esa-sceva/satcom-chunk-collection huggingface dataset:
# Superconducting Sub-Terahertz Oscillator with Continuous Frequency Tuning
## 3 Results and Discussion
### Modeling of Suppression Methods and Estimation of Parameters
It should be noted that attenuation layers significantly reduce the radiation power of the FFO. Thus, it is crucial to design a geometry with a low reflection ratio from the ends and low losses. As we can see from Figures 6 and 8, the suppression of the resonances can be improved, either by the attenuation layers near the line or by the modifications of the end (compare geometries #2 and #1). Therefore, we designed and performed a simulation of the structure with a Chebyshev impedance transformer connected to the end of the structure. The transformer was optimized to provide a match between the FFO and outer medium at frequency 300 GHz. Both the structures made of a superconductor and normal metal were simulated. In the structure with the superconducting transformer, any noticeable change in damping compared to geometry #2 was observed, but geometry with the transformer made of normal metal showed an additional decrease in amplitude by more than two times (see Mo-#3 curve in Figure 8). However, many effects regarding the real FFO are yet to be considered in simulations. First, our simulation did not involve any effects related to the fluxon dynamics and generation in the FFO. Actually, the generation occurred only when the fluxon reached the end of the junction and far from the ends the FFO behaved like a passive transmission line. However, standard excitation port in Ansys HFSS was still a good approximation. Second, in a real structure, there is an overlapping region made of a superconductor, which covers the FFO itself and the metallization layers (in corresponding geometries). As shown in a study by [PERSON] et al. [65], this may greatly contribute to the propagation velocity depending on the width of the overlap. Moreover, the proximity effect [73] significantly impacts the properties of normal metal and poses some restrictions on the layer thickness.
## 3 Results and Discussion
### Computational Overhead
#### 3.1.1 Key Generation  
Figure 6 shows the comparison of key generation time for different access schemes. It is observed that the time for generating keys increased concerning attributes. However, in multi-authority and attribute-based encryption, the key generation is four times higher than the proposed MCP-ABE-BG for 100 attributes. This shows that the proposed MCP-ABE-BG requires 0.8 s less to generate a key with 100 attributes.
# Improved Durability of Wood Treated with Nano Metal Fluorides against Brown-Rot and White-Rot Fungi
###### Abstract  
Low-water soluble metal fluorides such as magnesium fluoride (MgF\({}_{2}\)) and calcium fluoride (CaF\({}_{2}\)) were evaluated for decay protection of wood. Initially, the biocidal efficacy of nano metal fluorides (NMFs) against wood destroying fungi was assessed with an _in-vitro_ agar test. The results from the test showed that agar medium containing MgF\({}_{2}\) and CaF\({}_{2}\) was more efficient in preventing fungal decay than stand-alone MgF\({}_{2}\) or CaF\({}_{2}\). These metal fluorides, in their nanoscopic form synthesized using fluorolytic sol-gel synthesis, were introduced into the sapwood of Scots pine and beech wood and then subjected to accelerated ageing by leaching (EN 84). MAS \({}^{19}\)F NMR and X-ray micro CT images showed that metal fluorides were present in treated wood, unleached and leached. Decay resistance of Scots pine and beech wood treated with NMFs was tested against wood destroying fungi _Rhodonia placenta_ and _Trametes versicolor_ in accordance with EN 113. Results revealed that mass losses were reduced to below 3% in wood treated with the combination of MgF\({}_{2}\) and CaF\({}_{2}\). It is concluded that NMFs provide full protection to wood even after it has been leached and can be used as wood preservatives in outdoor environments.  
