esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Several studies have investigated the effect of hyperparameters on the performance of deep learning models for object detection and classification, but only a few have focused on performance for multi-crop classification in a mixed farm context. For example, in [14] the authors investigated the effect of different hyperparameters on the performance of YOLOv3 for crop classification using remote sensing data. They found that increasing the batch size improved the accuracy of the algorithm. The authors also found that increasing the number of anchors improved the recall rate of the algorithm. Similarly, a study by [15] investigated the effect of hyperparameters (training epochs) on the performance of YOLOv5 for multi-crop classification in a mixed farm. The authors used remote sensing data to classify five crop types and tuned the number of training epochs from 100 to 1000. They found that increasing the number of epochs improved the accuracy of the algorithm until the algorithm became saturated at the 700 th epoch, when the model performance began to decline. Several studies have investigated the impact of batch size on the performance of deep learning models. In [16], the authors found that increasing batch size can lead to better generalization performance, while [17] found that larger batch sizes can lead to lower generalization errors. However, other studies have shown that the optimal batch size varies depending on the dataset and network architecture [18, 19, 20]. Several studies have investigated the impact of batch size on the performance of YOLOv3 and YOLOv4. In a study by [21], the authors found that larger batch sizes can lead to better performance on the COCO dataset. Similarly, [22] found that increasing batch size can lead to improved accuracy and speed in object detection tasks. Hence, this study focuses on investigating the effect of varying batch sizes on the performance of YOLOv8 for multi-crop classification in a mixed farm.
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 1 Introduction
The engineering exploration of THz communication technology began in the 1970s. First, in 1970, Bell Labs developed the first continuous-wave THz laser (methanol laser, 1-5 THz) using gas laser pumping technology. Although constrained by cryogenic operation and low efficiency, this breakthrough marked the inception of artificially controllable THz signal generation. Subsequently, Nippon Telegraph and Telephone Corporation (NTT) achieved the world's first long-distance THz communication experiment in 2006, demonstrating 10 Gbps over 15 km line-of-sight (LoS) using the 120 GHz band, validating THz technology feasibility for satellite backhaul and wide-area coverage scenarios. Further, during the 2008 Beijing Olympics, NTT deployed a 120 GHz THz communication system to successfully achieve real-time broadcast of 8K ultra-high-definition video signals, proving its commercial potential for applications with extreme bandwidth demands for the first time. Finally, in 2017, the IEEE published the first international THz communication standard (802.15.3d), specifying the physical layer protocol and frame structure for the 252-325 GHz band, laying the groundwork for chip design, device manufacturing, and the 6G standardization process [22; 43; 44; 45]. This paper summarizes a series of advanced methods and architectures that achieved historic, record-breaking ultra-broadband THz signal transmissions, providing critical technological support and spectrum expansion for the transition from 5G-Advanced to 6G. In Section 2, we discuss the generation of broadband THz signals using simple and cost-effective schemes. Section 3 summarizes key enabling technologies comprehensively considering power consumption, equalization, algorithms, and structure to increase the transmission distance of THz communication systems. To explore the role of THz systems within the integrated Space-Air-Ground (SAG) strategy, Section 4 presents simulations of satellite-to-ground communications. The final section provides a summary of the paper and outlines predictions and plans for the next phase of long-distance THz communication system development.
# Online Control for Biped Robot with Incremental Learning Mechanism
## 4 Experiment Results and Analysis
### Simulation: Climbing Stairs  
In this case, we consider the new robot BRZ-5, whose basic parameters Table 3. As indicated in Figure 8, BRZ5 is 121 cm in height and 14.9 kg in weight. The whole simulation contains two kinds of gaits. One is walking gait and the other is climbing stairs gait. Specially, every gait includes six step cycles. In this simulation, the robot is required to climb stairs after walking six steps on flat ground. Moreover, some comparative simulations between our proposed method with the one in [26] are conducted on Pybullet which is a real-time physics simulation platform. To facilitate the comparison and analysis, we keep the setting parameters and initial conditions as the same.  
Figure 6: ZMP trajectories for BRZ-4 with two methods.  
Figure 7: Yaw moment evolutions for BRZ-4 with two methods.  
