esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 5 Novel Thermal Practices
### Radio Frequency (RF)
#### 5.2.2 Application of Radio Frequency in Meat Products  
As per different examinations, the impact of fat level, both with and without the expansion of salt, on the cooking design and physicochemical properties of ground pork parties cooked by microwave energy has been found. The authors observed that tempera  
\begin{table}
\begin{tabular}{c c c} \hline
**Properties** & **Radio-Frequency** & **Microwave** \\ \hline Work frequency & 13.56, 27.12, 40.68 MHz & 915, 2450 MHz \\ In vacuum wavelength & 22.1, 11.1, 7.4 m & 0.33, 0.12 m \\ In tap water (penetration depth) & 1.58, 0.79, 0.53 m & 0.02, 0.01 m \\ Prime heating mechanism & Migration of ionic charge & Dipole H\({}_{2}\)O molecule agitation \\ System construction & Simple & Complicated \\ \hline \end{tabular}
\end{table}
Table 2: Differentiation of major properties between radio frequency (RF) or microwave heating in the food industry [24].  
tures at the edge of the pork patties expanded quicker than those at the middle or in the mid-manner positions inside the microwave cooker. Absolute cooking loss, drip loss, and the decrease in diameter or thickness were higher in patties with 20% fat contrasted with those with 10% fat content, along these lines demonstrating the impact of fat substance and salt on cooked meat utilizing microwave energy. The radio waves has longer wavelength and low frequency as compared to microwaves thus enabling them to penetrate deeply in to product resulting in the uniform heating.In this sense, RF is more reasonable and powerful for huge breadth staples such as meat items [1,42].  
Obviously, the nature of heated items by RF turns into the critical factor. [PERSON], [PERSON] and colleagues, 2016, explored the impact of RF heating on the color, water holding limit, and surface of three kinds of meat items (communuted, ground or muscle). Their examination tracked down that the edible nature of some meat items was unfavorably influenced, particularly the texture [43]. Different authors compared the quality of meat items prepared by RF heating and steam heating. Curiously, they found that meat warmed by RF had more enthusiastic consistency than that heated by steam. Additionally, specialists had the option to recognize RF-cooked and steam-cooked samples. Regarding time, a shorter cooking time of the hams was needed with RF cooking [44]. Despite the fact that reviews have discovered that RF cooking enjoyed benefits of a more limited cooking time, decreased juice loss, and satisfactory color and surface, the viability of RF cooking on the inactivation of E. coli in ground hamburger has made a correlation between the timeframe of realistic usability of ground meat cooked by RF and that by heated water bath. They discovered that the two strategies were powerful in lessening microbial defilement; in any case, RF heating required a more limited cooking time and brought about more uniform heating, and along these lines, the RF cooking of meat is best in the meat industry [1].
# New Methods for Assessing External Sulfate Attack on Cement-Based Specimens
## 2 Materials and Methods
### Sampling and Exposure Conditions  
After one month of curing in water, the specimens were immersed (to a depth of 1 cm) for a period of 24 weeks in a solution of Na\({}_{2}\)SO\({}_{4}\) at a concentration of 15 g per liter, which was demonstrated by several studies to be the most appropriate for ESA testing [10,11]. The immersion depth of the specimens was only 1 cm, corresponding to a partial immersion usually referred to as semi-immersion. The rest of the sample depth (4 cm) was exposed to air. The test solution was maintained at a pH of 8, which is believed to be representative of field exposure conditions, especially in the case of partially immersed RC structures.  
As part of the acceleration method, it is necessary to maintain pH at 8 (\(\pm\)0.1), and so the set-up shown in Figure 2 was developed and used with a titration system injecting sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) solution at 0.02 M. This technique ensures that the sulfate ion concentration remains approximately constant throughout the entire experiment. The specimens were exposed in baths connected to a central underside bath with a capacity of 80 L where the titration was performed. In the central bath, a 750 L per hour pump delivers the solution to the exposure baths at the same concentration and flow rate as the central bath. An additional supply of sulfate ions was provided by renewing the solution every two weeks. Detailed information about the developed device can be found in [10].  
