esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results Analysis
when the time constant is increased from its original value than when it is decreased. When the time constant decreases, the system tends to become unstable. A sweep was conducted for the parameter value of \(\beta\), with the limitation that it can only take values between 0 and 1. The results obtained from the simulation with all arbitrarily considered values are shown in Figure 11.4 The settling time is approximately the same in the three scenarios. However, the percentage overshoot is affected by the parameter variation but the zero steady-state error is still achieved. The time constant parameter \(\tau_{4}\) is more sensitive (meaning it has a greater impact on the stability of the system) than the input coefficient \(\beta_{45}\). An analysis of the obtained results is presented below, along with relevant comments and requisite considerations, while acknowledging that this is an approximation of a complex system such as human behavior. Despite the limitations of the mathematical modeling of human behavior, initial estimates of the MPC controller's parameters were made based on the model obtained. Open-loop tests were conducted to establish the natural and uncontrolled response of the model, followed by closed-loop tests to evaluate the system's response to gradual and  
Figure 11.5 Effects of the time constant parameter \(\tau\) on the number of steps. The controller’s response shows a significant improvement when the time constant is increased from its original value, as opposed to the situation in which it is decreased. It is worth noting that a decrease in the time constant may cause the system to become unstable. Figure 11.6 Effects of modifications of the input coefficient \(\beta_{45}\) parameter on the number of steps. continuous changes in input. The controller successfully regulated both step and ramp signals, although the step response showed a relatively high overshoot. Implementing saturation-limiting elements could address this issue. Finally, closed-loop tests were conducted for different scenarios and the controller was effective in absorbing variations in sensitivity levels depending on the parameter being varied. We should note that the time constant parameter \(\tau_{4}\) appears to have a considerably more significant impact on the stability of the system compared to the input coefficient \(\beta_{45}\). This suggests that it exhibits a higher level of sensitivity concerning the stability of the system.
## 3 Results and Discussion
### Mass and Wave Velocity Damage Analysis
Longitudinal wave propagation in sandstone encountering discontinuity surfaces will be refracted, emitted, and bypassed, resulting in a change of propagation trajectory and speed. Therefore, the smaller the wave velocity inside the sandstone, the more severe the damage. From Figure 10, the Wave Velocity Damage Factor (D\({}_{\rm v}\)) shows a linear increasing trend, indicating that the number of cracks inside the sandstone continuously increases. In conjunction with Figure 10, the sandstone wave velocity is more sensitive to changes in the freeze-thaw cycles, showing an overall near-linear increase. In contrast, the mass damage shows significant changes only at higher numbers of freeze-thaw cycles.
## 2 Materials and Methods
### Destruction Removal Efficiency (DRE)
#### 2.2.2 Calculation of Destruction Removal Efficiency (DRE)  
The average volume flow rate of the exhaust gas from the process can be calculated from the measured concentration of the tracer gas using the QMS based on Equation (1).  
\[\mathrm{F}=\frac{S_{f}}{C_{Kr}\times 10^{-6}} \tag{1}\]  
F: the average volume flow rate of exhaust gas from the process based on a single concentration data points (L/min);  
\(S_{f}\): the volume flow rate of tracer gas injected using a MFC (L/min);  
\(C_{Kr}\): the measured concentration of tracer gas using the QMS (\(\upmu\)mol/mol).  
The average volume flow rate is calculated using Equations (2) and (3);  
\[F_{m}=\frac{n}{1}\frac{F_{i}}{n} \tag{2}\]  
\[\sigma_{Fm}=\sqrt{\frac{1}{n}\frac{\sum_{1}^{n}(F_{i}-F_{m})^{2}}} \tag{3}\]  
\(F_{m}\): the average volume flow rate of tracer gas from 1-\(n\) times measurements (L/min);  
\(F_{i}\): the measured volume flow rate of process emission gas from \(i\) time measurements (L/min);  
\(n\): number of measurements;  
\(\sigma_{Fm}\): relative errors.  
