esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Fatty Acid Profile of Milk after Pasteurization  
Figure 7 illustrates the results of our investigation into the fatty acid content in milk samples subjected to pasteurization techniques of PEF, MF, LTLT, HTST. No significant differences were observed between the control and treated samples. In control samples, average omega-3 content was 0.51%, omega-6 content 1.81%, and omega-9 content 19.04% of the total fatty acid content. In PEF-treated samples, omega-3 content was 0.50%, omega-6 1.85%, and omega-9 19.05%. For MF-treated samples omega-3 content was 0.47%, omega-6 1.82%, and omega-9 19.01%. LTLT-treated samples demonstrated similar results, with omega-3 content of 0.47%, omega-6 1.82%, and omega-9 19.01%. In HTST-treated samples, the average omega-3 content was 0.52%, omega-6 1.85%, and omega-9 19.02%.  
Figure 6: DPPH radical scavenging activity, expressed as a percentage, across various pasteurization methods: RM—raw milk (control); PEF—pulsed electric field; MF—microfiltration; LTLT—low temperature, long time; HTST—high temperature, short time.  
Figure 7: Fatty acid content (% of total fatty acid) in milk samples subjected to various pasteurization methods: Control (raw milk); PEF—pulsed electric field; MF—microfiltration; LTLT—low temperature, long time; HTST—high temperature, short time.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 2 Background and Related Works
Neural networks were used in [28] to forecast mm-wave beams with low training overhead utilising received signals from nearby gNBs. Ref. [31] analysed voice signals for a multiple-access network consisting of many gNBs. The mid-band 5G frequency was only discussed in the [33] framework in a single gNB scenario. Deep neural networks, which need channel knowledge to make decisions, were used in [33] to perform joint beamforming and interference cooperation at mm-wave. The effectiveness of deep neural networks for beamforming without using reinforcement learning was examined in [34]. These works have deployed machine learning algorithms to implement beamforming to solve the interference problem. Although beamforming improves the SINR of the system and resists interference, it is very complicated and expensive in terms of the hardware to be deployed. Advanced techniques such as double deep Q-learning were discussed in [35]. The main difference between DQL and double deep Q-learning (DDQL) is that DDQL uses two separate deep neural networks to estimate the Q-values, whereas DQL uses only one. The idea behind DDQL is to reduce the overestimation of Q-values that can occur in DQL. However, in some cases, such as when dealing with interference in 5G networks, overestimation may not be a problem and may even be desirable. In the case of interference in 5G networks, the goal is to minimise the interference between different channels and users. This can be achieved by optimizing the transmission power and frequency allocation of different users. In this scenario, DQL may be more suitable than DDQL because it allows for more exploration of the state-action space, which can lead to better performance in complex environments. Additionally, DQL has been shown to be more computationally efficient than DDQL, which can be a critical factor when dealing with large-scale 5G networks with many users and channels. The literature review is summarised in Table 1 and the outcomes are discussed in the following section. Outcome  
UAVs are frequently touted as a promising solution for a wide range of applications, and, as such, the future 5G networks are expected to be able to support UAVs flying at higher altitudes and speeds with minimal interference. Within the existing literature, many works discuss interference mitigation techniques; however, they also have certain drawbacks and unsuitability to the problem at hand. Methods such as ICIC, as discussed in [17; 18; 19; 20], are effective for reducing interference via coordination; however, in dynamic and unpredictable environments, such as UAVs in 5G networks, they would be ineffective. In addition, they also generate high overhead, thus
## 7 Discussions and Findings
### Building Information Modelling (BIM)  
BIM, presenting elaborated real-time construction information, has been widely used in modular construction. To date, BIM application in modular construction focuses on modular design. It is advantageous to improve the modular building design by the reduction in design coordination errors. BIM-based parametric design takes full advantage of the characteristics of BIM to create  
Figure 5: Module installation at site (photo by [PERSON]).  
