esa-sceva/satcom-chunk-collection huggingface dataset:
# Dilated Multi-Temporal Modeling for Action Recognition
## 4 Results
### Training and Inference  
#### 4.2.1 Training  
In our experiments, we utilized pre-trained weights from ImageNet as the initial weights for our models. This approach is commonly used in deep learning as a means of transferring knowledge from a pre-trained model to a new task. To ensure that the models were well suited for action recognition, we trained them using both 8 and 16 frames as inputs, allowing us to evaluate the impact of input length on performance. For the FineGym99 and SthV1 datasets, we sampled frames at equal intervals from all the video clips. For the Kinetics400 dataset, following the practice in [29], the frames were sampled from 64 consecutive frames in the video. To augment the data, we applied techniques such as resizing the shorter side of the frames to 256, applying multi-scale cropping, and randomly flipping the frames horizontally. The resulting cropped frames were then resized to 224 for network training. For FineGym99, we trained the models for 50 epochs with an initial learning rate of 0.002, which was decreased by a factor of 10 at the 40 th epoch. For SthV1, we trained the models for 50 epochs with an initial learning rate of 0.01, which was decreased by a factor of 10 at the 20 th and 40 th epochs. For Kinetic400, we trained the models for 100 epochs with an initial learning rate of 0.01, which was decreased by a factor of 10 at the 50 th, 75 th, and 90 th epochs. To prevent overfitting, we used SGD [30] with a momentum of 0.9 and a weight decay of 1\(\times\) 10\({}^{-3}\) for SthV1 (weight decay of 1 \(\times\) 10\({}^{-4}\) for FineGym99 and Kinetics400) during training.
## 4 Simulation Experiments
### Performance Comparison for Varying Pilot Signal Intervals  
In this subsection, we examine the beamforming performance of the proposed and conventional schemes considering varying pilot signal intervals. For both schemes, we consider three experiments based on scenarios with interval values of 10 ms, 50 ms and 200 ms. The UAV is flying along a curve with 80 m radius, and the speed is fixed at 160 km/h. The initial distance between the UAV and the BS is 100 m. The SINR outputs of the LCMV and DL beamformers are shown in Figure 7c,d, respectively.  
It is observed that the proposed scheme maintains higher performance for both beamformers. Furthermore, the stability of the proposed method is convincingly demonstrated here since the three cases show very similar DoA estimation and beamforming performance, for both the LCMV and DL beamformers. Thus, we conclude that an FSI exchange frequency of 5 Hz is sufficient to achieve  
Figure 7: Beamforming performance for varying pilot signal transmission intervals. (**a**) LCMV beamformer. (**b**) DL beamformer.  
high and stable beamforming performance. In this way, the proposed algorithm can save significant training overhead. Accordingly, we compare the training overheads of the two schemes. Based on the previous analysis, we assume that the proposed method receives telemetry messages every 200 ms. Given that the telemetry information already exists on the Command&Control link for UAV-cellular communications, the corresponding overhead is incurred even if the FSI is not applied for DoA estimation; therefore, the traditional method will also require the consumption of a minimum of 5 Hz for telemetry messages. Thus, the proposed method can achieve overhead reductions of 100, 20 and 5 pilot signals per second relative to the conventional scheme when using pilot signal intervals of 10 ms, 50 ms and 200 ms, respectively.  
We also note that the pilot signal transmission interval mainly affects the conventional scheme by magnifying the accumulated error of the improved MUSIC algorithm. In the traditional scheme, the DoA information cannot be updated until a new pilot signal is received and processed. Thus, this scheme suffers from large errors when the DoA estimation interval is 200 ms, and the gain also decreases dramatically.
## 2 Methods
### Data Extraction and Data Synthesis  
Data extraction involved systematically collecting key information from each included study, such as the study design, population characteristics, intervention details, the outcomes measured, and the findings relevant to the research question. A standardized data extraction form was used to ensure consistency across studies. For data synthesis, the findings were organized by outcome categories, allowing for comparison across the studies. Qualitative synthesis was employed to interpret patterns, variations, and gaps within the data, providing a comprehensive overview of the evidence related to the intervention's effectiveness. The quantitative findings, where applicable, were summarized to highlight trends and significant results across the included studies. This approach enabled a structured and coherent synthesis of the data to answer the research question.