fluoride; nanoparticles; fluorolytic sol-gel synthesis; brown-rot fungi; white-rot fungi; basidismycetes; wood protection; biocidal efficacy +
Footnote †: journal: _Sciences_
# FEA for Optimizing Design and Fabrication of Frame Structure of Elevating Work Platforms
## 2 Materials and Methods  
### Experimental Design  
This study applied Finite Element Analysis (FEA) to examine the structural performance of the telescopic arm frame of an Elevating Work Platform (EWP) constructed with four different materials, in accordance with UNI EN 280 (Figure 1). The objective was to identify the optimal material based on stress distribution, deformation, and load-bearing capacity under simulated operational conditions and the own weight of the structure in order to make it more manageable during transport and handling phases. The materials selected for this study include the following:  
1. Aluminium Alloy (EN-AW 1200): good resistance to corrosion, but not high resistance to stress. Known for its relatively low cost, mild steel offers moderate strength and ductility, making it a common choice in EWP frames;
2. Aluminium Alloy (EN-AW 2014): Alloy highly appreciated for aeronautical and mechanical constructions where good toughness and good resistance to crack propagation are required;High-Strength Low-Alloy Steel (Fe275 JR): structural steel with a low carbon content. Also called \"construction steels\", as they are mainly used in the construction of building works;
4. High-Strength Low-Alloy Steel (S700): structural steel. High strength low alloy for cold forming. Exceptional weldability properties. Excellent load-bearing capacity.  
The analysis follows a structured approach to evaluate how each material affects the overall performance, focusing on load distribution, safety factors, and deformation patterns under standard operating conditions.
## 5 Experimental Results
These characteristics enable fuzzy controllers to outperform conventional PI controllers in a wide range of applications. The practical effectiveness of fuzzy control can be validated through numerical simulations, performance metrics--such as integral of absolute error (IAE), integral of squared error (ISE), and integral of time-weighted absolute error (ITAE)--as well as experimental verification. These methods provide an objective framework for evaluating control strategies in terms of steady-state error, response time, overshoot, and overall system stability under specific performance requirements. Therefore, the adoption of fuzzy control should be based on a comprehensive understanding of the system's dynamic behavior and real-world application demands, rather than being driven solely by the goal of simplifying controller design. This study posits that, if computational and experimental results demonstrate that fuzzy control can significantly enhance performance under defined conditions, then its integration into the controller is not only reasonable, but also academically sound, engineering-justifiable, and forward-looking. The input AC current, which closely approximated a sinusoidal waveform and was in phase with the input AC voltage, is shown in Figure 10. Under the conditions of _Vo_ = 24 V and _Io,cc_ = 70 A, the root mean square values of the input AC voltage and current were _vac_ = 220 Vrms and _iac_ = 9.1 Arms, respectively. Therefore, the power factor was approximately unity. The charging voltage corresponding to the SOC is shown in Table 2.  
Using the fast Fourier transform function of the digital oscillator (Model number: SDS2104X Plus, Siglent Tech. Corp., Solon, OH, USA), the total harmonic distortions (THDs) of _vac_ and _iac_ were analyzed, as shown in Figure 11. The THD of the AC input voltage was primarily concentrated in the fundamental component _H_\({}_{\text{71}}\). In contrast, the AC input current exhibited significant components at the fundamental, third, fifth, and seventh harmonics (_H_\({}_{\text{c1}}\), _H_\({}_{\text{c3}}\), _H_\({}_{\text{c5}}\), and _H_\({}_{\text{c7}}\)), with corresponding amplitudes of 9.1 Arms,0.86 A\({}_{\rm rms}\), 0.34 A\({}_{\rm rms}\), and 0.07 A\({}_{\rm rms}\), respectively. By employing an EMI filter, the high-order harmonics can be effectively attenuated to comply with various safety standards. Figure 12 shows the prototype of the battery charger and the experimental platform, including the input AC source, the charger prototype, and the solid-state lithium battery pack.
# Predicting Location of Tweets Using Machine Learning Approaches
## 5 Experiments and Results
### Impact of Data Quality and Machine Learning Models  
The accuracy of location prediction is dependent on the quality of the available data and the machine learning models used. To mitigate this, we performed data cleaning and preprocessing steps, such as removing irrelevant tweets and normalizing the text, to improve the quality of the dataset.  
Our experiments (see Table 7) evaluated the impact of different machine learning models and features on location prediction accuracy. The random forest (RF) and support vector machine (SVM) models performed the best in terms of accuracy, with an average accuracy of over 67% when predicting locations within a range of 140 km. We also observed that the accuracy gains beyond 140 km were limited, which suggests that the social factor of language may play a role in location prediction accuracy as shown in Figure 2.  
