esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition
## 2 Related Work
In these kind of systems, data is processed in a sequential way, such that at each time t, it gets input from the previous hidden state \(s_{t-1}\) and obtains new data \(x_{t}\). Most of the state-of-the-art methods [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2019); [PERSON] et al. (2020); [PERSON] et al. (2021) have proposed their own recurrent networks by leveraging CNNs and RNNs for action recognition, and have achieved impressive performance. However, due to the large number of calculations of parameters, and negligence of effect of initial input after few layers, vanishing gradient problems occurred. The solution to this problem is LSTM [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2020), which has the ability to capture long-term dependencies and preserve sequence information over time by integrating memory units. LSTM was first introduced by [PERSON] et al. (2018); it has been successfully adapted to many sequential modelling tasks such as speech recognition, visual description and machine translation, and has achieved encouraging performance. In most of these networks, the inputs to the LSTM are the high level features captured from a fully-connected layer of CNN. LSTM units use multiplicative gates to control access to the error signal propagating through the networks. In this paper, we propose a 3 Dconv-based iterative training method to generate the motion map, enabling the use of existing CNN models directly on video data with fine-tuning. Our model efficiently integrates the temporal information of the motion map and video frames and generates the arbitrary length of the motion map. The Combination of CNN-RNN provides effective representation for long-term motion and modeling of the sequential data, each of which has a time relationship with adjacent points. (RNN uses the extracted C3D features as inputs and models more robust, longer-range features.) The C3D network is able to encode local temporal features within each video unit; it cannot model across the multiple units of a video sequence. We thus introduce LSTM to capture global sequence dependencies of the input video and cues on motion information. The fused spatio-temporal features are processed by LSTM, which helps recognizing complex frame-to-frame hidden sequential patterns. After conducting extensive experiments, we observed that our method is very effective for videos of various lengths, and shows significant improvement in action recognition.
## 2 Literature Review
### Quantum Key Distribution BB84 Protocol
This robustness arises from any effort to intercept or measure the qubits, inevitably altering their polarization, thus alerting the receiver. Consequently, any eavesdropping attempt is promptly detected, preventing the establishment of the shared secret key. Nevertheless, the BB84 protocol is not without limitations. A primary drawback is its reliance on a costly and challenging-to-implement quantum channel. Also, it requires a classical channel for error correction and communication which is susceptible to potential attacks [33]. To avoid additional overheads like key reconciliation and privacy amplification in the classical channel, there are some authenticated encryption schemes which can be used in the classical channel using the PQC algorithms introduced by NIST [34].
## 4 Experiments and Results  
In general, since voxels located on the side of the tomographic area do not travel by any ray, the water vapor density of these voxels can be estimated based on only constraint equations, which reduces the accuracy of the tomography solutions. The primary contribution of the side signals is to ensure that voxels not passed in the traditional model can be crossed, which allows the accuracy and stability of tomographic results to be improved. To test the HFM and evaluate the efficiency of side signals, four special experimental schemes were performed separately. Section 4.1 describes the implementation of the four experimental schemes; the contribution of the side signals to the tomography solutions is analyzed in Section 4.2. On the other hand, the existing improved model combining the side observations was reestablished using the GNSS data [14], and is compared to the proposed HFM in Section 4.3.  
It should be noted that when assessing the accuracy of tomography solutions, the vertical profiles obtained from radiosonde were used as reference values. Although the radiosonde data has been made use of to calculate the isotropic height factor, its main contribution is to provide the vertical distribution characteristics of atmospheric water vapor, not to participate in the tomographic processing. For instance, the tomographic time was set to August 2017, according to the HFM, and the optimal tomography top boundary was set to 11 km. Additionally, the height factor model \(\lambda_{iso}(h)=1.121\cdot e^{-0.051h}-0.191\cdot e^{-1.019h}\) was applied to calculate the isotropic height factor. Consequently, only GNSS observations went sent to the tomography system as input information for reconstructing the three-dimensional water vapor field, which shows that the proposed HFM does not interfere with the accuracy assessment of tomography results.
