esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion on the Results
### Potential Undesired Compressor Failures  
Measured and analytically determined cooling water flow rates, including the water temperature increase, are represented in Figure 18. The comparison indicates that the measured flow rates are higher than the flow rates that were identified by calculations (except for the last measurement at 13:17). During this measurement, there was an unexpected closure of the regulating valve that resulted in a decrease in the pressure difference between the cooling water inlet and the water outlet. This reduced the rate of the cooling water flow into the intercooler. The flow rate decrease resulted in an increase in the cooling water temperature at the outlet from the aftercooler, as well as an increase in the temperature at the outlet from the cooling of the cylinders. If such a state lasted for a longer period of time, then it would result in an increase in the cooling water temperature at the outlet from the cylinders above 50 \({}^{\circ}\)C and in the emergency stop of the compressor.  
The water flow rate can be approximately controlled by using its dependence on the pressure difference between the cooling water inlet and outlet, as described by the formula, determined by the regression analysis between the pressure difference and the measured flow rate, as shown in Figure 5.  
\[Q_{V}=1.048\cdot\sqrt{\Delta p}-4.961, \tag{15}\]  
Figure 17: Recommended minimum cooling water flow rate, depending on the inlet water temperature.  
Figure 18: Measured and analytically identified minimum flow rate.  
Such dependence is only applicable within the pressure difference range from 80 to 255 kPa. The pressure difference depends on the degree of presence of impurities in the water circuit; thus, the procedure that is described above is only approximate.  
When the cooling water flow rate decreases, there is also an increase in the consumption of electric power input into the electric motor. An extreme case occurs when the cooling water supply is completely stopped. In such a case, the heat is no longer withdrawn and adiabatic gas compression occurs.
## 2 Related Work
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Reference** & **Year** & **Research Idea** & **Datasets** & **Feature Selection** & **Implementation** & **Accuracy** \\ \hline
[14] & 2025 & Disease detection in economically important Crops & PlantVillage (maize, apple, grapes, tomato, and bell pepper) & NA & 7 ML techniques (best results with RF) & 91.80\% \\ \hline
[15] & 2025 & Classification of soybean leaf disease diagnosis & PlantVillage, Al2018, Embra & NA & SWIN transformer + CNN & 98.00\%, 97.00\%, 76.00\%, and 92.00\% \\ \hline
[16] & 2025 & Impact of transfer learning on fruit detection & Fruits-360 & NA & CNN with VGG16, ResNet50, MobileNet-V2, and EfficientNet-B0 & 65\% to 99\% \\ \hline
[23] & 2025 & Intrusion detection & UNSW-NB15 and CIC-IDS2017 datasets & Hybrid of crow search algorithm and flower pollination algorithm & AdaBoost & 99.14\% and 97.98\% \\ \hline
[25] & 2025 & EEG signal-based emotion classification & DEAP and MAHNOB-HCI & Gray Wolf Optimization & XGBoost & 89.63\% and 84.54\% \\ \hline
[26] & 2025 & Leukemia classification & ALLID\_B1, ALLID\_B2, C\_NMC & Entropy-controlled firefly feature selection & Multi-class SVM & 99.64\%, 98.96\%, 96.67\%, and 98.89\% \\ \hline
[18] & 2024 & Fruit image classification & Fruits-360 & NA & CNN with EfficientNet-B0, MobileNetV2, and ResNet50V2 & 97.15\% \\ \hline
[17] & 2024 & A CNN-based implementation of fruit recognition & Fruits-360 & NA & CNN & 98.17\% \\ \hline
[28] & 2024 & UC MERCED land use dataset classification & UC MERCED LandUse & NA & VGG19, VGG16 & 95.07\% and 91\% \\ \hline
[27] & 2024 & Mango leaf disease classification using a hybrid-tuned neural network model & PlantVillage & Hybrid Coyote-Gray Wolf Optimization & NN model & 96.71\% \\ \hline
[20] & 2024 & Grape leaf disease detection & PlantVillage (Leaf flight, Black measles, Black not, and healthy) & Gray Wolf Optimization & Ensemble classifier & 95.69\% \\ \hline
[19] & 2024 & Plant leaf disease detection & PlantVillage & Hybrid ABC & HYgmABC & 98.53\% \\ \hline
[21] & 2024 & Plant leaf detection framework & Plant leaf diseases (apple scalp, apple black rot, apple cedar rust, and healthy apple) & RFO & SVM & 97.70\% \\ \hline
[24] & 2024 & Cloud-IDS by NI-FS techniques & UNSW-NB15, CIC-DDoS2019, and CIC Bell DNS & Highly of Grasshopper Optimization Algorithm (GOA) & Random Forest & 98\%, 99\%, and 92\% \\  & & EXF 2021 & and Genetic Algorithm (GA) & & \\ \hline
[29] & 2024 & COVID-19 detection using medical images & Custom dataset & Hybrid of cuckoo search optimization and learning-based optimization & XGBoost & 95.99\% \\ \hline
[22] & 2023 & Weed classification & Weed Detection & NA & RCNN & 99.40\% \\ \hline
[30] & 2022 & Plant leaf disease detection through CNN & Plant leaf diseases & NA & CNN & 98.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of research on classification and detection techniques.