prefabricated components with complete attributes. [PERSON] et al. [79] developed a BIM-based process of parametric design optimising traditional parametric design processes, considering manufacturability and assemblability. [PERSON] et al. [80] proposed a BIM-based automatic design application improving the quality of buildings embedded with wood panels. However, few studies concentrated on the application of BIM for high-rise modular buildings. In accordance with findings of the case study, BIM-based parametric design could improve the issues including C12, \"poor design flexibility\", and C11 \"unable to freeze design early\". On the other hand, BIM as a real-time information sharing platform makes a significant contribution to multi-disciplinary collaboration and coordination in conventional construction [81, 82]. In contrast to conventional construction, modular construction is a typical process-intensive approach, with a relatively interdependent supply chain that needs a mature solution for coordination and communication among all stakeholders throughout the entire life cycle [83]. As a consequence, BIM has great potential to act as a communication platform during modular construction that is a possible solution to C9, \"lack of coordination and communication\", the foremost constraints from the questionnaire.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Sample Homogenization--Dry, Wet and Cryogenic Grinding  
In this section, we offer a glance at various homogenization techniques, namely, dry, wet and cryogenic grinding techniques. Aflatoxins have been found in a variety of foods, including peanuts, tree nuts, oil seeds, cereal grains and miscellaneous commodities such as dried figs, milk, cheese and spices. Most samples such as peanuts and in-shell nuts can be directly ground and blended until sample particles are small enough to meet a predefined degree of fineness (e.g., pass a No. 20 sieve) [33], minimizing the impact of heterogeneous distribution of aflat on quantitation. Depending on the size of a sample and available milling devices, the sample could be first coarse ground to pass through a bigger sieve (e.g., No. 8 or 10). Sub-samples are then removed and blended for fine grinding to pass through a No. 20 sieve [34]. Alternatively, the entire coarse ground sample is finely ground before the test portion or analytical sample is removed [33; 35]. Dried commodities (e.g., corn kernels) can be ground with the addition of water [36]. Similarly, green coffee beans or dried fruits are cryogenically ground using dry ice in place of water. [37].
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 6 Limitations  
Despite its many advantages, adopting ML in WSNs has certain limitations, including the following:  
* Because machine learning (ML) algorithms need to learn from prior data, they do not yield correct predictions right away. The quantity of previous data affects the system's performance. Large data sizes also result in excessive energy consumption during data processing. Stated differently, a trade-off exists between the high computational complexity of the ML algorithm and the energy limits of WSNs. The ML algorithms must operate centrally in order to overcome this trade-off.
* It is a laborious process to validate the predictions made by the machine learning algorithm in a real-time setting.
* Finding a specific machine learning method to solve a problem in a wireless sensor network (WSN) can be hard at times.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 4 System Design and Implementation
### Data Collection
There is a similar dataset for Philadelphia [[https://www.opendataphilly.org/dataset/vacant-property-indicators](https://www.opendataphilly.org/dataset/vacant-property-indicators)]([https://www.opendataphilly.org/dataset/vacant-property-indicators](https://www.opendataphilly.org/dataset/vacant-property-indicators)) (accessed on 19 May 2023) that enjoys more or less the same characteristics, although with a rather limited size. We also created a small dataset of hand-labeled features for the city of Volos. For the second method, we obtained data for nonvacant buildings from OpenStreetMaps using the Turbo Overpass API. These data include spatial information for residential areas, public facilities (e.g., schools), and amenities (e.g., cafes). As for the abandoned buildings' data, we hand-crafted a small dataset by searching through the city of Volos and generating their coordinates through geojson.io.
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 3 Measurement Implementation and Verification
### ROI Edge Extraction Analysis  
As shown in the comparison of edge detection times in Figure 14, when processing a 6144 \(\times\) 4096 high-resolution image, the fastest measurement times for the sub-pixel edge  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement Position**} & \multirow{2}{*}{**Part**} & **Three Coordinate Measuring Machine (mm)** & **This Article’s Algorithm (mm)** & **Sub-Pixel Edge Detection (mm)** & **Obsu-Canny Edge Detection Algorithm (mm)** & **Canny Edge Detection Algorithm (mm)** \\ \hline \multirow{4}{*}{Inner roundness} & part 1 & 0.067 & **0.062** & 0.061 & 0.050 & 0.052 \\  & part 2 & 0.062 & 0.068 & **0.063** & 0.130 & 0.048 \\  & part 3 & 0.069 & **0.062** & 0.079 & 0.092 & 0.095 \\  & part 4 & 0.043 & **0.043** & 0.708 & 0.069 & 0.057 \\  & part 5 & 0.070 & **0.072** & 0.068 & 0.082 & 0.078 \\ \hline \multirow{4}{*}{Outer roundness} & part 1 & 0.073 & 0.780 & **0.075** & 0.126 & 0.095 \\  & part 2 & 0.082 & **0.070** & 0.026 & 0.022 & 0.042 \\ \cline{1-1}  & part 3 & 0.076 & **0.073** & 0.097 & 0.058 & 0.062 \\ \cline{1-1}  & part 4 & 0.077 & **0.070** & 0.039 & 0.031 & 0.035 \\ \cline{1-1}  & part 5 & 0.070 & 0.062 & 0.076 & 0.067 & **0.073** \\ \hline \hline \end{tabular}  
* In the table, the best relative precision is in bold.  