The volume flow rate of FCs can be determined based on the concentration of GHGs that are measured with FT-IR (Equation (4)).  
\[V_{i}=\sum_{l=1}^{N}F_{m,l}C_{i,j}=F_{m,i}\sum_{l=1}^{N}C_{i,j} \tag{4}\]  
\(V_{i}\): the volume flow rate of FC gas \(i\) (L/min);  
\(F_{m}\): the average volume flow rate of tracer gas from 1-\(n\) times the number of measurements (L/min);  
\(C_{i}\): the concentration of FC gas \(i\) (\(\upmu\)mol/mol);  
\(C_{i,j}\): the concentration of FC gas \(i\), \(j\) entering, or being emitted from, the abatement equipment (\(\upmu\)mol/mol);  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gas** & **DRE** \\ \hline CF\({}_{4}\) & 0.89 \\ C\({}_{2}\)F\({}_{6}\) & 0.96 \\ C\({}_{3}\)F\({}_{8}\) & 0.95 \\ C\({}_{4}\)F\({}_{6}\) & 0.99 \\ c-C\({}_{4}\)F\({}_{8}\) & 0.98 \\ C\({}_{4}\)F\({}_{8}\)O & 0.98 \\ C\({}_{5}\)F\({}_{8}\) & 0.98 \\ CHF\({}_{3}\) & 0.98 \\ CHF\({}_{2}\) & 0.98 \\ CH\({}_{3}\)F & 0.98 \\ CH\({}_{2}\)F\({}_{5}\) & 0.95 \\ NF\({}_{3}\) & 0.95 \\ SF\({}_{6}\) & 0.95 \\ N\({}_{2}\)O & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Default DRE factors for GHG emissions in semiconductor and display industries.  
\(j\): the concentration of gas entering, or being emitted from, the abatement equipment.  
Using the volume flow rate of the entering and emitting FCs from Equation (4), the DRE is determined using Equation (5).  
\[\text{DRE}=\left(1-\frac{V_{i,out}}{V_{i,\ in}}\right)\times 100(\%) \tag{5}\]  
\(V_{i,in}\): the volume flow rate of FC gas \(i\) that flows into the abatement equipment per unit time under normal operating conditions (L/min);  
\(V_{i,out}\): the volume flow rate of FC gas \(i\) that flows out of the abatement equipment per unit of time under normal operating conditions (L/min).
# Analysis of Cost Functions for Reinforcement Learning of Reaching Tasks in Humanoid Robots
## 4 Analysis of Influence of Different Cost Function Terms on the Learnt Motions
### Experimental Setting
#### 4.1.4 Systematic Search for the Optimal Weights of the Stability Cost Term  
Based on the findings presented in Sections 4.1.2 and 4.1.3, the trajectory similarity term and the smoothing term did not enhance the task performance. On the other hand, Section 4.1.1 shows that it is important to consider the stability terms. Thus, the most suitable weight configuration includes the weights for position and orientation reaching errors and the stability cost term. As discussed in Section 4.1.1, the weights of position and orientation terms in the cost function can be set to preselected constant values. Here, we discuss how to automate the selection of optimal weight for the stability cost term. To do this, we conducted a grid search across stability weights, ranging from 0 to 60 in increments of 5.  
In Figure 11, we present the average cost associated with each stability weight from the conducted grid search. Clearly, the relationship between the stability weight and the defined cost function as well as the specific cost terms is non-convex. Having a non-convex relation discards the applicability of most of the traditional optimization techniques, such as bisection and gradient-decent approaches. Therefore, a random search or grid search are the simplest approaches to get a better insight of the parameter impact. For the grid search conducted in this experiment, the weight \(w_{stab}\) that results in the minimal overall cost is \(w_{stab}=40\).  
Figure 11: Average cost value over different stability weights. The points represent an average value of the overall task cost \(C\) calculated from the last 50 updates along with the average cost values of the separate cost terms, i.e., \(C_{stab}\), \(C_{pos}\), and \(C_{ori}\) across the last 50 updates.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Position [m]**} & \multicolumn{2}{c}{**Orientation [rad]**} & \multicolumn{2}{c}{**Smoothing Cost**} \\  & **Avg** & **Min** & **Avg** & **Min** & **Avg** & **Min** \\ \hline \(w_{acc}=0.0\) & 0.0020 & 0.0003 & 0.0047 & 0.0008 & 1.4340 & 1.4215 \\ \hline \(w_{acc}=1.0\) & 0.0041 & 0.0015 & 0.0077 & 0.0024 & 1.3749 & 1.3733 \\ \hline \(w_{acc}=1.5\) & 0.0047 & 0.0005 & 0.0109 & 0.0021 & 1.1814 & 1.1779 \\ \hline \(w_{acc}=10\) & 0.0414 & 0.0402 & 0.1195 & 0.1114 & 0.8862 & 0.8851 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical analysis of the reaching accuracy and smoothing cost when using different smoothing cost weights to optimize the reaching motion. The weights of the other cost terms were set as follows: \(w_{pos}=20,\ w_{ori}=4,\ w_{stab}=20,\ w_{traj}=0.0\).
