respect to our unpublished work [27], the current article presents an original contribution in Section 3: the application of RNA methods to accelerate the convergence of the SK algorithm._
## 3 Precise Orbit and Clock Product Introduction and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Orbit [m]**} & \multicolumn{4}{c}{**Clock [ns]**} \\ \cline{2-7}  & **R** & **T** & **N** & **3D** & **RMS** & **STD** \\ \hline GPS(XRS) & 0.0115 & 0.0132 & 0.0146 & 0.0233 & 0.242 & 0.051 \\ GPS(WUM) & 0.0127 & 0.0121 & 0.0132 & 0.0225 & 0.198 & 0.052 \\ \hline Galileo & 0.0183 & 0.0241 & 0.0296 & 0.0431 & 0.338 & 0.085 \\ BDS-2-GEO & 0.394 & 1.353 & 1.024 & 1.742 & – & 1.31 \\ BDS-2-IGSO & 0.103 & 0.340 & 0.343 & 0.494 & – & 0.16 \\ BDS-2-MEO & 0.115 & 0.213 & 0.230 & 0.334 & – & 0.14 \\ BDS-3-RAFS & 0.118 & 0.225 & 0.242 & 0.351 & – & 0.17 \\ BDS-3-PHM & 0.119 & 0.209 & 0.215 & 0.322 & – & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Averaged values of comparison results of XRS and WUM products. Figure 1: Ground station distribution of XRS POD products, where the blue circles denote stations that can track BDS-3 signals.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 9 Lessons Learned, Suggestions, and Recommendations
### Suggestions and Recommendations
#### 9.2.1 Cryptographic Suggestions  
These suggestions include secure communication as it is a key component that enables smart grid components to communicate with each other. It is essential to present different robust security measures to maintain the necessary level of communication efficiency,  
Figure 7: Smart grid future directions, lessons learned, suggestions, and recommendations.  
safety, security, and performance. This also includes secure data availability in transit and at rest as smart grid data integrity and confidentiality are ensured by secure data availability, which is achieved through robust data handling protocols and encryption. Robust cryptographic techniques encrypt data as it is transmitted, guarding against unwanted access and alteration. Data is safeguarded from unauthorized access and tampering while at rest by being stored in secure environments with access controls, which preserves the confidentiality and integrity of smart grid activities. Moreover, it includes a multi-factor authentication protocol by enforcing users to submit two or more verification factors, such as combining their identity (biometric scan) with their smart card or token (smart card), or something they know (password), which increases the security of smart grids. These protocols are known as multi-factor authentication (MFA) protocols. This method enhances authentication integrity against cyberattacks targeting smart grid infrastructure while reducing the likelihood of unauthorized access.
## 3 Results and Discussions
### Physical and Mechanical Performance
#### 3.2.2 Shrinkage Rate  
The smaller shrinkage rate is conducive to the grouting and reinforcement of stone relics so that the slurry and the rock can be closely conglutinated to achieve the ideal reinforcement effect [29]. Figure 5 shows the shrinkage rates of three hydraulic lime mortar blocks at 3, 7, 14, 21, and 28 days.  
Figure 4: Compressive strength of the specimens.  
Figure 3: Flexural strength of the specimens.  
The shrinkage rates of the specimens are small for all hydraulic lime types. The shrinkage rates of SK and SL2 are between 0.15 and 0.25%, and that of SL5 is 0.11% at 28 days. Therefore, SL5 has a smaller shrinkage rate than SK and SL2. Although SK has a longer hydraulic phase, there is no calcium oxide after slaking. In addition to the hydration reaction in SK, moisture can volatilize and cause shrinkage of the sample. There is moisture consumption via calcium oxide and hydration reactions in SL2 and SL5, which prevents the shrinkage of the sample.
