esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Multimodal Feature Fusion
#### 2.3.1 Classification Based on the Fusion Stage  
The fusion stage can be categorized into three types, as illustrated in Figure 4: feature-level fusion, model-level fusion, and decision-level fusion.  
Feature-level fusion, or early fusion, involves extracting different modality features and concatenating them to form an overall multimodal feature representation. Since various modality information often exhibits high correlation, extracting this correlation after feature-level fusion can be challenging. Therefore, this method may not fully capture the correlation between different modalities, and in the temporal dimension, simple feature fusion may not achieve cross-temporal fusion of multimodal data. As the number of modalities increases, concatenating feature vectors may lead to high-dimensional features, difficulty in training models, and information redundancy.  
Model-level fusion involves merging two features at an intermediate stage in the model. Afterward, independent models continue to extract the features, and finally, both types of features are combined for the classification task before the final classification. Taking the example of Multi-Layer LSTM (ML-LSTM), this approach combines multiple layers of neural networks with a traditional LSTM (Long Short-Term Memory) model. The fusion process is as follows: The text features are put into the first LSTM layer (Layer 1), producing hidden layer states for each neuron. Subsequently, the audio features are concatenated with the hidden layer states from Layer 1 and put into the second LSTM layer (Layer 2), generating hidden layer states for each neuron in the second layer. The visual features are then concatenated with the hidden layer states from Layer 2 and put into the third LSTM layer (Layer 3), producing hidden layer states for each neuron in the third layer. Finally, the fused features are input into the fully connected layer (FC) to obtain the prediction result.  
Figure 4: Multimodal fusion method. AU is an analysis unit.  
Decision-level fusion, also known as late fusion, primarily involves using different network architectures for feature extraction and textual and audio information fusion. Decision-level fusion models each modality separately, treating different modalities as mutually independent. The features are extracted for each modality, and the emotion recognition results for individual modalities are obtained through emotion classifiers. Subsequently, a decision method is applied to recognize the results of each modality, ultimately yielding the final emotion classification result. Designing the decision rules for decision fusion is a challenging task. If the decision rules are too simple, they may not accurately reflect the correlation between different modalities.
## 2 Materials and Methods
### A Description of GPU-Accelerated Block-Matching Algorithm in USE
#### 2.1.1 A Standard Protocol for Calculating NCC  
Given one reference signal \(f\) and one target signal \(g\), the NCC function over a search range \((\tau_{x},\tau_{y},\tau_{z})\) can be calculated as follows:  
\[R_{\text{NCC}}\left(u,v,w,\tau_{x},\tau_{y},\tau_{z}\right)\] \[=\frac{\sum_{m=u}^{u+W_{x}-1}\sum_{n=v}^{v+W_{y}-1}\sum_{k=v}^{u+W _{z}-1}\left\{f\left(m,n,k\right)\bullet g\left(m-\tau_{x},n-\tau_{y},k-\tau_{ z}\right)\right\}}{\sqrt{\sum_{m=u}^{u+W_{x}-1}\sum_{n=v}^{u+W_{y}-1}\sum_{k=w}^{u+W _{z}-1}\left\{f^{2}\left(m,n,k\right)\right\}\bullet\sum_{m=u}^{u+W_{x}-1} \sum_{n=v}^{u+W_{y}-1}\sum_{k=v}^{u+W_{z}-1}\left\{g^{2}\left(m-\tau_{x},n- \tau_{y},k-\tau_{z}\right)\right\}}} \tag{1}\]  
where the dimensions of the reference and target windows (tracking kernels) are \([u,u+W_{x}-1]\), \([v,v+W_{y}-1]\), \([w,w+W_{z}-1]\) in the lateral, axial and elevational directions, respectively. Similarly, \((W_{x},W_{y},W_{z})\) are the tracking kernel dimensions in lateral, axial and elevational directions, respectively. In Equation (1), the origin of the reference window (tracking kernel) is \((u,v,w)\) and \([\tau_{x},\tau_{y},\tau_{z}]\) is the search range defined below,  
\[(\tau_{1}\leq\tau_{x}\leq\tau_{2},\tau_{3}\leq\tau_{y}\leq\tau_{4},\tau_{5} \leq\tau_{z}\leq\tau_{6}) \tag{2}\]  
In the block-matching algorithm, \([\tau_{1},\tau_{2}]\), \([\tau_{3},\tau_{4}]\) and \([\tau_{5},\tau_{6}]\) are pre-determined by the algorithm. For each 3-D shift \((\tau_{x},\tau_{y},\tau_{z})\), Equation (1) can be used to obtain one NCC value. Thus, looping through the entire search range yields a 3-D NCC map.  
