esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerodynamic Characteristics of a Square Cylinder with Vertical-Axis Wind Turbines at Corners
## 2 Numerical Simulation Configurations
### Simulation Cases  
As shown in Figure 4, two types of simulation cases, stationary and rotating wind turbines, are considered. For the stationary wind turbines, their orientation related to the wind flow direction may have a significant effect on aerodynamic characteristics of the cylinder. Similarly, for the rotating wind turbines, it is anticipated that the rotation speed of the turbines is a key factor which affects the aerodynamic characteristics of the cylinder. In order to study the effects of these two factors, 4 different orientations of the stationary wind turbines and 9 different rotation speeds are studied, as shown in Tables 3 and 4. The orientation of the stationary turbine is denoted by \(\theta\), which represents the angle of rotation of the wind turbines. Two wind turbines rotate inward relative to the square cylinder. \(V_{t}\) is the rotation speed of the wind turbines and \(f_{s}\) is the vortex shedding frequency of the square cylinder calculated according to Strouhal number of 0.132 for the square cylinder provided by [22]. The rotation speed ranges from 0 to 4 times that of \(f_{s}\), and a rotation speed of 0 refers to a stationary case. In this study, the turbine rotates independently from the wind speed. According to [30], the rotation speed of the Savonius wind turbine is about 19.5 (r/s), which is closest to the rotation speed of R6. Thus, R6 is considered as an actual case. In order to conduct a comparative study on the influence of different rotational speeds on the aerodynamic characteristics of the square cylinder, we created 8 other working conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data from Previous** & **Re** & \(\overline{C_{d}}\) & \(C_{l}^{RMS}\) & \(S_{t}\) & \(\delta_{cd}\) \\ \hline Experimental results: & & & & & \\
[24] & \(1.76\times 10^{5}\) & 2.05 & - & 0.122 & 2.93\% \\
[26; 27] & \(2.14\times 10^{4}\) & 2.1 & - & 0.132 & 5.24\% \\
[28] & \(2.20\times 10^{4}\) & 2.09 & 0.95 & 0.123 & 4.78\% \\ Simulation results: & & & & & \\
[22] & \(6.0\times 10^{4}\) & 2.26 & 1.52 & 0.132 & 11.9\% \\
[25] & \(1.9\times 10^{4}\) & 2.37 & 1.37 & 0.122 & 16.0\% \\
[29] & \(2.2\times 10^{4}\) & 2.26 & 1.60 & 0.136 & 11.9\% \\ Present simulation results: & \(6.0\times 10^{4}\) & 1.99 & 1.29 & 0.123 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of force coefficients between previous studies and present numerical simulations.  
Figure 3: Comparisons of mean pressure distribution between previous studies and present numerical simulations.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 4 Results
### Path Simulation Comparison
#### 4.2.4 Verification and Analysis of Results in the Rectangular Scenario  
To verify the comprehensive performance of the method proposed in a warehouse environment, the process will be compared and verified with the improved algorithm and the GWO, DSA-EGA, LM-GWO, and CWSO algorithms. The performance of the three algorithms will be evaluated by indicators, such as path length, planning time, success rate, and collision rate, respectively, as shown in Figure 9a-e.  
As can be seen from Figure 9 and Table 5, the paths planned by the LAPGWO algorithm have significantly fewer turning points, are smoother, have the shortest average path length, and maintain a certain safe distance from obstacles. The average computation time of the LAPGWO algorithm is improved by 9.1%, 12.3%, and 6.2% compared with the DSA-EGA algorithm, the LM-GWO algorithm, and the CSWO algorithm, respectively. Path smoothness increased by 36.6%, 47.8%, and 29.4%, respectively. The search time, the number of iterations, and the path length of the method proposed in this paper are all superior to those of the DSA-EGA algorithm, the LM-GWO algorithm, and the CSWO and DSA-EGA algorithms. This highlights the advantages of the algorithm in this paper in a simple warehouse environment.