Figure 1: Sample preparation: (**a**) cement paste cylinders obtained after casting; (**b**) rotation device used to treat the cylinders and avoid bleeding; (**c**) cylinder cutting; and (**d**) coated specimen.  
Figure 2: (**a**) Semi-immersion exposure; (**b**) photo of the pH-control device developed in the laboratory and used for this study.
## 4 Application to a Plate Girder Bridge under Vehicle Load  
### Collecting Training and Test Data  
In Section 3.1., the proposed optimization method was verified using a simple beam. In this section, an I-shaped steel plate 5-girder bridge was utilized for verification of the proposed optimization method. [PERSON] et al. [18] confirmed the predictability of displacements from the strains of a bridge under vehicle load in a previous paper. Eleven displacements were predicted from eleven strains per girder. In this paper, the number and locations of strain-measurement points needed to predict eleven displacements for the same bridge model were optimized by GA. The specifications of the bridge model are shown in Table 4.  
Figure 8: Overall displacements shape at t = 1 s (max. displacement).  
Figure 7: Displacements over time at the beam center for test dataset.  
Three types of vehicles--passenger cars, buses, and trucks--were considered for vehicle load. Additionally, it was assumed that they moved at constant speeds (40 km/h, 60 km/h, and 80 km/h) across the bridge. The numerical models of cars, buses, and trucks were made by vehicles proposed by [PERSON] and [PERSON] [56], [PERSON] et al. [57], and [PERSON] [58]. The numerical model was verified by comparison with experimental values [59; 60]. Figure 9 shows the numerical bridge model and section of girder.  
Utilizing a Pearson type III traffic distribution theory [61], a total of 25 load scenarios were created for each vehicle speed. Using the 25 load scenario for each speed, numerical analysis was carried out. From the analysis, eleven strains and displacement measurement points were selected at uniform intervals of 3.6 m below the flange of each girder (Figure 10) to obtain ANN training and test data. Therefore, a total of 55 strains (11 points \(\times\) 5 girders, input) and displacements (11 points \(\
## 2 Materials and Methods
### Research Procedure  
Power and energy consumption measurements were carried out using the HIOKI PW 3390 power analyzer [36], equipped with three HIOKI CT6843A current probes. The power analyzer was powered by an additional 12 V 62 Ah battery via a WHITENERGY 800 W inverter. The total weight of the tested vehicle was 38.3 kg.  
Figure 15: The information flow diagram (**upper figure**) and the simplified block diagram (**lower figure**) of the control system utilizing the camera.  
The power analyzer and current probes used were characterized by the measurement accuracy outlined in Table 2. The relative error in the measurement of maximum power was \(\pm\)4%.  
The vehicle moved within a room along a specified path measuring 16.0 m in length, as shown in Figure 16.  
The tests were conducted in a laboratory at an ambient temperature of 25 \(\pm\) 3 \({}^{\circ}\)C.  
Current measurements were taken simultaneously on the wires powering the entire vehicle (control system along with the drive system) and the wires powering both motors. Additionally, the voltage on the battery and both motors was measured using the power analyzer. These parameters were recorded at a frequency of 10 Hz.  
For each sensor, two distances (250 mm and 350 mm) from the vehicle's drive axis with the drive wheels were considered (Figure 9). The distance of the reflective optocouplers from the vehicle's drive axis in the horizontal plane is referred to as SDt (Sensor Distance transceiver), while the distance of the camera lens from the vehicle's drive axis is referred to as SDc (Sensor Distance camera).  
