esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Air-to-Space Channel Models
### Rain Attenuation
For a given outage probability, as the UAV velocity increases, the required fade margin decreases. However, if a UAV preserves a velocity lower than the advection of the raincells, the required fade margin increases compared to the fixed-satellite system. The time-series synthesizer in Figure 4 can also be applied in A2A wireless links. In this case, the \(u_{M}\) in (4) takes into account the combined vector from the velocities of two UAVs. Therefore, \(u_{M}=\sqrt{u_{a}^{2}+u_{b}^{2}-2u_{a}u_{b}\cos\theta}\), where \(u_{a}\) and \(u_{b}\) are the velocities of the first and second UAV, respectively, and \(\theta\) the relative angle between their velocity vectors. If one of the UAVs is stationary, then \(u_{M}=u_{a\parallel b}\). The results for the attenuation of the A2A channel are similar to those presented above, since the raincell advection is now associated with the combined velocity vector. Since the proposed model is a physical mathematical model, its limitations lie in its basic assumptions, which are mostly related to the statistical distribution of the rain attenuation on a fixed link, which is considered log-normal distribution. Obviously, this needs experimental validation. With the employment of new UAS systems and the launching of satellites with new technologies, experimental campaigns on the evaluation of the rain attenuation in these links are expected. Figure 6: Long-term exceedance-probability distribution of rain attenuation for UAV terminals: (**a**) Athens, GR, in Ka band; (**b**) Milan, IT, int Ka band.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 3 Methods
### Evaluation Metrics  
Common performance metrics for object detection and recognition algorithms include Precision, Recall, Average Precision (AP), and mean Average Precision (mAP). These metrics are derived from four fundamental components: True Positives (TPs): Correctly predicted positive samples; True Negatives (TNs): Correctly predicted negative samples; False Positives (FPs): Incorrectly predicted positive samples (false alarms); and False Negatives (FNs): Incorrectly predicted negative samples (missed detections).  
The formulas for these metrics are as follows:  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{FN}+\text{FP}+\text{ TN}) \tag{3}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{4}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{5}\]  
\[\text{AP}=\int_{0}^{1}\text{Precision}(\text{Recall})\text{dRecall} \tag{6}\]  
\[\text{mAP}=\frac{\sum_{\text{i=0}}^{\text{p}}\text{AP}(\text{i})}{\text{n}} \tag{7}\]  
Precision represents the ratio of correctly identified true ship targets to the total number of detected ships. The false alarm rate is defined as \(1-\text{Precision}\), reflecting the proportion of incorrect detections among all predictions. Recall measures the ratio of correctly identified true ship targets to the total number of actual ship targets in the dataset. The miss rate (\(1-\text{Recall}\)) quantifies the proportion of undetected true targets.  
Average Precision (AP) is determined by calculating the area under the Precision-Recall curve for each class, which integrates both Precision and Recall values across varying confidence thresholds. AP comprehensively evaluates the performance of an object detection network for a specific class. To assess overall model performance, the mean Average Precision (mAP) is computed by averaging AP values across all classes. Higher AP and mAP values indicate superior detection accuracy and robustness of the network.
# Improved Duplication-Transfer-Loss Reconciliation with
## 3 Materials and Methods  
Our algorithms for computing and sampling optimal DTLx reconciliations build upon corresponding dynamic programming algorithms for computing and sampling optimal DTL reconciliations as described in [7,11]. These algorithms for DTL reconciliation performa nested post-order traversal of the gene tree and species tree and compute, at each step, an optimal solution to a subproblem. Specifically, given any \(g\in V(G)\) and \(s\in V(S)\), the subproblem \(c(g,s)\) is defined to be cost of an optimal DTL reconciliation of the subtree \(G(g)\) with \(S\) under the constraint that \(g\) maps to \(s\). As shown in [7], optimal solutions for these subproblems can be efficiently computed based on previously computed optimal solutions for smaller subproblems, and the final optimal reconciliation cost for \(G\) and \(S\) is then simply \(\min_{s\in V(S)}c(rt(G),s)\).  
