esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
###### Abstract  
Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. Taking the southern part of China's North-South Seismic Belt (20\({}^{\circ}\) N\(\sim\)30\({}^{\circ}\) N, 96\({}^{\circ}\) E\(\sim\)106\({}^{\circ}\) E), where strong earthquakes frequently occur, as an example, we used the sliding time window method to calculate 11 seismicity indicators from the earthquake catalog data as the characteristic parameters of the training model, and compared six machine learning models, including the random forest (RF) and long short-term memory (LSTM) models, to select the best-performing LSTM model for predicting the maximum magnitude of an earthquake in the study area in the coming year. The experimental results show that the LSTM model performs exceptionally well in predicting earthquakes of magnitude 5 \(<\) ML \(\leq\) 6 within the time window of the test set, with a prediction success rate of 85%. Additionally, the study explores how different time windows, spatial locations, and parameter choices affect model performance. It found that longer time windows and key seismicity parameters, such as the b-value and the square root of total seismic energy, are crucial for improving prediction accuracy. Finally, we propose a magnitude interval-based assessment method to better predict the actual impacts that different magnitudes may cause. This method demonstrates the LSTM model's potential in predicting moderate to strong earthquakes and offers new approaches for earthquake early warning and disaster mitigation.  
China's north-south seismic zone; machine learning; seismicity parameters; prediction of maximum magnitude +
Footnote †: journal: Applied Sciences  
1
## 4 Results and Discussion
### The Influence of Chain Length on the Execution Time
During our experiments, SHA-512 demonstrated the highest execution time due to its increased number of rounds and larger block size, making it computationally intensive. In contrast, MD5 and NTLMv2 exhibited the lowest execution times, due to their smaller block sizes, fewer rounds and simpler computational structures. These factors directly affect execution time, as hash functions with greater complexity require more processing cycles. The increased number of computational steps, such as additional rounds and larger block sizes, results in higher memory and CPU utilization, leading to longer processing times. The trade-off between security and performance is evident: SHA-512 offers superior cryptographic strength at the cost of higher computational demands, while MD5 and NTLMv2 provide faster processing speeds but lack security resilience.
# Signal Quality Assessment for Transdermal Optical Wireless Communications under Pointing Errors +
## 3 Average SNR  
Based on the previous analysis, and by assuming an intensity modulation and direct detection (IM/DD) method, the instantaneous SNR can be obtained as [26]  
\[\gamma=\frac{R^{2}\exp\left(-\alpha(\lambda)\delta\right)h_{p}^{2}\bar{P}_{s} }{2|e|RP_{b}+2|e|I_{DC}+N_{0}}, \tag{18}\]  
where \(\bar{P}_{s}\) and \(N_{0}\), respectively, represent the signal and noise optical power spectral density (PSD).  
Theorem 3.1: _The average SNR, can be analytically evaluated as_  
\[\tilde{\gamma}=\frac{R^{2}\exp\left(-\alpha(\lambda)\delta\right)\bar{P}_{s} }{2|e|RP_{b}+2|e|I_{DC}+N_{0}}\frac{\xi A_{0}^{2}}{\xi+2}. \tag{19}\]  
Proof: Please refer to the Appendix.  
From Equation (19), it is evident that the average SNR depends on the transmission PSD, the skin particularities, namely skin attenuation and thickness, the RX's characteristics, and the intensity of the pointing errors. Moreover, note that, since the skin attenuation is a function of the wavelength, the average SNR is also a function of the wavelength.