First, tests were conducted on the vehicle equipped with reflective optocouplers. The vehicle was placed on the transit path as shown in Figure 16. Then, the measured parameters were registered in the power analyzer, and the drive system was activated. For each distance SDt, three measurements were performed. Based on these, the average values of selected parameters were calculated, and analyses of instantaneous power and energy consumption were carried out.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Apparatus** &
\begin{tabular}{c} **DC Power Accuracy** \\ **(+\% of Reading \(\pm\) \% of Full Scale)** \\ \end{tabular} \\ \hline Power analyzer HIOKI PW 3390 & \(\pm\)0.05\% rdg. \(\pm\) 0.07\% f.s. \\ Current probe HIOKI CT6843A & \(\pm\)0.20\% rdg. \(\pm\) 0.02\% f.s. \\ Total accuracy & \(\pm\)0.25\% rdg. \(\pm\) 0.47\% f.s. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power measurement accuracy.  
Figure 16: Dimensions of the movement path.  
In the second phase, a camera was installed on the vehicle. Similarly, for each value of SDc, three passes were carried out. The subsequent course of the tests was identical to that of the vehicle equipped with reflective optocouplers.
# PrismParser: A Framework for Implementing Efficient P4-Programmable Packet Parsers on FPGA
###### Abstract  
The increasing complexity of modern networks and their evolving needs demand flexible, high-performance packet processing solutions. The P4 language excels in specifying packet processing in software-defined networks (SDNs). Field-programmable gate arrays (FPGAs) are ideal for P4-based packet parsers due to their reconfigurability and ability to handle data transmitted at high speed. This paper introduces three FPGA-based P4-programmable packet parsing architectural designs that translate P4 specifications into adaptable hardware implementations called base, overlay, and pipeline, each optimized for different packet parsing performance. As modern network infrastructures evolve, the need for multi-tenant environments becomes increasingly critical. Multi-tenancy allows multiple independent users or organizations to share the same physical network resources while maintaining isolation and customized configurations. The rise of 5G and cloud computing has accelerated the demand for network slicing and virtualization technologies, enabling efficient resource allocation and management for multiple tenants. By leveraging P4-programmable packet parsers on FPGAs, our framework addresses these challenges by providing flexible and scalable solutions for multi-tenant network environments. The base parser offers a simple design for essential packet parsing, using minimal resources for high-speed processing. The overlay parser extends the base design for parallel processing, supporting various bus sizes and throughputs. The pipeline parser boosts throughput by segmenting parsing into multiple stages. The efficiency of the proposed approaches is evaluated through detailed resource consumption metrics measured on an Alveo U280 board, demonstrating throughputs of 15.2 Gb/s for the base design, 15.2 Gb/s to 64.42 Gb/s for the overlay design, and up to 282 Gb/s for the pipelined design. These results demonstrate a range of high performances across varying throughput requirements. The proposed approach utilizes a system that ensures low latency and high throughput that yields streaming packet parsers directly from P4 programs, supporting parsing graphs with up to seven transitioning nodes and four connections between nodes. The functionality of the parsers was tested on enterprise networks, a firewall, and a 5G Access Gateway Function graph.  
SDN; packet parser; P4 language; FPGA; overlay +
Footnote †: journal:
## 2 Materials and Methods
### Data
#### 2.2.4 MODIS Data  
The MODIS sensor is carried by two satellites, Terra and Aqua. Data products are timely and abundant. In the field of crop yield prediction, MODIS data are used more commonly. In this study, _EVI_, _GPP_ and _SAVI_ from MODIS data were selected.  
Enhanced Vegetation Index _EVI_, an improved and optimized version of NDVI, can more accurately reflect vegetation growth changes in areas with high vegetation cover [37]. MOD13A2 V6 product provides _EVI_ with an average of 16 days and 1 km resolution. Equation (1) shows the formula for _EVI_, where _NIR_, _RED_ and _BLUE_ are the near-infrared and red light bands provided by the MOD13A2 V6 product.  
\[EVI=2.5\times\frac{NIR-RED}{NIR+6.0 RED-7.5 BLUE+1} \tag{1}\]  
Gross Primary Productivity _GPP_, which represents the total amount of organic carbon fixed by photosynthesis per unit time per unit area of green plants, is widely used in crop yield estimation [38]. The MOD17A2H V6 product provides 8 days of cumulative total primary productivity data with 500 m resolution. Equation (2) shows the formula for _GPP_, where _NIR_, _RED_ and _BLUE_ are the near-infrared and red light bands provided by the MOD13A2 V6 product. Equation (2) shows the formula for GPP, where _APAR_ is photosynthetically active radiation absorption ratio by vegetation, and \(\epsilon\) is the realistic light energy utilization based on the _GPP_ concept.  