## 3 Numerical Simulations
### Optimization of the Lengths of the Undulators  
After passing through the radiator, the well bunched electron beam is sent into the afterburner to lose again. According to Figure 3b, the bunching factor increases along the radiator of the ESASE section, giving rise to higher power in the afterburner. However, with an increased length of the radiator, the duration of the bunching profile is also increased, and the signal to noise ratio is dramatically decreased, as shown in Figure 3d. To achieve radiation pulse with high intensity and short duration, the lengths of the radiator (z1) and the afterburner (z2) need to be optimized. Simulations are performed for different cases and the results are presented in Figure 4. When z1 is constrained to 14 m < z1 < 16 m, the peak power from the afterburner can be over 1 GW and the pulse duration can be less than 400 as, with a proper length of the afterburner (0.6 m < z2 < 1 m). In the following simulations, the length of the radiator and the afterburner is chosen to be 14.76 m and 0.72 m, respectively.  
The power of the superradiance generated with the pre-bunched electron beam is proportional to the product of the beam current squared and the bunching factor squared: \(\text{P}\text{o}\text{I}^{\text{2b}}\)[43; 44]. The duration of the output pulse from the radiator is about 700 as, as shown in Figure 5a. In comparison, Figure 5a also shows the profiles of the current peak, bunching factor, and the \(\text{I}^{\text{2b}}\text{I}^{\text{2}}\) by the end of the radiator. Although the duration of the current peak and the bunching profile is 600 as and 1 fs, correspondingly, the distance between them leads to a much shorter duration of about 350 as compared with the \(\text{I}^{\text{2b}}\text{I}^{\text{2}}\) profile. Driven by the large bunching factor, intense radiation is immediately generated at the beginning of the afterburner. Figure 5b shows the radiation pulse from the afterburner, where the pulse duration is 396 as and the peak power is 1.47 GW.  
Figure 4: Output peak power (**a**) and pulse duration at the main current peak (**b**) from the proposed technique as a function of the lengths of the radiator and the afterburner.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 2 Performance Evaluation
### Parameters to Evaluate Radiation Performance  
Radiation effects are a vast topic [21; 22] even particularized for ADCs; this section provides a condensed summary that should be sufficient to understand radiation data presented afterwards.  
Radiation effects on electronic devices can be classified in two main categories: CE and SEE (cumulative and single event effects, respectively). CE, in turn, are classified as TID effects if they are caused by ionizing radiation, or as DD (displacement damage) if radiation is non-ionizing. On the other hand, SEE are discerned into destructive or non-destructive, and can also be referred as hard or soft errors, respectively.  
Figure 1: Applied methodology in the survey of radenv-ADCs.  
The radiation characterization of an ADC is performed by measuring the threshold of the physical magnitude provoking each effect. However, it is not necessary to reach the threshold if it is below the environmental specifications of the target application; in that case, the maximum tested value is reported. Bearable non-destructive effects are characterized such that the degradation (in case of CE) and the statistical error rate SER (in case of non-destructive SEE) during operation can be extrapolated; for those effects, the threshold and the behavior above it are reported.  
