esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Gene Annotations and Functional Pathways  
We identified _CTNNA3_ as a possible candidate gene. _CTNNA3_ is part of the _CTNNA_ family with three catenin subtypes (_CTNNA1_, _CTNNA2_, and _CTNNA3_) displaying close functional relationships [41,42]. The catenin subtypes effect a wide range of diseases, such as Alzheimer's disease or cancer development in humans [42]. CTNNA3 is a key protein of the adherens-junctional complex in epithelial cells and plays a central role in cellular adherence [41]. It also acts as an activator in the MAPK (mitogen-activated protein kinase) pathway [42]. MAPK signaling further regulates the production of target inflammatory genes during clinical mastitistis [43].  
_EVA1A_ is involved in autophagy and programmed cell death [44,45]. Since the invasion of a pathogen into the udder activates phagocytic cells, which afterwards have to be eliminated by apoptosis [46], it is possible that _EVA1A_ is also involved in this process.  
_HEMK1_ was identified as a possible candidate gene. The SNP located in _HEMK1_ was significant for the interaction effect, indicating alterations of gene activities with farming system particularities. _HEMK1_ acts as a regulator in the JAK-STAT (Janus kinase/signal transducer and activator of transcription) pathway. The JAK-STAT pathway is pivotal for the development and function of the immune system, displaying different mechanisms in different studies [47]. Hence, it is not surprising that _HEMK1_ is differently regulated in different housing systems, such as compost and cubic, due to different environmental stressors. Previous studies demonstrated that immune-related genes such as _IL17_, _IL17F_, and _LIF_ indicate GxE [48]. Furthermore, these genes also influence fertility traits, depending on environmental conditions [48].  
The _CHL1_ gene on chromosome 22, belonging to the L1 family cell adhesion molecule [49], was also detected on the basis of SNP by housing system interactions. In humans, _CHL1_ plays a role in diseases triggered by chronic stress [49]. [PERSON] et al. showed that _CHL1_ expression in monocytes was significantly downregulated in depressed patients with chronic stress [50]. In addition, these patients showed a reduced number of positive CD19\({}^{+}\) and CD20\({}^{+}\) B cells. The downregulation of these two immune cells has a negative effect on the immune system and implies an increased disease susceptibility [50]. Due to improved animal welfare and lower stress levels in compost barns [7], we assume that a possible upregulation of _CHL1_ is associated with a positive effect on the immune system and therefore on under health. A gene expression analysis was conducted in relation to different floor types in Holstein dairy cows [51]. Various types of floor implied different comfort behaviors of cows. Improper lying and walking areas increased stress levels and triggered several cellular processes. In this way, the expression of various genes may be influenced [51; 52]. Consequently, a rapid increase in the expression of the gene _HSPA1A_ during stress was identified, with ongoing effects on immune system functions [51].
# UAV Support for Mission Critical Services
###### Abstract  
Mission critical solutions are essential for providing communications and services in the case of the troubles with connectivity that are often found in infrastructure-based solutions. Such solutions are typically used in the case of disasters, lack of energy, etc. There exist several narrowband solutions that provide countrywide coverage in certain countries. In recent years, the activities related to creating mission-critical broadband solutions based on Long Term Evolution (LTE) have led to the definition of LTE Mission Critical (LTE-MC). Both solutions ignore virtualization and require dedicated mobile terminals as a part of the mission-critical communication solution. This paper describes the opportunities, open issues and a proposal of a solution that exploits Unmanned Aerial Vehicles (UAVs) and network virtualization for mission-critical services. The presented approach combines Cloud/Edge and Fog orchestration to efficiently use all the available resources, including virtualized resources of the end-user devices.  
mission-critical; UAV; LTE-MC; TETRA; MEC; network virtualization +
Footnote †: journal: Physics Letters
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 2 Materials and Methods
### Spectra Measurement  
Visible-near infrared (Vis-NIR) absorption spectra of the samples were measured using a FOSS XDS Rapid Content Analyzer (FOSS NIRSystems, Hillerod, Denmark) across a wavelength range of 400-2499.5 nm, with a spectral resolution of 0.5 nm, covering a total of 4200 wavelengths. Each sample was scanned twice, with the sample container rotated between scans to ensure that each measurement captured a different area of the soil sample. This rotation minimized the effects of sample heterogeneity, ensuring more representative spectral measurements. In this study, the average of the two spectra was used for each sample to further reduce noise and improve measurement reliability.  
