esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Thus, all of the sensor nodes only need to support the same data rate as the worst node so that some sensor nodes may have energy remaining after transmitting their sensing data to the CH. On the other hand, the CH needs more processing for the reception and aggregation of multiple sensing data and has to transmit the aggregated data to the HAP via an uplink; thus, it requires more energy in general (i.e., the CH becomes the highest energy-consuming node in the cluster with high probability). Considering this situation, we apply the SWIPT technique to the considered WPSN so that the sensor nodes could transfer their remaining energy to the CH while transmitting data in a cooperative way. This approach can increase the sensing data rate in the cluster while guaranteeing the ENO of sensor nodes because the CH receives additional energy from its member nodes and the sensor nodes give up only the remaining energy. The objective of our study is to maximize the achievable rate of sensing data while guaranteeing ENO in the considered WPSN. The main contributions can be summarized as follows:  
* We design a frame structure to operate SWIPT in the hierarchical WPSN structure. The frame is divided into WET, SWIPT, and WIT slots, and each sensor node uses either a PS or TS method in the allocated SWIPT slot. * We numerically express the achievable rate of sensing data collected in each cluster subject to the guarantee of ENO of the sensor nodes and obtain the total energy dissipated in the cluster depending on the use of SWIPT. * We develop an algorithm that finds the optimal SWIPT ratio in terms of PS and TS to maximize the achievable rate of sensing data in the cluster. * Finally, we design a clustering and CH selection algorithm based on the K-means clustering algorithm to maximize the achievable rate in the entire network. The rest of this paper is organized as follows. In Section 2, we survey related previous studies and explain the originality of our study. In Section 3, we describe the considered WPSN system and introduce our basic approach. In Section 4, we explain the proposed ENO framework in terms of frame structure, the details of ENO, the optimal SWIPT ratio, and clustering and CH selection. In Section 5, the simulation results are discussed. Finally, we present the conclusions in Section 6.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Determining the EMG-COP Coordination  
Ten 6-s subsets of EMG and COP data, each comprising 1500 time points, were extracted from the central 60 s of the recording [28,41]. The 6-s cross-correlation analysis window was chosen to focus on short-term temporal relationships between EMG signals and COP displacements, minimizing the impact of long-term lags that are less relevant for examining EMG-COP coordination [28]. Although the full 60-s data were used, dividing them into smaller subsets helped refine the results and reduce noise [28]. The strength of the correlation between individual EMG signals and COP displacements within each subset was assessed using normalized cross-correlation analysis, which measures the relationship between two signals [17]. This method improved the reliability of calculating the cross-correlation coefficient (r) at a specific time delay (\(\tau\)) [28]. Correlation values of \(r\) (\(|r|\)) with absolute values ranging from 0.1 to 0.3 were classified as minor, 0.3 to 0.5 as moderate, and 0.5 to 1.0 as large [28]. For statistical comparison, the average \(|r|\) of each participant's subsets was computed, and [PERSON]'s r-to-z transformation was applied to standardize the data [28].
## 1 Introduction
### Contributions of This Study
#### 1.1.2 Fine-Tuning Large Language Models Using LoRA
* To further optimize the usage of parameter space in the transformer model, we apply Low-Rank Adaptation (LoRA), a parameter-efficient tuning method that increases adaptability with a reduced computational footprint.
* All experiments indicate LLaMA 2 fine-tuned with LoRA had the highest score of 96.58% in the F1-score, not meeting that of other approaches.
