esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Statistical Methodology
### General Guidelines for the Choice of \(\xi\) and \(\tau\)  
The selection of the representative variables directly depends on the parameters \(\xi\) and \(\tau\). As explained in Section 2.3, a CORE-cluster \(S_{\xi,\tau}\) is indeed a connected variable subset with a size higher than \(\tau\) and a coherence \(\mathbf{c}(S_{\xi,\tau})\) higher than a threshold \(\xi\). In order to estimate pertinent representative variables, we recommend to use the following guidelines: (1) First compute how the edge weights \(w_{i,j}\) of the graph \(G(N,E)\) are distributed. The coherence \(\mathbf{c}(S_{\xi,\tau})\) represents the weakest connection between the variables of \(S_{\xi,\tau}\), so the threshold \(\xi\) should be relatively high regarding the different values of \(w_{i,j}\). A value of \(\xi\) equal to the 80 th percentile of the edge weights \(w_{i,j}\) appears to be reasonable. (2) Choosing a suitable minimal amount of variables \(\tau\) in \(S_{\xi,\tau}\) is more subtle, as this choice both depends on the complexity of the relations expressed in \(G(N,E)\), and how the number of observations \(n\) is low compared with the observations dimension \(p\). In all generality, tuning \(\tau\) as equal to \(p/10\) is reasonable as a first guess. (3) If no CORE-clusters are found with the initial parameters, the user may try to run again the CORE-clustering procedure with lower parameters \(\xi\) and \(\tau\).  
From our experience, we recommend in all cases not using values of \(\xi\) lower than the 40 th percentile of the edge weights or values of \(\tau\) lower than 10. The CORE-clusters would be likely to contain variables with strongly heterogeneous behaviors or false positive connections in these cases. Note finally that when several connected CORE-clusters are identified with a given parametrization, it is interesting to test whether stronger CORE-clusters would be locally found by using higher values of \(\xi\) or \(\tau\). This is illustrated in Section 4.3.
# Multi-Modal Sentiment Analysis Based on Interactive Attention Mechanism
## 5 Analysis of Experimental Results  
### Results Analysis of Baseline  
The experiment will evaluate the LG-BERT model on the CMU-MOSI dataset; the experimental results are shown in Table 1.  
First, in the processing of multi-modality data based on LSTM, TFN uses the form of a tensor cross-product to fuse feature vectors of different modes. Since it is in the form of a cross-product of feature vectors, the amount of feature data resulting from fusion is N3. LMF degrades the tensor matrix on the basis of the TFN model. Both models use the traditional LSTM model, and the innovation lies in the way of feature fusion. As a result, the experimental effect is not good, and its accuracy is 77.1% and 76.4%, respectively. Based on LSTM, GME-LSTM introduces reinforcement learning to update the gated information. Since the decision classification problem, which reinforcement learning is good at, is not introduced, its accuracy is 76.5%. MFM optimizes the joint generation identification model in cross-modal data. Generative factors are shared across channels and contain joint multi-modal characteristics, and discriminant factors contain information about generated data. RMFN divides the fused features into different stages, focusing only on a small number of features in each stage. MCTN is a learning method of joint feature presentation between different modes. These models all bring forward the innovation of feature fusion, but they do not improve the degree of feature extraction.  
MulT uses a bidirectional cross-modal attention mechanism that enables learning at each time step. By improving the training model, the accuracy is improved. When using BERT's model, the results of CM-BERT+, the mask attention mechanism model and BERT-TA+, the two-mode tensor fusion model based on self-attention, reached 82.65% and 83.38%, respectively, which was 3-5% higher than that of LSTM. This shows that the BERT model is powerful for feature extraction. Experiments have found that our model performs better than traditional models. The reason may be that the embedded dimension of audio data is very small, and it is easily affected by noise when passing through the attention module.  
