esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 3 Database Establishment and Analysis  
### Feature Parameter Selection  
Slope stability is a complex problem with multisystem, nonlinear, and multifactor influences. The multi-index system is based on the M-C strength criterion, and the BKA-MLP model is adopted to predict the safety factor of slope stability. The selected characteristic parameters should not only accurately reflect the failure characteristics of slope deformation and displacement characteristics but also cover the common characteristics of most slopes as comprehensively as possible. At the same time, the real conditions of the slope cannot be ignored [34]. Considering the influencing factors of slope stability and landslide mechanism, six influencing factors, namely two parameters of the M-C strength criterion (shear strength \(\tau\) and effective stress \(\sigma^{\prime}\), kPa), the bulk density of slope rock mass (\(\gamma\), kN/m\({}^{3}\)), slope geometry parameters (slope height H (m) and slope inclination \(\alpha\)(\({}^{\circ}\))), and pore ratio \(\tau_{\rm u}\), are selected as the feature inputs of the prediction model. They are represented by X\({}_{1}\) to X\({}_{6}\). Compared with the traditional features of cohesion and internal friction angle, the use of shear strength and effective stress as features has more direct physical significance. The shear strength reflects the ability of rock and soil to resist shear failure, while effective stress is the key to affecting this ability. The two are directly related to the stability of the slope, and their introduction can not only improve the accuracy, reliability, and practicability of the model; but also make the model more interpretable.
# Hybrid Renewable Energy Systems--A Review of Optimization Approaches and Future Challenges
## 4 HRES Optimization
### Weakness and Limits of Optimization Methods  
The reviewed methods, referenced in more than one article from 2017 to 2024, reveal significant limitations in addressing complex optimization challenges in energy systems. HOMER Pro 3.12-3.16 struggles with modeling advanced utility billing structures, multi-objective optimization, and machine learning capabilities, while MILP, despite its efficiency, faces scalability issues and lacks integration with energy management systems (EMS). PSO and Constrained Multi-Objective Particle Swarm Optimization (CMOPSO)-Multi Strategy Integration (MSI) algorithms are hindered by slower convergence, sensitivity to parameter settings, and high computational demands. NSGA-II and SSA suffer from weak convergence and high computational costs, with SSA particularly limited in mitigating local optima and optimizing load-shifting tasks. GWO and CSA exhibit challenges in scalability, dynamic adaptation, and balancing exploration with exploitation. Additionally, TOPSIS lacks a robust distance-weighting mechanism, further had the need for more comprehensive and efficient approaches to solving real-world energy system problems (see Table 1). \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Method** & **Weakness/Limits** \\ \hline \hline \multirow{8}{*}{[81]} & & The system faces significant limitations, including an inability to model various electric utility billing structures and complex pricing methodologies and the absence of machine learning-based predictive modeling capabilities. It lacks novel storage systems and advanced thermal modules utilizing heat pumps, along with optimization and modeling for these systems. High costs restrict accessibility for low-resource laboratories and individuals, while the system cannot perform multi-objective optimization or support innovations in HRES design and operation. Users encounter challenges in defining specific prices and costs in inputs, further limiting their flexibility and practicality. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Weakness and limits of optimization methods (structured by the authors).
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 6 Security Analysis
### Security Properties  
Table 3 presents the better security properties ensured by the proposed scheme compared to related schemes [6, 8, 15, 16]. The existing schemes are insecure various attacks and their scheme cannot ensure mutual authentication and user anonymity. In contrast, the proposed scheme can provide essential security properties and can achieve user anonymity and mutual authentication.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 4 Discussion
### Single-band and Multi-band DTW  
NDVI was used for the single-band DTW approach because of its power in identifying crop classes. For example, [PERSON] et al. [78] found NDVI to have a prediction power of around 50% amongst 20 ASTER vegetation indices for crop identification. For multi-band approach, we addedGNDVI, NDRE, SAVI, and NDWI, computed using most of the Sentinel-2 spectral bands. Red-edge bands proved to be very efficient for different vegetation mapping applications due to their potential to estimate vegetation chlorophyll content [48; 79; 80]. The suitability of red-edge and short-wave infrared (SWIR) bands of Sentinel-2 had been shown in different applications including chlorophyll content and leaf area index (LAI) estimation of various crops including corn, sugarbeet, onion, garlic, and wheat [81] and LAI estimation of wheat and potatoes crops [82], estimation of crops and grass chlorophyll and nitrogen content [83], and crops and tree species mapping [84]. SWIR spectral bands quantify canopy reflectance in relation to moisture content and their importance for different vegetation mapping applications was proven in previous studies [85].  
