esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia
## 4 Indoor and Outdoor Probability Models  
The performance of wireless channels is influenced by the indoor and outdoor LOS and NLOS probabilities, which are key parameters in channel estimation. The probability of an LOS path between the transmitter and receiver antennas is defined as the LOS probability [15]. When any path is obstructed by buildings, trees, or other obstacles, it is termed an NLOS path. In this paper, we consider both indoor and outdoor probability models at mmWave frequencies. In indoor environments such as shopping malls, offices, residential areas, etc., obstructions such as walls, furniture, ceilings, and partition sheets may lead to NLOS propagation, reducing the LOS probability. As the LOS probability decreases, the path loss increases, resulting in weaker signal strength. Therefore, LOS probability models play a crucial role in path loss estimation in 5G networks. In this paper, we consider the existing ITU-R model, the WINNER-II A1 model, and our proposed model to estimate indoor probabilities in mmWave frequency bands and determine the optimal distance to maximize the probability and enhance the path loss [43]. The mathematical expressions of the three indoor probability models are provided below [43]. ITU-R Model:  
\[P(R) = 1,R\leq R_{a} \tag{5}\] \[= exp(-R(R-R_{a})/a),R_{a}<R\leq R_{b}\] \[= b,R>R_{b}\]  
WINNER II-A1 Model:  
\[P(d) = 1,R\leq R_{c} \tag{6}\] \[= 1-K\Bigl{(}-(R-Z.log10(R))^{3}\Bigr{)}^{1/3},R>R_{c} \tag{7}\]Proposed Model:  
\[p(d) = 1,R\leq R_{d} \tag{8}\] \[= 1-L\Big{(}-(R-Tlog10(R))^{3}\Big{)}^{1/3},R_{d}<R\leq R_{e}\] (9) \[= X.exp\bigg{(}\frac{-R(R-R_{e})}{B}\bigg{)},R>R_{e} \tag{10}\]  
The outdoor probability can be estimated using 3 GPP models. There are four 3 GPP models in the literature [44]. The mathematical expressions of these four models are as follows. 3 GPP 3D model:  
\[P(R)=min\bigg{(}\frac{18}{R},1\bigg{)}\bigg{(}1-exp\bigg{(}\frac{-R}{63}\bigg{)} \bigg{)}+exp\bigg{(}\frac{-R}{63}\bigg{)} \tag{11}\]  
3 GPP R1/R2 Model:  
\[P(R)=min\bigg{(}\frac{R_{1}}{R},1\bigg{)}\bigg{(}1-exp\bigg{(}\frac{-R}{R_{2}} \bigg{)}\bigg{)}+exp\bigg{(}\frac{-R}{R_{2}}\bigg{)} \tag{12}\]  
Squared Model:  
\[= exp(-R(R-R_{a})/a),R_{a}<R\leq R_{b}\]0  
Inverse Exponential Model:  
\[= exp(-R(R-R_{a})/a),R_{a}<R\leq R_{b}\]1  
In the outdoor region, the path loss probability depends on the type of geographic location, such as urban, semi-urban, or rural, along with the antenna characteristics, such as the location of the antenna, type of antenna, angle of tilt, etc. According to the location, obstructions such as ground surfaces, buildings, trees, advertising signs, bollards, etc., may change, affecting the path loss probability. In this paper, we consider urban and highway scenarios along with vehicle density to estimate the outdoor LOS and NLOS probabilities. The mathematical expressions for the outdoor LOS and NLOS probabilities for the highway and urban scenarios are shown in Table 2[17].