The performance under radiation of an ADC is bound to the technology and libraries of devices used to implement it; this information is critical to plan effectively the radiation test campaign of an ADC, where accelerated tests (with harsher radiation conditions than during operation) are required to predict its behavior throughout its lifetime in a reasonable time. Below in this section, we bind some radiation effects with recurrent devices used in ADCs' implementations.
## 1 Introduction  
Honey is one of the oldest foods used in traditional medicine for the treatment of different human ailments, including infectious diseases [1, 2]. The antibacterial activity of honey was reported by [PERSON] in 1892 [3], and many studies have described antimicrobial activity in honeys with different botanical origins and the underlying mechanisms [4, 5, 6]. Nowadays, the emergence of multi-drug-resistant bacteria has promoted the reintroduction of natural products, including honey, as antibacterial agents [7].  
The mechanism for the antibacterial activity of honey is not yet fully understood. It has been reported that degradation of bacterial DNA is promoted by the presence in honey of hydrogen peroxide, phenolics, flavonoids, and other unknown components that act as pro-oxidants [8; 9]. The sensitivity of bacteria to honey appears to be related to certain physicochemical characteristics [10] that include sugars, protein, melanoidin, and minerals, as well as water activity, acidity, viscosity, color, and conductivity [11; 12]. For example, it has been reported that the antibacterial effect of honey increases with increasing content of water and reducing sugars, while it decreases with increasing electrical conductivity (EC) [13]. Further, a high correlation between conductivity and mineral content with antibacterial activity that is boosted by the presence of essential oils has been described [12]. Methylglyoxal and bee defensin-1 have also been reported to be involved in the antimicrobial activity of some honeys [14]. In addition, the parameters that determine the antibacterial activity of honey depend on the specific bacterial strain that is targeted. For example, minimum pH values of 4.3, 4.0, and 4.4 are required for inhibiting the growth of the pathogenic bacteria _Salmonella_ spp., _Pseudomonas aeruginosa_, and _Streptococcus pyogenes_, respectively [5].  
Zantaz honey is a dark-colored, monofloral honey from the prickly shrub _Bupleurum spinosum_ Gouan (Apiaceae) that is produced in the Atlas Moroccan Mountains and is considered a traditional geographical specialty by the authorities. The scientific characterization of its health-promoting properties is of great interest in order to promote its consumption, which would help the economy of local low-income families. The cellular antioxidant and antiproliferative activities of this honey have been recently described [15]. The objective of this work was to determine the possible antibacterial activity of Zantaz honey, and to explore whether it correlates with physicochemical parameters.
# Microscopic Analysis of Structure and Wear for Metallic Materials Using SEM
## 2 Materials and Methods
### Wear of Metal Pipes  
Wear of metal materials can occur from various environmental influences, and it depends on the specific application and liquid environment. The typical main wear mechanisms of pipes for aqueous media are the following:  
* The abrasion--surface wear due to friction with other materials or particles in the internal (liquid) environment;
* The adhesive wear--friction between liquid versus metal surfaces can cause a change in profile (reduction in flow diameters) and surface geometry;
* The corrosion wear--change in the surface microstructure to brittle corrosion products (oxides) which are released and exfoliated into the liquid medium, thus becoming abrasive particles;
* The fatigue wear--gradual weakening of the material as a result of repeated loading with liquid and changes in temperature and pressure;
* The cavitation wear caused by the change in local pressures in different micro-volumes of the liquid, resulting in the implosion of bubbles that tear it from the surface due to its dynamic impact on the material, and thus it becomes an abrasive particle [6; 7; 10; 11].
## 3 Kinematics and Dynamics Analysis
### Kinematics  
To explore more about the mechanism and provide some motion properties for design, the kinematic model of the VGS finger mechanism was established, as shown in Figure 5. The position and length requirements of the linkages in the mechanism were given in order to have a straight-line trajectory. Several symbols and their corresponding physical meanings are shown in Figure 5.  
Figure 4: The architecture of the proposed VGS finger.  
Figure 3: The length of the elastic component under different situations.  