Finally, by optimizing the BERT model, the accuracy of the LG-Bert model proposed is improved by 0.44% compared with the BERT model, which provides an idea for further research on the optimization of the BERT model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Modality** & **ACC/\%** & **F1** \\ \hline TFN [5] & T + A + V & 77.1 & \\ LMF [6] & T + A + V & 76.4 & 75.7 \\ GME-LSTM [3] & T + A + V & 76.5 & \\ MFM [20] & T + A + V & 78.1 & 78.1 \\ RMFN [21] & T + A + V & 78.4 & 78.0 \\ MCTN [22] & T + A + V & 79.3 & 79.1 \\ MulT [23] & T + A + V & 83.0 & 82.8 \\ CM-BERT+ & T + A & 82.65 & 82.64 \\ BERT-TA+ & T + A & 83.38 & 83.45 \\
**LG-BERT (ours)** & **T + A** & **83.82** & **83.91** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental results of different models on the CMU-MOSI dataset, “+” represents the code duplicated by ourselves.
# Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games
## 1 Introduction
### Audio in Games  
Modern video games feature increasingly elaborate soundscapes, making it intriguing to investigate how various components of these soundscapes affect different aspects of the game experience.  
Audio backgrounds in most video games can be split into three different types of sounds: background music, which sets the atmosphere of the game scene; ambient sounds, which are all sounds displaying the surroundings in the game (such as wind or birds); and character sounds, which are all sounds evoked by the action of a character (such as steps or attacks). The influence of background music is relatively well explored, owing to its long-standing research interest in the film industry [14]. In games, background music has been found to lead to higher immersion [15; 16], enhanced emotions and enjoyment [17], and increased experiences of flow [18]. The thematic overlap between music and games is also important for the experience of atmosphere and immersion [19; 20].  
However, the distinct impacts of character and ambient sounds, separately, have not gained much attention yet. While sound effects in general have received some recognition, it remains unclear which specific sound effects have been evaluated. In general, sound effects have been linked to higher immersion and fun [21; 22], but there has been no differentiation between which type of sound influences which aspect of the game experience. To our knowledge, the first study, which investigated the different influences of ambient and character sounds, found a significant increase in immersion through ambient sounds and higher avatar identification with character sounds in a puzzle-platformer game [23]. Therefore, examining these differential effects of different sound types on players' game experiences in other game genres to assess their generalizability is an important next step.
# A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO
## 5 Conclusions  
The results shown here are quite encouraging. However, the conclusions in this study are based on only one case study. More experiments with additional cases for a variety of tropical weather systems are needed to demonstrate a thorough evaluation of the consistent impacts of the CYGNSS observations to NWP forecasts. New versions of the CYGNSS data (v3.0 and Climate Data Record--CDR v1.0) are expected to be available soon; v3.0 includes a near-real-time corrections for variations in the transmit power of the GPS satellites, and is expected to improve the temporal and spatial sampling properties, as well as the accuracy. CDR data include track-wise debiasing that is expected to improve wind speed calibration. In both new data versions, a reduction is expected in the uncertainty of the wind speed products for both high and low wind speed [40]. Therefore, future research should be conducted with WRF simulations and data assimilation experiments for new versions of CYGNSS data to further explore the application of the latest products. In addition, the frequent revisit time of CYGNSS is one of the great advantages of this instrument. Hence, it would be of great interest to conduct continuous assimilation of CYGNSS data and to investigate the benefit it may bring to midrange to subseasonal forecasts of tropical convection and MJO events.  
Conceptualization, X.L., J.R.M., and T.J.L.; Methodology, X.L., J.R.M., and T.J.L.; Software, X.L. and T.J.L.; Validation, X.L., J.R.M., and T.J.L.; Formal analysis, X.L., J.R.M., and T.J.L.; Investigation, X.L., J.R.M., and T.J.L.; Resources, X.L., J.R.M., and T.J.L.; Data curation, X.L. and T.J.L.; Writing--original draft preparation, X.L., J.R.M., and T.J.L.; Writing--review and editing, X.L., J.R.M., and T.J.L.; Visualization, X.L., [PERSON], and T.J.L.; Supervision, [PERSON] and T.J.L.; Project administration, [PERSON] and T.J.L.; Funding acquisition, [PERSON], [PERSON], and T.J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by NASA, grant number NNM11 AA01A.  
