esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Research Motivation  
To address these limitations, this study focuses on three key challenges in OR-based WSNs:  
* Highly dense networks: excessive node density leads to multiple receivers per packet, causing data redundancy, network congestion, and energy inefficiency (Figure 1) [8].  
Figure 1: Routing opportunistically in an asynchronous WSN.  
Non-uniform node distribution: inaccurate density estimations result in poor forward selection, increased routing loops, and packet isolation issues [7].
* Long transmission ranges: a higher number of potential forwarders leads to packet duplication, excessive collisions, and reduced network efficiency [9].
## 2 Related Work
[30] proposed an algorithm that integrates adaptive synthetic sampling (ADASYN) with random forest. ADASYN is an oversampling technique that assigns weights to minority-class samples based on their distribution density in the majority-class sample space [31]. This dynamic weighting helps determine the number of synthetic samples to generate. The researchers evaluated the proposed method on the CIC-IDS2017 dataset, and the experimental results showcased its superiority over traditional ML algorithms and the random forest algorithm using different sampling methods. The proposed method achieved an F1 score of 95.3%. However, ADASYN may introduce noise, potentially affecting the quality of the dataset. Hence, [PERSON] et al. [32] proposed a data resampling technique that combines ADASYN with the Tomek links algorithm. Tomek links can remove overlapping samples between adjacent classes to enhance class boundaries and reduce noise. The proposed method was evaluated by employing the NSL-KDDdataset. The experimental results clearly indicate an improvement in the detection rate of the minority class. In the context of multi-classification, the proposed method achieved an accuracy rate of 99.9%. [PERSON] et al. [33] tackled the issue of imbalanced datasets by utilizing a generative adversarial network (GAN). They utilized GAN to augment the samples of the minority class, followed by applying the random forest algorithm to classify the new dataset. The researchers evaluated the proposed method on the CIC-IDS2017 dataset, and the experimental results revealed a substantial improvement in the performance of the random forest classifier when using the GAN-based approach. The method they proposed achieved an accuracy of 99.83%, surpassing the performance of the original dataset. The studies mentioned above have made substantial contributions to the advancement of the NIDS field and have provided inspiration for this research. This paper addresses the challenge of imbalanced datasets at the algorithmic level. Cost-sensitive learning is one algorithmic technique that adjusts the decision boundary of the classifier by assigning varying weights to samples from different classes while maintaining the distribution of the imbalanced datasets [34; 35]. This adjustment raises the cost of misclassifying minority class samples, thus enhancing classification performance. [PERSON] et al. [36] proposed focal loss, which is based on cost-sensitive learning, as a solution to the class imbalance issue in object detection. In this study, we utilize focal loss to tackle the challenge of imbalanced datasets in IDS. In order to assess the effectiveness of CBF-IDS, we selected three widely used imbalanced datasets. These datasets cover various attack scenarios and are suitable for evaluating the performance and generalization capability of CBF-IDS in the presence of class imbalance.
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 5 Multi-Hazard Assessment and Vibration Control of Buildings
### Multi-Hazard Vibration Control of Buildings  
Vibration control of buildings subjected to wind or seismic forces has been extensively researched. Vibration control of buildings in multi-hazard scenarios is, on the other hand, not as extensively studied. Some important studies in this area are summarized in Table 2. Performance assessment of control devices, their optimization, and life-cycle cost analysis are the main issues that have been addressed in these studies. Wind and earthquake forces are the most considered hazard in these studies. Most of these studies present traditional control systems such as passive TMDs, passive energy dissipation devices, viscous fluid dampers, multiple tuned passive TMDs, etc. Some recent advances in this area include inerter-based TMDs ([PERSON] et al., [140]; [PERSON] et al. [141]; [PERSON] et al. [142]; [PERSON] and [PERSON] [143]), glass curtain wall TMDs ([PERSON] and [PERSON] [144]), and sliding floor isolators ([PERSON] and [PERSON] [139]; [PERSON] and [PERSON] [145]).
# Dynamic Analysis and Security Characteristics of Carrier-Based Aircraft Arresting in Yaw Condition
## 5 Calculation and Analysis
### The Influence of Yaw Angle on Kink-Wave  
In the case of right yaw arrest, the triple kink-wave on the right side ends first, and then the third kink-wave on the left side ends. The influence of yaw angle on the resistance of both sides during the first triple kink-wave was analyzed, and the results are shown in Figures 10 and 11.  
Figure 10: Port tension during kink-wave period.  
Figure 9: Curves comparison of arresting force–displacement in simulation and test.  
The results of the time length of the triple kink-wave on both sides at different yaw angles are listed in Table 2 for comparative analysis.  