## 2 Related Work
### Physically-Based Foam Modeling Approaches  
Several studies have been published on the expression of foam effects in the field of physics-based simulation. [PERSON] et al. analyzed the motion of the underlying fluid based on curvature and expressed splash and foam effects by applying the state change rules to the area with large motion [28]. However, their method is not sufficient to express various foam effects because it generates motion similar to grains of sand rather than foam. [PERSON] et al. proposed a multi-layer method to efficiently process the foam generation process and expressed foam, splash, bubble, and mist individually in each layer [29]. However, in this method, the quality of the result is low because the foam particles are clumped, and it is not sufficient to realistically express the foam effects because its focus is only on generating and rendering the foam without the advection process.  
When using the Eulerian approach, secondary effects such as splash are expressed usually using particle level-set [30] or marker-particle [31] techniques. [PERSON] et al. introduced a way to express splashes and bubbles without discarding marker-particles away from water surfaces [32]. Since the foam particles generated through this method have a ballistic motion, it was difficult to express detailed foam effects. [PERSON] et al. expressed splash by improving this method through particle level-set technique, and applied SPH technique to splash particles to improve ballistic-like motion [33]. This technique expresses diffuse phenomena that are intermediate between air and splash particles, which are difficult to express in existing particle approaches. However, this method focuses only on splash, and foam effects are expressed using texture.  
[PERSON] et al. solved the dissolved gas problem based on SPH and realistically expressed carbonated drinks, which were difficult to express in the past [34]. [PERSON] et al. proposed a unified framework that can express splash, foam, and bubble using only SPH, a meshless framework [25]. However, since the quality of the results depends a lot on the number of water particles, a huge number of SPH particles are required to produce high quality results, and the calculation time increases accordingly. Additionally, since the SPH kernel is isotropic, the foam particles are clumped. [PERSON] et al. [35] improved the method of [PERSON] et al. [25] efficiently by proposing a hybrid technique that mixed SPH and Lattice-Boltzmann techniques. However, splash effects were mainly expressed, and foam or bubble effects were insignificant.
## 4 Security Proofs
### Secure Mutual Authentication
If \(e_{1}\) is non-negligible, \(\mathcal{S}_{MA}\) has non-negligible advantage \(Adv^{IND-CCA}_{\mathcal{S}_{MA}}(k)\) to output a guess bit \(b^{\prime}\) such that \(b^{\prime}=b\) where \(Adv^{IND-CCA}_{\mathcal{S}_{MA}}(k)=e^{\prime}_{1}-\frac{1}{2}\) and  
\[ \begin{array}{ll}e^{\prime}_{1}&=\frac{e_{1}+(1-e_{1})\frac{1}{2e_{1}}}{q_{ 2}q_{3}}+\frac{(1-e_{1})\frac{1}{2e_{1}}-1}{2q_{2}q_{3}}+\frac{(q_{2}q_{3}-1)} {2q_{2}q_{3}}\\ &>\frac{2e_{1}}{q_{2}q_{3}}+\frac{(1-e_{1})\frac{1}{2e_{1}}+(1-e_{1})\frac{2e_ {1}}{2e_{1}}}{2q_{2}q_{3}}+\frac{(q_{2}q_{3}-1)}{2q_{2}q_{3}}\\ &=\frac{e_{1}}{2q_{2}q_{3}}+\frac{1}{2}.\end{array} \]  
In Case 2, \(E\) has to send a valid 4-tuple \((\alpha,\theta_{1},\hat{T},\theta_{2})\) to \(\Pi^{v}_{V_{j},MS_{j}}\) first and then respond a valid string \(\hat{r}_{5}\) after receiving \((\hat{r}_{1},\hat{r}_{4})\) from \(\[PERSON]^{v}_{V_{j},MS_{j}}\). Let \(S_{T}\) be the set of \(T\)'s obtained by \(\[PERSON]^{u}_{MS_{i},V_{j}}\). The followings are two sub-cases when \(E\) successfully impersonates \(\[PERSON]^{u}_{MS_{i},V_{j}}\). 1. \(\hat{T}\
otin S_{T}\): Assume that \(\hat{T}=(F_{1}(\hat{m}),\hat{\delta},\hat{\sigma},\hat{\gamma},\hat{s})\). Thus, \(\mathcal{S}_{MA}\) can obtain \(\hat{m}=\hat{r}_{5}\oplus F_{3}(r_{2}||r_{4})\) and forge a ticket \((\hat{m},\hat{\delta},\hat{\sigma},\hat{\gamma},\hat{s})\). However, we have proved the security of ticket unforgeability in Section 4.3. Hence, the probability of that \(E\) is successful in this sub-case is negligible. 2. \(\hat{T}\in S_{T}\): In the sub-case, \(E\) successfully swindles \(\hat{T}\) which is owned by \(\Pi^{u}_{MS_{i},V_{j}}\). We have proved the security of ticket swindling resistance in Section 4.5. Consequently, the probability of that \(E\) is successful in this sub-case is also negligible. Therefore, the probability \(\epsilon_{2}\) is negligible. We conclude that _No Matching\({}^{E}(k)\)_ is negligible because \(\epsilon_{1}\) and \(\epsilon_{2}\) are both negligible.