# Experimental Study on the Effect of Different Parameters on Rotor Blade Icing in a Cold Chamber
## 3 Experimental Results and Analysis
### Effect of Icing Temperature on Ice Shape  
In order to study the effect of icing temperature on rotor blade icing, we compared the experimental results of cases 1, 2, and 3. Figure 7 shows the ice shapes under cases 1, 2, and 3. It can be seen that different icing temperatures led to different ice types. Figure 7b shows the ice shape at \(-5\)\({}^{\circ}\)C. This is known as glaze ice, and the ice shape was typical horn ice. The amount of ice near the stagnation point was relatively small, while the icing on the upper and lower sides was thicker. This was because when the icing temperature is high, the liquid water collected on the blade surface flows to upper and lower sides under the action of surrounding airflow and freezes gradually. The ice shape at \(-15\)\({}^{\circ}\)C is shown in Figure 7c. The icing mainly accumulated near the leading edge, and the ice thickness was the largest near the stagnation point. This was a case of rime ice, which is white and opaque in appearance. The droplets froze rapidly after impacting on the surface, and there was no liquid water flowing on the surface. Figure 7a shows the rotor icing at \(-10\)\({}^{\circ}\)C. Although icing also occurred near the leading edge, it was more transparent in terms of its appearance. This ice type has the common characteristics of glaze ice and rime ice, and is known as mixed ice. It can be seen that the trend of experimental results was the same as the theoretical analysis, which indicates that there are different ice types in rotor blade icing under different temperature conditions, which should be considered in the design of rotor anti-icing and de-icing systems.  
Figure 6: Ice shape of rotor blade.  
Figure 7: Ice shapes under different icing temperatures: (**a**) case 1: icing temperature was \(-10\)\({}^{\circ}\)C, mixed ice; (**b**) case 2: icing temperature was \(-5\)\({}^{\circ}\)C, glaze ice; (**c**) case 3: icing temperature was \(-15\)\({}^{\circ}\)C, prime ice.
# Integrated System for Official Vehicles with Online Reservation and Moving Path Monitoring
## 2 System Architecture
### Cloud Server  
The cloud server stores data by interfacing with Line API so that managers can conduct data analysis of the vehicles. The familiar interface of Line allows users to use the system easily to reserve the use of the cabinet. The diagram of the cloud server is shown in Figure 12. For making a reservation, a user opens the cabinet door through LINE and takes the key and STB. At the same time, the information of the user is sent back to the cloud server. In the background management system, the use of official vehicles is managed and monitored.  
The back-end management in the cloud server has the following nine functions (Figure 13):  
1. Historical records such as date, user name, the license plate, number of uses, and period of use;
2. Vehicle route query for total time and distance of a trip;
3. Manager account for uses including information of people, groups, and administrator accounts;
4. Group management for users by teams or departments;
5. Employee's account for the mobile phone number, name, and employee number;
6. Vehicle management for recording total and recent mileage of a vehicle and warning for vehicle maintenance;
7. Vehicle reservation for showing the status of vehicles;
8. Field management for checking the status of keys and STBs;
9. Mileage check by vehicles, groups (departments) in a certain period.  
To manage the huge amount of data in the system efficiently, formalized work is indispensable to avoid data duplication and waste storage. During the normalization process, the data structure is adjusted to reduce the abnormal data. These processes include the first normal forms of 1 NF, 2 NF, and 3 NF, and the Boyce-Codd Normal Form (BCNF). In addition to the above four, 4 NF, 5 NF, Domain/key Normal Form, and 6 NF can be added. Each normalization reduces the chance of occurrence of abnormal data. Through the process, the database satisfies 3 NF with high regularization. Although it has better constraints on data association, it increases the number of data association tables for the input and output of the database. Therefore, the proposed system uses the third level of normalization. Figure 14 presents a schematic diagram of the normalization level.  
Figure 12: Cloud system structure diagram.
## 4 Parametric Analysis  
Some studies have shown that the main factors that have a great impact on the cutting force are depth of cutting, width, rake angle, friction coefficient (tensile strength, flow shear strength), material strength and cutting speed. However, by analysing the buffer force-stroke curve in Figure 5a, it is found that the cutting speed decreases during the collision process but the cutting force remains basically unchanged. Therefore, it can be considered that the average buffer force of the buffer structure under large cutting depth is affected by the cutting speed. Assuming that the average buffer force \(F\) of the energy-absorbing structure is a function related to the structural parameters of the cutting depth \(D\), the width \(W\), and the rake angle \(A\), the general model is established as follows: \(c\) is constant, \(x_{1}\), \(x_{2}\), \(x_{3}\) are exponent, and \(\sigma\) is material strength (Formula (1)).  
