esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The normal operation of spaceborne parabolic cylindrical reflector antennas under various operating conditions relies on maintaining the root mean square (RMS) of the reflector surface's deformation within reasonable limits. In engineering practice, the designing of reinforced ribs is the primary way to control the RMS of the reflector surface. However, the layout and dimensions of reinforced ribs for many existing designs rely on the experience of the designer and lack a theoretical foundation. This leads to suboptimal layouts and dimensions in many designs, deviating from the optimal design. To address these concerns, this study proposes a comprehensive design approach that combines both topology optimization and parametric analysis. Optimization and parametric analysis were conducted for a large-sized spaceborne composite parabolic cylindrical reflector antenna. The layout and dimensions of the reinforced ribs were reconstructed based on the optimization results and parametric analysis. This study also obtained the influence of the height and thickness of the reinforced ribs on the RMS of the reflector surface. Subsequently, utilizing antenna temperature field simulations as thermal excitation inputs, finite element thermal distortion analyses were conducted for the reflector surfaces without reinforced ribs, with the original reinforced ribs designed based on empirical methods, and with optimized reinforced ribs. In comparison to the original design of the reinforced ribs, the optimized design, without an increase in the volume of the reinforced ribs, reduced the RMS of the reflector surface from 0.6025 mm to 0.5561 mm, resulting in an optimization ratio of 7.7%. Moreover, when compared to the reflector surface without reinforced ribs, the optimized design achieved a 17.9% reduction in RMS.  
parabolic cylindrical reflector antenna; topology optimization; parametric analysis; reinforced ribs +
Footnote †: journal: Applied  
12 January 2024  
Received: 3 January 2024  
Accepted: 3 February 2024  
Published: 8 February 2024
## 3 Methodology of TSDCA-BA Model
### Structural Design of the TSDCA-BA Model
#### 3.1.2 SDCA-BA Module  
Figure 3 shows the proposed SDCA-BA (Sub-band-aware Dual-Channel Attention with Band-wise Attention) Module consists of two primary components: a sub-band-aware dual-channel attention (SDCA) block and a subsequent Band-wise Attention (BA) Module.  
The SDCA module is based on the DSDDB module proposed in the literature [28; 29] and has been further optimized for lightweight design. The input features first pass through two consecutive one-dimensional convolutional layers with kernel size 3, each followed by batch normalization and a nonlinear activation function. This process effectively captures local temporal information and projects the input into a higher-level feature space. To enhance feature discriminability, a channel attention mechanism is introduced using a Squeeze-and-Excitation (SE) module. Specifically, global average pooling is performed along the temporal dimension to aggregate global contextual information for each channel. The aggregated features are then passed through two fully connected layers with a bottleneck structure and a sigmoid activation function to generate channel-wise attention weights.  
Figure 2: The overall architecture of the proposed Extract stats Module.  
Finally, these weights are used to adaptively recalibrate the feature channels, thereby enhancing the representation of key informative channels while suppressing irrelevant ones.  
Following the SDCA block, a Band-wise Attention Module is introduced to model frequency-band dependencies. Motivated by the observation that different frequency bands in speech signals contain varying levels of semantic and perceptual importance--e.g., low-frequency bands typically carry energy and voicing cues, whereas high-frequency bands often encode fine details--the BA aims to assign dynamic importance to each sub-band. To achieve this, the channel dimension is partitioned into non-overlapping sub-bands, and the mean activation within each sub-band is computed across time. These values are computed using a sigmoid activation function. The attention weights are then broadcast and applied to the corresponding sub-band features through element-wise multiplication. This process enables the model to suppress frequency bands heavily contaminated by noise or interference and to enhance those that contribute more significantly to speech intelligibility, especially in adverse conditions such as strong background noise or multi-speaker scenarios.  
By combining temporal convolution, channel-wise recalibration, and frequency-aware modulation, the SDCA-BA module improves the network's capability to focus on both informative time-frequency regions and robust feature representation, leading to enhanced speech enhancement performance.
