esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 6 Discussion
### Implications for Badminton Training and Performance  
These findings have significant implications for badminton training and performance evaluation:1. QE-based Training Programs: Our results suggest that incorporating QE training into existing programs could significantly enhance players' shot accuracy.
2. Real-time Feedback Systems: The real-time capabilities of our model open up possibilities for immediate performance feedback during training and competitions.
3. Personalized Coaching: The individual differences observed in QE strategies suggest the potential for developing personalized visual training programs tailored to each athlete's unique characteristics and skill level.
4. Technology-enhanced Coaching: Our model demonstrates how advanced analytics can augment traditional coaching methods, providing objective, data-driven insights to optimize athlete performance.
## 2 System Modelling
### Power Balance Modelling  
In residential homes, examples of both shiftable and non-shiftable appliances include lights, kitchen appliances (ovens, stoves, kettles), washing machines, dishwashers, air conditioners, and EVs. Kettles, stoves, refrigerators, and air conditioners are examples of non-shiftable equipment that have a steady power output for a predetermined amount of time. However, appliances, such as washing machines and EVs can be used at other times. For instance, shiftable equipment (washing machines) can be used to conserve energy during non-peak times.  
Energy costs can be reduced by rescheduling shiftable equipment. However, this may impede user comfort. Furthermore, heavy electrical demand occurs between 5:00 and 9:00 p.m. as grid power prices steadily rise to their maximum. Therefore, a more flexible strategy is necessary to satisfy the comfort and financial requirements of residential cus  
Figure 1: (**a**) A schematic of the RL-HCPV concept. (**b**) DL and RL in RL-HCPV.  
tomers. SB energy storage systems are an excellent choice for home power demand for peak shifting or shedding reduction. They can be classified as time and power-shiftable loads or power supply resources. In this study, SB and V2H units are utilized to balance demand and supply, reducing both expense and discomfort. The limits for PV, SB, and EV battery charging and discharging power and utility costs are provided by Equations (4)-(7).  
\[0\leq Pr_{pp}(t)\leq Pr_{pv,max} \tag{4}\]  
\[SoC_{SB,max}\geq SoC(t)\geq SoC_{SB,min} \tag{5}\]  
\[SoC_{EV,max}\geq SoC(t)\geq SoC_{EV,min} \tag{6}\]  
\[C(t)\leq C_{1}(t)\leq C_{SG-max}(t) \tag{7}\]  
where, \(Pr_{pp}(t)\) is the power of PV at time \(t\) and \(Pr_{pv,max}\) is the maximum generated power by the PV panels. \(SoC_{SB,max}\) and \(SoC_{SB,min}\) are the maximum and minimum SOC limitations for the SB to avoid battery degradation. Likewise, \(SoC_{EV,max}\) and \(SoC_{EV,min}\) are the maximum and minimum SOC limitations for the EV battery. \(C_{SG-max}\) and \(C_{MG-min}\) are the maximum and minimum utility costs. Equation (8) presents the power balance over the operation.  
\[Pr_{compt}(t)=Pr_{SG}(t)+Pr_{pp}(t)+Pr_{EV}(t) \tag{8}\]  
\(Pr_{compt}\) represents the appliances' power consumption at time \(t\) as the total power supplied by \(Pr_{SG}\), \(Pr_{pv}\), \(Pr_{EV}\); the grid, PV panels, and EV, respectively.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment
### Analysis of Simulation Results
#### 5.2.4 Performance Trend Analysis with BTS Network Density  
The impact of BTS network density on algorithm performance is examined by increasing the BTS density from 0.02 to 0.1, with the results shown in Figures 10 and 11 using bar charts with error bars. Each bar represents the average hop count of an algorithm for a specific BTS network density, and the error bars indicate the standard deviation of 10 independent runs, reflecting the variability in algorithm performance due to different initial conditions.  
Figure 10: Hop count variation with BTS network density.  
Figure 9: Time consumption trend with increasing number of base stations.  
As shown in Figure 10, the average hop count for all algorithms decreases as the BTS network density increases. When the density reaches 0.06 to 0.08, the hop count for all algorithms converges to 1, indicating that nearly all BTS pairs become interconnected. Despite this convergence, QSA consistently achieves a lower hop count across different densities, with smaller error bars compared to other algorithms. This reaffirms QSA's efficiency in reducing latency and its robustness under varying network conditions.  