\[GPP=APAR\times FPAR\times\epsilon \tag{2}\]  
The soil-adjusted vegetation index _SAVI_ is also commonly used for crop yield prediction with an increased soil adjustment factor to mitigate the effect of soil noise on the vegetation index [39]. MODIS products do not provide the relevant data directly. It is obtained by waveband calculation. Equation (3) shows the formula for _SAVI_, where \(L\) isthe soil conditioning factor, and \(NIR\) and \(RED\) are the near-infrared and red light bands provided by the MOD13A2 V6 product, respectively.  
\[SAVI=(1+L)\frac{NIR-RED}{NIR-RED+L} \tag{3}\]
# Laser Scanning versus Wide-Field--Choosing the Appropriate Microscope in Life Sciences
## 6 Representative Applications
### Small Organisms  
For imaging small organisms up to about 1 mm in size, Light Sheet Fluorescence Microscopy (LSFM), as well as Confocal Laser Scanning Microscopy (CLSM), have proved to be appropriate methods. As a further technique, Optical Projection Tomography (OPT) should be mentioned, where a sample is illuminated from various sides, and projections from individual planes are calculated. These projections are further combined in order to recover a high-quality 3D image (for a review, see [61]). However, in most cases, either high transparency or, again, the application of clearing techniques is required, if viability is not a main criterion.  
Examples are given in Figure 11 of a copep stained with Acridine Orange (imaged by light sheet microscopy) and a tick (autofluorescence, imaged by confocal laser scanning microscopy). Specimens are kept in glycerol with a refractive index n = 1.473  
Figure 10: Light Sheet Fluorescence Microscopy of a 1 mm-thick mouse brain slice stained with Sytox Orange (DNA dye) and cleared with the Cubic clearing method [58]. A z-stack of 31 images with 50 \(\upmu\)m spacing was recorded. Representative images from the stack are depicted at depths of 0, 250, 500, and 750 \(\upmu\)m; scale bar: 500 \(\upmu\)m. Images recorded in cooperation with [PERSON] and [PERSON], IMB Mainz.  
well suited to the refractive indexes of the organisms, as well as the surrounding glass capillaries. For further details, the reader is referred to [62], also describing the rotation of the specimens within a set of two glass capillaries (a cylindrical rotatable capillary surrounded by a fixed rectangular capillary with a glycerol immersion).  
Microscopy of small organisms includes the large field of developmental biology, wherein particular embryos of zebrafish, insects, and even mammalians (mice) are usually observed over a longer period. Due to its low light exposure, Light Sheet Microscopy (LSFM) is often the method of choice (for a review, see, e.g., [63; 64; 65; 66]), but also Multiphoton Laser Scanning Microscopy (with its high penetration depth in tissue) is reported in the literature [67]. Recent developments include a combination with super-localization microscopy [68], real-time imaging [69], and multi-view light sheet microscopy [70]. A detailed description of all these methods and applications, however, would exceed the scope of this manuscript; therefore, the reader is referred to more specific literature.
