esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Overview
### Multi-Agent Reinforcement Learning  
In this study, each video client running in the network is regarded as an independent agent. These agents regard other agents as a part of the environment. Each agent selects actions and obtains rewards only based on its own observations. Although this method is easy to deploy and decentralized, it also brings some problems. For a given agent \(i\in\{1,\ldots,n\}\), there is no way to distinguish the randomness of the environment from the behavior of other agents \(i\in\{1,\ldots,i-1,i+1,\ldots,n\}\). In addition, because the reward \(R_{i}\) is not all from the actions performed by the agent, it is difficult to assign the correct reward to each action agent, resulting in an inability to learn the optimal strategy.  
[PERSON] et al. [15] found that when Independent Proximal Policy Optimization (IPPO) uses more comprehensive global features as the input of the neural network, it can often obtain better performance than QMix [16]. Whether the input of the value network contains the characteristics of the global state is the fundamental difference between Multi-Agent Reinforcement Learning and completely independent Distributed Reinforcement Learning. It is essential for the DRL algorithm in the multi-agent scenario to ensure that the input parameters of the neural network contain enough global information [17].  
To solve the convergence problem of an algorithm based on distributed bandwidth allocation and improve the algorithm's performance, in this paper, we attempt to make the neural network input contain as many global features as possible and reduce the extra communication cost from parameter transmission. Therefore, we constructed two global parameters counted by the server, namely coefficient of variation of QoE \(C\) and QoE level of client \(L\), which are defined as follows:  
\[C=\frac{\sigma_{QoE\
eq{}^{\prime}\
eq{}^{\prime}\
eq{}^{\prime}}}{\mu_{QoE \
eq{}^{\prime}\
eq{}^{\prime}}} \tag{6}\]\[L=\frac{QoE_{i}^{\gamma,h}}{\frac{1}{n}\sum\limits_{i=0}^{n}QoE_{i}^{\gamma,h}} \tag{7}\]  
where \(\sigma_{QoE_{i}^{\gamma,h}}\) represents the standard deviation of the historical discount QoE for the past \(h\) video blocks of all video clients served by the current server; \(\mu_{QoE^{\gamma,h}}\) represents the mean value of the historical discount QoE of the past \(h\) video blocks of all video users. QoE coefficient of variation is used to indicate the dispersion degree of QoE score distribution of video stream users. The greater the value, the greater the dispersion degree. The generation of the above two parameters only involves QoE user information transmission. As described in Section 3.2, the transmission opportunity only occurs when the client sends a new GET request to the server. Therefore, the agent can obtain the overall state information of the current system with minimal communication and calculation cost.
# A 28 nm Bulk CMOS Fully Digital BPSK Demodulator for US-Powered IMDs Downlink Communications
## 3 Characterization of the US Data Link
In fact, it is worth remembering that the distance between the last sound pressure's maximum value and the sound source is called the near-field length, which is expressed by \(N\), and the area within the \(N\) is called the near-field area. The region where the distance from the axis of the wave source is greater than the length of the near-field region is called thefar-field region. The ultrasonic near-field area can be calculated by the following equation [35]:  
\[N\approx\frac{D^{2}}{4\lambda}=\frac{A}{\pi\lambda}\sim 3.7\,\mathrm{mm} \tag{2}\]  
where \(D\) is the diameter of the material, and \(A\), its area, equals to about \(9\,\mathrm{mm}^{2}\), while \(\lambda\) is the US-wavelength in tissues. This proves that the system works in the far-field region. The impedance characterization of the devices was carried out using a VNA (E5061B provided by Keysight Technologies), measuring out both the magnitude of the impedance, \(|Z|\), and the phase \(\phi\) as functions of the frequency. Both piezo devices were connected to the VNA, and the equivalent Butterworth-Van Dyke (BVD) electric circuits were carried out. Furthermore, resonance doublets were depicted by using two parallel RLC series circuits but, as claimed before, for the power transfer to the implanted device, just the electric resonance is important. The piezo devices, under the electrical resonance condition, presents finite impedances (in the order of magnitude of 0.1-1 k\(\Omega\)), when acting as a passive component (e.g., the TX-PIEZO). On the other hand, the Thevenin equivalent model deals with the concept of active generation when resonance for the RLC series circuit occurs (see Figure 5c)). The received voltage amplitude falls in the following range \(V_{PZT}\in[2.0\)-\(4.5]\,\mathrm{V}\), while the received power in \(P_{RX}\in[1.0\)-\(3.0]\,\mathrm{mW}\) and the equivalent series resistance is \(R_{PZT}\in[0.5\)-\(1.5]\,\mathrm{k\Omega}\). A peak-to-peak sine signal applied to the TX-PZT equals to \(20\)\(V_{pp}\), and the electrical resonance frequency is the same as that of the RX-PZT, \(f_{0}=2\,\mathrm{MHz}\).