## 2 Materials and Methods
### Canopy Characterization and Environmental Data  
Leaf chlorophyll content was estimated with a chlorophyll meter SPAD-502 (Minolta, Ramsey, NJ, USA), and is expressed here in SPAD units, which presents a curvilinear relationship with chlorophyll content and roughly corresponds to it in \(\upmu\mathrm{g\cdot cm^{-2}}\)[34]. Leaf fluorescence spectra under natural light were acquired with a specific laboratory-made device (leaf spectro-fluorometer, LSF) described elsewhere [13; 35]. Briefly, it consists of a focusing optic that projects blue-green filtered sunlight on the leaf, so that the illumination intensity is equivalent to the outdoor incident photosynthetically active radiation (PAR). The fluorescence emission spectrum is detected from 660 nm to 800 nm with a compact spectrometer coupled with a fibre optic. Mean canopy height, fresh and dry above-ground biomass, and leaf area index (LAI) were measured at periodic intervals during crop development (Table 1). LAI was assessed using a LI-3100 (Li-Cor, Lincoln, NE, USA) area meter. Incident PAR was measured at the same sampling rate as the vegetation radiance acquired by TriFLEX (see Section 2.3.1) with a quantum sensor (JYP 1000, SDEC, Reignac sur Indre, France). Half-hourly diffuse fraction of incident PAR was measured using a BF3 light sensor (Delta-T Devices, Cambridge, UK) and air temperature with a HPM35D sensor (Vaisala, Helsinki, Finland) (see Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **2 March** & **24 March** & **14 April** & **7 May** & **26 May** & **9 June** \\ \hline Day of Year & 34 & 83 & 104 & 127 & 146 & 160 \\ Height (cm) & 8.6 & 18 & 40 & 69 & 86 & 86 \\ LAI (m\({}^{2}\cdot\mathrm{m^{-2}}\)) & 0.15 & 0.89 & 3.32 & 6.88 & 5.74 & 1.07 \\ Fresh biomass (kg\(\cdot\mathrm{m^{-2}}\)) & 0.091 & 0.58 & 2.13 & 4.26 & 4.32 & 3.50 \\ Dry biomass (kg\(\cdot\mathrm{m^{-2}}\)) & 0.018 & 0.116 & 0.384 & 1.02 & 1.51 & 1.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Height, leaf area index (LAI), and aerial biomass of the crop at different dates during the measurement campaign.  
Figure 1: Development of the wheat crop during measurement campaign: open circles: vegetation height; filled squares: leaf chlorophyll (Chl) content. Bars indicate \(\pm\) one standard deviation; gray zone indicates measurements campaign.
# Tax or Subsidy? Optimal Carbon Emission Policy: A Supply Chain Perspective  
[PERSON]  
1 School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Marketing and Logistics Management, Nanjing University of Finance and Economics, Nanjing 210023, China; [EMAIL_ADDRESS]  
[PERSON]  
3 School of Management and Engineering, Nanjing University, Nanjing 210093, China; [EMAIL_ADDRESS]  
[PERSON]  
4 Correspondence: [EMAIL_ADDRESS]; Tel.: +86-025-83597496444544  
55.50 per 0.1 mpg for each product. After Finland and Sweden introduced a carbon dioxide tax in 1990s, carbon pricing systems, including taxes and cap-and-trade measures (which work similarly), have been adopted or planned in 40 countries and more than 20 cities, according to the World Bank. In Canada, carbon emissions in British Columbia dropped between 5% and 15% because of the tax [8]. Therefore, how to make appropriate policy to reduce carbon emissions is a big challenge facing governments.  
We investigated this problem from a supply chain perspective. Specifically, we considered a two-tier supply chain consisting of one government policy-maker and one manufacturer. The key questions we aimed to answer included: (i) Which policy (tax or subsidy) should be adopted by the government? (ii) How will different factors (e.g., environment externality, the cost of new technology, etc.) affect the policy making? (iii) How will the manufacturer react in response to the government policy?  
We established a stylized model setting to investigate the above questions. The government, as the [PERSON] leader, decides whether to tax or subsidize the firm in a particular industry, considering his possible reactions to different policies. The firm, as the [PERSON] follower, decides greening degree of his product, considering the consciousness of consumers to environmental protection (e.g., carbon emissions, pollutants, etc.). Our main findings are three-fold: Firstly, the government should adopt a tax policy if consumers are rather sensitive to the greenness level of products, the cost of greening is high, or the negative impact due to carbon emission is large, and subsidize the firm otherwise. Secondly, the firm will adopt a greener technology no matter the which policy (tax or subsidy) the government adopts compared with the situation without any government intervention. Lastly, the less sensitive consumers are toward the greenness of products, or the lower the cost of greening, the higher the consumers' surplus.  
In Section 2, we discuss the body of literature relating to this subject. In Section 3, we conceptualize and formulate the model used in this article and give the main results. In Section 4, we extend our model setting, incorporating the endogenous pricing issue. We conclude the paper in Section 5.