Incorporating elements of perplexity and burstiness, which are measures of text complexity and frequency, respectively, may further improve location prediction accuracy. However, these factors were not explicitly considered in our study, and future research could explore their impact on location prediction accuracy.
# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
## 4 Experimental Results and Analysis
### Range and Position-Varied Environment  
The volunteer was asked to hold the smartphone and walk away from the AP in this experiment so that both the position and the distance between the smartphone and the AP were varied. In this condition, the ground truth distance was not available as it varied according to the time even though we controlled the walking speed of the volunteer as uniformly (stable) as possible. Hence, Wi-Fi RTT distance was compared to the reference distance. Similar to previous experiments, the offsets were applied to the Wi-Fi RTT distance. The RMSE between the Wi-Fi RTT distance and the reference distance were calculated and listed in Table 4. It can be seen that the RMSE is similar when the signal is in LOS condition or blocked by wooden and glass blockers. However, a huge error occurs when the blocker is metal. As visualized in Figure 12, compared to the reference distance, the Wi-Fi RTT distance shows some acceptable variations when the smartphone is moving away from the AP in LOS condition. There are also some expected outliers shown in NLOS condition with wooden and glass blockers. Nevertheless, it can be seen that there is a significant number of outliers in the NLOS condition with metal blockers. As the standard deviations are listed in Table 5, the outliers show a severe impact on the stability of the Wi-Fi RTT distance measured by all three smartphones when the metal blocker blocks the signal. Unlike the fluctuations caused by the motion of smartphones, such variations are mainly caused by the outliers, which are far from the ground truth. Although such outliers are usually the minority in the measured Wi-Fi RTT distance in one journey of each smartphone, their impact on average error is huge.  
Figure 11: Wi-Fi RTT distance and error in RFPV and NLOS condition: (**a**) Wi-Fi RTT distance from Google Pixel 2; (**b**) Wi-Fi RTT distance from Xiaomi Redmi K20; (**c**) Wi-Fi RTT distance from LG V50S; (**d**) RMSE of Wi-Fi RTT distance from Google Pixel 2; (**e**) RMSE of Wi-Fi RTT distance from Xiaomi Redmi K20; (**f**) RMSE of Wi-Fi RTT distance from LG V50S.  
It can be concluded that when the smartphone is in this environment, the dominant error becomes the outliers rather than the fluctuations. It can also be inferred that the NLOS effects could be exacerbated by the motion effects, which lead to high Wi-Fi RTT distance dispersion. In addition, according to the skin effects in the NLOS condition, the higher the conductivity of the blocker, the more outliers would appear in Wi-Fi RTT distance. Moreover, it can be seen from the results that LG V50S provides the lowest error in most of the cases. We infer that the phone embedded with a better CPU performance could provide more stable and accurate measurements in such a critical environment.
# Comparison of Various Models for Optimal Water Distribution in Canal Systems
## 2 Materials and Methods
### Irrigation Model 4 (Rotational Irrigation Between Groups, with Continuous Irrigation Within Groups)  
In contrast to Irrigation Model 3, Model 4 adopts the method of rotational irrigation between groups, with continuous irrigation within groups [7]. The model is established as follows:  
(1) Calculation of rotational irrigation groups  
The number of irrigation groups is calculated as follows:  
\[ceil[\frac{\sum_{j=1}^{N}0.6*q_{jd}}{Q_{d}}]\leq M\leq floor[\frac{\sum_{j=1}^{ N}q_{jd}}{Q_{d}}] \tag{8}\](2) Decision variables  
Decision variables are the group conditions of canals and the water distribution flow rate of canals. The decision variable \(X_{ij}=\{0,1\}\) represents the switch state of the outlet \(j\) of irrigation group \(i\). \(X_{ij}=0\) indicates that the water outlet is closed. \(X_{ij}=1\) indicates that the water outlet is opened. The decision variable \(q_{j}\) represents the flow rate of canal \(j\) (m\({}^{3}\)s\({}^{-1}\)). (\(i\) = 1, 2,... M is the number of irrigation groups; \(j\) = 1, 2,... N is the number of lower-level canals).  
