esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Latency Mitigation Strategies
### Initiatives, Standardisation Efforts, and Guidelines
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithms** & **Applicability** & **Pros** & **Cons** \\ \hline Visual enhancement & Perception & Improves situational awareness & Sensitive to noise and may not be suitable for dynamic data \\ \hline MPC-based & Control & Applicable for short and long delays & It is computationally expensive \\ Fuzzy logic & Control & Robust to uncertainties & Less precise \\ Active estimation & Control & Optimal and accurate solution & Complex, computationally expensive, heavily depends on system model \\ Adaptive-based estimation & Control & Robust to system dynamics & Complex, computationally expensive, Heavily depends on system model \\ Passive estimation or passivity-based & Control & Maintains stability of system & Does not have predictive capability \\ Local or onboard autonomy & Control & Backup, real-time response & Affected by delay and requires computation, maintenance, and updates \\ Supervisor control & Control & High-level commands & No continuous commands \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._Co-Pilot Level 2 [153], Mercedes-Benz's Distronic Level 2 [154] and Drive Pilot Level 3 [155], Audi A8 Level 3, Baidu Apollo Level 4 [156], Google Waymo's World's Most Experienced Driver Level 4 [157], Ford's and Volkswagen's Argo AI Level 4 [158], General Motors Level 4 [159], Uber [160], Tesla, and Mobileye [3; 4; 161]. Most level 2 systems rely on cameras and radar sensors, while levels 3 and 4 additionally use lidar sensors to perceive the environment. Regarding automated control, all these levels have distance keeping, lane following and keeping. The level 2 vehicles, in contrast to 3 and 4, do not have lane-changing autonomy [4]. A few of these initiatives have considered remote operations [14]. Examples include Nissan Seamless Autonomous Mobility [162], ARGO AI remote system [158], Aurora Teleassist [163], Voyage Telesisst [164], Zoxc TeleGuidance [165], and UBER remote system for remote guidance and assistance. Likewise, Valeo [166] has Drive4U Remote service and Waymo level 4 ADS has human input for fleet response. Meanwhile, companies such as Bosch [167], Baidu Apollo, Einride [168], Phantom Auto [169], Ottopia [170], Fernridge [171], and Vay [172] have considered remote driving solutions [14].
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 6 Conclusions  
This paper proposes a resource- and neighbor-aware observation transmission scheme (RNA-OTS) where each observation satellite periodically decides whether or not to exploit its own resource and the resource of the relay satellite. For the optimal decision of each observation satellite in a distributed manner, the operation of observation satellites is modeled by means of a constrained stochastic game, and a best-response-dynamics-based algorithm is devised to find the Nash equilibrium. The evaluation results demonstrate that RNA-OTS can decrease the delay to deliver the observation to the destination by up to 87% compared to a relay-satellite-based scheme while providing a sufficient low average utilization of computational and storage resources. Moreover, it can be observed that RNA-OTS optimizes its transmission policy by considering its operating environment (e.g., connection duration between the ground station and the observation satellite). In our future work, we will extend the proposed scheme in order to learn the optimal policy without prior information on the operational environment. In addition, we will conduct the route optimization of the observation satellites to further reduce the observation delivery delay.  
Conceptualization, H.K.; methodology, H.K.; validation, H.K.; formal analysis, H.K. and Y.K.; investigation, H.K. and Y.K.; resources, H.K. and Y.K.; data curation, H.K. and Y.K.; writing--original draft preparation, H.K.; writing--review and editing, H.K. and Y.K.; visualization, H.K.; supervision, Y.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation (NRF).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
Figure 7: Effect of the unit latency when handling one observation in the relay satellite on the average delay.  
