esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
For example, if the electricity-consumption data are included in the BIM environment, along with the potential power generation presented in this study, the energy self-sufficiency rate, which indicates production surplus in relation to demand, can be efficiently predicted and support the maintenance and planning of energy infrastructure systems. In this respect, this research may not only help to investigate suitable regions for PV plants using PV power-output maps at a regional and national scale but also be potentially utilized for the energy management and planning of highway-network construction and maintenance. Although this study can efficiently search for suitable sites for installing solar PV plants with geo-spatial analysis using horizontal-based highway data as polygon shapes, future studies need to address the following issues. The proposed method should be developed by using 3D spatial analysis that can reflect the features' 3D geometry exactly in the site selection or solar-energy estimation, which could not be represented by 2D or 2.5D analysis using DEM with the BIM-based highway network data and geo-spatial data (e.g., topography and meteorological data) [28]. This is because the current approach cannot be fully considered or defined for vertical surfaces, such as the height difference between highways and solar PV plants. For instance, the horizontal-based highway data did not reflect obstacle factors preventing solar energy from reaching the candidate sites for PV plants, such as L-type side gutters, soundproof walls, and changes in highway elevations. Furthermore, this paper should replace the highway objects that display as subregion shapes imported from 2D polygon with data using the 3D model-based highway design to visualize the installation of PV plants near the highway roads. Due to the limitations of the collected 2D data that is not given in the same section, it makes it challenging to assign an elevation value to the highway data in the same section. Thus, the existing data in this paper was converted into a subregion format and limited to highway data displayed in a 3D environment. Accordingly, future studies should be used to the level of detail technique (LOD 350-level 3D design data that include slopes, side gutters, soundproofing, and elevations with industry foundation classes (IFC)) for integrating GIS and BIM environments to implement a 3D-based geo-spatial analysis and more accurate visualization of installation of PV plant for energy management in 3D environments.
## 3 Results
### Effect of Diabetes Type on Foot Joint ROM and Gait Kinematics
The tarsometatarsal joint in type 1 and type 2 was significantly more inverted than that in controls throughout the whole gait cycle. As to the metatarsus to calcaneus frontal-plane rotation, one supra-threshold cluster (55-65% of gait cycle) exceeded the critical threshold and the CaMe angle in type 2 was significantly more exerted than that in controls (\(p<0.05\)). In the transverse plane, one supra-threshold cluster (65-75% of gait cycle) exceeded the critical threshold and the ankle in type 1 was significantly more adducted than in controls (\(p=0.041\)); the ankle of type 1 was also significantly more adducted than in type 2 at 50-60% of gait cycle (\(p=0.046\)). The midtarsal joint of type 2 was significantly more adducted than in controls for most of the gait cycle; the midtarsal joint of type 2 was overall more adducted than in type 1 with two clusters exceeding the threshold. As to the metatarsus to calcaneus transverse-plane rotation, both type 1 and type 2 were more abducted than controls at 50-60% of gait cycle (\(p=0.050\); \(p=0.049\)). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Type 1**} & \multicolumn{2}{c}{**Type 2**} & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**ANCOVA**} \\  & & & & & & & & **(\textgreater{} 0.05)** & \\ \hline
