esa-sceva/satcom-chunk-collection huggingface dataset:
# Sliding Balance Control of a Point-Foot Biped Robot Based on a Dual-Objective Convergent Equation
## 3 QP Controller with Multiple Constraints
### Dynamic Constraints  
The dynamics of the model with the configuration described in Section 2.1 are expressed by the Euler-Lagrange equation:  
\[D(q)\ddot{q}+C(q,\dot{q})\dot{q}+G(q)=B\tau+J(q)^{T}F, \tag{38}\]  
where \(D(q)\in R^{5\times 5}\) is the inertia matrix, \(C(q,\dot{q})\in R^{5\times 5}\) is the matrix of Coriolis and centrifugal terms, \(G(q)\in R^{5}\) is the gravity vector, \(\tau\in R^{2}\) is the torque of joints, and \(B\in R^{5\times 2}\) is the generalized force mapping matrix. For a point-foot model, \(F=\big{[}F_{x},F_{y}\big{]}\) is a vector comprising the x-axis and y-axis components of the contact force, and \(J(q)\in R^{5\times 2}\) is the Jacobian of coordinates of the contact point.  
During the process of high-speed sliding, the contact force needs to satisfy (7) and (8) so that the dynamics Equation (38) can be rewritten as  
\[D_{sl}\ddot{q}+C_{sl}\dot{q}+G_{sl}=B\tau, \tag{39}\]  
where  
\[D_{sl}=D(q)-mJ(q)^{T}\bigg{[}0^{2\times 2}\quad\begin{matrix}-\mu J_{y}\\ J_{y}\end{matrix}\bigg{]}, \tag{40}\]  
\[C_{sl}=C(q,\dot{q})-mJ(q)^{T}\bigg{[}0^{2\times 2}\quad\begin{matrix}-\mu J_{y} \\ J_{y}\end{matrix}\bigg{]}, \tag{41}\]  
\[G_{sl}=G(q)-mJ(q)^{T}\bigg{[}\begin{matrix}-\mu g\\ g\end{matrix}\bigg{]}. \tag{42}\]Similarly, according to Equations (8) and (16), the dynamic equation for standing is rewritten as  
\[D_{st}\ddot{q}+C_{st}\dot{q}+G_{st}=B\tau, \tag{43}\]  
where  
\[D_{st}=D(q)-mJ(q)^{T}\bigg{[}0^{2\times 2}\begin{array}{c}J_{x}\\ J_{y}\end{array}\bigg{]}, \tag{44}\]  
\[C_{st}=C(q,\dot{q})-mJ(q)^{T}\bigg{[}0^{2\times 2}\begin{array}{c}J_{x}\\ J_{y}\end{array}\bigg{]}, \tag{45}\]  
\[G_{st}=G(q)-mJ(q)^{T}\bigg{[}\begin{matrix}0\\ g\end{matrix}\bigg{]}. \tag{46}\]  
Using \(X\) to represent \(sl\) and \(st\), the dynamic Equations (39) and (43) are rewritten in the standard form of dynamic constraints in QP:  
\[\big{[}D_{X}\quad-B\quad 0^{1\times 2}\big{]}u=-C_{X}\dot{q}-G_{X}. \tag{47}\]
## 5 Experiments
### Experimental Setting  
In this paper, the dataset is randomly divided into 10 subsets, each time using 8 subsets as the training set, 1 subset as the validation set, and 1 subset as the test set. To ensure result stability, the random division is repeated 10 times. All variables, except for the diagnostic category, undergo z-normalization. In both the validation and test sets, each sample's first half of time points are utilized to forecast the second half of time points for the same subject. If the number of time points is odd, the last time point is excluded from the prediction. Performance evaluation metrics are computed for each division, and the average of these metrics over the 10 repetitions is considered as the final result.  
