esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution Approach
### Assumptions  
In order to allocate parking lots, some assumptions [40; 41; 42] are taken into account as follows:  
* This study assumes that the distribution company is responsible for supplying customer demand, installing parking lots, and controlling the charging and discharging of EV batteries. DISCO aims to fulfill these responsibilities while minimizing costs and improving the quality and reliability of customer service.  
Figure 2: Different phases in the suggested solution approach.  
* It is important to note that in calculating profits, the study assumes that DISCO does not receive compensation from EV owners for battery charging required for driving purposes. Additionally, any vehicle degradation costs due to vehicle-to-grid (V2G) operations are covered by DISCO and paid to EV owners. These assumptions are made to incentivize EV owners to park their vehicles in the parking lot during days with high-priced peak electricity.
* All vehicles are assumed to be charged and discharged at their maximum charging rate. It is important to note that this assumption is common in several EV studies.
* In the modeling of parking lots, it is assumed that the initial state of charge of EVs has three levels (Figure 1). However, the proposed model can be adapted for use with other SOC levels as well. The initial SOC of vehicles can be adjusted using a suitable distribution function, and parking lots can be optimally placed considering this function.
* Another assumption made in the modeling of parking lots is that all batteries are the same size. As a result, the output power of the parking lot is assumed to be constant during the discharging state.
* Under traditional approximations used by utilities, there might be 200 peak hours in a year during which an incremental kW h of electricity would be worth USD 0.5/kW h [18]. Therefore, the maximum hours that vehicles deliver power to the network is assumed to be 200 h in a year.
# Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm
## 5 Conclusions  
In this study, a missing DTS estimation method based on the ISSA-DELM is proposed by using the conventional petrographical data in a block of the Ordos Basin in China. The ELM, DELM, GA-DELM, PSO-DELM, and SSA-DELM algorithms are used to assess the precision and generalizability of the proposed model. The application of the LightGBM  
Figure 9: The comparison between the measured and anticipated DTS during the testing phases in Well N predicted by the proposed models. (**a**) ELM, (**b**) DELM, (**c**) GA-DELM, (**d**) PSO-DELM, (**e**) SSA-DELM, (**f**) ISSA-DELM.  
algorithm in conjunction with the ordinary model (DELM) demonstrates that as the estimator model inputs increase, modeling errors decrease. However, the error drop trend is only slight after five input variables. As a result, the optimal modeling inputs are the variables including DT, RHOB, CAL, NPHI, and RT. In comparison to the other models proposed in this research, the ISSA-DELM model has the highest accuracy (R-square: 0.9916) and the lowest error (RMSE: 6.1255 and MAE: 4.1369). Additionally, when the reservoir DTS encounters the local mutation, the ISSA-DELM model performs better at recreating local DTS characteristics. The reason is that the ISSA-DELM model uses the ISSA to optimize the initial weights, which can more efficiently extract the spatial features of the log curve and enhance the model's capacity for prediction. As a result, it can be confidently stated that the ISSA-DELM model is more suitable for missing the DTS prediction compared to the other models introduced.  
Conceptualization, L.Q. and Z.J.; methodology, L.Q.; software, L.Q.; validation, L.Q., Z.J. and Y.C.; formal analysis, H.S.; investigation, K.X.; resources, K.X.; data curation, K.X.; writing--original draft preparation, LQ.; writing--review and editing, L.Q.; visualization, H.S.; supervision, Z.J.; project administration, Z.J.; funding acquisition, K.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (2016 YFC0600201), the Academic and Technical Leader Training Program of Jiangxi Province (20204 BCJ23027) and the Joint Innovation Fund of State Key Laboratory of Nuclear Resources and Environment (2022 NRE-LH-18).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the confidentiality requirements of the data provider.  
China petrochemical corporation (Sinopec) is gratefully acknowledged for providing useful data and valuable support.  