Figure 1: An illustrative diagram show how the calculation of NCC values with and without sum-tables are integrated into ultrasound-based motion tracking. RF signal stands for radiofrequency ultrasound echo signal.
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 2 Proposed Design Strategy
### Improved Canny Edge Detection Algorithm  
In all hardware implementations of the Canny edge detection algorithm, the method of selecting the threshold can be roughly divided into two types, namely, fixed and adaptive thresholds. The adoption of a fixed threshold will undoubtedly reduce algorithm complexity and hardware resource consumption. However, when the object to be detected changes or the external environment of the detected object changes, such as light, the threshold needs to be reset. This obviously cannot meet the requirements of real-time performance. In contrast, an adaptive threshold is a good solution to the above problems. In this section, we introduced three approaches to improve the Canny edge detection algorithm for hardware implementation, namely, [PERSON]'s algorithm, which is used to automatically select the threshold, low complexity gradient magnitude and direction calculation method, and logarithmic approximation, which is used to reduce the complexity of [PERSON]'s algorithm. Figure 2 shows an overview of the improved Canny edge detection design strategy. A detailed description of the proposed Canny edge detection algorithm is given below.
# Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
## 3 Results and Discussions
### Discussions
Their inclusion in future work could improve seasonal robustness and help model short-term variability. Lastly, while multivariate linear regression yielded high accuracy and interpretability, the complex, nonlinear nature of HAB processes may be better captured by advanced machine learning or hybrid modeling frameworks [77, 78]. Prior research has demonstrated that the emerging approaches using remote sensing data, such as deep learning-based tools for detecting algal bloom via optical and SAR sensors [79] or RGB imaging for turbidity classification [80], offer greater flexibility for spatially and temporally dynamic environments. Addressing these limitations through the integration of broader datasets and advanced hybrid modeling techniques will be essential for scaling this framework to broader geographic areas and improving real-time HAB prediction.
