esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Propagation with Non Line of Sight (NLOS)  
It is important to study the effects of vegetation on a signal because many applications require the use of the microwave frequency band (0.3-300 GHz). In addition, depending on the thickness of the vegetation and the frequency of operation, a signal could travel along diffracted trails reducing the range of radio equipment communications. Quantitative knowledge of the excess of propagation loss suffered by radio waves due to the presence of vegetation is essential to plan a communication link in any wooded land [52; 53; 54; 55]. Inside a greenhouse, in addition to attenuation in free space, electromagnetic waves are vanished by mechanisms that include diffraction, reflection, and dispersion produced by the leaves, branches, and stems of crops, distributed at random, between the transmitter and receiver, having an unknown effect on the exact propagation of radio waves (see Figure 1) [56]. To determine these changes, propagation models are used that estimate the radio coverage area of a transmitter and show the strength of the signal between the transmitter and receiver [11; 12; 36; 57].
## 2 Design Rules of Small Cell Enhancement Systems
### Channelization of Link Budget
#### 2.2.1 Path Loss  
The Friis free-space equation [16] can be seen in Equation (1):  
\[P_{r}(d)=P_{t}G_{t}G_{r}(\lambda/4\pi d)^{2}L \tag{1}\]  
The formula under consideration is given by \(P_{r}(d)=P_{t}\ G_{t}\ G_{r}\ (\lambda/4\pi d)^{2}\ L\), where \(P_{r}(d)\) represents the received power at a distance of \(d\) from the transmitter, \(P_{t}\) denotes the transmitter power, \(G_{t}\) and \(G_{r}\) stand for the respective antenna gains of the transmitter and receiver, \(\lambda\) is the wavelength of the signal, \(d\) represents the distance between the transmitter and receiver, and L represents the system loss factor.  
Based on the Fraunhofer distance [17], this can be written as Equation (2):  
\[d_{f}=2D^{2}/\lambda^{2} \tag{2}\]  
Here, \(D\) is the largest physical linear dimension of the antenna. Additionally, in the far-field region, \(d_{f}\) is the Fraunhofer distance, which must meet the following two conditions:  
* \(d_{f}>D\), \(d_{f}>\lambda\).  
It is clear that Equation (1) does not hold for \(d=0\). For this reason, the propagation mode for large-scale power reception uses a near-field distance \(d_{0}\) as the 20 to 30 \(\lambda\) reference.  
* \(d>d_{0}>d_{f}\).  
At any distance greater than \(d_{0}\), the received power, \(P_{r}(d)\), may be related to the power \(P_{r}\) at \(d_{0}\). The value of \(P_{r}(d_{0})\) predicted by Equation (1) can be determined by averaging the received power at multiple points located at a near-field radial distance of \(d_{0}\) from the transmitter. It is important to select a reference distance that lies in the far-field region, that is, \(d>d_{f}\), and \(d_{0}\) should be smaller than any practical distance used in the wireless communication system. Hence, Equation (1) can be used to calculate the received power in free space at a distance \(d_{0}\), where the received power, \(P_{r}(d)\), is equal to \(P_{r}(d_{0})\) multiplied by \((d_{0}/d)^{2}\).  
In wireless communication systems, it is not unusual for the received power, \(P_{r}\), to vary by several orders of magnitude across a typical coverage area of several square kilometers, leading to a large dynamic range of received power levels that can be expressed in units of decibel-milliwatts. When these conditions are met, the received power can be written as Equation (3):  
\[P_{r}(d)\text{dBm}=P(d_{0})+20\log(d_{0}/d) \tag{3}\]
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Smart-Grid-Based Cybersecurity Standards
#### 2.5.2 IEEE 2030  
IEEE 2030 is a standard that focuses on the implementation of layered cybersecurity architecture and modular deployment to ensure alignment with IEEE 1547 [48], IEC 61850, and IEC 62351, thus promoting interoperability and cybersecurity in smart grid systems such as microgrids, distributed generation, and energy storage, as well as EV infrastructure. However, its adoption remains a challenge that persists in small-scale systems due to deployment complexities [49].
# Screening Mechanism and Properties of a Cantilevered Vibrating Sieve for Particles Processing
## 3 Screening Mechanism of Cantilevered Vibrating Sieve (CVS) Based on Motion Characteristics  
### Motion Characteristics of Cantilevered Vibrating Sieve (CVS)  
Any cantilevered sieve rod of the CVS is a thin and long rod with a certain degree of elasticity. Given that the vibration direction of the screen body is not vertical to sieve rods, a tangential additional bending vibration relative to sieve rod section may be very easily yielded in the vibration process from Position A to Position C, as shown in Figure 3.  