## 3 Proposed Method
### POI Relationship Modelling  
This paper calculates the scenic score of the edge according to the POI scenic score of the adjacent edge. However, in the actual road network, there are often multiple POIs adjacent to the same edge. At the same time, due to the different characteristics of POIs, the scenic score will be lost when multiple POIs are combined. Therefore, to calculate the scenic score for the edge more accurately, the relationship between POIs is mined and modelled as follows:  
\[r(i,j)=\sqrt{\text{Co\_VP}(i,j)\times Sim(i,j)} \tag{14}\]  
where \(r(i,j)\) represents the correlation between \(POI_{i}\) and \(POI_{j}\), which is jointly determined by the user's common access probability \(Co\_VP(i,j)\) to POIs and the feature similarity \(Sim(i,j)\) between POIs. The larger \(r(i,j)\) is, the closer the relationship between \(POI_{i}\) and \(POI_{j}\), and the less the loss of scenic score when they are combined. The specific calculation of \(Co\_VP(i,j)\) and \(Sim(i,j)\) is shown in Formulas (15) and (16), respectively.  
\[\text{Co\_VP}(i,j)=\alpha\frac{N_{i,j}}{N_{i}+N_{j}+N_{i,j}}+(1-\alpha)\frac{ \sum_{k=1}^{m}\min\left(uc_{k,j},uc_{k,j}\right)}{\sum_{k=1}^{m}\left(uc_{k,j}+uc _{k,j}\right)} \tag{15}\]  
where \(Co\_VP(i,j)\) is the co-visit probability between \(POI_{i}\) and \(POI_{j}\), \(N_{i,j}\) is the number of users who have visited \(POI_{i}\) and \(POI_{j}\) at the same time, \(N_{i}\) is the number of users who have visited \(POI_{i}\) but not visited \(POI_{j}\), \(uc_{k,j}\) is the number of times user \(k\) has signed at \(POI_{i}\), \(m\) is the number of users, and \(\alpha\) is the weighting coefficient, set at 0.5 here. The larger \(Co\_VP(i,j)\), the more likely the user is to visit the two POIs at the same time, that is, the closer the relationship between the two POIs from the user's perspective.  
\[Sim(i,j)=\cos\theta(T_{i},T_{j})=\frac{T_{i}\cdot T_{j}}{\|T_{i}\|\|T_{j}\|} \tag{16}\]  
where \(Sim(i,j)\) is the cosine similarity between the eigenvectors of \(POI_{i}\) and \(POI_{j}\), \(T_{i}\) is the eigenvector of \(POI_{i}\). The larger \(Sim(i,j)\) is, the closer the relationship between \(POI_{i}\) and \(POI_{j}\).
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 3 Countermeasures against Web Tracking
### Challenges
#### 3.3.3 Effectiveness (Blocking and Preventing Tracking)  
[PERSON] et al. [50] argued that advancements in technology made personal information more susceptible to fingerprinting. They provided an example of how trackers could easily track browsers with installed plugins, even if a user changed the value of fingerprintable elements like the user-agent or canvas or emptied the list of plugins. Most browsers have one or more plugins, making them easy targets for fingerprinting. [PERSON] et al. [69] developed FP-Scanner to detect the spoofing elements used in existing countermeasures against fingerprinting. Through the tool, they confirmed the mismatch of fingerprinting attributes, which showed that trackers could recover key attribute values. For instance, when a Firefox user on Windows uses Random Agent Spoofer (RAS) to spoof as Safari, trackers can easily recognize it as the agent information changes but elements like the OS or font remain the same. Therefore, more comprehensive countermeasures, such as randomizing browser elements to realistic values or randomized values for each session, will be necessary in addition to further research. To address the limitations in the effectiveness previously mentioned, several recommendations can be made. First, holistic tracker detection should be implemented by combining multiple detection layers, including DNS-based filtering, behavior blocking, and fingerprint resistance. A detection system can be deployed to flag suspicious third-party requests or tracking pixels when they are dynamically loaded. Second, cross-layer protection: Protection should be integrated across the browser, network, and device levels to prevent reliance on a single layer (e.g., cookies) for tracking. For instance, encrypted DNS (DNS-over-HTTPS) combined with network-level traffic analysis can stop DNS-based trackers from bypassing other blockers. Lastly, enhanced user education: Tools should be made more intuitive while offering better insights into what is being blocked. For example, integrating a \"privacy score\" can help users understand their overall risk level, enabling them to adjust their behavior or tools accordingly.
# Aerodynamic Benefits by Optimizing Cycling Posture
## 4 Application to Ironman Hawaii
### Wind Data for IM Hawaii  
Wind and weather conditions at IM Hawaii are well known and documented. The event takes place on the exact same weekend every year in October--there are no seasonal changes. Compared to other IM courses, IM Hawaii has a much stronger crosswind [20]. To analyze the wind distribution in more detail, French carbon wheel manufacturer Mavic executed a field study in 2013. They obtained wind measuring points by riding the entire course on 9 October 2013, 3 days prior to IM Hawaii at the same time of day as when the professional athletes would be on course. Mavic stated that the wind conditions were similar to those usually met at that time on race day. The exact time of day during the measurement was highly important, as the wind speed has strong day-time variation [21].  