\[F=c\times D^{x1}W^{x2}A^{x3}\sigma \tag{1}\]  
In order to better reflect the influence of various parameters on the average buffer force, a total of 28 sets of simulation tests have been designed in this paper. The relevant parameters and simulation results are shown in Table 2. The average cutting force in the table is a calculated value. The total kinetic energy absorbed is divided by the total energy absorption stroke. The Formula (2) is as follows:  
\[F=E/L \tag{2}\]  
The collision simulation deformation diagram and force-displacement curve of each parameter are shown in Supplementary Materials. It can be seen from Table 2 that the energy absorption of the material can be changed according to the parameter change. When the device is applied to a rail vehicle as a shock absorption device, the structural size of the device can be reversely deduced according to the mass and running speed of the rail vehicle. Thereby, it can achieve the purpose of protecting rail vehicles.  
Figure 7: Cutting deformation process diagram under simulation. (a) Deformation process under simulation; (b) displacement force and energy curve.
## 2 Materials and Methods
### Pilot Equipment  
In order to obtain results similar to real industrial conditions, a pilot-scale production apparatus was constructed.  
Building on the insights gained from producing composite test specimens using a 10-liter laboratory-scale device, a pilot-scale system was developed for larger-scale testing. The main challenges encountered during laboratory experiments included slow heating, primarily due to the low thermal conductivity of the raw materials, and the high viscosity of certain mixtures, particularly those containing SBS roofing materials. These issues not only hindered efficient mixing, but also limited the volume of material that could be processed and further slowed the heating process.  
Figure 1: BBRM waste from a demolition site.  
To address these challenges, the pilot plant was designed with gas heating, enabling faster thermal response. Additionally, a high-torque agitator was incorporated to enhance mixing efficiency and minimize the risk of agitator jams, ensuring smoother operation even with highly viscous materials (Figure 2).  
The mixer was an experiment in itself, as it was a gas-heated bitumen mixer built without an oil jacket. The purpose of the oil jacket would have been to prevent localized overheating beyond the self-ignition point, which could potentially lead to an explosion. The decision to use gas was driven by uncertainty regarding the availability of industrial electric power in various locations and, on the other hand, the ready availability and cost efficiency of gas energy. With electricity, there would have been no risk of explosion and no construction complexity. Nevertheless, the new machine operated satisfactorily.  
A novel feature is a spiral beater made from round steel, designed to test the mixing of textiles with bitumen without the material wrapping around the blades (Figure 2). However, this did not help; even the fiber contained in SBS (styrene-butadiene-styrene) was sufficient to entangle both the blades and the shaft.  
The press molds were initially designed to withstand a pressure of 50 tons, which later proved excessive. Warm bitumen is easily molded with pressures ranging from 10 to 20 tons. The mold surfaces were successfully made of bitumen-repellent using a coating of silicone dissolved in octane.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Selfish Nodes  
Selfish nodes pose a significant challenge to MANETs. Unlike malicious nodes that actively disrupt the network, selfish nodes prioritize their own benefit over network goals. They selectively participate in routing protocols, forwarding packets only when it serves  
Figure 6: Delay variance attack.  
their needs. This behavior often involves dropping other packets to conserve resources like battery power. While selfish nodes may not directly target specific users, their actions harm the entire network by increasing overall overhead and reducing efficiency. Network performance suffers as other nodes must work harder to compensate for dropped packets and find alternative routes [35; 36].