(3) Objective function  
The minimum time of the water distribution is calculated thus:  
\[minT=\sum_{i=1}^{M}maxX_{i\;j}t_{j} \tag{9}\]  
\[t_{j}=\frac{w_{j}}{q_{j}} \tag{10}\]  
(4) Constraints  
Water outlet status constraint: Each water outlet can only be opened once during the entire water distribution process.  
\[\sum_{i=1}^{M}X_{i\;j}=1 \tag{11}\]  
Decision variable value constraint:  
\[X_{i\;j}=\{0,1\} \tag{12}\]  
Flow rate of lower-level canal constraint: the flow rate of water distribution for any canal should be within 0.6-1.0 times its design flow rate.  
\[0.6q_{j\;d}\leq q_{j}\leq q_{j\;d} \tag{13}\]  
Water balance constraint: At any time, the sum of the flow of the lower-level canal shall equal the flow of the upper-level canal.  
\[Q_{t}=\sum_{t=1}^{T}q_{j} \tag{14}\]  
Flow rate of upper-level canal constraint:  
\[0.6Q_{d}\leq Q_{t}\leq Q_{d} \tag{15}\]
## 3 Extensive Air Showers: The Heitler-Matthews Model
### Longitudinal Development  
The longitudinal development of a hadronic shower is dominated by the parallel e.m. sub-showers produced by the \(\pi^{0}\) decays in the first interaction, at an atmospheric depth \(X^{*}=\lambda_{p-air}\cdot\ln\ 2\approx 55\) g/cm\({}^{2}\). In a good approximation following cascades can be neglected. The energy of the single photon is \(E_{\gamma}=\frac{E_{\mathrm{g},\beta}}{2}=\frac{E_{0}}{3}\frac{2}{N_{ch}}= \frac{E_{0}}{3N_{ch}}\).  
From Equation (3), we have  
\[X_{max}^{p} =X^{*}+X_{0}\cdot\ln\left(\frac{E_{0}}{3N_{ch}\cdot e_{c}^{em}}\right)\] \[=X^{*}+X_{max}^{em}-X_{0}\cdot\ln(3\,N_{ch})\quad\mathrm{g/cm^{2}}\]  
where \(X_{max}^{em}\) is the atmospheric depth of the maximum of \(\gamma\)-induced showers with \(E_{0}\) primary energy and \(N_{ch}\) is the multiplicity of charged pions in the first interaction. The elongation rate for showers induced by protons is then  
\[\Lambda^{p}=\Lambda^{\gamma}+\frac{d}{d\,\log_{10}E_{0}}\left[X^{*}-X_{0}\cdot \ln(3\,N_{ch})\right]=58\quad\mathrm{g/cm^{2}\ per\ decade}, \tag{16}\]  
reduced from the elongation rate for purely e.m. showers. This estimation verifies [PERSON]'s elongation rate theorem [41], which points out that e.m. showers represent an upper limit to the elongation rate of the hadronic showers. The shower maximum is expected to be influenced by the elasticity of the first interaction, \((1-\kappa)=E_{lead}/E_{0}\), where \(E_{lead}\) is the energy of the leading particle. For interactions with \((1-\kappa)>0.5\) most of the primary energy will be transferred deeper into the atmosphere and correspondingly the shower maximum will be deeper.  
The extrapolation to a primary particle with mass \(A\) with the superposition model yields  
\[X_{max}^{A}=X_{max}^{p}-X_{0}\cdot\ln A \tag{17}\]  
Detectors able to observe the longitudinal air shower development can estimate the primary energy with good accuracy measuring the so-called _calorimetric energy_, that is, the energy of the e.m. component. With this estimator of the energy of the primary particle, the orthogonal variable sensitive to its primary mass is the depth of the shower maximum in terms of the number of particles, \(X_{max}\).  
Therefore:  
* \(X_{max}\) is smaller for heavier nuclei (logarithmic dependence on \(A\))
* \(X_{max}\) is the same for same \(E_{0}/A\) but different \(E_{0}\). As a consequence, the proton-induced showers result, on average, in a larger number of particles at the observation level compared to iron-induced events. However, the shower-to-shower fluctuations are as large as the shift of \(X_{max}\) between proton and iron thus limiting an event-by-event assignement of a primary mass.  
Despite the simplicity and the approximations of the toy model, the main characteristics of the EAS development are quite well reproduced. Obviously, a detailed description of the cascade, in particular for what concern the role of fluctuations, can be provided only by detailed Monte Carlo simulations.