The recorded raw data even showed yaw angles \(>\)20\({}^{\circ}\) which seem fairly rare in actual cycling. Additionally, the wind distribution seems to be asymmetrical. Noteworthy is also that the wind direction changes as the athletes move along the course due to local thermal conditions. Traditionally, the highest yaw angles show up after the turning point in Hawai. The course is also slightly downhill at this point, and athletes are therefore traveling at high speeds, which is likely favorable for the DB and TT positions that showed the best sailing effects (_the sailing_ effect is a somewhat inaccurate phenomenon: emergence of lift may reduce drag if projected in a specific direction) in the simulations [22]. Data from [21] were clustered. The low share of yaw angles \(>\)20\({}^{\circ}\) was not taken into account further. Note that 45% of the wind yaw is seen at angles higher than 10\({}^{\circ}\) with a calculated average yaw angle of 10.8\({}^{\circ}\). This average value was later used for the _Average CdA Bike Split Model_. For the _Weighted CdA Bike Split Model_ the probability of wind yaw was evaluated with the weighted wind distribution presented by Mavic. Yaw angles were binned to simplify the calculation procedure; see Figure 9.  
Figure 8: IM Hawaii course map overall. Scale: approximately 1:850,000.
## 1 Introduction
### Reference PD Pulses and Pulsating Noise Trains
#### 2.1.4 Noise Pulsating Signals
The IDE function with the time parameters \(1/\alpha\) = 44 ns and \(1/\beta\) = 9.9 ns is used as the reference PD pulse waveform to be generated (see Figure 5a) in PD recognition and PD location tests, because in these tests no PD clustering is required. This reference pulse corresponds to a T\({}_{\text{PD}}\) width of 75 ns. The parameter T\({}_{\text{PD}}\) is defined as the width of the equivalent rectangular pulse having the same charge and peak values as the generated pulse. \[i(t)=i_{peak}\cdot k\cdot\frac{1}{e^{\alpha\cdot t}+e^{-\beta\cdot t}} \tag{3}\]  
where:  
\[k=\frac{\beta+\alpha}{\beta}\cdot\left(\frac{\beta}{\alpha}\right)^{\frac{k} {\beta+\alpha}} \tag{4}\]  
To carry out the PD clustering test, various pulse waveforms following a damped sinusoidal waveform modulated by IDE pulses are assigned to the PD pulses or pulsating noise signals, according to Equation (5) (see Figure 5b). By doing this, the waveforms emulate the specific traveling wave condition from each PD pulse or noise source. Multiplying in Equation (5), the sinusoidal function by Equation (3), the usual oscillations found in the PD pulses recorded during online PD measurements are reproduced. \[i(t)=i_{peak}\cdot sin(2\cdot\pi\cdot f\cdot t+\varphi)\cdot k\cdot\frac{1}{e^ {\alpha\cdot t}+e^{-\beta\cdot t}} \tag{5}\]
# Prediction of Lower Leg Swelling in Driving Posture
## 1 Introduction  
### Passenger Physical Fatigue  
The development of automobile seats is important for a reduction in passengers' physical fatigue [1]. In the previous studies, the effectiveness of physical fatigue reduction of car seats was evaluated by measuring the muscle electrical potentials and lower leg swelling of passengers seated for a short period of approximately 10 min [2; 3; 4]. The average operating time exceeds 1 h [5; 6; 7]; therefore, measurements over a longer period are necessary. However, fatigue evaluations for many design parameters in automobile seat development are difficult to conduct over a long period. Therefore, if a prediction equation for physical fatigue can be constructed from data such as height, weight, and body pressure distribution while seated, this could improve the development of automobile seats and enhance the fatigue reduction effect. The following is an overview of the physical fatigue of passengers.  
Passenger physical fatigue can be categorized into the following two types: musculoskeletal load and contact load [8]. Musculoskeletal load refers to the physical strain on muscles and joints caused by driving posture. Prolonged exposure to such loads leads to muscle fatigue, which results in body pain and discomfort [9; 10]. Muscle fatigue cannot be measured directly and is often estimated from the reduction in amplitude of the muscle electrical potential and the amount of muscle activity [11; 12]. Contact load refers to the pressure exerted on soft tissues and blood vessels by the body surface in contact with the seat surface. This load mainly affects the thighs. Prolonged exposure to such pressure leads to lower leg swelling, which causes leg lethargy and discomfort [13; 14]. Since lower leg swelling can bemeasured directly by the circumference, volume, or bioimpedance in the lower leg [15; 16; 17], this study focused on lower leg swelling as the passengers' physical fatigue.  