The spectral data in this study are presented in wavelengths rather than wavenumbers, a convention commonly used in Fourier transform infrared (FTIR) spectroscopy. Representing data in wavelengths offers a practical advantage in Vis-NIR spectroscopy, aligning with standard practices and ensuring consistency with the instrumentation and methodologies applied in this study. However, this approach may differ for FTIR users, as spectral resolution in terms of energy or wavenumber is not uniform when using wavelengths. Despite this, wavelength-based representation provides straightforward integration with Vis-NIR models and maintains compatibility with the analytical framework of this research.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 3 Dataset  
### Cseic-Ids2018  
In 2018, the Canadian Institute of Cybersecurity (CIC) collaborated with the Communications Security Establishment (CSE) to develop the CSECIC-IDS2018 dataset [36]. Initially intended for evaluating intrusion detection research, it has evolved into a crucial benchmark for assessing IDSs. This carefully curated dataset mirrors real-world cyber threats and attacks, offering a range of scenarios for thorough analysis. Its importance stems from its ability to simulate intricate network environments, enabling both academics and practitioners to effectively assess and enhance intrusion detection systems. The dataset was gathered over a span of ten days, encompassing 80 columns, seven attack families, and fifteen distinct attack types. As shown in Table 1, it meets critical criteria such as overall traffic volume, a variety of attack types, and thorough labeling. The simulated attacks targeted an infrastructure consisting of 50 machines within an organization composed of five departments, 420 machines, and 30 servers. The dataset contains detailed records of network traffic and system logs for individual machines, utilizing CICFlowMeter-V3 to extract 80 attributes. The CSECIC-IDS2018 dataset comprises ten CSV files, encompassing 16,232,943 instances and 80 features. The distribution of attacks is shown in Figure 3.
# Numerical Estimation of Bending in Holographic Volume Gratings by Means of RCWA and Deep Learning
## 1 Introduction
In the present work, we propose a novel method for predicting bending parameters from theoretical data using deep learning tools. This study continues our previous work on the estimation of characteristic parameters of holographic volume gratings based on machine learning, as presented in [21], where we first demonstrated the feasibility of using deep learning models to estimate grating parameters. Here, we extend that foundation by investigating optimal architectures for predicting bending parameters and comparing the performance of a Feedforward Neural Network (FNN) and a hybrid Convolutional Neural Network (CNN)-FNN model. Slight improvements in accuracy and robustness were observed in the hybrid model, even when a smaller number of weights was applied. FNNs and CNNs have played pivotal roles in advancing machine learning capabilities, offering robust solutions to vast real-world problems [22; 23; 24; 25]. FNNs, also known as dense networks, consist of layers where each neuron is connected to every neuron in the subsequent layer, with intertwined nonlinear activation functions. This architecture enables FNNs to learn intricate relationships within data, making them suitable for various tasks such as classification, regression, and pattern recognition [26; 27; 28]. On the other hand, CNNs leverage convolutional layers to extract spatial features from input data. Although CNNs are not as commonly used for regression as for classification due to their design being intrinsically well suited for pattern extraction and hierarchical feature learning, they have proven effective for regression tasks like image super-resolution [29; 30], depth estimation [31], Image Reconstruction [32], and others. Regarding the structure of this paper, in the first section, we will set the theoretical basis for developing our RCWA-Shooting method approach and the theoretic basis for the implemented deep learning solutions. The results of our RCWA-SM study on the standard RCWA and FDTD models will be presented. Then, the application and results of the deep learning solutions will be discussed. Finally, and serving as the conclusion, a discussion on the validity and suitability of both approaches will be conducted.
## 5 Results and Discussion
### Skid Resistance  
The skid resistance of the pavement is directly related to the safety of vehicular traffic, and the pavement requires sufficient frictional resistance even in wet conditions [41]. Hence, it was crucial to conduct tests on the skid resistance of asphalt concrete. Figure 9 provides the results of the pendulum friction test.  
Figure 9: Skid resistance test results.  
