esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 1 Introduction
Microbiological food spoilage is caused by the growth of microorganisms which produce enzymes that lead to objectionable by-products in the food. Chemical food spoilage occurs when different components in the food react with each other or with some added component which alter the food's sensory characteristics. Examples of this include oxidation, enzymatic browning, and nonenzymatic browning. Physical food spoilage results when moist foods are excessively dehydrated, or dried foods absorb excessive moisture. Among all foods, fish is one of the most important commodities, which is popular all over the world due to its high nutritional value, being rich in proteins, vitamins and essential amino acids. In addition, fatty fish species are acknowledged as a natural food resource of omega-3 polyunsaturated fatty acids (PUFAS), such as eicosapentaenoic acid (EPA) and docosahexaenoicenoic acid (DHA), which cannot be synthetized by the human body and must be included in the diet. However, fish is highly perishable with a short shelf-life and depends heavily on post-harvest preservation methods, which is a challenge for the fish industry to avoid microbial spoilage while retaining high sensory quality parameters. Nowadays, consumers are conscious of healthy food products with enhanced safety, great nutritional value, premium sensory quality, apparent freshness and less or no chemical additives. To meet consumers' preferences, non-thermal food processing technologies, such as high-pressure processing (HPP), ultrasound (US), pulsed electric fields (PEF), pulsed light (PL), cold plasma (CP) and ozone have attracted extensive attention from the food industry as well as researchers. These technologies have been shown to be effective on the inactivation of spoilage and pathogenic microorganisms while maintaining nutritional and sensory properties for a wide range of food products, such as fruit and vegetables, fish, or fresh and ready-to-eat meat products [6].
# Application of Bonded-Block Models to Rock Failure Analysis
## 4 Analysis of Underground Structures
### Underground Opening Model  
The circular tunnel model shown in Figure 3 was analyzed considering the two types of bonded-block patterns described in the previous section, the Voronoi polygon and triangular-block shapes, with the properties obtained in the calibration process. The elastic blocks surrounding the bonded-block model were assigned a global Young's modulus of 20 GPa. A vertical overburden stress of 15 MPa was considered, with the horizontal stress component equal to half this value. Therefore, the largest compressive hoop stresses are expected to develop at the tunnel springline. Under these stresses, tunnel excavation without including any support leads to the failure of the blocks in the stressed sidewall regions. The failure mechanisms obtained by the two models involve block movements in the same region, but they are clearly influenced by the type of discontinuity network. The polygonal-block system (Figure 6a) is more likely to develop vertical fractures, followed by the failure of the columns into the opening. The triangular system (Figure 6b) allows more continuity of sliding, inducing the formation of a wedge that progressively detaches. The choice of the block pattern is an open issue that still requires further research. It is necessary to extend the comparative assessment of the performance of the various types of random networks intended to represent block fracture to practical situations of interest. It is also important to analyze how this internal block fracture system interacts with rock mass discontinuities, namely, when they are generated as discrete fracture networks.  
An alternative tunnel shape with vertical side walls was also analyzed. The failure modes obtained with the two types of bonded-block patterns are compared in Figure 7. The development of vertical fractures parallel to the walls is dominant, with the triangular pattern again showing more sliding on inclined joints.  
Figure 5: Numerical uniaxial compression test. (**a**) Triangular-block pattern; (**b**) failure mode.  
Figure 6: Failure modes of circular tunnel. (**a**) Voronoi block model; (**b**) triangular-block model.