Adapting this approach to computing DTLx reconciliations requires several modifications and extensions to the basic dynamic programming framework. In particular, (i) hidden nodes in the augmented gene tree must be handled differently in the dynamic programming framework than the other nodes, (ii) mappings to extra leaves in the species tree must be handled differently than mappings to other nodes, and (iii) the new evolutionary events \(\mathbb{TL}\) and \(\mathbb{TX}\) must be integrated into the cost calculations performed within the dynamic programming framework.  
Recall that a DTLx reconciliation of \(G\) and \(S\) is a reconciliation between an augmented gene tree \(G^{\prime}\) and the augmented species tree \(S^{\prime}\). Accordingly, our algorithms for computing optimal DTLx reconciliations perform a nested post order traversal of a _fully augmented gene tree_, denoted \(G^{\prime\prime}\), and the augmented species tree \(S^{\prime}\). This fully augmented gene tree is defined as follows:  
**Definition 8** (Fully augmented gene tree).: _Given a gene tree \(G\), the fully augmented gene tree, denoted \(G^{\prime\prime}\), is defined to be the tree obtained from \(G\) by subdividing each edge in \(E(G)\) by a hidden node such that each edge \((g,g^{\prime})\in E(G)\) is replaced by the two edges \((g,h)\) and \((h,g^{\prime})\), where \(h\) is a new hidden node._  
The fully augmented gene tree \(G^{\prime\prime}\) thus contains all possible hidden nodes and allows the algorithms to consider the possibility of \(\mathbb{TL}\) and \(\mathbb{TX}\) events occurring along each edge of \(G\). Hidden nodes of \(G^{\prime\prime}\) that are not ultimately labeled as representing a \(\mathbb{TL}\) or \(\mathbb{TX}\) event are suppressed to yield the final augmented gene tree \(G^{\prime}\).  
Next, we present our algorithm for the O-DTLx problem.
# Matheuristics for the Design of a Multi-Step, Multi-Product Supply Chain with Multimodal Transport
## 4 MILP Model and Matheuristics
### Matheuristics
#### 4.2.1 Allocation Model  
Allocation model 2.1 defines the decision variables \(u_{uc}^{pt}\) which due to the nature of the problem is the largest set of variables since the number of these variables is defined by the expression \(|W|\cdot|C|\cdot|P|\cdot|T|\) assuming that you always have more customers than warehouses or factories.  
In the case of the instances used for this work, the smallest number of variables \(u_{uc}^{pt}\) used in one instance was 6000, so defining those variables alternately greatly reduces the number of branches explored by the branching and cutting algorithm. This is used in its default configuration in the IBM ILOG program CPLEX to solve mixed-integer linear programming models. The allocation model is presented below.  
_Variables_  
\[u_{ad}^{t}=\left\{\begin{array}{ll}1&\text{if origin $o$ supplies destination $d$ by mode of transport $t$,}\\ &((o,d)\in\{(S,F)\cup(F,W)\cup(W,C)\};t\in T)\\ 0&\text{other case}\end{array}\right.\]  
The constraints used in the original model are constraints (9), (10), (14) and (21)  
\[u_{uc}^{t}\in\{0,1\}\quad w\in W,c\in C,t\in T \tag{21}\]  
And the objective function is the following (22):  
\[Min\sum_{p\in P}\sum_{c\in C^{p}}\sum_{u\in W^{p}}\sum_{t\in T_{uc}}DEM_{c}^{ pt}TC_{uc}^{pt}u_{uc}^{t} \tag{22}\]
## 1 Introduction
As you can see, sensor nodes located on board are also connected to related objects such as rails, tunnels, locomotive, wagons and bridges, and transmit the collected data by Bluetooth or Wi-Fi technology to the base station. The base station forwards the data collected by sensors by using general packet radio service GPRS and/or satellite to the central control server. Figure 1: General methods used for signaling and monitoring in railways. Supervision on railway lines is one of the important plans of wireless sensor networks. Sensor network techniques assist in diagnosing any defect in the railway lines and preventing any probable event. As could be seen in Figure 3, in the discussed plans [1; 2; 4; 6; 7; 8; 9; 10], a base station or sink in the central control department delivers data collected by existing sensors in the route or in the train to the central server for data analysis and issuing necessary commands. Considering the advantages of a mobile sink, including longer network life, lower power consumption, load balancing by moving the sink, ease of data collection, and so forth, the method was proposed based on the moving sink that is optimal for the plan, as indicated in Figure 4.  