**Acknowledgments:** This research was supported by the National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIP) (No. 2022R1F1A1063183).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
### Convolutional Neural Network
During the drilling process, the abrasion of the drill bits is much greater than that of the bearings and gears. The health condition of the drill directly affects the success or failure of drilling. Additionally, drilling is one of the significant processes of rock mechanics and mining engineering. Mining engineers are interested in identifying geological information by monitoring the vibration signals from the drilling process. In recent years, [PERSON] et al. reported the logical relations between rock characterization and acoustic frequencies by monitoring the acoustic signals generated during the drilling operation [20]. Furthermore, by analyzing acoustic signals, various physical characteristics of sedimentary rocks and carbonate rocks can be determined [20; 21]. [PERSON] et al. used the acoustic signal's root mean square (RMS) to identify the uniaxial compressive strength; the Brazilian tensile strength; the Schmidt rebound number; and the longitudinal wave velocities of sedimentary, metamorphic, and igneous rocks [22]. These approaches have significant potential for their future use in the industry for field identification and classification of rock. However, the premise of all of these applications is to use a non-damaging drill bit. Therefore, establishing a highly effective condition monitoring and failure detection system is essential and helps to reduce unnecessary material and time consumption and protects the machine from damage. This study demonstrates a drill bit failure diagnostic system by monitoring vibration patterns during the drilling process. Vibration signals were collected using a SmartCheck system, and drill bits with different health conditions were used. Short-time Fourier transform was performed on the raw data to create a spectrogram image for each measurement data set. After processing the data, CNN algorithms were applied for defect classification and diagnosis (Figure 1). Additionally, 1D CNN and 2D CNN were used for data processing. Finally, the characteristics of the two algorithms were compared.
## 1 Introduction  
Transportation infrastructure is at the core of an economy's development and the general life of civic societies. At the same time, rapid urbanization, the growing ownership of vehicles, and concerns for sustainable development have led to a rise in complications in managing these systems. For example, the transportation network in China comprises approximately 5.35 million kilometers of roads and 155,000 km of railways. All these put together create a vast network that faces many challenges, like the severe congestion of traffic, inequalities in the standards of infrastructure, and increasing maintenance costs [1]. The population and number of vehicles have registered a steep increase, especially in urban areas, putting a heavy burden on the available infrastructure. The need for such infrastructure far exceeds the supply, causing traffic jams that continue to worsen day by day, which are a waste of time and energy and are also contributory factors to environmental pollution. Added to this is the abominable condition of the roads, insufficient public transport systems, and inefficient traffic management that further worsen the problem [2]. However, traditional methods of infrastructure management are for the most part reactive; for instance, the damage or congestion of roads usually must reach a critical level before any attempt is made to resolve it. This has been a very costly and inefficient practice that can affect the lifespan and safety of infrastructure [3]. With increasing complexity and scale in transport systems, the need has emerged to adopt more innovative approaches, like digital twin (DT) technology, which may help enhance transportation infrastructure management, monitoring, and maintenance.
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 2 Methodology
### Selection of Measurement Device
#### 2.1.3 Radar  
Radar devices utilise electromagnetic waves that travel at the speed of light, 3 \(\times\) 10\({}^{8}\) m/s, for level measurement. Radar devices operate similar to that of the ultrasonic sensor, sending out a direct microwave signal from the device which, as shown in Figure 3, is reflected from the water surface and received back by the device [16]. The delay of the signal is proportional to the depth of the liquid and the signal is converted into a very accurate measure of the liquid level.  
Advantages of the non-invasive radar include that it is lightweight and easy to operate. The high frequency microwave signal is not affected by conditions such as humidity and mist, eliminating many of the false-positive readings returned by ultrasonic devices. The disadvantages include generation of false positives if the device is set up incorrectly and is more expensive than the alternatives.  
We provide a summary of the radar operating principal in Section 6 (Background). Based on the above, a radar was selected for non-contact measurement, and Figure 4 shows a typical manhole setup with a radar sensor attached at the top.  
Figure 3: Function of a non-invasive radar.
## 3 Results and Analysis
### Test 3: LED Current Control in Buck Mode  
This third test evaluates the 24 V commercial lead-acid battery UPS-BAT/VRLA-WTR under the same LED current and voltage conditions (Figure 12). Therefore, the converter must operate in buck mode.  