Figure 2: Schematic diagram of particles passing through screen holes of (**a**) a traditional rectangular screen and (**b**) a CVS. Generally, the screening body of a vibrating screen usually oscillates in a simple harmonic motion. Assuming the vibration equation of screen body with respect to time it is \(y_{s}(t)=D\sin\omega t\), where \(D\) is the vibration amplitude and \(\omega\) is the vibration circular frequency. The included angle between the vibration direction of screen body and sieve rods is \(\alpha\), the sieve inclination angle is \(\beta\), and thus the vibration direction angle is \(\gamma=\alpha-\beta\). The length and cross-sectional area of the cantilevered sieve rod are L and A, respectively. Thus, in the coordinate system xoy of the screen body, the partial differential equation of additional bending vibration \(y(x,t)\) is:  
\[EJ\frac{\partial^{4}y(x,t)}{\partial x^{4}}+\rho A\frac{\partial^{2}y(x,t)}{ \partial t^{2}}=-\rho A\cdot\frac{\mathrm{d}^{2}y_{s}(t)}{\mathrm{d}t^{2}} \cdot\sin\alpha\quad(0\leq x\leq L), \tag{4}\]  
where \(E\) and \(\rho\) are the Young's modulus and the density the rod material, respectively, \(J\) is the second moment of the cross-sectional area with respect to the neutral axis. For Equation (4), the additional bending vibration equation of cantilevered sieve rods can be assumed as [22]:  
\[y(x,t)=\sum_{i=1}^{\infty}C_{i}Y_{i}(x)\sin\omega t, \tag{5}\]  
where \(Y_{i}(x)\) is the ith-order natural mode of vibration. For constant-section homogeneous rods like the cantilevered sieve rods, the following relationship holds:  
\[Y_{i}(x)=\cosh\beta_{i}x-\cos\beta_{i}x-\frac{\sinh\beta_{i}L-\sin\beta_{i}L}{ \cosh\beta_{i}L+\cos\beta_{i}L}(\sinh\beta_{i}x-\sin\beta_{i}x), \tag{6}\]  
where the value of \(\beta_{i}\) is determined by the formula \(\cos\beta_{i}L\cdot\cosh\beta_{i}L+1=0\). The solution to this nonlinear equation is shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \(i\) & **1** & **2** & **3** & **>3** \\ \hline \(\beta_{i}\) & 1.8751/\(L\) & 4.6941/\(L\) & 7.8548/\(L\) & (2i-1)\(\pi\)/2\(L\) \\ \hline \end{tabular}
\end{table} 
Table 1: Parameterized representation of the solution. Figure 3: Cantilevered vibrating sieve (CVS) vibration model during screen body motion.
## 1 Introduction
Although the requirement to check container weight before loading was a significant step forward, there are still some vague points in the container packing process and incidents still take place where the weight of a container is believed to have been declared incorrectly (CMA CGM George Washington, MSC Zoe) [29]. A proper loading plan requires determining factors and environmental parameters in which the vessel is to operate. For this reason, many researchers have addressed this problem. For example, [PERSON] in his work analyzes data on the influence of wavelength and wind force in the context of the stability of ship-going vessels [30]. A considerable contribution is a work presented by [PERSON] (now in its seventh, completed edition), which provides a comprehensive introduction to all aspects of stability and strength, ship squat, and the effects of used shipbuilding materials [31]. Numerous practical examples are included to help officers gain qualifications related to this subject. [PERSON] and [PERSON] in their study [32] described the effect of ballast water exchange operations at sea on ship stability and strength. [PERSON] et al. [33] characterizedthe BLOCK Algorithm, which helps to solve ship reloading problems, related to container jamming in the hold or on deck. [PERSON] [34], on the other hand, pointed out that the stability of a vessel is also affected by auxiliary equipment (commonly used in small general cargo ships and small container ships). Model tests confirmed that Ultra Large Container Ships are more sensitive than expected to parametric rolling because of low GM values in full load conditions combined with low speeds due to port congestion and Easterly swells in the wintertime. This shows how important it is to properly assess the ship's stability [35]. Moreover, the EMSA 2019-2024 strategy prioritizes specific safety issues related to container vessels--in particular, loss of containers and fires onboard [9]  
One of the reasons why regulations can be difficult to supervise is that there are different methods for calculating the weight of containers. Partly, this is because the system is filled with differing procedures and also differing qualities of equipment and devices, e.g., weight bridges, depending on the financial capacity of the parties concerned. Improved enforcement of regulations would increase their effectiveness, so it is desirable that the provision for verifying the weight of a container is further revised. Therefore, in this paper, it has been decided to simulate (using the MACS3 program) the degree of irregularities in container loading in relation to their planned and actual weight. The differences are indicated in the criteria affecting the ship's stability and the consequences of incorrect load on the stability of container ships.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 6 Applicative Example
### Our Protocol
#### 6.2.3 Over the Core Network  
As it was mentioned earlier, \(5G\) technologies will bring a lot of virtualization, implying more uncertainty on the location of the involved physical equipment. In cases where the Mobile Network Operator (MNO) has not full control over this physical equipment (i.e. if other companies are providing them to the MNO), our protocol can help to detect non-advertised changes if those changes create an impact on RTT, which is probably the case if the infrastructure provider gets compromised. The Core Network is an IP private network, hence, it should be a highly stable area time-wise, which makes our protocol easily applicable between two nodes of the Core Network such as the User Plane Function (UPF) entry node and the UPF exit node.