## 5 Numerical Results
Figure 7: Average BER performance of AO algorithm with different numbers of IRS units, where \(M=12\) and \(K=4\). algorithm, where \(M=12\), \(N=36\), and \(K=4\). It can be seen from Figure 8 that, under different turbulence intensities, the BER performance of the proposed scheme shows a downward trend. However, under stronger turbulence intensities, a higher \(E_{b}\) must be used if the same BER target needs to be achieved. Figure 9 shows the effect of background radiation intensity on the average BER performance of the system, where \(M=16\), \(N=40\), and \(K=4\). The experimental results show that our proposed scheme can resist the influence of strong background radiation. Specifically, even under strong background light of \(E_{b}=-164\) dBJ, the BER of the system can reach \(10^{-4}\). In addition, as the background radiation increases, higher signal energy is required to achieve the same BER. When the background radiation energy per bit \(E_{nb}\) increases from \(-172\) dBJ to \(-168\) dBJ, the required signal energy \(E_{b}\) for BER \(=10^{-4}\) only increases by about \(1.2\) dB. These results demonstrate the tolerance of the proposed scheme to background radiation. In Figure 10, we consider the impact of channel uncertainty on system performance. Imperfect CSI may be caused by estimation errors or delayed feedback from the receiver, which can lead to the incorrect generation of the precoding matrix and thus affect the performance of the algorithm. We characterize the channel irradiance as \(\hat{h}_{k,m}=\delta(h_{k,m})\), where the estimation error \(\delta\) follows an independent and uniformly random distribution within the interval \([-\delta,\delta]\), with \(\delta\) representing the maximum error percentage [44]. In Figure 10, \(\delta=0\) represents perfect CSI, and it can be observed that the proposed algorithm exhibits excellent robustness. Specifically, even with an estimation error percentage as high as \(\delta=0.8\), our scheme can achieve a BER performance of \(10^{-3}\) at \(E_{b}=-164\) dBJ. Figure 8: Average BER performance of AO algorithm under different turbulence channels, where \(M=12\), \(N=36\), and \(K=4\).
## Appendix B Derivation of the Model of the Leakage Rates
### Calculation of Contact Pressure \(p_{contact}\)  
In a non-actuated state, the valve is open, and the fluid can pass through. Applying a positive voltage to the piezoceramic causes a deflection of the actuator towards the valve seat, whereas a negative voltage actively opens the valve and decreases its fluidic resistance. The stroke volume, which is the volume displaced by the piezoelectric actuator, dependsapproximately linearly on the electrical excitation and the applied pressure difference at the diaphragm [31]:  
\[V(p,E_{z})\ =C_{E}\cdot(E_{z}-E_{0})+C_{p}\cdot(p-p_{0}). \tag{100}\]  
Here, \(E_{z}-E_{0}\) is the electric field applied to the piezoceramic with \(E_{0}=0\), \(p_{0}\) is the atmospheric pressure and \(C_{E}\) and \(C_{p}\) are the volumetrical-electrical coupling coefficient and the fluidic capacitance, respectively. In [31], both coefficients are calculated for a circular piezoceramic with radius \(R_{1}\), which is bonded to a circular diaphragm with radius \(R_{2}\) clamped at \(r=R_{2}\). The expressions depend on the thicknesses of the piezoceramic and the diaphragm, their elastic moduli, the Poisson ratios of the piezoceramic and the diaphragm, and the piezoelectric coefficient \(d_{31}\), that quantifies the stretching of the piezoceramic in the \(x_{1}\)-direction for an electric field applied in the \(x_{3}\)-direction.  
To ensure blocking of the fluid, the force of the actuator must be larger than the force exerted to the diaphragm by the pressure of the fluid. The threshold pressure that is necessary to close the channel is called the blocking pressure. It depends on the properties of the piezoceramic material and the difference \(\Delta U\) between the maximal actuation voltage and the minimum actuation voltage. The blocking pressure is defined as the pressure, where the stroke volume is zero and can be derived according to [31]:  
\[p_{\text{block}}=-\frac{C_{E}^{*}}{C_{p}}(U_{+}-U_{-})+p_{0}. \tag{101}\]  
In the equation above,\(C_{E}^{*}=C_{E}/d_{p}\) with \(d_{p}\) is the thickness of the piezoceramic layer, and \(U_{+}\) and \(U_{-}\) are the electric voltages corresponding to the minimal and maximal position of the diaphragm. The contact pressure derived from contact mechanics is related to the blocking pressure. Since the blocking pressure is defined as the actuator force on the diaphragm, but the contact area is smaller than its total surface, the contact pressure is given by the blocking pressure multiplied by the ratio of these two areas. Therefore, the expression for the contact pressure is given by:  
\[p_{\text{contact}}=\frac{A_{diaphrag}}{A_{\text{contact}}}p_{\text{block}} \tag{102}\]  
with \(A_{diaphrag}/A_{\text{contact}}\approx 1.06\). This expression can be inserted into Equation (5) to calculate the average surface separation, which is the key parameter for estimating leakage flow rates in metal seals.