Using a multi-band DTW increased the accuracy by 6.1% in California by reducing confusion between alfalfa and vegetables, alfalfa and sugarbeets, and other hay and sod/grass seed. In Texas, multi-band DTW had 1.5% lower accuracy than single-band DTW. In the latter case, although it reduced many instances of class confusion, it increased confusion between corn and cotton, two classes that shared a similar pattern and the usage of multiple vegetation indices might have smoothed their temporal profiles. Unlike other classifiers, DTW does not compute a variable importance measure for multi-band DTW so we cannot have a deeper knowledge of the importance of each vegetation index used. However, DTW computed a measure that can act as a measure of confidence for the classification, namely the DTW dissimilarity value [25].
## 4 Materials and Methods
### Gene Differential Expression and Enrichment Analysis  
HTSeq v0.6.0 was used to count reads for each gene in each sample, and transcripts per million reads (TPM) were calculated to estimate the expression level of genes [53]. Uni-genes with a total TPM across the five groups of less than five or uni-genes with more than three groups having no detectable expression were filtered prior to clustering [53]. An estimated false discovery rate (FDR) was assigned to each gene and adjusted with the Benjamini and Hochberg approach to control the FDR [54]. Genes with an FDR \(\leq\) 0.05 and a \(\mid\)log2 ratio \(\mid\)\(\geq\) 1 were identified as DEGs.
# The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
## 2 Methods  
### Theoretical Considerations  
The Taylor expansion method was applied to convert the WJ distribution to the form of a Gaussian function. The expansion was performed around three representative positions, i.e., around \(x=0\) (Method I), \(x=v\) (Method II) or \(x=x_{m}\) (Method III). The expansion outcomes were appropriately elucidated with numerical solutions. The fitting methods adopted were nonlinear regressions, and the overall goodness-of-fit criterion was the correlation coefficient \(R^{2}\) (COD, Coefficient of Determination), supported by the analysis of the residual errors for individual points.
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 4 Discussion and Related Work
### Related Work for CWE Detection in Source Code
A series of supervised learning classifiers have been tested with this approach, such as random forest, support vector machines, decision tree, logistic regression, K-nearest neighbors, neural networks, showing that the classifier used to identify security patterns using the Intermediate Representation is vital. The security pattern identifier is qualitative enough to complement Static Application Security Testing solution. We have documented through practical experiments that theproposed multi-language CWE scanner is highly accurate in detecting security weaknesses (CWE) in source code, when it is well supported by training samples. For Java source code, we have obtained a f1 parameter in value of 0.955 and a recall value of 0.990 (recall is an indication for the rate of true positives detection), when the training set is highly supportive (more than 5000 vulnerable samples per each trained CWE). These values show that security analysts are not missing almost any weakness in their code. The performance of our model (F1 > 0.95) seems to be better than the one provided by [PERSON] et al. [9] by using deep learning on NIST dataset (F1 = 0.84), when using CWE discovery at function level. If choosing the code snippet level, the performance of our method is lower (F1 = 0.77), with a recall value remaining high (recall = 0.899). However, even when there are not so many supporting code snippets, the results are good enough to complement the SAST technologies that are based on deterministic algorithm to detect security vulnerabilities (e.g., regex based or compiling the code before the analysis). For more than 100 samples with vulnerable code, security analysts are benefiting of a good f1 value of 0.873 and a good recall value of 0.948 when scanning is executed close to function level (as soon as the function code is developed).
## 3 Results and Discussions
### Bitumen-Aggregate Bond Strength Analysis  
The results of the bitumen bond strength test with aggregate after both dry and wet conditions are shown in Table 6 with failure pattern. In this table, the addition of CNTs improved the adhesion of bitumen with aggregate. Burst pressure obtained by PATTI was converted into pull off tensile strength using following relationship.  
\[POTS=\frac{\left(BP*A_{g}\right)-C}{A_{ps}} \tag{2}\]  
where \(BP\) is burst pressure, \(A_{g}\) is the contact area between gasket and reaction plate, \(C\) is piston constant and \(A_{ps}\) is area of pull off stub. For F4 type of stub, \(A_{g}\) is 4.06 in\({}^{2}\), \(C\) is 0.286 and \(A_{gs}\) is 0.1963 in\({}^{2}\).  
Figure 14: Master curve of G*/Sin\(\delta\) for CNT-modified and unmodified bitumen at 58 \({}^{\circ}\)C.  
Figure 15: Black diagram for unmodified and CNT-modified binder.  