## 2 Driverless Car Technology and the Organ Donor Supply  
### Driverless Car Technology Explained  
According to SAE International, the J3016 standard determines the level of automation primarily by the extent to which the human driver or the vehicle itself controls the dynamic driving task [4]. The dynamic driving task is defined as operational tasks such as steering and braking and tactical assessments such as determining when to signal, turn, or change lanes but not strategic decisions such as choosing a destination. Within the six-level standard, the crucial distinction occurs between Level 2 and Level 3. At Levels 2 or below, the human driver performs at least part of the dynamic driving task; at Levels 3 or above, the vehicle's automated driving system performs the entire dynamic driving task (Table 1). At a more technical level, driverless cars function by utilizing a system of sensors that monitor the vehicle's surroundings to generate data, allowing the automated driving system to operate the vehicle amid other cars as well as varying road and weather conditions. More specifically, autonomous vehicles employ radar sensors to monitor the location of nearby vehicles. Video cameras are utilized to detect traffic lights, read road signs, and track vehicles, pedestrians, and obstacles. Lidar sensors assist with detection of road edges and lane markings by bouncing light pulses off the car's surroundings. Ultrasonic sensors are employed in the wheels to detect the location of curbs, vehicles, and other obstructions while parking. Controlling driverless cars mean more connectivity and will also allow more passenger freedom, permitting travelers to engage in other activities while leading to a reduction in auto accidents, especially those due to distraction and cellphone use (Figure 2).
## 4 Experimental Results
### Resonant Vibration Case  
Resonant vibration excitation (f = 1.5 Hz; T = 50 s) was applied to the model, where the excitation frequency matched the model's fundamental natural frequency, and the displacement responses under resonant conditions were recorded in Figure 7. The results demonstrate that the peak displacements under uncontrolled and controlled states reached 18.00 mm and 2.5 mm, respectively, achieving an 86.13% vibration-reduction rate. This conclusively verifies the SPPD's effectiveness in suppressing structural resonant responses.  
Figure 6: Response of the model in the free vibration test.
# A Parallel Monte Carlo Algorithm for the Life Cycle Asset Allocation Problem
## 2 Materials and Methods
### Description of Monte Carlo-Based Algorithm for Solving the Bellman Equation
These levels change over time due to investment returns, consumption, and labor income. Capturing these dynamics is essential for optimizing long-term decisions. In dynamic programming, the pension wealth is the state variable. It describes the current financial status. The state transition equation dictates the changes over time, guiding optimal strategy calculations. The utility function is central to dynamic programming and the Bellman equation. It measures satisfaction in each state. In our pension problem, it reflects preferences for consumption and risk aversion. The utility function quantifies immediate utility and expected future utility. Immediate utility indicates satisfaction from current consumption. Expected utility combines immediate utility with the future value function. Monte Carlo simulations help calculate this. Interpolating the future state's value function aggregates immediate utility and the weighted future value. This provides a comprehensive measure of overall utility. Managing the exponential expectation terms in Equation (8) required special handling. Avoiding zero values was crucial, as zero's negative exponentiation can lead to undefined results. To explore the implications of different consumption paths on the overall utility, we set the initial value function for the final year as the terminal value of the utility function. This approach ensures that the terminal value reflects the cumulative effect of all prior consumption decisions. Initialization: set the initial value function at T using the terminal utility function \(U_{T}\). Random Generation of C: for period T, generate \(C_{T}\) randomly from a chosen probability distribution. This Monte Carlo-based algorithm for solving the Bellman equation operates on the principle of learning from complete episodes of interaction with an environment. At each state within an episode, a decision is made to take an action, guided by the probability \(\varepsilon\), leading to a subsequent state and an associated reward. This sequence continues until a terminal state is reached, capturing a full trajectory of states, actions, and rewards. The goal is not to evaluate the quality of a strategy based solely on immediate rewards but to consider the long-term payoffs, which are captured in the state-action value function Q. This function estimates the expected return of taking an action in a given state and following the current policy thereafter. By averaging the returns over multiple samples, the algorithm approximates the expected value, thus providing an estimate of the state-action value function that underlies optimal decision-making according to the Bellman equation. This stochastic approach, incorporating randomness in the strategy choice, is critical for exploring the action space to find an optimal strategy among all possible ones.
## 2 Materials and Methods
### Expert Rule-Based Classification  
Based on the test results, we defined an expert-driven scheme to categorize each athlete's overall functional movement profile. The logic was developed a priori through structured expert consultation. Specifically, two strength and conditioning specialists and one sports physiotherapist with experience in testing and working with youth athletes were consulted in two rounds of meetings. In the first round, experts reviewed test definitions, thresholds, and operational criteria based on normative data and current evidence [1; 11; 14]. In the second round, the classification rules were finalized through consensus using example athlete profiles.  
