esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Framework
### Overview
The culmination of this process is the construction of comprehensive group representations by concatenating the representations across all factors, thus reflecting the collective group intents pertaining to various attributes. **Predictor:** With the disentangled group representations at our disposal, we proceed to predict the group preference distribution. This entails employing a predictor that effectively maps the group intents associated with specific attributes to the overarching group preferences for the items, thereby completing the inference process. This operation is essentially the converse of the initial encoding step and aims to recombine the disentangled factors into coherent inference outcomes (from \(d\) to \(|\mathcal{I}|\)). Reflecting on the food attribute example, this predictor translates the group's attribute-based intents into concrete preferences for the available food options. Similarly, the user preference distribution is predicted via an auto-regressive behavior to regularize the training process. As illustrated in Figure 2, Embedding1 and Embedding2 are the regressive preference distributions which are predicted by a shared dimensionality reduction encoder (from \(d\) to \(|\mathcal{I}|\)). **Optimization Objectives:** The DAGA framework integrates all the aforementioned modules and is trained utilizing a set of optimization objectives that encompass both supervised learning (Objectives 1, 2 and 3 in Figure 2) and SSL loss functions (Objective 4 in Figure 2). These are designed to leverage historical interactions and the mutual information inherent within the contextual data. The supervised loss components are derived by quantifying the congruence between the predicted preference distributions and the historical preference data for both users and groups, employing KL divergence as a measure due to its efficacy in comparing probability distributions. In parallel, a contrastive SSL loss function is formulated to identify and differentiate between positive and negative samples intra- and inter-group, where the group with its members is regarded as a set of positive samples while different groups with their members constitute the negative samples. This is achieved by adhering to the principle of mutual information maximization, ensuring that the disentangled representations align with positive samples while diverging from negative ones. The intricacies of these optimization strategies are expounded in subsequent sections.
## 4 Experiments
### Datasets and Indicators
Furthermore, the three generalization testing sets only contained AGDs, so only the recall rate was considered. The definitions of the indicators are as follows:  
**Accuracy (A):** The ratio of the correctly classified samples to the total samples, which represents the confidence of the classification. \[A=\frac{TP+TN}{TP+FP+FN+TN} \tag{1}\]  
**Precision (P):** The ratio of the true positives (\(T\)Ps) to the sum of the \(T\)Ps and the false positives (\(F\)Ps), which represents the confidence of the classification. \[P=\frac{TP}{TP+FP} \tag{2}\]  
**Recall (R):** The ratio of the \(T\)Ps to the sum of the \(T\)Ps and the false negatives (\(FN\)s), which represents the completeness of the classification. \[R=\frac{TP}{TP+FN} \tag{3}\]  
**F-Measure (F1):** The harmonic mean of the precision and recall, which represents the synthesis of the performance of the classification. \[F1=\frac{2*P*R}{P+R} \tag{4}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Composition** & **Description** \\ \hline Dictionary-based & Total: 210,000 & 11 families of dictionary-based AGDs from \\ dataset & AGD: 110,000 & UMUDGA and random selected 100,000 benign \\  & Benign: 100,000 & domain names from Alexa \\ \hline Character-based & Total: 790,000 & 39 families of character-based AGDs from \\ dataset & AGD: 390,000 & UMUDGA and random selected 400,000 benign \\  & Benign: 400,000 & domain names from Alexa \\ \hline Full dataset & Total: 1,000,000 & Equal to the union of the dictionary-based dataset \\  & AGD: 500,000 & and the character-based dataset \\  & Benign: 500,000 & \\ \hline DGA Archive & AGD: 146,648 & Families contained in UMUDGA \\ in UMUDGA & AGD: 137,457 & Families not contained in UMUDGA \\ \hline Netlab 360 & AGD: 20,950 & Latest AGDs from Netlab 360 DGA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptions of datasets. where _TP_ represents the number of samples correctly classified as positive, _FN_ represents the number of samples incorrectly classified as negative, _FP_ represents the number of samples incorrectly classified as positive, and _TN_ represents the true negatives that are correctly classified as negative.