# Current Applications of Ultrasound in Fruit and Vegetables Osmotic Dehydration Processes
## 3 Sonication
### Structure Changes of Material Subjected to Sonication and Osmotic Process
In addition, the use of coating with sodium alginate on pumpkin resulted in decrease of porosity of cell structure, when coating with higher percentage (3%) was applied [73]. The entity of changes in the plant tissue structure following the treatments by US, OD or UOD depends on many factors (processing time, US frequency, type and concentration of the solution, etc.). With short time of OD, better preservation of the kiwifruit structure subjected to the novel method of osmotic-dehydrofreezing with US enhancement was observed. For kiwifruits subjected to 120 min of either OD or UOD significant differences were observed in their structure as compared to fresh samples. However, for longer OD with US assistance (180-300 min), cells of kiwifruit tissue became distorted and formed large intercellular spaces [74]. A similar effect was observed in cranberry samples treated by a combination of cutting or blanching with US assisted OD process, even if the UOD treatment was set up to 30 (Figure 3b) and 60 min. Moreover, these treatments promoted a merging of the neighboring cells and creation of microchannels. Furthermore, with the thermal treatment (blanching), the tissue was characterized by more porous structure [75]. The sonication applied during OD caused also changes in microstructure in coconut tissue [76], pineapple [31] and cherries [77]. In addition, the frequency of US used during OD process impacts on microstructural alterations. OD assisted by US with high frequency (130 kHz) caused serious ruptures and damages in cranberries tissue microstructure, while at lower frequency (35 kHz) only few microchannels were observed. The significant changes in structure resulted in texture ruptures, which could not preserve the puffiness formed by steam pressure caused by microwave treatment [78]. Figure 2: The influence of ultrasound treatment on the structure of: apple (**a**); and potato (**b**). The magnification is \(100\times\) and \(200\times\), respectively. Own unpublished data. The white arrows indicate microchannels. The concentration of osmotic solution and method of OD conduction is also of great importance. [PERSON] et al. [79] observed less damages of the potato tissue, while a lower concentration of osmotic solutions was applied. In comparison to OD process, UOD with 15% salt or 50% sugar solution resulted in mechanically damaged and broken cells. Furthermore, the black jamun subjected to OD process in atmospheric conditions and under vacuum with (UVOD) and without (VOD) simultaneous application of US was characterized by different microstructure, depending on the process applied. The samples subjected to OD had oval pores, while those treated by VOD had small and irregular intercellular spaces and UVOD treated samples presented a shrinkage of the cellular structure [80].
# Modeling the Directional Clumping Index of Crop and Forest
## 4 Sensitivity Analysis
### Factors Influencing Forest Clumping Index Variation
#### 4.2.2 Tree Distribution Pattern  
Non-random tree distribution causes clumping and more apparent shadowing among crowns, which also affects canopy \(\Omega(\theta)\) at large zenith angles. Two tree distribution functions were compared: Poisson and Neyman distributions, since these represent random and non-random distributions, respectively [46].  
Neyman distribution included \(m_{2}=1\), 2, 5, 10, 20, as shown in Figure 16. When \(m_{2}=1\), the gap fraction distribution is similar to, but not the same as, the Poisson distribution. As \(m_{2}\) increases, the clumping effect between trees and intra-crown gap fraction increase. However, this combined effect on \(\Omega(\theta)\) is not large. When \(m_{2}\leq 5\), \(\Omega(\theta)\) difference between sample lines \(<\)0.02. This demonstrates that the slight non-random distributions in natural forests has little influence on canopy level \(\Omega(\theta)\). But, if the non-random distribution is dominant, its influence on \(\Omega(\theta)\) cannot be ignored. For example, the \(\Omega(\theta)\) difference between the sample line with \(m_{2}=5\) and that with \(m_{2}=20\) can reach 0.08 at large zenith angles.
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 2 Isolation of Proteins and Functional Peptides from Microalgae  
Although the incorporation of whole microalgae biomass into food and feed is well established, for the production of microalgae protein isolates and their subsequent successful incorporation into food products, the development of robust and feasible processes is required [20]. After the protein extraction, the solubility is increased and undesired color is removed, which leads to easier integration into the food product [120]. Firstly, to improve the efficiency of any extraction process, it is necessary to disrupt the cells and release the intracellular content to buffers of solvents. The composition of the microalgae cell wall is specific to each species, so the selection of a suitable disruption technique must also take into account, among other things, the cell wall composition [20]. The use of conventional extraction techniques generally results in low yields caused by protein degradation due to extreme temperatures and pH conditions used during the processes. Therefore, researchers are currently mainly focused on the development of novel, non-thermal extraction methods that employ enzymes and \"green technologies\" to increase extraction efficiencies and lower negative impacts on the environment [22]. During most of the extraction procedures, proteins are co-extracted with sugars, polyphenols and other compounds. For this reason, subsequent isolation and purification procedures are necessary. For the scheme of the whole process of protein extraction and the production of bioactive peptides, see Figure 1.
## 1 Introduction
### Key Contributions  
Our research advances the state-of-the-art through four principal contributions.  