## 3 Experimental Tests
### Consistency Properties  
The initial phase of binder testing focused on evaluating the basic physical properties that are essential for determining the consistency and performance of asphalt binders. This phase included conducting the penetration test [42], which measures the depth that a standard needle penetrates the asphalt binder under specific conditions, offering insights into the binder's consistency. Simultaneously, the softening point (SP) test was carried out according to [45] to determine the temperature at which the asphalt binder transitions from a semi-solid state to a liquid state, providing a critical measure of the binder's susceptibility to high temperatures. Additionally, the Penetration Index (PI) was calculated to quantify the asphalt binder's sensitivity to temperature variations. This index, derived from the results of the softening point and penetration at 25 \({}^{\circ}\)C, is calculated using the following formula:  
\[\text{PI}=\frac{1952-500\text{log}\,P25-20 SP}{50\text{log}\,P25-SP-120} \tag{1}\]  
where P25 denotes the penetration value at 25 \({}^{\circ}\)C, and SP signifies the softening point. These tests collectively form the cornerstone of binder assessment, establishing a foundational understanding of the asphalt binder's physical characteristics and its expected performance in various environmental conditions.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 3 Proposed Methodology
### Energy Model  
The nodes' energy is primarily due to biological data acquisition, transmission, reception, and idle interception. The majority of them are related to biological data transmission requiring energy. Because this work focuses on transmission among the nodes, only the energy utilization of transmitting and collecting is considered, while data reception and idle interception are ignored. The amount of energy required for both sending and receiving n-bit biological data is represented in the following Equations (1) and (2):  
\[E_{RX}(n)\ =\ E_{ele}\times n \tag{1}\]  
\[E_{TX}(n,L)\ =\ E_{ele}\times n+E_{amp}\times n\times L^{2} \tag{2}\]  
where \(E_{RX}(n)\) represents the energy absorbed by obtaining n bits of biological data and \(E_{TX}(n,L)\) represents the energy absorbed when transmitting n bits of biological data to a distant node. \(E_{ele}\) represents the energy the circuit absorbs when the node transmits or receives biological data, and \(E_{amp}\) representsthe energy the power amplifier absorbs when transferring biological data.  
If \(E_{j}^{initial}\) is the node's initial energy, \(E_{j}^{con}\) is the consumed energy, and \(E_{j}^{res}\) is the residual energy, then the mathematical relationship is as follows:  
\[E_{j}^{con}\ =\ E_{RX}+E_{TX} \tag{3}\]  
\[E_{j}^{res}\ =\ E_{j}^{initial}-E_{j}^{con} \tag{4}\]  
This architecture's power amplifiers and electrical circuits operate with energy from the transmission device and the sensor. Equations (1)-(4) demonstrate the energy utilization of receiving and sending n-bit biological information at the distance between the transmitter and the receiver. Assuming the range between the transmitter and receiver is less than the threshold distance, each node's energy consumption is required based on the energy model.  
Since data transmission power grows exponentially with distance, the range threshold is proportional to it. Alternative procedures, including multi-hop routing, are used when communication energy consumption exceeds the threshold. In large-scale WBANs, in particular, the choice of L affects the network topology. Though it may enhance complexity and lead to lower energy usage per hop, a more densely linked network with a smaller L would need more frequent multi-hop communication. Network congestion and interference may raise the energy required for transmission and reception; thus, these factors should also be included in the energy consumption formulae. Sensors with power-saving modes and adaptive transmission power regulation, which dynamically modify energy consumption depending on distance and network circumstances, are examples of energy-saving protocols that might be related to the notion of L.
## 3 Results
### Statistical Analysis: Horizontal Wind Speed Gradients  
As mentioned in Sections 2.1 and 2.3, SAR derived 10 m wind speeds over a period of four years are investigated. In the same way as in Section 3.2, the horizontal wind speed gradients are estimated considering the transect boxes set up in Section 2.3 for offshore wind directions. With respect to easterly winds with wind directions between 60\({}^{\circ}\) and 120\({}^{\circ}\), 70 profiles are found for the entire four-year period (Table 1). A plot of all the wind speed transects of \(u_{10}\) for easterly winds is displayed in Figure 9a-c. The \(u_{10}\) curves show a general trend of the wind field from the coast to offshore. The mean wind speed gradient (red curve in Figure 9a) exhibits an overall progressive intensification of the wind speed with distance from the shore. On average, the wind speed increase is higher than 1 m/s in terms of absolute values (red curve, Figure 9b) and the relative normalised increase is about 20% (red curve, Figure 9c) beyond roughly 80 km offshore. The empirical model  
Figure 8: (**a–c**): Vertical profiles of wind speed from wind LIDAR on 25 August 2019 before the time (**a**), at the time (**b**) and after the time (**c**) of satellite measurement. Black dots and red star symbols represent, respectively, the wind speed from SAR and DWD at the location of the LIDAR.  