# A Disturbance Rejection Control Method Based on Deep Reinforcement Learning for a Biped Robot
## 3 Method  
Since the simplified model of the model ZMP control method is the cart-table model, we took the model as the research object for analysis. The acceleration of the car represents the acceleration of the upper body of the robot. Only when moving at an appropriate acceleration, the table has the possibility to maintain balance for as long as possible, and the counterpart is the biped robot to maintain balance. The present study considers a force of rolling friction between the wheels of a cart and table and assumes that there is no relative sliding. [PERSON] et al. [21] stated that the table remains upright if the cart is moving with proper acceleration, while the table rises with excessive acceleration and the table sinks with insufficient acceleration, as shown in Figure 1.  
The table tilts forward an angle \(\theta\) relative to the vertical position. The cart acceleration \(\ddot{x}\) corresponds to the body acceleration. [PERSON] et al. [22] considered the physical meaning of model ZMP control using a version of the cart-table model shown in Figure 2 and adopting [PERSON]'s method:  
\[\left\{\begin{array}{l}\big{(}x^{2}+z_{c}^{2}\big{)}\ddot{\theta}+\ddot{x}z_ {c}-g(z_{c}\sin\theta+x\cos\theta)+2x\dot{x}\dot{\theta}=\tau/M\\ \ddot{x}+\ddot{z}_{c}-\dot{\theta}^{2}x+g\sin\theta=f/M\end{array}\right., \tag{5}\]  
where \(\tau\) is the torque acting on the support point of the table, and \(f\) is the force that accelerates the cart on the table. Equation (6) is the result of linearizing Equation (5) around \(\theta\), \(\dot{\theta}=0\) and setting \(\tau=0\):  
\[\Big{(}x^{2}+z_{c}^{2}\Big{)}\ddot{\theta}=gx+gz_{c}\theta-z_{c}\ddot{x} \tag{6}\]  
Figure 1: Relationship between the tilt angle \(\theta\) and the acceleration \(\ddot{x}\) of the cart.  
Now, we assume that the target position of the cart \(x_{d}\) is generated by the dynamics of the linear inverted pendulum  
\[\ddot{x}_{d}=\frac{g}{z_{c}}(x_{d}-p_{d}), \tag{7}\]  
where \(p_{d}\) is the target ZMP. We, therefore, draw the following conclusions.  
1. The acceleration of the cart is increased when the target ZMP is set behind the current ZMP (\(p_{d}<0\)).
2. The acceleration of the cart is decreased when the target ZMP is set in front of the current ZMP (\(p_{d}>0\)).  
The main method of this research is to send the desired driving force to the cart's wheels within each control cycle, so as to continuously control the acceleration of the cart at any time (i.e., in any control cycle). Assuming that \(x\) in Equation (6) is the position of the cart \(x_{d}\) and substituting Equation (7) into Equation (6), it follows that  
\[\ddot{\theta}=\frac{g}{x_{d}^{2}+z_{c}^{2}}p_{d}+\frac{gz_{c}}{x_{d}^{2}+z_{c} ^{2}}\theta \tag{8}\]  
Equation (8) shows that the tilt angle \(\theta\) of the table can be controlled by changing the position \(p_{d}\) of the target ZMP. In other words, the ZMP of the biped robot model can be controlled by the acceleration of the cart.
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 3 Proposed Algorithm for MIMO Detection
### Parameter Selection  
In general from the existing iterative MIMO detection algorithms aim at the small number of iterations. In this case, number of approximate solutions obtained by the iterations is also limited. Thus, in this part, we only consider the value of \(m\) as 2, 3 and 4. Firstly, we focus on the complexity of calculating the correction coefficient. In each iteration process, we need to compute the correction coefficient \(a_{1},\cdots,a_{m}\), which can be written as:  
\[\begin{array}{l}\mathbf{y}_{q}=-\mathbf{Q}_{q}^{+}\mathbf{r}_{q},\\ a_{q}=1-\sum\limits_{i\
eq q}a_{i},\end{array} \tag{14}\]  
where \(q\in\{1,\cdots,m\}\). For illustrative purposes, we consider the \(q\) as \(q=1\), then Equation (14) can be rewritten as:  
\[\begin{array}{ccc}\left[\begin{array}{cccc}a_{2}&\cdots&a_{m}\end{array} \right]=-\left[\begin{array}{cccc}\mathbf{r}_{2}-\mathbf{r}_{1}&\cdots& \mathbf{r}_{m}-\mathbf{r}_{1}\end{array}\right]^{+}\mathbf{r}_{1}.\end{array} \tag{15}\]  
Case 1: for \(m>2\), it should be noted that we need to calculate a Moore-Penrose inverse to obtain a correction coefficient, the required number of complex-valued multiplication is \(O\left(mN_{t}^{2}\right)\)[24].  