The authors declare no conflict of interest.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 1 Introduction
Opcode n-gram techniques, meanwhile, disregard important contextual features like operand relationships. For this reason, this study aims to both create malware images using PE files with a large new dataset and to classify them with a hybrid method. First, PE files are converted into assembly code. Then, the assembly code data is visualized, aiming to classify malware types more accurately and robustly. The main contributions of this article are as follows:  
This study presents an innovative methodology for the visual-based classification task using static analysis. Although various methods address the task of malware classification, there is a lack of a model that effectively visualizes assembly code sequences and classifies different malware families. This study aims to address this deficiency and proposes a new convolutional architecture based on deep learning. The proposed architecture first converts software into assembly code and then converts these codes into three-channel images. This architecture, which is validated with tests on a new dataset, has made significant contributions to the existing literature. This study investigates the potential of vision-based approaches and convolutional neural networks (CNNs) for malware detection using a novel dataset. We introduce MalevisAsm, an enhanced dataset created by merging the existing MaleVis collection with newly acquired benign samples. This augmentation improves the model's generalization capacity for more effective malware threat detection and classification. This paper is structured as follows: Section 2 provides an overview of existing malware classification approaches. Section 3 presents the rationale behind our proposed methodology. The architectural details and implementation of our model are explained in Section 4. Section 5 evaluates the experimental results and compares them with state-of-the-art methods. The article concludes with a summary of contributions in Section 6. The limitations of the current study are discussed in Section 7.
## 3 Concept
### Propulsion
#### 3.3.3 Excitation System  
A linear synchronous motor requires a strong excitation system to bridge the large airgaps, compared to rotational machines. Exciting the stator with a strong magnetic field leads to higher voltages and lower currents in the active windings, and therefore a higher efficiency, as well as a decreased amount of copper in the infrastructure. On the other hand, reaching strong magnetic fields at the stator often requires laminated iron in the guideway, increasing the infrastructure costs. Generally, there are three design options available.  
Ordinary electromagnets onboard have the advantage that the magnetic field can be turned off, which is beneficial during assembly, transportation, and maintenance. However, the achievable magnetic field strength is comparatively low. Also, electromagnetic excitation can be combined with an electromagnetic levitation system, like in the Transrapid. This concept requires a heteropolar levitation coil arrangement, so there are alternating magnetic poles in driving direction. For high speeds, this leads to more eddy currents in the iron and therefore requires expensive lamination of the iron. This is one reason for separating the levitation and propulsion systems. Other reasons include independent testing and development, as well as the option to implement propulsion in parts of the track only.  
With neodymium permanent magnet arrays in a Halbach configuration, stronger magnetic fields are feasible at comparable mass. Additionally, no power is required on the pod side, leading to less heat. However, a significant amount of rare earth material is required. Also, the reachable field strength enables an ironless stator with sensible effort only in a sword configuration, and the magnetic field cannot be turned off during handling and maintenance.  
Superconducting excitation promises strong magnetic fields by cooling down the system to cryogenic temperatures and harnessing the superconductive property of certain materials. In contrast to the Japanese L0, high temperature superconductors operate at higher temperatures and require less intensive cooling. Nevertheless, this concept is the most complex and technically challenging design but enables a simple ironless stator. Thus, the concept shifts the complexity and the costs from the tube infrastructure to the pods, aligning with the defined design goals.
## 3 System Model
### An Adaptive Packets Segmentation Scheme Based on the Irregular Gradient Algorithm
After multiple TTIs, the segmented packets transmitted by different satellites and beams are cached to the receiver. When all the segmented packets of one packet are transmitted to the receiver, the segmented packets are sorted at the MAC layer according to their sequence numbers and reassembled. The reassembled packets are transmitted to the upper layer. Figure 6: Adaptive task block segmentation scheme based on irregular gradient algorithm.