## 4 Evaluation Study
### Data Collection and Analysis
#### 4.3.2 Qualitative Data Collection and Analysis  
The qualitative data comprise mainly interview data. We used various coding techniques (e.g., Structural, In Vivo, and Evaluation coding) described by [PERSON] [62] to qualitatively analyze the interview transcripts, aiming to assist in the meaningful interpretation of participants' collaboration. Finally, 136 codes were identified from the transcribed interviews. Then, we sorted the codes for further analysis and theme generation, as suggested by [PERSON] [63]. A total of 32 nodes emerged during our qualitative coding process and can be classified into eight categories, as shown in Table 4. Additionally, Appendix B lists the complete qualitative coding results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Themes** & **Categories (Frequency Count)** & **Nodes (Frequency Count)** \\ \hline \hline Theme 1 & Game experience (25) & Novel and interesting (9) \\  & & Challenging (8) \\  & & Intense (3) \\  & & Enjoy (3) \\  & & Competing and exploring (2) \\  & & Spatial presence (5) \\  & & Exquisitely crafted visuals (5) \\  & & Realistic special effects (5) \\  & & Natural feedback (4) \\  & & Authentic story (5) \\ \hline Theme 2 & Co-presence (14) & Observing teammates’ positions (2) \\  & & Seeing avatars’ actions (3) \\  & & Interacting with teammates (6) \\  & & Moving realistically (1) \\  & & Face-to-face negotiation (2) \\  & Co-located (11) & Safety issues (6) \\  & & Real and warm (2) \\  & & Form strategies quickly (3) \\  & Pre-training (22) & Lack of understanding of game rules (9) \\  & & Unfamiliarity with VR devices (1) \\  & & Effective observation and learning (11) \\  & & Prior experience (1) \\ \hline Theme 4 & Technological issues (7) & Unstable real-mapping technology (5) \\  & & Unstable game functionality (2) \\  & Communication issues (14) & Channels of communication (5) \\  & & Timeliness of communication (5) \\  & & Flexibility in communication (4) \\  & Game issues (19) & Lack of dynamic information cues (10) \\  & & Lack of level challenges (3) \\  & & Lack of audio cues (2) \\  & & Lack of key content prompts (3) \\  & & Lack of personalized choices (1) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of themes, categories, and nodes for VR experiences.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constructs** & **Number of Items** & **Cronbach’s Alpha** & **CR** & **AVE** \\ \hline Basic information & & & & \\ \hline Personality & 5 & 0.708 & n/a & n/a \\ \hline Collaborative competence & 19 & 0.944 & n/a & n/a \\ \hline VR readiness & 8 & 0.809 & n/a & n/a \\ \hline Collaboration experience & & & & \\ \hline Collective efficacy & 8 & 0.942 & 0.922 & 0.748 \\ \hline Social experience & 6 & 0.902 & 0.914 & 0.641 \\ \hline IVR presence & & & & \\ \hline Spatial presence & 5 & 0.836 & 0.831 & 0.632 \\ \hline Social presence & 3 & 0.763 & 0.770 & 0.408 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structure of questionnaire and its reliability and validity.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 4 Experimentation and Results
### Field Test  
For this last trial, a relatively controlled environment was needed to avoid any possible loss of the end devices. Thus the beach of El Tecolote (La Paz, B.C.S., Mexico) was selected, due to its ease of access and deployment. In this location, three buoys where placed approximately 60 m away from the beach (24.33657, \(-\)110.32229 UTM), with a separation of a few meters amongst each other, capturing environmental and positioning data every minute, granting high amounts of information in a short period of time (Figure 14).  
Prior to their placement, all the slave nodes (sensor nodes and monitor) were configured at the 433 MHz frequency band, with a spreading factor of 128 chips/symbol, a bandwidth of 125 kHz and a coding rate 4/5, which provides a high data transfer rate at the expense of transmission range reduction, with only the minimum of noise immunity. The maximum amount of bitrate was estimated using tools and formulas provided by the transceiver's manufacturer [44]. Although with other LoRa RF configurations increased distances are attainable, these settings allow the validation of the systems functionality as a whole.  
To verify the proper communication between the UAV and buoys, a network monitor was placed at the beach connected to a computer. Given that the buoys and the network monitor were fairly close to each other, once the network monitor lost the reception of data from the UAV, it could be inferred that the same would happen with the buoys. After a given time in which the buoys had collected enough data, the UAV with the master node was launched from the beach, in automatic mode, following pre-established waypoints (Figure 15).  
Figure 14: Drifting Davis buoy placed in El Tecolote (Mexico) beach for environmental data collecting.  
After the UAV had been launched, in the network monitor it could be viewed how the master node periodically located the three buoys and extracted the data. A sample of these can be seen in Figure 16.  
Figure 16: Data extracted by the master node from one of the buoys.  
Figure 15: UAV flight path for the trials at El Tecolote beach. The furthest waypoint was set at 8.62 km away from the launch area and the UAV flew at a maximum height of 30 m.  
It is worth noting that the signal from the LoRa master node was lost at approximately 4 km in distance, which is reasonable given the LoRa RF configuration set on the end devices. As verified in the pre-field tests, the maximum expected range was 5 km. Additionally, in this trial the UAV was limited to a reasonable distance, although it is able to travel double the distance with battery to spare.  
The buoys, which were closely located to the beach, only drifted approximately 50 m each since the surface layer currents at that location were very weak. Yet, since there was a constant communication with the master node which was viewed with the monitor node, it can be concluded that the system as a whole is a valid one.