Case 2: when \(m=2\), the correction coefficient \(a_{1}\) and \(a_{2}\) can be rewritten as  
\[a_{2}=-(\mathbf{r}_{2}-\mathbf{r}_{1})^{+}\mathbf{r}_{1}=-\frac{(\mathbf{r}_{ 2}-\mathbf{r}_{1})^{H}\mathbf{r}_{1}}{\|\mathbf{r}_{1}-\mathbf{r}_{2}\|_{2}^{2 }},a_{1}=-\frac{(\mathbf{r}_{1}-\mathbf{r}_{2})^{H}\mathbf{r}_{2}}{\|\mathbf{ r}_{1}-\mathbf{r}_{2}\|_{2}^{2}}. \tag{16}\]  
The calculation of correction coefficient requires performing two vector multiplication and one norm calculation with the complexity is \(O\left(N_{t}\right)\). The second case requires far fewer computations than the first case to realize coefficient calculation. Therefore, in terms of computational complexity, \(m=2\) is more advantageous for the iteration process than \(m=3\) and 4. Next, the detector performance is shown in terms of the bit error rate (BER) performance over a Rayleigh-fading channel to illustrate the effect of m value on performance. We consider an uplink massive MIMO scenario with \(128\times 32\) antenna configuration and 64-quadrature amplitude modulation (QAM) modulation scheme. In addition, the number of iterations is set as \(K=3\).  
Figure 2 and Table 1 show that the proposed algorithm with \(m=3\) and \(m=4\) does not result in an appreciable gain in performance with respect to the case \(m=2\). We can observe that the proposed algorithm with \(m=3\) only outperforms the proposed algorithm with \(m=2\) in 0.12 dB for BER = \(10^{-5}\) and its complexity is \(3N_{t}\) times higher. The proposed algorithm \(m=4\) even has the same BER of the proposed algorithm with \(m=2\). As a consequence, we choose the value of \(m\) is 2. In Algorithm 2,the pseudo code of the proposed algorithm with \(m=2\) is presented, where the algorithm terminates at the \(K\)-th iteration.
# PAPR Reduction in UFMC for 5G Cellular Systems
## 4 Numerical Results and Discussions
### The CCDF-PAPR Performance  
This section investigates the CCDF-PAPR performance when testing the UFMC system by applying two methods. The first method is the normal SLM (NSLM), and the second is the proposed Dcomp-SLM scheme, as shown in Figures 3 and 4. Figure 3 shows two important observations; the first is that the UFMC technique suffers from a high PAPR even more than the OFDM technique. Second, the proposed technique is capable of alleviating this difficulty, and producing results that are better than the conventional SLM, besides the simulated results of Dcomp-SLM match the theoretical results given by Equation (10). Furthermore, in Figure 4, the UFMC and OFDM performances were shown at different numbers of candidate phase vectors \(U\), which is \(U=4,6,\) and \(8\) vectors. As can be observed clearly, while the NSLM can improve the original CCDF-PAPR performances for all _Us_ values, the proposed Dcomp-SLM shows further PAPR reductions for all cases. For example, Dcomp-SLM gains about \(1\) dB over the NSLM at \(\mathrm{Pr}(\mathrm{PAPR}>\mathrm{PAPR}_{0})=10^{-4}\), \(N=512\) sub-carriers and input-back-off (IBO) = \(7\) dB and \(U=6\) phase vectors.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **SLM-UFMC System** \\ \hline Number of data sub-carriers,\(N_{sc}\) & 300 \\ FFT size, \(N\) & 512 \\ Cyclic prefix length, \(L_{cp}\) & 128 \\ Modulation order, \(M\) & QPSK and 16-QAM \\ Filter length, \(L_{f}\) & 37 \\ Number of sub-bands, \(B\) & 30 \\ Sub-band size, \(N_{b}\) & 10 \\ High power amplifier (HPA) & SSPA (Rapp’s Model) \\ Channel model & Rayleigh fading channel \\ No of paths in fading channel, \(L\) & 64 \\ Number of phase vectors, \(U\) & 4, 6, and 8 \\ Filter type & Dolph-Chebyshev \\ Side lobe attenuation, \(\beta\) & 60 dB \\ input back off (\(IBO\)) of SSPA & 5, 7, and 9 \\ Smoothness factor of SSPA, \(p\) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The SLM-UFMC system parameters.  