**Remark 2**.: _A brief analysis is given to the above comparative results. As we know, the strong environment adaptive ability is one of the keys to realize the large-scale application of biped robots. However, it is almost impossible to handle all kinds of dynamic disturbances from environment with only one well-trained model. Unlike the approach in [26], an incremental updating mechanism is newly integrated into our scheme. With the aid of an iteration algorithm for updating, the new incoming data can be used straightforwardly to update into the original well-trained model, which successfully avoids the entire retraining from beginning. Thus, with this incremental updating mechanism, the adaptive capacity of robot is further improved._
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 3 Superimposed Perfect Binary Array-Aided OTFS Channel Estimator
### Minimum Mean Square Error Estimator  
Recall from (21) that the threshold method of estimation is influenced by the interference term \(v_{q}\). To reduce this interference and achieve more accurate channel estimation, we consider the characteristics of wireless channels and use a minimum MSE (MMSE) estimator to smooth the threshold method estimation \(\hat{h}_{q}\).  
The MMSE channel estimate is given as  
\[\hat{h}_{q}=\left(\frac{\sigma_{h_{q}}^{2}}{\sigma_{h_{q}}^{2}+\sigma_{v_{q}} ^{2}}\right)\hat{h}_{q}, \tag{35}\]  
where \(\sigma_{v_{q}}^{2}\) is the MSE of \(\hat{h}_{q}\) in the threshold method, as shown in (22). Similar to (21), the MMSE channel estimation result can be given as  
\[\hat{h}_{q}=h_{q}+\vartheta_{q}, \tag{36}\]where \(\bar{o}_{q}\) denotes the channel estimation error with the MMSE estimator.  
Because of the orthogonality of the MMSE estimator [17], the MSE of \(h_{q}\) in (35) can be expressed as  
\[\sigma_{\bar{o}_{q}}^{2}=\frac{\sigma_{\bar{o}_{q}}^{2}\sigma_{h_{q}}^{2}}{\sigma _{\bar{o}_{q}}^{2}+\sigma_{h_{q}}^{2}}, \tag{37}\]  
while the MSE of MMSE channel estimation is  
\[\sigma_{\text{MMSE}}^{2}=\sum_{q=1}^{P}\sigma_{\bar{o}_{q}}^{2}=\sum_{q=1}^{P} \frac{\sigma_{\bar{o}_{q}}^{2}\sigma_{h_{q}}^{2}}{\sigma_{\bar{o}_{q}}^{2}+ \sigma_{h_{q}}^{2}}. \tag{38}\]  
As \(\sum_{q=1}^{P}\sigma_{h_{q}}^{2}=\sigma_{H}^{2}\), by utilizing the Lagrange multiplier method, we can obtain the following upper bound on (38):  
\[\begin{split}\sigma_{\text{MMSE}}^{2}\leq\overline{\sigma^{2}}_ {\text{MMSE}}&=\frac{\sigma_{H}^{2}\sigma_{\bar{o}_{q}}^{2}}{ \sigma_{\bar{o}_{q}}^{2}+\sigma_{H}^{2}/P}\\ &=\frac{\sigma_{H}^{2}\cdot\text{MSE}(\hat{h})}{\text{MSE}(\hat {h})+\sigma_{H}^{2}/P}.\end{split} \tag{39}\]  
This upper bound is obtained when the power of each path is equal, which means that \(\sigma_{h_{1}}^{2}=\sigma_{h_{2}}^{2}=\cdots=\sigma_{h_{P}}^{2}\). By comparing (23) and (39), it can be seen that \(\sigma_{\text{Thr}}^{2}\geq\overline{\sigma^{2}}_{\text{MMSE}}\). This result shows that the MMSE estimator can successfully improve the channel estimation accuracy.
# Agile Stage-Gate Approach for Design, Integration, and Testing of a 1U CubeSat
## 2 Materials and Methods
### Conceptualization and Design
These actuators are activated by sending I2C signals, which must be synchronized to avoid interfering with other subsystems, as these signals represent the default protocol for internal communication. Serial communication was also tested for monitoring variables because it is the default protocol of communication for the payload module. The FlatSat used in the project can integrate three systems at the same time and allows subsystems to be easily assembled and disassembled when necessary. The FlatSat uses the PC/104 standard as a connector for the nanosatellite bus and complies with the IPC-A-610 electronic assembly acceptance standard. The system uses banana plugs for external power and Molex PicoBlade connectors for I2C digital communication. To assess each subsystem board, tests were performed using the FlatSat. However, each board included a Molex PicoBlade UART connector to allow the console to interface with a PC for testing and control.
## 3 Performed Full-Scale/Model Test Comparisons
### Low-Rise Buildings  
According to Ref. [7], many full-scale measurement campaigns have been launched for researching wind effects on some benchmark low-rise buildings in the last four decades. In the early 1970s, the Building Research Establishment (BRE) in the UK began a full-scale measurement program on an experimental building of two stories in Aylesbury, England. In the late 1980s, a full-scale experiment on a low-rise building was conducted by the Texas Tech University in Lubbock, TX, USA. At the same time, a full-scale experiment was conducted in Silsoe, UK, which utilized a steel frame building with changeable eaves. At the beginning of the twenty-first century, a 6 m cube was constructed at the Silsoe Research Institute in open-country terrain. These full-scale measurement campaigns were followedby the corresponding wind tunnel tests, which allowed researchers to validate the modeling technique via full-scale/model test comparisons.