Figure 11 shows that as BTS density increases, the computational time required by all algorithms generally decreases. This suggests that both QSA and other clustering-based optimization approaches adapt well to varying BTS densities, demonstrating strong scalability and flexibility in edge network deployments.
## 3 Results and Discussion
In the simulation study where graphene is applied, the return loss is quite good, while the radiation efficiency and radiation power are lower than the metal patch and no-patch conditions. To summarize, it is not very useful to use graphene to reduce the size of the antennas or change their geometry, but it is seen that it is mechanically flexible, has optical transparency, is easy to produce with a 3D printer, and even creating a conductive patch with graphene and metal mixture provides better performance. It is known that these examples are valid for simple monoatomic graphene layers, but this situation changes in multi-layer graphene applications with an increase in radiation efficiency [109].
## 5 Simulation and Performance Evaluation  
In order to validate the performance of the MCC-ACO algorithm fully in this study, we carried out multiple Monte Carlo simulation experiments for underwater (3D) node deployment in a MATLAB simulation environment. First, we simulated the node deployment optimization process for a three-dimensional underwater sensor network and analyzed the MCC-ACO algorithm when one instance was run on the node deployment platform. Subsequently, The NDSBAalgorithm [20] and the ACO-MCC3D algorithm [21] are typical ACO-based deployment algorithms, so these two algorithms were chosen for comparisons. The performance of the NDSBA algorithm, the ACO-MCC3D algorithm, and the MCC-ACO algorithm were compared using the following evaluation indicators: moving distance, number of node deployments, and node distribution density. The computing platform for the operating algorithm simulation was an Intel(R) Core(TM) i7-3770 CPU @ 3.40 GHz (4 CPUs) processor, 8 GB RAM, and Windows 7 operating system. During the simulation, we referred to [20; 21], and the parameters of this algorithm and the experimental parameters were set as: \(\lambda=1\), \(\gamma=0.2\), \(\Psi=86\), \(c=5\), \(\alpha=1\), \(\beta=5\), \(r^{s}=15\) m, \(r^{c}=15\) m, \(\rho=0.5\), and \(N_{max}=50\).
## 6 Evaluation
### Experiments of Encrypted Traffic Detection  
As the core of offline validation, the validity of encrypted traffic detection directly determines the authenticity of encrypted traffic samples. If there are some flows with unknown protocols, that is, if the HVP that can be resolved is only TCP or UDP, it is difficult to determine whether the current flow is unencrypted. Unencrypted flow should not interfere with encrypted traffic analysis because there are more direct and definitive analysis schemes. The presence of unencrypted traffic samples affects the authenticity of the samples. Therefore, we compared A3C's encrypted traffic detection module with the current state-of-the-art HEDGE [32] method. HEDGE uses approximate entropy test, block bit frequency test, and cumulative sum test to determine whether a flow is encrypted. The dataset used for encrypted traffic detection and comparison was collected by multiple devices in the national education network environment by the A3C system in December 2021. We conducted several experiments in environment W, each with a sample space of 3000. The average experimental results are shown in Table 7.  
Figure 11: Manual method packets toxic ratio in Windows.  
It can be seen from the results that the SED-CapsNet in A3C is superior to HEDGE in other indexes, except the precision rate is slightly lower than HEDGE, and the detection time is much faster than HEDGE (only nearly 1/8). The three measures used by HEDGE cost many calculation resources. On the contrary, A3C encrypted traffic detection mainly spends time on feature extraction of segmented entropy distribution, and this part of the cost can be further compressed in engineering implementation.