Figure 3: CCDF-PAPR of UFMC format and orthogonal frequency division multiplexing (OFDM)with \(N=512\) and 16-QAM modulation, at U = 6 phase vectors for normal SLM (NSLM) and Dcomp-SLM schemes.  
Furthermore, the proposed Dcomp-SLM technique is better than the E-SLM and P-SLM approaches that were proposed in [20; 37], respectively, by approximately 0.5 dB at \(10^{-4}\) CCDF under the same simulation parameters.
## 3 Challenges in FSO Communication
### Atmospheric Channel Effects Phenomena
#### 3.1.4 Reliability and Security Concerns
Furthermore, key PLS metrics such as secrecy outage probability (SOP), average secrecy capacity (ASC), and the probability of strictly positive secrecy capacity (SPSC) are unified in the context of FSO communication in [59; 60; 61]. In [61; 62; 63], the secrecy of a mixed RF-FSO system is analyzed, where an adversary intercepts secure information via the FSO link by capturing a fraction of the optical power due to beam scattering. The findings in [62], supported by simulations, demonstrate that factors such as photo-aperture size, fading parameters, and the average eavesdropper signal-to-noise ratio (SNR) are crucial for securing RF-FSO communications. Specifically, the results indicate that under most weather conditions, and with a photo-aperture size of approximately 200 mm, mixed RF-FSO relaying is advantageous for designing a secure RF-FSO relay network. Furthermore, refs. [59; 64] propose a hybrid RF/FSO system utilizing RIS reflection to interfere with eavesdroppers in FSO eavesdropping scenarios. The system's PLS performance is also analyzed and optimized. The performance of PLS in mixed FSO-RF systems has been the focus of several studies. In [50], a PLS secrecy analysis for a mixed FSO-RF system under a \(\Gamma\Gamma\) and Rayleigh fading scenario is conducted. The study in [65] analyzes the secrecy performance of a mixed FSO-RF simultaneous wireless information and power transfer (SWIPT) system using a decode-and-forward (DF) relaying scheme. These studies consider eavesdropping on the FSO side with full channel state information (CSI) of the eavesdropper's channel. However, the studies reported in [54; 63; 65; 66; 67; 68; 69; 70] are limited to PLS secrecy analysis under the assumption of ideal power amplifier (PA) deployment at the RF front-end. In practice, the non-negligible power consumption by PAs significantly impacts the secrecy performance, making the effects of PA efficiency crucial in PLS secrecy analysis. Although a few studies have examined the impact of hardware imperfections on mixed RF-FSO systems' performance, such as [71; 72], only a limited number have done so. To model these hardware imperfections, [72] considers soft envelope limiter (SEL) PA nonlinearities, while [71] uses a degradation PA model to characterize the hardware impairments. The study in [73] analyzes the secrecy performance of a hybrid RF-FSO system under hardware impairments using the degradation PA model, focusing on scenarios where only the RF eavesdropper intercepts secure information. Additionally, ref. [49] propose a different system structure for a mixed FSO-RF system under hardware imperfections, where all RF Rxs use the SWIPT technique to collect energy from the relay's wireless signals. This study also assumes that both FSO- and RF-side eavesdroppers can overhear the intended information separately and simultaneously.
