esa-sceva/satcom-chunk-collection huggingface dataset:
# Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry
## 2 Principles of Industry 4.0 from Food Perspectives and Meat 4.0 Concept
### Food Industry 4.0 Enablers
#### 2.2.3 Internet of Things  
IoT is related to transferring data between interconnected computer devices and machinery [24]. It consists of physical devices that collect data, a network that transmits the collected data, and an application layer which includes IoT applications and services.  
Figure 3: Robotics and automation in the meat sector (**(a)** bovine hindquarter cut; **(b)** lamb ventral cut; **(c,d)** automated poultry line).  
Thus, IoT has favoured the spread of interconnected devices fostering an increase in the employ of several smart IoT applications [39]. Moreover, IoT is widely applied in the supply chain to enhance transparency and traceability [30]. The horsemeat scandal in 2013 was caused when several adulterated meat products were identified, resulting in the public losing trust in them [40]. IoT-enabled wearable devices allow real-time monitoring and tracking, employee safety, productivity, and food safety [41]. For instance, the use of hyperspectral imaging systems in combination with IoT could help to monitor the components/ingredients of food, thus improving food safety [42]. Some researchers used IoT technology to reduce food waste in food processing factories [43; 44].
## 6 Comparison of LW-CCA with ContikiMAC  
We evaluated the proposed method LW-CCA and compared it with ContikiMAC using the Cooja simulator. Tmote-Sky is used as a hardware platform for nodes on the network. The network protocol for all nodes is the same as in Table 3. Figure 1 illustrates the network scenario with respect to the RPL routing protocol graph. It is a random scenario with a variety of hops and neighbors consists of:  
* 1 emulated node that is programmed as a sink that plays the role of the root node for RPL in the network graph. In fact, it is a UDP server that collects data from client nodes;
* 21 emulated duty cycle nodes as UDP clients in network graph that send data to sink;
* power consumption of the nodes being estimated by the Energest module available at Contiki.  
The LW-CCA method is compared with the ContikiMAC in terms of the average CPU ticks in CPU, LPM, Rx and Tx, CPU Power, LPM Power, Rx and Tx Duty-Cycle(%), Rx and Tx power, total power consumption, and PDR. LW-CCA and ContikiMAC nodes are evaluated based on the two data transmission rates, once every 60 s (low rate) and once every 30 s (high rate). Simulation time for each of low rate and high rate scenario is 4 h. The node number \"\(1\)\" is considered as a sink and its radio is turned on during the network time, so the network status analysis is done based on the sender nodes from 2 to 21. All of the scenarios outputs are based on Collect-View in Cooja. The equations are based on embedded equations for power consumption in the Collect-View source code [31]. The power parameters for simulation are listed in Table 2.  
Figure 8: Diagram of the LW-CCA method.
## 5 Review of Considered Study
### Medical Treatment Phobia
#### 5.4.2 Dental Phobia  
Virtual reality exposure therapy was implemented to treat dental phobia (dentophobia) using an Oculus head-mounted display (HMD). The researchers were also able to see and monitor what patients experience during the therapy through a computer monitor. The session starts by asking the patient to sit in the dental chair and wear the HMD. The scene builds by containing all the dental clinic elements in 3D. Moreover, the patient was exposed to five scenarios including seating with no dental tools, mouth mirror and oral cavity, injection, using the muted drill, and using a drill with sound. Each scenario was repeated many times until the therapist noticed the progress, before moving to the next scenario. The evaluation was conducted on two dental phobia patients aged between 24 and 56 years. The baseline level of anxiety was determined using a VAS. Furthermore, the dental anxiety level was determined using a dental fear survey (DFS) and a modified dental anxiety scale (MDAS). At the end of the exposure, the progress of exposure therapy was measured using a SUD [14]. Another study was conducted by [38] to show the effectiveness of using virtual reality exposure therapy to treat a dental phobia, in addition to highlighting the limitations of other previous studies. A single-blind trial of 30 dental phobia patients was randomized to either an informational pamphlet (IP) condition or virtual reality exposure therapy. However, there were two outcomes of this study: the primary outcomes and the secondary outcomes. The primary outcomes compute and compare the anxiety scores before the intervention and the follow-up. The secondary outcomes find the difference between the VRET and the IP condition, behavioural avoidance, heart rate variation, temporal variation, and acceptance of dental treatment. However, several hypotheses were tested to determine the VRET's effectiveness. The evaluation of VRET was done using an Oculus development kit 2 HMD (head-mounted display), which displays five scenarios in one session on the dental chair, while using dental odours to enhance the immersion. The five scenarios are the same as the one used earlier in [14], and the patient must be exposed to the five in the same session. On the other hand, the IP condition was asking the patient to sit in the dental chair for 45 min while hearing guidelines about overcoming the dental fear [38]. Table 5 summarizes how specific phobia was approached in each paper using augmented reality and virtual.