# ResnetCPS for Power Equipment and Defect Detection
## 4 Experiment
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Sharing** & \multirow{2}{*}{**Params**} & **Mem** & **Model** & **Top-1** \\  & **Ratio** & & **R + W** & **Size** & **Acc** \\ \hline \multicolumn{2}{c}{resnetrs50} & 35.69 M & 273.64 M & 279 M & 85.52 \\ \hline \multirow{3}{*}{\begin{tabular}{c} ResNet- \\ cps50 \\ \end{tabular} } & 1/16 & 35.41 M & 273.02 M & 277 M & 85.65 \\  & 1/8 & 35.13 M & 271.81 M & 275 M & 86.39 \\  & 1/4 & 34.58 M & 269.39 M & 271 M & **86.95** \\  & 1/2 & **33.46 M** & **264.56 M** & **262 M** & 86.79 \\ \hline \multicolumn{2}{c}{resnetrs101} & 63.62 M & 526.85 M & 498 M & 85.65 \\ \hline \multirow{3}{*}{\begin{tabular}{c} ResNet- \\ cps101 \\ \end{tabular} } & 1/16 & 62.16 M & 521.01 M & 486 M & 86.62 \\  & 1/8 & 60.69 M & 514.32 M & 475 M & 87.36 \\  & 1/4 & 57.77 M & 500.95 M & 452 M & **87.39** \\  & 1/2 & **51.92 M** & **474.21 M** & **406 M** & 87.26 \\ \hline \multicolumn{2}{c}{resnetrs152} & 86.62 M & 1.0 G & 678 M & 84.75 \\ \hline \multirow{3}{*}{\begin{tabular}{c} ResNet- \\ cps152 \\ \end{tabular} } & 1/16 & 84.15 M & 1015.25 M & 659 M & 86.34 \\  & 1/8 & 81.68 M & 1001.51 M & 639 M & **87.95** \\  & 1/4 & 76.73 M & 974.03 M & 601 M & 86.32 \\  & 1/2 & **66.85 M** & **919.06 M** & **523 M** & 85.57 \\ \hline \multicolumn{2}{c}{resnetrs200} & 93.21 M & 1.31 G & 730 M & 86.35 \\ \hline \multirow{3}{*}{\begin{tabular}{c} ResNet- \\ cps200 \\ \end{tabular} } & 1/16 & 90.46 M & 1.29 G & 708 M & 87.26 \\  & 1/8 & 87.71 M & 1.28 G & 687 M & **87.69** \\  & 1/4 & 82.21 M & 1.24 G & 644 M & 87.16 \\  & 1/2 & **71.21 M** & **1.17 G** & **558 M** & 87.08 \\ \hline \multicolumn{2}{c}{resnetrs270} & 129.86 M & 1.72 G & 1017 M & 87.76 \\ \hline \multirow{3}{*}{\begin{tabular}{c} ResNet- \\ cps270 \\ \end{tabular} } & 1/16 & 125.84 M & 1.7 G & 985 M & **88.36** \\  & 1/8 & 121.82 M & 1.68 G & 954 M & 88.20 \\  & 1/4 & 113.78 M & 1.63 G & 891 M & 88.15 \\  & 1/2 & **97.69 M** & **1.53 G** & **765 M** & 87.64 \\ \hline \multicolumn{2}{c}{resnetrs350} & 163.96 M & 2.54 G & 1.284 G & 87.71 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} ResNet- \\ cps350 \\ \end{tabular} } & 1/16 & 158.49 M & 2.51 G & 1.241 G & 88.60 \\  & 1/8 & 153.02 M & 2.48 G & 1.198 G & **89.33** \\ \cline{1-1}  & 1/4 & 142.09 M & 2.4 G & 1.113 G & 88.39 \\ \cline{1-1}  & 1/2 & **120.23 M** & **2.26 G** & **942 M** & 88.33 \\ \hline \multicolumn{2}{c}{resnetrs400} & 191.89 M & 3.53 G & 1.502 G & 84.76 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} ResNet- \\ cps400 \\ \end{tabular} } & 1/16 & 185.24 M & 3.48 G & 1.450 G & 85.01 \\  & 1/8 & 178.59 M & 3.43 G & 1.398 G & 85.86 \\ \cline{1-1}  & 1/4 & 165.29 M & 3.34 G & 1.295 G & **86.32** \\ \cline{1-1}  & 1/2 & **138.69 M** & **3.14 G** & **1.087 G** & 84.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Resnetcps model scale with different sharing ratios.