## 2 Materials and Methods
### Acquisition of Visible/Near Infrared Spectra  
In 2016, after homogenizing the samples, the ground frozen and barn-stored forages were placed in a 50 mm-diameter ring cup and scanned in reflectance mode at 2-nm intervals from 400 to 2500 nm using a Foss NIRSystems model 6500 scanning VIS/NIR spectrometer (Foss NIRSystems, Silver Spring, MD, USA). Spectra and reference values were recorded using ISscan version 2.21 software (Infrasoft International, State College, PA, USA). Each spectrum was averaged from 32 scans. A reference scan (using the internal ceramic reference tile) was taken before each sample. The reflectance (R) values were converted into absorbance (A) values using the formula A = log (1/R). The raw NIR spectroscopy data were pre-processed using the standard normal variate and de-trending scatter correction procedure [23]. The spectra were then transformed using a mathematical second-order gap derivative (2,8,8,1), where the first digit is the order of the derivative, the second is the gap over which the derivative is calculated, the third is the number of datapoints in the first smoothing, and the fourth is the number of datapoints in the second smoothing. Then, the average spectrum for the samples under the same storage treatment (frozen or barn storage) was calculated. Finally, the difference between the average spectra of the storage methods was computed.
## 3 Novelty and Contribution  
This research conducts a comparative analysis of Li-ion and LA batteries under permissible SoC limits established through a Battery Management System (BMS) to observe their behaviour and find the most applicable battery under such constraints. The study demonstrates that the battery as a storage apparatus has contributed significantly to the renewable generation-based power system. Nevertheless, it is apparent from the literature that LA batteries have most frequently been utilized in these implementations.  
Consequently, considering their significant technical parameters, it is important to conduct a comparative analysis of the Li-ion energy storage for VER applications. The prime contributions of this study are defined as follows:  
* To analyze the behaviour of the proposed grid-tied microgrid system utilizing a variable load depiction, resources' information, and apparatus.
* To analyze the effect of utilizing LA and Li-ion batteries as an electrical energy storage system (EESS) for the same grid-tied microgrid system.
* To conduct a technical study and distinguish the operation of Li-ion and LA batteries in the suggested power grid.  
The remainder of the paper is in the following order: Section 4 demonstrates the modelling of apparatus in the microgrid. Section 5 demonstrates the simulation results, while a results comparison, related future works, and the conclusion are depicted in Sections 6-8, respectively.
## 3 Overview on Antiresonance Assignment through the Active or Semi-Active Tuned Mass Dampers  
### The Tuned Mass Damper: An Introduction  
The tuned mass damper (TMD), also called dynamic vibration absorber (DVA) in the literature [7; 8; 9], is the oldest and most widespread device used to create antiresonance for suppressing oscillations [1; 10]. A TMD is an external vibrating system, made by some masses, springs and sometimes dampers, that is properly attached to the primary system to absorb its vibrations. Some review papers have been already proposed (e.g., [11; 12]), listing thousands of papers. Therefore, this section aims at discussing some of the main results and the related equations provided by the milestone achievements in TMDs that could be useful for future developments in this research area.  
The original idea of the TMD, as well as the subsequent early developments, have been conceived as passive devices, where the external structure is composed only of masses with passive dampers and springs. More recently, active TMDs (ATMD) have been proposed by adopting actuators, controlled through feedback, as active dampers and springs. A third \"hybrid\" approach has been developed too, leading to semi-active TMDs in which the dampers or springs of the TMD are changed during the operation to adapt them.
## 5 Discussions and Conclusions
### Conclusions  
In summary, we propose a bidirectional quantum communication protocol using the composite GHZ-GHZ state as quantum channel. Two arbitrary nodes in a quantum communication network can transmit qubits to each other based on quantum measurements performed in parallel by all the nodes involved. The original qubits can be retrieved locally at the destination site based on the measurement results and the types of GHZ channel. Information leakage is avoided in our scheme, and it has a shorter delay for the end-to-end quantum communication, in contrast to the hop-by-hop qubit transmission scheme.  
There are many avenues for future work and extensions. One aspect is the imperfection of quantum channel, which is crucial for the efficiency and fidelity of qubit transmission. Another important aspect is the extension to multi-user quantum communication, which is a realistic scenario for future quantum networks.  