In line with the TADPOLE challenge, we employ two metrics, namely multiclass area under the operating curve (mAUC), and balanced class accuracy (BCA), to assess the accuracy of diagnosis classification. A higher value of BCA and mAUC indicates superior performance in terms of classification accuracy.  
mAUC can be calculated as:  
\[mAUC=\frac{2}{K(K-1)}{\sum_{i=2}^{K}{\sum_{j=1}^{i}{\hat{A}\big{(}c_{i},c_{j} \big{)}}}} \tag{18}\]  
where \(K\) denotes the number of classes, \(\hat{A}\big{(}c_{i},c_{j}\big{)}\) is the mean AUC of any two classes, and \(\hat{A}\big{(}c_{i},c_{j}\big{)}\) can be calculated as:  
\[\hat{A}\big{(}c_{i},c_{j}\big{)}=\frac{\hat{A}\big{(}c_{i}|c_{j}\big{)}+\hat{A }\big{(}c_{j}|c_{i}\big{)}}{2} \tag{19}\]  
\(\hat{A}\big{(}c_{i}|c_{j}\big{)}\) is the AUC of a class \(c_{i}\) against another class \(c_{j}\), its calculation formula is as follows:  
\[\hat{A}\big{(}c_{i}|c_{j}\big{)}=\frac{S_{i}-n_{i}(n_{i}+1)/2}{n_{i}n_{j}} \tag{20}\]  
where \(n_{i}\) and \(n_{j}\) represent the quantities of data points associated with classes \(i\) and \(j\), respectively. \(S_{i}\) denotes the cumulative sum of the ranks assigned to the test points from class \(i\) after arranging all the data points from classes \(i\) and \(j\) in ascending order based on the probability of belonging to class \(i\).  
BCA can be obtained as:  
\[BCA=\frac{1}{K}{\sum_{k=1}^{K}{BCA_{k}}} \tag{21}\]  
where \(BCA_{k}\) is the balanced accuracy for class \(k\):  
\[BCA_{k}=\frac{1}{2}\bigg{[}\frac{TP}{TP+FN}+\frac{TN}{TN+FP}\bigg{]} \tag{22}\]where TP, FP, TN, and FN represent the quantities of true positives, false positives, true negatives, and false negatives, respectively, for classifying as class \(k\).  
The MAE can be calculated as:  
\[MAE=\frac{1}{N_{p}}\sum_{i=1}^{N_{p}}\left|\widetilde{M}_{i}-M_{i}\right| \tag{23}\]  
where \(N_{p}\) is the number of data points acquired by the time the forecasts are evaluated, \(M_{i}\) is \(i\)-th individual's true value, and \(\widetilde{M}_{i}\) is the corresponding predicted score.  
It is important to mention that all experiments were conducted on a personal computer equipped with an AMD Ryzen 7 4800 CPU and an NVIDIA RTX 2060 GPU.
# Experimental Study on Streamwise Vortex-Induced Vibration of a Flexible, Slender Cylinder
## 1 Introduction
One mode, i.e., the SA mode (treated as a special case of the A-II mode), was found in the second excitation region. The competition between the S-I and A-II modes in the first excitation region and transient behaviors of the wake were further analyzed in [PERSON] and [PERSON] (2012). It was found that the first excitation region was actually dominated by the A-II mode, characterized by alternate vortex shedding, which contradicted the common assumption that the vortex shedding was symmetric in the entire first excitation region. Later work by the same researchers investigated the streamwise VIV of a 2-DOF pivoted cylinder. It was found that the 2-DOF response regimes were similar to the previous 1-DOF results, and the additional degree of freedom did not significantly alter the vortex-shedding process ([PERSON] et al., 2013). Investigation on the streamwise VIV of a rigid, circular cylinder has proliferated in recent years. By contrast, only a few researchers have investigated the streamwise VIV dynamic mechanism of flexible, slender cylinders. [PERSON] et al. (2013) experimentally investigated the streamwise VIV of a cantilevered flexible cylinder with an aspect ratio of 41 in a water channel facility. It was found that large amplitude vibration could be excited at low reduced velocities when \(C_{n}\) was smaller than \(1.0\). Few researchers have focused on the prediction of streamwise-only VIV of flexible cylinders, such as marine riser and submarine free spanning pipeline. Several prediction models were proposed to analyze the features of streamwise response undergoing vortex shedding ([PERSON] and [PERSON], 2012; [PERSON] and [PERSON], 2013; [PERSON] and [PERSON], 2014). Until now, experimental studies on the streamwise VIV of a flexible circular cylinder, especially with a large aspect ratio and a low mass ratio, were lacking. A series of laboratory experiments were conducted in a towing tank facility to investigate the streamwise VIV of a flexible slender cylinder with a large aspect ratio and a low mass ratio. The statistical characteristics and instantaneous dynamic behaviors of the cylinder model undergoing streamwise VIV were presented and discussed. The organization of the rest of this paper is as follows: Section 2 presents a description of the experimental set-up; in Section 3, a modal analysis method for the measurement data is introduced;experimental results and discussions are presented in Section 4; finally, some conclusions are drawn in Section 5.