## 1 Introduction
For these reasons, digital PID controllers are preferred in the industrial control field. With the popularity of MCU chips, a simple PID controller can be easily realized by running the digital PID algorithm in cooperation with the ADC and DAC modules of the MCU. In PID theory, the PID parameters determine the performance of the control system, making the search for the optimal PID parameters for the system the most critical step. The optimal PID parameters must enable the control system to stabilize at the desired point as quickly as possible without oscillations and overshoots. For analog closed-loop sensors, the PID parameters are constant when the hardware parameters remain unchanged. This means the optimal PID parameters can be regarded as a system property that does not vary with the system inputs. Various tuning methods can be used to find the optimal PID parameters, such as trial and error methods, experiment-based methods, model-based analytical methods, and automatic tuning methods [27]. The design and analysis of fluxgate current sensors present challenges due to the non-linearity of the B-H curve of the core material [28; 29]. The non-linear characteristics make model-based analytical methods more difficult and complex. Hence, most commercial PID controllers employ automatic methodsthat can adjust the controller's parameters dynamically if there is a change in the system. Integrating more artificial intelligence methods into traditional fluxgate current sensors can significantly improve the sensors [30]. This paper proposes an automatic tuning method called the single-neuron-based self-pre-optimization algorithm to find the optimal PID parameters of the system in a faster and more intelligent way. The developed closed-loop fluxgate current sensor incorporates the single-neuron-based self-pre-optimization algorithm and the conventional PID algorithm to realize precise closed-loop control. The rest of this paper is organized as follows: Section 2 describes the fluxgate technology, focusing on the zero-flux principle under a closed-loop. In Section 3, the developed sensor structure is presented. Subsequently, Section 4 details the closed-loop control algorithms, which contain the conventional positional PID algorithm and the single-neuron-based self-pre-optimization algorithm. Finally, Section 5 presents and analyzes the experimental results, discusses the problems that arise, and proposes countermeasures.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 6 Implementation and Tests with Real Hardware  
In this section, we present some results of the OTA updates, measured in terms of total required time. A real setup is employed with low-end IoT devices based on the ARM-Cortex-M processor, a popular chip family which is optimized for low costs and energy efficiency. In this case, the _only unicast_ version of the OTA procedure is implemented: in our work, there are nodes with different versions of the firmware because they have different sensors and implement various functions. The _only unicast_ option is, therefore, implemented, as it simplifies the management of the firmware versions.  
In addition to the architecture of the system, we will detail the update procedure as it happens in the node. In the last subsection, we will present some results of firmwareupdates performed with real hardware in our lab. In this case, the results will not be presented in terms of the total update time: a real setup has some limitations that do not appear in the simulation environment: number of devices, a high update time caused by duty cycle limitations, and interference, etc.
## 3 Foundation
### Transformers  
The integral components of transformers are (i) self-attention and (ii) multi-headed attention, which is described below.  
* **Self-Attention:** The self-attention estimates the significance of one item with others, explicitly modeling the interactions among them for structured prediction, updating each component via global information aggregation from the entire input sequence as shown in Figure 2. Consider a sequence of \(n\) items with \(d\) embedding dimension i.e., \(X=\{x_{1},x_{2},\cdots,x_{n}\}\in R^{n\times d}\) then the aim is to capture all the interaction, encoding each entity in terms of the global contextual information by three learnable weight matrices, including _Keys_ (\(W^{K}\in R^{n\times d_{k}}\)), _Queries_ (\(W^{Q}\in R^{n\times d_{q}}\)) and _Values_ (\(W^{V}\in R^{n\times d_{c}}\)), then projecting \(X\) on the mentioned matrices to obtain \(K=XW^{K}\), \(Q=XW^{Q}\), and \(V=XW^{V}\) as \[S_{a}=softmax\Bigg{(}\frac{QK^{T}}{\sqrt{d_{q}}}\Bigg{)}V\] (1) Here \(S_{a}\in R^{n\times d_{c}}\) is the self-attention layer's output achieved by computing the dot-product of the query with all keys for a given item; furthermore, softmax is applied to get the normalized attention scores where individual items become the weighted sum of all items. It is to be noted that the attention scores provide weights.
* **Multi-headed Attention:** The multi-head attention shown in Figure 3 is composed of multiple self-attention modules to capture multiple complex relationships between various items in a sequence, where each modules learns the weight matrices \(W^{Q}_{i},W^{K}_{i},W^{V}_{i}\), and \(i=[0,\cdots,(h-1)]\). At the end of multi-head attention, the \(h\) self-attention modules are concatenated \([S_{a0},S_{a1},\cdots,S_{a(h-1)}]\in R^{n\times h\cdot d_{c}}\) and then projected onto a \(W\in R^{h\cdot d_{w}\times d}\) weight matrix.  
