esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation
## 1 Introduction
SCCD only uses a sparse vector of length \(N\), and storing such a vector requires only a memory size depending on the number of non-zero values, whereas LBI requires an additional vector of length \(N\) which is dense and requires a most costly update [23, 24]. Based on these characteristics, SCCD can be considered a more suitable candidate for the frequency estimation problem [12]. In this letter, a new version of SCCD based on an adaptive frequency grid is proposed. The proposed method inherits the characteristics of the original formulation and in addition employs several strategies for accelerating the convergence and reducing the number of required mathematical operations. The main idea of the proposed method is to effectively reduce the size of the grid (i.e., to reduce the number of columns of the matrix \(\mathbf{A}\)) for estimating the frequency parameters. This will allow one to reduce the computational complexity, and subsequently the number of required mathematical operations in comparison to the original formulation. Therefore, it enables a significantly better complexity/precision trade-off. As our approach mainly consists of complex rotations, it is easily implementable in fixed-point digital hardware with only moderate precision requirements. The proposed method also achieves similar performance to the Root-MUSIC algorithm, but with significantly less computational complexity.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.3 Effects of Different Parameters on CBZ Adsorption  
Influence of pH  
The effect of four different pH values (3, 5, 7, and 9) on the adsorption of CBZ onto the biochar TN < 75 \(\upmu\)m and SARATECH(r) was investigated (Figure 7). The highest adsorption capacity for the biochar was obtained at pH 5, while for SARATECH(r), the maximum was observed at pH 9. For the SARATECH(r), there was an increase in the adsorption capacity from pH 5 to 9. This increase was less pronounced between pH 7 and 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Kinetic Models** & \\ \hline
**Adsorbents** & Pseudo first order & Pseudo second order \\ \hline SARATECH® & – & – \\ \hline \multirow{3}{*}{Biochar < 75 \(\upmu\)m} & R\({}^{2}\) = 0.998 & \\  & q\({}_{\text{e}}\) = 0.769 mg/g & – \\  & k\({}_{\text{1}}\) = 1388 min\({}^{-1}\) & \\ \hline \multirow{3}{*}{Biochar 1–2 mm} & R\({}^{2}\) = 0.916 & R\({}^{2}\) = 0.908 \\  & q\({}_{\text{e}}\) = 0.137 mg/g & q\({}_{\text{e}}\) = 0.153 mg/g \\  & k\({}_{\text{1}}\) = 0.119 min\({}^{-1}\) & k\({}_{\text{2}}\) = 1.00 g/mg\text{\text{\textIn general, the variation of the pH of the solution may affect the functional groups of both the pharmaceutical and the adsorbent; however, in the pH range studied, only the adsorbent might be influenced. CBZ is a neutral compound in the entire studied pH range, and its adsorption behavior is different from ionized compounds, which can be affected by electrostatic forces. One should also take into consideration a phenomenon affecting both neutral and ionizable compounds that consists of the size exclusion of large sorbate molecules from microporous carbonaceous adsorbents. The NH\({}_{2}\) functional group in CBZ can interact with oxygen-containing functional groups of the adsorbents, such as OH and CO, through hydrogen bonding [33; 50].  
At lower pH values, functional groups on adsorbents and CBZ can interact with H\({}^{+}\) more easily due to their abundance in the solution, which decreases hydrogen bonding between the adsorbents and CBZ, and consequently decreases adsorption efficiency [33]. In contrast, as the concentration of H\({}^{+}\) is reduced at higher pH levels, hydrogen-bonding donor groups on CBZ can interact with hydrogen-bonding acceptors or donors in the adsorbents, and therefore the adsorption efficiency is expected to be enhanced [33].  
For the 3D experiments, pH 7 was chosen after considering the common pH of domestic wastewaters and the obtained results in the optimization of adsorption and 2D studies.