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 4 Performance Results
### Outdoor Experiments
#### 4.1.1 Outdoor Passive Detection of a Low-Altitude Signal Source
The reference distance for distance estimation is 500 m. These results show that the difference between the estimated and actual distances increases with distance. Table 2 shows the corresponding parameter estimation errors for the proposed, HOC, and CFAR methods. These results indicate that the proposed method provides better performance, particularly at long distances. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Distance between Rx and \(c\) (m)**} & \multicolumn{4}{c}{**UAV Present**} & \multicolumn{4}{c}{**UAV Not Present**} \\ \cline{2-9}  & \begin{tabular}{c} **Max** \\ \(R_{jk}\) \\ \end{tabular} & \begin{tabular}{c} \(f_{start}\) \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} \(f_{range}\) \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} \(\dot{p}\) (\({}^{\circ}\)) \\ \end{tabular} & \begin{tabular}{c} \(d\) (m) \\ \end{tabular} & \
\begin{tabular}{c} **SNR** \\ \end{tabular} &
\begin{tabular}{c} **Max \(R_{jk}\)** \\ \end{tabular} \\ \hline
500 & 2.907 & 2.401776 & 0.00954 & 90.22 & 512.56 & 0.6634 & 1.381 \\
1000 & 2.87 & 2.401746 & 0.00953 & 94.58 & 986.27 & 0.4901 & 1.469 \\
1500 & 2.84 & 2.401764 & 0.00955 & 96.09 & 1520.38 & 0.2940 & 1.149 \\
2000 & 2.708 & 2.401782 & 0.00951 & 97.83 & 1972.53 & 0.2216 & 1.32 \\
2500 & 2.352 & 2.471985 & 0.00953 & 36.43 & 2531.74 & 0.1649 & 1.647 \\
3000 & 2.295 & 2.471981 & 0.00954 & 38.35 & 3040.13 & 0.1083 & 1.285 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Estimated parameters with the proposed method in an outdoor environment. UAV: unmanned aerial vehicle; \(R_{jk}\): improved cross-correlation matrix; \(f_{start}\): the start of the UAV signal; \(f_{range}\): the range of the UAV signal; \(\dot{p}\): estimated direction of arrival; \(d\): the distance between the source and receiver; SNR: signal-to-noise ratio. Figure 12: The results for the constant false alarm rate (CFAR) method outdoors at distances of (**a**) 500 m, (**b**) 1000 m, (**c**) 1500 m, (**d**) 2000 m, (**e**) 2500 m, and (**f**) 3000 m.
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 2 Materials and Methods
### Method and Model  
#### 2.2.1 Swarm-C Satellite Surface Model  
The coordinate system of each normal panel vector is the star-fixed coordinate system of the SWARM-C satellite. The star-fixed coordinate system is centered on the satellite's center of mass, and the three coordinate axes, X, Y, and Z, form a right-handed coordinate system. In this paper's calculation, the illumination radiation pressure modeling needs to use the attitude data of the space-borne camera to realize the conversion between the star-fixed coordinate system and the inertial coordinate system.  
In Swarm's manual, ESA provides each panel's size and optical characteristic parameters. Therefore, compared with GRACE, Swarm's panel composition is more complex. For example, the Swarm-C satellite contains 15 panels in total. Figure 5a shows the simplified shape diagram of the Swarm-C satellite. Figure 5b shows the Swarm-C satellite's side panel and bottom panel schematic diagram.  