(yellow dotted line) indicates increases of the parameters \(\Delta u\), \(R_{max}^{f}\), \(x_{95\%}\) of about 1.5 m/s, 22% and 84 km, respectively.  
Figure 9: (**a–c**) 10 m wind speed derived from Sentinel 1A/B (dots) for easterly winds for 2017–2020 along the transect “Ref” in Figure 2b (**a**). The corresponding parameters \(\delta u_{10}\), \(R_{u_{10}}\) are displayed in (**b**,**c**), respectively. (**d–f**): The same as (**a–c**) but for southerly winds for 2017–2020 along the transect “Ref” (**d**) and corresponding \(\delta u_{10}\) (**e**) and \(R_{u_{10}}\) (**f**). The red, yellow and white dashed curves represent, respectively, the mean value, the standard deviation and the empirical model fits (Equations (10), (14) and (15)).  
Similarly to the eastern wind speed gradients, southerly offshore winds are also analysed (Figure 9d-f). In this case the average wind transects of \(u_{10}\) (red curve in Figure 9d) is also characterised by a rapid increase of the wind speed close to land. The wind speed increase \(\delta u_{10}\) exceeds 1.3 m/s beyond \(\sim\)50 km offshore (Figure 9e). Consistently, the empirical model indicates values for \(\Delta u_{10}\) and \(x_{95\%}\) of about 1.4 m/s and 38 km, respectively. The average relative increase \(R_{u_{10}}\) (red curve, Figure 9f) displays an overall growth above 18%, which is consistent with the estimate \(R_{max}^{f}\) of 20%.
###### Abstract  
Aim of the study was to develop a standardized model system to investigate endodontic irrigation techniques and assess the efficiency of different activation methods on the removal of hard tissue debris in complex root canal systems. Mesial roots of mandibular molars were firstly scanned by micro-computed tomography (uCT) and allocated to three groups of irrigation activation: sonic activation (EDDY, VDW, Munich, Germany), laser activation (AutoSWEERS, FOTONA, Ljubljana, Slovenia) and conventional needle irrigation (control). Roots were fixed in individual 3D-printed holders to facilitate root canal enlargement under constant irrigation with NaOCI (5%). To enable standardized quantification of remaining debris, BaSO\({}_{4}\)-enriched dentine powder was compacted into the canals, followed by another uCT-scan. The final irrigation was performed using 17% ethylenediaminetetraacetic acid (EDTA) and 5% sodium hypochlorite (NaOCI) with the respective activation method, and the volume of remaining artificial debris was quantified after a final uCT-scan. The newly developed model system allowed for reliable, reproducible and standardized assessment of irrigation methods. Activation of the irrigation proved to be significantly more effective than conventional needle irrigation regarding the removal of debris, which persisted particularly in the apical third of the root canal in the control group. The efficiency of irrigation was significantly enhanced with laser- and sonic-based activation, especially in the apical third.  
disinfection; sodium hypochlorite; ethylenediaminetetraacetic acid; lasers; computed tomography +
Footnote †: journal: Applied Sci.  