Figure 10(a) reports a behavior similar to that obtained with the 12 V and 18 V batteries, as the current reference of \(i_{LEDref}\) is the same. Therefore, the current of the LED \(i_{LED}\) and its voltage should be the same, but Figure 10(b) reports that the buck-boost converter behaves only as a reducer. This checks that the drivers impose the desired current for any  
Figure 11: Results for the LED current control in buck-boost mode with a 18-volt battery.  
voltage value in the battery, thus allowing proper regulation of the LEDs with commercial batteries of various values. Figure 11(c) shows that the duty cycle has a lower value than when used with 12 and 18 volt batteries, without saturation and with fixed switching frequency. Figure 11(d) shows the \(i_{L}(t)\) current, which has a greater ripple than the \(i_{L}(t)\) current compared to the previous two cases, which is due to the increase in voltage in the battery. In any case, the current \(i_{L}(t)\) effectively follows the reference \(i_{Lref}(t)\).  
Finally, for the three batteries, the system controls the current \(i_{LED}\), which maintains the luminous flow at the value desired by the user. In any case, the temperature compensation must be checked (Test 5). The current \(i_{L}(t)\) is also correctly controlled with the ZAD at a fixed switching frequency with the three batteries.
## 2 Materials and Methods
### Machine Learning Prediction and Performance Evaluation
#### 2.5.4 Cross-Validation  
Cross-validation was a statistical technique employed to assess the performance of machine learning models and select appropriate parameters [51; 52]. Among these techniques, K-fold cross-validation was the most commonly utilized approach. The dataset was randomly divided into K subsets, with K \(-\) 1 subsets used for training the model and the remaining subset used for evaluating the model's performance. This process was repeated K times, during which each iteration employed a different subset as the validation set. Finally, the results of the K evaluations were averaged to obtain the final performance indicator.  
The specific process is as follows (as shown in Figure 9).  
Data partitioning: The procedure of K-fold cross-validation includes segregating the training dataset into K subsets, wherein K = 5 is chosen in this study. Each subset is used as a part of the validation set, while the remaining K \(-\) 1 subsets constitute the training set.  
Model training and verification: For each iteration of cross-validation, the random forest model optimized using the DBO optimization algorithm is trained. K \(-\) 1 subsets are employed as the training set during each iteration, and one of the remaining subsets is used as the validation set to assess the model's performance.  
Performance evaluation: For each cross-validation iteration, the performance metrics of the model on the validation set, i.e., accuracy and precision, are recorded. Finally, the results of the K evaluations are averaged to obtain the final performance metrics as the evaluation results of the model.  
Parameters tuning: Cross-validation can be utilized to select appropriate parameters. By trying different parameter combinations and evaluating the model's performance using cross-validation results, the best-performing parameter combination can be chosen.  
Using cross-validation performance metrics provided a more objective reflection of the model's generalization ability. It effectively avoided overfitting or underfitting issues on local data and improved the model's generalization ability and stability. In this study, a 5-fold cross-validation method was employed, and the results from each iteration were recorded and statistically analyzed to further validate the reliability and accuracy of the constructed prediction model.