**Joint** & **Plane** & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** & \\ \hline \multirow{3}{*}{**ShFo**} & Sagittal & 22.6 & 4.2 & 21.2 & 3.5 & 25.2 & 3.4 & Control \textgreater{} type1 \\  & Frontal & 12.0 & 3.0 & 12.5 & 3.2 & 11.9 & 2.3 & \\  & Transverse & 16.0 & 3.9 & 13.9 & 4.1 & 18.4 & 3.8 & Control \textgreater{} type2 \\ \hline \multirow{3}{*}{**ShCa**} & Sagittal & 16.2 & 2.9 & 14.8 & 2.9 & 16.6 & 3.7 & \\  & Frontal & 7.0 & 2.0 & 6.8 & 1.8 & 6.8 & 1.5 & \\  & Transverse & 12.3 & 3.1 & 11.2 & 3.8 & 13.6 & 3.3 & \\ \hline \multirow{3}{*}{**CaMi**} & Sagittal & 13.2 & 4.3 & 14.3 & 4.2 & 17.0 & 4.7 & Control \textgreater{} type1 \\  & Frontal & 7.9 & 1.8 & 8.6 & 2.4 & 9.5 & 2.0 & Control \textgreater{} type1 \\  & Transverse & 7.8 & 2.5 & 7.2 & 2.3 & 8.6 & 2.5 & \\ \hline \multirow{3}{*}{**MiMe**} & Sagittal & 11.6 & 2.9 & 12.6 & 4.1 & 15.5 & 3.5 & Control \textgreater{} type1 \\  & Frontal & 6.1 & 1.2 & 6.0 & 1.6 & 6.0 & 1.5 & \\  & Transverse & 7.4 & 1.7 & 8.9 & 2.9 & 8.5 & 2.9 & \\ \hline \multirow{3}{*}{**CaMe**} & Sagittal & 19.6 & 4.4 & 19.4 & 5.6 & 23.1 & 4.7 & Control \textgreater{} type1 \\  & Frontal & 9.1 & 2.4 & 8.7 & 3.1 & 10.6 & 2.7 & \\  & Transverse & 11.1 & 3.1 & 11.4 & 3.7 & 15.1 & 3.9 & \\ \hline
**1 st MTPj** & Sagittal & 44.2 & 7.3 & 38.9 & 8.2 & 41.0 & 8.1 & type1 \textgreater{} type2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean and standard deviation of foot joints ROM [deg] in stance in type 1, type 2 and control groups. The last column is reporting the ANCOVA statistically significant differences between groups using walking speed, BMI, disease duration as covariates.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### System-Level Techniques
#### 2.5.5 Off-Line Techniques  
For the reason of simplicity, many researchers used off-line techniques [39]. This optimization technique is based on MIxed Linear Programming (MILP) and a Directed Acyclic graph (DAG). It militates for a combination of DVFS and DPM for a set of dependent tasks (precedence constraints), periodic, with a deadline on request and running on multiprocessor systems. Because of the extreme disparity in application scenarios that are constantly and dynamically changing, this solution is inappropriate and sub-optimal facing the importance of energy efficiency. In addition, it is not able to grasp the dynamic behavior of the entire software/hardware system and incurs the tasks with a worse case behavior (WCET), which is not efficient, leading to a negative energy balance especially when the dynamics of the system are important.
# Robust Hessian Locally Linear Embedding Techniques for High-Dimensional Data
## 4 Robust Hessian Locally Linear Embedding
### Noise Reduction Using Local Linear Smoothing  
When the data have a large amount of small noises, we employ the local linear smoothing [18] method for noise reduction. The local smoothing algorithm is similar in spirit to local polynomial smoothing applied in nonparametric regression [34]. The basic idea of local linear smoothing is to project each point \(x_{i}\) to an approximate tangent space computed by the weighted PCA. The main steps of local linear smoothing, for each iteration, are as follows [18]:  
1. For each sample \(x_{i}(i=1,2,\ldots,N^{{}^{\prime}})\), find the \(k\) nearest neighbors of \(x_{i}\) including itself: \(X_{i}=[x_{1}^{i},x_{2}^{i},\ldots,x_{k}^{i}]\)
2. Compute the weights \(w_{j}^{i}(j=1,\ldots,k)\) using an iterative weight selection procedure.  
Compute an approximation of the tangent space at \(x_{i}\) by a weighted PCA according to Equation (13);
4. Project \(x_{i}\) to the approximate tangent subspace to obtain: \[\hat{x}_{i}=\hat{x}_{w}^{i}+U^{i}(U^{i})^{T}(x_{i}-\hat{x}_{w}^{i})\]  
As pointed out in [18], when increasing the number of iterations in local linear smoothing, local linear smoothing will suffer from the well-known \"trim the peak and fill the valley\" phenomenon [34] for regions of the manifold where the curvature is large. Bias correction can also alleviate this problem for some applications. However, bias-correction is also possible to overshoot the correction. Moreover, it is difficult to choose a suitable scaling parameter to balance between the bias-correction and the overshooting. Therefore, we employ for the local linear smoothing only one iteration in our algorithm. Figure 2f shows the result of the noise reduction using one iteration of local smoothing on the \"Swiss roll with a hole\" dataset whose outliers have been removed by our fast approximate algorithm. As we can see from this, most of the noise-corrupted points have been moved close to the underlying manifold surface, and only a few points in the blue ellipse still deviate from the manifold surface. Nevertheless, as we will present in the next subsection, our Robust HLLE algorithm can further reduce the undesirable effects of outliers and noises on the embedding result.
