esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Case-Study: Parallel Workflows
### Experiment 1: Evaluating Performance of Parallel Workflows
#### 6.1.2 Potential Deadlocks Caused by Capping  
While resource capping offers benefits, there is a potential drawback in the context of Federated Learning: the possibility of deadlocks. This arises from the combination of three factors. First, training rounds commence only when all clients have joined the server. Second, resource reservation employs \"thick-provisioning\", allocating the full requested resources upon creation. Third, each workflow requires the total available resources from the node. The problem occurs when the shared physical resources become insufficient to serve both workflows simultaneously. If a node from workflow B interferes with the scheduling of workflow A, the resources on the node may deplete before all clients of workflow A are scheduled. Consequently, _the federated learning server will not dispatch jobs to  
Figure 14: Per-client CPU utilization for parallel workloads.  
Figure 13: Average CPU utilization across all clients of parallel workloads. Properly capping the CPU can yield more predictable and stable utilization profiles.  
the scheduled clients, rendering them idle or wasting resources, and mitigation of the deadlock risks needs careful resource management and allocation strategies._
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
###### Abstract  
This paper proposes a dynamic high-type control (DHTC) method based on an interval type-2 fuzzy logic controller (IT2 FLC), which is used in the photoelectric tracking system to improve the steady-state accuracy and response speed. Adding integrators to the traditional multi-loop feedback control loop can increase the system type, thereby speeding up the response speed and improving the steady-state accuracy, but there is a risk of integral saturation. Switching the type dynamically according to the system state can avoid integral saturation while retaining the advantages of the high-type. Fuzzy logic control (FLC) can dynamically change the output value according to the input change and has the advantages of fast response speed and strong ability to handle uncertainties. Therefore, in this paper, the FLC is introduced into the high-type control system, and the output of the FLC is used as the gain of the integrator to control the on-off to achieve the goal of dynamic switching type, which is successfully verified in the experiment. IT2 FLC introduces a three-dimensional membership function, which further improves the FLC's ability to handle uncertainties. From the experimental results, compared with T1 FLC, IT2 FLC's ability to handle uncertainties is significantly improved. In addition, in order to speed up the calculation speed of IT2 FLC, this paper proposes an improved type-reduction algorithm, which is called weighted-trapezoidal Nie-Tan (WTNT). Compared with the traditional type-reduction algorithm, WTNT has faster calculation speed and better steady-state accuracy, and has been successfully applied to real-time control systems, which has good engineering application value. Finally, in order to reduce the interference of human factors and improve the automation level of the system, a multi-population genetic algorithm (MPGA) is used to iteratively optimize the parameters of the FLC, which improves the output accuracy. On the experimental platform of the flexible fast steering mirror (FFSM), the control effects of the traditional controller, T1 FLC and IT2 FLC are compared, which proves that the IT2 FLC-DHTC system has a faster response performance, higher steady-state accuracy, and stronger ability to handle uncertainties.  
dynamic high-type control (DHTC); fuzzy logic control (FLC); interval type-2 fuzzy logic controller (IT2 FLC); photoelectric tracking system; multi-population genetic algorithm (MPGA)
## 3 Incremental Nonlinear Dynamic Inversion Control Design
### INDI Acceleration Controller  
In this section, we design an INDI control law to track the desired trajectory. We define the state \(x=\begin{bmatrix}p&v\end{bmatrix}^{T}\). As given by (8), \(u\) is a vector composed of the squares of motor speeds. From (1) and (2) we can obtain:  