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 4 Results and Discussion
### Subjective Results
#### 4.4.3 Cybersickness
Figure 17: Overview of self-reported cybersickness occurrence for each of the four scenarios in terms of the general VRSQ (**a**) and the oculomotor (**b**) and disorientation (**c**) sub-scales. Higher scores indicate a higher occurrence of given symptoms. The full orange line indicates the median, the green dotted line the mean. The bullets indicate outliers, which are defined as samples laying outside the interval \([Q1-1.5\cdot\text{IQR},Q3+1.5\cdot\text{IQR}]\), with IQR the Interquartile Range defined as \(\text{IQR}=Q3-Q1\). Similar observations can be made for scenarios B (burst) and C (latency). Once again, we hypothesize that this is a direct result of the nature and interactivity of the particular role at hand. As User B's tasks induce more movement and interaction, they become more prone to cybersickness. This occurs in both a direct matter (due to the additional movement) and an indirect matter as the increased awareness of network distortions (Section 4.4.1) also alters the risk of cybersickness occurrence.
# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 2 Literature Review, Theoretical Analysis, and Research Hypotheses  
### Research Questions and Gaps in the Literature  
The global conversation on AI and employment has largely focused on job creation and displacement, with limited attention paid to the quality of employment, particularly in developing economies. While existing studies highlight AI's potential to enhance productivity and innovation, they also raise concerns about skill polarization, income inequality, and regional disparities in employment outcomes [2,3]. However, several gaps remain in the literature:  
1. Regional Disparities: Most studies focus on aggregate effects or case studies in developed economies, neglecting the heterogeneous impact of AI across regions with varying economic structures and levels of technological adoption [4,5].
2. Employment Quality: The concept of employment quality, which encompasses factors such as job security, income distribution, and working conditions, has received limited attention in the context of AI adoption, particularly in developing countries [6,7].
3. Policy Interventions: While the importance of policy interventions in mitigating the negative effects of AI is widely acknowledged, there is a lack of empirical evidence on the effectiveness of specific policies in different regional contexts [8,9].  
This study aims to address these gaps by examining the regional disparities in the impact of AI on employment quality in China, with a focus on the role of industrial structure, economic development, and policy interventions. By doing so, it seeks to provide a nuanced understanding of how AI adoption affects employment outcomes across different regions and offers actionable insights for policymakers.
## 4 Results
### Variations in Vegetation Distribution and Leaf Area Index  
The alterations in vegetation distribution characteristics and LAI provide a more detailed representation of the impacts that bar and vegetation dynamics have on the river channel. Utilizing the ML-based LAI inversion model, variations in vegetation distribution and LAI in the MYR after the TGD operation are depicted in Figures 4 and 5.  
Figure 4 shows the fluctuations in LAI within the MYR subsequent to the commencement of the TGD operation. The results reveal an overall 24% augmentation in LAI. More specifically, the LAI in sub-reaches 1-9 have undergone changes of +79%, +15%, +66%, +18%, +7%, +7%, \(-3\%\), +34%, and \(-3\%\), respectively. Despite sub-reach 1 having the least dense bar vegetation (LAI = 0.60), its LAI growth rate is the most substantial among all the reaches (+79%). Simultaneously, sub-reach 6 possesses the densest bar vegetation (LAI = 1.40), exhibiting only a slight LAI growth rate following the TGD operation (+7%). Furthermore, while the bar vegetation in most sub-reaches typically displays a trend towards density, the bar vegetation in sub-reaches 7 and 9 exhibits a relatively sparse trend, with LAI growth rates both equal to \(-3\%\).  
Figure 5 displays the alterations in LAI distribution for four representative bars within the MYR after the TGD operation. These four representative bars are selected due to two main reasons. First, the four bars chosen are situated hundreds of kilometers apart, as the downstream influence of water and sediment conditions stemming from the dam exhibits a discernible spatial impact on the riverine geomorphology. Second, since channel type can influence the bar evolution process, the four bars are located in different channel types covering straight, braided, and meandering channels. Obviously, most vegetations have exhibited a tendency to expand and become denser, irrespective of various river and bar types. In terms of vegetation zone morphology, a vegetation expansion transpired from the tail to the head, as well as from the edge to the center, while maintaining a slight shrinkage of the bar area.