###### Abstract
IOSs have been widely used in recent years in the manufacture of implant prostheses [22]. In the digital impression process, after removing the healing abutment (HA), the first scan is performed to record the transgingival profile. After the scan body is connected, additional scanning is performed to accurately reproduce the three-dimensional (3D) position of the implant. However, the transgingival profile is less important when implants are placed in posterior areas, and gingival molding using temporary teeth is rarely performed due to the aesthetics being far less important. In addition, after removing the HA, there is concern that the accuracy of the scan may be reduced because only residual ridges or structures with motion frenum remain and landmarks for use in the bite registration process disappear. Therefore, some clinicians perform a primary scan with the HA in the posterior region. In such cases, it is necessary to scientifically verify whether the presence of the HA improves the accuracy during intraoral scanning and buccal bite registration, which is used to align the upper and lower arches using the virtual interoclusal record (VIR). While many studies have investigated the accuracy of IOS scans, investigations of the VIR have been insufficient [23; 24]. In particular, there is a need for investigations of the effects of landmarks on the accuracy of the VIR on dedentulous areas [18]. The present study aimed to determine the accuracy of intraoral scanning data and the jaw relationship between quadrant maxillary and mandibular models according to whether or not there is an HA at the implant placement site, and elucidate the distribution of distortion on the arch span when using three types of IOSs. The first null hypothesis of this study was that the accuracy of the scan data does not differ significantly between the presence or absence of an HA in a quadrant implant model. The second null hypothesis was that the distortion in the accuracy of the scan does not vary from anterior to posterior tooth positions.
## 1 Introduction
In [30], measurements in Germany and France were reported which lead to a strong surface-to-tunnel reduction to be used for the prediction in the British Highspeed2 project. The Austrian Institute of Technology has performed a detailed measurement campaign with train and shaker excitation of a deep tunnel and a surface line [31]. All these measurements in [29; 30; 31] and the BAM measurements in [2; 32; 33] will be consistently evaluated in this contribution. Moreover, the frequency-dependent characteristics presented in this article give the missing reasons for the differences in the vibrations between tunnel and surface lines. A unique measurement campaign has been performed by BAM (Section 2). The different measurement results for the vehicle, track and ground are described by one-third octave band spectra (Section 3). The special effects of the bridge and the tunnel line are analysed in detail by time histories and Fourier spectra (Section 4). The surface-to-tunnel reduction is evaluated for the present measurement campaign with low train speeds up to 160 km/h. The results are compared with similar measurement results for high-speed trains which have been performed by BAM and other institutes (Section 5). The measurement results are compared with theoretical results to give some explanation of the observed reductions for the tunnel and the observed amplifications for the bridge (Section 6). The significant contributions of this article (the explanation of the bridge resonance by the axle sequence spectrum and the tunnel-to-surface reduction by the scattered axle impulses) are summarized in the conclusion.
## 2 Literature Review
### Airport Location-Allocation Strategies  
For airport site selection, numerous studies have employed multi-criteria decision-making (MCDM) methodologies such as the analytic hierarchy process (AHP). [PERSON] (1980) established AHP as a systematic approach that decomposes complex decision-making problems into a hierarchical structure and derives the relative importance of factors through pairwise comparisons by experts [43]. [PERSON] (1994) applied AHP to the airport facility location problem in the United States, organizing key considerations for airport expansion into four hierarchical levels and analyzing the relative weight of each element [44]. [PERSON] (2003) proposed an MCDM-based evaluation model for airport siting using the island of Samothraki in Greece as a case study, incorporating factors such as local demand, tourism capacity, environmental impact, and technical feasibility [45].  
Furthermore, a range of airport site selection studies have proposed more complex and sophisticated models beyond AHP. For instance, [PERSON] and [PERSON] (2014) proposed an airport site selection method using triangular fuzzy numbers to address complex multi-attribute decision-making problems [46]. [PERSON] et al. (2019) applied a multi-objective genetic algorithm to determine the optimal locations for general airports, designing a multi-objective optimization model that considers obstacle constraints and flight safety [47]. [PERSON] et al. (2018) utilized the analytic hierarchy process (AHP) to determine the optimal locations for military airports, setting 9 primary criteria (e.g., climate, geographical features, infrastructure) and 33 sub-criteria for evaluation [48].  
More recent studies have introduced hybrid or spatially enhanced approaches. For example, [PERSON] and [PERSON] (2013) used lattice order decision making to integrate subjective judgment with normalized data [49], while [PERSON] et al. (2020) developed the MESA (Metodologia de Escolha de Stios Aeroporutarios) framework for selecting regional airport locations in Brazil, integrating geographic information system (GIS) analysis with AHP [50]. [PERSON] et al. (2016) proposed the PRIME-LS model, a probabilistic mobility-aware approach that introduced the MinMax Radius metric for candidate site filtering [51]. [PERSON] et al. (2009) addressed the optimal location selection (OLS) problem in spatial databases by defining an optimization metric based on distances between spatial objects, refining and filtering candidate sites to enhance computational efficiency [52].  