\[\dot{x} =\begin{bmatrix}\dot{p}\\ \dot{v}\end{bmatrix} \tag{14}\] \[=\begin{bmatrix}0&1\\ 0&0\end{bmatrix}\begin{bmatrix}p\\ v\end{bmatrix}+\begin{bmatrix}0\\ g+m^{-1}(Rf_{T}e_{3}+f_{ext})\end{bmatrix}\] \[=\underbrace{\begin{bmatrix}0&1\\ 0&0\end{bmatrix}\begin{bmatrix}p\\ v\end{bmatrix}}_{f(x)}+\underbrace{\begin{bmatrix}0\\ g+m^{-1}(R\begin{bmatrix}0\\ 0\\ B(1:)\end{bmatrix}\begin{bmatrix}\omega_{1}^{2}\\ \omega_{2}^{3}\\ \omega_{4}^{3}\end{bmatrix}+f_{ext})\\ g(x,u)\end{bmatrix}}_{g(x,u)}.\]  
We denote \(F=Rf_{T}e_{3}\in\mathbb{R}^{3}\). \(F_{0}\) and \(f_{ext0}\in\mathbb{R}^{3}\) denote the force from propellers and external disturbances at the current moment, respectively. \(\Delta F=F-F_{0}\), \(\Delta f_{ext}=f_{ext}-f_{ext0}\). To obtain a stable command signal while flying at a large attitude, the control command is designed as the control force in the inertial frame. Similar to (13) we can take a Taylor series expansion:  
\[\dot{v}=\dot{v}_{0}+m^{-1}(\Delta F+\Delta f_{ext}), \tag{15}\]  
\[\dot{v}_{0}=g+m^{-1}F_{0}+m^{-1}f_{ext0}, \tag{16}\]  
where \(\dot{v}_{0}\in\mathbb{R}^{3}\) denotes the derivative of velocity at the current moment and can be measured directly. Defining \(v_{d}\in\mathbb{R}^{3}\) as the reference velocity, the velocity error \(e_{v}\in\mathbb{R}^{3}\) can be written as follows:  
\[e_{v}=v-v_{d}. \tag{17}\]  
Similarly, the position error is defined as \(e_{p}\in\mathbb{R}^{3}\). The increment of the control force is designed as:  
\[\Delta F=m(-k_{x}e_{p}-k_{y}e_{v}-\dot{v}_{0}+\dot{v}_{d}), \tag{18}\]  
where \(k_{x}\in\mathbb{R}^{3\times 3}\) is the diagonal gain matrix in the position loop and \(k_{v}\in\mathbb{R}^{3\times 3}\) is the diagonal gain matrix in the velocity loop. Take the derivative of both sides of (17):  
\[\dot{e_{v}}=-k_{x}e_{p}-k_{v}e_{v}+m^{-1}f_{ext0}, \tag{19}\]  
where \(k_{x}\) and \(k_{v}\) are chosen based on the conclusions in [15] to ensure that the system is asymptotically stable when it is free of disturbance. For instance, \(k_{x}\) and \(k_{v}\) are Hurwitz. Then the command force \(F_{c}\in\mathbb{R}^{3}\) will be  
\[F_{c}=F_{0}+\Delta F. \tag{20}\]  
Once the force is determined, we can calculate the input motor speeds. The incremental property enables the controller to control the acceleration in the presence of disturbances or model errors.
###### Abstract
Nevertheless, the accuracy of BIA in predicting body fat percentage (BF) may decrease in athletes with higher statures and longer body segments, due to overestimation of lean body mass [11]. This critical aspect may be accounted for by evaluating taller samples, such as basketball players, and highlights the need to develop specific equations. An alternative methodological approach, qualitative analysis through Bioelectrical Impedance Vector Analysis (BIVA) overpasses typical concerns associated with the use of regression equations for predicting body composition and consists of the interpretation of the raw BIA parameters (resistance R and reactance Xc) related to the subject's stature [8]. BIVA's ability to properly assess body composition over time was compared with gold-standard methods, such as the four-compartment model [7], dilution technique [11], and Dual Energy X-ray Absorptiometry (DXA) [12], showing comparable results when assessing body fluid and soft-tissue changes in athletes during the competitive season. BIVA data are scarce for athletes, particularly in adolescence, and their complementary use may be helpful for the evaluation of changes in nutrition and hydration states during growth and maturation. Considering the rapid evolution of basketball in recent years, and how athletes' physical and physiological profiles have changed, it is necessary to have up-to-date benchmarks, possibly on the best-performing population, also considering a qualitative approach, never applied to basketball players. Thus, the present study aimed to determine the morphological characteristics of a sample of young elite basketball players, according to the somatotype and the BIVA methods, and develop reference tolerance ellipses for the BIVA point graph. In addition, it experiments with a new simple method to synthesize and easily acquire somatotypes. Reference values for basketball players are needed by researchers, coaches, and practitioners alike when evaluating players for talent identification and development.