# Numerical Analysis of the Kinematic Accuracy of the Hermetic Harmonic Drive in Space Vehicles
###### Abstract
Harmonic drives have been used for years in manipulators and wheel drives of space vehicles moving on the surface of Mars. In these cases, the tightness of the construction allows for securing the sensitive toothed wheel rim or elastic bearing from damaging dust and mechanical impurities. The combination of these exceptional features, i.e., kinematic accuracy and tightness, makes it so that the hermetic harmonic drive could be used in steering and regulating mechanisms used in an unfavorable environment (for example, outside the Earth's atmosphere).
# _Sci_
## 3 Results
### Technical Challenges of Employing Lasers versus Radio Frequency Communications
#### 3.2.3 Atmospheric Impacts
Since the size of rain drops relative to optical wavelengths is relatively larger than the instance of fog, the attenuation in rain is less pronounced, ranging from one to ten decibels per kilometer [95].
## 4 Experimental Results
### Feature Importance  
Feature extraction was performed using the XGBoost algorithm, with important features chosen based on F-scores. These important features were then used as input for the TCN-Attention model. Figure 7 shows the feature scores of the synthetic data for garlic and chili data.  
Figure 6: Results of t-SNE: (**a**) garlic; (**b**) KL of garlic; (**c**) ginger; and (**d**) chili.
## 4 Experimental Work
### Operation of the Embedded ANN on the PLC  
The designed ANN-based data-driven control algorithm was tested on the fluidic muscle station with twenty different positions and demonstrated over two different cylindrical test pieces, as shown in Figure 13, with a height of 25.5 mm for the aluminum part and 29.2 mm for the polymer-based part. Since the initial distance between the fixed lower support and the movable upper part of the pressing unit was 40.4 mm, the displacements required for the pressing process were 11.2 mm for the aluminum test piece and 14.9 mm for the polymer-based test piece.  
Figure 11: Simulink model of the controller: (**a**) subsystems; (**b**) masked main controller.  
Figure 12: Embedded ANN-based data-driven controller on a PLC: (**a**) function block; (**b**) HMI design.  
As shown in Figure 13, the position values used in the test parts are data not previously used in neural network training, and the desired position was achieved with the signal generated for the proportional pressure controller. The performance of the PAM-actuated pressing unit was observed to be satisfactory when positioning the aluminum test piece, achieving a displacement of 11.2 mm as desired. However, when attempting to position the polymer-based test piece, which required a displacement of 14.9 mm, the desired position could not be attained as it exceeded the contractile limits of the PAM. Nevertheless, the control algorithm managed to produce a result through extrapolation. The force exerted by the PAM due to contraction, necessary for positioning the polymer-based test piece, was estimated to be 81.7 N as a secondary output, whereas it was 12.68 N for the aluminum test piece.
###### Abstract  
Traffic prediction is a popular research topic in the field of Intelligent Transportation System (ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve road traffic efficiency. Graph neural networks are widely used in traffic prediction because they are good at dealing with complex nonlinear structures. Existing traffic prediction studies use distance-based graphs to represent spatial relationships, which ignores the deep connections between non-adjacent spatio-temporal information. The use of a simple approach to fuse spatio-temporal information is not conducive to obtaining long-term deep spatio-temporal dependencies. Therefore, we propose a new deep learning model Multi-Stage Spatio-Temporal Fusion Diffusion Graph Convolutional Network (MFDGCN). It generates multiple static and dynamic spatio-temporal association graphs to enhance features and adopts the multi-stage hybrid spatio-temporal fusion method. This promotes the effective fusion of a spatio-temporal multimodal and uses the diffuse convolution method to model the graph structure and time series in traffic prediction, respectively. The model can better predict both long and short-term traffic simultaneously. We evaluated MFDGCN using real road network traffic data and it shows good performance.  
traffic prediction; spatio-temporal prediction; graph convolutional network; temporal convolutional network; multi-head attention +
Footnote †: journal: Applied  
1
Footnote 1: Department of Computer Information Engineering, Cheongiu University, Cheongiu 28503, Korea; [EMAIL_ADDRESS] (Z.C.); [EMAIL_ADDRESS] (J.Z.)  
2
Footnote 1: Department of Computer Information Engineering, Cheongiu University, Cheongiu 28503, Korea; [EMAIL_ADDRESS] (Z.C.); [EMAIL_ADDRESS] (J.Z.)  