# AAGA: A Priori Adaptive Splitting Algorithm for the Split Delivery Vehicle Routing Problem
###### Abstract
The obtained solution for the resulting CVRP is then translated back to the original SDVRP. Regarding the splitting strategy, the solutions proposed in both [30; 31] are based on a static splitting rule without taking into account the specific characteristics of the problem, such as customers' location and demand. In the method proposed by [30], a fixed splitting rule that is inspired by the US coin denominations is applied to all customer demands and the VRPH solver introduced by [32] is used to solve the resulting CVRP instance. We refer to this algorithm as VRPHAS in the rest of this paper. The numerical evaluation of VRPHAS illustrates its ability to produce acceptable sub-optimal solutions in much less time compared to the state-of-the-art heuristic algorithms. However, the adoption of the introduced splitting rule is not well justified. Instead of applying a fixed rule to all customers, a splitting rule that is adaptive, based on the specific characteristics of a customer can potentially result in an improved solution for the SDVRP. In this paper, we study the problem of defining a good splitting rule for each customer. To this end, we introduce an a priori adaptive splitting algorithm (AASA). In combination with the VRPH solver, AASA results in an improved performance in terms of the optimality gap without significantly increasing the computational complexity of VRPHAS. AASA achieves this by taking into account the distance between the customers and the depot, the value of the customers' demand along with the vehicles' capacity. Similar to VRPHAS, AASA can be used with any CVRP solver. Thus, it provides a method to solve the SDVRP that is easily understandable, can be implemented simply, and generates high-quality solutions very quickly. The rest of the paper is organized as follows. In Section 2.1, we provide a formal definition of the SDVRP. Our proposed solution is explained in Section 2.2. Section 3 presents the numerical results. Finally, the conclusion is discussed in Section 4.
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 2 Measurement Algorithm
### ROI-Based Canny Edge Detection
#### 2.4.1 Gaussian Filtering for Smoothing Images  
Before performing edge detection, the noise in the ROI of the image usually needs to be smoothed. The Gaussian filter is a linear filter obtained by normalizing the two-dimensional Gaussian function after sampling. The formula is in the form of the two-dimensional Gaussian function used by the Gaussian filter in the Canny operator:  
\[G(x,y)=\frac{1}{2\pi\sigma^{2}}e^{(-\frac{x^{2}+y^{2}}{2\sigma^{2}})} \tag{13}\]  
where \(G(x,y)\) is the value of the Gaussian function at the coordinate point (\(x,y\)), and \(\sigma\) (_sigma_) is the standard deviation of a Gaussian function, which determines the width and smoothness of the function.
## 3 Results and Discussion
### Clinker Characterization
#### 3.2.2 XRD Analysis of Clinker  
The mineral phases of clinkers prepared with four biomass alternative fuels at a 20 wt% blending ratio were analyzed using XRD, with the corresponding patterns presented in Figure 6. As shown in Figure 6, the predominant mineral phases in the clinker samples include alite (C\({}_{5}\)S), belite (C\({}_{2}\)S), aluminate (C\({}_{3}\)A), and ferrite (C\({}_{4}\)AF). In the H\({}_{0}\) sample, C\({}_{3}\)S and C\({}_{2}\)S are identified as the dominant mineral phases, characterized by prominent peaks and a normal distribution. Following the addition of biomass alternative fuels, the clinker samples H\({}_{5Y2}\), H\({}_{JM2}\), and H\({}_{YMX2}\) exhibit a weakened characteristic peak for C\({}_{3}\)S and an enhanced peak for C\({}_{2}\)S. This phenomenon occurs because alkali metal oxides in the alternative fuel ash either promote the decomposition of C\({}_{3}\)S or inhibit its formation, leading to the fixation of more SiO\({}_{2}\) in C\({}_{2}\)S [54]. In the H\({}_{0}\) sample, the peaks of C\({}_{3}\)A and C\({}_{4}\)AF are more pronounced, suggesting that aluminate and aluminoferrite are formed under normal conditions. Following the incorporation of biomass fuel, a reduction in peak intensity for the H\({}_{5Y2}\), H\({}_{JM2}\), and H\({}_{YMX2}\) samples was observed, suggesting that some Al\({}_{2}\)O\({}_{3}\) was consumed by alkali sulfates or other by-products. f-CaO and f-MgO are residual phases resulting from incomplete calcination or alkali-induced interference reactions. In the H\({}_{YMX2}\) sample, the characteristic peak of f-CaO is markedly weaker compared to the other four samples, suggesting that an appropriate amount of alkali reduces the formation of free calcium oxide and enhances the burnability of raw meal, a result further corroborated by the free calcium oxide content data presented earlier. In the H\({}_{\text{DG2}}\) sample, a prominent characteristic peak for CaSiO\({}_{3}\) was observed, attributed to the high content of siliceous components in rice straw ash, which led to the formation of more inert CaSiO\({}_{3}\).  