Figure 2: **Self-Attention:** The convolutional features that are key, query and value are computed. The attention is calculated next and applied to reweight the values. An output projection is employed to obtain output features of the same size as the input.
## 3 Research Methodology
### QPSO Process  
The role of the QPSO in data analysis in this research consists of searching for an appropriate set of four variables that define the tessellation properties of the search space, namely the tile width and height, and the horizontal and vertical positions with respect to the bottom-left corner point.  
In most \"swarm-based\" optimization processes, a population of candidate solutions is first created within a multidimensional search space. Every candidate solution, also named \"individual\" or \"particle\", is associated with a cost value, which is evaluated according to the particular optimization task. The resulting individual in this QPSO process thus becomes a four-dimensional vector of homogeneous geometrical features. At every iteration, each individual evolves its position in the search space with a law of motion linked to two attractors, namely its own best value and the overall best value that is calculated among the whole population and updated at every loop.  
The QPSO algorithm, first introduced in 2004 [36; 37], is inspired by the Quantum Mechanics concept that every particle is associated with a wave and the fact that quantum waves cover all the search space. The position of each individual is updated in the spirit of Quantum Mechanics and relies heavily on the generation of random numbers. The position update for each individual is governed by two attractors, the average value of all \(N\) individuals' best positions:  
\[\mathbf{m}=\frac{1}{N}{\sum_{i=1}^{N}\mathbf{b}_{i}} \tag{1}\]  
and a linear combination with random coefficients \(c_{1}\) and \(c_{2}\) of the individual and global best:  
\[\mathbf{p}_{i}=\left(c_{1}\mathbf{b}_{i}+c_{2}\mathbf{g}\right)/\left(c_{1}+c_{2}\right) \tag{2}\]  
The position update for iteration \((n+1)\) is then provided by the following expression, which identifies the maximum likelihood coordinated where to find the individual:  
\[\mathbf{x}_{i}(n+1)=\mathbf{p}_{i}+\beta(n){\cdot}\text{sign}(c_{3}-0.5){\cdot}\text{ ln}(c_{4})\left|\mathbf{m}-\mathbf{x}_{i}(n)\right| \tag{3}\]  
where \(c_{3}\) and \(c_{4}\) are uniformly distributed random coefficients and \(\beta(n)\) is a uniformly decreasing parameter that traditionally starts at \(\beta(1)=1\) and reaches \(\beta=0.1\) after a preset number of iterations, at which the algorithm ends.  
Considering that the QPSO procedure involves individuals and positions evaluated by a linear combination of vectors, its applicability is naturally taken into consideration, as in this work the search space is a subset of the multidimensional set \(\mathbb{R}^{4}\). The cost function \(f(x_{i})\) was evaluated for each individual \(x_{i}=(w_{i},\ h_{i},\ u_{i},\ v_{i})\) using an MLP neural network, which was designed to provide the most accurate positioning achievable through appropriate network training.
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 2 Experimental
### Measurements  
Highly reactive HONO was measured with a tunable infrared-laser differential absorption spectrometer (TILDAS) with applied quantum cascade (QC) lasers. QC-TILDAS, developed by the Aerodyne Research Incorporation, is designed for measuring real-time HONO with a high sensitivity (\(\sim\)0.1 ppbv) and short response time (1-10 Hz). It determines the mixing ratio of the measured trace gases by monitoring the absorption of the molecule's radiation at 1273 cm\({}^{-1}\) for measuring HONO. The absorption amount was then compared with the theoretical spectrum of the HIgh-resolution TRANSmission (HITRAN) database to calculate the mixing ratio of HONO. The TDL Wintel data acquisition program installed in the instrument is designed to perform a frequency scan and acquire the resulting absorption spectrum for analysis of the spectra obtained by QC-TILDAS [31]. Dry N\({}_{2}\) (99.999%) background gas was injected at 5-min intervals to clean out the multipass cell (MPC) while stabilizing the baseline for measurement.  