It can be seen from the results that there is no difference in the time length of the first kink-wave between the left and right sides, both of which are about 0.0993 s. After the propagation of the first kink-wave, the time length of the left kink-wave increases slightly with the increase of eccentricity, and the time length of the right kink-wave decreases slightly with the increase of yaw angle.  
The main reason is that when the first kink-wave propagates, the initial length of the arresting cables on either side is the same, and the kink-wave velocity is slightly different, so the time of the first kink-wave propagation is basically the same. After the end of the first kink-wave, the length of the arresting cables on either side is different. When the right yaw arrests, the right arresting cable is shorter than the left, the kink-wave propagation time is shorter, and the time difference of the same kink-wave on either side is obvious.  
When the yaw angle is larger, the fluctuation of the first kink-wave on the right side is obviously weakened. When the yaw angle reaches 7\({}^{\circ}\), the fluctuation is smaller. When the yaw angle reaches 9\({}^{\circ}\), the fluctuation of the basic first kink-wave is not obvious.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Yaw Angle** & \begin{tabular}{c} **First Kink-Wave** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Second** \\ **Kink-Wave (s)** \\ \end{tabular} &
\begin{tabular}{c} **Third Kink-Wave** \\ **(s)** \\ \end{tabular} \\ \hline \multirow{2}{*}{0\({}^{\circ}\)} & Port & 0.0994 & 0.2055 & 0.3320 \\  & Starboard & 0.0994 & 0.2055 & 0.3320 \\ \hline \multirow{2}{*}{3\({}^{\circ}\)} & Port & 0.0993 & 0.2071 & 0.3360 \\  & Starboard & 0.0992 & 0.2035 & 0.3264 \\ \hline \multirow{2}{*}{5\({}^{\circ}\)} & Port & 0.0993 & 0.2083 & 0.3390 \\  & Starboard & 0.0992 & 0.2023 & 0.3227 \\ \hline \multirow{2}{*}{7\({}^{\circ}\)} & Port & 0.0993 & 0.2094 & 0.3419 \\  & Starboard & 0.0992 & 0.2010 & 0.3189 \\ \hline \multirow{2}{*}{9\({}^{\circ}\)} & Port & 0.0993 & 0.2106 & 0.3450 \\  & Starboard & 0.0992 & 0.1998 & 0.3152 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time length comparison of kink-wave in yaw arrest.  
Figure 11: Starboard tension during kink-wave period.
# A Reconfigurable Radio-Frequency Converter IC in 0.18 \(\upmu\)m CMOS
## 2 Broadcast Architecture
It is important to mention that the demodulated RF signal is a standard television signal and, therefore, it can be fed to any television set without the need for a decoder as it happens with IPTV, thus facilitating the implementation and use of this service. Figure 2: Scheme of the structure of a GPON to implement triple-play services using the third lambda at 1550 nm. In this work, we present an integrated reconfigurable RF converter to tune digital video broadcasting terrestrial (DVB-T) television signals sent in a triple-play architecture over GPON using the third lambda carrier at 1550 nm and encryption by spectrum inversion. The system, whose conceptual principle of operation is shown in Figure 3, takes the DVB-T input, which is multiplexed in frequency (WDM) in the range from 47 MHz to 1000 MHz in channels of 8 MHz, and spectrally inverted. Then, to separate the desired channel from the rest of the signal, it up-converts its frequency to the band-pass of a highly selective surface-acoustic wave (SAW) filter centered at 1.3 GHz. Finally, the filtered signal containing only one broadcast channel is down-converted, spectrally inverted and centered in one of the channels of a television set, which range from 470 MHz to 868 MHz.
## 2 Methods
### Theoretical Calculation of the Sectional Coal Pillar Retaining Size under Noncut  
_Roof Conditions_  
The reasonable width of the coal pillar is a critical factor for roadway stability. The theoretical model of a reasonable width of a coal pillar in roadway protection is shown in Figure 5, and the calculation formula is  
\[B=x_{0}+x_{1}+x_{2} \tag{1}\]  
where \(x_{0}\) is the width of the plastic zone of the coal pillar; \(x_{1}\) is the length of the bolt (cable); and \(x_{2}\) is the coal pillar safety zone, generally 0.15\(\sim\)0.35 (\(x_{0}+x_{1}\)).  
\begin{table}
\begin{tabular}{|c|c c c c|} \hline \hline \multirow{2}{*}{**Density/[kg.m\({}^{-3}\)]**} & **Bulk Modulus** & **Shear Modulus** & **Dilatancy** & **Frictional Angle** \\  & **[GPa]** & **[GPa]** & **Angle [\({}^{\circ}\)]** & **[\({}^{\circ}\)]** \\ \hline
1700 & 10 & 6 & 10.3 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of goaf.  