The most important method in routing protocols for a mobile sink is hierarchical structure that aims to reduce the burden of the sink position notification to the network. In this study, we propose a new hierarchical routing protocol for sensor network with mobile sink, which is a reliable and energy-efficient routing protocol that provides rapid delivery of information. The proposed routing, by combining a routing protocol with a mobile sink, called Ring Routing, and a gravitational search optimization algorithm for the initial selection of ring, increases the lifespan of a sensor network. For better performance of Ring Routing, the primary selection of ring is conducted by using a gravitational search algorithm causing an increase in the sensor network's lifespan. In addition to increasing the lifespan, railway monitoring by the mobile sensor network causes:  
1. Detection of maintenance requirements;
2. Accurate information to be obtained from trains on the move and rail paths and stations on the way to be checked to send the necessary commands;
3. maintain process tolerances;
4. Saved money and time for businesses;
5. Reduction of the number of incidents relating to signaling faults and failures;
6. Better communication between drivers and signalers at any time from any point of the station. Figure 3: Current used plan. Figure 2: Situation monitoring by a wireless sensor network.
## 6 Outcome of the Study and Future Works
### Future Works  
For a thorough evaluation of network performance, the two metrics (latency and throughput) must be evaluated using deterministic dimension order routing and adaptive routing algorithms [30; 31]. Statistically, the TFBN is less traffic congestive with a high packing density; however, an implementation of prototype by FPGA is necessary for the TFBN cost analysis [32].
## 1 Introduction
The LESO is embedded into current controller to realize robustness in the full-speed range. In [30], an ADRC controller using ESO is proposed for elevator traction machine. The nonlinear disturbance during elevator startup is estimated, which improves the dynamic response without using weight sensor. As we know, current loop also have disturbances, such as unmodeled dynamics of back electromotive force (EMF), current fluctuation, stator resistance change. Also active disturbance compensation is widely used in PMSM systems, but, almost all disturbance compensation is for speed loop and position loop, few papers focuses on current loop. With the development of hardware, it has been reported that disturbance compensation technology is used in current loop. In [31], ESO is introduced to estimate the disturbance of \(q\)-axis current loop, and the disturbance is compensated by feed-forward. Simulation and experiment show that feed-forward compensation of current loop can effectively improve the anti-disturbance performance. In [32; 33], a current loop controller based on sliding mode control is proposed, and simulation results show that the new current controller is more accurate then classical PI controller. In this paper, by introducing sliding mode control and feed-forward compensation, a composite controller is designed for not only speed loop but also \(q\)-axis current loop. The lumped disturbances of speed loop, which include parameters variations and load change, are estimated by ESO. ESO-based feed-forward compensation can actively reject disturbances. Moreover, the dynamics of \(q\)-axis current loop are also considered in this paper, the lumped disturbances of \(q\)-axis current loop, which include unmodeled dynamics of back electromotive force and resistance variations, are estimated by ESO for active compensation. Here, a composite control law using SMC and ESO is designed for speed loop. An ESO-based proportional-integral control law is designed for \(q\)-axis current loop. A tracking-differentiator is used as transition process. The rest of this paper is organized as follows: in Section 2, mathematical model of PMSM and problem formation of PMSM drive system are given. In Section 3, a composite control law using SMC and ESO for speed loop and an ESO-basedproportional-integral for \(q\)-axis current loop are designed. Moreover, a transition process of reference signal is introduced to replace step reference signal. In Section 4, simulation and experiment results are presented. Finally, conclusions are shown in Section 5.