2
Footnote 2: Division of Software Convergence, Cheongiu University, Cheongiu 28503, Korea; [EMAIL_ADDRESS]
## 6 Other Technologies
### ANT Technology  
ANT is a proprietary technology featuring a wireless communication protocol stack thought for ultra-low power networking applications. It is designed to run using low cost, low power microcontrollers and transceivers operating in the 2.4 GHz ISM band. The ANT WSN protocol has been engineered for simplicity and efficiency, resulting in an ultra-low power consumption, maximised battery life, a minimal burden on system resources, simpler network designs and lower implementation costs. ANT also features low latency, the ability to trade-off data rate against power consumption, and support for broadcast, burst and acknowledged transactions up to a net data rate of 20 kbit/s (ANT's over the air data rate is 1 Mbit/s). Different topologies could be established: peer-to-peer, star, tree and other types of mesh network.  
ANT nodes are capable of acting as slaves or masters within a network and swapping roles at any time. This means the nodes can act as transmitters, receivers or transceivers to route traffic to other nodes. ANT is a good protocol for practical networks because of this inherent ability to support ad hoc interconnection of tens or hundreds of nodes.  
ANT allows a system to spend most of its time in an ultra-low power sleep mode, wake up quickly, transmit for the shortest possible time and quickly return back to an ultra-low power sleep mode. This implies that ANT is one of the most energy-efficient available technologies. While Bluetooth is designed for rapid file transfer between devices in a PAN, its average power consumption is 10 times greater with respect to ANT and the hardware costs are 90 percent higher. With respect to IEEE 802.15.4 ANT presents a larger data rate of 1 Mbit/sec and is relatively less complex.  
However, being a proprietary technology, ANT lacks interoperability.
## 3 Methods  
### Remotely Sensed Imagery  
To identify lineament feature of the Wangjiabian-Houjiawan Fault (F1-1a), high-resolution satellite imagery from Google Earth and Digital Elevation Model (DEM) data at 5 m, 12.5 m and 30 m resolutions were analyzed. Aerial photographs (resolution less than 0.1 m pixel) in the study area taken by an unmanned aerial vehicle (UAV) and Structure from Motion (SfM) technology data were also acquired. A Digital Surface Model (DSM, resolution less than 0.1 m pixel) from the SfM data was then generated. The 12.5 m- and 30 m-resolution DEM data are freely available from NASA and USGS [17; 18], while the 5 m-resolution DEM data are confidential. The aerial photos and the DSM data could be available from the first author on reasonable request.
## 2 Designed Photovoltaic System
### Environmental Data  
The designed system includes real-world environmental data for Ankara province using PVGIS (Photovoltaic Geographic Information System) monthly averages of solar irradiation, temperature and sunlight hours. These parameters are critical for accurately modeling the performance of the PV array and optimizing the MPPT algorithms. Table 5 below summarizes the monthly averages of these environmental parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Power Demand & 2 kW (constant) \\ Daily Energy Consumption & 48 kWh \\ \hline \end{tabular}
\end{table}
Table 4: Parameters of the load.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Capacity & 20 kWh \\ Type & Li-Ion \\ Efficiency & 95\% \\ Depth of Discharge (DoD): & 80\% \\ State of Charge (SOC) Range: & 20\% to 90\% \\ Initial SOC & 50\% \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the battery.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Month** & \multicolumn{2}{c}{**Solar Irradiance**} & \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Sunlight Hours**} \\  & **(kWh/m\({}^{2}\)day)** & & **(hours/day)** \\ \hline January & 1.9 & 0.3 & 3.1 \\ February & 2.7 & 2.1 & 4.2 \\ March & 4.0 & 6.7 & 6.1 \\ April & 5.4 & 12.1 & 8.1 \\ May & 6.5 & 17.2 & 10.0 \\ June & 7.3 & 21.5 & 11.0 \\ July & 7.5 & 23.5 & 11.2 \\ August & 6.8 & 23.0 & 10.5 \\ September & 5.4 & 18.5 & 8.5 \\ October & 3.8 & 12.5 & 6.2 \\ November & 2.4 & 6.0 & 4.0 \\ December & 1.8 & 1.0 & 3.1 \\ \hline \end{tabular}
\end{table}
Table 5: Environmental parameters.  
Temperature:  
* The efficiency of PV cells is temperature-dependent, with higher temperatures typically reducing performance.