0021
## 4 Results
### Experimental Results and Analysis
#### 4.3.2 Improved Strategy Ablation Experiment  
To verify that the proposed improvements have a positive impact on the results, we conducted ablation experiments using a controlled variable approach. Ablation experiments involve systematically removing model components to evaluate the contribution of each component to the overall performance. In this study, we independently assessed the impact of replacing the ResNet backbone with MLP-MIXER, optimizing the Multi-Head Self-Attention mechanism, and adjusting the loss function. The results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **AP50** & **AP50:95** & **AR50:95** & **FPS** & **\
\begin{tabular}{c} **Parameters** \\ **(Millions)** \\ \end{tabular} &
\begin{tabular}{c} **FLOPs** \\ **(Billion)** \\ \end{tabular} \\ \hline DETR & 0.852 & 0.842 & 0.862 & 20 & 41.5 & 244 \\ Faster R-CNN & 0.865 & 0.815 & 0.845 & 24 & 134.0 & 150 \\ YOLOv8 & 0.886 & 0.852 & 0.893 & 33 & 64.9 & 139 \\ SSD & 0.835 & 0.793 & 0.820 & 28 & 26.3 & 31 \\ RetinaNet & 0.861 & 0.812 & 0.847 & 22 & 36.8 & 138 \\ OrthoDETR (Ours) & 0.897 & 0.864 & 0.895 & 26 & 39.7 & 123 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Experimental Results of Different Models.  
The ablation study results show that after replacing the ResNet backbone with MLP-MIXER, OrthoDETR's detection speed increased by approximately 20%, while the mean Average Precision (mAP) only decreased by 1.5%. This suggests that MLP-MIXER has a positive impact on improving detection speed. On the other hand, optimizing the Multi-Head Self-Attention mechanism improved OrthoDETR's performance in handling occlusion and background interference, leading to an mAP increase of around 3.2%. Lastly, by adjusting the loss function, OrthoDETR demonstrated a stronger robustness in addressing scale variations and diversity issues, resulting in a further 2.1% increase in mAP.
## 2 Internet of Things
Physical items can connect to the virtual world, be controlled remotely and act as physical access points to Internet services. Currently, IoT can be viewed as a network of networks. There are three visions for IoT [26]:  
\(\bullet\) Internet-oriented: In the internet-oriented vision, it is needed to make smart objects. The objects must use the specification of IP protocols. \(\bullet\) Semantic-oriented: In Semantic-oriented vision, the number of available sensors will be vast, and their collected data will be huge. Thus, the raw data needs to be managed and processed for better representations and understanding. * Things-oriented: In the things-oriented vision, we can track any object using sensors and pervasive technologies. Any object can be identified uniquely using an electronic product code (EPC). EPC is extended using sensors. IoT has three important characteristics [27]:  
1. Comprehensive sense: Using sensors to collect information from any objects anytime and anywhere. 2. Intelligent processing: Using techniques such as cloud computing to analyze huge amounts of data to control objects. 3. Reliable transmission: Accurate and real-time data transmission via communications networks and the Internet. Cisco has created a dynamic connections counter to track the estimated number of connected things from December 2012 until July 2020 [28]. In Dec. 2012, there were 8.7 billion connected objects globally while in May 2014, the number is exceeding 12.3 billion. Cisco has conducted analysis on the potential economic impact of the Internet of Everything, and their analysis indicates that there is as much as $14.4 trillion of potential economic \"value at stake\" for global private-sector businesses over the next decade, as a result of the emergence of the Internet of Everything [29]. Looking to the future, Cisco Internet Business Solutions Group (IBSG) predicts there will be 25 billion devices connected to the Internet by 2015 and 50 billion by 2020 [30]. A separate analysis from Morgan Stanley predicts that number can be as high as 75 billion and claims that there are 200 unique consumer devices or equipment that could be connected to the Internet that have not yet done so [31]. [PERSON] [32] in his report in Progressive Policy Institute describes how technological innovation, particularly as it relates to the Internet of Everything (IoE), could lead America's economy out of a \"slow-growth rut.\" The European Commission (Information Society and Media DG) predicted 50 to 100 billion devices to be connected by 2020 [33]. Future technological developments in IoT and its research needs that have been foreseen for the next 20 years are outlined in Tables 1 and 2[33]. Since there is a growing interest in using IoT technologies, the number of industrial IoT projects and the number of IoT publications is quickly growing.
