esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Response Surface Parameters
### Effects of Geometric Factors on the Displacement Response  
The ANOVA in Table 4 of Section 2.3.2 has shown the buried depth of the small-radius curved tunnel to be the greatest factor affecting displacement. The angle of the curved tunnel and the radius of the curve do not offer significant implications for the displacement response. Figure 4 gives the variation of the displacement response of the small-radius curved tunnel under seismic excitation with respect to the buried depth and the angle of the curve. As can be seen from the figure, the displacement response of the small-radius curved tunnel under seismic excitation decreases as the buried depth increases, while the displacement response of the curved tunnel increases slightly as the angle increases. From Figure 4, it can be seen the minimum displacement response of the small-radius curved tunnel under seismic excitation is the maximum buried depth and minimum curve angle for the parameter design.  
Based on the results of the experimental design, the influence of geometric parameters on the displacement response and acceleration response of the small-radius curved tunnel at point B is further studied through parameter analysis. The ANOVA in Table 6 already shows the buried depth of the small-radius curved tunnel to be the greatest factor affecting the displacement at point B. The angle of the curved tunnel is more consequential on the displacement response and the radius of the curve does not offer significant implications for the displacement response. In Figure 5, it can be seen the variation of the displacement response at point B under seismic excitation in the small-radius curved tunnel with respect  
Figure 4: Variation of the displacement response with buried depth and angle at point A.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Depth (m)** & **Angle (deg)** & **R (m)** & & **s (m)** & **_a (m/s\({}^{2}\))** \\ \hline \multirow{4}{*}{20} & \multirow{4}{*}{60} & \multirow{4}{*}{338.24} & FEM Results & 0.116 & 3.532 \\  & & & RSM & 0.114 & 3.487 \\  & & & Prediction & 1.75\% & 1.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between FEM results and RSM prediction results of point A.  
to the buried depth and the angle of the curve. From Figure 5, it can be seen that the displacement response of the small-radius curved tunnel under seismic excitation decreases as the buried depth increases, while the displacement response of the curved tunnel increases slightly as the angle increases. From Figure 5, it also can be seen that the minimum displacement response of the small-radius curved tunnel under seismic excitation is the parameter design for the maximum buried depth and the minimum curve angle. This conclusion is consistent with the analytical results for point A.
## 1 Introduction
The accuracy of the identified model is validated by using both numerical and experimental methods, and the results indicate that the NARX model of the nonlinear, rotor-bearing system can be used to represent different system characteristics, which contribute to the analysis, design, and fault diagnosis of the rotor-bearing system. This paper is organized as follows. In Section 2, a typical nonlinear, rotor-bearing system is established and the inherent characteristics of the system are revealed. Section 3 introduces a new approach of identifying the NARX model of nonlinear systems based on a multi-harmonic input, which is discussed under different working conditions (such as: low speed, critical speed, and over critical speed). The NARX model is evaluated by two different validation methods and an error criterion. An experimental validation is discussed in Section 4, in which a test rig connected with the Labview test system and two eddy current displacement sensors are used. Finally, the three main conclusions are presented in Section 5, which provide an efficient method for numerical modeling of nonlinear, rotor-bearing system.
## 3 Proposed Methodology
### Model Training
#### 3.3.4 Hybrid LSTM-Volume  
Our final model, which we referred to as Hybrid LSTM-Volume, integrated the benefits of the Multi-LSTM-Sentiment architecture (from Section 3.3.1) while optimising performance through refined training and hyperparameter adjustments. This model's training differed as it used a multivariate input feature set, including historical Bitcoin price as well as the total volume of tweets.  
The architecture consisted of three LSTM layers, each containing 50 neurons. A dropout rate of 30% was applied after each LSTM layer to improve generalisation. The input sequence length remained at 48 time steps, maintaining a two-day temporal context for learning meaningful relationships among features. A dense output layer with a single neuron was employed to produce the final Bitcoin price prediction.  
By combining tweet volume with historical price, the model exploited a broader set of features, potentially improving prediction accuracy. The choice of neuron count and increased dropout rate reflected an emphasis on preventing overfitting while maintaining sufficient model capacity for capturing complex relationships. Retaining three layers allowed us to balance model depth and computational efficiency.  