## 3 Wireless Body Area Network (WBAN)  
### Characteristics of WBAN  
This section investigates the characteristics of WBAN, distinguishing it from MANETs and WSNs. WBANs are interconnected devices mounted on, in, or around the human body. Medically implanted and wearable sensors are wildly popular in the context of E-Healthcare. IEEE 802.15.6 is an alternative name for the WBAN standard created by the IEEE 802.15 working group [21]. The structure of a WBAN typically includes the following components [16; 22]:  
1. Medical sensors: These small, low-power devices collect data from the human body. They can be attached to the skin, implanted in tissues, or ingested by the patient. 2. Wireless transceivers: The hardware responsible for sending and receiving data between the sensors and the central monitoring system. Various wireless communication technologies, including Bluetooth, Zigbee, or Wi-Fi, can be supported by the WBAN transceivers. 3. Central monitoring system: The network sink, i.e., the coordinator device receives and processes the data from the distributed medical sensors. It has higher communication  
Figure 1: Classification of e-health systems. capabilities and supports multiple communication interfaces. A body control unit (BCU), a body gateway, or a sink are all terms used to describe this equipment. A personal digital assistant (PDA) or smartphone can sometimes be used instead of a dedicated unit. The primary goal of this unit is to collect all of the data collected by the sensors and actuators and convey it to the user (patient, nurse, etc.) over an external gateway. This gadget has a power unit, a huge processor, vast memory, and a transmitter. Several important distinct characteristics distinguish WBAN, summarized in Table 1[23; 24; 25]. Numerous benefits of WBANs make them more appealing to researchers and the industry compared to traditional medical strategies and old sensor networks [22]. They are non-surgical networks that autonomously monitor and transmit the patient's health status to a nearby coordinator device. Then, this device transmits medical information to hospital-based healthcare professionals for further analysis to make the appropriate medical decision for the patient. Such monitoring saves time and money because it permits early detection and intervention of health issues without violating patient privacy or employing full-time medical personnel [16]. WBANs have many applications; however, most are related to simple and traditional healthcare monitoring [26]. The structure of a WBAN is designed to enable continuous and noninvasive monitoring of a patient's health status. This can be particularly useful for people with chronic medical conditions or those requiring close monitoring during surgery recovery [27]. Few recent applications have seen the development and deploy
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 3 Model Construction  
### Distance Calculation Method  
In our study, in order to simplify the analysis and better focus on the core issue of the vehicle path problem, we assume that the vehicle travelling speed is constant, and do not take into account the influence of traffic congestion and other external environments. Based on this assumption, we chose Euclidean distance as the method to calculate the distance between customer points.  