Data curation, S.S. and N.C.; methodology, S.S. and N.C.; supervision, B.Y.; validation, S.S.and N.C.; visualization, B.Y.; writing--original draft, S.S.; writing--review and editing, N.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61701285).  
The authors declare no conflict of interest.
# Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells
## 1 Introduction
The Kalman filter, complementary filter, and gradient descent are popular methods for denoising raw data [15; 16; 17; 18; 19]; existing results show that the Kalman filter is better than the others under the condition of a simple system with no multiple state variables. Many dumbbell workouts focus on different parts of the body, which can be divided into different primary groups: those vital involving the biceps, deltoids, and triceps. In this study, we selected six acts for each muscle named bicep, deltoid, tricep, shoulder, squat, side and lunge actions. The aim of this study is to create a new smart dumbbell that can monitor and identify the user's state as well as manage the accuracy of the dumbbell movement. We improved the identification quality of Support Vector Machine (SVM) in the Machine Learning method and proposed a new algorithm to recognize the modes of exercises with high efficiency. The proposed new algorithm could keep tracking and classifying the movement of the dumbbell in order to correctly and effectively train certain muscles in real time.
# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 5 Experimental Results
### Comparison of Model Convergence Performance  
Select the best model in the cross-validation experiment for each network and plot its accuracy curve on the validation set, as shown in Figure 6. SE_Xception has the fastest convergence speed and highest accuracy rate among all of the models on the validation set. SE_miniXception has similar convergence performance with MobileNetV1 and SE_Xception2. In addition, the learning curve of SE_Minixception fluctuates slightly more than that of SE_Xception, and the convergence speed is slower than the latter, but it converges around 20 epochs. To conclude, the convergence speed of SE_Xception is better than all the other compared models, and the compressed SE_miniXception model still has an ideal convergence.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & **Number of** & \multirow{2}{*}{**FLOPs (M)**} & \multicolumn{2}{c}{**Training**} & \multirow{2}{*}{**Recognition**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**\(F_{1}\) Score**} \\  & **Parameters** & & & **Time for 80** & & **Speed** \\  & **(M)** & & & **Epochs (min)** & & **(ms/image)** \\ \hline ResNet50 & 23.60 & 47.63 & 72 & 15 & 95.56 & 94.99 \\ DenseNet201 & 18.33 & 38.73 & 209 & 36 & 96.88 & 96.34 \\ Xception & 20.87 & 42.24 & 92 & 10 & 97.41 & 96.78 \\ SE\_Xception & 23.85 & 48.15 & 122 & 15 & 99.40 & 99.10 \\ SE\_Xception2 & 21.20 & 42.88 & 103 & 13 & 98.02 & 97.52 \\ MobileNetV1 & 3.23 & 42.24 & 41 & 6 & 95.41 & 94.64 \\ ShuffleNet & 1.95 & 6.67 & 106 & 20 & 95.79 & 95.49 \\ SE\_miniXception & 0.61 & 0.13 & 23 & 7 & 97.01 & 96.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of compared models for ATLDD dataset.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 3 System Model
### Channel Models  
Since there is no consensus on a proper path loss model for UAV-enabled networks [28], we selected two popular path loss models [29; 30] and applied them in our network simulations. In our simulation environment, the data rates of connections are probabilistic, and the probability values are obtained from the Received Signal Strength Indicator (RSSI) transmission mode matrix. The matrix values contain the probability of the Wi-Fi rate at the given RSSI value, i.e., 6, 9, 12, 18, 24, 36, 48 and 54 Mbps for the IEEE 802.11g standard. The RSSI values for each connection are estimated based on the utilized channel models. The channel model equations used in our system are given in Appendix A.
# A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems
## 2 The Proposed Mechanism
### Management of Mobile Sinks by Sensor Nodes  
Typically, mobile sink selects a sensor node as its communication agent node and gets data from the agent node. The procedure of data transfer starts in source node, via main forwarding, branch forwarding, and agent, ends in the mobile sink. For the real-time data transmission, the agent should relay the holding data with the highest priority (almost no delay).  