## 3 Results
### Production of VOCs by Calla Lily and by Cabbage in Response to Incoulation of Pectobacterium  
VOCs are produced by plants as part of the early reaction to and interaction with microorganisms [29; 30]. VOCs emitted from calls lily or cabbage leaves following inoculation with either PC16, WPP14 or DDW (mock-inoculated control) were contained for 24 h. Thermal desorption served to release adsorbed VOCs that were produced for 24 h by the treated leaves in a sealed glass jar for 24 h and adsorbed onto SPME fibers. GC-MS was employed in an effort to identify VOCs in all six samples. In calls lily samples, a total of 16 VOCs were detected, 11 of which were identified (Figure 3 and Table 3). Four VOCs were common to all calls lily samples, while two, 4-methylheptane and 2, 4-dimethyl-1-heptene, were present only in calls lily + WPP14 and the calla lily control. Toluene was also identified, but only in the mock-inoculated calls lily control. Three VOCs, 4-methyloctane and two branched alkanes, were only detected following infection of calls lily with WPP14 samples. The headspace of calls lily + PC16 samples contained six VOCs that were unique to that interaction: methyl acetate, ethyl acetate, isopentyl alcohol, isobutyl acetate, isopentyl acetate and a compound similar to sandaracopimaradiene (Table 3). Figure 3: Chromatograms of volatile organic compounds (VOCs) produced by leaf samples, (**A**) calla lily and (**B**) cabbage samples (double-distilled water (DDW)-black, Pc16- red, WPP14-green), following infiltration with bacteria, and incubation in airtight jars at 28 \({}^{\circ}\)C for 24 h, and adsorption onto a SPME fiber. The adsorbed compounds were analyzed using Agilent GC 7890A and 5975C MSD, and mass spectra were acquired in positive electron-impact (EI) scan mode (m/z 20–350). Identification of the VOCs was performed by comparing their mass spectra and retention times with the NIST/EPA/NIH Mass Spectral Database (NIST 05, National Institute of Standards and Technology, Gaithersburg, MD, USA; see Tables 2 and 3). Representative chromatograms from two independent experiments are presented. In the cabbage samples, a total of 21 VOCs were detected, 12 of which were identified (Figure 3 and Table 2). Eleven of the VOCs detected were found in all of the cabbage samples, and three VOCs were detected for both cabbage + WPP14 and the cabbage control: two branched alkanes and 2, 6, 10, 14-tetramethyl heptadecane. The remaining seven VOCs were detected only following the inoculation of cabbage with WPP14: isopentyl alcohol, methyl disulfide and five unidentified compounds (Table 2). Using 6 commercially available compounds that were suggested by the GC-MS library search we were able to confirm the identification of four compounds, namely methyl acetate, ethyl acetate, isopentyl alcohol and isobutyl acetate, which eluted at RT 2.36, 2.78, 3.80 and 4.28, respectively. These phytochemicals were only produced by calls lily that was challenged with PC16. Similarly, we confirmed four compounds at RT 2.10, 2.78, 3.84 and 4.20 as ethanol, ethyl acetate, isopentyl alcohol
# Swarm of Drones in a Simulation Environment--Efficiency and Adaptation
## 4 Simulation Experiments  
During experiments, we carried out a series of tests where a swarm of drones navigated in a straight line from point A to point B (each point being 50 m apart). The experiments considered flights at varying speeds of 1, 2, 3, 4, and 5 m/s. Additionally, the impact of various levels of GPS error, including errors of 0 cm, 5 cm, 50 cm, and 100 cm, on the accuracy and safety of the swarm's flight was examined. The experiments anticipated different communication delays, at levels of 0 ms, 50 ms, 100 ms, 200 ms, 300 ms, 400 ms, and 500 ms. The aim of the experiment was drone swarm synchronization and coordination assessment. During the experiments two scenarios  
Maintaining a grid formation of 20 m \(\times\) 20 m with distances of 5 m between drones was considered: a grid formation with the leader in the upper left corner (Figure 4) (only two drones at a time focus on each other, each drone looks at the leader) and a formation with leaders propagating the flight trajectory, where each subsequent drone follows its predecessor (Figure 5). In the second scenario, the errors were expected to propagate as each drone in the sequence looked to its predecessor. Such a solution potentially amplifies the positioning and communication errors.  
The aim of these experiments was to determine the minimum distances that must be maintained between drones in a given formation to avoid collisions. The study results helped to determine how positioning errors and communication delays affect the safe minimum distance. The purpose of this work was to verify the mechanisms developed in simulation conditions (before tests with real drones in real-world conditions). Additionally, these studies allowed determining the control mechanism input data, which assured the safe execution of missions (the speed of the swarm's movement and the distance between drones). Based on real experiments, we assumed a safe distance of 2 m between drones to allow avoiding collisions, considering their physical sizes.  
Figure 3: Communication system and information exchange for swarm drone.
## 4 Performance Evaluation Results and Discussion
### Experiments and Results
#### 4.2.2 Result of Experiment 2
Figure 8: Performance when applying SDK-Injection on univariate data compared with Base-CNN with multivariate data: (**a**) accuracy when applying Base-CNN (sky blue) and SDK-Injection (red) to univariate inputs AF-D1 and AF-D2, respectively, compared with the accuracy of the Base-CNN with dual-variate input composed with AF-D1 and AF-D2 (yellow); (**b**) additionally, training time for the same situation. AF-D2 than when the Base-CNN with multivariate input is applied. Additionally, when the SDK-Injection is applied to AF-D1, all metrics except the precision are the same or higher than when the Base-CNN with multivariate input is applied. This shows the possibility that the SDK-Injection method can be applied to smart hospital or healthcare services in a wearable-based environment.