## 3 The Results and Discussion  
### MICaFVi Basic Principle  
The method of detection developed in this study (MICaFVi) consisted of three main steps: enrichment, neutralization, and detection (Figure 1). The sensitivity of viral diagnostic assays, including RT-qPCR or qPCR, is tightly linked to sample quality; at low viral concentrations, the accuracy of these techniques drops dramatically. To overcome this problem, in the design of our detection method, we included an enrichment step based on viral immuno-capture using iron-oxide MNPs coated with the anti-spike polyclonal antibody (AS-MNPs). Since it is the most immuno-genic and the only surface glycoprotein, the spike protein constitutes the best candidate for coronavirus antigen-based diagnosis [45; 46; 47; 48]. Moreover, the use of polyclonal antibodies in the enrichment step will permit viral immuno-capture with high specificity and sensitivity. Anti-spike antibodies used for immuno-capture were linked to MNPs, permitting magnetic precipitation of the captured viral particles (Figure 1A). The enrichment step concentrates viral particles found in prospective camel/patient samples in a very low volume, and subsequently increases the sensitivity of detection, especially in samples with a low viral titer (early infection). Following the enrichment step, the captured viruses on the surface of AS-MNPs were neutralized by the addition of paraformaldehyde (PFA). This step not only permits the cross-linking of viral particles to AS-MNPs but also the complete neutralization of live viruses, which allows for the safe handling of samples in subsequent analysis steps.  
For the detection of captured viral particles, the complex virus/AS-MNPs was stained with a combination of two antibodies: (i) FITC-labeled monoclonal anti-spike antibody, and (ii) a secondary anti-rabbit APC-labeled antibody specific to AS-MNPs (Figure 1B). After the staining step, the AS-MNPs were analyzed via flow cytometry: the detection of double-positive events indicates virus/AS-MNPs complex formation, which demonstrates the presence of a target virus in the analyzed sample. In the case of a viral-free sample, AS-MNPs will only be APC-positive (Figure 1B).
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 4 OCTA Algorithms
### Phase-Based OCTA Algorithms  
Phase-based OCTA algorithms rely on \(\varnothing(\mathrm{x},\mathrm{z},t)\) in Equation (1) to distinguish dynamic and static tissue. Doppler OCT, a category of phase-based OCTA, uses a deterministic Doppler phase shift for in vivo blood flow measurements [52; 53]. While Doppler OCT can quantify flow, visualization applications are limited due to its angle dependence (Figure 5A). For instance, retinal blood vessels are nearly perpendicular to the optic axis, particularly outside of the optic nerve head, yielding insufficient phase shifts for Doppler measurements [42]. Power Doppler [54; 55] and phase variance imaging [56] represent alternative approaches that are sensitive to decorrelation, or random non-deterministic Doppler shifts. In 2007, [PERSON] et al. [57] proposed phase variance for motion contrast. In [58], the phase variance at position \((\mathrm{x},\mathrm{z})\) is defined as:  
\[\mathrm{PV}(\mathrm{x},\mathrm{z})=\frac{1}{\mathrm{N}-1}\sum_{t=0}^{(\mathrm{ N}-2)\mathrm{T}}[\Delta\varnothing(\mathrm{x},\mathrm{z},t)-\overline{\Delta \varnothing(\mathrm{x},\mathrm{z})}]^{2}. \tag{6}\]  
The phase difference at a given location is given by:  