Figure 4: Comparison between accelerometer data correction results and ESA (**a**) during magnetic storm day (Kp = 8, magnetic storm level: G4); (**b**) during nonmagnetic storm day.  
Figure 5: Simplified satellite parameters. (**a**) Simplified shape model of Swarm-C satellite; (**b**) schematic diagram of the side panel and bottom panel of Swarm-C satellite.  
Taking the motion direction as the \(x\)-axis, the normal vector of each panel of the Swarm-C satellite can be calculated. Table 1 shows the normal vectors of each panel of the Swarm-C satellite.  
Because there are many panels of the Swarm-C satellite, and some panels are not perpendicular to the coordinate axis, it will be much more challenging to model the surface of the Swarm-C satellite and judge the shadow area.
# Sentiment Analysis of Twitter Data
## 6 The Approaches for Twitter Sentiment Analysis
### Machine Learning-Based Approach
#### 6.1.4 Decision Tree Classifier  
This is a non-parametric approach of supervised learning, in which the feature space is continually partitioned into sub-feature spaces for classification and regression. The goal of this approach is to use decision rules to forecast the class label of the feature.  
The supervised learning-based method is efficient for sentiment analysis; however, it is difficult to manually prepare labeled data for the classification system. An unsupervised learning-based approach has been developed to solve the problem, which identifies the degree of polarity by subjective indicators generated from the sentiment lexicon [9].
# Research on the Accumulated Plastic Strain of Expansive Soil under Subway Loading
## 3 Analysis of Test Results
### Analysis of Accumulated Plastic Strain Behavior of Expansive Soil under Intermittent Loading  
#### 3.2.1 Analysis of the Influence of Intermittent Loading Ratio on the Accumulated Plastic Strain  
Figure 6 shows the development curve of accumulated plastic strain with the number of cycles under different intermittent loading ratios. Under other conditions, including the same cyclic stress ratio and static deviatoric stress, the evolution of accumulated plastic strain under intermittent loading conditions follows a similar pattern. Initially, during the early stage of cyclic loading, the accumulated plastic strain increases rapidly with the number of vibration cycles. After a certain number of loading cycles, the rate of development curve of the accumulated plastic strain begins to slow down, and the slope of the curve decreases as the number of vibration cycles increases. Eventually, the cumulative plastic strain approaches a stable value.  
However, it can be seen from Figure 6a,b that under the same number of cycles, the accumulated plastic strain decreases with the increase in the intermittent loading ratio. This is mainly because, for saturated soil samples, although the dissipation of pore pressure can promote strain growth, the amount of pore pressure dissipation does not continuously increase. Instead, it gradually diminishes over time. With the increase in the intermittent  
Figure 6: Development curve of accumulated plastic strain with the number of cycles under different stress ratios of intermittent loading. (Sample numbers are S9, S12, S15, S18, S21, S24, S27, S30). (a) \(\eta_{d}=0.4~{}\sigma_{s}=\)15 kPa; (b) \(\eta_{d}=0.4~{}\sigma_{s}=\) 30 kPa.  
duration, the growth of accumulated plastic strain no longer depends on the strain growth transformed by the dissipation of pore pressure. At this time, because the recovery of strain when the loading is stopped accounts for the main part, the accumulated plastic strain will ultimately decrease with the increase in the intermittent loading ratio.
## 3 Proposed Model
### U-Net Architecture  
The U-Net architecture is a fully convolutional network (FCN) originally developed for biomedical image segmentation. Proposed by [PERSON] et al. [24], U-Net is an end-to-end learning model designed to perform pixel-wise segmentation with high precision. The model extends the concepts introduced by [PERSON] and [PERSON] [25], incorporating contemporary classification networks such as AlexNet, VGGNet, and GoogLeNet into a symmetric encoder-decoder structure.  
\begin{table}
\begin{tabular}{c c} \hline
**Group** & **Data Labeled by Damage Type** \\ \hline
**GT Sample Data** & **Data Labeled by Damage Type** \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._Table 5 presents the experimental environment, including the software and hardware configurations used in this study. Based on this environment, the U-Net-based deep learning model was trained and evaluated for damage segmentation and analysis.  