To operationalize strength and balance cut-offs, we applied a Z-score threshold of \(-0.5\) (i.e., one-half the standard deviation below the sample mean) to flag moderate underperformance. This threshold was chosen based on expert agreement as a simple, interpretable, and team-friendly benchmark rather than an empirically optimized cut-off. It reflects a practical compromise--identifying athletes performing clearly below group norms while minimizing false positives.  
In the next step, we implemented a set of hierarchical rules applied to the soccer data (later also to handball for validation). Each player was assigned to one of four mutually exclusive profile categories in the order of precedence presented in Figure 1.  
This rule hierarchy was intentionally designed to reflect the single most-limiting functional domain in each athlete--mirroring real-world practice where a primary deficit typically guides intervention. Although athletes may present with multiple suboptimal areas, assigning one dominant profile simplifies interpretation and prioritizes individualized training or rehabilitation. We selected the \(-0.5\) SD cut-off a priori to capture mild-to-moderate underperformance without overflagging typical variability. Exploratory tests with stricter (\(-1.0\) SD) and more lenient (\(-0.25\) SD) thresholds showed that the \(-0.5\) SD value preserved interpretability while avoiding excessive reclassification.
## 4 Discussion
### Color Values of SSEFs in Different Conditions  
#### 4.2.1 Color Values at Different pH  
The increases in the concentration of the SWE under the same pH conditions decreased the L* value of the film, while the a* and b* values increased. Considering the pH value of the color of the SSEF, the increases in the SWE concentration contributed to the increasing darkness, redness, and yellowness of the SSFE. As shown by the results of the color measurement, the values of the a*, which indicates the sample's redness, were increased by the increasing pH values. Moreover, the yellowness, indicated by the b* value, decreased with the increasing pH values. The brazilin appeared yellow when the environment was acidic; the color changed to red as the pH rose to an alkaline condition. The lowering L* value also reflected the increased darkness caused by increase in the pH into the alkaline region. The protonation and deprotonation of the hydroxyl (OH) group of polyphenic compounds (such as anthocyanins and other flavonoids), which frequently occurred upon the shifts in the pH, resulted in alterations in the molecular structure of the brazilin and caused changes in the color value [20].
## 5 Conclusions  
1. In view of the narrow density window problem faced by deep well drilling and cementing operations, this study establishes a new calculation model for cementing ECD. This new model does not adopt the assumption of a uniform wellbore but instead establishes a multi-layer structure of the wellbore based on an irregular wellbore, further clarifying the impact of irregular well diameter factors on both the downhole and wellbore ECD. The comparison between the model calculation results and the conventional, temperature, and pressure models proves that an irregular well diameter significantly impacts the downhole and wellbore ECD and provides new ideas for the study of deep well ECD control methods.
2. Based on the random forest method, a quantitative analysis of the calculation results of the new model found that the density of cement slurry and drilling fluid has the most significant impact on the maximum ECD, with the impact reaching 0.3142 and 0.2902, respectively. Moreover, the impact of spacer fluid density, rheological changes, and irregular well diameter cannot be ignored. The main factors that affect the minimum ECD are the density and rheological changes in the drilling fluid, reaching 0.7014 and 0.2846.  
Conceptualization, F.Y. and Z.L.; Methodology, J.S.; Software, H.L. and Y.S.; Validation, J.S. and J.Z.; Data curation, Y.S. and J.Z.; Writing--original draft, F.Y. and H.L.; Writing--review & editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research study was supported by the scientific research starting project of SWPU (No. 2021 QHZ029), the National Natural Science Foundation of China (52274010), and the Sichuan Science and Technology Program (2020 JDTD0019).  
Not applicable.  
**Informed Consent Statement:** Since this study does not involve humans, Informed Consent Statement were waived for this study.  
Figure 14: Weight analysis of influencing factors for the minimum ECD in the annulus.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by PetroChina Tarim Oilfield Company, CNPC. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
###### Abstract  
With the advancement of technology, biometric recognition technology has gained widespread attention in identity authentication due to its high security and convenience. Finger vein recognition, as a biometric technology, utilizes near-infrared imaging to extract subcutaneous vein patterns, offering high security, stability, and anti-spoofing capabilities. Existing research primarily focuses on improving recognition accuracy; however, this often comes at the cost of increased model complexity, which, in turn, affects recognition efficiency, making it difficult to balance accuracy and speed in practical applications. To address this issue, this paper proposes a high-accuracy and high-efficiency finger vein recognition model called Faster Multi-Scale Finger Vein Recognition Network (FMFVNet), which optimizes recognition speed through the FasterNet Block module while ensuring recognition accuracy with the Multi-Scale Convolutional Attention (MSCA) module. Experimental results show that on the FV-USM and SDUMLA-HMT datasets, FMFVNet achieves recognition accuracies of 99.80% and 99.06%, respectively. Furthermore, the model's inference time is reduced to 1.75 ms, representing a 20.8% improvement over the fastest baseline model and a 62.7% improvement over the slowest, achieving more efficient finger vein recognition.  