Existing literature on airport siting has predominantly focused on the formulation of decision variables and the advancement of methodological frameworks for site-specific evaluations. Nevertheless, in the absence of any operational vertiports, such approaches remain insufficient for application to vertiport site allocation at the current stage.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 4 Results and Discussion  
In this section, the simulation results are presented to evaluate the performance of our proposed UAV-assisted ocean monitoring radar-communication system. Thanks to [59] for the proposed platform which provides some basic algorithm codes that simplify some of our repetitive work. We investigate the optimal position of the UAV and its impact on the performance of the system under different buoy distribution methods. Employing a UAV as an independent data collector to gather data from the transmitting buoys is a reasonable approach in practical applications and the configuration of four buoys and one UAV is a common deployment setup in practical communication and radar systems [9; 12]. To better showcase the superiority of our proposed system without sacrificing generality, and to facilitate future extensions of the proposed system, four buoys are distributed on the sea surface in a two-dimensional area of 1 km \(\times\) 1 km. The scope of the entire system we have devised aligns with the majority of application scenarios introduced in Section 1 ([6; 9; 11; 16]). Furthermore, it is well suited for deployment and can be easily extended to several working environments. Unless otherwise stated, the default minimum power difference for SIC is set as 2 dB, the bandwidth is set as 50 MHz, the default target location is presented in Location 1, the buoy distribution situation is random, and the maximum buoy transmit power is 18 dBm. Moreover, the UAV is assumed to fly at a constant height. The remaining parameters are summarized in Table 2. To ensure the effectiveness and comparability of the experiments, relevant communication parameters such as AWGN power and UAV altitudes are kept consistent with the [32]. This is carried out to maintain the validity and continuity of the results obtained. In order to enhance the experimental and facilitate accurate comparisons, careful attention is given to aligning these specific parameters with those reported in the relevant paper.
# Updates on Impact Ionisation Triggering of Thyristors
## 3 Results
### Boosted Gate Driver  
The following measurements are taken at the corresponding test points marked \"TP\" in Figure 6, with a 50 \(\Omega\) attenuator, with the remaining circuit disconnected at each test point.  
The output at the first avalanche transistor level is a pulse of 300 V in amplitude and a rise time of 1.2 ns; at the second avalanche transistor level, there is a pulse of 540 V in amplitude and a rise time of 1.5 ns (Figure 12). At this stage, the pulse does not rise quickly enough as required: after the initial steep ascent, the rate of dU/dt decreases.  
After the two avalanche transistors, the addition of a GaN HEMT operated at 640 V makes the driver able to deliver >620 V onto a 50 \(\Omega\) load (Figure 12) at \(\approx\)1 kV/ns in its fastest section (120-240 ps). These are sufficient conditions to trigger a MOS component in the gate-boosted mode.  
In an attempt to find the limit of our approach, we operated our setup above the recommended derated voltage in irradiated facilities [38]. We consider operating at 640 V as safe since the GaN HEMT has a significant drain-source voltage margin (\(\approx\)30%), as stated in its datasheet [29], and GaN technology devices are supposed to be immune against the single-event burnout (SEB) phenomenon. This is an abrupt failure caused by a high-energy hadron (proton or neutron), such as cosmic rays or, in accelerator facilities, stray radiation from the accelerator, striking a power semiconductor device, triggering an instantaneous breakdown of the device and compromising its reliability.  
Figure 11: The output voltage of the six parallel IMBF170R450M1 SiC MOSFETs on a 4 \(\Omega\) load (TP5, Figure 3), charging voltage 1.7 kV. The maximum voltage amplitude is 1.6 kV; hence, there is a calculated output current of 400 A.  
In summary, our driver comprising two avalanche transistors and a GaN HEMT in series enables a sharp rising edge, achieving slew rates exceeding \(1\,\mathrm{kV/ns}\) for the driving pulse. This is crucial for subsequently triggering high-voltage MOS components in the gate-boosted mode.