The variation of modifier content in the binder did not noticeably affect the skid resistance of HSACs and ESACs. The BPNs of HSACs were all above 90, exhibiting excellent skid resistance. In contrast, the BPN of ESAC was much lower, with BPNs of 58, 59, 61, and 60 for ESAC35, ESAC40, ESAC45, and ESAC50, respectively. On the one hand, compared to the dense graded EA-10, SMA-13 was a gap grading, and the proportion of coarse aggregate in HSAC was higher than that in ESAC, which could increase the pavement structure depth and thus its macroscopic texture was rougher; on the other hand, for the same gradation, the larger the nominal maximum aggregate size, the better the skid resistance of the concrete. As a result, the skid resistance of HSAC with SMA-13 grading was significantly better than that of ESAC with EA-10 grading. The Chinese specification (JTG 3450-2019) requires that the BPN of the pavement should be \(\geq\)45 [33], and the skid resistance of all HSACs and ESACs could meet the requirement. However, the aggregate on the pavement was subjected to the combined effects of friction, impact, and crushing of vehicle tires for a long time in the service process, which easily led to a continuous decline in skid resistance [42]. Thus, it is necessary to choose a gradation with superior initial skid resistance for the top layer to help enhance driving safety and extend the service life of the pavement, which is the crucial reason for using SMA-13 gradation in this project.
## 4 Results and Discussion
### Results
#### 4.2.6 Price-Inducing Effects
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sectors** & \multicolumn{2}{c}{**South Korea**} & \multicolumn{1}{c}{**Japan**} \\ \cline{2-5}  & **Value** & **Rank** & **Value** & **Rank** \\ \hline
1. & Agricultural, forest, and fishery goods & 0.01248 & 27 & 0.00516 & 27 \\
2. & Mined and quarted goods & 0.00126 & 32 & 0.00024 & 32 \\
3. & Food, beverages, and tobacco products & 0.03669 & 15 & 0.04287 & 7 \\
4. & Textile and leather products & 0.01647 & 25 & 0.00031 & 26 \\
5. & Wood, paper products, printing, and reproduction of recorded media & 0.02031 & 23 & 0.00939 & 24 \\
6. & Petroleum and coal products & 0.02272 & 19 & 0.00099 & 31 \\
7. & Chemical products & **0.09422** & 3 & 0.0533 & 6 \\
8. & Non-metallic mineral products & 0.02736 & 17 & 0.01208 & 20 \\
9. & Basic metal products & **0.0119** & 2 & 0.0038 & 5 \\
10. & Fabricated metal products, except machinery and furniture & 0.04660 & 11 & 0.01895 & 15 \\
11. & Computing machinery, electronic equipment, and optical instruments & 0.04354 & 13 & 0.01721 & 16 \\
12. & Electrical equipment & 0.03403 & 16 & 0.01310 & 19 \\
13. & Machinery and equipment & 0.04022 & 14 & 0.02896 & 13 \\
14. & Transport equipment, manufacturing services, and repair services of industrial equipment & 0.07923 & 5 & **0.09890** & 3 \\
15. & Other manufactured products & 0.00688 & 30 & 0.0257 & 29 \\
16. & Electricity and steam supply & **0.01319** & 1 & 0.01590 & 17 \\
17. & Water supply, sewage, waste treatment, and disposal services & 0.01055 & 29 & 0.00966 & 23 \\
18. & Construction & 0.06646 & 8 & 0.04120 & 8 \\
19. & Wholesale, retail trade, and commodity brokerage services & 0.07507 & 9 & **0.01606** & 2 \\
20. & Transportation & 0.04650 & 12 & 0.01969 & 14 \\
21. & Food services and accommodation & 0.07215 & 7 & **0.01495** & 1 \\
22. & Communications and broadcasting & 0.02146 & 21 & 0.01561 & 18 \\
23. & Finance and insurance & 0.0220 & 20 & 0.00903 & 25 \\
24. & Real estate services & 0.02718 & 18 & 0.01071 & 22 \\
25. & Professional, scientific, and technical services & 0.09381 & 4 & 0.01193 & 21 \\
26. & Business support services & 0.01147 & 28 & 0.03170 & 11 \\
27. & Public administration, defense, and social security services & 0.02092 & 22 & 0.02975 & 12 \\
28. & Education services & 0.04959 & 10 & 0.03427 & 10 \\
29. & Health and social care services & 0.07555 & 6 & 0.07114 & 4 \\
30. & Art, sports, and leisure services & 0.01926 & 24 & 0.00424 & 28 \\
31. & Other services & 0.01621 & 26 & 0.03977 & 9 \\
32. & Others & 0.00226 & 31 & 0.00167 & 30 \\ Sum(A) & & 1.62724 & & 0.057915 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Supply shortage effects of natural gas supply sector in South Korea and Japan.