First, we develop and validate a multi-algorithm ML framework comprising four synergistic algorithms: Kalman Filter Timing Correction (KFTC) optimized for Gaussian-distributed timing errors and white noise conditions, Recursive Least Squares Timing Correction (RLSTC) designed for linear drift and time-varying parameter scenarios, Fuzzy Logic Timing Correction (FLTC) targeting non-linear error dynamics and burst interference patterns, and Hybrid Adaptive Timing Correction (HATC) implementing meta-learning for dynamic algorithm selection across mixed error conditions--each seamlessly integrated within real-time constraints.  
Second, our comprehensive evaluation, validated through rigorous statistical analysis, demonstrates significant improvements across all metrics. The ML-enhanced system achieves a 31.01% timing correction success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline methods with a large effect size (Cohen's d = 0.82, \(p<0.001\)). The security enhancements are equally impressive, with an 88.3% average attack detection rate across six distinct attack vectors, reducing the average CVSS score from  
Figure 1: SMART DShot system overview with ML-enhanced signal processing, real-time attack detection, and adaptive timing correction.  
7.3 (High) to 3.1 (Low). Statistical validation through ANOVA (F(3,1996) = 30.30, \(p\) < 0.001) confirms these improvements are both statistically significant and practically meaningful.  
Third, FPGA synthesis across multiple device architectures confirms practical feasibility with minimal resource overhead (2.16% LUT utilization) and efficient power consumption (16.6 mW per channel), allowing immediate industrial deployment.  
Fourth, we establish mathematical frameworks characterizing the performance of timing correction in various noise models, with empirical validation showing that the performance enhancement is achieved through intelligent algorithm selection.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 4 Our Research on IPMN Standards
### Hybrid Indoor Positioning System Architecture  
Our IPMN system architecture, as shown in Figure 3, is very similar to the indoor navigation architecture for ITS. Typically, an IPMN system contains a backend layer, which represents different servers in the cloud computing environment, and a client layer, which represents the applications for smartphones or mobile robotic devices. Large data, such as static indoor map data, positioning fingerprints database, containing all kinds of informa  
Figure 2: Standard system architecture.  
tion (sensors, energy efficiency, etc.) about smartphones, and so on, should be stored on the backend side and should be updated frequently. The client can download needed data when it generates coordinates or runs a path-finding algorithm using various positioning and GIS technologies. Sometimes, a client needs to update the device location to the server for monitoring or tracking services. The key characteristic of our architecture is that the GIS technologies, and functionalities such as semantic analysis, are integrated into the individual positioning technology for enhanced accuracy and reliable positioning.  
In this paper, we discussed the standards for the architecture. We classified the standards used in this architecture into three parts:  
1. Standards for indoor map data.
2. Standards for positioning technologies.
3. Standards for navigation service.  
In the following sub-section, we will briefly discuss some standards used for our project, including the:  
* Content model for indoor mapping.
* Data collection for indoor maps.
* GIS model for indoor spatial data.
* Indoor map symbols.
* Specification for digital indoor map products.
* Multi-source fusion positioning data interfaces.
* Seamless cooperative positioning service interfaces.
# Effect of Plate Curvature on Heat Source Distribution in Induction Line Heating for Plate Forming
###### Abstract  
Line heating is an essential process in the formation of ship hull plates with a complex curvature. Electromagnetic induction heating is widely used in the line heating process. In electromagnetic induction heating, the shape of the coil and the air gap between the inductor and workpiece could influence the heat source distribution. Moreover, in the line heating process, the change of curvature of the plate will cause a change of the air gap of the inductor. Magnetic thermal coupling calculation is an effective method for simulating induction heating. This paper used the finite element method to calculate the distribution of heat sources in different initial plate curvatures and coil widths. The changes in heat source distribution and its laws were investigated. The results show that when the coil width is less than 100 mm, the effect of plate curvature on heat source distribution and strain distribution is not apparent; when the coil width is greater than 100 mm, the plate curvature has a visible effect on the heat generation distribution. In the case of a curvature increasing from 0 to 1 and a coil width equal to 220 mm, the Joule heat generation in the center of the heating area is reduced by up to 21%.  
Keywords:plate forming; line heating; induction heating; structural deformation; heat distribution +
Footnote †: journal: Applied Sciences