## 3 Efficiency Analysis of Herringbone Planetary Gear
### Power Loss Due to Gear Meshing
#### 3.2.3 Calculation of Power Loss Due to Gear Transmission Windage  
As free space inside the gearbox is reduced during power transmission, the contact area between the surrounding air and a rotating body, such as a gear moving at a higher rotational speed, increases relative to the volume reduction, resulting in windage. This increases the relative motion between rotating parts, which is accompanied by agitation of the lubricating oil. Furthermore, windage heats the inside of the gearbox, which can lead to the formation of oil mist. Heating, agitation, and other phenomena become more serious as the gear rotational speed increases.  
According to [PERSON] et al. [33], the gear windage power loss can be calculated as  
\[P=1.047\times 10^{-4}T\cdot n \tag{32}\]  
Moreover, torque \(T\) can be expressed as  
\[T=\frac{\rho\alpha^{2}R^{5}}{2}\bigg{(}1+2.3\frac{b}{R}\bigg{)}\frac{0.09}{R^{ 0.2}} \tag{33}\]  
where \(R_{e}\) is the Reynolds number, which can be expressed as \[R_{e}=\frac{\rho\omega R^{2}}{\mu} \tag{34}\]  
Thus, the windage power loss calculation model can be obtained.  
\[P_{w}=C_{1}\Big{(}1+2.3\frac{b}{R}\Big{)}p_{eq}^{0.8}r^{2.8}R^{4.6}\mu_{eq}^{0.2} \tag{35}\]  
\[C_{1}=2.04\times 10^{-8} \tag{36}\]  
\[\rho_{eq}=\frac{\rho+34.25\rho_{air}}{35.25} \tag{37}\]  
\[\mu_{eq}=\frac{\mu+34.25\mu_{air}}{35.25} \tag{38}\]  
where \(b\) is the tooth width, \(\mu_{eq}\) is the dynamic viscosity of the air-oil mixture, \(\mu_{eq}\) is the air-oil equivalent density, \(\rho_{air}\) is the air density, \(\mu_{air}\) is the air dynamic viscosity, and R is the pitch radius.
# Enhanced Curvature-Based Fabric Defect Detection:
## 2 HIL (Hardware in the Loop) Implementation  
The experimental setup illustrated in Figure 1 models a knitting machine with helical rotation. A camera system is positioned in the center of a drum that moves continuously up and down to follow the fabric's movement. The fabric is mounted on the drum, with its rotation speed (up to 36 rpm) controlled via an analog output. The vertical section of the knitted fabric is captured by the camera as it moves, while stitched images track the rotation of the sample textile. Anomaly localization is determined by elapsed time and rotational speed, enabling defects to be accurately marked. By controlling the speed of both the drum rotation and the camera's movement, various fabric knitting scenarios can be replicated, allowing defects arising from different configurations to be captured. The rotational velocity of the fabric system generates 15 frames per second, producing approximately 30 MB of image data for processing. After detection, these images, with or without defects, are used to generate input data for an artificial intelligence system to classify anomalies. Customized lighting is carefully designed in this setup to enhance specific defects and capture high-quality images during high-speed rotations. All image data are transferred to a development board where artificial intelligence and other detection algorithms (e.g., Gabor Transform, Discrete Curvature Algorithm) are implemented.