The productions of HONO from the surfaces inside of the chamber were tested along with varying NO\({}_{2}\) concentrations (up to 300 ppbv) under 11.3 W m\({}^{-2}\) of TUV and 500 ppbv of toluene. The highest measured HONO concentration in the blank tests was 0.01 ppbv, which is below the detection limit (\(\sim\)0.1 ppbv) of QC-TILDAS. Although we couldn't quantify the exact HONO productions in our chamber surfaces due to analytical limitation, it was possible to assume that they were zero, or at most, they statistically insignificant.  
Field observations were performed with QC-TILDAS at two different locations in Seoul, Korea. The first field study was conducted during the KORea USA-Air Quality (KORUS-AQ) campaign from May 19 to June 12, 2016; a container top at the Olympic Park site (37.52\({}^{\circ}\) N, 127.12\({}^{\circ}\) E), which has dense vegetation and is located in a highly populated area with heavy traffic surrounding the park, was studied. Another study was conducted on the fourth floor of a building (37.59\({}^{\circ}\) N, 127.08\({}^{\circ}\) E), located in a commercial area of Jungnang district with high levels of traffic and large buildings surrounding the measurement site, from May 1 to June 9, 2019.
# Waste Rubber-Modified Cement-Bound Base Course: Laboratory Characterisation and Field Application
## 2 Laboratory Testing Programme  
### Materials and Test Methods  
As a preliminary research programme, laboratory testing was carried out to investigate the properties monitored during the construction of a cement-bound base course (CBC) within a construction quality control programme. Firstly, the compaction characteristics were determined and analysed following the determination of mechanical properties, in line with a standard field test programme conducted on CBC (7- and 28-day compressive strength test and resistance to freezing and thawing). Within this research, CBC mixtures consisted of equal proportions of three gravel fractions and one fraction of river sand. They were stabilised with various amounts of cement (3%, 5%, and 7% by total aggregate mass) and various amounts of waste rubber (WR) (10%, 20%, 30%, and 40% by sand volume) as a fine fraction (sand) volume substitute. The natural aggregate included gravel and sand from the Sava and Drava rivers, respectively; Portland cement of grade 32.5R (CEM II B/M (P-S)) was used as a binder. The densities of the natural aggregates used (0-4 mm, 4-8 mm, 8-16 mm and sand) were 2.96, 2.63, 2.70, and 2.86 g/cm\({}^{3}\), respectively; WR was 1.123 g/cm\({}^{3}\), as determined by EN 1097-6:2013 [24]. The reference mixture comprised equal proportions of each natural aggregate fraction (25% mass each) with a volume substitution of sand by WR, as presented in Table 1, due to its similar grain size distributions (Figure 1). A Scanning Electron Microscopy (SEM) image (at a 100 times magnification) of sand and WR is presented in Figure 2.  
Figure 1: Grain size distribution of sand and WR.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mix** & **\%Vol Sand Replacement by WR** & **Cement Content (\%Mass of Aggregate)** \\ \hline C0R0 & 0 & 0 \\ C3R0 & 0 & 3 \\ C5R0 & 0 & 5 \\ C7R0 & 0 & 7 \\ \hline C3R10 & 10 & 3 \\ C3R20 & 20 & 3 \\ C3R30 & 30 & 3 \\ C3R40 & 40 & 3 \\ \hline C5R10 & 10 & 5 \\ C5R20 & 20 & 5 \\ C5R30 & 30 & 5 \\ C5R40 & 40 & 5 \\ \hline C7R10 & 10 & 7 \\ C7R20 & 20 & 7 \\ C7R30 & 30 & 7 \\ C7R40 & 40 & 7 \\ \hline \end{tabular}
\end{table}
Table 1: CBC mix composition.  
Figure 2: _Cont._CBC is constructed of well-graded aggregate, a hydraulic binder (cement), and an appropriate amount of water, as determined by a modified Proctor compaction test [25] in laboratory conditions using material compaction parameters. However, in field conditions, granular soils are mostly compacted with vibration compaction using vibrating rollers, potentially making the Proctor test inadequate for obtaining realistic results because it does not use any vibrations during specimen compaction. As an alternative to sample preparation by the Proctor compaction method, the vibratory hammer compaction method [26] was used in this research.