## 3 Key Techniques
### Determination of the Initial Moment of Collapse  
According to the proposed hybrid framework, the FE simulation is conducted before the initial moment of collapse \(T_{\mathrm{c}}\), and the physics engine simulation is conducted after \(T_{\mathrm{c}}\). Therefore, the determination of \(T_{\mathrm{c}}\) is essential. Several collapse criteria have been proposed [44; 45; 46; 47; 48; 49]. There are various structural collapse modes, including lateral collapse, vertical collapse, and so on. No matter which collapse mode is, the vertical displacements of structural members will exceed a specific value when the structures collapse [47; 48]. Therefore, the definition of collapse depends on the moment when \"the deformation of the structure is insufficient to maintain a safe use space\" [47]. Besides, [PERSON] et al. [46] studied four collapse criteria, including the following: Criterion 1) According to Chinese seismic code [44], the earthquake-induced inter-story drift ratio of reinforced concrete frames should not be greater than 1/50; Criterion 2) Federal Emergency Management Agency (FEMA) 356 [45] recommended that the inter-story drift ratio limit for the collapse-prevention performance level of concrete structures be 4%; Criterion 3) According to [PERSON] [49], when the tangent slope of the incremental dynamic analysis (IDA) curve is lower than 20% of the slope of the initial elastic stage, the structure will collapse; Criterion 4) \"The vertical collapse displacement of the main structural members exceed[ing] 1 m\", is considered as Criterion 4 [46]. The work of [PERSON] et al. [46] shows that the conventional collapse criteria (i.e., Criteria 1-3) significantly underestimate the collapse resistance of the frames. Compared with the other three collapse criteria, Criterion 4 can better reflect the extreme nonlinear behavior of the collapse limit state. Therefore, collapse Criterion 4, proposed by [PERSON] et al. [46], is selected as a rule for determining \(T_{\mathrm{c}}\) in this work.  
Figure 3: Rotation method for inclined elements.
# A Review on SIW and Its Applications to Microwave Components
## 10 SIW Multiplexer  
Multiplexers are microwave components employed for both breaking a frequency spectrum into two or more sub-spectrums or for merging two or more sub-spectrums into one wide spectrum. Diplexers are commonly employed in satellite communication applications to merge the transmit and the receive antennas on space vehicles [17]. This marginally lowers the size of the space vehicle by a sizeable quantity. Triplexers, on the other hand, are for linking three separate networks with distinct operational frequencies to one port [72]. Multiplexers are normally used in the RF front end of cellular radio base stations to divide the transmit and the receive channels.  
Conventional SIW multiplexers (including diplexers and triplexers) are composed of several coupled single-mode SIW cavities/resonators with separate paths and a matching network as reported in [17]. Multi-mode resonators, such as the dual-mode resonator proposed in [74] and the triple-mode cavity reported in [75], have been investigated and implemented to enormously miniaturise the end device, and reduce the extent of losses recorded on the transmit/receive paths. SIW diplexers based on circular triplet combine filters have been proposed in [76]. The involvement of the cross-couplings in the circular triplet section was used to realise high rejection in the diplexer device. A tuneable multi-layer SIW diplexer has been reported in [77] with simulation and measurement responses shown in Figure 9. The multi-layer architecture in the design facilitated the easy control of the electric and magnetic coupling via circular slots. This allowed for a wide stopband to be achieved in the SIW diplexer component. A performance comparison of published works on SIW diplexers is given in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \(\mathbf{f_{0}}\) & **Filter** & **No. of** & **Insertion** & **Return** & **Dimension** \\  & **(GHz)** & **Order** & **Bands** & **Loss (dB)** & **Loss (dB)** & **(\(\mathbf{\lambda_{o}}\times\mathbf{\lambda_{o}}\))** \\ \hline
[67] & 1.7 & 3 & 1 & 1.3 & 22 & 0.27 \(\times\) 0.63 \\ \hline
[69] & 25/27 & 4 & 2 & 1.7/1.6 & 14 & - \\ \hline
[70] & 7.5 & 5 & 1 & 1.5 & 19 & - \\ \hline
[71] & 8/11.4 & 3 & 2 & 2.3/3.1 & 1
## 5 Results  
### Performance Metrics  
The following performance metrics are measured in this simulation:  