Sometimes, each individual mobile sink might temporally be out of the movable area due to obstacles on the path. That is, deviations from environmental factors could be occur. When the mobile sink leaves the movable area, the sink selects the node closest to the edge of the movable areas as inner agent to report its location. In addition, if the sink is out of the communication range of inner agent, the node that exists within the communication range of inner agent is selected as outer agent. It ensures that the sink has a connectivity to receive data even if it is out of the movable area. When the inner agent receives a data packet, the agent forwards the packet to the outer agent using geographic routing. It is possible to relay with more distance because there might be remaining distance to the maximum distance (\(D+\sqrt{2}R\)). The remaining distance could be represented as follows,  
\[f_{r}(\theta)=\sqrt{2}R+Rcos\theta-|Rsin\theta|. \tag{10}\]  
Although the remaining distance is positive, it cannot be enough to support the out-of-range mobile sink. In order to support the mobile sink with higher remaining distance and higher probability, the location of the out-of-range mobile sink is shared with neighbor boundary nodes and the data packet is relayed via one of the multiple boundary nodes by multipath routing as shown in Figure 5.  
Figure 4: Real-time scheduling in branch points.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 5 Discussion  
Our work focuses on distributed processing of static range monitoring queries over moving objects. To utilize the computational capability \(o.cap\) of each moving object \(o\) by using the SSP-tree, we fix the system parameter \(\theta\) in advance to the minimum number of query segments \(o\) should process by assuming the server lets \(o\) to select one of the predefined values as \(o.cap\) when \(o\) is registered at the server, so that \(o.cap\geq\theta\). This is because when a new moving object \(\delta\) with its capability \(\delta.cap<\theta\) is registered at the server, the SSP-tree needs to be reconstructed from the scratch, similarly to the index structures used in the existing distributed methods for processing static range monitoring queries in Euclidean space [2; 3]. Figure 11a shows the effect of the number of queries on the initial  
Figure 10: Effect of the minimum computational capability on the communication cost and the server computation cost. (**a**) Total number of messages vs. the minimum computational capability; (**b**) The amount of CPU-time vs. the minimum computational capability.  
construction time of the SSP-tree. When the number of queries is 5000 (the default value of this parameter), the construction time of the SSP-tree takes 8.7 s, which can be amortized by the long running time of the range monitoring queries if \(\theta\) is fixed or is not frequently changed. However, for the case where \(\theta\) is dynamically changed, more investigations should be done.  
In addition, different from the centralized methods [4; 5; 6; 7], in our work, moving objects may suffer from the additional computational burden of participating in query processing tasks. Figure 11b shows the effect of the minimum computational capability of each moving object \(o\) on the amount of CPU-time consumed by \(o\). It can be easily observed from the figure that as the value of the minimum computational capability is increased (i.e., as the value of \(\theta\) is increased), the computational burden on \(o\) is also increased. Therefore, there needs to be future consideration of how to enable the moving objects to voluntarily participate in query processing tasks so that the advantages of the proposed method can be fully realized.  
Finally, another issue that needs to be addressed is the energy efficiency of moving objects. With regard to identifying the location of moving objects, our work makes the same assumption that the centralized methods do, namely, that each moving object \(o\) periodically measures its current location through GPS. However, GPS is an energy-intensive module, and thus its periodic usage can be a major energy drain for \(o\). To achieve the energy efficiency of each moving object \(o\), the strategy that makes \(o\) allow the GPS module to sleep should be considered. This would entail developing the method for determining a proper sleep duration \(\Delta t\) for \(o\), so that \(o\) can move freely without sensing and checking its current location against nearby queries during \(\Delta t\).