# A Reinforcement-Learning-Based Distributed Resource Selection Algorithm for Massive IoT
## 2 IoT System
### System Model  
In this work, the studied IoT system model is considered to be maximally reasonably simple for the target applications of massive IoT, such as a monitoring application. First, an IoT network, where IoT devices that can only select one of multiple available channels to deliver the frame each time, was established. Extremely simple applications such as a switch or a sensor were mainly considered for each resource-constrained IoT device. Therefore, it was not intended for exchanging numerous frames with much resource usage and large memory capacity. Consequently, these IoT devices were resource constrained, using a battery, equipping one simple half-duplex transceiver, and turning off the radio periodically to save energy. Thus, it was practically difficult to run a complex algorithm on it.  
Moreover, generally, IoT network topologies are star- or tree-based, with the data collected by groups of sensors and sent to a collector or border router. In this work, the IoT devices are assumed to be associated with a gateway with a star topology, as shown in Figure 2, because the star topology is simple enough so that power consumption could be reasonably low for resource-constrained IoT devices. To minimize power consumption, resource-constrained IoT devices assumed in this work do not conduct any complicated process such as channel measuring, data frame receiving, or forwarding.  
In addition, each IoT device accesses the channel for transmission following the procedure of IEEE 802.15.4 non-beacon-enabled mode, in which synchronization is not necessary. Each time an IoT device wakes up when it has to transmit a data frame, it selects a channel for the data frame transmission and performs the CSMA/CA algorithm (see Figure 1), as discussed in Section 2.1, to send the data frame. Regardless of the success of the current frame transmission, the IoT device goes to the sleep mode for a predetermined \"sleep interval\" duration to save power.
## 2 Materials and Methods
### Fabrication of PDMS-Glass Microfluidic Device  
The PDMS-Glass microfluidic devices with proposed printed connectors were fabricated by soft lithography process [32; 33], as shown in Figure 3. Firstly, the silicon wafer was coated by photoresist (AZ4620) with a thickness of 7 \(\upmu\)m. After the photolithography (SUSS Microtec MA6, Garching, Germany) and development (FHD-5), the micro-channels layout was patterned on the silicon wafer. Secondly, the micro-channels pattern at a depth of 100 \(\upmu\)m was etched by the DRIE process. With PR stripping, the silicon wafer could serve as the master mold for PDMS casting. To release the PDMS replica from the mold easily, the surface of the silicon master mold should be hydrophobically modified by the FAS (Fluoroalkylsilane) coating. Therefore, the silicon master mold was put in a vacuum chamber with 100 \(\upmu\)L of Trichlor (1H,1H,2H,2H-perfluorooctyl) silane 97% for 2 h for surface treatment. Thirdly, a PDMS mixture was prepared with a monomer and curing agent at a ratio of 10 to 1. After mixing and degassing (Mazerustar\({}^{\textregistered}\) planetary mixer, Technic Inc.,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Refractive Index at 365 nm** & **Absorption Coefficient at 365 nm (mm\({}^{-1}\))** \\ \hline Glass & 1.54 & 0.001 \\ Aluminum & 0.40 & 1.52 \(\times\) 10\({}^{5}\) \\ Silicone & 1.54 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input parameters in Tracepro ray-tracing.  
Figure 2: The converged UV-LED light source design and its simulation results: (**a**) the converged UV-LED light source attached to the coaxial nozzle; (**b**) dimension clarification of the converged UV-LED; (**c**) the total UV radiation area in an ellipse shape with a length and width of around 13 and 10 mm, respectively; and (**d**) the UV radiation for the pre-curing, which the intensity was estimated to an average of 1.45 W/cm\({}^{2}\) with the area of 1 \(\times\) 6 mm\({}^{2}\).  