## 3 Experimental Section
### Experimental Set-Up
#### 3.2.2 Recovery Assays and Method Validation  
In a typical assay, ultra-pure water (25 mL, pH 5.5) spiked with a mixture of the five working standards at a desired concentrations, a microextraction bar device and conventional Teflon magnetic bar were introduced into a glass flask. For the optimization purposes assays were performed in a Variomag multipoint 15 agitation plate (Thermo Fisher Scientific Inc., Waltham, MA, USA) at room temperature. Parameters such as agitation speed (750, 1,000 and 1,250 rpm), extraction time (1, 2, 3, 4 and 16 h), matrix pH (2, 5, 8 and 10), organic modifier (MeOH; 5%, 10% and 15%, v/v) and ionic strength (NaCl; 5%, 10% and 15%, w/v) were systematically studied in triplicate. For back-extraction (LD), the microextraction bars were removed from the samples with clean tweezes and placed into a 2 mL vial containing 1.5 mL of the stripping solvent, ensuring their total immersion prior to ultrasonic treatment at a constant temperature (25 \({}^{\circ}\)C). To evaluate the best LD conditions, several assays using MeOH, ACN and a mixture of both (1:1) with different desorption times 30, 45 and 60 min) were performed in triplicate. After back-extraction, the microextraction bars were removed by clean tweezes, the stripping solvent was evaporated until dryness under a gentle stream of purified nitrogen (\(>\)99.5%), followed by reconstitution with 200 \(\upmu\)L of mobile phase. The vials were then sealed and placed on the auto-sampler for HPLC-DAD analysis. For the method validation experiments, 25 mL of ultra-pure water were spiked with 200 \(\upmu\)L of the working standard mixture at desired concentrations, and the extraction and back-extraction experiments were performed as described above under optimized conditions. For real sample assays, 25 mL of water sample and the standard addition methodology (SAM) were used, following by the same procedure employed for the validation experiments. Blank assays were also performed using the procedure above described without spiking.
## 2 Materials and Methods
### Removal of Debris and Smear Layer  
The teeth, prepared to an apical size of 35/.06 or 50/.06 and irrigated according to the procedures mentioned (i.e., Manual, Ultrasonic, Sonic) (\(n=5\)/group), were longitudinally split using a chisel. The morphology of the canal surface was accessed by scanning electron microscopy (SEM, FEI Quanta 400 FEG/ESEM) without any additional manipulation. Prior to imaging, the specimens were sputter-coated (SPI-Module) with a thin film of gold/palladium. To evaluate the superficial debris and smear layer, a previously published four-score index system was adopted [22]. Succinctly, the index used for debris evaluation ranged between score 1: none to slight presence of superficial debris covering up to 25% of the dental surface; score 2: little to moderate presence of debris covering between 25-50% of the surface; score 3: moderate to heavy presence of residual debris covering between50-75% of the surface; score 4: heavy amount of aggregated or scattered debris covering over 75% of the surface. The index used for smear layer evaluation ranged between score 1: little or no smear layer, covering less than 25% of the specimen with tubules visible and patent; score 2: little to moderate or patchy amounts of smear layer, covering between 25-50% of the specimen with many tubules visible and patent; score 3: moderate amounts of scattered or aggregated smear layer, covering between 50-75% of the specimen with minimal to no tubules visible or patent; score 4: heavy smear layer covering over 75% of the specimen with no tubule or pitent. The evaluation was conducted by two calibrated observers ([PERSON] and L.G.).
## 3 Results and Discussion
### Evolution of the UCS  
A uniaxial compression test was carried out on CGB after chemical corrosion. A typical stress-strain curve of CGB after different times of erosion was obtained, as shown in Figure 4a. It can be seen from the figure that the shape of the curve shows different trends with the increase in the CGB erosion time. When the erosion time reached 60 days, the uniaxial compressive strength of the specimen increased by 16% compared with that of the  
Figure 3: Mass change in CGB after chemical erosion.  
Figure 2: The apparent morphology of the sample after soaking at different times.  
initial specimen. When the erosion time increased to 150 days, the uniaxial compressive strength decreased by 8% from the initial specimen and by 20% from that after 60 days. As the erosion time increases, the initial compressive and yield stages of the stress-strain curves are lengthened, while the elastic phase is shortened. The peak strain of the CGB increases, the ratio of peak stress to strain decreases with the erosion time, and the curve segment from the beginning of the loading to just before the peak stress flattens out. The reason for this is the increase in erosion time and a large number of \(\text{SO}_{4}{}^{2-}\) and \(\text{H}^{+}\) invaded, resulting in the formation of more micropores on both the surface and the interior of CGB so that the original pores of the specimen are enlarged and the chemical erosion reaction zone is enlarged. Macroscopically, it is manifested in the reduction in brittleness, the enhancement of plasticity, and the increase in the deformation of CGB when subjected to a load. It can be seen that the erosion of acidic aqueous solution has a significant effect on the mechanical properties of CGB, resulting in changes in its mechanical response to external loads.