# Antioxidant and Antimicrobial Activity of Porcine Liver Hydrolyates Using Flavourzyme
## 2 Materials and Methods  
### Materials, Chemicals, and Apparatus  
Raw porcine livers were acquired from local meat industry (Carnicas M. Boo, San Cibrao das vinas, Ourense, Spain). Flavourzyme 1000L was supplied by Novozymes (Bagsvaerd, Denmark). The following bacterial species were distributed by Microkit (Madrid, Spain): _Escherichia coli_ (DSMZ 1103/WDCM 00013), _Listeria monocytogenes_ (MKTD 11994/WDCM 00019), _Salmonella enterica_ (MKTD 17058), _Staphylococcus aureus_ (MKTD 799/WDCM 00032), and _Pseudomonas aeruginosa_ (MKTD 1128/WDCM 00026). _Brochothrix thermosphacta_ strain (CECT 847) was dispensed by CECT (Valencia, Spain). Molecular weight cut off (MWCO) regenerated cellulose membranes (5, 10, and 30-kDa) were provided by Millipore (Jaffrey, NH, USA) and Amicon Stirred Cell 400 mL (76 mm membrane diameter) by Millipore (Munich, Germany). 2,2'-Azino-bis(3-ethylbenzothazoline-6-sulphonic acid) diammonium salt (ABTS) 98%, 2,4,6-Tri(2-pyridyl)-1,3,5-triazine (TPTZ) 98% and L-(+)-Ascorbic acid 98% were procured by AlfaAesar (Kandel, Germany). (\(\pm\))-6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox) 97% and \(\alpha\),\(\alpha^{\prime}\)-Azodiisobutytramidine dihydrochloride (AAPH) 98% were purchased from Acros Organics (New Jersey, USA). Folin-Ciocalteu's reagent, Iron (II) sulphate heptahydrate, iron (III) chloride hexahydrate, potassium peroxodisulphate, and sodium acetate were acquired by VWR chemicals. Potassium dihydrogen phosphate reagent grade, di-potassium hydrogen phosphate anhydrous extra pure, gallic acid monohydrate extra pure, hydrochloric acid 37% and sodium hydroxide 32% were supplied by Scharlau S.L. (Barcelona, Spain). Fluorescein sodium was purchased from Fisher (Loughborough, UK) and methanol from Chem-Lab NV (Zedelgem, Belgium). Gentamicin sulfate antibiotic was served by Biowset (Nuaille, France). The bacteriocin nisin produced by _Lactococcus lactis_ bacteria and 2,2-Diphenyl-1-picrylhydrazyl (DPPH) were procured by Sigma Aldrich (Munich, Germany).  
All spectrophotometric data were acquired using a UV-1800 Shimatzu spectrophotometer (Kyoto, Japan) and a Fluorometer (Synergy H1 Hybrid Multi-Mode Reader, BioTek Instruments, Inc., Winooski, VT, USA). Free amino acids were analyzed by Waters Alliance 2695 HPLC Separations Module with a Waters 2475 Multi Fluorescence Detector. The analysis was performed using a reverse-phase column (\(3.9\times 150\) mm, Waters AccQ-Tag amino acids).
## 3 Results and Discussions
### Final Products Characterization
#### 3.2.4 Total Phenolic Content of Final Products  
The content of polyphenols is closely related to the antioxidant capacity; baking causes partial loss of polyphenols and therefore of the antioxidant capacity [56; 60]. According to [PERSON] et al. [61] and [PERSON] et al. [64] values of total polyphenols in baked products are lower than in flour and dough.  
The lowest result obtained in this research was for the control sample--20.33 mg GAE/100 g (Table 8) which is in accordance with the value reported by [PERSON] et al. [7] (29.1 \(\pm\) 0.6 mg GAE/100 g).  
The addition of 10%, 20% and 30% SF increased the total phenolic content by 2.37 (48.33 mg GAE/100 g), 4.03 (82 mg GAE/100 g) and 7.39 (150.33 mg GAE/100 g) times compared to wheat-based products. Total phenols content of the samples supplemented with BW were higher compared to the product based on wheat flour, but lower than the SF final baked products (Table 8). This could be explained through the chemical composition of SF which was higher in total phenols content compared to BW (Table 5). No significant difference (\(p<0.05\)) in total phenols content were found between samples with guar gum and improver, respectively.  
The current state of art regarding the phenolic content in bakery products manufactured with BW, reported different values for total phenolic content. For instance, [PERSON] et al. [61] obtained a value of 12 \(\pm\) 24 mg GAE/100 g for control, 25 \(\pm\) 31 mg GAE/100 g for products with 15% buckwheat flour and 46 \(\pm\) 62 mg GAE/100 g for bread with 30% buckwheat.  
In the study carried out by [PERSON] et al. [64] the total phenolic compounds were 1.7 \(\pm\) 0.07 mg GAE/g for the control sample, 2.1 \(\pm\) 0.08 mg GAE/g for bread with 15% buckwheat and 2.65 \(\pm\) 0.1 mg GAE/g product with 30% buckwheat.  
To the best of our knowledge, regarding the determination of total polyphenolic content for the bakery products fortified with SF, no reports were found.