## 2 Governing Equations and Material
\begin{array}{l}f^{\prime\prime}-(f^{\prime})^{2}+ff^{\prime\prime}+\alpha_{ 1}\big{[}f^{\prime 2}f^{\prime\prime}-2 ff^{\prime}f^{\prime\prime}\big{]}-\alpha_{2}\Big{[}3f^{2 }(f^{\prime\prime})^{2}-f^{3}f^{i\prime\prime}+\\ 2f(f^{\prime})^{2}f^{\prime\prime\prime}\big{]}+\alpha_{3}\big{[}(f^{\prime \prime})^{2}-ff^{i\prime\prime}\big{]}+f^{\prime\prime}+\alpha_{4}\big{[}fff^{ \prime i^{\prime\prime}}-2 ff^{\prime\prime}f^{\prime\prime}+\\ f^{2}f^{\prime\prime}+(f^{\prime})^{2}f^{\prime\prime}+f^{\prime}(f^{\prime \prime})^{2}\big{]}-M^{2}\big{[}f^{\prime}-\alpha_{1}ff^{\prime\prime}+\alpha_{ 2}f^{2}f^{\prime\prime}\big{]}+M^{2}K+\\ K^{2}+\delta\big{[}\theta-Nr\phi-Nc\chi\big{]}=0,\end{array}  \tag{8}\]  
\[ \begin{array}{l}\Big{(}1+\frac{4}{3}Rd\Big{)}\theta^{\prime\prime}+Pr(\delta \theta+f\theta^{\prime})+Pr\lambda_{T}\Big{[}\delta f\theta^{\prime}+Nt( \theta^{\prime})^{2}-Nb(f\theta^{\prime}\phi^{\prime\prime}-\\ f\theta^{\prime\prime}\phi^{\prime})+2 Ntf^{\prime}\theta^{\prime}\theta^{\prime \prime}-\big{(}f^{2}\theta^{\prime\prime}-ff^{\prime}\theta^{\prime}\big{)} \big{]}=0,\end{array}  \tag{9}\]  
\[ \begin{array}{l}\phi^{\prime\prime}+LePr\Big{[}-f\phi^{\prime}-\sigma^{*}(1+ \mu\theta)^{n}exp\Big{(}\frac{-E}{1+\mu\theta}\Big{)}\phi^{-}\lambda_{C}K_{c}f \phi^{\prime}\Big{]}+\\ LePr\lambda_{c}\Big{[}-f^{2}\phi^{\prime\prime}-ff^{\prime}\phi^{-LePr\frac{Nt} {Nb}}(\theta^{\prime}+f^{\prime}\theta^{\prime\prime})\Big{]}=0,\end{array}  \tag{10}\]  
\[\chi^{\prime\prime}+Lbf\chi^{\prime}-Pe\big{[}(\chi+\omega)\phi^{\prime\prime}+ \chi^{\prime}\phi^{\prime}\big{]}=0. \tag{11}\]with boundary conditions  
\[\left. \begin{array}{l}f=0,\ f^{\prime}=1,\ \theta^{\prime}=-\gamma_{1}(1-\theta( \xi)),\ \phi^{\prime}=-\gamma_{2}(1-\phi(\xi)),\\ \phi^{\prime}=-\gamma_{2}(1-\phi(\xi)),\ \chi^{\prime}=-\gamma_{3}(1-\chi(\xi))\ at\ \ \xi=0,\\ f^{\prime}\to K,f^{\prime\prime}\to 0,\ \theta\to 0,\ \phi\to 0,\ \chi\to 0\ as\ \xi\to\infty.\end{array} \right\} \tag{12}\]  
Diverse non-dimensional factors occurring in (8)-(12) are provided below:  