## 4 Network Self-Organization  
The goal of network self-organization is to achieve automatic configuration and management of the entire network through autonomous collaboration and coordination among nodes, thereby providing conditions for routing. This paper divides the network self-organization process into topology discovery and network clustering. In the topology discovery stage, a small number of anchor nodes are used, and the required number of anchor nodes is calculated to meet discovery needs while minimizing discovery complexity and reducing network costs. In the clustering stage, adjacency information is obtained  
Figure 2: The workflow of thick LWSN.  
through communication between nodes and used as one of the critical factors in electing cluster heads. Unequal clustering is implemented to balance network energy, and a timing mechanism is used to determine the order in which nodes become cluster heads.
## 3 Predictive Equations for Shear Spring Parameters
### Development of Predictive Equations  
A regression analysis was conducted to establish prediction equations based on specimen properties, utilizing the calibrated shear spring parameters from Section 3.2. The considered properties include column section width (\(b\)), section height (\(h\)), column length (\(L\)), transverse reinforcement spacing (\(s\)), clear cover (\(cc\)), gross sectional area (\(A_{g}\)), concrete compressive strength (\(f_{c}^{\prime}\)), yield strengths of longitudinal and transverse rebars (\(f_{yl}\), \(f_{yt}\)), a confinement factor (\(\kappa_{c}\)) calculated by the Cusson-Paultre model [30], longitudinal and transverse reinforcement ratios (\(\rho_{l}\), \(\rho_{t}\)), and the ratio of applied axial load to the axial compressive capacity of the column (\(\rho_{Pn}\)). Linear regression analysis with exponential and natural logarithmic functions was employed to develop prediction equations for shear spring parameters. Regression analysis for backbone curve parameters was performed exclusively for three points (crack, yield, and maximum). For the residual strength point, the force was assumed to be zero, and the displacement was assumed to be 1.5 times the maximum point displacement; thus, a separate regression analysis was not conducted for this point. The pinching parameter, potentially ranging from \(-1\) to 1, was adjusted by adding 1 to accommodate potential negative values. Similarly, the degradation parameter was adjusted by adding 1 to avoid zero values. The proposed prediction equations for each parameter are shown in Equations (12)-(23). Figure 6 presents the regression analysis results of the proposed prediction equations for shear spring parameters. The coefficient of determination (\(R^{2}\)) for the backbone curve parameters ranges from 0.8078 to 0.9522, indicating a generally good predictive capability. However, the accuracy for predicting \(F_{cr}\) and \(D_{cr}\) is comparatively lower.
## 8 Calibration of Instrumentation for Aerodynamic Testing
### Multichannel Aerodynamic Pressure-Sensing Instrument: General Description
The signals from all the pressure-measurement channels are sampled simultaneously, and the readout of the measurement data from the analog-to-digital converters is carried out by the data processing and communication module, where digital signal processing is performed in real time. Simultaneous measurement on all pressure channels is an important advantage of the instrument's architecture for two reasons: (1) it eliminates the multiplexing of sensor signals, and thus its inherent disadvantages (parasitic effects, limited scanning speed); (2) it enables the performance of spatially coherent measurements in aerodynamic experiments that involve unsteady flow fields and transient phenomena. The data processing and communication module is based on a highly integrated system-in-package (OSD32 MP157, Octavo Systems LLC, Houston, Texas, USA), which contains a microprocessor (two ARM Cortex-A7 application cores and one ARM Cortex-M4 real-time core), RAM, and power management circuitry. This system-in-package occupies a significantly smaller surface area compared to functionally equivalent solutions based on separate components. It was chosen in order to minimize the dimensions of the data processing and communication module, thus contributing to the instrument's miniaturization. The Cortex-A7 application cores perform digital signal processing of the measurement data by applying sensor-correction algorithms based on the parametric models of sensing elements and the algorithms presented in this work. With operating frequencies of up to 800 MHz, these processor cores have high computing power, and thus do not pose any limit to the achievable measurement throughput of the instrument. The data processing and communication module communicates with external computing equipment, and transfers the processed measurement data to it via the computer network. This module also contains all the necessary power supply and interface circuitry. A diagram showing the flow of sensor signals and data in the instrument is given in Figure 10.  