## 4 Test and Analysis Methods
### Gray Correlation Analysis (GCA) Method  
Gray correlation analysis (GCA) is a relatively new factorial analysis method that can quantitatively assess the degree of correlation among factors in the system dynamics. The GCA method is a combination of quantitative and qualitative analyses. The basic theory of GCA is to determine the degree of correlation between factors based on the degree of similarity in series curves. GCA has the advantage of a small sample size and less calculation and, therefore, is generally used in many areas including natural and social sciences [22; 23]. The major steps of GCA are as follows:  
1. Determination and standardization of reference series and comparative series. Determine reference series: \(X_{0}=\left\{\left.X_{0}(k)\right|\,k=1,2,\cdots n\right\}\) Determine comparative series: \(X_{i}=\left\{\left.X_{i}(k)\right|\,k=1,2,\cdots n\right\}i=1,2,\cdots n\) Standardize reference series: \(X_{0}^{\prime}=\left\{\left.X_{0}(k)/\overline{X_{0}}\right|\,k=1,2,\cdots n\right\}\) Standardize comparative series: \(X_{i}^{\prime}=\left\{\left.X_{i}(k)/\overline{X_{i}}\right|\,k=1,2,\cdots n \right\}i=1,2,\cdots n\)
2. Calculation of the differences: \(\Delta_{i}(k)=\left|X_{0}^{\prime}(k)-X_{i}^{\prime}(k)\right|\)
3. Calculation of the maximum and minimum differences: \(M=\max\limits_{i}\max\limits_{k}\Delta_{i}(k)\); \(M=\min\limits_{i}\min\limits_{k}\Delta_{i}(k)\)
4. Calculation of correlation coefficient: \(\xi_{i}(k)=\frac{m+\rho M}{\Delta_{i}(k)+\rho M}\), \(\rho\in(0,1)k=1,2,\cdots n\), \(i=1,2,\cdots n\)
5. Calculation of relevancy: \(\xi_{i}=\frac{1}{n}\sum\limits_{k=1}^{n}\xi_{i}(k)\), \(i=1,2,\cdots n\)
6. Ranking of the calculated relevancies: the larger the relevancy \(\xi_{i}\), the higher the degree of correlation between comparative series \(X_{i}\) and reference series \(X_{0}\).
7. Calculation of correlation degree (\(\gamma\)): \(Y_{i}=\sum\limits_{k=1}^{5}b_{ij}\times(\text{test result of each index})\), \(i=1,2,\cdots 9\) \(b_{ij}=\frac{\text{the proportion of each index}}{\text{the difference between max and min valus of each index}}\), \(j=1,2,3,4,5\)
# Study of Gravelly Soil Core Material Using a Large-Scale Triaxial Wetting Test
## 3 Results
### Discussion on Wetting Mechanism  
The interaction of water with soil is what causes wetting deformation; as water enters soil particles, the strength and coefficient of the friction of the particles tend to decrease [14]. This weakening of the material is thought to be the source of wetting deformation. During the wetting process, soil particles soften upon encountering water, leading to changes in the original soil structure. Pre-existing microcracks within the soil particles may also rapidly expand, causing particle fracturing and a rearrangement into a new stable structure [15; 16].  
As shown in Figure 7, the dry specimen is in an equilibrium state before wetting. When wetting occurs, the strength of the specimen is reduced due to softening, the equilibrium state is broken, and additional stresses within the particles are generated. Thus, the specimen will be deformed until it reaches a new equilibrium state again under a constant external load. During this process, the particles are broken and rearranged; the weakened bearing capacity is reinforced. In the process of wetting, the sample is subjected to a cycle of water immersion, softening, particle breakage, and structural rearrangement. Due to the external load acting on the specimen being kept constant, the reduced bearing capacity due to the material weakening is equal to that restored via the rearrangement of the specimen particles. The specimen is returned to its pre-wetting strength through the two effects. Therefore, wetting does not affect the final bearing capacity of the specimen.  
In the dry-state loading stage, particle rearrangement can increase the compactness of the specimen, leading to a denser initial state when wetting begins under a high stress level. Under high axial stress, there can be significant particle rearrangements, resulting in greater wetting deformation. However, the wetting direction is limited by high axial stress; the wetting deformation increase along the stress direction may be relatively small. Despite the particle rearrangement and denser initial state, wetting deformation may not exhibit a large change in the stress direction. Conversely, at low stress levels, the wetting direction is more free, allowing for a more pronounced incremental change along the stress direction. In this case, the wetting deformation may exhibit a more noticeable change along the applied stress direction. Therefore, the direction of wetting is significantly affected by the stress level.