## 5 Online Classification Experiment
### Results and Discussions
#### 5.2.5 Erroneous Classification  
Further analysis shows that erroneous classification sometimes occurs for motions associated with fingers. As shown in Figure 15, for example, when the participant flexes his/her ring finger, the ANN sometimes misclassifies the motion as flexion of the middle finger. This happens because as the participant tries to flex the ring finger, the middle finger also flexes as well. Another example is when the participant extends his/her index finger or performs hand opening motion, the ANN misclassifies the intended motion as extension of the ring finger. In this case, misclassification happens because the muscles associated with the movement of these fingers are very close to each other which makes it difficult to obtain distinguishable EMG patterns. Interestingly, the motions that often get misclassified are different depending on who the user is. This may be due to the fact that each participant executes each motion in a different manner. An extension to this study would be to discuss ways to deal with these erroneous classifications in order to improve the accuracy.  
Figure 14: Comparison of mean correct rates of each classification scenario (9 movements). (*) represents \(p<0.05\).  
Figure 15: Confusion matrix of classification performance across subject. The 17 motions include pronation (pro), supination (sup), flexion (flex), extension (ext), hand grasping (grasp), hand opening (open), thumb flexion (TF) and extension (TE), index finger flexion (IF) and extension (IE), middle finger flexion (MF) and extension (ME), ring finger flexion (RF) and extension (RE), little finger flexion (LF) and extension (LE), and rest state (rest).
# A Study on the Impact of Blue Light and Its Brightness on Long-Term Memory
## 1 Introduction
(2017) reported the superiority of individuals exposed to blue light for 30 min after completing learning tasks assessing verbal memory's delayed performance compared to those exposed to amber light for the same duration [24]. On the other hand, [PERSON] et al. (2017) reported that a 210 min exposure to blue light during the daytime did not result in any improvement in the participants' declarative memory performance [25]. In addition, previous studies on the effect of blue light on memory did not employ its brightness as an experimental parameter [16; 17; 21]. Brightness is among the key parameters of light that significantly affect attention, working memory, and long-term memory. Some studies have concluded that the brighter the light is, the more positively it affects attention and memory [2; 12; 21], but other studies have reported that there is no such effect of light brightness on memory or even demonstrated the opposite results [26; 27; 28]. For example, [PERSON] & [PERSON] (2020) argued that the best performance in attention was achieved at the highest illuminance of 1000 lux compared to other conditions of 300, 400, and 500 lux, but long-term memory performance was the highest at the lowest illuminance of 400 lux [11]. In this context, both light type (blue light) and brightness should be comprehensively considered to gain accurate insights into the influence of blue light on memory. In this study, the effect of blue light and its brightness on long-term memory is experimentally evaluated, and the results are compared with those measured using red light to confirm the validity of the experimental results. This study also focuses on confirming whether blue light has the same effect on long-term memory as it does on attention and working memory and further determines whether the same relationships are found under red light conditions. Additionally, the effect of the brightness of both blue light and red light on memory enhancement is explored. Two different brightness levels of 1000 lux and 400 lux were applied, and red light (3000 K) was employed as a comparison group to systematically assess the effect of blue light (7000 K) and its brightness on long-term memory.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 4. Results and Discussions
### Results of Baselines  
Firstly, the results of the experiment for the selection of a baseline described in Section 3.4.1 are shown in Table 3. In this table, the MAE of the LSTM technique was 27.12, which was better than the result for ARIMA, SVR, and ConvLSTM. Thus, the LSTM model was selected as the baseline.
# Stability Analysis of Soil Flow Protector and Design Method for Estimating Optimal Length
## 3 Results and Analysis
### Optimal Length Ratio  
The optimal length ratio (OLR) is the installation length of the SFP divided by the height (\(h\)) of the box structure; this is used in practical field applications. It is difficult to apply the results of scaled model experiments to a full-size structure. Therefore, it is advantageous to apply the OLR while considering the height of the structure.  
To calculate the OLR of the SFP, previously reported results and those from the scaled model experiments conducted in this study can be depicted as a graph of the relationship between the surface settlement ratio and the ratio of the lower length of the SFP to the box structure height, as shown in Figure 10. The relationship is represented by Equation (1), where \(\alpha=1.394\) and \(\beta=0.297\). The results show that the surface settlement decreases with the increase in the length, and the trend gradually converges above a certain length. The optimum surface settlement ratio and the ratio of the lower length of the SFP to the height of the box structure (\(h=250\) mm) are 0.34 and 0.45, respectively.  