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 2 Methodology
### ST-GAT-Based PID Controller  
The following flowchart presents the proposed method for predicting and controlling wall deflections (Figure 5). First, a servo axial force system is introduced as it allows for the adjustment of steel support axial forces according to project requirements, thereby actively controlling the excavation-induced deformations. This servo system is a prerequisite for the implementation of the proposed method. Following this, the dataset is collected, including the axial forces of four servo steel supports from one inclinometer along with the wall deflection data from nearby monitoring points. These data are preprocessed through min-max normalization to standardize the scale, preventing certain features with larger or smaller values from dominating the model training. The correlation between the axial forces and deflection at the same monitoring point is analyzed to explore the effect of axial forces on wall deflections, while the correlation between different monitoring points is examined to understand the spatial relationships among them. A graph attention structure is then constructed, assigning varying weights to each monitoring point based on its spatial significance. These spatially informed outputs are subsequently fed into a temporal GRU network to establish the ST-GAT model. Based on the hyperparameter-optimized ST-GAT model, a wall deflection proactively control method is proposed. Specifically, the ST-GAT model generates predicted wall deflections, which are then used to replace the real-time deflection measurements in the PID controller. This enables the controller to use predicted values for more accurate and timely adjustments of the servo axial forces rather than relying solely on current deflection records.  
Figure 4: Implementation process of the PID controller.  
Figure 5: Proposed ST-GAT-based PID controller.
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 4 Multicast Mechanism
### Joining a Group
After receiving the JOIN REQUEST message sent from [PERSON], [PERSON] finds that the NFT load of the next router (N2) from it to N6 is less than the threshold. Thus, [PERSON] inserts the forwarding interface (2) into the out-interface list of the NFT entry and then replies to [PERSON] with a JOIN RESPONSE message, in which the State Flag is set to stateful and Parent NA is set to N\({}_{\text{NA1}}\). After receiving the JOIN RESPONSE message, [PERSON] first registers the mapping between MGEID\({}_{1}\) and N\({}_{\text{NA2}}\) with NRS. Then, N2 finds that the NFT load of the next router (N4) from it to N6 reaches the threshold, so it adds the NFT entry \(<\)NA\({}_{\text{NA1}}\), MGEID\({}_{1}\), \(>\) and adds the GMT entry \(<\)MGEID\({}_{1}\), RID\({}_{\text{N6}}\)\(>\). Finally, [PERSON] sets the State Flag in the message to stateless, modifies the Parent NA of the message to N\({}_{\text{NA2}}\), and forwards the message to N6. [PERSON] just forwards the JOIN RESPONSE message with the stateless flag to N6. When [PERSON] receives the JOIN RESPONSE message, it sets the Parent NA of the NFT entry to N\({}_{\text{NA2}}\). Figure 3: The example of the join process in AHHM. (**a**) R1 joins the multicast group; (**b**) [PERSON] joins the multicast group; (**c**) [PERSON] joins the multicast group. As shown in Figure 3b, when [PERSON] is notified that R2 wants to join the multicast group identified by MGEID\({}_{1}\), it first initializes the NFT entry <-, MGEID\({}_{1}\), 2>. Then, N7 obtains that MJNs of the group are NA\({}_{\text{N1}}\) and NA\({}_{\text{N2}}\) by querying NRS. [PERSON] selects the nearest NA (NA\({}_{\text{N1}}\)) as target MJN and sends a JOIN REQUEST message to the N1. After receiving the JOIN REQUEST message, [PERSON] finds that the NFT load of the next router (N3) from it to N7 reaches the threshold. Thus, [PERSON] adds the GMT entry <MGEID\({}_{1}\), RID\({}_{\text{N7}}\)> and replies to [PERSON] with a JOIN RESPONSE message in which the State Flag is set to stateless and Parent NA is set to NA\({}_{\text{N1}}\). N3 and N5 directly forward the JOIN RESPONSE message with the stateless flag to [PERSON]. After receiving the JOIN RESPONSE message, [PERSON] sets the Parent NA of the NFT entry to NA\({}_{\text{N1}}\). As shown in Figure 3c, when R3 informs N7 that it wants to join the multicast group identified by MGEID\({}_{1}\), [PERSON] directly inserts the interface (3) connected to R3 into the out-interface list of the NFT entry corresponding to MGEID\({}_{1}\).