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 4 Some Studies of Fitts' Law in HCI Scenes
### VR Scenes  
Although VR devices provide a novel means of engagement, they are not yet able to offer tactile input that sets them apart from touchscreens. On the other hand, VR devices are widely used for a variety of visual experiences in various settings [43]. Even though VR provides immersive digital experiences, problems have surfaced. Variations in the size of objects in virtual space can impact our capacity to target and interact with them more quickly [66]. Furthermore, compared to a hand-tracking system, a head-tracking system's lag has a greater influence on performance prediction in the use of VR devices. Since most VR device interactions are hand-based, even modest delays can significantly reduce performance, especially when aiming at small objects [67].  
Apart from its size, a target's shape is a major factor in estimating our reaction time. To describe the selection of targets with different shapes in VR environments, [PERSON] et al. presented a prediction approach that makes use of probabilistic fitting principles [68]. Expanding upon this methodology, a motion target selection prediction model was created by integrating probability fitting laws and a decision tree [69]. This model aims to improve the precision of target selection predictions, ultimately producing a more lifelike VR experience. Auditory signals influence our performance forecasts in VR environments as well. High-pitched error feedback has been shown in studies by [PERSON] to negatively impact user performance in terms of throughput and time. On the other hand, the research discovered that adaptive audio feedback lowers the mistake rate connected with performing activities at top speed without sacrificing task completion time [70]. These research findings on VR can help with the development and design of various VR systems.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
###### Abstract
The model consists of three components, i.e., an adaptive mask memory capsule network (MemCapsNet) for extracting the textual features associated with the affective entities, an aspect-guided capsule network (AgCapsNet) for capturing the image emotional features guided by the aspect phrases, and a multimodal fusion module for interactively learning the textual and visual capsule features and exploring the cross-modal interaction and intermodal emotional enhancement. Since no more publicly available datasets exist for multimodal emotion analysis, we built a large-scale text-image aspect sentiment dataset, named MTCom, by crawling the website of Meituan to obtain the review data of Chinese restaurants and reintegrating and annotating the obtained data using six aspect labels. The main contributions of this paper are summarized as follows:  
* We present MemCapsNet and AgCapsNet structures, which use an adaptive mask memory attention and aspect-guided attention to extract the aspect emotion features of text and image on the pose matrix of the capsule, respectively. * We design a visual enhancement network to explore the cross-modal interaction between text and image and the emotional enhancement of image to text. * We propose a novel VECapsNet model that uses the capsule features of text and images for aspect-based multimodal sentiment analysis. The empirical results show that our model performs satisfactorily on both the Multi-ZOL dataset and our MTCom dataset. The subsequent sections of the paper are organized as follows: Section 2 summarizes the related work, including the studies on single-modal sentiment analysis, multimodal sentiment analysis, aspect-based sentiment analysis, and multimodal fusion. Section 3 proposes a VECapsNet model for aspect-based multimodal sentiment analysis. In Section 4, we perform the experiments and analyze the experimental results. Finally, Section 5 concludes the whole paper and puts forward future work.