Figure 4: Model schematic diagram.  
Figure 3: Comprehensive bar chart of the 1301 working face.  
According to the geological report of the #15 coal seam, the coal seam is buried at a depth \(H=300\) m; the average bulk density of the overlying strata \(\gamma=26\) kN/m\({}^{3}\); the pillar height is the height of the mining roadway \(m=2.8\) m; and the cohesion and internal friction angle at the junction of the coal pillar and roof and floor are \(C_{0}=0.82\) MPa and \(\phi=25^{\circ}\), respectively. The supporting resistance of the coal pillar on one side of the goaf is \(P_{X}=0\); the coefficient of the stress concentration \(K=2.3\); the Poisson's ratio for coal #15 is \(\mu=0.31\); the lateral pressure coefficient \(\lambda=\mu/(1-\mu)=0.45\); and the length of the bolt of the coal pillar at the side of the roadway \(x_{1}\) is \(2.0\) m. The above data are substituted into the following equation:  
\[x_{0}=\frac{m\lambda}{2\tan\phi_{0}}\ln\frac{K\gamma H+\frac{C_{0}}{\tan\phi_{ 0}}}{\frac{C_{0}}{\tan\phi_{0}}+\frac{P_{X}}{\lambda}} \tag{2}\]  
Then, \(B=x_{0}+x_{1}+x_{2}=3.3+2.0+0.35\times(2.7+2.0)=7.15\) m.  
Through the above calculation, it is theoretically analyzed that the minimum size of the section coal pillar should not be less than 7.15 m when roof cutting measures are not taken to relieve pressure. If the roof cutting operation is taken, the size of the section coal pillar can be further reduced.
# Comparison of the Biological Potential and Chemical Composition of Brazilian and Mexican Propolis
## 3 Discussion
### Antiprotozoan Activity  
Some scientific articles have identified the effect of different concentrations of extracts (methanolic, ethanolic, and supercritical) of different propolisis (brown, green, and red) against protozoa were identified. The extracts showed good inhibition against these organisms (_Trypanosome brucei_, _Trypanosoma cruzi_ Y, _Leishman_ (V.) _braziliensis_, _Leishman amazonenses_) particularly when using the green and red propolis extracts from Brazil [26; 34; 35; 37]). Red propolisis extract showed a better reduction of L. (V) _brazilensis_ comparing with green propolisis extract, corroborating the results that red propolisis had more effective antioxidant activity [26; 34; 35; 64; 118].  
For Mexican propolisis, only one scientific report on the effect of propolisis on protozoa was found, and a good inhibition of _Giardia lamblia_ was verified.
# Predicting Missing Values in Survey Data Using Prompt Engineering for Addressing Item Non-Response
## 5 Limitations and Future Works  
### Limitations  
While our proposed method demonstrates significant improvements in predicting item non-responses in survey data, there are several limitations that should be acknowledged:  
1. Challenges with Questions Having Numerous Options: The predictive accuracy of our method decreases when dealing with survey questions that offer a wide range of answer choices. As the number of options increases, the complexity and ambiguity in distinguishing between them can lead to difficulties in accurately predicting the correct response. This is particularly evident in questions where the options are nuanced or overlap in meaning.
2. Requirement for Hyperparameter Tuning: The optimal values for hyperparameters such as top-k (the number of top relevant questions) and n-shot (the number of few-shot examples) can vary between different survey questions. Each question may require a unique set of hyperparameters to achieve the best performance, necessitating a search process to identify these values.
3. Dependence of Linguistic Relevance: The effectiveness of our method relies heavily on the linguistic relatedness of the survey questions. For questions that are not semantically rich or lack clear connections to other items in the survey--such as \"How did you access this survey?\"--our method may struggle to provide accurate predictions. In such cases, the lack of relevant contextual information hinders the model's ability to infer missing responses effectively.
4. Limitations with Smaller Language Models and Long Contexts  
When utilizing language models with a smaller number of parameters, such as LLaMA38\({}_{B}\), performance issues become more pronounced as the context length increases. These smaller models are more susceptible to decreased accuracy and a higher incidence of hallucinations when processing long prompts.