\[y=y_{0}-\frac{x}{\alpha x+\beta} \tag{1}\]
## 5 Case Study
### Impact of Energy Storage Capacity  
From the result of Section 5.2, although community operator 1 and 2 have equipped gas boiler and energy storage, they can only participate in the 1 st grade of DR resource in a few time slots. The main reason is due to the limitation of percentage of heat demand and discharging power of energy storage. Considering the percentage of heat demand in the whole demand is generally stable within a certain range, hence, it is difficult for community operator with gas boiler to further improve the grade of DR resource. However, community operator with energy storage can further improve the grade of resource by configuring larger discharging capacity. Hence, in this section, discharging power of energy storage will be discussed to analyze the influence on the bidding strategy and profit of community operators.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Community Operator** & **Operator 1** & **Operator 2** & **Operator 3** \\ \hline Profit from DR purchaser & 31.54 & 36.41 & 9.97 \\ Profit from gas boiler & 1.23 & 0 & 0 \\ Profit from energy storage & 0 & \(-\)0.63 & 0 \\ \hline Total profit & 32.77 & 35.78 & 9.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Profit of 3 community operators (\(\times 10^{3}\) RMB).  
Figure 3: Bidding amount of 3 community operators.  
Total profit of 3 community operators and bidding amount of 3 grades of DR resource for different storage capacity are presented in Figures 4 and 5, respectively. In the simulation, discharging energy of storage in 15 min is set to 0 MWh, 0.2 MWh, 0.4 MWh, 0.6 MWh, and the corresponding storage capacity is set to 0 MWh, 2.5 MWh, 5 MWh, 5 MWh, respectively. From Figure 4, one can see that with the increasing of storage capacity, the profit of community operator 2 is increased gradually, while the profits of community operator 1 and 3 are affected in different degree. It is obvious that with the increasing of storage capacity, the grade of DR resource has been gradually improved from 3 rd grade to 1 st grade. Hence, community operator 2's profit has a sustained growth. However, since the market price of 3 grades of DR resource will be affected with the change of storage capacity, the other 2 operators' profits will also be affected. For example, when discharging energy is changed from 0.2 MWh to 0.4 MWh, DR resource in most of time has been improved from 3 rd grade to 2 nd grade. Consequently, the market price for 3 rd grade will be increased while for 2 nd grade will be declined. Therefore, operator 1's profit will be declined and operator 3's profit will be increased. The bidding amount of different grades of DR resource in Figure 5 has verified the result of community operators' profit in Figure 4. By analyzing the impact of energy storage capacity on the bidding strategy and profit, it shows that the configuration of storage capacity is an important research point for the proposed DR program, which will be our next research direction in the future.  
Figure 4: Total profit of 3 community operators for different storage capacity.
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 4 Discussion
### Tactile Warnings as the Best Potential Unimodal Warnings  
To investigate which unimodal warning system facilitates drivers' response time to the best degree, comparisons between visual, auditory and tactile warnings were conducted. The results verified our hypothesis, in which among the three unimodal warnings, tactile warnings can best facilitate drivers' task performance.  
Further comparison between auditory and visual warnings showed that there was no significant difference between these two warning conditions. Noting that auditory warnings require a higher level of cognitive demand [106; 107] which is limited during the driving process. As proposed by previous researchers, auditory stimuli in the driving environment (e.g., mobile phone prompts, conversations, music etc.) would occupy auditory attention resources, leading to cognitive overload similar to the visual attention overload [16; 80]; therefore impairing the effectiveness of the auditory warnings. Besides the fact that a high cognitive level is needed for auditory information processing, the nature of the auditory warning itself is also a factor to be considered. [PERSON] and [PERSON] pointed out that adjusting parameters such as the frequency of sound helps strengthening individuals' ability to respond [108]. Nevertheless, tactile warnings can hardly be interfered with during its execution as the information in the driving environment is mostly auditory or visual [75]. Overall, the current results showed that tactile warnings have more advantages over auditory warnings and visual warnings in facilitating drivers' response time, allowing tactile warning systems to potentially be the best unimodal warning system.
# Development of a Staggered PCD End Mill for Carbon Fiber Reinforced Plastic
## 6 Effects of Fiber Cutting Angle on Milling Force
### Influence of Trimming Fiber Orientation on Milling Force  
The staggered PCD end mill is used in the milling of unidirectional fiber laminates. The milling parameters are: \(\
u=100\mathrm{m}/\mathrm{min},\ a_{e}=2\ \mathrm{mm}\) and \(f_{z}=0.01,\ 0.02\ \mathrm{and}\ 0.03\ \mathrm{mm}/\mathrm{z}\). The fiber orientation angle is \(30^{\circ}\), \(60^{\circ}\), \(90^{\circ}\), \(120^{\circ}\) and \(150^{\circ}\). The influence of fiber-cutting angle on the main cutting force is shown in Figure 10.  