Euclidean distance is a straight-line distance between two points, it is the simplest and most intuitive distance measure, and due to the universality and simplicity of Euclidean distance, the algorithm using it as a distance measure can be more easily accepted and adopted by other researchers. Supposing there are two points, and the coordinates of point  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & \multicolumn{1}{c}{**Definition**} \\ \hline \(N\) & Node combination, \(N=\{0,1,2\ldots n\}\), where \(0\) is the distribution center and \(1-n\) are customer points \\ \(A\) & Arc set, \(A=\{(i,j)|i\
eq j,\ i,j\in N\}\). \\ \(K\) & Vehicle set, a total of \(K\) vehicles, \(K=\{1,2,\ldots k\}\). \\ \(d_{ij}\) & Distance between node \(i\) and node \(j\). \\ \(Q\) & The nominal capacity of the vehicle. \\ \(q_{i}\) & Demand of customer point \(i\). \\ \(t_{ij}\) & The time taken by the vehicle to travel from node \(i\) to node \(j\). \\ \(h_{ik}\) & The moment when vehicle \(k\) arrives at customer point \(i\). \\ \(s_{k}\) & The duration vehicle \(k\) stays at customer point \(i\). \\ \(w_{k}\) & The waiting time of vehicle \(k\) after reaching customer point \(i\). \\ \(a_{i}\) & The earliest time a customer can be served. \\ \(b_{i}\) & The latest service time a customer can receive. \\ \([a_{i}\,b_{i}]\) & Time window of the customer. \\ \(f_{k}\) & Vehicle fixed cost. \\ \(c_{k}\) & Unit transport cost of vehicle \(k\). \\ \(\delta\) & CO\({}_{2}\) emission factor. \\ \(F_{e}\) & Fuel consumption per kilometer. \\ \(E_{c}\) & Carbon emissions per kilometer. \\ \(E\) & Cost of carbon emissions. \\ \(P_{0}\) & Fuel consumption per unit distance when empty. \\ \(P_{1}\) & Fuel consumption per unit distance when fully loaded. \\ \(c_{m}\) & Cost of carbon emissions per unit. \\ \(y_{k}\) & Customer \(i\) is serviced by vehicle \(k\). \\ \hline \multicolumn{2}{c}{Description of symbols} \\ \hline \(x_{ijk}\) & Vehicle \(k\) travels between nodes \(i\) and \(j\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of symbols.  
A are (\(x_{1}\), \(y_{1}\)) and the coordinates of point B are (\(x_{2}\), \(y_{2}\)), then the Euclidean distance d between these two points can be calculated by using Equation (1):  
\[d=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}} \tag{1}\]
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Hop Count and Content Store Size
#### 5.5.2. Hop Count and Content Store Size for Overall Traffic  
QLPCC outperforms the EQPR scheme in the range of 57% to 60%, the PRR scheme in the range of 57% to 60%, the probability cache scheme in the range of 65% to 67%, the LFF scheme in the range of 66% to 72%, and the LFU scheme in the range of 66% to 72%. The QoS-node content store size ranges from 50 MB to 250 MB, as shown in the X axis of the graph provided in Figure 10.  
Figure 10: Hop count vs. content store size for overall traffic. (**a**) Hop count vs. content store size (overall traffic) for \(|M|\) = **500. (b)** Hop count vs. content store size (overall traffic) for \(|M|\) = **1000.**
## 2 Critical Literature Review
### Data Types Associated with Autonomous Vessels
#### 2.5.2 Personal or Non-Personal Data  
Autonomous operation systems are the sum of integrated digital technologies combining multisensor cognitive technology, risk avoidance and optimal response technology, optimal route and status technology, integrated control technology, and satellite communication, network, and security technology [14; 16]. Advances in digital technology have transformed the maritime industry ecosystem's data platforms to facilitate the continuous production, storage, and use of data at different levels and locations. There are also ongoing transformations related to the ownership and usage rights of the data.  