## 2 The Proposed VA-RAIM
### Va-Raim
#### 2.3.1 Protection Level and Availability  
Protection level computation is in essence a performance safeguard to evaluate the power of fault detection. Denote \(N_{I(k)}=N_{G(k)}+N_{V(k)}\) is the sum number of satellites and landmarks, \(H_{slope}(i)\) and \(V_{slope}(i)\) (\(i=1,2,\,\ldots,\,N_{I(k)}\)) of the integrated system is obtained as [21]:  
\[\textit{Hslope}(i)=\frac{\sqrt{\mathbf{A}_{i}^{2}+\mathbf{A}_{{}_{Nj}}^{2}}}{\sqrt{S_{ {}_{i}}}} \tag{20}\]\[Vslope(i)=\frac{\left|A_{y_{i}}\right|}{\sqrt{S_{ii}}} \tag{21}\]  
where \(A_{1i}\), \(A_{2i}\), and \(A_{3i}\) are the elements of matrix \(\mathbf{A}_{I(k)}=(\mathbf{H}_{h\odot}^{T}\mathbf{\Sigma}_{h\odot}^{-1}\mathbf{H}_{h\odot})^{-1 }\mathbf{H}_{h\odot}^{T}\mathbf{\Sigma}_{h\odot}^{-1}\), \(S_{ii}\) is the \(i^{th}\) diagonal elements of matrix \(\mathbf{S}_{I(k)}=\mathbf{I}_{N_{I(k)}}-\mathbf{H}_{h\odot}\mathbf{A}_{I(k)}\), \(\mathbf{I}_{N_{I(k)}}\) is a \(N_{I(k)}\) by \(N_{I(k)}\) identity matrix.  
Then the calculation of horizon/vertical PL (HPL/VPL) is given as  
\[HPL=\max(\mathrm{H}slope(i))T(N_{I(k)},P_{FA})+k(P_{MD})\sqrt{J_{11}+J_{22}} \tag{22}\]  
\[VPL=\max(Vslope(i))T(N_{I(k)},P_{FA})+k(P_{MD})\sqrt{J_{33}} \tag{23}\]  
where \(T(N_{I(k)}\), \(P_{FA})\) is a threshold value, which is a function of \(N_{I(k)}\) and the probability of false alarm (FA) \(P_{FA}\), and follows a chi-square distribution with \(N_{I(k)}-4\) degrees of freedom. \(k(P_{MD})\) is the number of standard deviations corresponding to a specified probability of missed detection (MD) \(P_{MD}\). \(J_{11}\), \(J_{22}\), and \(J_{33}\) are the diagonal elements of matrix \(\mathbf{\Delta}_{I(k)}=(\mathbf{H}_{h\odot}^{T}\mathbf{\Sigma}_{h\odot}^{-1}\mathbf{H}_{h \odot})^{-1}\).  
If the calculated HPL/VPL is larger than the horizon/vertical alert limit (HAL/VAL), _i.e._, \(HPL>HAL\) or \(VPL>VAL\), the system is not available and an alert is provided to the user. Otherwise, the fault detection procedure will be processed as follows.
## 1 Introduction  
Over the last few decades, wind energy has been attracting great attention as an alternative energy resource to limit climate changes caused from fossil fuels. The total energy capacity of the existing wind energy conversion system has grown from 23.9 GW in 2001 and to 591 GW in 2019; approximately a 25-times increase has been achieved, and there is still a strong demand as it is one of the promising sustainable energy systems internationally. Even up to 1787 GW capacity by 2030 is expected along with the reduced levelized cost of energy (LCOE) through the improved technologies, including better understanding of fluid behavior, composite materials, and blade design [1, 2, 3, 4].  
Wind turbines generally consist of three main components such as the rotor blade, nacelle, and tower. A rotor blade is the most important component to harvest energy from wind efficiently. A recent rotor blade enhances the power performance with an increased blade length and improved aerodynamic design of the blade, achieving higher annual energy production (AEP) while satisfying aerodynamic thrust force constraints [5]. Most wind turbine rotor blades of horizontal axis wind turbines (HAWT) have been basically designed by blade element momentum theory (BEMT) with the wind tunnel experiment data of airfoils [6]. However, it is difficult for BEMT to evaluate the three-dimensional aerodynamic effect of the rotor resulted from three-dimensional flow fields [7, 8, 9]. Recently, along with the evolution of computing power, the numerical approaches from computational fluid dynamics (CFD) can accurately evaluate the aerodynamic performance of the rotor blade within the three-dimensional flow fields [10; 11; 12]. Although vortical flow phenomena in the wind turbine were addressed by previous studies, the detailed three-dimensional structures and relationship between vortical flow phenomena and aerodynamic performance were not completely addressed [13]. With the detailed investigation of accurate three-dimensional (3D) vortical flows around a rotor blade, it is possible to explore the ways to increase the power performance while reducing aerodynamic loads.  