In order to have a straight-line trajectory, the lengths of the linkages should satisfy the following requirements [21].  
\[\|CF\|:\|C\|I\|:\|FG\|=1:1.5:6 \tag{1}\]  
The direction of the straight-line trajectory is perpendicular to the direction decided by point C and F. If a global coordinate system were set on point C, the distance between the straight-line trajectory and point C should be,  
\[d=5\|CF\| \tag{2}\]  
Since points I, G, F are on the same line, when the active linkage (represented by point C and D in Figure 5) is driven by a constant angular velocity (\(\omega=10^{\circ}\)/s), then the movement of point G, which is fixed on distal phalanx can be obtained by simulation, as shown in Figure 6.  
In the simulations, the lengths of the linkages meet the requirements indicated by Equations (1) and (2). Meanwhile, one set,  
Figure 5: Kinematic model of the VGS finger mechanism.  
Figure 6: Kinematic details of the distal phalanx.  
\[\|CF\|=100\text{ mm},\;\|CD\|=\|DG\|=450\text{ mm} \tag{3}\]  
According to Figure 6, when the finger mechanism starts to work, point G has no displacement in the y-direction, which indicates that the distal phalanx can move along a straight-line trajectory. In addition, the acceleration of the distal phalanx in both x and y directions are smaller than 0.1 m/s\({}^{2}\), which allows the proposed robot finger to avoid a big peak force during the grasping process and makes it possible for the proposed finger to deal with fragile objects.
# The Bearing Performance and Sectional Design Method of Yielding U-Shaped Steel Support
###### Abstract  
The bearing capacity and yieldable performance of yielding U-shaped steel support are difficult to be fully exerted in roadways under complex conditions, and serious deformation and damage occur frequently. Taking the mining roadways of Tangkou Coal Mine in a kilometer-deep well as the engineering background, this paper summarizes and analyzes the typical failure modes of the on-site yielding U-shaped steel support. By utilizing the independently developed full-scale arch frame test system, the bearing performance tests of arch frames with different sectional methods were carried out. The results show that compared with the three-section U-shaped steel support, the yieldable performance of the four-section support is increased by 21.8%, while the bearing capacity is only decreased by 1.9%. Furthermore, numerical tests on yielding U-shaped steel support under different load patterns and different cross-sectional forms were conducted to clarify the deformation characteristics and internal force distribution laws of U-shaped steel support under complex stress conditions. Finally, a sectional design method for yielding U-shaped steel support and on-site engineering suggestions were put forward. Based on this methodology, it is feasible to optimize the support structure scheme that better matches the engineering geological conditions, thereby fully utilizing the yielding characteristics and load-bearing capacity of the support. This approach effectively prevents premature local failure of the support, extends its service life, and enhances the safety of roadway support engineering while achieving significant economic benefits.  
deep soft rock; yielding U-shaped steel support; bearing performance; sectional design +
Footnote †: journal: Applied sciences
## 2 Numerical Methods and Theoretical Model  
### Governing Equations  
In this paper, the thermal effect is neglected, mixture multiphase is applied. The continuity and momentum equations are given below.  
\[\frac{\partial\rho_{m}}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho_{m} \mathbf{U}_{m})\ =\ 0 \tag{1}\]  
\[\begin{array}{cc}\frac{\partial(\rho_{m}\mathbf{U}_{m})}{\partial t}+&\frac{ \partial(\rho_{m}\mathbf{U}_{m}\mathbf{U}_{m})}{\partial x_{j}}\ =\ -\
abla p+\rho_{m}\mathbf{g}+\mathbf{F}\\ &+\
abla\cdot\left[\mu_{m}\big{(}\
abla\mathbf{U}_{m}+\
abla\mathbf{U}_{m}^{T} \big{)}\right]+\
abla\left(\sum\limits_{k\ =\ 1}^{n}\alpha_{k}\rho_{k}\mathbf{U}_{dr,k}\mathbf{U}_{dr,k}\right)\end{array} \tag{2}\]  
where \(\rho_{m}\) is the mixture density, \(\mathbf{U}_{m}\) is the mass-averaged velocity, \(\alpha_{k}\) is the volume fraction of phase \(k\).  