###### Abstract  
Locusts are agricultural pests found in many parts of the world. Developing efficient and accurate locust information acquisition techniques helps in understanding the relation between locust distribution density and structural changes in locust communities. It also helps in understanding the hydrothermal and vegetation growth conditions that affect locusts in their habitats in various parts of the world as well as in providing rapid and accurate warnings on locust plague outbreak. This study is a preliminary attempt to explore whether the batch normalization-based convolutional neural network (CNN) model can be applied used to perform automatic classification of East Asian migratory locust (AM locust), _Oxya chinensis_ (rice locusts), and cotton locusts. In this paper, we present a way of applying the CNN technique to identify species and instars of locusts using the proposed ResNet-Locust-BN model. This model is based on the ResNet architecture and involves introduction of a BatchNorm function before each convolution layer to improve the network's stability, convergence speed, and classification accuracy. Subsequently, locust image data collected in the field were used as input to train the model. By performing comparison experiments of the activation function, initial learning rate, and batch size, we selected ReLU as the preferred activation function. The initial learning rate and batch size were set to 0.1 and 32, respectively. Experiments performed to evaluate the accuracy of the proposed ResNet-Locust-BN model show that the model can effectively distinguish AM locust from rice locusts (93.60% accuracy) and cotton locusts (97.80% accuracy). The model also performed well in identifying the growth status information of AM locusts (third-instar (77.20% accuracy), fifth-instar (88.40% accuracy), and adult (93.80% accuracy)) with an overall accuracy of 90.16%. This is higher than the accuracy scores obtained by using other typical models: AlexNet (73.68%), GoogLeNet (69.12%), ResNet 18 (67.60%), ResNet 50 (80.84%), and VggNet (81.70%). Further, the model has good robustness and fast convergence rate.  
**Keywords:** locust; grasshopper; monitoring and forecasting; image processing; deep learning; CNN
## 3 Result and Discussion
### Physicochemical Properties  
#### 3.3.1 Viscosity  
Yield stress is used to determine the extent of cross-linking in yoghurt, and this is translated as the firmness of yoghurt [48]. Usually the use of a single strain of _Lactobacillus_ is known to produce the lowest firmness and curd tension in yoghurt production [49]. Both of the inoculated milk samples produced from the control and the thermo-tolerant strains were incubated at 42 \({}^{\circ}\)C for 24 h for stable texture and viscosity and to minimise syneresis. As shown in Figure 3, both samples have shown a typical trend of shear thinning and yield stress behaviour, as reported by [PERSON] and [PERSON] (1995) [50]. The control sample required approximate 2 Pa of yield stress applied before the flow. The entangled molecules in the milk gel structure have started to untangle from stress rate of 0 to 72.72 s\({}^{-1}\) and became less resistant to flow, clearly demonstrating a shear-thinning behaviour (Figure 3a). The control sample had higher yield stress compared to that of the thermo-tolerant strain and this has prevented syneresis, whey separation and breaking down in structural alignment, as described by [PERSON] et al. (2020) [33]. Samples made from thermo-tolerant strain showed a lower yield stress, 0.5 Pa. Compared to the control sample, the flow curve appeared to be more linear. The observations over the shear-thinning behaviour may be explained by weakened alignment of the biopolymer-biopolymer molecule interactions or biopolymer molecules with the shear field [48; 51] and from the aggregation of casein particles influencing the rheological behaviour of inoculated milk overall [47; 52]. The shear-thinning behavior can further be linked to the WHC data in the next section.