## 6 Experiments and Results
### AtomGID in an Activity Recognition Context  
The final set of experiments were designed to test the AtomGID in an activity recognition context with the overall system. As described earlier, for this purpose, we leveraged a clustering method that our team developed using flocking [45]. This enables us to detect activities that are performed in the smart home in a fully unsupervised manner without relying on a large dataset. For this experiment, a spatial dataset of five ADLs realized ten times was collected in our smart home infrastructure. Keep in mind that the gestures are only extracted for a single active object in the kitchen area. It is the zone we selected to install a higher number of RFID antennas to be able to perform precise localization necessary for that task. The reader should also keep in mind that even if we had an unlimited number of RFID antennas, trilateration would have to be restrained to specific areas to avoid being overwhelmed by collisions. Indeed, each RFID system functions in round robin, but the brand we own can only support four antennas per system.  
The ADLs selected are fine-grained activities performed exclusively in the kitchen: preparing a bowl of cereal, preparing an instant coffee, making a burger, preparing pasta, and washing hair (in the kitchen sink). This dataset is still strongly imbalanced since the degree of complexity and the number of sensors activated vary a lot. The localization algorithm is the module generating the most data. There are about 20 tagged objects in the kitchen at the time of data gathering which are all generating positions. We drastically reduced the complexity by assuming that only one object at the time is moving (or is active) and feeding the list of positions to AtomGID. The dataset is split randomly, and 2/3 is used for the training. The procedure is repeated ten times to average the results. On most executions, flocking rapidly converges over 80% accuracy. The average final accuracy of the trained models is 86.73%. One interesting observation our team made is that flocking converges in around the same amount of time in all the experiments. It seems to indicate that the basic computation fee is higher than the linearity of the dataset.
## 4 Interaction Between Parameters of New and Existing Piles
### Behavior of Existing Piles  
As new piles are introduced, the stress redistribution in the surrounding soil exerts pressure and remolding effects on adjacent existing piles [40; 41]. Figures 10 and 11 display the settlement results of existing piles under different \(h_{\text{N}}(\text{s})\) and \(d_{\text{N}}(\text{s})\) of the new piles. The results indicate a negative correlation between the settlement of existing piles and the increase in \(h_{\text{N}}\). The normalized settlement variation ranges from 0.46 to 0.41, with a stable gradient of change.  
Figure 10: Relationship between settlement of existing pile foundations and \(h_{\text{N}}(\text{s})\): (**a**) existing pile (pile-f); (**b**) existing pile (pile-b).  
In the group pile model, the introduction of new piles will alter the load-sharing mechanism of the soil. Among them, an increase in \(h_{\text{N}}\) can redistribute the overlying load, reducing the load borne by the existing pile and positively affecting the settlement control of the existing piles. The settlement of existing piles shows a positive correlation with the increase in the \(d_{\text{N}}\). After normalization, the settlement range of existing piles close to the new piles varies between 0.450 and 0.454, with only a small discrepancy across different parameters, ultimately stabilizing at 0.45.  
The increase in \(d_{\text{N}}\) reduces the net distance between piles. When new piles are introduced, the soil around the piles moves downward with pressure and friction, with the effect becoming more pronounced as the \(d_{\text{N}}\) increases. This is evident from Figure 9. Consequently, adjacent existing piles also experience downward movement due to the influence of the moving surrounding soil. However, the impact of the increasing \(d_{\text{N}}\) on the surrounding soil is limited, including the lateral influence range. As seen in Figure 11, the settlement influence of existing piles stabilizes at \(d_{\text{N}}\) of \(1.2d\). Meanwhile, the existing pile (Pile b) near the new pile exhibits a settlement pattern along the depth direction that initially decreases and then increases with changes in pile diameter. However, the variation difference becomes significantly less than the new pile. By the time it reaches pile-f, this pattern has almost disappeared.