\[\left.\begin{array}{l}\alpha_{1}=c\lambda_{1},\ \alpha_{2}=c^{2}\lambda_{2},\ \alpha_{3}=c\lambda_{3},\ \alpha_{4}=c^{2}\lambda_{4},\ M=\sqrt{\frac{ \sigma p_{D}^{2}}{\rho_{f}}},\\ \lambda=\frac{(1-\text{C}_{\infty})\theta^{\ast\ast}\theta^{\ast}(\text{T}_{ \theta}-\text{T}_{\infty})}{c^{2}x},\ Nr=\frac{(\rho_{P}-\rho_{f})\theta^{\ast}( \text{C}_{\infty}-\text{C}_{\infty})}{(1-\text{C}_{\infty})\theta^{\ast\ast} \rho_{f}(\text{T}_{\theta}-\text{T}_{\infty})},\\ \delta=\frac{Q_{0}}{c\rho_{C}},\ \Lambda_{T}=c\lambda_{t}^{\ast},\ Pr=\frac{\
u}{ \alpha},\ Nc=\frac{(\rho_{m}-\rho_{f})\gamma(\text{N}_{\infty}-\text{N}_{\infty} )}{(1-\text{C}_{\infty})\theta^{\ast\ast}\rho_{f}(\text{T}_{\infty}-\text{T}_ {\infty})},\\ Nb=\frac{\tau D_{B}(\text{C}_{\infty}-\text{C}_{\infty})}{\
u},\ Rd=\frac{4\sigma^{\ast}T_{\lambda}^{\ast}}{K^{\ast}},\ Nt=\frac{\tau D_{T}(\text{T}_{\mu}-\text{T}_{\infty})}{ \tau D_{\mu}},\\ \lambda_{\text{C}}=c\lambda_{e}^{\ast},Le=\frac
###### Abstract  
This cross-sectional study examined gastrocnemius medialis fascicle length (FL) and anatomical cross-sectional area (ACSA) in female volleyball athletes aged 8-18 years, in an attempt to investigate morphological growth changes during childhood and adolescence. Methods: Eighty-nine athletes were assigned to five groups: 8-10, 10-12, 12-14, 14-16 and 16-18 years old. Height, body mass, leg and calf length were measured, and maturity offset was calculated from anthropometrics. FL and ACSA were assessed using ultrasonography. Results: FL was longer and ACSA was larger in the 12-14 y group compared with the 8-10 y group (4.31 \(\pm\) 0.60 vs. 3.66 \(\pm\) 0.40 cm, \(p\) = 0.008 and 13.08 \(\pm\) 3.01 cm\({}^{2}\) vs. 9.98 \(\pm\) 2.07 cm\({}^{2}\), \(p\) = 0.018, respectively). However, there were no differences in FL or ACSA among the groups aged between 12 and 18 years (\(p\) > 0.573 and \(p\) > 0.352). Notably the percentage increase in ACSA between the youngest (8-10 y) and the oldest group (16-18 y) was twofold greater than the percentage increase in FL (49% vs. 24%). Significant correlations were found between anthropometric characteristics FL and ACSA (\(r\) = 0.366-760, \(p\) < 0.001). Conclusions: These data indicate that there is a rapid increase in FL and ACSA from the age of 8 y until the age of 14 y, which is a crucial time frame for muscle growth.  
