esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Influence of Bridge Tower Deformation
#### 3.3.1 Shift Response of the Top Part of the Bridge Tower  
Through comparative analysis of the displacement response of the bridge's top under different peak accelerations of transverse seismic waves, Figure 8 presents the time-history curve for the bridge's top, while Table 6 displays both maximum and residual displacements.  
The displacement response of the top of the bridge tower under different peak accelerations exhibited overall consistency, as evident from Figure 8 and Table 6. Moreover, the displacement gradually increased with an increase in the peak earthquake acceleration. Notably, when the peak seismic acceleration was below 0.4 g, the displacement remained within acceptable limits for tower collapse. However, at a peak seismic acceleration of 0.6 g, the maximum displacement at the top of the bridge tower measured 0.811 m, surpassing the prescribed limit of 0.72 m for tower collapse. Subsequently, as peak acceleration continued to rise, so did the displacement at the top of the tower. At a peak acceleration of 1.0 g, this led to a maximum displacement at the top of the tower reaching 1.34 m--significantly exceeding specified limits. When subjected to a complete seismic wave, the bridge tower underwent plastic deformation, as evidenced by a certain displacement of its top relative to the initial position. At a peak acceleration of 0.2 g, the residual displacement at the top of the tower was minimal. However, as the peak acceleration increased, there was an augmented distance between the tower top and its original position, leading to residual displacement once plastic deformation set in.  
\begin{table}
\begin{tabular}{c c c} \hline
**Peak Acceleration** & **Maximum Displacement (m)** & **Residual Displacement (m)** \\ \hline
0.2 g & 0.268 & 0.026 \\
0.4 g & 0.544 & 0.165 \\
0.6 g & 0.811 & 0.333 \\
0.8 g & 1.071 & 0.527 \\
1.0 g & 1.340 & 0.824 \\ \hline \end{tabular}
\end{table}
Table 6: Maximum and residual displacement at the top of the bridge tower.  
Figure 8: Time-history curve of the top part of the bridge tower.
## 3 Results and Discussion
### Proximate Composition
(2019) [28], by [PERSON] and [PERSON] (2018) [37] for quinoa (1.8 to 3.60%), for whole amaranth flour (2.4 to 6.86%), for buckwheat (1.51 to 3.05%) and also for barley [50]. Many minerals in quinoa are found at concentrations greater than those reported for most grain crops [23]. High ash content, resulting from quinoa flour addition to wheat flour, could involve an increased amount of minerals in the composite flour samples. Crude protein content of quinoa flours with different particle sizes (11.96-18.86%) is comparable to the values (12.0-16.0%) reported in other studies [11; 14; 20; 28]. The crude protein content of the flours was significantly different (\(p<0.05\)) from each other. It can be seen from the data presented in Table 2 that the protein content from quinoa flours has the same trend as the ash content. The highest content of crude protein was observed for particle size QM (18.86%). Other authors found a direct increase of protein content with a decrease of particle size [31], but [PERSON] et al. (1962) [53] opened that the change of protein with particle size is not universal and it depends on the structure of endosperm and type of endosperm cells. The data obtained by [PERSON] et al. (2002) [54] indicated that proteins are found especially in the embryo (23.5%) whereas in perisperm are only 7.2%. [PERSON] et al. (2019) [55] found higher concentrations of up to 38% in the embryo and less than 5% in the perisperm. The low amount of proteins in the perisperm was also confirmed by [PERSON] et al. (2005) [56] and [PERSON] et al. (1992) [57]. These data suggest that quinoa seeds store proteins in the embryo to provide nutrients for growing and development [58]. This result indicates that the substitution of wheat flour with medium particle size of quinoa flour (QM) would increase the protein content of final products, producing more shelf stable products due to their lower moisture content. The fat content of the quinoa flours fractions ranged from 6.36 to 6.44%, but the particle sizes did not show significant differences in lipid content (\(p>0.05\)). The content of lipids increased with particle size increase. Small quinoa particle size (QS) has been considered an alternative oilseed crop due to its lipidic fraction [59]. Similar data for fat content, which ranged between 1.8 and 9.5%, were reported earlier in other studies [59; 60]. [PERSON] et al. (1998) [61] showed that lipids are stored in the cells of the endosperm and embryo tissues, which explains this variation. The carbohydrate content ranged from 61.49 to 69.39%. The carbohydrate contents for quinoa particle size flours were significantly different (\(p<0.05\)) from each other. The largest particle (QL) showed a very high percentage of carbohydrate content of 69.38%. While the protein and ash content of samples increased, a decrease in carbohydrate content for the flour samples was observed (Table 2).