Cranston, RI, USA), the PDMS mixture was poured on the silicon master mold and then cured on a hot plate at 80 \({}^{\circ}\)C for 2 h. After that, the cured PDMS layer could be peeled off from the mold and cut into individual pieces. The last step was the PDMS-Glass microfluidic chip assembly. At first, hollow connectors were directly printed on the glass slide with matching holes (\(\O\) 2.5 mm) in advance. The target length of printed connectors was set to 5 mm for sufficient sealing performance, as referred to the typical design of reported microfluidic connectors [8,17]. Then, the glass slide with printed connectors was bonded with an individual PDMS piece by plasma treatments. In detail, bonding surfaces of the glass slide and the PDMS piece were exposed in oxygen plasma for 90 s and then aligned and contacted under a microscope. Finally, the assembled PDMS-Glass device was put on the hot plate at 80 \({}^{\circ}\)C for 1 h for bonding strengthening. Through commercial silicone tubes fitted on the printed connectors by compression sealing, PDMS-Glass microfluidic chips could be connected with the macro world for liquid/gas passage.
## 4 Trade-Offs among Performance Parameters
### Reliability  
The reliability of the traffic behavior of information or video frames is being questioned in the wireless scenario of AR/VR network design due to the temporary outages occurring from the impairments in the signal-to-interference-plus-noise ratio (SINR). However, increasing the reliability always results in additional delay as re-transmission to send the rectified frames at the physical layer by using the parity increases the latency. The most-important reliability aspect in 5G to ensure a smooth and immersive AR/VR experience is obtaining an ultra-high success rate of tracking message signaling with a maximum packet error rate (PER) of \(10^{-5}\). Hence, these packets have to be delivered with ultra-high reliability to ensure a smooth VR service. Based on these requirements, a scheduling optimization framework was shown in [95] to satisfy the trade-offs between stringent latency and reliability constraints for an immersive VR gaming experience in the case of an unsuccessful frame delivery. The HD frames corresponding to the user's upcoming movement and head rotation are rendered by a joint proactive computing and caching scheme following different priority levels for real-time computing. To allocate the mmWave transmission resources to users by prioritizing their requests with tight latency deadlines, a matching algorithm based on the deferred acceptance (DA) matching [107] was considered for matching preferences to meet the requirements for the reliability and latency constraints.
## 5 Proposed Novel Architecture for Stock Market Prediction
### Price Prediction Phase  
In this crucial phase, historical data are meticulously gathered and utilized to generate accurate price predictions. Recognizing the inherent complexity of stock price data, our approach employs long short-term memory (LSTM) due to its efficacy in handling temporal dependencies within time series data. Stock prices often exhibit noise, making direct analysis challenging. To mitigate this challenge, the raw signal undergoes a preprocessing step using variational mode decomposition (VMD) before being fed into the LSTM network as illustrated in Figure 7. VMD plays a pivotal role in enhancing the quality of our predictions. Its unique ability lies in effectively handling noisy data and isolating essential features. Unlike other methods, VMD excels in feature selection, making it robust against noise interference. By identifying the intricate relationship between the asset and market sentiment, VMD provides a solid foundation for our analysis. The architecture leverages the VMD component to address the complexities of real-world signals, which often comprise multiple frequency components. VMD achieves this by employing distinct filters to separate these components. The filtering process, based on intrinsic mode functions (IMFs), proves instrumental in denoising the signals, ensuring that the subsequent time series data are clear and reliable. During the VMD phase, the input signal is intelligently divided into five sub-signals. Each of these sub-signals undergoes meticulous analysis, enabling the generation of precise predictions using the LSTM model. The resulting predicted prices, derived from these sub-signals, are subsequently fed into our deep reinforcement learning (DRL) model, forming a critical link in our comprehensive analysis. It is imperative to note that the VMD phase is executed using the \"vmdpy\" library in Python, ensuring a robust and efficient preprocessing step in our price prediction methodology. This meticulous approach enhances the accuracy and reliability of our predictions, laying a solid foundation for our subsequent analyses.