## 2 Materials and Methods
### Spectral Preprocessing  
Spectral preprocessing played a crucial role in improving the quality of spectral data by eliminating various types of noise present in the raw spectra [25]. This noise included background information from the sample, noise from the spectrometer itself, and stray light from the surrounding environment. Such noise could interfere with the signal and reduce the accuracy and reliability of the spectra. By applying various preprocessing techniques, such as smoothing, normalization, and denoising, the spectral data were effectively processed to enhance signal quality and minimize the impact of noise on subsequent analyses. This preprocessing established a solid foundation for further spectral analysis, quantitative modeling, and chemometric analysis. High-quality preprocessing not only improved the accuracy of the analytical results but also enhanced the robustness of the model, making it more adaptable under different conditions.
###### Abstract  
As various fields and industries have progressed, the use of drones has grown tremendously. The problem of path planning for drones flying at low altitude in urban as well as mountainous areas will be crucial for drones performing search-and-rescue missions. In this paper, we propose a convergent approach to ensure autonomous collision-free path planning for drones in the presence of both static obstacles and dynamic threats. Firstly, this paper extends the jump point search algorithm (JPS) in three dimensions for the drone to generate collision-free paths based on static environments. Next, a parent node transfer law is proposed and used to implement the JPS algorithm for any-angle path planning, which further shortens the planning path of the drones. Furthermore, the optimized paths are smoothed by seventh-order polynomial interpolation based on minimum snap to ensure the continuity at the path nodes. Finally, this paper improves the artificial potential field (APF) method by a virtual gravitational field and 3D Bresenham's line algorithm to achieve the autonomous obstacle avoidance of drones in a dynamic-threat conflict environment. In this paper, the performance of this convergent approach is verified by simulation experiments. The simulation results show that the proposed approach can effectively solve the path planning and autonomous-obstacle-avoidance problems of drones in low-altitude flight missions.  
static obstacles; dynamic threats; path planning; track optimization; jump point search algorithm; artificial potential field +
Footnote †: journal: Applied
## 6 Results
### Adjustments to CI and FI Propagation Models  
The values of PLE and the coefficients alpha (\(\alpha\)) and beta (\(\beta\)) are crucial for improving the accuracy of signal propagation predictions in real environments, which enhances the performance of wireless networks in vegetated areas. All values of PLE for the CI model and the coefficients (\(\alpha\) and \(\beta\)) and initial distance (\(d_{0}\)) for the FI model are displayed in Table 5.  
According to the values of FI(\(\beta\)) and CI (PLE), at a height of 6 m (tree trunk), the propagation loss for both VV and HH polarizations is similar. However, at heights of 12 m (beginning of the tree canopy) and 18 m (middle of the tree canopy), the VV polarization has the lowest decay with distance.
## 3 Results and Discussion
### Color Intensity Measurements  
The color intensity of the ferroin complex was measured in both mean gray and integrated density. Results show that both measurements successfully measured color intensity on silica substrate. Table 1 shows R\({}^{2}\) values calculated from calibration of Fe color intensity measurements in mean gray and integrated density. The average for mean gray measurements was 0.9752, while for integrated density measurements it was 0.9867. While in Figure 3 mean gray appeared to be a better tool of measurement, results of 10 experiments show a statistical difference between the measurement tools (Student's \(t\) test, \(p=0.018\)), indicating that measurements are generally better done in integrated density. In other studies, mean gray has been the choice of measurement when using this software due to the accurate results it yields [19; 49; 50; 51; 52; 53; 54; 55], while not much is reflected in the literature regarding integrated density as a color intensity measurement tool. Typically, integrated density takes into account the area of the selection for calculations. This way, if only a portion of the drop reaches the detection point, the area of the color development is used to correct for the calculated intensity.  
A trend was also observed where images from the box enclosure (higher background noise) were statistically best measured in mean gray, while those from the tabletop scan (lower background noise) were best measured in integrated density (Table 1). According to the literature [39; 56], integrated density captures bright and dim colors more accurately, while mean gray \"corrects\" the dim colors to be brighter and the brighter colors to be dimmer [39; 56]. Essentially, while integrated density corrects for area, mean gray corrects for brightness.