\[\Delta\varnothing(\mathrm{x},\mathrm{z},t)=\varnothing(\mathrm{x},\mathrm{z}, t+T)-\varnothing(\mathrm{x},\mathrm{z},t), \tag{7}\]where T is the time lag. Equation (6) is a temporally averaged, variance-based algorithm without normalization. Phase-based OCTA algorithms are advantageous over amplitude- and intensity-based algorithms if phase changes but intensity and amplitude do not. However, phase-based OCTA loses information about the OCT signal amplitude and intensity. Moreover, phase-based OCTA may not detect changes in the presence of a large static scattering component. Similar to amplitude- and intensity-based algorithms, phase-based OCTA is sensitive to decorrelation (Figure 4E,F). However, as phase is particularly sensitive to axial motion, additional bulk motion phase correction is typically required. In [57], before phase variance analysis, [PERSON] et al. removed the bulk motion phase change:  
\[\Delta\varnothing^{\text{corr}}(\text{x},\text{z},\text{t})=\Delta\varnothing( \text{x},\text{z},\text{t})-\Delta\varnothing^{\text{bulk}}(\text{x},\text{t}), \tag{8}\]  
where \(\Delta\varnothing^{\text{corr}}(\text{x},\text{z},\text{t})\) denotes the corrected phase change, and \(\Delta\varnothing^{\text{bulk}}(\text{x},\text{t})\) represents the phase change due to bulk motion, estimated as:  
\[\Delta\varnothing^{\text{bulk}}(\text{x},\text{t})=\frac{\sum_{\text{z}=\text{ a}}^{\text{b}}\left[|\text{S}(\text{x},\text{z},\text{t})|\Delta\varnothing( \text{x},\text{z},\text{t})\right]}{\sum_{\text{z}=\text{a}}^{\text{b}}\left[ |\text{S}(\text{x},\text{z},\text{t})|\right]} \tag{9}\]  
The phase change due to bulk motion is thus calculated by a weighted mean from \(\text{z}=\text{a}\) to \(\text{z}=\text{b}\) in one A-scan. Note that bulk phase change estimation based on cross-correlation is also possible [16; 59].
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 2 BDPE Receiver Architecture
The details of these filters are not further discussed in this work, but all of them rely on a proper particle weight update. The particle weight update function determines the weight change of each PVT particle and takes the MC values as input. Afterwards, the final PVT solution (yellow circle in **4**) is computed by constructing a probability density function using the particle weights and calculating the final PVT solution using a weighted mean over all particles. To illustrate the implementation, Figure 2 depicts the normalized logarithmic particle weights \(\widetilde{w}_{k}^{i}\) from Equation (35) using the optimal particle weight update from Equation (34) but without information from the previous epoch \(\widetilde{w}_{k-1}^{i}=0\). Thus, there is no influence from the past and the Figure shows the results from one epoch only. In contrast to it, Figure 3 shows an improper aligned clock error where the GNSS signals do not overlap in the position domain. In BDPE, the implemented Bayesian filter plays an important role as it estimates the PVT solution from all available measurements. The following chapter discusses in detail the Bayesian framework and its role in the concept of DPE. Figure 2: The normalized logarithmic particle weights \(\widetilde{w}_{k}^{i}\) from Equation (35), assuming a uniform distribution from the previous epoch, i.e., \(\widetilde{w}_{k-1}^{i}=0\) and using the optimal particle weight update from Equation (34) are shown. The particles are equidistantly distributed over a grid in the northeast plane. The lines through the plot correspond to the weighted correlation function in the position domain and thus refer to a GNSS signal. In a proper case (correct user velocity, clock error and drift), the lines overlap at a distinct point in the position domain, which is in this case the northeast plane. The resulting peak represents the probability of the 2D position. Note that the plotted weights are normalized and in the logarithmic scale, thus the peak has the maximum value of 0. The coherent integration time for this plot was set to \(T_{coh}=2\) ms. The processed data refers to open sky. It was recorded at latitude LAT = 47.06446263 deg, longitude LON = 15.40777110 deg on the rooftop of Reininghausstrasse 13a, Graz, Austria.