biometrics; finger vein recognition; deep learning Article
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 3 Data Sources and Benchmarking
### Benchmark Datasets
#### 3.2.2 Datasets  
Artificially generated datasets are not the only type of dataset for stream classification. Often, datasets collected in real-world settings are used as well, as observed in Table 3. The underlying classification tasks range from predicting numerical price changes \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{**Generator**} & **Description** & **Attributes** & **No. Classes** & **Concept Drift** \\ \hline \multirow{7}{*}{**Semi-2017**} & BG-FD [49] & Binary Generator with Feature Drift & 2 categorical & 3 & Feature \\  & Circle [47] & Four contexts defined by four circles & 2 numerical & 2 & Gradual \\  & Gauss [47] & Normally distributed data & 2 numerical & 2 & Abrupt \\  & Mixed [47] & Different functions used & 2 numerical, 2 categorical & 2 & Abrupt \\  & RandomRBP [50] & Random Radial Basis Function & Simple: 10 numerical & Arbitrary & None \\  & R-RBF with drift [50] & Random RBF with drift & Simple: 10 numerical & Arbitrary & Gradual \\  & RRG [51] & Rotating Hyperplane Generator & Arbitrary & Arbitrary & Gradual, Incremental \\  & RRG with drift [50] & RHG with drift for each attribute & Arbitrary & Arbitrary & Incremental \\  & RTG [51] & Random Tree Generator & Simple: 10 numerical, 10 categorical & Arbitrary & Abrupt \\  & RTG-FD [49] & RTG with Feature Drift & Simple: 20/Complex: 100 & 2 & Features \\  & SEA [52] & Streaming Ensemble Algorithm & 3 numerical & 2 & Abrupt \\  & SEA-FD [48] & SEA with Feature Drift & 3 numerical & 2 & Feature \\  & Sine [47] & Points below or above sinus curve & 2 numerical & 2 & Abrupt \\  & STAGER [53] & Boolean function & 3 categorical & 2 & Abrupt \\ \hline \multirow{7}{*}{**Semi-2017**} & AGrawal [54] & Loan applications approval & 6 numerical, 2 categorical & 2 & None \\  & LED [55,56] & Digit prediction on LED display & 24 categorical & 2 & Abrupt \\  & LED with drift [50,56] & LED generator with drift & 24 categorical & 2 & Arbitrary \\  & Rotating checkerboard [57] & Generates virtual drift of checkerboard & 2 numerical & 2 & Gradual \\  & WaveForm [48,55] & Detect Wave Form & Simple: 21 numerical & 3 & Abrupt \\  & WaveForm-FD [48,55] & Detect Wave Form with Feature Drift & Simple: 21 numerical & 3 & Feature \\  & & Complex: 40 numerical & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Synthetic data generators from different application scenarios of stream classification. Above the horizontal line, we summarized artificial generators, while we considered application-oriented generators below.
## Appendix A Illustrative Example of IGLOO's Performance on the Alanine Dipeptide  
To illustrate the behavior of IGLOO, Figure 11 presents results obtained along several iterations of the algorithm to find low-energy conformations of the alanine dipeptide. This molecule corresponds to an alanine residue acetylated in its N-terminus and methyl-lamidated in its C-terminus. Due to its relatively complex energy landscape, despite its small size, alanine dipeptide has been frequently used as test-system in theoretical work (e.g., [49; 50; 51; 52]). It is well known that the two backbone dihedral angles of the alanine residue, \(\Phi\) and \(\Psi\), are good descriptors of the conformational space of this molecule. The background images in Figure 11 represent projections of the energy landscape on these two variables for four different values of an energy threshold: (a) 0 kcal/mol, (b) \(-\)20 kcal/mol, (c) \(-\)35 kcal/mol, (b) \(-\)40 kcal/mol. These landscapes were obtained by systematic grid sampling of the \(\Phi\) and \(\Psi\) angles with constant 1\({}^{\circ}\) step-size followed by local minimization of the other variables. Note that these two-dimensional (2D) landscapes were computed for illustrative purposes, but they were not used for the exploration performed by IGLOO, which operates in the full-dimensional space. In this particular case, IGLOO explores a seven-dimensional space, corresponding to all the variable dihedral angles of the alanine dipeptide.  
The four images in Figure 12a-d represent the result of the exploration of IGLOO at the end of four iterations. They show its ability to explore globally but roughly during the first iterations (Figure 12a,b), before being focused on the lowest-energy basins (Figure 12c,d), with the aim to accurately identify a representative set of distinct low-energy conformations.