It can be seen from the results that POTS for CNT-modified asphalt binders were higher than for the unmodified asphalt binder in both dry and wet conditions. In dry conditions, most of the failure was cohesive failure, while adhesive failure was observed in most of the samples tested after wet conditioning due to the penetration of water at the interface between bitumen and aggregate, which weakened the bond and resulted in reduction of POTS [47]. CNT-modified bitumen showed less susceptibility to moisture as compared to unmodified bitumen due to the hydrophobic nature of CNTs [48]. In dry condition, addition of 0.5% of CNTs resulted in an increase of POTS by 19% as compared to base binder. As the bitumen content increased, POTS tended to increase. With 3% addition of CNTs, POTS increased by a value of 52%, while, in wet mixing, 3% CNTs increased the POTS by 45%.
## 2 Technologies in DTx
### Data Analysis
#### 2.2.1 Machine Learning for DTx  
In previous research fields, medical data analysis has mainly focused on statistical analysis techniques. However, the medical AI field is expanding with the development of various machine learning algorithms that show higher classification performance by repeatedly learning hidden data features that human eyes cannot recognize. By training data features through deep neural network structures, it expanded its scope to specific medical fields such as radiology [24], pathology [25], neuroscience [26], genetics [27], mental illness [28], and various other chronic diseases.  
DTx products utilize diagnostic or classification algorithms to analyze targeted illnesses through medical AI technology and design their systems. When analyzing medical image data, convolutional neural network (CNN)-based techniques such as prediction,classification, segmentation, and augmentation are performed to obtain meaningful information and make decisions from the original input image. In addition, recurrent neural network (RNN)-based algorithms and transformer-based encoder-decoder-structured algorithms that can be applied to time-series data analysis are used because it is important to check the state changes rather than diagnosing with a user's static state.  
Medical imaging is where machine learning techniques that identify data characteristics and help make final decisions are currently being applied the most [29]. The paper shows that learning techniques such as support vector machines and relevance vector machines are commonly used in the fields of computer-aided diagnosis (CAD) and functional brain mapping. Through this, lesions and abnormalities are efficiently found in medical images containing noise. In addition, similar machine learning algorithms have been used in various ways in the field of lung disease brought on by COVID-19. Research has emerged to find infected parts, evaluate the severity [30], and see accurate lesion positions through lung CT images and X-ray images [31; 32]. With this research, it has been confirmed that machine learning algorithms can efficiently help humans to make medical decisions.
# Security Risk Analysis of LoRaWAN and Future Directions
## 6 Security Suggestions for the Improvement of LoRaWAN
### Nonce Related Improvement  
Each end-device may have nonce which might be associated with the serial number of itself. The \(NS\) holds the list of nonces (_Nonce-List_) and revokes each nonce whenever its used first time.  
Nonces can be updated by hand-shaking, after successful key initiation phase. Both \(NS\) and the end-device might agree on a new nonce and that nonce might be written to a _non-volatile memory_ of the end-device. The old nonces would be moved to the \"used nonces list\" and the new nonce should be moved to \"valid nonces list\".  
By this way, any attempt to use an old nonce would not cause any security breach in the network. Besides, these attempts can easily be detected by the \(NS\) by comparing the nonce with the nonce lists. The join request can be granted if only the submitted nonce is in the _valid nonces list_ otherwise, if the nonce is in the _used nonces list_, then this is a possible situation of cloning, node-capture or replay attack!
## 4 Enhancing the Dynamic Multi-Hop Routing (EDMR) Protocol  
This paper presents new modifications to the DMR protocol. It aims to minimize the total energy consumption while preserving the WSN's performance, stability, and throughput. For this reason, it proposes a new approach that retains the network architecture introduced in the DMR protocol; however, this approach makes adjustments to the number of clusters that should remain active for transmitting data toward the BS. Thus, the proposed protocol incorporates a sleep/wake mechanism to reduce the total energy consumption of the WSN, which in turn extends the network lifetime and improves the network throughput. It also considers the types of applications that use the WSN. The EDMR protocol divides the network lifetime into rounds. Each round includes two general phases: a network architecture phase and a data-routing phase, each consisting of a number of sub-phases, as shown in Figure 3. The following section provides explanations of the protocol's phases.
###### Abstract  
This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP in terms of economic cost (EC) and power quality (PQ). Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, \"improvisational\" and \"foresighted\", are compared by testing on the Institute of Electrical and Electronic Engineers (IEEE) 118-bus benchmark system. It is revealed that \"foresighted\" strategy, which incorporates LSTM prediction and bi-objective optimization over a 5-h receding horizon, takes 10 Pareto dominances in 24 h.  
multi-energy virtual power plant; economic cost; power quality; bi-objective dispatch; long short-term memory; multi-objective particle swarm optimization
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 3 Sensing Matrix Optimization of Compression Acquisition  
In this section, we take the Frobenius norm of the difference between the Gram matrix and the approximately ETF [27] as the objective function to optimize the sensing matrix of compression acquisition.
# Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach
## 3 Materials and Methods  
### Study Area  
Spain has remarkable landscape diversity and a wide range of ecosystems due to its geographic position in southwestern Europe, climate, geological features and relief, with elevation varying from sea level to 3479 m. A vast plateau surrounded by a number of mountain ranges dominates the central part of Spain. Synoptic air masses create a NW to SE gradient of water availability (total annual precipitation varies from 2000 mm to 120 mm), ranging from Atlantic humid climate zones on the north coast to Mediterranean climate on the east coast, the SE corner becoming the most arid zone of Europe [37]. Spain has a mosaic of land cover that includes significant areas of traditional and newly developed agriculture (49% of land), dominated by non-irrigated land. These areas are embedded in a matrix of natural and semi-natural vegetation (47% of land), mainly occupied by shrublands, and broadleaved or coniferous forests [38]. Vegetation distribution is strongly controlled by aridity, and drought conditions have a marked influence on vegetation cover and activity. The droughts that affect the semiarid areas represent an important factor in environmental degradation.  
Figure 1 shows a precipitation map obtained averaging the annual precipitation of the eight years analyzed in this study (2005-2012), an average annual PAR image and the reference annual GPP for 2011 (both products will be described later).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hypothesis** & & **Description** \\ \hline H1 & \(\mathrm{PAR}^{Y}_{i}=\mathrm{PAR}^{\circ}_{i}\) & \begin{tabular}{c} PAR inter-annual variations effects on annual GPP are \\ negligible. A typical or representative PAR series, \(\mathrm{PAR}^{\circ}_{i}\), \\ can be used to characterize its seasonal pattern, \\ independently of the year. \\ \end{tabular}, \\ \hline H2 & \(f^{Y}_{\mathrm{APAR},i}=a\,+b\,\Pi^{Y}_{i}\) & \
\begin{tabular}{c} A linear relation with a VI (such as NDVI and EVI) is \\ a reasonable approximation for \(f_{\mathrm{APAR}}\) evaluation. \\ \end{tabular} \\ \hline H3 & \(\epsilon^{Y}_{i}=\epsilon_{\mathrm{max}}\) &
\begin{tabular}{c} The conversion efficiency is independent of time and \\ approximated by the ecosystem maximum efficiency at \\ annual scale. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hypotheses underlying the estimation of annual GPP by means of the semi-empirical model.
# Incisors Extrusion with Clear Aligners Technique: A Finite Element Analysis Study
## 4 Discussion
### Study Limitations  
FEM studies represent one of the best ways to analyze force systems delivered by orthodontic appliances. However, in vitro and in vivo study results may differ. When analyzing aligner deformations, we should always keep in mind that polymer material, friction phenomena, thermoplastic material properties, thermoforming procedures, and insertion and removal of the appliance are all factors acting on aligner mechanical properties. Unfortunately, most of those factors are patented and not disclosed by companies, and therefore cannot be used to increase the complexity of FEM analyses. Another limitation  
Figure 3: Amount of deformation based on the analyses of the OTT model.  
of FEM studies in orthodontics is the approximation in modelling the material properties of biologic components, in particular PDL. Despite the increase in scientific evidence in this field, modelling of PDL properties remains an issue to consider when interpreting FEM studies' results. Therefore, future FEM analyses should also consider those effects in analyzing orthodontic tooth movement biomechanics.  
It is now possible to answer the clinical/research questions:  
* Are aligners efficient in performing upper incisor extrusion?  
As stated previously, the contemporary upper incisor extrusion biomechanics in aligner orthodontics is largely unpredictable. Extrusion of the four upper incisors results in central incisor retroclination, with lateral incisor flaring. Therefore, clear aligners seem to be not so efficient in controlling pure extrusion movements of the upper incisors.  
Does the number, position, and shape of attachments influence the force system?  
RETT and PALAT configurations show better results in terms of force system, with minimal deformation in aligner's gingival portion. This could lead to better control of lateral incisor movement. From a clinical perspective, clear aligners could be indicated in the treatment of open-bite patients with protruded upper incisors.  
Is posterior anchorage preserved during incisor extrusion?  
There is a tendency of buccal tipping of the posterior teeth during extrusion. However, there is no evidence that this can cause a lack of posterior anchorage. Furthermore, the most efficient configurations showed the need for the use of attachments on posterior teeth in order to obtain better anchorage performances.