As shown in Figure 2, the network is named \"U-Net\" due to its U-shaped architecture. The model consists of a contracting path (encoder) that captures contextual information and a symmetric expanding path (decoder) that enables precise localization. This structure allows U-Net to learn detailed spatial features, making it suitable for fine-grained segmentation tasks in complex environments such as damaged bridge surfaces.  
The U-Net model follows a two-part structure comprising an encoder that captures essential features and a decoder that reconstructs spatial details. At the beginning, convolutional layers extract key representations from the input image. Downsampling, which is achieved through pooling, reduces data dimensionality while preserving crucial characteristics. The decoder then performs upsampling operations to progressively regain the original resolution, allowing for precise localization. Through this process, the target is predicted.  
As illustrated in Figure 2, U-Net derives its name from its U-shaped design. The encoder path captures semantic features via convolution and pooling operations, while the decoder restores the spatial resolution through upsampling layers. This architecture allows the model to capture both context and detail, making it suitable for precise segmentation tasks on complex surfaces such as damaged bridge structures.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Computational Environment** & **Details** \\ \hline CPU & Intel(R) Core(TM) i9-9900 KS CPU @ 4.00 GHz \\ GPU & NVIDIA GeForce RTX2080 Ti \(\times\) 2 \\ Memory & 128 GB \\ Programming language & Python 3.11 \\ Software for compilation & PyCharm 2021.3 \\ Deep learning framework & TensorFlow 2.7.0 \\ CUDA & CUDA 11.5 \\ Batch size & 50 \\ Learning rate & 0.0001 \\ Epoch & 55 \\ Input size & 320 \(\times\) 320 pixel \\ Loss function & CrossEntropy, Focal, mIoU \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computational environment.  
Figure 2: U-Net architecture consisting of a contracting encoder path and an expansive decoder path.
# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 2 UAV System Model
### UAVs Classification
\begin{table}
\begin{tabular}{l c l} \hline
**Sensor/Camera** & **Utility** & **Ref.** \\ \hline \hline \multirow{8}{*}{RGB Camera} & \(\bullet\) & Visual analysis \\  & \(\bullet\) & Mapping \\  & \(\bullet\) & Landing \\  & \(\bullet\) & Object classification \\  & \(\bullet\) & Vehicle detection \\  & \(\bullet\) & Tracking animals and people \\ \hline \multirow{8}{*}{UAV LiDAR} & \(\bullet\) & Vegetation canopy analysis \\  & \(\bullet\) & Estimation of forest carbon \\  & \(\bullet\) & Absorption \\  & \(\bullet\) & Mapping cultural heritage \\  & \(\bullet\) & Building information \\ \hline \multirow{8}{*}{Hyperspectral sensors} & \(\bullet\) & Land cover/land use mapping \\  & \(\bullet\) & Vegetation indices estimation \\  & \(\bullet\) & Biophysical, physiological, or biochemical parameter estimation \\ \cline{1-1}  & \(\bullet\) & Agriculture and vegetation disease \\ \cline{1-1}  & \(\bullet\) & Detection, including disaster damage \\ \cline{1-1}  & \(\bullet\) & Assessment \\ \hline \multirow{8}{*}{Lightweight cameras} & \(\bullet\) & Visual analysis, vegetation detection and analysis, crop monitoring \\  & \(\bullet\) & Mining \\ \cline{1-1}  & \(\bullet\) & Soil moisture estimation, fires \\ \cline{1-1}  & \(\bullet\) & Detection, water level measurement \\ \cline{1-1}  & \(\bullet\) & Land cover/land use mapping \\ \hline \multirow{8}{*}{Lightweight thermal infra-red sensors} & \(\bullet\) & Tracking creatures \\ \cline{1-1}  & \(\bullet\) & Volcanoes \\ \cline{1-1}  & \(\bullet\) & Detection, including forest fires \\ \cline{1-1}  & \(\bullet\) & Hydrothermal studies, urban heat monitoring \\ \cline{1-1}  & \(\bullet\) & Islands and hazardous area tracking \\ \hline \end{tabular}
\end{table} 
Table 1: Examples of UAV onboard sensors and cameras.