## 5 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Device/** & **Attack** & **Algorithm** & **Accuracy** & **TP** & **FP** & **FN** & **TN** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline \multirow{9}{*}{TCP} & RF & 0.999292 & 3639 & 2 & 2 & 2007 & 0.999451 & 0.999451 & 0.999451 & 0.999451 & 0.99907 \\  & DT & 0.998584 & 3623 & 4 & 4 & 2019 & 0.998897 & 0.998897 & 0.998897 & 0.999863 \\  & kNN & 0.999646 & 3598 & 1 & 1 & 2050 & 0.999722 & 0.999722 & 0.999722 & 0.999781 \\  & XGBOOST & 0.998938 & 3592 & 3 & 3 & 2052 & 0.999166 & 0.999166 & 0.999166 & 0.999396 \\  & SVM & 0.996106 & 3560 & 8 & 14 & 2068 & 0.997758 & 0.996083 & 0.99692 & 0.998166 \\ \cline{1-1} \cline{2-11}  & RF & 0.999872 & 7545 & 2 & 4 & 1999 & 0.999835 & 0.99987 & 0.999603 & 0.99983 \\  & DT & 0.999267 & 7497 & 5 & 2 & 2046 & 0.999334 & 0.999733 & 0.999533 & 0.999701 \\ \cline{1-1}  & kNN & 0.998953 & 7451 & 5 & 5 & 2089 & 0.999329 & 0.999329 & 0.999329 & 0.999833 \\  & XGBOOST & 0.999581 & 7444 & 1 & 3 & 2102 & 0.99866 & 0.999597 & 0.999731 & 0.999515 \\ \cline{1-1}  & SVM & 0.997592 & 7412 & 6 & 9 & 7412 & 0.998867 & 0.996796 & 0.99783 & 0.998597 \\ \cline{1-1} \cline{2-11} \cline{2-11}  & RF & 0.999529 & 6479 & 2 & 2 & 2017 & 0.999691 & 0.999691 & 0.999691 & 0.999993 \\ \cline{1-1}  & DT & 0.999412 & 6461 & 2 & 3 & 2034 & 0.999691 & 0.999536 & 0.999613 & 0.999737 \\ \cline{1-1}  & kNN & 0.999412 & 6437 & 3 & 2 & 2058 & 0.999534 & 0.999689 & 0.999612 & 0.999959 \\ \cline{1-1}  & XGBOOST & 0.999412 & 6421 & 2 & 3 & 2074 & 0.999689 & 0.999533 & 0.999611 & 0.999982 \\ \cline{1-1}  & SVM & 0.998353 & 6405 & 7 & 10 & 6405 & 0.998409 & 0.998409 & 0.998409 & 0.9991113 \\ \hline \hline \multirow{9}{*}{Camcorder/} & RF & 0.999249 & 5978 & 4 & 2 & 2006 & 0.999331 & 0.999666 & 0.999498 & 0.99968 \\ \cline{1-1}  & DT & 0.998999 & 5959 & 4 & 4 & 2023 & 0.999329 & 0.999329 & 0.999329 & 0.999963 \\ \cline{1-1} \cline{2-11}  &  
Figure 3: _Cont_. Figure 2: Comparison of different MLA efficiencies (decision tree—DT, random forest—RF, K-nearest neighbor—KNN, extreme gradient boosting—XGBoost, support vector machine—SVM) for Router/Mirai botnet detection: (**a**) TCP attack; (**b**) UDP attack; (**c**) HTTP GET attack; (**d**) DNS tunneling.
## 3 The Selection Parameters of the MPPT Algorithms
### Implementation Complexity  
Implementation complexity refers to the ease or difficulty of implementing the MPPT algorithm in hardware or software. Simpler algorithms are easier to implement but may be less efficient under certain conditions. More complex algorithms require more advanced coding, hardware, and tuning. The tradeoff involves balancing performance with the development effort, debugging, and system integration.
# Numerical Modeling of Heat and Mass Transfer during Cryopreservation Using Interval Analysis
## 2 Methods
### Example of Computations  
At the stage of numerical computations, the homogenous cylindrical articular cartilage sample of dimensions \(H\) = 1 mm and \(R\) = 3 mm has been considered. In the numerical example, thermal conductivity \(\overline{\lambda}=[\lambda-0.05\,\lambda,\ \lambda+0.05\,\lambda]\) and the volumetric specific heat \(\overline{c}=[c-0.05\,c,\ c+0.05\,c]\) were assumed as interval numbers, where \(\lambda\) = 0.518 \(W\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) and \(c\) = 3.924 \(\times\) 10\({}^{6}\) J\(\cdot\)m\({}^{-3}\cdot\)K\({}^{-1}\). Additionally, the following input data were introduced: initial temperature \(T_{0}\) = 22 \({}^{\circ}\)C, the initial concentration \(C_{0}\) = 0 % (_w/w_), heat transfer coefficient \(\alpha\) = 525 W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\)[28]. To determine the diffusion coefficient (Equation (5)), the following parameters were used: the Boltzmann constant \(k_{\text{B}}\) = 1.38 \(\times\) 10\({}^{-23}\) J\(\cdot\)K\({}^{-1}\), radius of the spherical particle molecule \(r_{\text{s}}\) = 2.541 \(\times\) 10\({}^{-10}\) m and the dynamic viscosity \(\eta\) = 1.996 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s [28,61].  
The experiment using the LT approach was carried as follows: The cylindrical sample of articular cartilage was immersed in a bathing solution whose temperature and concentration were regulated using computer control. The cryoprotectant was delivered to the sample in such a manner that prevented ice crystals forming without causing toxicity. This is the sample temperature being close to the liquidus temperature during the process (the melting point of the sample was changed by the cryoprotectant's impact) [21,22].