# DTFS-eHarris: A High Accuracy Asynchronous Corner Detector for Event Cameras in Complex Scenes
## 4 Experiments and Results
### Real-Time Performance  
As event cameras are often used in highly dynamic scenes, detection efficiency is important for event-based corner detection methods and data throughput is an important real-time performance evaluation metric. Table 1 shows the real-time performance of different corner detection algorithms, incorporating the average time required to process a single event (\(\upmu\)s) and the maximum number of events that can be processed per second in millions of events per second (Meps). The first three rows in the table are for the normal detection method and the last two rows are for the hybrid detection method. Our method improves by about 20% faster than FA-Harris and 4\(\times\) faster than eHarris\(\dagger\). However, Arc* is 3\(\times\) faster than ours since we apply the gradient-based method to refine corners for higher accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Time per Event** & **Max. Event Rate** & **Red.** \\  & **[us/Event]** & **[Meps]** & **[\%]** \\ \hline eFAST\(\dagger\) & 0.862 & 1.150 & 95.69 \\ G-eHarris\(\dagger\) & 3.558 & 0.280 & 95.06 \\ Arc* & 0.260 & 3.830 & 90.62 \\ \hline FA-Harris & 0.975 & 1.025 & 97.28 \\ Ours & **0.781** & **1.278** & **98.80** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Throughput and Reduction Rate performance. Two hybrid methods are listed separately. The best result of the hybrid method is made in bold.  
Figure 8: Ground truth collection. Firstly, a Harris detector then LK tracker are used to detect and track corners. The trajectory is the blue line segment. Taking this segment as the center, two concentric cylinders are constructed with radius of 3.5 and 5, respectively. The events within 3.5 pixels away from the trajectory are labelled as true ground-truth corners (green) and those between 3.5 and 5 are labelled as false corners (red).
## 4 Results and Discussion
### Block Size and Complexity Analysis
Sorting algorithms, such as block based or binary tree, present a complexity of \(\mathcal{O}(N_{b}^{2}\log N_{b}^{2})\). Therefore, the complexity will depend on the number of symbols and employed bins. In our case, we employed 100,000 symbols and 40 bins, and as a consequence, the histogram building process was the dominant term. The complexity of the second stage, that is finding the closest high density point to a given symbol, depends on the number of high density points. Furthermore, this number will vary depending on the shape of the clusters. In particular, the noisier the clusters are, the larger the areas of relative high density and the number of points with high density are. If we assume that we have a set of \(S_{hd}\) of \(N_{hd}\) points of high density, then for each symbol, we need to calculate \(N_{hd}\) distances (indeed, it is possible to calculate the square of the distance):  
\[D=d^{2}[k]=(s_{i}[k]-u_{i}[m])^{2}+(s_{q}[k]-u_{q}[m])^{2},\text{ where }u[m]=u_{i}[m]+j \cdot u_{q}[m]\in S_{hd}. \tag{3}\]  
This distance required five real valued operations, as we needed to calculate 2 subtractions, 2 multiplications, and 1 addition. The complexity of this stage was then \(\mathcal{O}(N_{s}\cdot N_{hd})\), which was higher than that of k-means, where the number of distances to be calculated corresponded to the number of clusters. However, it should be noted that this comparison only considered the distance calculation and not the number of operation to find the centroids. The complexity of HBC was also higher than that of simple nonlinear rotation, which was as small as two real valued operations.
## 2 Theoretical Background
### Linear-Quadratic Optimal Control (LQ) Framework  
The novel feature of the OPTCON algorithm is the combination of a nonlinear dynamic system and the above-mentioned stochastics when solving optimal control problems. Before we can discuss this technique in detail, a few words should be said about the underlying linear-quadratic optimal control framework. The dynamic system is given in linear form as  
\[x_{t}=Ax_{t-1}+Bu_{t}+c_{t}. \tag{5}\]  
The corresponding optimal control objective function can be written in the same form as in (3) and (4). Using well-known LQ optimization techniques, the rules for defining control variables can be found iteratively backwards in time using dynamic Riccati equations. We start from the familiar LQ framework (e.g., [20]), which was adapted to economic models by [15; 21; 22]. The same standard technique is applied in the OPTCON algorithm whenever an optimal control for a linearized system should be obtained.  