As before, the Adam optimiser was used for gradient-based learning, and the MSE was used to measure the model's performance. The model was trained for 50 epochs, with  
Figure 4: Training and validation error trends for the Bidirectional LSTM-Sentiment model across metrics: MSE, MAE, and MAPE. The decreasing trends indicate effective learning and generalisation.  
a batch size of 25. The 20% validation split was retained, allowing the evaluation of the model's ability to generalise to unseen data throughout training. Figure 5 presents the training and validation error trends for the Hybrid LSTM-Volume model across MSE, MAE, and MAPE metrics.
## 4 Discussion
The changes in THs, e.g., increase of T3 and decrease of T4, are well supported by significant upregulation of _deio_2 gene that were observed in the present study. In fish, three types of deio enzymes, namely deio1, deio2, and deio3, play different roles in regulating TH levels. For example, deio1 affects iodine recovery and TH removal [39], deio2 catalyzes the production of active T3 from T4 [40], and deio3 plays a role in the inactivation of enzymes [41]. Given the increase of T3 and _deio_2 gene transcription, and the decline of T4 observed in this study, it can be suggested that PHMG-H can increase whole-body T3 concentration by increasing _deio_2 activities. Thyroid peroxidase (tpo) catalyzes the iodination of thyroglobulin (tg), which is stored for THs [42]. A previous study demonstrated that tpo inhibition could result in reduced production of T4 [43]. In the present study, decreased transcription of _tpo_ and _tg_ genes was observed in fish that were exposed to Vegetable HD and PHMG-H, suggesting the reduction of TH synthesis and inhibition of tg production. The decrease of T4 levels could either be caused by TPO inhibition, by deio induction, or by a combination of both mechanisms. In conclusion, the results of this study demonstrate the acute mortality, developmental toxicity, and thyroid endocrine disruption of Vegetable HD and PHMG-H. Exposure to an amount that is recommended by the manufacturer of Vegetable HD may result in acute mortality. The sub-lethal concentration of PHMG-H affects the feedback regulatory circuits of THs, which delays the development of zebrafish larvae. The major reason of differences in toxicity between the two counter ions of PHMG warrant further investigation. Conceptualization, S.P., H.K. and K.J. ; methodology, S.P. and H.K. ; investigation, S.P. and H.K. ; writing--original draft preparation, S.P. and H.K. ; writing--review and editing, K.J. ; supervision, K.J. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Research Foundation of Korea (NRF; Project no. 2019R1A2C1002712). Not applicable. The authors declare no conflict of interest.
## 2 Methods
### Standardized Precipitation Evapotranspiration Index (SPEI)  
SPEI requires monthly PET data in addition to monthly precipitation data, as it calculates the climatic water balance by subtracting PET from precipitation [16]. Unlike precipitation, the water balance can have values less than zero; therefore, a three-parameter distribution--the log-logistic distribution--is applied to calculate SPEI [16]. As this research focuses solely on meteorological drought, SPEI at a 3-month timescale from 1902-2015 is used to represent drought patterns in Pakistan, with low SPEI corresponding to drought conditions. In total, 327 grid cells cover the entire area of Pakistan. The climatic water balance, D, for the month i is calculated using the equation below where P\({}_{i}\) represents precipitation and PET\({}_{i}\) represents potential evapotranspiration:  
\[D_{i}=P_{i}-PET_{i}. \tag{1}\]  
The values of \(D_{i}\) are then aggregated at varying temporal scales just like SPI. The values of SPEI can be computed for different time scales by fitting a three-parameter probability distribution unlike SPI, which can be calculated using a two-parameter (gamma or Pearson Type III) distribution. This is because three-parameter distribution allows negative values, which is common when computing climatic water balances, whereas in the two-parameter distributions used for precipitation, the lower limit is 0. The three-parameter distribution that fits well to the D series at all time scales and in any region in the world is the log-logistic. The resultant probability distribution function of the D series is given by the following formula [16]:  
\[F(x)=[1+(\frac{a}{x-y})^{\beta}]^{-1}. \tag{2}\]  
The SPEI, then, is the standardized value of \(F(x)\) and is obtained by the approximation of [PERSON] and [PERSON] (1965) given below:  
\[SPEI=W-\frac{C_{0}+C_{1}W+C_{2}W^{2}}{1+d_{1}W+d_{2}W^{2}+d_{3}W^{3}}. \tag{3}\]  
In this equation, \(W=\sqrt{-2}\)ln(P) for \(p\leq 0.5\) and \(C_{0}\), \(C_{1}\), \(C_{2}\), \(d_{1}\), and \(d2\) are constants. P here is the probability of exceeding a determined D value, \(p=1-F(x)\). If \(p>0.5\), then \(p\) is replaced by 1-\(p\). The sign of the SPEI is then reversed [16].