## 7 New Challenge: Multimodality
### V2V and V2I Communication  
V2V and V2I systems use wireless communication (e.g., Dedicated Short-Range Communications or 5G) to share real-time data (speed, position, or road hazards) between vehicles [83] and infrastructure (e.g., traffic lights) [84]. Safety issues here include the following:  
* Cybersecurity Risks: These communication channels are vulnerable to hacking. A compromised system could broadcast false information, such as a nonexistent obstacle, causing vehicles to brake suddenly or swerve dangerously.
* Data Overload: With potentially hundreds of vehicles and devices communicating simultaneously, an AV's AI might struggle to filter relevant signals from noise, leading to delayed or incorrect responses.
* Interoperability: Different manufacturers may use incompatible protocols, reducing the effectiveness of V2V/V2I networks and creating blind spots in situational awareness.
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 4 Numerical Analysis for an Optical Transmission System
### Multi-Span Fiber System Setup
#### 4.2.1 AIR Results  
In Figure 9, two sets of results are shown for AIRs versus launch power over two multi-span transmission link, respectively. The first set (see Figure 9a) show the AIR for SE of 14 bits/sym, i.e., 128 QAM and 128-GS, over 4 \(\times\) 80 km SSMF and 6 \(\times\) 80 km SSMF. Around the optimum launch power regime where linear and nonlinear propagation effects are comparable, 128-GS modulation gives 0.24 bit/sym and 0.5 bit/sym AIR gains with respect to 64 QAM for over 4 \(\times\) 80 km SSMF and 6 \(\times\) 80 km SSMF, respectively  
The second set (see Figure 9b) show the AIRs for SE of 12 bits/sym, i.e., 64 QAM and 64-GS, over \(10\times 80\) km and SSMF \(15\times 80\) km SSMF. At the optimum launch power, 64-GS modulation gives 0.17 bit/sym and 0.19 bit/sym AIR gains with respect to 256 QAM for over \(10\times 80\) km SSMF and \(15\times 80\) km SSMF, respectively Note that for higher launch power regime where nonlinearity is dominant, performance improvements are still remaining.
# Discrete Lattice Element Model for Fracture Propagation with Improved Elastic Response
## 3 Numerical Simulations
### Uniaxial Compression Test  
In the uniaxial compression test, the specimens possess identical mechanical and geometrical properties, along with the same meshes from the uniaxial tension test. This test shows the discrete lattice model's capability to accurately simulate local deformations in the elastic regime and post-peak softening behavior, but under uniaxial compressive loading conditions. Simulations in the compression test are conducted using imposed displacement control, as illustrated in Figure 14. Figure 15 displays the displacement plot at an imposed displacement level of 0.004 cm, where the specimen remains intact and in the linear elastic regime.  
Figure 14: Uniaxial compression test meshes: (**a**) Mesh1-1210; (**b**) Mesh2-2570.  
Figure 13: Macro-crack at the end of tension test for: (**a**) Mesh1-1210; (**b**) Mesh2-2570.  
The plots in Figure 15 reveal that the specimen undergoes lateral expansion, and the ratio of horizontal to vertical displacements aligns with the desired Poisson ratio of 0.2. This accurate deformation in the lateral direction is achieved through the application of the fictitious-force approach, as discussed previously. Both meshes respond effectively to the introduced fictitious forces in the internal force vector, ensuring the proper linear elastic response.  
However, the initiation of the first fractures causes the specimen to transition into a discontinuous state until total failure. The macroscopic responses in terms of the reaction-displacement curve in Figure 16 illustrate the material softening during the failure process. Slight differences in the amount of total dissipated energy and softening are observed between the two meshes due to a slightly varied failure mechanism arising from internal heterogeneity and the randomness of the lattice network.  
Figure 16: Comparison of results for compression test for two different meshes.  
Figure 15: Displacement of Voronoi nodes for compression test for imposed displacement 0.004 cm for \(v=0.2\): (**a**) displacement in direction x-Mesh1; (**b**) displacement in direction y-Mesh1; (**c**) displacement in direction x-Mesh2; (**d**) displacement in direction y-Mesh2.  
The final failure patterns illustrated in Figure 17 reveal a typical uniaxial compression test failure mechanism, where the dominant macro-crack takes a diagonal form due to shear sliding. The diagonal form of the dominant crack in the uniaxial compression test is typical for quasi-brittle materials such as concrete, as shown in the experiment provided in [45].  
Similar failure patterns are observed with both meshes, although the length and angle of the macro-cracks exhibit slight differences due to internal randomness. Despite these variations, the proposed discrete lattice model effectively captures the essential aspects of the uniaxial compression test while being robust in simulating complex fracture behaviors under applied loading conditions.