Figure 5: f-CaO content of clinker produced at 1350, 1400, and 1450 \({}^{\circ}\)C.  
Figure 6: XRD of clinker mixed with 20 wt% biomass fuel. 1: C\({}_{3}\)S, 2: C\({}_{2}\)S, 3: C\({}_{3}\)A, 4: C\({}_{4}\)AF, 5: f-CaO, 6: f-MgO, 7: CaSiO\({}_{3}\).
## 1 Introduction  
Wind energy, as a sustainable power source, has become a key component of energy systems worldwide [1]. To improve the efficiency of wind energy utilization, the size of wind turbine blades has been continuously increasing, and the complexity of their material properties and structural design has also advanced. Enhancing blade performance to withstand extreme loads and extending the fatigue life are key challenges in current wind turbine blade design. Owing to the complexity of blade fatigue performance, the accurate prediction of fatigue life through theoretical calculations alone is difficult, and engineering insights or expertise from other fields frequently lack direct applicability to blade fatigue prediction. Therefore, full-scale fatigue testing is commonly used in engineering to validate whether wind turbine blades can meet the service life requirement of 20 years [2].  
To accurately predict the fatigue life, it is essential to establish a reliable model for fatigue damage evolution. In this context, residual strength and stiffness are key parameters for assessing the damage state of composite materials, both of which gradually degrade with the accumulation of fatigue cycles. For engineering components with complex geometry and a large size, residual stiffness can be assessed without the need for destructive testing. Therefore, stiffness is more suitable for evaluating the fatigue damage of the blades [3, 4]. If the residual stiffness of the blades can be predicted via early data during fatigue tests, the cost associated with blade evaluation could be significantly reduced.
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 5 Simulation and Evaluation of DEC and DEC-KM Protocols
### Simulation and Evaluation of DEC-KM
#### 5.2.6 Network's Stability and Lifetime  
Evaluation of the network's stability and lifetime was performed on the basis of computer simulations conducted for both protocols for 5000 rounds and parameters given in Table 1 and the base station located in the middle of the field.  
The following performance metrics were considered:  
* FSD--number of rounds until a first sensor node dies--this parameter defines the network's stability period [17];
* LSD--number of rounds until the last sensor node dies--the period between FSD And LSD defines the network's instability period [17];
* PSA--number of rounds until 90% of sensor nodes are still alive;
* Chart of the number of alive sensor nodes in a round, i.e., the whole number of sensor nodes whose energy is greater than zero;
* Chart of the number of dead nodes in a round.  
The results of the simulations are shown in Table 7 and in  
The obtained results indicated that the DEC-KM ensures reduced energy consumption, outwits standard DEC and extends the network's stability period and the network's lifetime.
# Understanding the Influence of the Head Coach on Soccer Training Drills--An 8 Season Analysis
## 2 Materials and Methods
### Statistical Analysis  
Following satisfactory checks for normality of distribution, linear mixed models were used to assess differences in physical outputs whilst accounting for the repeated measurement of individuals and the unbalanced number of observations. Separate models were constructed for each dependent variable, with 'player identity' being included in all models as a random effect. Training 'drill category,' 'head coach' at the time of data collection and the primary 'position' of the individual player, were specified as fixed effects to assess their influence. A step-up approach to model construction was used, whereby fixed effects were sequentially added to the model in the order specified above and were retained if they demonstrated statistical significance that is \(p<0.05\), and improved the model fit based on an Akaike's information criterion assessment [23]. In the event of significant fixed effects, Bonferroni-adjusted pairwise comparisons and standardized effect sizes, ES, with associated 90% confidence intervals, CI, were calculated to investigate differences between the levels of each fixed effect. Analyses were completed using R Studio (V 3.6.1) using the _lme4_ and _emmeans_ packages and ES were interpreted as: _trivial_ [ES \(<\) 0.2], _small_ [0.2 \(\leq\) ES \(<\) 0.6], _moderate_ [0.6 \(\leq\) ES \(<\) 1.2], _large_ [1.2 \(\leq\) ES \(<\) 2] and _very large_ [\(\geq\)2] effects [24].