## 3 Mathematical Modelling
### Empirical Equation for Density Estimation of Blends Versus Temperature  
For blends, [PERSON] et al. [44] propose empirical equations to calculate the overall density based on the individual densities of diesel and biodiesel volume percentages. Each volume percentage (diesel or biodiesel) has its own density, which is affected by factors like temperature. Therefore, according to [PERSON] et al. [41], empirical equations can be used for predicting the density of each volume percentage (diesel/biodiesel) separately, as well as equations for diesel/biodiesel blending:  
\[\rho_{biodiesel}=-0.69T+1075 \tag{6}\]  
\[\rho_{diesel}=-0.657T+1051 \tag{7}\]  
\[\rho_{mix}=(-0.033T+24.4)\,X-0.7950T+1052 \tag{8}\]  
where \(\rho_{biodiesel}\) is the density of biodiesel, \(\rho_{diesel}\) is the density of diesel, and \(\rho_{mix}\) is the density of their mixture measured in g/cm\({}^{3}\). \(T\) is the temperature in K, and \(X\) is the percentage volume (v%) of biodiesel in the mixture.  
Preliminary modelling tests on the experimental data of this study using Equation (8) showed large discrepancies. Therefore, it was decided to set the variables \(a\) and \(b\) (Equation (9)) instead of the fixed values \(-0.033\) and \(-0.7950\), respectively. Parameters \(a\) and \(b\) were evaluated using the fitting process to the experimental data.  
\[\rho_{mix}=(aT+24.4)\,X-bT+1052 \tag{9}\]
## 1 Introduction
The study generates the situational awareness output by synthesizing the threat-unit attribute set in a specific sensing interval, and generates the situational awareness elements by multiplying the threat-unit attributes by the situational awareness matrix, in a bottom-up order. However, the method uses per-count statistics in terms of cybersecurity situational awareness results, which has certain shortcomings in terms of identification accuracy and efficiency. [PERSON], [PERSON] [5] proposed a network security policy consistency detection method based on the knowledge graph, which brings new ideas to the research in this field, but there are still some limitations. The construction of the ontology model relies on the experts' long-term accumulated industry experience and their deep understanding of the internal network structure, security monitoring projects, etc., and in the short term, it can only rely on manual preparation by experts and practitioners. This approach is not only inefficient and costly, but also susceptible to subjective factors, that may lead to deviations in the accuracy and completeness of the ontology model. If the experts do not have a sufficient understanding of certain domains, the model may not accurately reflect the actual situation, affecting the reliability of the subsequent recognition and analysis. [PERSON], [PERSON] [6] mentioned the performance advantages of the architecture, but large data processing and complex model training still consume a lot of computational resources. For example, LSTM network training requires a large amount of high-quality data and time, and with the increase in data size and network complexity, the demand for storage and computational resources continues to increase. In addition, the architecture proposed in this paper aims to improve the real-time processing capability, but delays may still occur when processing large datasets. When faced with high-rate and large-scale network traffic data, data collection, storage, analysis, and alerting may not be able to meet strict real-time requirements. For example, in a DDoS attack scenario, it may not be possible to respond and defend in time due to processing delays. Considering the variety and volume of data in the process of network security situational awareness, this paper proposes a network situational awareness method that combines an improved Particle Swarm Optimization algorithm with Long Short-Term Memory (BiLSTM) neural networks [5] to enhance the efficiency and accuracy of network security situational recognition.
## 5 Results
### Relationship between the Fracture Network and Karstification  
Due to the influence of significant compressive stress, an anticline structure is a favourable zone for karst development in the axial section of the structure. A series of faults and fractures evolved in the direction parallel to the anticline hinge during the formation of the anticline. Since fractures are prone to karstification, the karst features developed along the weak fracture paths, particularly where the fracture sets intersected.  