* _Normalized throughput_: This is calculated by dividing the total number of packets received (\(P_{total}\)) by the number of packets that can be sent in a given simulation time (\(T_{sim}/F\)), and then multiplying it by the ratio of the packet payload (\(L_{pkt}\)) to the frame size (\(F\)). Where \(T_{sim}\) represents the maximum simulation time and \(F\) indicates the frame size. Normalized throughput is calculated using Equation (1). \[Normalized\ Throughput=\left[\frac{P_{total}}{\left(\frac{T_{sim}}{F}\right)} \right]\times\left[\frac{L_{pkt}}{F}\right]\] (1)
* _[PERSON]'s fairness index (JFI)_: Average fairness among nodes queued for channel access. This is obtained using Equation (2), where \(n\) is the number of stations and \(t\) is the time for the current experiment. \[Average\ Fairness\ Index=\left[\frac{\sum_{t}\left(\frac{\left(\sum_{t=1}^{n}x_{t} \right)^{2}}{n.\left(\sum_{t=1}^{n}x_{t}^{2}\right)}\right)}{t}\right]\] (2)
* _Channel access delay_: Average queuing delay for a node to access the channel. The channel access delay is computed from Equation (3), where \(N\) is the number of stations and \(d_{n}\) is the queuing delay of packets of \(n\)th station. \[Channel\ access\ delay=\frac{\sum_{n=1}^{N}d_{n}}{P_{total}\times 1000}\] (3)
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 2 Background
### 5G Use Cases in Healthcare  
When it comes to digital healthcare transformation, 5G is at the forefront, and its potential applications in the healthcare sector are exciting. High-speed data transfers and real-time, high-quality internet access are made possible by 5G, allowing both local and distant authorities to link their medical equipment and workers on a centralized network [17; 18; 19]. Some critical use cases of 5G in healthcare include (a) telemedicine in which health-related information and services are sent remotely via data networks and telecommunications systems, (b) remote patient monitoring and surgery using wearables and eHealth devices, (c) real-time patient data/information (sensors and HD videos) flowing between the hospital and the ambulance to connect the ambulance service, etc. [20; 21; 22; 23]. Some other use cases of 5G in healthcare are shown in Figure 2.
# Novel Technologies Used in the Assessment of Patellofemoral Pain: A Scoping Review
###### Abstract  
This scoping review aims to present existing evidence on new technologies reported recently to assess patients with patellofemoral pain (PFP). The literature search was conducted in September 2023, and search engines were Medline (via Pubmed), Scopus, and Cochrane Central. The preferred search term was \"patellofemoral pain\", as the 2016 PFP consensus statement recommended, and several subgroups were arranged to find any possible technology-related assessment. The total number of articles found was 7927. After eliminating duplicates, 2058 articles remained for the title and abstract screening. Methods sections of the articles were investigated for data charting. Among the 652 full-text articles, 8 met our inclusion criteria on gait analysis, 34 on imaging, and 95 on EMG. However, only 5 included innovative technology, 2 used cone-beam CT, 1 used a device in medical imaging to apply stress to the patella in anatomical directions, and 2 used a novel EMG electrode system based on a high-density linear array. The results of this review demonstrate the large use of innovative technologies in PFP, particularly using medical imaging and state-of-the-art gait analysis, sometimes used together for thorough biomechanical studies. Because modern technology can provide precise and detailed information, exploiting these to design more effective prevention campaigns and patient-specific rehabilitation programs is fundamental. Investigations are becoming increasingly translational and multidisciplinary as a fusion of technological and clinical perspectives brings significant insights to PFP.  
patellofemoral pain; technology-based assessment; biomechanics; medical imaging; gait analysis; electromyography; clinical evaluation
## 3 Model Assumption and Toss-and-Catch Algorithm
### Gathering of Information about the Route Ahead and the SBSs Deployed around This Route
#### 3.1.1 Track Path Calculation and Coordinate Conversion
After obtaining the track equation, the relative relationship between the track and nearby SBSs can then be established through the kernel coordinate system, such that SBS categorization and SBS coverage adjustments can be appropriately carried out.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 4 Implementation
### Experimental Settings  
To conduct our experiments, we used a machine equipped with an Intel(R) Core(TM) i7-11800H @ 2.30 GHz CPU and 32 GB of RAM. The simulations were performed using ndnSIM 2.8 [42] on Ubuntu 20.04 LTS. We developed additional modules and modified some of the original code in ndnSIM 2.8.  