## 4 Results of the Experiment  
The obtained load-displacement diagram is shown in Figure 17, with the highest loading force peaking at 1612.2 N. However, during the experiment, the lower bone segment failed at the site of placement of one of the staples (lateral staple). This means that the staples are capable of withstanding even higher load than measured. The failure of the lower bone segment was also likely caused by the fragility of the artificial bone (i.e., the mechanical difference between the real and the artificial bone). The bone failure occurred at the maximum loading force.  
Figure 18 shows the artificial model bone after the experiment, along with the detail of the failure of the lower bone segment at the lateral staple site.  
Figure 16: The average distance between the proximal and distal bone segment of the tibia (mm), deformation scale 10:1.  
Figure 17: Experiment—Tensile force Fz (N) vs. separation of jaws (mm).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Value** & **Place** \\ \hline Maximal Equivalent von & 682.39 MPa & Radius of medial staple, outside of tibia; see Figures 13 and 14 \\ \hline Maximal Displacement & 0.2 mm & Apex of medial staple, inside of tibia; see Figure 15 \\ \hline Average distance between the & & \\ proximal and distal bone & 0.3 mm & Epiphysis, inside of tibia, see Figure 16 \\ segment of the tibia & & see Figure 16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: FEA results.  
Apart from this experiment simulating a real-world application of Blount staples, additional experiments were performed using solid foam blocks with bone-like properties procured from SAWBONES; see [27]. The block was cut into smaller pieces to fit into our testing machine. Two pieces of the foam block were connected by a Blount staple and, as such, they were inserted into the testing machine using screws from the abovementioned jig and pulled apart. This experiment yielded similar results--the foam block always failed in the place of staple insertion, leaving the staples' structural integrity intact (similarly to the experiment on the model of the tibia); see Figure 19.  
Because it was the bone blocks and not the staples that failed every time, presenting measured data for these blocks would not provide any valuable information. However, evaluation of experiments with the bone blocks (including nonlocking and locking plates, not only Blount staples) will be presented in our future work.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 4 Proposed Methodology  
Many researchers have developed fall detection systems using machine learning (ML) and deep learning (DL). However, there has been limited work on using multimodal datasets. In this study, we propose a novel multimodal fall detection system that combines skeleton and sensor data. The skeleton data are processed using GSTCAN [46], while the sensor data are handled through an integration of Bi-LSTM and a channel attention (CA) network. By combining these two types of data, the system enhances the accuracy and reliability of fall detection. The structure of the proposed system is shown in Figure 3, and sensor model details are shown in Figure 4.  
Figure 1: Visualization of the hand gesture: (**a**) X, Y, Z axis sensor-based signal; (**b**) X-axis ankle acceleration signal.  
Figure 2: (**a**) A wearable sensor is located on the human body. (**b**) Layout of sensor and camera for the experiment in the UP Fall dataset [53].  
The GSTCAN model works directly on the raw skeleton and motion data without the need for dimensionality reduction. This allows the system to capture both the spatial and temporal patterns in the data [46]. This approach, inspired by recent work in human activity recognition and skeleton-based fall detection [41; 54; 55], helps the system learn detailed motion patterns directly from the raw data. The novelty of the skeleton and stream data lies in its ability to avoid dimensionality reduction and learn complex movement patterns from the raw data, which makes it more efficient in capturing detailed and meaningful motion patterns. For sensor data, we used the Bi-LSTM to extract sensor features, followed by CA [56] to refine them. This integration enhances the system's ability to understand time-varying human motion characteristics. The novelty of our sensor stream module lies in combining the Bi-LSTM's ability to capture long-range and temporal dependencies with the feature refinement of CA. This approach improves feature extraction and strengthens the model's performance in dynamic and noisy environments, making it particularly effective for detecting events such as falls, where both temporal context and sensor channel importance are crucial.  