## 6 Discussion
### Challenges
#### 6.2.6 Increased Latency and Limited Throughput  
A higher network delay is a drawback of integrating the blockchain into KDN. Reducing the block capacity too much and increasing the block size of blockchains too much can result in higher propagation delays. Introducing a new block to the digital ledger requires starting a transaction procedure, and it must be propagated in the network, be verified using the digital signature, and then undergo a consensus process that involves mining, block creation, block propagation, and block validation steps. Therefore, due to the distributed approach to adding blocks to the blockchain, the latency introduced to the system cannot be prevented. When there are many transactions pending verification and addition to the blockchain, the processing time of these transactions will be high. Researchers have introduced sharding and pruning as solutions for reducing the delay in processing transactions on the blockchain [394]. However, even with such approaches, it is difficult to prevent the additional delay caused by the use of blockchains for intelligent networking. In fact, this delay increases with the increment in network size and may negatively affect delay-critical applications in KDN such as autonomous driving, remote surgery, etc.  
Since both Bitcoin and Ethereum have a transaction throughput of under 100 transactions per second, blockchains are renowned for having poor transaction throughput [395]. Increasing the block size boosts throughput. However, increasing the block size causes an additional requirement for storage resources, such that nodes with limited storage resources are negatively affected. Therefore, in intelligent knowledge-based networks, applications that require low latency and high throughput can be negatively affected due to the combination of blockchain technology if the real-time network data reach the machine learning models through a blockchain network. However, recent research has proposed high-throughput blockchains such as Conflux [396], which has a tree graph-based ledger structure capable of processing concurrent blocks to achieve fast consensus and has a transaction throughput of 3480 transactions per second. Thus, these types of blockchains employing a DAG-based structure have shown high transaction throughput due to the parallel computation capability of transactions. Therefore, low-latency techniques such as shardingand fast-throughput DAG-based blockchain frameworks such as Conflux are recommended when applying blockchain technology in time-critical knowledge-based networks.
## 5 Evaluation
### Metrics  
To evaluate the performance of semantic segmentation, a confusion matrix was used. The confusion matrix is illustrated in Table 3.  
In binary classification tasks, the relationship between the GT and prediction (pred) comprises four possible outcomes, as shown in the table. Because the objective of this study was to detect weeds, we defined the positive class as weeds and the negative class as the background. Accordingly:  
* True Positive (TP): The number of pixels correctly predicted as weeds when the GT was also weeds.
* False Negative (FN): The number of pixels incorrectly predicted as the background when the GT was weeds.
* False Positive (FP): The number of pixels incorrectly predicted as weeds when the GT is the background.
* True Negative (TN): The number of pixels correctly predicted as background when the GT is also the background.
## 4 Biological Activities of Ginger Polysaccharides
### Anti-Tumor Effects  
Ginger polysaccharide plays a vital role in controlling the proliferation and metastasis of tumor cells and promoting their apoptosis. In one study, the anti-tumor effect of ginger polysaccharide was evaluated by using MTT method. Three kinds of crude ginger polysaccharides (HCGP, ECGP, and UCGP) can be obtained from ginger rhizome after hot water extraction (HWE), enzyme assisted extraction (EAE) and ultrasonic cell grinder extraction (UCGE). After purification and separation, five purified polysaccharides were obtained, namely, HGP, EGP1, EGP2, UGP1 and UGP2. Studies have shown that the purified polysaccharide has a greater inhibitory effect on the proliferation of tumor cells than the corresponding crude polysaccharide. Among the five types of purified polysaccharides, UGP1 and UGP2 have a strong inhibitory effect on the proliferation of tumor cells. Among the five types of tumor cells (H1975, Hela, HCT116, B16, and MCF-7), UGP1 and UGP2 have significantly different inhibitory effects on their proliferation. UGP1 has a strong inhibitory effect on these kinds of tumor cells such as Hela and HCT116, of which the inhibitory effect of UGP1 on the human colon cancer HCT116 cell linen is relatively high (56.843 + 2.405%), which indicates that UGP1 may be a potential drug for the treatment of colon cancer [43].