# Energy Efficient Communications for Reliable IoT Multicast 5G/Satellite Services
## 1 Introduction
Besides, satellites can simultaneously deliver software/firmware update towards a large number of cars or multicast traffic and road conditions information, together with indicating of alternative routes by guaranteeing the continuity of communications, without the risk of losing the connection, as can happen in terrestrial wireless network due to sudden traffic peaks. Moreover, the satellite high speed backhaul connectivity allows multicasting the same content (video or HD/UHD TV) since content distribution remains mainly a key satellite-based service. In particular, for these bandwidth-hungry services, the satellite can be used for off-loading the terrestrial 5G networks traffic. In this paper, we apply an advanced Network Coding (NC) scheme to reliable multicast, typically for content delivery or reconfiguration to a M2M/IoT group-based devices, and we propose an approach to optimize the overall end-to-end energy consumption for a hybrid IoT 5G/satellite scenario. The adopted NC method assures an efficiency improvement because the acknowledgments, usually needed from each terminal, can be avoided, thus minimizing both the delivery delay and the energy consumption to decode the data flow. In addition, to minimize the required energy for the overall multicast packet transmission, an optimized transmission scheme is introduced, where multiple data symbols are replicated over multiple subcarriers of orthogonal frequency division multiplexing (OFDM) signal, by varying: (i) the adopted modulation schemes according to the channel conditions; and (ii) the number of coded packets \(K\) needed to be transmitted to all the on-Earth devices with the constrained of a target outage probability for the group-based sensors/devices. In [8], a similar reference scenario for reliable multicast content delivery applications and NC technique is also introduced. However, in this paper, a completely different methodology is proposed as follows: (i) NC operations are carried out at the ground control center (CC) as more transparent Geostationary Earth Orbit (GEO) satellite is considered while in [8] a satellite with on-board processing capabilities for NC is assumed; (ii) data symbols are replicated in the frequency domain, while in [8] a rate reduction factor is used to increase the time duration of data symbols; and (iii) we stress that our goal is different from the in [8] because here the repetition factor is used to enforce the transmission for minimizing the overall delivering energy of the hybrid satellite/terrestrial system. The evaluation and performance assessment of the proposed scheme are addressed by providing numerical results. Finally, the conclusions are outlined.
## 3 Research Method  
The design of the proposed scheme is displayed in Figure 3. The character image was supplied for individual feature-extraction schemes. In the DWT-based scheme, the BDWT was applied on a given image to collect a feature-vector in the form of wavelet coefficients. The same image was given to individual pre-trained DCNN models (VGG-19 Net, ResNet-50, and InceptionV3-Net) to receive respective feature-vectors. At the end of the feature-extraction stage, we received four different feature-vectors related to the given image; they are labelled as F1, F2, F3, and F4. Next is the dimensionality reduction stage, where the PCA method was used to reduce the size of individual feature-vectors; they were reduced to equal sizes and marked as W, V, R, and I. The reduced-sized feature-vectors were supplied to the feature-fusion stage, where hybrid feature-vectors were developed with all possible combinations of W, V, R, and I. At this stage, 15 new datasets (D1 to D15) were created by collecting respective hybrid feature-vectors for all the images of input dataset. The performance of individual datasets was estimated over MLP and SVM classifiers. The respective results were collected and analyzed in the output stage.  
The experimental framework was developed in a Python environment. Several open-source libraries were used for the task: open-cv, python-imaging-library, py-wavelet, keras, numpy, pandas, scikit-learn, seaborn, python-time-module, etc. The experiments were simulated on the powerful Google Co-laboratory platform. Co-laboratory is supported with tesla k-80, 2496 CUDA cores, 12 GB GDDR5-VRAM GPU, hyper-threaded Xeon processor, 12.6 GB RAM, and 33 GB storage capacity.