## 2 Materials and Methods  
### Dataset  
#### 2.1.1 Gfs, GDAS Reanalysis Dataset  
The Global Forecast System (GFS) ([[http://rda.ucar.edu/datasets/ds083.2/](http://rda.ucar.edu/datasets/ds083.2/)]([http://rda.ucar.edu/datasets/ds083.2/](http://rda.ucar.edu/datasets/ds083.2/)) (accessed on 19 June 2023)) [41] and Global Data Assimilation System (GDAS) ([[http://rda.ucar.edu/datasets/ds083.3/](http://rda.ucar.edu/datasets/ds083.3/)]([http://rda.ucar.edu/datasets/ds083.3/](http://rda.ucar.edu/datasets/ds083.3/)) (accessed on 19 June 2023)) [42] are two meteorological reanalysis datasets developed and maintained by the National Oceanic and Atmospheric Administration (NOAA) of the United States. These two datasets contain variables such as temperature, wind speed, humidity, precipitation, cloud cover, etc. The GFS and GDAS datasets used in this study were from October 2021, with a temporal resolution of 6 h and a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\).
## 3 Proposed Model and its Phases
### Cluster-Head Selection Phase (CHSP) in the First-Level Nodes  
In the CHSP in the first level, \(m\) homogeneous nodes placed in a specific region near the BS and the network control are handed over to the BS, which plays a central role in selecting suitable cluster heads by the central property of the proposed hybrid algorithm. In this level, as BS keeps entire knowledge of the region, after computation the average energy in each round, BS compares the residual energy of each node to average energy and computes the weight of CHs selection in the central region. Meanwhile distributed area networks compute the probabilistic  
After calculating the \(ECR\) of each node, the distance from the BS (\(d_{toBS}\)) is calculated to determine the suitable eligible nodes selected as the CHs in the first level.  
\[Suitability(m)=\frac{E_{r}}{ECR\times d_{toBS}} \tag{5}\]  
By substituting the value of Equation (4) in Equation (5):  
\[Suitability(m)=\frac{E_{r}}{\frac{E_{0}}{E_{0}-E_{r}}\times d_{toBS}} \tag{6}\]  
Suppose \(p\) is the desired percentage of CHs in \(m\) nodes. In that case, the BS will select \(m\times p\) CHs, which have more energy than the average residual energy of \(m\) nodes and have the highest suitability ratio within the first level of the network area.
# A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers
## 3 Models and Simulation Setup
### Global Queue Systems  
In the global queue model (Figure 1a), the tasks are kept in the global FIFO pool of tasks where each idle worker pulls if the queue is not ready. With more than one idle worker, the task can be selected randomly by any idle worker. In a real (non-simulated) global queue architecture, the arrival rate \(\lambda\)i of each worker i cannot be determined centrally. The worker's schedule of pulling times is determined by the system load and the worker's processing rate. An optimal worker's schedule is when workers' idle time is minimized. An optimal task assignment for workers is to minimize the time tasks, wait in a queue, and reduce the idle workers' free time. A TB event occurs when a task is assigned to a random idle worker out of others available at the same time. A \"wrong\" timing is when a task is chosen by one idle worker, causing another idle worker to wait, which may cause higher waiting times for the pending tasks. Thus, many TB events lead to different schedules of pulling times. Our simulation determines how different schedules of pulling times impact the arrival rate \(\lambda\)i of each worker and tasks' waiting times. Specifically, we check a schedule where the task is pulled in FIFO order by a worker who is idle for a longer period than others.  
In a homogenous environment, all workers have the same mean processing rates. In this work, we assume that the processing rates are distributed using an exponential distribution with an average of one. The schedule determined by the pulling times of homogenous workers in the global queue setting is considered optimal because when the queue is not empty while a worker pulls out a new task just after terminating the process of the previous task, the worker's wait time for the next task is minimized. There are very few TB events, and statistical changes cause them.