Furthermore, for computational reasons it is useful to transfer the objective function from the quadratic tracking form as used in Equation (4) into the general quadratic form:  
\[L_{t}(x_{t},u_{t})=\frac{1}{2}\left(\begin{array}{c}x_{t}\\ u_{t}\end{array}\right)^{\prime}W_{t}\left(\begin{array}{c}x_{t}\\ u_{t}\end{array}\right)+\left(\begin{array}{c}x_{t}\\ u_{t}\end{array}\right)^{\prime}\left(\begin{array}{c}w_{t}^{x}\\ w_{t}^{u}\end{array}\right)+w_{t}^{c}, \tag{6}\]where  
\[\left(\begin{array}{c}w_{t}^{x}\\ w_{t}^{u}\end{array}\right)=-W_{t}\left(\begin{array}{c}\hat{x}_{t}\\ \tilde{u}_{t}\end{array}\right)\;\;\text{and}\;\;w_{t}^{c}=\frac{1}{2}\left( \begin{array}{c}\xi_{t}\\ \tilde{u}_{t}\end{array}\right)^{\prime}W_{t}\left(\begin{array}{c}\hat{x}_{ t}\\ \tilde{u}_{t}\end{array}\right).\]  
The equivalence between the quadratic tracking form and the general quadratic form is shown, for instance, in [23].
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 3 Background
### Reinforcement Learning  
Reinforcement learning (RL) has recently attracted a lot of attention. It is one of the three main categories of machine learning, alongside supervised and unsupervised learning. The intention is to have an agent learn how to achieve goals in a complex environment subject to uncertainty.  
RL consists of two main components: an environment and an agent. The environment encapsulates the problem statement and the rules about the world that can be observed through a state \(S\). The agent observes the state and interacts with the environment through a predefined set of actions \(A\).  
The interaction between the environment and the agent can be broken down into four steps:  
1. The agent receives an observable state \(S\), provided by the environment.
2. The agent executes an action \(A\), based on the received observation.
3. The environment provides a reward \(R\), indicating how favorable the performed action was.
4. The agent uses the reward \(R\) to tune its action selection process by maximizing the cumulative return.  
An action \(A\) carried out by the agent might change the environment's state \(S\). This process is repeated until the agent's performance converges. The output of the RL training process is an agent that has learned over time by interacting with the environment.  
As a further enhancement, we apply an actor-critic (AC) approach that combines both a value-based and a policy-based learning strategy, as displayed in Figure 1. This work examines a natural actor-critic (NAC) algorithm and a proximal-policy optimization (PPO) algorithm, both of which build on the AC approach.
## 4 AC/DC Converter
### Architectures: Evaluation and Selection
#### 4.4.2 Secondary Side Diode-Based Topology  
In order to verify the maximum output power and efficiency of the architecture, simulations were performed changing the values of different variables, such as switching frequency, duty-cycle, and resistive load.  
Figure 32 shows the simulation results (output power, output voltage, generator efficiency, converter efficiency and complete system efficiency) with different wind velocity. Sweeps were carried out with resistive loads between 100 \(\Omega\) and 2100 \(\Omega\), duty-cycles between 0.1 and 0.9, and switching frequencies of 200 kHz and 333 kHz for 3 m/s wind velocity, and 500 kHz and 1000 kHz for 10 m/s wind velocity. The switching frequency depends on the ambient conditions because the input range of wind speed is wide, thus a shift of the frequency for each case is required. The maximum output power and system efficiencies are obtained by sweeping the variables in the simulation for different test conditions.  
Figure 31: Output power, voltage and efficiency values with different resistive loads at 3 m/s and 10 m/s wind velocities. (**a**) Pout (mW). (**b**) Voltage (V). (**c**) Efficiency of the system (%): Generator + Conversion system.  
Figure 32: Cont.  
Figure 32: Three phase diode bridge converter simulation results. The (**a,c,e**) figures are with 3 m/s wind velocity and 200 kHz switching frequency. The (**b,d,f**) figures are with 10 m/s wind velocity and 500 kHz switching frequency. (**a**) Output power (3 m/s, 200 kHz). (**b**) Output power (10 m/s; 500 kHz). (**c**) Output voltage (3 m/s, 200 kHz), Red plane: 3 V as minimum. (**d**) Output voltage (10 m/s, 500 kHz), Red plane: 3 V as minimum. (**e**) Generator + Converter efficiency (3 m/s, 200 kHz). (**f**) Generator + Converter efficiency (10 m/s; 500 kHz).  