# Equivalence between Fuzzy PID Controllers and Conventional PID Controllers
## 4 Conclusions  
This paper proposed and clearly identified the equivalence relationship between a conventional PID controller and a FLC. The derived equivalence equation is straightforward, so a well-designed conventional PID controller can be easily transformed to an equivalent FLC by simply defining the input/output operating ranges and following the Sugeno-style inference. The knowledge base for the equivalent FLC can be built by a cube FAM, instead of the combination structure of PI-type or PD-type FLCs. Simulation results demonstrate the effectiveness of the proposed approach, where system responses with the conventional PID controller or the equivalent FLC are similar. Moreover, the equivalent FLC implemented in discrete form was also provided and simulated, with comparable system responses to the original conventional PID controller. Based on the result of this study, the mature design results of traditional PID controllers can be applied as prior knowledge for an FLC design. Subsequently, a nonlinear FLC can outperform a traditional linear PID controller by changing fuzzy rule design or fuzzy membership functions. The proposed equivalent FLC can be set as the initial design for some learning-based techniques or evolutionary algorithms, which may achieve the optimal FLC design and considerably improve the convergence speed. Our future work will involve developing an optimal fuzzy PID controller with a simpler structure and fewer parameters, which will be designed to be more efficient for practical applications.  
The authors would like to express the sincere thanks to the reviewers for their invaluable comments and suggestions. This work is supported by the Ministry of Science and Technology, Taiwan, under Grant no. MOST 103-2221-E-218-027.  
[PERSON] and [PERSON] developed the methodology and drafted the manuscript. Moreover, [PERSON] and [PERSON] implemented the Matlab/Simulink simulations. The authors approved the final manuscript.  
The authors declare no conflict of interest.
###### Abstract  
While foot orthosis is suggested to improve rearfoot motion in running, little information is known about forefoot biomechanics. The objective of this study was to examine the effects of arch-support orthoses with various heel lift manipulation on the loading rate, spatiotemporal, and forefoot joint mechanics using a skin marker set model. Fifteen male habitual rearfoot strikers ran at their selected speeds on an instrumented treadmill in four foot orthoses conditions: flat-control, D2 (2 mm heel lift, arch-support), D6 (6 mm heel lift, arch-support), and D10 (10 mm heel lift, arch-support). A repeated measures ANOVA was performed to examine any significant difference in each of the tested variables, with \(\alpha\) = 0.05. Wearing D10 led to smaller maximum loading rate than D2 (\(p\) < 0.001) and control (\(p\) = 0.002). For sagittal plane, D10 had larger rearfoot touchdown dorsiflexion than D2 (\(p\) = 0.027) and control (\(p\) = 0.007) and larger in D6 than control (\(p\) = 0.025). For frontal plane, wearing D10 demonstrated larger rearfoot frontal RoM than D2 (\(p\) = 0.018) and peak forefoot version than D6 (\(p\) = 0.047) and control (\(p\) = 0.048). Furthermore, the forefoot frontal range of motion was lowest when wearing D6. For joint moment, wearing control orthosis exhibited larger peak rearfoot aversion moment than D6 (\(p\) = 0.035), but smaller peak knee extension moment than D2 (\(p\) = 0.025) and D10 (\(p\) = 0.010). These findings indicate that the use of arch-support orthoses would alter the running mechanics that are related to injury potential. Lower heel lift orthoses led to alternations to most of the biomechanical variables than higher heel lift orthoses. Further longitudinal study seems necessary to optimize arch-support orthoses design in running.  
metatarsophalangeal joint; impact; skin marker; frontal kinematics; forefoot; heel-toe drop
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 3 Biofilms in Food Industry
### Conventional Approaches to Biofilm Removal  
A common and readily available solution for biofilm eradication is the use of chemicals. According to the European Chemicals Agency, a few active compounds used as biocides have been approved for application in food and feed areas [132]. Biocides are usually a mixture of reagents with antimicrobial properties (active chlorine, aldehydes, peroxides) and surfactants (quaternary ammonium salts). The high efficiency of biofilm removal from the surface is only guaranteed under specific conditions, such as pH [133], temperature [134], velocity of liquid, shear stress, or the composition of the biofilm [65]. The combined application of chemical disinfection with other anti-biofilm practices has been tested in experiments, where the synergistic effect of the active chlorine with enzymes [135], essential oils [136], or ultrasounds [137] was observed. Other interesting disinfection procedures were examined in lab-scale experiments, such as the application of saturated steam [138], plasma-activated water [139], ozone [140], or LED light [141].