\end{table}
Table 4: Comparison Table of Roundness Results.  
detection algorithm and the Otsu-Canny edge detection algorithm are 0.17 s and 1.57 s, respectively. In contrast, the fastest test time for our algorithm is 0.16 s, which is faster than the other three detection methods. The first two edge detection methods scan the entire image, requiring the processing of large amounts of data, computation, and time. In comparison, our method accurately locates and extracts the ROI in the image through CAD model drawings. By reducing the processing scope and computational complexity, a faster processing speed is achieved. The targeted ROI processing reduces the impact of pattern processing and uneven illumination on the ROI area. Only the edge features related to part size measurement are processed, eliminating the need to handle background noise and other infrared-related areas, thereby improving edge recognition accuracy. This provides reliable data support for subsequent size measurements and defect detection.
## 3 Results and Discussion
### Composites Characterization
#### 3.2.2 Fourier Transform Infrared Spectroscopy (FTIR)  
The FTIR spectrums of neat PP and BC/PP composites are shown in Figure 5. The FTIR of composites shows mainly the characteristics peaks of the neat PP. The strong peak at 3000-2800 cm\({}^{-1}\) is contributed by the C-H stretching vibrations of PP chains. The peaks at 1457 cm\({}^{-1}\) and 1376 cm\({}^{-1}\) are assigned to -CH\({}_{2}\) and -CH\({}_{3}\) bending vibrations, respectively. Moreover, small peaks at 1167 cm\({}^{-1}\), 998 cm\({}^{-1}\), and 973 cm\({}^{-1}\) are attributed to -CH\({}_{3}\) symmetric deformation and -CH\({}_{3}\) rocking vibrations, receptively [31]. The biochar peaks with very small intensity at 1693 cm\({}^{-1}\) and 760 cm\({}^{-1}\), due to stretching vibrations of the carbon double bond (C=C-), were also present in the composites. This indicates that biochar are well present in the composites.
# Study of the Process of Packet Arrival at a Multiservice Node
## 2 Mathematical Model of a Multiservice Node
### Accuracy Criteria for Solving the Problem
For the _G/M/1_ model, the inverse problem is solved in an elementary way, but the result is two values of the required service intensity, of \(\mu_{1}\) and \(\mu_{2}\):  
\[\mu_{1}=\frac{1}{S^{(1)}(1-\sigma)},\;\mu_{2}=-\frac{\ln(1-p)}{t_{p}(1-\sigma)} \tag{13}\]  
Since both limitations should be met, the value of \(\mu\) is chosen as the maximum one from the pair \(\mu_{1}\) and \(\mu_{2}\). It follows from Relationships (13) that the values of \(\mu_{1}\) and \(\mu_{2}\) are inversely proportional to the normalized value that can be denoted by the letter \"\(z\)\". The rest of the variables included in Relationships (13) represent a certain constant. Let us denote the relative error in assessing the \(z\) as \(\delta_{z}\). After a series of simple transformations, the following assessment can be obtained for the relative error in the calculation of the service intensity, \(\delta_{\mu}\):  
\[\delta_{\mu}\leq\delta_{z}\frac{2}{(1-\delta_{z})}. \tag{14}\]  
In the area of generally accepted levels of \(\delta_{z}\) (units of percents), when solving the inverse problem, the relative error doubles. This conclusion should be taken into account when carrying out calculations related to the design of those hardware and software facilities, the models of which can be represented by a queuing system.
# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 1 Introduction
The proposed model involves the recovery of the input image using three consecutive coding structure blocks (CS blocks) with different dilation rates. Thereafter, the feature maps of the submodules are blended, and the recovered image is generated through the scale blend block (SB block) and simple mathematical operations. The contributions of this study are as follows:  
1. A U-Net-based [17] encoder-decoder submodule called a CS block is proposed. Unlike a typical U-Net, the feature maps of the encoding steps are passed through a densely connected network [18] before being merged in the corresponding decoding steps. Three CS blocks are arranged consecutively in ascending order of dilation rate. 2. To effectively utilize the feature maps of the CS block, a cross-stage feature fusion block (CSFF block) is proposed for providing information to subsequent CS blocks. 3. A scale-blending operation is proposed and implemented in the model. 4. To balance the tradeoff between pixel-level accuracy and structural accuracy, the model is trained using the mean square error (MSE) loss and structural similarity (SSIM) loss [19] functions.