# Indirect Assessment of Railway Infrastructure Anomalies Based on Passenger Comfort Criteria
## 4 Indirect Method for Infrastructure Condition Assessment Based on Comfort Criteria
### Whole-Body Vibration Evaluation  
The ISO 2631 standard quantifies WBV concerning comfort, health, and motion sickness. Frequencies between 0.5 and 80 Hz affect the body as a whole. Thus, these frequencies are defined as the most relevant ones. The standard defines 3-axial measurements on the vibration transmission interfaces: floor, seat surface, and seatback. The root-mean-square (rms) acceleration is calculated for each axis from the measurements. Depending on the human body characteristics, vibrations with similar intensities but different spectral content will induce different dynamic responses. To quantify this effect, the standard determines the application of weighting curves, assigning different weights to the rms acceleration and rating its impact on the human body [46; 47; 48; 49]. The weighting process is calculated according to Equation (1):  
\[a_{w}=\left[\sum(W_{i}a_{i})^{2}\right]^{\frac{1}{2}} \tag{1}\]  
where \(W_{i}\) represents the weighting frequencies and \(a_{i}\) represents the rms accelerations. Weighting curve application relies on the measurement location and purpose. This way, the effect of the frequencies most influencing passengers' discomfort is amplified, while  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Train** & **Primary Suspension** & **Secondary Suspension** \\ \hline \multirow{4}{*}{AP} & 16 helicoidal springs & 12 flexi-coil springs \\  & + & + \\  & 4 vertical hydraulic dampers & (2 vertical, 2 transversals, 2 anti-yaw) \\  & 8 helicoidal springs & 4 helicoidal springs \\ IC & + & + \\  & 4 vertical hydraulic dampers & (2 vertical, 2 transversal) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary and secondary elements of AP and IC train suspension systems.  
that of the frequencies with less of an impact on discomfort is reduced. The total vibration \((a_{v})\) is obtained following Equation (2):  
\[a_{v}=\left(k_{x}^{2}a_{ux}^{2}+k_{y}^{2}a_{uy}^{2}+k_{z}^{2}a_{uz}^{2}\right)^{ \frac{1}{2}} \tag{2}\]  
where \(a_{w}\) is the rms accelerations for each axis and \(k\) represents the multiplying factor dependent on the measuring position according to Table 2.  
Lastly, based on \(a_{v}\), a defined scale evaluates discomfort levels (see Table 3), in which values higher than 0.315 m/s\({}^{2}\) are considered uncomfortable.  
As mentioned, the standard determines acceleration measurements at interface surfaces where vibrations are transmitted to the user. However, it is well known that the seat modifies vibration transmission [50; 51; 52; 53]. Additionally, the floor represents the closer location between the users and the carbody. This way, the present research applies the calculation of instantaneous floor discomfort by performing the standard analysis per second and applying the recommendations concerning floor measurement location.
## 4 Results and Analysis  
To rigorously evaluate the encryption strength and network performance of the implemented GA-based hybrid encryption system within the SDN, we conducted a series of controlled experiments. The dataset consisted of 31 distinct text messages, each varying in length from 6 to 256 characters. The selection of messages was influenced by the need for controlled testing, imposed by limited resources in the simulation environment. While larger sample sizes could have allowed for more thorough and detailed assessments, the  
Figure 5: Egress decryption logic at the controller. The dashed box represents the group of processes the controller handles to deploy the policy of egress decryption.  
Figure 6: SDN network topology simulated in Mininet, consisting of two OpenFlow switches and four hosts for evaluating encryption performance.  
chosen 31 messages represented a diverse range of alphanumeric characters, symbols, and byte distributions. This allowed for meaningful cryptographic analysis. Despite variations in content, the messages followed a similar format, ensuring comparable byte distributions. This design allowed for testing of encryption's ability to provide randomness and assess performance under diverse data conditions in SDN. The messages were transmitted across the network using the netcat command. This method helped model real-world network traffic and measure performance metrics. The variation in message length and complexity allowed us to check the robustness of the algorithm. This also tested the system's ability to handle different loads. Logs of encryption and decryption processes were recorded in detail, along with CPU and memory utilization. Instances of packet loss or latency issues were also noted. This systematic analysis provided a clear and broad understanding of the proposed encryption scheme's effectiveness and its impact on overall network performance.