**Acknowledgments:** The authors also acknowledge the NASA CYGNSS science and operations team for providing the observational data. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to make the paper having a much improved quality.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Objective Evaluation
### Comparison Stereo Systems  
The necessary phase and/or level differences for a stereophonic recording as mentioned in Section 2.3 can also be obtained by only two microphones. Different angles and distances between these two microphones, as well as different microphone directivity patterns are possible, as described, for example, by the A-B or the X-Y technique [12]. A unified theory of these two-microphone systems for stereophonic sound recording can be found in [6].  
Assuming no phase differences, this theory states that a level difference of \(\Delta Level=\pm 15\,\mathrm{dB}\) determines the left or right lateral shift towards the loudspeakers of a phantom sound source in the playback situation. This level difference is achieved in the recording situation with different angles between two microphones with specific directivity patterns. The angle covering this level difference is called recording angle \(\phi_{rec}\). If \(\phi_{rec}>\phi_{base}\), the recorded sound scene is compressed in the playback configuration, whereas \(\phi_{rec}<\phi_{base}\), the recorded sound scene is expanded [6]. Therefore, we can assume that if we have \(\phi_{rec}=\phi_{base}\), the recorded spatial properties are the same after playback. Table 1 shows the possible microphone directivities and base angles between the microphone pairs.  
The microphone array stereo system described in this study records less reverberant sound than these state-of-the-art two-microphone stereo systems. As a measure, we choose a modified definition of the directivity index \(DI_{mod}\), which is the logarithm of a modified directivity \(D_{mod}\), mentioned in Section 4.1:  
\[D_{mod} =\frac{\sum_{m=0}^{M-1}2\max_{\phi_{m}}(b_{mic1}(\phi_{m})^{2})}{ \sum_{m=0}^{M-1}b_{mic1}(\phi_{m})^{2}+b_{mic2}(\phi_{m})^{2}} \tag{28}\] \[DI_{mod} =10\log_{10}(D_{mod})\]  
Figure 6: Directivity index \(DI(f_{p})\) of a linearly-spaced array (\(l_{spread}=1,s=0.125\,\mathrm{m}\)) (dashed line) and the logarithmically-spaced one (\(l_{spread}\approx 35,s=0.01\,\mathrm{m}\)) (solid line) with the same total length of \(Length=1\,\mathrm{m}\).  
where \(b_{mic1}(\phi_{m})\) and \(b_{mic2}(\phi_{m})\) are the directivity patterns of the first and the second microphone, respectively. The modified directivity index includes the sum of the directivity patterns of the two microphones. The modified directivity index considers the angle between these two directivity patterns, which determines the percentage of recorded reverberant sound in addition to the directivity pattern itself. As shown in Table 1, the proposed microphone array stereo system is, in fact, more directive than the two-microphone stereo ones, taking also into account the angle between the two microphone arrays.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 3 Parameters to Be Considered for the Design and Analysis of a Flexible Antenna
### Gain  
Another figure of merit that describes the performance of a flexible antenna is the gain. The antenna must have an acceptable gain for the intended use to be accepted in the wearable market. A flexible antenna should exhibit a positive gain to become more efficient for wearable applications. An average gain of more than 3 dBi is needed for the flexible antenna for the smooth working in 5G applications.  
Incorporating array, as well as MIMO structures, can improve the gain of the antenna. A greater patch volume can also increase the gain, but an increase in the antenna volume would not be a good choice for wearable applications where the antenna has to be conformal and should accommodate in less space.
# Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning
## 3 Methodology
### Proposed Framework  
Our framework comprises a botnet dataset, botnet training models, and botnet detection models. The botnet dataset consists of four subdatasets of N-BaIoT. We select devices that include all 10 attack samples described in Table 6 in the N-BaIoT, such as a doorbell (Ennio), baby monitor (Philips B120N/10), security camera (Provision PT-838), and webcam (Samsung SNH 1011 N). Table 7 shows the number of samples in the four datasets according to the device type we used.  