## 7 Conclusions  
To prevent the affecting of missing flight data on flight information analysis, the polynomial fitting model based on the least squares method was employed to fit and correct the missing flight data. For the flight data of aircraft engine exhaust gas temperature (EGT) is often lost because of the immature detection technology, a series of multi-order polynomials are established by the relationship of aircraft engine exhaust gas temperature (EGT) and revolutions per minute (RPM). By comparing the error of every polynomial, the four-order polynomial has more obvious advantages for correction and fitting of flight Data EGT based on RPM, and it is verified that the polynomial least squares analysis method in correction and fitting civil aviation flight data EGT based on RPM is simpler and more effective than the previous method, has universal practicability, and has engineering application value in-flight data fitting and correction. Subsequent research will be applied to aircraft operation reliability assessment and pilot flight quality monitoring on the basis of filling and fitting data.  
Conceptualization, N.C. and Y.-C.S.; data curation, N.C. and Z.W.; methodology, N.C. and C.P.; formal analysis, N.C. and C.P.; writing--original draft preparation, N.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number U2033202; Key R&D Program of Sichuan Provincial Department of Science and Technology (22 ZDYF2942); Safety Capability Fund Project of Civil Aviation Administration of China (2022)026).  
Not applicable.  
The data used to support the findings of this study are included within the article.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **Real Value** & **Predicted Value** & **Error (\%)** \\ \hline two & 956.81 & 953.6518 & 0.3 \\ three & 956.81 & 953.612 & 0.3 \\ four & 956.81 & 959.1251 & 0.2 \\ five & 956.81 & 954.4378 & 0.3 \\ six & 956.81 & 937.3811 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The error of different multi-order polynomial for EGT4.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 1 Introduction
Contributions: The contributions of our work have been summarized in the itemized text below: (1) The optimal set of mapped values for number of potential forwarding nodes and number of suppressed nodes has been investigated; (2) Significant energy is saved due to optimal route discovery mechanism; (3) Additional energy conservation achieved by dividing forwarder region into transmission power levels; (4) An optimal solution provided to cater for the problem of voids and energy holes; (5) Performance parameters included in formulating the holding time i.e., number of potential forwarder, number of suppressed nodes, hop count; (6) Significant improvement in end-to-end delay achieved by readjusting the position of sink nodes; (7) Traffic congestion sorted out by averaging potential forwarding nodes which forms the basis of item 2. To implement the above mentioned contributions, we follow the following steps:  
* Selection of forwarder by computing the optimal average number of PFNs of the forwarding nodes,
* Calculating optimal transmission power adjustment based upon more distant node from the source node in potential forwarding region,
* Finding the alternate node from the suppressed region for the case if source node is in a void,
* Carrying out packet holding time calculations to assign priorities,
* Finding the nearest sink for the case if one of the sink is embedded underwater. The rest of the manuscript has been structured as follows. The detailed discussion regarding previous work on routing protocols in UWSNs is carried out in Section 2. Section 3 is about the identification of the problem and present the problem statement. The system model is explainedin Section 4. The experimental setup and simulation outcomes of our approaches are described in Section 5. Performance comparison and analysis discussed in Section 6. Finally, a brief conclusion is presented in Section 7.
## 2 Materials and Methods
### Landslide Conditioning Factors
#### 2.3.3. Hydrological Factors  
In this study, we consider drainage proximity, the stream power index (SPI), the topographic wetness index (TWI), and rainfall patterns observed in the months following seismic activity as the hydrological factors. During ground-shaking events, slopes composed of saturated or partially saturated soils undergo loss of shear strength, sometimes also behaving akin to liquid. This phenomenon exacerbates the potential for slope instability and subsequent landslides. The topographic wetness index (TWI) is a particularly noteworthy factor employed to quantify the influence of topography on hydrological processes [29]. It provides valuable insights into the distribution and movement of water across a landscape, thereby helping predict areas of potential slope instability. Figure 7a-d present the hydrological layers, and details of the stations and their measurements are listed in Supplementary Table S2.  
Figure 5: Geological map of the study area with lithological geocodes and their descriptions.  
Despite the critical role of hydrological factors in triggering landslides, many existing studies have overlooked the contribution of precipitation in modeling co-seismic landslide susceptibility. A significant innovation of this study is the incorporation of rainfall data not only from April 2015 (the month of the earthquake) but also from the two subsequent months (May and June 2015), when numerous delayed landslides were reported. Rainfall data were obtained from 10 ground-based meteorological stations managed by the Department of Hydrology and Meteorology (DHM), Nepal. The data were spatially interpolated using the Inverse Distance Weighting (IDW) method to generate continuous rainfall surfaces. This station-based integration offers a more physically realistic representation of post-seismic hydrological conditions, which is crucial for accurate susceptibility modeling.  
Figure 6: Topographic factors: (**a**) aspect, (**b**) curvature, (**c**) elevation, and (**d**) slope.