In the Subis Limestone build-up, potential karst depressions are distributed around the fracture zones (Figure 8), indicating that their formations are strongly associated with the faults and fractures. Surface and underground water infiltrates along faults and fractures, dissolving the carbonate along the pathway. Dissolution pores along the fractures formed in the early stages can enhance the porosity and permeability of the carbonate reservoirs. Large-scale caves easily form at the intersection of multiple groups of faults or fractures. For instance, the orientation of the passages of the Great Cave and the north-south trending isolated passage of the Traders' Cave match the major regional lineament sets.  
The studied outcrop has well-developed fractures, vugs, caves, and passages, which can become potential pathways or capacities for the reservoirs if they are unfilled, semi-filled, or filled with permeable sediments. The connectivity of the fracture network can be enhanced or reduced by considering the interplay of the matrix porosity in the DFN modelling.  
The observed fractures were mainly infilled by calcite minerals, with some passages and caves being partially filled by boulders. Large limestone boulders were found in the southern part of the Traders' Cave and were thought to represent the remains of the collapsed ceiling during the karst maturation phase. The cave is open to the east, which is believed to be caused by paleo-weathering at the north-south trending cliff. The boulders at the bottom of the dolines provided similar evidence of ceiling collapse (dolines) in the Great Cave. The collapse event in karst structures is a process of karst maturation that continues until the erosional process is complete. Karst dissolution primarily occurs along the fractures and enlarges the pathway through time.  
In offshore carbonate build-ups, the void system and karst caverns partially collapse during subsequent burial, potentially resulting in karst-related traps. Using the Subis Limestone near-surface karst system as an analogue, the karst cavern zones in Central Luconia are likely to extend horizontally rather than vertically while oriented parallel to major structural lineaments.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 4 Results and Analysis
### Spatial Quantitative Analysis of UBD and LST  
The results of the comparison between OLS and GWR were obtained by using the GWR module of Arcgis10.3 software. The results are shown in Table 5.  
The GWR model had a larger R\({}^{2}\) and smaller Akaike information criterion (\(AIC_{\text{c}}\)) and sigma, indicating that the LST and UBD fitting effects based on the GWR model were better than those based on the OLS model. First, according to the GWR regression results, the overall effect of this model is significant, and UBD can explain 54.2% of the LST total variation. In the OLS model, UBD can only explain up to 33.8% of the LST variance. Figure 13a,b shows the residual spatial distributions of OLS and GWR; compared with the results of OLS, the residuals dropped from 43.13 to 27.96. It is not suitable for the global model to be established based on the OLS method because there is a strong spatial correlation. Although spatial autocorrelation still exists in the GWR model residual, it is lower than that in the OLS method and presents a more random spatial distribution.  
At the same time, the Akaike information criterion (\(AIC_{\text{c}}\)) of GWR dropped significantly from \(-717.2\) to \(-1464.9\). The \(AIC_{\text{c}}\) is a measure of the fitting degree to a statistical model. According to the evaluation criteria proposed by Fotheringham, a difference between the two of greater than 3 is the optimal bandwidth, and the lower the value, the better the fitting effect. Even considering the complexity of the GWR, the GWR model is better than the OLS model, and the difference between the two models far exceeds this value.  
The spatial distribution of the local fitting result of the GWR model (Local R2) is shown in Figure 14. Where the subsurface of the suburbs is dominated by grassland and cultivated land, the LST is mainly affected by vegetation and the GWR model has a high fitting degree. However, in the central urban area where buildings are concentrated, the LST is affected by the buildings and the fitting degree of the GWR is relatively low.  
Figure 13: Schematic diagram of standard residuals for UBD and LST: (**a**) Standard residual plot of OLS; and (**b**) Standard residual plot of GWR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(AIC_{\text{c}}\) & \(R_{adj}^{2}\) & **Sigma** & **Residual** \\ \hline OLS & \(-717.2\) & 0.338 & 2.237 & 43.13 \\ GWR & \(-1464.9\) & 0.542 & 1.753 & 27.96 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation effect by Geographically Weighted Regression (GWR) and a comparison with ordinary least squares (OLS).