For the lowest wind velocity (3 m/s) the maximum efficiency of the generator is 18.06% for a 100 \(\Omega\) load and 0.3 duty-cycle. The efficiencies of the converter are higher than 40%, achieving a maximum efficiency of 81.09% with a duty-cycle of 0.6 for a 1100 \(\Omega\) resistive load. Moreover, the complete system maximum efficiency is 10.66% for a 300 \(\Omega\) load and 0.4 duty-cycle.  
For the highest wind velocity (10 m/s) the maximum efficiency of the generator is 13.31% for a 1700 \(\Omega\) load and 0.4 duty-cycle. The converter achieves a maximum efficiency of 81.94% with a duty-cycle of 0.2 for a 100 \(\Omega\) resistive load. Moreover, the complete system maximum efficiency is 10.46% for a 100 \(\Omega\) load and 0.4 duty-cycle.
## 2 Literature Review
### Key Concepts
#### 2.1.2 Augmented Reality  
Previous research built various empirical studies on AR shopping applications and their positive effects. Existing research explores AR applications and products using online experiments, lab experiments, and surveys [13]. The underlying processes examinedinclude self-viewing, environmental embedding, spatial presence, perceived augmentation, interactivity, flow, and more. As outcome measure variables, we can mention purchase intention, app and brand attitude, recommendation and reuse intention, user satisfaction, and more. Overall, the studies demonstrate the positive effects of AR on various outcome variables. For example, AR positively affects purchase intention, app attitude, user satisfaction, and user experience--the spatial presence induced by AR enhances brand attitude and product likeability [13; 26]. The perceived informativeness, enjoyment, interactivity, and inspiration mediate positive effects. However, some studies show mixed results regarding the effects of AR on brand attitude [13].  
AR is mainly used to visualize products better rather than enhance the brand, and a single-time app use may be unlikely to change positions towards well-established brands. The positive effect on brand reactions varies depending on the type of AR. Personalization leads to positive brand responses for a self-augmentation app (makeup). In contrast, spatial presence is conducive to positive brand reactions for a surrounding augmentation app (furniture [13]).  
The personal and familiar atmosphere of users' homes allows them to engage with a brand in a manner that encourages self-expression and experimentation. Through AR apps, brands integrate with consumers' physical traits, resulting in a fusion where the consumer's interests take precedence while the brand takes a supportive role [27; 28].  
AR could enhance these experiences. While online shopping is an individual activity, interaction, standards, help, and communication significantly influence consumers' purchase intentions and decision making. Customers often interact with shopping assistants for aid and share information through recommendations and word of mouth, driven by inherent needs for socialization and relatedness. AR offers unexplored opportunities for synchronous and asynchronous interactions between users and virtual content. Buyers can communicate viewpoints, messages, comments, and advice and collaborate on modifying virtual products. These capabilities could improve social knowledge during e-commerce, leading to critical outcomes that may increase technology adoption [29].  
Despite the significant investment required to adopt AR in e-commerce, retailers can benefit from using AR to increase consumer interest in their products. They need to understand the potential of AR technologies and collaborate with technology developers to enhance shopper experiences [30].  
The interactive experience of mobile AR applications has great potential for use in many fields, including education, art, entertainment, medical treatment, and tourism [31].
## 3 Results and Discussion
### Storage Stability  
The storage stability test, depicted in Figure 7, was conducted to assess the success of binder modification and its stability during storage. Acceptable storage stability demands a minimal temperature difference between sample top and bottom. All asphalt binders showed adequate stability without significant separation, owing to slight variations in softening points within 2.2 \({}^{\circ}\)C. Virgin and WMA-modified asphalt binders exhibited lower storage stability, with a 0.5 \({}^{\circ}\)C difference. RECR asphalt binder exhibited the highest storage asphalt due to the crumb rubber's contribution. Elevated crumb rubber content decreased modified binder stability, aligning with prior studies [21].