The features from both data streams (skeleton, motion, and sensor) are then fused, combining complementary information from each modality. This fusion of features provides a more comprehensive and accurate understanding of human motion. After fusion, the combined features are passed through a fully connected layer for final classification. The fusion approach helps overcome the limitations of unimodal systems by providing richer, more reliable feature representations. In this study, we used video data from UP fall incidents to extract skeletal points for each frame, producing 18 points, including the nose, mouth, shoulders, elbows, wrists, hips, knees, and ankles (as shown in Figure 5). However, we selected 14 points by omitting the eyes and ears from both sides.  
Figure 3: Proposed working flow diagram [46].
# A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy
## 2 Multi-Dimensional Cloud Model
In addition, we introduce information entropy and mutual information to measure the importance of features and select features with high information entropy. Subsequently, the data can be mapped from a high-dimensional feature space to a low dimensional space  
Figure 4: The convergence of different algorithms with different sizes. through information entropy features. As a result, the joint entropy of two variable random variables can be expressed as follows. \[H(X,Y)=E\bigg{[}\log\frac{1}{p(x,y)}\bigg{]}=\sum_{x}\sum_{y}p(x,y)\log\frac{1}{p( x,y)} \tag{17}\]  
Under the condition that random variable \(X\) is determined, the conditional entropy of random variable \(Y\) can be expressed as follows. \[H(Y|X)=\sum_{x}\sum_{y}p(x,y)\log\frac{1}{p(y|x)}=\sum_{x}p(x)\sum_{y}p(y|x) \log\frac{1}{p(y|x)} \tag{18}\]  
Through the above analysis, it can be expressed as follows. \[H(Y|X)=\sum_{x}p(x)H(Y|x)=E\bigg{[}\log\frac{1}{p(y|x)}\bigg{]} \tag{19}\]  
To solve the above problems, we propose a hybrid, high-dimensional, multi-objective PSO clustering algorithm based on the multi-dimensional cloud model and entropy theory(HHCE-MOPSO) and the feature selection algorithm based on correlation information entropy and the particle swarm optimization algorithm. Simultaneously, the particle position vector is used as the attribute weight vector and the information entropy is used as the attribute weight evaluation function. In addition, the gradient descent method is used to minimize the attribute weight evaluation function. In the process of clustering, the influence of intra class entropy and inter class entropy on attribute weight is comprehensively considered and a group of optimal attribute weight values are finally obtained through iteration. Thus, the global search ability and convergence speed of the algorithm are improved. In the meantime, the hybrid method firstly uses correlation-based information entropy to reduce the high dimension of features quickly. Then, the optimization features are searched by the PSO algorithm. Similar or redundant features are divided into one feature class by information entropy to reduce the search time and space. The particle swarm is prevented from falling into local optimization to obtain the optimal solution of the final feature set. Finally, we will consider large-scale, high-dimensional data that is difficult to process by existing particle swarm feature selection algorithms and propose anovel hybrid, high-dimensional, multi-objective PSO clustering algorithm based on the cloud model and information entropy.
_Appl. Sci._**2012**, \(2\), 479-484; doi:10.3390/app2020479  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Closed Form Representations of Some Series in [PERSON]'s Model for Squeeze Film Damping with a Rectangular Plate**  
**[PERSON]**  
Department Mathematik, Friedrich-Alexander Universitat Erlangen-Nurnberg, Cauerstr. 11, 91058 Erlangen, Germany; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-9131-85-67130;  
Fax: +49-9131-85-67134  
_Received: 25 April 2012; in revised form: 11 May 2012 / Accepted: 14 May 2012 / Published: 21 May 2012_  
**Abstract:** Compressible squeeze film damping is a phenomenon of great importance for micromachines. For example, for the optimal design of an electrostatically actuated micro-cantilever mass sensor that operates in air, it is essential to have a model for the system behavior that can be evaluated efficiently. An analytical model that is based upon a solution of the linearized Reynolds equation has been given by [PERSON]. In this paper we explain how some infinite sums that appear in [PERSON]'s model can be evaluated analytically. As an example of applications of these closed form representations, we compute an approximation for the critical frequency where the spring component of the reaction force on the microplate, due to the motion through the air, is equal to a certain given multiple of the damping component. We also show how some double series that appear in the model can be reduced to a single infinite series that can be approximated efficiently.  