Thus, we need to distinguish between personal and non-personal data in order to promote data usages linked to autonomous vessels [21]. The personal data associated with MASSs can vary according to the number and type of people on board, and the vessels' degree of autonomy. However, the data would include at least some information about the passengers, ship owners, the cargo owners (and possibly the buyers), any backup crew, and other data collected through various internal systems or equipment. The non-personal data is the data provided from external sources after reprocessing (e.g., anonymizing or deidentification) and includes information related to the cargo, navigation, and the ship, without the conflict of rights relations regarding external exposure. However, for merchant vessels, it can be difficult to clearly distinguish between personal and nonpersonal information. Therefore, we need to acknowledge that nonpersonal data might contain personal information in our discussions of and deliberations on establishing a relevant legislative framework.
# Dynamic Pressure Analysis of Hemispherical Shell Vibrating in Unbounded Compressible Fluid
## 2 Control Equation and Solution
### Discussion  
For the incompressible fluid condition, Equation (33) shows that it has an important influence, as the incompressible assumption was adopted, and the pressure is contributed from two parts. The first part is the local acceleration, which is proportional to both the boundary acceleration and the reciprocal of the radius of the fluid domain. Another part is proportional to the square of the fluid velocity. As for compressible fluid, according to the solution, \(\Delta p=\rho_{0}c\cdot u\), it can be concluded that the pressure is proportional to the velocity of fluid. The ratio is the product of the sound speed and the density. In such case, it can be obtained from Equation (1) that the pressure effect of fluid on the structure is equivalent to the viscous damping whose value is \(\rho_{o}c\), exactly.  
In the physical sense, it can be seen that between times \(\Delta t\), the amount of fluid that can be squeezed by the structure is \(\frac{4}{3}\pi[(r_{s}+u_{s}\Delta t)^{3}-r_{s}^{3})]\), and can be spread to the range of \(\frac{4}{3}\pi[(r_{s}+c\Delta t)^{3}-r_{s}^{3}]\). When \(\Delta t\to 0\), the fluid compression ratio is:  
\[\lim_{\Delta t\to 0}\frac{\frac{4}{3}\pi[(r_{s}+u_{s}\Delta t)^{3}-r_{s}^{3})]}{ \frac{4}{3}\pi[(r_{s}+c\Delta t)^{3}-r_{s}^{3}]}=\frac{u_{s}}{c} \tag{34}\]  
Moreover, the fluid compression modulus is \(\rho_{o}c^{2}\), Therefore, according to [PERSON]'s law, the pressure of compressible fluid is:  
\[p=c^{2}\cdot\frac{u_{s}}{c}=\rho_{o}c\cdot u_{s}, \tag{35}\]  
The pressure of compressible fluid is identical to Equation (26) exactly. The discussion above shows that in the condition of compressible fluid, the motion of boundary propagates to the far field at the speed of sound. At the beginning, the motion of the shell compresses the air (fluid), which will be transferred to the far field afterward. The compression of air produces the local acceleration pressure, which is proportional to the fluid velocity.
## 4 Discussion
### Benchtop Transceiver Testing Discussion  
The amplifier characterization simulation shows a simulated amplifier design that can close all three of the optical link scenarios. The amplifier verification test was conducted to demonstrate that the amplifier design is able to achieve the expected performance at low pump powers. While the repetition rate was lower, the seed peak was lower, and the pulse width was wider for the hardware demonstration than the MOCT simulation, the optical amplifier test had \(\sim\)21.4% higher peak power than the simulation. Higher repetition rates are associated with shorter guard times, so it is expected that the amplifier demonstration (51.2 ns guard time) would have a higher peak power than the simulation (50 ns guard time) if all other variables were the same. For a fixed pulse energy in an unsaturated amplifier, wider pulses are expected to yield lower peak powers, which did not seem to significantly impact transmit peak power here. Additional simulations and tests should be conducted inorder to better verify the amplifier's performance, investigate the seed laser influence on performance, and update the model where necessary to predict performance. The amplifier verification test did show that this design is able to transmit pulses with a peak power on the order of a Watt. This amplifier is in a single, backwards pumping configuration and as the pump and transmitted peak powers increase in future tests, it may be determined that a more complicated configuration is required.  