## 3 Deviation-Angle-Panel-Based Antenna Concept
### Deviation-Angle Panel  
Figure 5a shows the top view of a deviation-angle panel. To reduce the magnitude of \(\angle B_{0}A_{0}D_{0}\), deviate the radial line segment \(A_{0}D_{0}\) from the original position by angle \(\alpha\) (deviation angle) to obtain a new panel \(A_{1}B_{1}C_{1}D_{1}\), which is colored in yellow. In this way, if the value of \(\angle B_{0}A_{0}D_{0}\) on plane \(OXY\) decreases, so does the actual \(\angle B_{0}A_{0}D_{0}\) in space. If the deviation angle \(\alpha\) continues to increase until line segment \(A_{1}D_{1}\) is tangent to the central aperture, then line segment \(A_{1}D_{1}\) will reach its limit position \(A_{\rm m}D_{\rm m}\), and \(\alpha\) obtains its maximum \(\alpha_{\rm m}\), thereby obtaining the limit panel \(A_{\rm m}B_{\rm m}C_{\rm m}D_{\rm m}\), colored in purple. Let the deviation angle \(\alpha=k_{\rm d}\alpha_{\rm m}\), where \(k_{\rm d}\in[0,1]\). If \(k_{\rm d}=0\), the deviation angle is 0, if \(k_{\rm d}=1\), the deviation angle is \(\alpha_{\rm m}\) and if \(0<k_{\rm d}<1\), the deviation angle is between 0 and \(\alpha_{\rm m}\). Therefore, parameter \(k_{\rm d}\) is referred to as the deviation parameter. It is evident that the case \(k_{\rm d}=0\) corresponds to the regular panel; thus, our proposed panel is a generality of the regular panel described in Section 2.4, which is referred to as the deviation-angle panel. This type of segmentation scheme retains many advantages possessed by the regular one. For instance, all the deviation-angle panels are the same, and both the deployed and the folded configuration are axisymmetric about the \(Z\)-axis, exhibiting higher axisymmetry than other antenna concepts such as the sunflower concept, since the unit of the sunflower has three panels whereas the new segmentation scheme has only one panel. Additionally, because all panels are identical, the newly obtained panel has a lower manufacturing cost compared with other segmentation schemes such as the sunflower and SSDA; hence, only one set of mold is required for fabricating it. All of the specifications are listed in the last row of Table 1.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Antenna Concept**} & \multicolumn{6}{c}{**Specification**} \\ \cline{2-9}  & **Number of Units** & **Panel Number** & **Supporting** & **Deployment** & **Surface** & **Symmetry** & **Number of Molds** \\ \hline Sunflower & 6 & 3 & No & Complex & Medium & Medium & 3 \\ SSDA & 6 & 3 & No & Complex & Medium & Medium & 4 \\ Regular panel & 18 & 1 & Yes & Simple & High & High & 2 \\ Deviation-angle panel & 18 & 1 & Yes & Simple & High & High & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of antenna concepts.
# Application of Finite Layer Method in Pavement Structural Analysis
## 4 Numerical Analysis Using EasyFEM and EEP Techniques
### Analytical Verification of EasyFEM
The convergence rates between ordinary EasyFEM and EasyFEM with the EEP post-processing technique are compared in this section to show the impact of the EEP technique on the solution accuracy. To emphasize the significant improvement brought about by the application of EEP, a series of rough meshes with 2-node linear elements was adopted in the EasyFEM. In particular, three models with different meshes were created, i.e., each layer of the asphalt pavement was divided into one, two and four elements (\(N_{\text{e}}=1\), 2 and 4), respectively; the asphalt pavement had six layers resulting in a total of \(6\times N_{\text{e}}=6\), 12 and 24 linear elements in the three pavement models. EasyFEM with the EEP post-processing technique can produce strains and stresses with a higher accuracy, which is demonstrated by a number of numerical results. In Figure 7, vertical strains at the points located below the loading center and at the bottom of asphalt base course were compared between ordinary EasyFEM and EasyFEM with the EEP technique, where one, two and four linear finite elements were used in each layer along the \(y\)-direction, respectively. The results from the model with the fine mesh in ABAQUS created in Section 4.1 were taken as the reference. Since the stresses are calculated by multiplying strains with elastic constants, similar results can be obtained for vertical stresses as well. It can be seen that the EEP technique for FLM, which converges faster, can significantly improve the accuracy of vertical strains and stresses of a plain EasyFEM. With the EEP technique, the EasyFEM can offer reliable results much more quickly and efficiently and is thus suitable to be applied in pavement engineering practice. Figure 7: Comparison of the vertical strains at the points located below the loading center and at the bottom of asphalt base course obtained with ABAQUS, ordinary EasyFEM and EasyFEM with EEP (Element Energy Projection), (**a**) winter; and (**b**) summer.