As botnet training models, we use most widely used ML and DL algorithms. We employed not only five types of ML models (naive Bayes (NB), K-nearest neighbors (KNN), logistic regression (LR), decision tree (DT), and random forest (RF)) but also three types of DL models (convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM)). There are two types of botnet detection models: binary classification and multiclass classification. The binary classification classifies the N-BaIoT dataset into two categories: attack and benign. This classification does not consider different types of protocols that can be used for botnet attacks, while the multiclass classification distinguishes each of protocol used for the Bashlite and Mirai. Figure 1 shows our framework for developing ML- and DL-based IoT botnet detection models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Botnet** & **Attack** & **Explanation** \\ \hline \multirow{4}{*}{Bashlite} & Scan & Scans the network for vulnerable devices \\  & Junk & Sending spam data \\  & UDP & UDP flooding \\  & TCP & TCP flooding \\  & COMBO & Sends spam data and open connection of IP, port \\ \hline \multirow{4}{*}{Mirai} & Scan & Automatic scanning for vulnerable devices \\  & Ack & ACK flooding \\ \cline{1-1}  & Syn & SYN flooding \\ \cline{1-1}  & UDP & UDP flooding \\ \cline{1-1}  & Plain UDP & Less of an option of UDP flooding for higher packet per second \\ \hline \hline \end{tabular}
\end{table}
Table 6: Botnet and attack types used in this study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Botnet** & **Attack Type** & **Doorbell** & **Baby Monitor** & **Security Camera** & **Webcam** \\ \hline \multirow{4}{*}{**Bashlite**} & **Benign** & 14,954 & 52,369 & 29,668 & 5852 \\ \cline{2-6}  & **COMBO** & 17,866 & 17,241 & 17,352 & 17,923 \\  & **junk** & 8624 & 8638 & 8788 & 8248 \\  & **Scan** & 8960 & 8286 & 8329 & 8624 \\  & **TCP** & 27,574 & 27,886 & 26,770 & 29,217 \\  & **UDP** & 31,932 & 31,779 & 31,371 & 30,852 \\ \hline \multirow{4}{*}{**Mirai**} & **Ack** & 30,754 & 27,587 & 17,449 & 32,505 \\  & **Scan** & 32,359 & 31,037 & 29,123 & 13,164 \\ \cline{1-1}  & **Syn** & 36,788 & 35,525 & 18,653 & 36,507 \\ \cline{1-1}  & **UDP** & 71,330 & 64,998 & 47,514 & 47,273 \\ \cline{1-1}  & **Plain UDP** & 24,349 & 24,258 & 16,051 & 25,083 \\ \hline \hline \multicolumn{2}{c}{**Total**} & **305,490** & **329,604** & **251,068** & **255,248** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of samples used in this paper.
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 4 The Principle of Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
### Interferometric ArcSAR Extraction DEM of Scenes  
Since the method of extracting DEM of the scenes using InSAR is relatively mature [22; 23], we only briefly describe the main principle of interferometric ArcSAR in this section.  
The model of interferometric ArcSAR is shown in Figure 7. Firstly, the antenna scans the target P at two different heights to obtain the imaging results on the reference plane, which can be shown as:  
\[G_{1}=E_{\rm s}\exp\left(-j\frac{4\pi}{\lambda}R_{\rm SP}\right) \tag{20}\]  
\[G_{2}=E_{\rm s}\exp\left(-j\frac{4\pi}{\lambda}R_{\rm SP2}\right) \tag{21}\]  
where G\({}_{1}\) is the ArcSAR image taken when antenna is rotating on the _xoy_ plane and G\({}_{2}\) is the ArcSAR image taken after raising the antenna to leave the _xoy_ plane. The height baseline is \(\Delta z\). \(E_{\rm s}\) stands for the amplitude information of the SAR image. \(R_{\rm SP2}\) represents the distance from S\({}_{3}\) to the target P.  