The BER measurement test was conducted in order to evaluate the link performance when the detector receives an optical power that is expected during a communication. The link budget assumes that 100 nW is required for the APD to detect a pulse, so the BER was measured at 102 nW and 176 nW to see how increasing the power received influences BER. Increasing the optical power from 102 nW to 176 nW decreased the BER by about a factor of 3. It should be noted that the indicated powers were determined as the maximum power that could be captured by the APD, so better focusing the lens onto the APD or improving the alignment could increase the actual power seen by the APD. The BER could also be reduced by increasing slot width at the cost of lowering the data rate. Even if there are bit errors during communication on a mission, error correction codes can be implemented to help reduce bit errors. Further investigation will need to be conducted to sweep more optical powers, thresholds, and communication parameters to better determine what is the lowest BER MOCT can achieve and under what conditions.
## 3 Materials and Methods
### Experimental Design
#### 3.1.2 Vineyards  
The study was carried out in an experimental vineyard, \"El Chaparral,\" located in Bullas (Murcia, Spain), latitude 38.11179 and longitude \(-\)1.6808. Seeds from different crosses between Monastrell (M) and Tempranillo (T), Syrah (S), Verdejo (V), Cabernet Sauvignon (C) (named MC180, MC69, MS30, MS33, MS82, MT103, MV11, MV67, and MV7) were planted between 2007 and 2012 and the parentals in 1997. The plantation framework was 3 m \(\times\) 1.25 m with a density of 2670 vines/ha. Twenty plants per crossing were planted in a trellis system. The vines received deficit drip irrigation, with a maximum of 0.665 m\({}^{3}\)/year per plant, depending on annual rainfall.  
The climate of the growing area is categorized as semi-arid continental, and the soil as loamy sand. The weather conditions for the two years studied led to the harvest being later in 2018, when the herbaceous-growth period was wet in April, May, and June, and very sunny. In 2019, the herbaceous-growth period was slightly warmer and drier, with a significant amount of rain only falling in April, although it was also very sunny.
## 3 Results and Discussion
### Effect of Ball Diameter Ratio
#### 3.2.2 Friction Factor  
How the use of BTs with different ball diameter ratios (\(BDR\) = 0.5, 0.75 and 1) impacts on the friction factor is described in Figure 6a,b. Unlike the Nusselt number, the friction factor tends to decrease with the increase of the Reynolds number for all BTs. It is also evident that the use of BTs leads to a substantial increase in the friction factor over that in the plain tube. The increase of the friction factor caused by the BTs is about 3.74-10.27 times that of the plain tube. Moreover, it can be observed that at the given Reynolds number, the friction factor increases with the increase in the ball diameter ratio. This is because at a larger ball diameter ratio, a greater flow interruption tends to increase the shear force and the pressure drop, thereby resulting in a greater flow resistance. It is noteworthy that the BTs with the largest ball diameter ratio, i.e., \(BDR\) = 1, mostly yield a larger friction factor with a percentage of 34.6-46.2% and 51.1-63.4% higher than the cases of \(BDR\) = 0.75 and 0.5, respectively. This indicates that a smaller ball diameter ratio shows a higher capability to effectively reduce the friction factor. In addition, it is also inferred that the friction factor ratio (\(f\)/\(f_{\rm p}\)) significantly increases with the increase of the Reynolds number.
## 4 Results
### Multiple Electric Vehicle Connection and Disconnection
#### 4.3.4 Frequency Regulation  
Figure 14 shows the behavior of the output frequency in the three DGs and their variation with load changes. This figure is obtained by considering multiple variations in the power loads as previously described. The results show that the frequency of the three generators stabilizes in a single value a few seconds after the load changes. The larger the change of load in the EV connection, the more abrupt the change in frequency. However, no matter how high the change in the variable load (connection and disconnection of different EVs), the controller maintains the frequency close to 50 Hz, which is the desired value.