## 4 Current Approaches to Open Challenges of F Estimations from Proximal to Airborne Scale
### Computer Models' Bridging Scales  
Since laboratory and field scaling experiments are time-consuming and expensive, computer models for the radiative transfer of F emitted from PSI and PSII to the tops and bottoms of leaves and to the top of the canopy have been developed to facilitate this task. The first attempts to model F on both leaf and canopy levels were undertaken in [150], which built upon the Kubelka-Munk (KM) two-stream radiative transfer theory. The recently developed leaf-level chlorophyll fluorescence model FLUSPECT [64], being a 'clone' of the PROSPECT dicotyledon leaf reflectance and transmittance model [151] is also based on the KM theory. FLUSPECT was, for the purpose of canopy modeling efforts, coupled with the radiative transfer and energy balance model entitled 'Soil Canopy Observation, Photochemistry and Energy fluxes' (SCOPE; [152]), which originates from the four-stream 'Scattering of Arbitrarily Inclined Leaves' (SAIL) model of [PERSON] [153]. Both models, SAIL and SCOPE, belong to the category of one-dimensional models and enable the scaling of leaf optical properties, including F, through canopies represented by a homogeneous turbid medium of leaves. A new SCOPE extension,called mSCOPE [154] enables modeling F from several optically distinct superimposed homogeneous leaf layers, but it cannot reproduce architecturally distinct canopies consisting of several structurally different plant functional types. F of such complex canopies can be simulated with three-dimensional models, such as the Discrete Anisotropic Radiative Transfer (DART; [155]), the FluorFLIGHT model [149], or the Fluorescence model with Weight Photon Spread (FluorWPS; [156]). Although these models were designed specifically to scale F signals across spatial scales, while accounting for various canopy structures and solar illumination-canopy-sensor geometries, they are still mathematical constructs of simplified reality and cannot be fully trusted. Nonetheless, they can provide unique insights into the sensitivity of top-of-canopy F to various structural and spectral properties, which would be difficult to unambiguously identify via experimental measurements and can thus also help to identify important ancillary data. Additionally, it is important to note that, similar to the requirements for measurements, it is necessary to register the priors (field or literature-based) and boundary conditions of the model as metadata when using these models, to be able to reproduce the results. Uncertainty (quality flags) and representativeness indicators (n) for model parametrization should also be documented.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
###### Abstract  
In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites' high mobility and resultant insufficient time for downloading make this challenging. In this paper, we propose a deep-reinforcement-learning (DRL)-based cooperative downloading scheme, which utilizes inter-satellite communication links (ISLs) to fully utilize satellites' downloading capabilities. To this end, we formulate a Markov decision problem (MDP) with the objective to maximize the amount of downloaded data. To learn the optimal approach to the formulated problem, we adopt a soft-actor-critic (SAC)-based DRL algorithm in discretized action spaces. Moreover, we design a novel neural network consisting of a graph attention network (GAT) layer to extract latent features from the satellite network and parallel fully connected (FC) layers to control individual satellites of the network. Evaluation results demonstrate that the proposed DRL-based cooperative downloading scheme can enhance the average utilization of contact time by up to 17.8% compared with independent downloading and randomly offloading schemes.  
deep reinforcement learning (DRL); soft actor-critic (SAC); low earth orbit (LEO) satellite; graph attention network (GAT) +
Footnote †: journal: Sensors  
doi:10.3390/s22186853