Figure 6: Flow chart of the proposed high precision ArcSAR imaging method.  
Then, we can acquire the complex conjugate result of G\({}_{1}\)and G\({}_{2}\):  
\[G_{2}G_{1}^{*}=E_{\rm s}^{2}\exp(-j\frac{4\pi}{\lambda}R_{\rm sp2})\cdot\exp(j \frac{4\pi}{\lambda}R_{\rm sp})=E_{\rm s}^{2}\exp(j\frac{4\pi}{\lambda}\Big{(}R _{\rm sp}-R_{\rm sp2}\Big{)}) \tag{22}\]  
According to Equation (22), the interferometric phase \(p_{1}\) can be expressed as:  
\[p_{1}=\frac{4\pi}{\lambda}\Big{(}R_{\rm sp}-R_{\rm sp2}\Big{)} \tag{23}\]  
Based on the work in [22], plane wave approximation is used to simplify the derivation process. Thus, \(R_{\rm sp2}\) can be approximated as:  
\[R_{\rm sp2}=R_{\rm sp}-\delta \tag{24}\]  
where \(\delta\) is the difference of \(R_{\rm sp}\) and \(R_{\rm sp2}\), which can be expressed as  
\[\delta=\Delta z\frac{h}{R_{\rm sp}}=\Delta z\sin\beta \tag{25}\]  
Thus, the \(p_{1}\) is rewritten as:  
\[p_{1}=\frac{4\pi}{\lambda}\Delta z\sin\beta \tag{26}\]  
\(\beta\) is the angle between \(R_{\rm sp}\) and the reference plane, which can be expressed as:  
\[\beta=\sin^{-1}\Big{(}\frac{\lambda}{4\pi\Delta z}p_{1}\Big{)} \tag{27}\]  
The height of the target P can be expressed as:  
\[h=R_{\rm sp}\sin\beta=R_{\rm sp}\Big{(}\frac{\lambda}{4\pi\Delta z}p_{1}\Big{)} \tag{28}\]
## 5 Distributed Machine Learning Algorithms
### Decentralized Learning  
In decentralized learning, or DL, the main idea is that multiple learning agents are distributed within the scenario. Such distributed agents can interact, or not, among them by creating different types of distribution. DL approaches have been recently developed, gaining from the introduction of novel communication paradigms, as those defined in 5G, B5G, and forthcoming 6G systems [11]. At the same time, the use of a distributed approach can solve several issues arising from the centralized implementations. Wireless and intermittent connections, energy issues, and privacy concerns raise the necessity of developing algorithms that should not rely on the presence of a centralized node, but can still work independently or in a decentralized fashion.  
As an example, [PERSON] et al. [98] propose the use of an MEC infrastructure to deploy several deep learning agents. Each of the agents is capable of training a deep neural network for image processing. However, to avoid a very long convergence time, several agents can collaborate among them by elaborating a subset of the image so that the distributed fashion is able to completely train the network exploiting the parallelism. The output of the training can then be used for IoV applications. [PERSON] et al. [99] propose instead to deploy a system of distributed deep learning agents in an IoV scenario. Those nodes can interact among them by exchanging data and parameters so as to create a DL approach. However, due to the intermittent nature of the vehicular environment, a proper node selection could be performed. In the paper, a proper policy is developed allowing to select which of the nodes should collaborate in order to respect proper timeliness requirements. [PERSON] et al. (2019) propose instead a multi-layer hierarchical DL scenario. By exploiting the presence of different nodes (i.e., vehicles, VEC and cloud) able to execute reinforcement learning algorithms, the authors propose to exploit them for collecting data from the lower layer and send the outcome of the RL algorithm to the upper layer to create scenario-wide learning. The main challenges reside here in the connection among nodes, which is supposed to be implemented through proper V2X communication technologies. [PERSON] et al. (2019) propose to map a DL algorithm on a platoon on vehicle where the different nodes train the same model and exchange the parameters. Moreover, depending on the processing capability and availability of the different vehicles, proper scheduling is performed allowing us to optimize the distribution mechanism.