# Spitzer Resurrector Mission: Advantages for Space Weather Research and Operations
## 3 Mission Design
### Strawman Payloads
#### 3.2.2 Faraday Rotation Experiment (FRE)  
When linearly polarized radiation transverses magnetized plasmas, it undergoes rotation in its polarization plane. This effect, dubbed Faraday Rotation (FR) has been used for decades to remotely probe coronal and heliospheric structures (e.g., [PERSON] et al. 1969 [8]; [PERSON] et al. 2017 [9]; and Figures 4 and 5). FR is a particularly promising remote sensing technique to derive not only the strength but also the 3D configuration of the magnetic field in CMEs while they are still in the corona. This is a valuable capability for SWx operations, as the CME magnetic field is the dominant quantity in assessing a CME's geo-effective potential. The rising interest in SWx impacts across the U.S. civilian and military sectors has renewed interest in FR studies and space applications [10; 11].  
SRM offers an excellent opportunity to demonstrate the SWx value of the FR technique by deploying a radio experiment specifically designed for FR studies. We baseline a linearly polarized beacon at the X-Band (8.1 GHz) that transmits a prescribed signal pattern (i.e.,  
Figure 4: The Faraday Rotation Experiment (FRE) will probe the internal magnetic structure of Earth-directed transients while the Helio Imager provides the large-scale context and density information. FRE transmits an S-band linearly polarized signal that can be received by modestly sized (\(\sim\)15 m) ground stations around the world and used for SWx research and operations.  
Figure 5: The FRE technique has been proven with both spacecraft transmission (**left**: Pioneer 4, from Jensen & Russell 2008 [12] ©AGU) and astronomical sources (**right**: 0842, from [PERSON] et al. 2017 [9] © AAS).  
square wave) through the 75 cm high-gain dual-feed antenna (HGA). The dual-feed system can accommodate both the FRE and downlink from the rest of the payload. The signal pattern facilitates calibration, while the relatively large HGA lowers the power requirement for the transmitter. The system requires a straightforward linear polarimeter at the ground station and may not require the use of the DSN. In any case, [PERSON] et al. (2018) have already demonstrated space-to-ground FR measurements across 1.8 AU with Pioneer 4 using 1970's technology and thus the technical challenges are minimal. During the 3.5-year cruise phase, the HGA is mostly pointed to Earth, except for burns and occasional interruptions for other tests. The FRE beams continuously during this time and allows us to test various techniques for deriving the heliospheric density and magnetic field with the assistance of the HTI observations that provide both context and a line-of-sight density estimate. We will also test and develop methods for ingesting the FRE information into research and operational models of the heliosphere and for developing forecasting workflows with this novel information.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Encoder-Decoder Based Approaches
#### 3.1.5 Memory-Augmented Encoder-Decoder Models  
Memory-augmented encoder-decoder models have emerged as a powerful approach to enrich video captioning with external knowledge. These models integrate external memory to store and retrieve pertinent information, enabling them to capture implicit knowledge and produce more diverse captions. [PERSON] et al. [27] proposed a memory-attended recurrent network that leverages an external memory to store visual and textual context. This context is then accessed during caption generation to enhance the overall context of the captions.  
Building on this concept, [PERSON] et al. [28] proposed the Memory-based Augmentation Network (MAN), as shown in Figure 6. The MAN consists of two key components: a memory-augmented encoder and a decoder. The encoder integrates a visual memory module that stores diverse visual contexts from the dataset and employs contextual-aware attention to extract essential video-related features. Conversely, the decoder incorporates a symmetric textual memory to capture external linguistic cues from dataset descriptions, using linguistic-aware cross-attention to gather relevant language features for each token at every time step. By leveraging visual and textual memories learned from the video-language dataset, the MAN enhances contextual understanding, thereby improving the quality and diversity of generated captions. While the performance of such memory-augmented models depends on the quality and diversity of the stored information, and maintaining the memory can be computationally intensive, their ability to produce high-quality and diverse captions makes them a promising direction in video captioning research.  
Figure 5: Redrawn hierarchical encoder-decoder model (adapted from [26]): HRNAT overview for video captioning (vc-HRNAT) with Event-level (orange), Action-level (light red), and Object-level (green) detail.