# Measurements of Room Acoustic and Thermo-Hygrometric Parameters--A Case Study +
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Coefficient** & **P** & **RMSE** & **R\({}^{2}\)** & **MAE** \\ \hline T\({}_{30}\) 125 Hz & \(-\)0.0184 & 0.0076 & 0.4196 & 0.0273 & 0.2190 \\ T\({}_{30}\) 250 Hz & \(-\)0.0010 & 0.7312 & 0.1839 & 0.0056 & 0.1099 \\ T\({}_{30}\) 500 Hz & \(-\)0.0065 & 0.1322 & 0.2729 & 0.0175 & 0.1204 \\ T\({}_{30}\) 1 kHz & \(-\)0.0003 & 0.5315 & 0.0365 & 0.0095 & 0.0294 \\ T\({}_{30}\) 2 kHz & 0.0009 & 0.0020 & 0.0200 & 0.0348 & 0.0158 \\ T\({}_{30}\) 4 kHz & 0.0036 & \(1.10\times 10^{-41}\) & 0.0154 & 0.4618 & 0.0125 \\ EDT 125 Hz & 0.0086 & \(1.11\times 10^{-83}\) & 0.0216 & 0.7141 & 0.0171 \\ EDT 250 Hz & 0.0075 & \(2.59\times 10^{-24}\) & 0.0460 & 0.3003 & 0.0371 \\ EDT 500 Hz & 0.0064 & \(1.27\times 10^{-37}\) & 0.0295 & 0.4190 & 0.0232 \\ EDT 1 kHz & 0.0095 & \(9.79\times 10^{-50}\) & 0.0364 & 0.5214 & 0.0300 \\ EDT 2 kHz & 0.0040 & \(1.14\times 10^{-9}\) & 0.0441 & 0.1361 & 0.0369 \\ EDT 4 kHz & 0.0025 & \(1.92\times 10^{-6}\) & 0.0348 & 0.1098 & 0.0288 \\ G 125 Hz & 0.0093 & \(1.83\times 10^{-33}\) & 0.0467 & 0.3930 & 0.0358 \\ G 250 Hz & 0.0095 & \(2.28\times 10^{-25}\) & 0.0568 & 0.3045 & 0.0458 \\ G 500 Hz & 0.0168 & \(1.02\times 10^{-61}\) & 0.0545 & 0.5975 & 0.0428 \\ G 1 kHz & 0.0109 & \(2.37\times 10^{-13}\) & 0.0972 & 0.1725 & 0.0803 \\ G 2 kHz & 0.0221 & \(1.06\times 10^{-52}\) & 0.0806 & 0.5313 & 0.0669 \\ G 4 kHz & 0.0317 & \(2.47\times 10^{-91}\) & 0.0736 & 0.7470 & 0.0575 \\ C\({}_{50}\) 125 Hz & 0.0064 & \(3.91\times 10^{-12}\) & 0.0601 & 0.1461 & 0.0509 \\ C\({}_{50}\) 250 Hz & \(-\)0.0214 & \(1.25\times 10^{-33}\) & 0.1077 & 0.3760 & 0.0883 \\ C\({}_{50}\) 500 Hz & 0.0151 & \(3.79\times 10^{-12}\) & 0.1441 & 0.1573 & 0.1221 \\ C\({}_{50}\) 1 kHz & \(-\)0.0215 & \(1.44\times 10^{-29}\) & 0.1169 & 0.3572 & 0.0953 \\ C\({}_{50}\) 2 kHz & 0.0083 & 0.0016 & 0.1782 & 0.0321 & 0.1491 \\ C\({}_{50}\) 4 kHz & 0.0104 & 0.0032 & 0.2358 & 0.0378 & 0.1857 \\ C\({}_{80}\) 125 Hz & 0.0040 & 0.0027 & 0.0898 & 0.0310 & 0.0730 \\ C\({}_{80}\) 250 Hz & \(-\)0.0244 & \(5.96\times 10^{-52}\) & 0.0911 & 0.5289 & 0.0751 \\ C\({}_{80}\) 500 Hz & 0.0359 & \(8.50\times 10^{-42}\) & 0.1558 & 0.4561 & 0.1296 \\ C\({}_{80}\) 1 kHz & \(-\)0.0181 & \(1.08\times 10^{-29}\) & 0.0974 & 0.3419 & 0.0800 \\ C\({}_{80}\) 2 kHz & 0.0006 & 0.7205 & 0.1208 & 0.0144 & 0.0970 \\ C\({}_{80}\) 4 kHz & 0.0101 & 0.0016 & 0.2158 & 0.0706 & 0.1766 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 3 Biofilms in Food Industry