**Keywords:** squeeze film; optimal sensor design; resonant mass sensor; double series; efficient approximation of double series; evaluation of infinite series; [PERSON]'s model
## 2 Materials and Methods
### Data
#### 2.2.2 Auxiliary Datasets  
In this study, we use two kinds of data: meteorological datasets (land surface temperature (LST), precipitation (PR), downward surface shortwave radiation (SRAD)) and human intervention indices (land-use and land-cover change (LUCC), nighttime light data (LIGHT)).  
We use the land surface temperature from MOD11A2 as the temperature dataset, which includes data every eight days starting from 2000 to 2020 with a spatial resolution of 1000 m [23]. We use the CHIRPS precipitation product to evaluate the influence of precipitation [24]. The downward surface shortwave radiation is obtained from TerraClimate, which is a dataset of monthly climate and climatic water balance for global terrestrial surfaces [25]. We obtained the land-use and land-cover change data from the Resource and  
Figure 1: Location (**a**) and elevation (**b**) of the Beijing\(-\)Hebei\(-\)Tianjin Region.  
Environment Science and Data Center ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)) (accessed on 1 April 2022)), with a spatial resolution of 1000 m. The nighttime light data used come from an extended time-series (2000-2020) of global NPP-VIIRS-like nighttime light data from cross-sensor calibration [26].  
The satellite data used for the study (GPP, temperature, precipitation, downward surface shortwave radiation) were obtained using Google Earth Engine (GEE) [27]. All of these programs were coded in GEE Code Editor. The workflow of this research is shown in Figure 2.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Scalability and Real-World Applicability  
Many IMI techniques particularly those involving game theory or mathematical optimization [229, 230] perform well in controlled environments or simulations but may not scale efficiently in UD B5G deployments [231, 232]. These methods often assume ideal conditions, such as perfect channel knowledge or static user distributions, which may not hold in dynamic real-world networks.
## 1 Introduction  
In recent years, wind energy has become the focus of managers and researchers in the energy field, due to the advantages of wind power, such as renewable and cleanliness. In 2016, the global installed capacity of wind power exceeded 54 GW. This installed capacity is distributed between 90 countries, which have an installed capacity of more than 10 GW, with 29 countries having an installed capacity of 1 GW [1]. Cumulative installed capacity increased by 12.6%, and accumulated capacity reached 486.8 GW. In the United States, more than 53,000 wind turbines operate, generating more than 84.1 GW of electricity in 41 states [2]. On the other side of the globe, wind power may become China's second largest power source by 2050 [3]. Accurate forecasting of wind speed is an important prerequisite for using wind power due to the following reasons: 1) it reduces rotating and operating costs of wind-farm equipment; 2) it helps dispatching departments to adjust their plans in time; 3) it reduces the impact on the entire power grid;4) it effectively reduces or avoids the negative impact of wind farms on the power system, and 5) it improves the ability of wind power in power market competition. However, because of the chaotic and random fluctuations of wind speed, it is a challenging and difficult task to obtain satisfactory wind-speed forecasting results [4; 5; 6]. To have satisfying results and reduce errors, and improve the accuracy and stability of the forecasting results, various wind-speed forecasting methods have been proposed and developed by former researchers. The methods can be classified in four categories: 1) physical models; 2) statistical models; 3) artificial-intelligence models; and 4) spatial-correlation models [7]. The common physical models include weather research and forecast, the consortium for small-scale modeling, and mesoscale model 5 (MM5) [8]. These methods are based on physics and the atmosphere, and forecast wind speed with additional background information [8]. Statistical models include the fuzzy methods [9], Autoregressive Moving Average (ARMA) model [10], the Autoregressive Integrate Moving Average (ARIMA) model [11; 12], and the grey model [13], combined with neurofuzzy techniques [14] and Markov chains [15]. Statistical methods aim to mine the relationship between historical data and establish prediction models, including traditional statistical and machine-learning models, to describe the potential sampling of wind-speed forecasting [16]. In recent years, because of their powerful nonlinear character, many artificial-intelligence forecasting methods, such as artificial neural networks (ANN) [17; 18; 19; 20; 21; 22; 23; 24; 25] and support vector machines (SVM) [26; 27], have been widely applied to wind-speed forecasting. The wind speed of the spatial relationship between different sites is considered in spatial-correlation models [28] to forecast wind speed. However, these four categories of methods have disadvantages because of their character: 1) physical models need accurate numerical weather-prediction data and detailed information of wind-farm locality, input parameters, and data acquisition.
# Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
## 3 Results
### Experimental Setup
We constructed seven modified versions of the Mazurka Dataset to study alignment accuracy under controlled global time warping conditions. All recordings of the same Mazurka are time-scale modified to a target duration, where target durations are calculated as the median duration multiplied by a constant scaling factor (0.500, 0.630, 0.794, 1.000, 1.260, 1.588, 2.000). Recording durations for the seven modified datasets are shown in the table and are expressed in seconds. error tolerance (right), all six beat timestamps are considered correct, resulting in an error rate of 0%. In our experimental results in Section 3, error rates are computed across all predicted alignments in the benchmark, and we report error rates at multiple different error tolerances. There is one subtle yet very important detail regarding evaluation. Consider the evaluation of a predicted alignment between two recordings \(A\) and \(B\). At the ground truth beat timestamp in \(A\), let us say that the predicted alignment error in \(B\) is 50 ms. If we take the exact same predicted alignment but elongate the time axis in \(B\) by a factor of two, the predicted alignment error in \(B\) will be 100 ms. Clearly, the predicted alignment has not gotten any worse in quality--it has simply been projected into a time axis that is elongated. To account for this issue, when calculating the alignment error, we project all predicted alignments into the median_x1.000 time axis and then apply a fixed error tolerance. This ensures that alignment errors in different time scales are compared fairly. One way to think about this is that we are using an error tolerance that is (roughly) a fixed fraction of a musical beat, rather than a fixed absolute duration of time.
## 4 Multi-User MIMO System Model
### Sector Assignment  
Each of the \(K\) UTs placed inside the cell must be assigned to one of the three sectors. As the 3-D position of the UTs is not known at the BS, we exploit the channel matrices to evaluate the suitability of a certain UT to be served by the antenna array of a sector, i.e., the link quality between them. If we denote with \(\mathcal{U}=\{U_{1},U_{2},\ldots,U_{K}\}\) the set of all users to be assigned, and with \(\mathcal{A}_{1},\mathcal{A}_{2}\), and \(\mathcal{A}_{3}\) the set of users assigned to sectors 1, 2, and 3 respectively, a user can only be assigned to one sector, i.e., \((A_{i}\cap A_{j})=\varnothing\) and \(|\mathcal{A}_{1}\cup\mathcal{A}_{2}\cup\mathcal{A}_{3}|=K\), where \(|\mathcal{X}|\) denotes the cardinality of \(\mathcal{X}\). Then:  
\[U_{k}\in\mathcal{A}_{s},\quad s=\operatorname*{argmax}_{j}\sum_{q}^{Q}\| \mathbf{H}_{k,j}(q)\|_{F}^{2},\quad j=1,2,3 \tag{67}\]  
where \(\|\cdot\|_{F}^{2}\) indicates the Frobenius norm. Therefore the user is assigned to the sector whose channel has the highest energy.  
We could expect that the UTs in \(LOS\) will be assigned to the same sector as the one covering their \(x\)-\(y\) coordinates, except for the UTs in the sector borders, where some uncertainty is present. For \(NLOS\) UTs, the sector assignment could have even less or no correspondence to the actual placement in the hexagon. An example of the sector assignment result can be seen in Figure 6.  
In a real case scenario, for example in 5G NR, the sector assignment is commonly a consequence of the initial access procedure, where the UT chooses the sector depending on the synchronization signal strength that it experiences. Even if this procedure is different from the one considered in our scenario, they both strongly depend on the link quality between the BS and the UT. We reasonably expect that the result of our assignment does not remarkably differ from a real scenario.