In recent years, the fatigue evaluation based on the musculoskeletal load has transitioned from the visual and palpatory assessments conducted by specialists such as ergonomists and physical therapists to the simulation using virtual musculoskeletal models (digital twins). These models utilize the participants' geometric data (e.g., joint positions and angles) to calculate the musculoskeletal loads. Digital twin-based fatigue evaluation methods have been applied to assess the musculoskeletal loads during seated work [18; 19], standing work [20; 21], and athletic activities [22; 23]. However, this approach has not yet been extended to the fatigue evaluation caused by contact loads during seated work, which is the focus of this study. Developing a predictive equation for lower leg swelling (one of the objectives of this research) would allow for the assessment of swelling based on contact loads, such as thigh and buttock pressures, calculated through a digital twin.
## 3 Results and Discussion
### Factors Affecting Nitrogen Removal by Strain F19  
#### 3.2.1 Effects of C/N Ratio  
Figure 2A shows the effects of C/N ratio on the nitrogen removal efficiency. The utilization of ammonium was analyzed with various C/N ratios from 1 to 10 in batch experiments over 48 h. As the C/N ratio declined from 10 to 2, NH\({}_{4}\)+-N removal dramatically declined from 97.21% to 78.12% after 36 h of cultivation. When the C/N ratio was further decreased to 1, the NH\({}_{4}\)+ removal efficiency was only 35.97% after 48 h of incubation. However, more than 78.12% nitrogen removal activity remained at a C/N ratio of 2 (Figure 2A). The NH\({}_{4}\)+ removal efficiency of strain F19 exhibited a lower C/N ratio tolerance than has been observed for some other heterotrophic denitrifiers [25; 26; 27]. Furthermore, the accumulation of nitrite was analyzed, and it was not detected with increasing C/N ratio (data not shown). The differences in removal efficiency of NH\({}_{4}\)+-N could be due to the consequent delay in cell growth at low C/N ratios (Figure 2B). These results suggested that strain F19 was an excellent heterotrophic nitrification-aerobic denitrification, similar to _Agrobacteriumum sp._ LAD9 [28] and _Alcaligenes faecalis_[29]. Therefore, this strain may be suitable for nitrogen removal from domestic wastewater with a low C/N ratio.  
Figure 1: Phylogenetic tree of strain F19 and close relatives based on 16S rDNA gene sequences.
## 3 Results
### Sample Characterization
#### 3.1.1 SEM Analysis  
SEM images are shown in Figures 2-4. The SEM micrographs show a significant difference in the surface between before and after adsorption. The scanning electron microscopy (SEM) images of all activated samples exhibited an uneven and well-defined porous structure, suggesting a comparatively large surface area. Upon activation, the outer surface of activated carbon exhibits fissures, crevices, and a range of grain sizes within its macroscopic pores. The porous and defective structure of carbon is attributed to the liberation of non-carbon components, such as hydrogen, oxygen, and nitrogen, from the char's surface during the pyrolysis process [53]. This liberation leads to the creation of a stiff carbon framework with a rudimentary pore structure. An efficient adsorbent requires the presence of pores and an internal surface area. The formation of pores in activated carbon is crucial, as pores serve as active sites, playing a major part in the process of adsorption [54].  
Figure 3: Scanning electron micrographs (SEM) of (**a**) DAC before adsorption, (**b**) DAC after Cu, adsorption, and (**c**) DAC after Pb adsorption.  
Figure 2: Scanning electron micrographs (SEM) of (**a**) RAC before adsorption, (**b**) RAC after Cu, adsorption, and (**c**) RAC after Pb adsorption.  
After the adsorption of Cu and Pb, visible modifications in the surface morphology confirm the adsorption of metal ions onto the activated carbon. In the Cu-adsorbed samples, some blocked pores and increased surface roughness are observed, indicating that the copper ions penetrated the more compressed portions of the activated carbon. Copper ions are retained as coordination complexes by the oxygenated functional groups (-OH and -COOH). By contrast, the Pb-adsorbed samples exhibit greater surface aggregation and structural compaction. This behavior can be attributed to the precipitation of lead in the pores or the formation of surface complexes with functional groups. The surface morphology of bioadsorbents RAC, DAC, and PAC significantly differs due to their precursor materials. All three adsorbents exhibit substantial adsorption potential, making them viable candidates for metal removal.