## 1 Introduction
The system is also equipped with a wireless communication module that sends the measured walking parameters to a mobile device (smartphone/tablet). A dedicated application is installed on the mobile device, enabling online/offline monitoring and analysis of the patient's NW gait by a trainer/physiotherapist [21]. However, before using mechatronic poles in the study of gait biomechanics, it is essential to investigate whether equipping classic NW poles with sensors would cause changes in the gait pattern. Therefore, the study aimed to compare the biomechanics of free walking of older adults while practising Nordic Walking (NW) using both classic poles and mechatronic ones. It was hypothesised that equipping classic NW poles with sensors would not cause a change in the gait pattern compared to classic NW poles. The study seeks to address a gap in the literature regarding the use of NW for gait improvement in older adults, particularly regarding the effects of mechatronic NW poles on gait patterns. The study also highlights the potential benefits of real-time feedback for improving gait technique, particularly for older adults who may have difficulty learning correct gait techniques without assistance. The study provides vital information for healthcare professionals, researchers, and technology developers and contributes to the development of evidence-based interventions for improving gait and mobility in the older population.
## 4 Results
### Real Dual-Doppler Lidar Wake Measurements  
Figure 8a shows a 10 min wind field reconstructed by MuLiWEA, from data collected on 20 February 2014 at \"alpha ventus\". Within this time interval, the lidars cover the PPI sectors indicated in Figure 5 seven (WLS2) and five (WLS3) times, respectively. According to data from the FINO1 measurement mast (Forschungsplattformen in Nord- und Ostsee Nr. 1), a southerly wind (181.7\({}^{\circ}\)) with an undisturbed speed of 11.16 m\(\cdot\)s\({}^{-1}\) and a turbulence intensity of 5.88% at hub height were prevailing at the time. The wind turbine was operating under its rated conditions. In Figure 8b, the absolute error on the wind field can be seen. The basic error on the line-of-sight velocity is built up by the lidar measurement error and the numerical uncertainty of MuLiWEA. The lidar \(v_{LOS}\) uncertainty and pointing error are 0.2 m\(\cdot\)s\({}^{-1}\) and 0.11\({}^{\circ}\), respectively, according to the manual from Leosphere. The numerical uncertainty is estimated through analysis of the residuals of the linear system in Equation (7). Uncertainty propagation is used to combine all mentioned error sources to provide an estimate of the absolute error on the reconstructed wind vector value \(V\). This approach is explained in detail in Appendix A. Note that the wind field estimation within \(\pm 1D\) longitudinal distance of the wind turbine has a relatively high uncertainty due to laser signal reflection from the wind turbine. In the averaged wind field, no significant fluctuations in wind direction can be distinguished further downstream of \(1D\). However, the error shows an increasing trend towards the northeast, which is directly related to the relative azimuth angle \(\Delta\chi\) of the lidars. In the wake of the wind turbine, slight negative direction deviations were noticed, most probably caused by the wake rotation that  
Figure 7: Number of considered measurement points \(K\) per grid point for the: (**a**) volume scan (case 1); (**b**) single elevation scan (case 3) and the average measurement height offset w.r.t. hub height for the: (**c**) volume scan (case 1); and (**d**) single elevation scan (case 3).  
is impacting the horizontal wind speed at measurement heights above the hub height. More results from this measurement campaign were compared with LES by [PERSON] et al. (2014), which confirms the usefulness of the algorithm in combination with the measurement campaign for steady wake model validation on a 10 min basis.
Remote Sens._**2014**, \(6\), 5868-5884; doi:10.3390/rs6065868  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 1 and [PERSON] 4**  
Footnote 1: European Commission, Joint Research Centre, Institute for Environment and Sustainability,  
Via E. Fermi 2749, I-21027 Ispra (VA), Italy; E-Mails: [EMAIL_ADDRESS] (F.R.);  
[EMAIL_ADDRESS] (A.S.)  