\[\mathbf{U}_{m}\ =\ \frac{\sum\limits_{k\ =\ 1}^{n}\alpha_{k}\rho_{k}\mathbf{U}_{ k}}{\rho_{m}} \tag{3}\]  
\[\rho_{m}\ =\ \sum\limits_{k\ =\ 1}^{n}\alpha_{k}\rho_{k} \tag{4}\]where \(n\) is the number of phases, \(\mathbf{g}\) is the acceleration of gravity, \(\mathbf{F}\) is the body force, \(\mu_{m}\) is the viscosity of the mixture, \(\mathbf{U}_{dr,k}\) is the drift velocity for secondary phase \(k\).  
\[\mu_{m}\ =\ \sum_{k\ =\ 1}^{n}\alpha_{k}\mu_{k} \tag{5}\]  
\[\mathbf{U}_{dr,k}\ =\ \mathbf{U}_{k}-\mathbf{U}_{m} \tag{6}\]  
Transport equation for vapor volume fraction was used to simulate vapor generation and condensation rate in the cavitating flow, expressed as follows [16; 17; 18]:  
\[\frac{\partial\alpha_{v}}{\partial t}+\frac{\partial}{\partial x_{i}}(\alpha_ {v}u_{i})\ =\ F_{vap}\frac{2\alpha_{muc}(1-\alpha_{v})\rho_{v}}{R_{B}}\sqrt{\frac{2}{3} \frac{P_{v}-P}{\rho_{l}}}-F_{cond}\frac{3\alpha_{v}\rho_{v}}{R_{B}}\sqrt{ \frac{2}{3}\frac{P-P_{v}}{\rho_{l}}} \tag{7}\]  
where \(\alpha_{v}\) is the vapor volume fraction, \(R_{B}\) = 1 \(\times\) 10\({}^{-6}\) m is the radius of bubble, \(F_{vap}\) = 50 and \(F_{cond}\) = 0.001 are the empirical evaporation and condensation coefficients respectively. \(\alpha_{muc}\) = 5 \(\times\) 10\({}^{-4}\) m is the nucleation site volume fraction. \(P\) ad \(P_{v}\) are the local far-field pressure and saturation vapor pressure respectively, and \(\rho_{l}\) is the liquid density.
# Analysis of Traffic Organisation in the Kiss-and-Fly Zone of Krakow Airport: Eye-Tracking Study
## 5 Confusion Points of K&F Zone of Krakow Airport
### Exit from the K&F Zone
Both of them did not focus on any of four considered elements of the traffic organisation but searched for other stimuli. Unfortunately, perhaps due to these unfavourable weather conditions, nothing attracted their attention, and that is why they missed the first exit and used the second one at the last possible moment. When speaking about good conditions, first of all, we should mention one driver who also did not observe any traffic organisation stimuli and also had a similar course of action with the same result--the choice of the second exit. Three participants of Group II behaved similarly and logically: each of them conducted only one fixation on the 'turn left' arrow and then consequently turned left using the first exit. One driver looked only at the 'go straight' arrow and went to the second exit, which also could be considered as a logical behaviour. Apart from logical cases, there were also two confusing situations. In the first case, the driver looked first at the 'turn left' arrow and then at the 'go straight' arrow: the first exit was the final choice, but according to the eye-tracking data, the participant's doubts about such a decision are visible. In the second case, the driver had only three fixations on the 'turn left' arrow, but he finally selected the second exit: looking through the data, it is possible to conclude that in comparison to the other participants, this driver was paying attention to many more details of road surroundings and probably, in such way, missed the first exit. Summarising, even drivers familiarized with the investigated area still met problems and confusions, which confirms the necessity to improve the situation with the traffic organisation and signage. All three drivers who knew the route and the airport very well (Group III) chose the first exit regardless of sunshine glare conditions: two out of three of them looked at the 'left arrow'. This confirms that drivers' experience supports mainly the choice of the first exit, which is intended to be a logical one according to designers. Therefore, such a situation differs from the concept of self-explanatory roads [46]. A separate issue of the exit from the K&F zone is road sign number 12 (Figure 10). Although this sign is dedicated for drivers using the roadway leading directly to long-term car parking, it was observed by seven drivers from both Group I and Group II. Maybe it attracted their attention because of its size and/or its configuration, but in any way, such a situation should not be considered a normal one.