First, a dispatch table was introduced to each router. Next, we implemented a forwarding database associated with each network interface. Within this database, priority queues and data flow sub-queues were organized to categorize Data packets based on their priority and name prefixes. These implementations leverage the mapping template in C++ to enhance functionality. The structure of the dispatch table is shown in Figure 6.  
Second, we enhanced the structure of Data packets. Specifically, we added a field to represent priority in each Data packet. This field is a variable of type uint16_t, where a larger value indicates higher priority. We declared and defined read/write operations for this new field. Since ndnSIM adopts Type-Length-Value (TLV) encoding, we declared additional blocks for the new element and implemented the corresponding function operations. To encode the priority field, we used the function prependByteArrayBlock. Additionally, we decoded the priority field within the function Data::wireDecode(const Block& wire).  
In addition, a new flag, CanBeReplace of type bool, was added to each CS entry. This flag indicates that the Data packet cannot be replaced before being forwarded.  
Importantly, we modified the data forwarding process. We defined operations including updating, inserting, querying, and erasing for the dispatch table. Notably, the integration of a dispatch table represents an additional feature within the NDN architecture. We modified the data forwarding mechanisms within NDN to better address the unique characteristics of LEO satellite networks. Currently, the NDN architecture undergoes extensive testing and deployment in terrestrial environments. By incorporating a dispatch table, our method optimizes bandwidth allocation, thereby enhancing performance in LEO satellite networks.
# Audio-Visual Tensor Fusion Network for Piano Player Posture Classification
## 3 Proposed Audio-Visual Tensor Fusion Network
### Data Normalization  
The data of the skeleton extracted from the visual information were normalized in two steps. In the first step, we perform a Procrustes transformation. As the C3 Pap consists of piano playing videos collected from YouTube, the distance between the camera and the player and the direction of the camera differ in each video. If the data are not normalized, rather than training to classify postures according to the actual posture, the model will incorrectly train to classify postures according to the camera's location. To compensate for this, the skeleton coordinates of the entire frame are changed to match one frame. As such, by normalizing one frame to a standard, the camera direction and distance between the player and the camera can be unified to one standard.  
Recall from Section 2 that skeleton extraction may lead to the following problems. The first problem is the distortion caused by the hand covering the piano, and the second problem is low video quality in the videos collected from YouTube. Both lead to issues with the skeleton recognition rate, ultimately causing the incorrect skeleton to be recognized or the skeleton to not be recognized at all, resulting in a null value. We consider this value as an outlier. Thus, in the second step, when the skeleton recognition rate decreases, the interquartile range (IQR) is used to compensate by removing outliers and replacing them with an average of adjacent values [26]. Specifically, we replaced the null values with the average of the frames before and after, in which the null value occurs using IQR. This procedure is repeated while increasing the left index and right index by increments of 1 until they are within the IQR.
## 1 Introduction
The proposed model integrates a tailored loss function to enhance prediction accuracy, facilitating the spatiotemporal prediction of ionospheric scintillation with enhanced precision. In the results section, we first define the geographical scope of our study before presenting the numerical outcomes derived from the predictive model, showcased in a near-real-time format. This includes a display of observed values at key diurnal intervals and a detailed comparative analysis of the predicted versus observed values, emphasizing the accuracy and reliability of each model iteration. The evaluation section further employs rigorous statistical methods to assess the model's efficacy in predicting ionospheric scintillations, thus providing a comprehensive overview of the model's performance under various atmospheric conditions. This holistic approach underscores the model's potential to contribute to our understanding and forecasting of complex ionospheric behaviors.