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 4 Processing of UAV Fault Data
### Feature Processing  
The drone's log contains many features, but some are not universal, such as changes in the longitude and latitude over a certain period because during the training process it is impossible to simply determine that a UAV has malfunctioned based on its arrival at a particular latitude and longitude. Moreover, the time-dependent feature should be removed, as it only serves to mark the sample location. The feature processing method is shown in Algorithm 2.  
```
Input: Single CSV file with original features Output: CSV file with processed features for Feature\(i\) in CSV: ifFeature\(i\)Related to time \(||\)\(Feature_{i}\)Not universal:  Delete (Feature\(i\)); end if; end for; Merge the remaining Feature into one CSV; end;
```  
**Algorithm 2** Feature Processing  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Labels** & **Fault Type** & **Window Type** \\ \hline
0 & Normal & Fault0 \\
1 & GPS failure & Fault1 \\
2 & Accelerometer failure & Fault2 \\
3 & Engine failure & Fault3 \\
4 & Remote-Control System failure & Fault4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fault types represented by class labels.
## 4 Further Validation
### Comparison with Traditional Estimation Methods  
A comparison of the proposed method with some traditional methods, similar to that done in [32], is presented in Figures 6 and 7, in terms of the total enthalpy loss coefficient \(\xi_{tot}\) as function of blade aspect ratio \(h/C\) for typical nozzle and rotor cascades, respectively. The flow angles \(\lambda_{1}\) and \(\lambda_{2}\) are related to the tangential direction. The cascade solidity is considered to be the same for all methods, as given by the Zweifel criterion. Uncompressible flow conditions are assumed, chord Reynolds number is supposed to be 400,000 and trailing edge blockage is 10% in all cases. The following  
Figure 4: Comparison of simulated and experimental total pressure profile loss coefficient for (**a**) HS1C cascade [24]; (**b**) HS1D cascade [24].  
Figure 5: Comparison of simulated and experimental total pressure profile loss coefficient for (**a**) E1S50 cascade [25]; (**b**) E2S50 cascade [25].  
methods are used as references: [PERSON] [6], [PERSON] & Mathieson modified by [PERSON] [8], [PERSON] [10] and [PERSON] [11]. Computational implementation for reference and proposed loss calculation methods were implemented by the author in Matlab 2011b. Traditional estimation models implementation were checked against published results [33; 34].  
It can be observed that the present work seems to have improved the results of the [PERSON] [11] method towards the trusted methods of [PERSON] modified by [PERSON] and [PERSON] [8] for the nozzle blades, and [PERSON] [10] for rotor blades. The loss sensitivity to cascade solidity is also improved as it is shown to be matched to the level reported in [35], about 10% increase in loss under \(\pm\)30% deviation from optimum solidity. The proposed loss model is further validated from experimental results in turbine stages. Complete information on the loss model is presented in [36].
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 3 Geo-DMP Mobile System
### The Workflow of Geo-DMP
#### 2. Upon Receipt of Extrinsic Query  
When a received bundle is recognized as a query bundle from another node (i.e., its source EID is different from the local one), DTN Daemon will attempt to satisfy it after putting it into the Bundle List. Firstly, DTN Daemon gives the content name specified in the query bundle to the BPQ module. Secondly, the BPQ module matches this name with items in BPQ Cache to see if the desired content is locally cached. Thirdly, if found, the content body is replicated and assembled with information for the bundle header as a new response bundle, then it is pushed to the BPQ Cache and Bundle List. Whether this response bundle is forwarded to the current neighbor is subject to the decision of the routing protocol. Since the query bundle has accomplished its task of finding the content, it will be removed from the Bundle List. Figure 6 depicts this process where the green arrows represent the steps after the successful matching.  
Figure 5: The handling of newly generated query.  
3 Upon Receipt of Extrinsic Response  
After the identification, if the received bundle is a response bundle from another node, it will be stored in the Bundle List and BPQ Cache at first. Subsequently, the BPQ module will check whether this new content can be used to satisfy local awaiting queries. Successful matches will generate corresponding new response bundles. Again, these response bundles are pushed to the BPQ Cache and Bundle List while the satisfied query bundles are removed. The routing protocol then selectively forwards some of these response bundle to the neighbor in contact. This process can be seen in Figure 7.  
Figure 6: The handling of an extrinsic query.  
Figure 7: The handling of extrinsic response.