muscle architecture; muscle morphology; muscle growth; ultrasonography; childner; adolescents
## 2 Theoretical Design of the PI Controller  
The theoretical design of the controller was developed exploring the implementation of this in a low-cost microprocessor. In this system, the sampling period \((T_{s})\) for a complete cycle of control is around 150-200 ms. Therefore, the microprocessor can not sample a laser diode with a rise time on the order of tens or hundreds of nanoseconds or even a few microseconds. Thus, a viable model of the system behavior is given by the steady-state curves of the laser. The steady-state light curves for a laser diode are called _light-current curves_. These represent the relation between the emission power and supply current or forward current. The light-current curves (see Figure 1) show two typical emission zones of semiconductor lasers. These are the spontaneous and stimulated emission zones [8; 23]. In most applications, the laser must operate in the stimulated emission zone where the efficiency of the system is maximum and the relationship between the emission power, \(P\), and the forward current, \(I_{F}(t)\), is linear and described by:  
\[P=\eta(I_{F}(t)-I_{th}), \tag{1}\]  
where \(\eta\) is the slope efficiency, which characterizes the light efficiency of the laser. Additionally, the threshold current \((I_{th})\) states the theoretical beginning of the stimulated emission zone. Both parameters depend on the temperature [8,10]. The nonlinear function that describes the light-current curve has no explicit dependence on time. Thus, the emitted optical power and the forward current, which represents the output of the system \(y(t)\) and the control signal \(u(t)\), respectively, are defined by:  
\[y(t)=f(u(t)) \tag{2}\]  
In our design, the forward current is limited to operating in the stimulated emission zone. Thus, the current is restricted to \(I_{F}\in[I_{th},I_{F,max}]\), where \(I_{F,max}\) is the maximum forward current defined by the manufacturer. If the system control variable is \(u(t)=I_{F}(t)-I_{th}\) and the optical power (system output) is \(y(t)\), the Equation (1) is rewritten as:  
\[y(t)=\eta\,u(t) \tag{3}\]  
The PI controller (see Figure 2) stabilizes the optical output power around a constant reference, \(y_{sp}\), using a control variable defined in terms of the error signal, \(e(t)\), as follows:  
\[u(t)=K_{p}\bigg{(}e(t)+\frac{1}{T_{l}}\int_{0}^{t}e(\tau)\,d\tau\bigg{)} \tag{4}\]  
Figure 1: Steady state Light–Current curve for Laser Diode ADL-65052 TL at 15 \({}^{\circ}\)C.  
\(K_{p}\) is the proportional gain, and \(T_{i}\) the integral time. The theoretical design determines a set of rules for \(K_{p}\) and \(T_{i}\) that guarantee convergence to zero or an asymptotically stable behavior of the error signal.
# Research on the Influence Mechanism of Foam Slurry Wall Formation
## 4 Influence of Various Factors on Mud Cake Membrane Formation  
### Influence of Soil Properties on Membrane Formation  
When the soil particle size is uniform, the computational results from Schemes 1, 2, and 6 (Table A1) show that soil particle size has a significant impact on membrane formation. Smaller pore sizes result in longer accumulation times for bubbles at the end of the soil layer, leading to effective clogging, while larger pore sizes allow bubbles to pass through more quickly, reducing the clogging effect, as shown in Figure 5. The computational results from Schemes 3 to 8 (Table A1) indicate that the time required for foam slurry membrane formation increases linearly with increasing soil porosity, as shown in Figure 6.  
When the soil has a continuous gradation, the computational results from Schemes 9, 10, and 13 (Table A1) show that as the maximum particle size (dmax) of the soil decreases, the time required for foam slurry membrane formation decreases rapidly, as shown in Figure 7. The computational results from Schemes 11 to 15 (Table A1) indicate that as the gradation parameter b increases, the soil becomes progressively denser, pore size decreases,  
Figure 4: Formation of stable mud cake upon infiltration of foamed slurry (Scenario 6).  
and membrane formation time also decreases, as shown in Figure 8a. The computational results from Schemes 16 to 19 (Table A1) show that as the gradation parameter m increases, the soil becomes increasingly loose, and membrane formation time exhibits a positive correlation with m, as shown in Figure 8b.  
Figure 5: Seepage behavior of foamed slurry in different soil layers.  
Figure 6: Seepage behavior of foamed slurry in L6 soil layer.  
Figure 7: Seepage behavior of foamed slurry in soil layers with different gradation parameters.  
From the computational results, among the soil parameters, particle size, especially the maximum particle size (dmax), has the most significant effect on membrane formation, whereby as the soil particle size or maximum particle size (dmax) increases, the permeability of the soil increases significantly, making membrane formation more difficult. Changes in particle size not only affect the time required for foam slurry membrane formation but also determine whether membrane formation can occur. The impact of other soil parameters (such as porosity, gradation parameters b and m, etc.) on membrane formation is relatively minor, generally not affecting whether membrane formation can occur but mainly influencing the time required for formation. Therefore, soil particle size significantly influences the movement behavior of bubbles and the formation process of the mud cake.