It is seen that the sequence of the size of the main cutting force under different fiber-cutting angles is \(30^{\circ}>60^{\circ}>90^{\circ}>150^{\circ}>120^{\circ}\). That is, the cutting force under down milling (\(0^{\circ}<\theta<90^{\circ}\))  
Figure 10: Influence fiber-cutting angle on the main cutting force.  
is greater than it in up milling (90\({}^{\circ}<\) 0 \(<\) 180\({}^{\circ}\)). During down milling, tensile fracture of carbon fiber is found, and with the increasing of the fiber-cutting angle, tensile fracture of carbon fiber occurs more and more. During up milling, the fracture of the fiber is mainly bending fracture, and with the increase in the fiber-cutting angle, the bending fracture of the material occurs more and more. As the tensile strength of the material is higher than the bending fracture strength, in terms of cutting force, the cutting performance of up milling is better than down milling. During down milling, the cutting force increases firstly and then decreases with the increase in feed per tooth; during up milling, the cutting force decreases firstly and then increases with the increase in feed per tooth.
# A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexopod Robots
## 3 Hexapod Robot Kinematics Modeling  
The robot's kinematic model can help us quantitatively analyze the robot's velocity, acceleration, attitude, and so on from a mathematical point of view. To establish the kinematic model of the hexapod robot, for each leg \(i\), three coordinates are defined at the hip joint (\(\{O_{i1}\}\)), the knee joint (\(\{O_{i2}\}\)), and the ankle joint (\(\{O_{i3}\}\)), respectively, as depicted in Figure 4. The reference coordinate is \(\{O_{i0}\}\). For each coordinate \(\{O_{ij}\}(i=1,\cdots,6;j=1,\cdots,3)\), the \(Z_{ij}\) axis coincides with the axis of the \(j\)th joint. The \(X_{ij}\) axis is the common perpendicular between the axes of the \(j\)th joint and the \((j+1)\)th joint. The \(Y_{ij}\) axis is then determined according to the right-hand rule. In this paper, the kinematic model is based on the DH (Denavit-Hartenberg) method [23], and the parameters are provided in Table 2. Since the six legs are the same, we do not distinguish the leg number \(i\) anymore, and only the joint number \(j\) is considered in the description that follows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Structure**} & \multicolumn{3}{c}{**Parameters**} \\ \cline{2-4}  & & **Value** & **Unit** \\ \hline \multirow{2}{*}{Servo motor} & Number & 18 & \(\backslash\) \\  & Weight & 57.0 & g \\ \hline Power supply & Voltage & 7.4 & DC(V) \\ \hline \multirow{3}{*}{Body} & Length & 254.1 & mm \\  & Width & 179.3 & mm \\  & Height & 3.0 & mm \\ \hline \multirow{2}{*}{Leg \(Link_{i1}\)} & Length & 45.5 & mm \\  & Weight & 18.8 & g \\ \hline \multirow{2}{*}{Leg \(Link_{i2}\)} & Length & 75.0 & mm \\  & Weight & 14.7 & g \\ \hline \multirow{2}{*}{Leg \(Link_{i3}\)} & Length & 109.6 & mm \\  & Weight & 41.1 & g \\ \hline \multirow{3}{*}{Foot} & Radius & 9.5 & mm \\  & Length & 19.5 & mm \\ \cline{1-1}  & Weight & 6.3 & g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the hexapod robot prototype.  
Figure 4: Coordinates at different joints for each leg.  
The relative translations and rotations between the \((j-1)\)th and the \(j\)th joint coordinates are computed by the transformation matrix (1).  
\[\genfrac{}{}{0.0 pt}{}{j-1}{T=}\left[\begin{array}{cccc}\cos\theta_{j}&-\sin \theta_{j}&0&a_{j-1}\\ \sin\theta_{j}\cos\alpha_{j-1}&\cos\theta_{j}\cos\alpha_{j-1}&-\sin\alpha_{j- 1}&-\sin\alpha_{j-1}d_{j}\\ \sin\theta_{j}\sin\alpha_{j-1}&\cos\theta_{j}\sin\alpha_{j-1}&\cos\alpha_{j- 1}d_{j}\\ 0&0&0&1\end{array}\right]. \tag{1}\]