In this paper, the detailed three-dimensional vortical flow phenomena and the aerodynamic characteristics were first investigated by the unsteady Reynolds averaged Navier-Stokes (RANS) simulation with the shear stress transport (SST) turbulence model in a high resolution scheme [14; 15; 16]. Then, based on the closed investigation of vortical flows, the blade tip designs such as winglet and ogee types were proposed and optimized to improve aerodynamic performance while lowering thrust force on the structure. Additionally, the flow angle and turbulence intensity were investigated to find out the optimal location of the nacelle anemometer related to yaw error, which could be lowered by considering the intrinsic error of flow angle passing the anemometer device of a wind turbine generator (WTG) [17; 18; 19].
## 4 Numerical Model
### Verification of Numerical Model  
To ensure the authenticity and effectiveness of the simulation results, the validity of the numerical model was verified. The accuracy of the numerical calculation model was verified by comparing the settlement of the tunnel vault obtained from field monitoring and numerical simulation. In the field construction, the thickness of the grouting reinforcement layer was 1.5 m and the slurry water-cement ratio was 0.85. The settlement of the left tunnel vault at different distances from the tunnel portal after excavation was obtained through monitoring, as shown in Figure 12. During construction, the tunnel portal was reinforced, so the settlement of the tunnel vault near the portal was small. Additionally, there were differences in the mechanical properties of surrounding rocks at different positions from the portal, so the vault settlement was also different, and the variation range of vault settlement was 5.1-24.5 mm. In the numerical simulation, when the thickness of the grouting reinforcement layer was 1.5 m and the slurry water-cement ratio was 0.85, the settlement of the left tunnel vault was 25.8 mm. The tunnel vault settlement obtained by field monitoring and numerical simulation was relatively close. The accuracy of the numerical model was reliable.
# Adaptive Optimization Method for Piled Raft Foundations Based on Variable Pile Spacing
## 3 Adaptive Optimization Method
### Objective Function and Design Constraints  
In this study, the objective function of the AOM can be expressed as:  
\[\text{Minimize}\quad\Delta S=S_{max}-S_{min} \tag{11}\]  
where \(S_{max}\) and \(S_{min}\) are the maximum and minimum settlements of the raft, respectively. \(\Delta S\) is the differential settlement of the raft.  
Based on the design guidelines for a large pile group in the China code [30] and the general design practice [14; 31], several design constraints are imposed on the optimization foundation:  
1. Constraint 1: During the optimization process, the minimum pile spacing is no smaller than twice the pile diameter to satisfy the constructability and structural requirements [12; 14; 32]; otherwise, stop the optimization loop. Therefore, this paper suggests that the pile spacing should be no less than thrice the pile diameter in the initial design.
2. Constraint 2: The maximum load (\(P_{max}\)) on top of the piles after optimization design is no higher than its ultimate bearing capacity (UBC) (\(P_{u}\)). Otherwise, increase the number of piles and recalculate this iteration until \(P_{max}\leq P_{u}\).
3. Constraint 3: The differential settlement can only be reduced indefinitely and cannot be eliminated entirely because of the influence of the pile-soil interaction. Therefore, it is necessary to set an upper limit on the objective function to terminate the optimization loop. While the increment (\(\Delta F_{m+1}=F_{m+1}-F_{m}\)) of the optimization rate (\(F\)) of the differential settlement is smaller than 5%, stop the optimization; m represents the number of iterations; \(F\) is the optimization rate and defined as: \[F=\frac{R-R^{\prime}}{R}\] (12) where \(R\) and \(R^{\prime}\) are the results from the initial design and the optimized design, respectively. The results can be the settlement of the raft and the stress on top of the piles.  
The flow chart of AOM is shown in Figure 5.
# Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection
## 2 Proposed Methodology
### Rules for the Insert and Grade Selection  
According to Sandvik's machining technical guide, there are three types of cutting edge geometry inserts: light, medium, and heavy [20]. The classification is based on two important angles on the insert: the rake angle (\(\gamma\)) and the cutting edge angle (\(\beta\)). The working condition is one of the key factors for choosing insert edge geometry. For example, the light geometry has a large \(\gamma\) and small \(\beta\), which means a more positive but weaker edge. Thus, the light geometry is suitable for unstable conditions. The cutting tool manufacturers always use their own code name for the insert grade. However, they are also classified according to the international organization standard (ISO) grade range; for example, from P10 to P50 for steel workpiece materials, and from K10 to K40 for cast iron materials. For rough operations, the grade type P50 is recommended, and for finishing operations, the grade type P10 is recommended [19]. Based on these characteristics, basic rules are established to determine the suitable insert and grade in a specific cutting tool database.  
Figure 5: The proposed hierarchy structure for the shoulder milling feature.