### Biofilm Control Methods in Food Industry
#### 3.2.3 Microorganisms for Pathogen Biofilm Control  
It is known that various fungi, yeasts or bacteria successfully inhibit the formation of biofilm. Such microorganisms are producers of natural compounds which act as antimicrobial agents against both planktonic cells and can also penetrate into the biofilm matrix. Nisin, a bacteriocin produced by some strains of _Lactococcus lactis_, being recognized by the FDA as GRAS substance (CRC 184.1538), is widely used as an antimicrobial agent in the food industry. The anti-biofilm properties of nisin have been successfully examined against _Listeria monocytogenes_ on SS surfaces, where significant inhibition (4.6 log CFU/cm\({}^{2}\)) has been observed [121]. However, re-growth occurred after 24 h. Generally, the adaptation of biofilm-forming microorganisms towards sanitizing agents is the impetus for the search for new anti-biofilm products [122]. After growing various microorganisms in culture media, cell-free supernatant was obtained and used against biofilm-forming bacterial species. For instance, crude extracts of _Actinomycetes_ isolates inhibited the biofilm formed by _Bacillus cereus_ and _Shewanella putrefaciens_ on a SS surface [123]. _Bacillus_ sp. cell-free supernatant exhibited anti-biofilm and anti-QS activity against important fish pathogens [124]. In some papers, detailed analysis revealed particular compounds responsible for anti-QS activity [125] or for changing the surface characteristic of bacterial cells [126] in cell-free supernatants. The artificially developed biofilm of non-pathogenic species introduce another strategy of how to deal with the adhesion of pathogenic bacteria onto the surface of stainless steel. Soil bacterial species were frequently applied in laboratory experiments, where they showed promising outcomes [127; 128]. However, there is a possibility that such protective biofilm can be the cause of food spoilage or the source of toxic metabolites. Extensive research is still needed to overcome these problems.
## 3 Architecture Overview
### FSF Forwarding Strategy  
When a contact opportunity arises, FSF will forward a message if, and only if, the nodes are friends or acquaintances (strong social tie), the message relay is not selfish or it is socially selfish, and itsdevice has no resource constraints. On the contrary, the message will not be forwarded. For example, if a node A having a message M1 addressed to node B meets with node C, node A will forward M1 to node C if, and only if, the following assumptions are true: nodes B and C are friends or acquaintances of each other, node C is non-selfish or socially selfish, and the device resources of node C are not at critical levels. It is worth mentioning that, if node C is socially selfish, it will receive the message M1 only if B belongs to its friend's circle. Otherwise, the message is not forwarded. In addition, if node A forwards the message to node C, the message will not be deleted from the buffer of node A as a way to increase the message delivery probability. Finally, if the nodes B and C are unknown to each other, or node C is assessed as individually selfish, or the device resources of node C are at critical levels, the message will not be forwarded. Figure 1 shows the flowchart representing the FSF forwarding strategy.
# Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making
## 2 Literature Review
Recent studies have also explored the integration of MCDM methods with uncertainty modeling in emerging technological contexts. For instance, [PERSON] et al. [23] proposed a hybrid model combining the Ordered Weighted Averaging (OWA) operator with the TOPSIS method to evaluate key factors influencing the production of digital twins based on blockchain technology. Their approach leverages neutrosophic logic to manage uncertainty in expert judgments, offering a structured yet flexible decision-making framework. While the application domain differs from the present study, both approaches share a common goal: enhancing the reliability of TOPSIS in uncertain, multi-criteria environments. In contrast to neutrosophic sets, our method uses fuzzy numbers exclusively to express uncertainty during the evaluation phase, followed by crisp classification based on distance to predefined ideal centroids. This allows for improved interpretability and computational simplicity while maintaining robustness in decision support. Beyond the MCDM- and TOPSIS-focused research reviewed here, advanced studies in optimization, machine learning, and statistical modeling may inspire novel extensions to fuzzy clustering and logarithmic normalization approaches. Recent works on meta-learning for nonconvex optimization [24], few-shot identification for stochastic dynamical systems [25], robust kernel-based surrogate modeling [26], and Gaussian kernel similarity for multisource information fusion [27] illustrate how sophisticated algorithms can handle high-dimensional, uncertain data. Related efforts address robust statistical tests for heavy-tailed time series [28], supervised learning for complex tracking [29], adaptive opinion dynamics [30], and agent-based decision models leveraging deep reinforcement learning [31]. Although these advanced methods offer impressive capabilities, they often come with increased computational complexity and demand a high level of technical expertise for effective implementation. In contrast, our goal is to propose a more straightforward and practical approach, suitable for real-world scenarios, that balances the need to address data variability and uncertainty with simplicity and usability. Nonetheless, these sophisticated techniques highlight promising directions for future MCDM research, particularly in the integration of meta-learning, robust modeling, and adaptive information fusion to further improve clustering and normalization strategies in complex decision-making contexts.
## 5 Applications and Services  
In this Section, we explore RIS-assisted, UAV-enabled communication from the perspectives of applications, services, aerial packages, UAV design, and RIS technologies. We briefly highlight some works and their methods, with a comprehensive list of relevant studies provided in Table 1.
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 3 Encapsulation Methods
### Corn Starch  
In 2017 [PERSON] and [PERSON] produced antibiotic-loaded SNPs by an ethanolic microemulsion system. An aqueous phase was formulated containing corn starch in an alkaline urea solution. Meanwhile, the organic phase was composed of sunflower oil, ethanol, Tween 20 as an emulsifying agent, and the antibiotic solution in different quantities (streptomycinycin and penicillin were used). A small volume of the aqueous phase was pumped into the solution containing the antibiotic under constant stirring. Finally, the nanoparticles were washed twice with ethanol to remove unincorporated components [27]. [PERSON] et al. also used corn starch for the synthesis of SNPs by the microemulsion method for delivery of the histone deacetylase inhibitor CG-1521 in breast cancer. A mixture of starch and the drug dissolved in dimethyl sulfoxide (DMSO) was used as the organic phase, while polyvinyl alcohol (PVA) dissolved in distilled water was used as the aqueous phase. The starch solution was added to the aqueous phase with a homogenizer under nitrogen at room temperature. Finally, SNPs were formed when DMSO was diffused by adding distilled water followed by stirring and the SNPs loaded with the drug were washed with distilled water [31]. In addition, SNPs loaded with conjugated linoleic acid (CLA) were synthesized by an emulsion method where [PERSON] et al. used modified ocenyl succininic anhydride (OSA) waxy maize starch as the polymeric precursor of the SNPs and xanthan gum (XG) as a complex carrier to protect it. The aqueous phase consisted of the OSA starch and XG with different mass ratios kept under stirring and the organic phase contained the CLA. The emulsion was homogenized using a high shear mixer and passed twice through a high-pressure homogenizer. Finally, CLA-loaded SNPs were obtained by spray drying [81]. Also, [PERSON] et al. studied the capacity of SNPs as a drug delivery system using three different model drugs, flufenamic acid, testosterone, and caffeine. In this study, the organic phase was consisted of the corn starch derivative that was dissolved in ethyl acetate. This solution was added to an aqueous phase with different percentages of PVA obtaining a biphasic system that was emulsified with a high-speed homogenizer. Subsequently, highly purified water was added to force the complete diffusion of the organic solvent into the aqueous phase. The resulting SNPs were obtained when the organic solvent was completely evaporated [28].
# Research on Intelligent Perception Algorithm for Sensitive Information
## 4 Experiment
### Datasets and Parameters  
In order to compare different models and algorithms, we use the THUNews dataset from Tsinghua University as the white sample of the experimental dataset and the JWBD dataset, which was composed of data manually collected from overseas blogs and Wikipedia using a crawler program, as the black sample of the experimental dataset, and we also compose our sensitive library from the collected data. We used the crawler to crawl the data of several fields and filtered out the texts containing sensitive information in different fields as black samples. For example, the military category contains information about combat, the political category contains the words of officials, and the economic category contains the economic income of companies, etc. As indicated in Table 1, we split the dataset into training, validation, and test sets in the proportions of 3:1:1. The parameters of the experiment are listed in Table 2.