## 4 Discussion
### Greater SPM Hg Load during the Rainy Season All along the Water Shed  
Suspended Particulate Matter (SPM) increased along the Boulanger Creek, from 2 mg L\({}^{-1}\) at point B1 to a maximum of 18 mg L\({}^{-1}\) at B8. Downstream from there, values were between 4 and 8 mg L\({}^{-1}\) during the rainy season and 8-12 mg L\({}^{-1}\) during the dry season. Mining activity was low that year on the Boulanger Creek, which could explain the small difference between the two seasons, but also a natural dilution effect can reduce SPM concentrations. Due to the low erosion rates of the Precambrian Guiana Shield, French Guiana Rivers naturally have very low SPM values [34]. Amounts of SPMobserved in the present work are similar to average values reported by these authors along other Guiana Rivers, i.e., the Oyapock and the Maroni Rivers.  
During the rainy season, the Hg load in SPM increased by 2-3-fold at all sampling points except B1. During the dry season, concentrations were between 100 and 250 ng g\({}^{-1}\) while during the rainy season they were between 400 and 600 ng g\({}^{-1}\). It would seem that the SPM mobilized during the rainy season contains more Hg than during the dry season. [PERSON] et al. [33; 35; 36] reported THg concentrations from 167 to 425 ng g\({}^{-1}\) (dry weight) in SPM from clear waters (such as those in French Guiana), on a commercial sector of the Tapajos River, and from 154 to 355 ng g\({}^{-1}\) (dry weight) in a zone 50 km from the mining district. The THg concentrations in the present study were slightly higher than this, but clearly lower than those reported for the Rio Negro, where THg reached values up to 2070 ng g\({}^{-1}\)[32]. However, in 2006 [16], during the rainy season at point B5 (down-stream Leonce; Figure 1), THg concentrations were 1420 ng g\({}^{-1}\) (dry weight) i.e., three times higher than in the present study, which may be due to the fact that during this period there was active mining upstream from site B5. In other Amazonian watersheds, similar or higher values have been reported, e.g., between 400 and 4000 ng g\({}^{-1}\) were found along the ASGM-affected Malinoski-Tampota river system in Peru [27]. Furthermore, Hg concentration ranges of 3.2 to 133 ng g\({}^{-1}\) in SPM during the dry season, and 13.3 to 42 ng g\({}^{-1}\) during the rainy season were found along the Madre de Dios River [28]. In Brazil, along the Tapajos River, Hg in unfiltered water varied between 0.65 ng L\({}^{-1}\) and 23.8 ng L\({}^{-1}\) in tributaries closer to gold mining activities. Given that Hg is mainly transported in the particulate fraction, this maximum value corresponds to around 86 ng Hg g\({}^{-1}\) SPM [29].
###### Abstract  
The steep bedding rock slope (SBRS) is easily destabilized under earthquake action, so it is crucial to research the features of this kind of slope's seismic dynamic reactions in order to prevent and mitigate disasters. Few researchers have examined these slopes from an energy perspective, and the majority of recent research focuses on the displacement and acceleration response patterns of these kinds of slopes under seismic action. This work performed an extended study of a dynamic numerical simulation and systematically analyzed the dynamic response characteristics of this type of slope under earth quake conditions from the standpoint of energy utilizing the Hilbert-Huang transform (HHT) and marginal spectrum (MSP) theory. This was carried out in response to the slope's shaking table test from our previous work. The findings indicate the following: (1) The 'elevation effect' and'surface effect' are clearly seen in the acceleration amplification factor (_AAF_) of the slope during an earthquake. The selectivity of the slope acceleration's Fourier spectrum amplification impact indicates that the elevation amplification effect makes the high-frequency peak's amplitude more noticeable. (2) Although the effect of the weak layer is more pronounced in the high-frequency portion, both the elevation and the weak layer affect the seismic wave's Hilbert energy. As a result, the weak layer at the top of the slope is usually destroyed first during an earthquake. (3) Prior to the locked segment's penetration failure at the toe of the SBRS, the Hilbert energy of the high-frequency band of the marginal spectrum at the monitoring point on the top portion of the segment will rise sharply. This suggests that the upper portion of the locked segment has begun to sustain damage. There are antecedents even when there is no penetration failure.  
step bedding rock slope; locked segment; shaking table test; seismic load; Hilbert-Huang transform; marginal spectrum theory; numerical simulation +
Footnote †: journal: Applied sciences  
Article