* In Ankara, temperatures range from an average of 0.3 \({}^{\circ}\)C in January to 23.5 \({}^{\circ}\)C in July. Sunlight Hours:
* Daily sunlight hours determine the duration of energy generation by PV systems.
* In Ankara, sunlight hours are shortest in winter (3.1 h/day in December and January) and longest in summer (11.0-11.2 h/day in June and July).
## 3. Results and Discussion
### Optimal Film Usage  
For the combined 3D wrapping technique, the analysis of the optimal film consumption reached for \(D_{b}^{*}\), \(H_{b}^{*}\) was carried out in [19; 20], where it was shown, in particular, that for the bottom film layers \(p_{b}\gg p_{u}\) the optimal selection of bale diameter and height may result in a 3.37% to even 23.13% reduction in film usage depending on the number of global film layers and wrapping parameters, which means up to 23% film cost savings. Many conclusions regarding the impact of film width, pre-assumed bale volume and numbers of bottom and upper film layers on near-optimal bale dimensions and near-optimal film consumption were formulated [19; 20].
## 3 Results
### Evaluation Metrics  
In order to comprehensively and objectively evaluate the performance of MSFNet, four indicators, namely, accuracy, precision, recall, and \(F1\)-_score_, are selected for model evaluation. The specific calculation formulas are shown in Formulas (4)-(7).  
Accuracy measures the proportion of correctly classified samples, reflecting overall model performance. Precision represents the ratio of correctly predicted positives to all predicted positives, indicating the reliability of positive predictions. Recall (sensitivity) quantifies the proportion of correctly predicted positives among all actual positives, highlighting the model's ability to identify positive instances. Additionally, the \(F1\)-_score_, as the harmonic mean of precision and recall, offers a balanced evaluation, especially in imbalanced datasets.  
\[Accuracy =\frac{TP+TN}{TN+TP+FN+FP}\times 100\% \tag{4}\] \[Precision =\frac{TP}{TP+FP}\times 100\%\] (5) \[Recall =\frac{TP}{TP+FN}\times 100\%\] (6) \[F1\text{-}score =\frac{2\cdot Precision\cdot Recall}{Precision+Recall}\times 100\% \tag{7}\]  
where \(TP\) denotes the number of positive samples correctly predicted by the model, \(TN\) denotes the number of negative samples correctly predicted, \(FN\) represents the number of negative samples incorrectly predicted as positive, and \(FP\) indicates the number of positive samples incorrectly predicted as negative.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Central Processing Unit & Intel Xeon Platinum 8352 V \\ Graphics Processing Unit & RTX 3080 Ti \\ Operating System & Ubuntu 20.04 LTS 64-bit system \\ Version of PyTorch & 1.10.0 \\ Version of CUDA & 11.3 \\ Version of Python & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameter settings in experiments.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
###### Abstract  
How much sound can a building surface reflect to a source, the location of which is not exactly known? This paper considers this question particularly for a planar surface acting as an array of retroreflectors, or of focusing retroreflectors. The question is investigated using finite-difference time-domain acoustic simulation, using ideal retroreflective patches achieved by space-reversal, and focusing achieved by delays. Extensive (7.2 \(\times\) 7.2 m) and local (2.4 \(\times\) 2.4 m) ideal planar reflector arrays were investigated at distances of 1.5 to 4 m from sources that were within a 2.4 \(\times\) 2.4 m square plane. Patch sizes ranged from 0.3 m squares to the full reflector size. Physically realizable non-ideal focusing retroreflectors based on parabolic trihedra were also investigated. With sufficiently large patches, ideal focusing retroreflector arrays consistently outperform non-focusing retroreflector arrays. A large focusing retroreflector array has the potential to provide retroreflected energy levels (speech and A-weighted) from the first reflection to a source at 2 m distance comparable to the diffuse field energy level of acoustically supportive reverberant rooms. A small focusing retroreflector array returns less sound, but still much more than a single reflection from an equivalent specularly reflecting surface. Results from parabolic trihedra demonstrate that retroreflected energy levels similar to those from ideal surfaces can be achieved by architectural form. Challenges in translating these concepts to practical design solutions are discussed.  
Keywords:room acoustics; architectural acoustics; sound reflection; retroreflection; focusing retroreflector; acoustic focusing; voice support; FDTD +
Footnote †: journal: Applied Sciences