Footnote 2: South African National Space Agency, Pretoria 0087, South Africa;  
E-Mail: [EMAIL_ADDRESS]  
Footnote 3: Institute of Remote Sensing and Digital Earth, Chinese Academy Of Science, Dengzhuang South  
Road 9, Haidian District, Beijing 100094, China; E-Mail: [EMAIL_ADDRESS]  
Footnote 4: Ecological Monitoring Centre, Rue L on Gontan Damas, BP 15 532 Dakar, Senegal;  
E-Mail: [EMAIL_ADDRESS]  
Footnote *: Author to whom correspondence should be addressed;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +39-332-786-429; Fax: +39-332-789-029.  
_Received: 9 April 2014; in revised form: 5 June 2014 / Accepted: 6 June 2014 / Published: 20 June 2014_  
maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean \(r=0.78\)). The negative correlation between delays in SOS and CFAPAR is stronger (mean \(r=-0.71\)) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean \(r=0.75\)). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship.  
keywords: phenology; SPOT-VEGFATION; FAPAR; time series; biomass production; Sahel +
Footnote †: journal: Journal of Computational Biology
## 2 Materials and Methods
### Sequence Analyses  
Paired-end reads were assigned to samples based on their unique barcodes and truncated by cutting off the barcode and primer sequence. The paired-end reads were merged using FLASH (V1.2.7) to obtain raw tags. Then, quality filtering of the raw tags  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **City** & **Encode** & \
\begin{tabular}{c} **Average** \\ **Temperature** (\({}^{\circ}\)**C) **A** \\ \end{tabular} &
\begin{tabular}{c} **Average Relative** \\ **Humidity (\%)**A** \\ \end{tabular} & **Altitude (m)** \\ \hline \multirow{3}{*}{Basin} & Suining & BA & & \\  & Leshan & BB & 7–8 \({}^{\circ}\)C & 75–80\% & \textless{}500 m \\  & Chengdu & BC & & & \\ \hline \multirow{3}{*}{Mountains} & Yibin & MA & & \\  & Ya’an & MB & 9–10 \({}^{\circ}\)C & 75–80\% & 500–2000 m \\  & Xichang & MC & & & \\ \hline \multirow{2}{*}{Plateau} & Kangding & PA & \multirow{2}{*}{\(-1\)–\(0\)\({}^{\circ}\)C} & \multirow{2}{*}{50–55\%} & \textless{}2500 m \\  & Ma’er kang & PB & & & \\ \hline \hline \end{tabular}  
* All data come from the Sichuan Statistical Yearbook. \({}^{\text{A}}\) Results are expressed as a range of 5 years on average (2014–2018), and the data of each year only consist of December of the current year and January of the following year (the production period of Sichuan bacon).  
\end{table}
Table 1: The information of the sampling locations.  
was performed under specific filtering conditions to obtain the high-quality clean tags according to the QIME (V1.7.0) quality-controlled process. After chimeric sequences were identified and removed by the UCHIME algorithm (UCHIME Algorithm), effective tags were obtained and clustered into operational taxonomic units (OTUs) with a 97% similarity threshold. Taxonomy assignment of the OTUs was performed by comparing the sequences to the SILVA database.  
The abundance information of the OTUs was normalized by the sample with the least sequences. A Venn diagram was illustrated to display the similarities and differences of the communities among the regions. Alpha diversity (Chaol and ACE richness estimators, Shannon and Simpson diversity indices, and Good's coverage) was used to analyze the bacterial diversity in a single sample. The beta diversity based on the Bray-Curtis and Unifrac distance was used to evaluate the differences between the microbial communities. Principal component analysis (PCA), principal coordinate analysis (PCoA), and non-metric multidimensional scaling (NMDS) were performed using QIIME software (Version 1.7.0). In the meantime, linear discriminant analysis (LDA) effect size (LEfSe) was applied to illustrate statistically significant biomarkers in each region with LDA \(>\) 4 [21].