Figure 10: The flow of sensor signals and data in the instrument. Figure 9: Simplified block diagram of the novel multichannel aerodynamic pressure scanner.
## 2 Experimental Part  
The electrochemical behavior of magnesium was studied by linear voltammetry in a three-electrode electrochemical cell using AUTOLAB PGSTAT potentiostat-galvanostat (The Netherlands). The working electrode was the end face of the magnesium rod (99.98%), with an area of 0.17 cm\({}^{2}\). The counterelectrode was a platinum plate with an area of 6.3 cm\({}^{2}\) and Ag/AgCl sat. KCl electrode was used as the reference electrode (E = 0.197V vs. the standard hydrogen electrode). Potentiodynamic polarization measurements with linear potential sweep were carried out at 10 mV/s rate of potential overlap in \(\pm\)300 mV potentials' region from the stationary potential. All the measurements were performed three times, followed by statistical analysis on blunder and averaging of the obtained result (Tafel's coefficient).  
Electrochemical impedance spectroscopy (EIS) studies were performed in a cell with a coaxial arrangement of electrodes. The area of the magnesium electrode was 5 times smaller than the area of the counterelectrode. Scanning electron microscope (SEM) studies were performed for additional characterization of the corrosion film. Prior to each measurement, the magnesium electrode was treated with SiC paper (grain size 5-7 \(\upmu\)m), then degreased with ethyl alcohol. All the experiments were carried out in a thermostat cell at a temperature of 25 \({}^{\circ}\)C maintained with an accuracy of \(\pm\)0.1 \({}^{\circ}\)C.  
The following salts were used to prepare solutions of various concentrations: magnesium sulfate heptahydrate (99.5%), and sodium sulfate anhydrous (99.5%).  
Initial characteristics of solutions for corrosion studies are presented in Table 1.  
Magnesium samples were kept in solutions of magnesium sulfate and sodium sulfate for 1, 5, 10, 20, and 30 min and for longer intervals up to 1 month to form corrosion films on them for testing.
## 3 Modal Simulation Analysis
### Influence of Suspension Faults on Modal Parameters
When the right front wheel leaf spring fails (Figure 9b), the modal energy of the bounce mode decreases by about 10%, while the modal energy of the pitch mode increases from \(-\)5% to 3%, which is opposite to the trend presented in the two-order mode of the left front leaf spring. When the left rear leaf spring fails (Figure 9c), the modal energy of bounce mode increases by 3%, and the pitch mode increases from \(-\)7% to 3%. As shown in Figure 9d, the decrease in the stiffness of the right rear leaf spring causes a decrease in the modal energy of two modes, wherein the pitch mode decreases by 15%. It can be seen that the weakening of the leaf springs at different positions of the vehicle can cause different changes in modal energy, because the change of spring stiffness at different positions will cause the vehicle to roll in different directions. It can be concluded that the modal energy difference is sensitive to leaf spring failure in both the bounce and pitch modes. Moreover, the position of the fault and the magnitude of the change in stiffness can be determined by the change in the modal energy difference. In order to simulate the influence of the damper fault on the modal parameters, the damping value of the shock absorber is gradually reduced from the original 100% to 30%. As the damping of the left-front damper decreases, as seen in Figure 10a, the MED of the front and rear in the bounce mode presents a diametrically opposite trend. In the pitch mode, the MED of the front side gradually increases with the decrease of the damping value, and that of the rear side gradually decreases. If the right-front damper fails (Figure 10b), the MED in the bounce mode exhibits a tendency opposite to that of left-front side. In the pitch mode, unlike the left-front damper failure, the MED of the front and rear side increases slightly with the decrease of the damping. Figure 9: Change of modal energy difference (MED) caused by spring faults at different positions. (**a**): MED changes caused by faults in left front wheel; (**b**): MED changes caused by faults in right front wheel; (**c**): MED changes caused by faults in left rear wheel; (**d**): MED changes caused by faults in right rear wheel. It can be seen that the modal energy difference can clearly indicate the damping changes in a shock absorber and reveal the fault behind it. Therefore, the damper failure can be effectively monitored and diagnosed by the method proposed in this paper. To summarize the tendencies of stiffness and damping ratio in the Table 3, we can see the positive and negative direction is distinctive in every situation. This shows the proof that MED can indicate the faults. It strongly suggests that the modal energy differences are susceptible to stiffness changes and damping changes. Furthermore, the diverse trends of modal energy correspond to the occurrence of different faults, which help to achieve the purpose of fault diagnosis positioning and condition monitoring.
## 7 Conclusions  
Taking the small radius curve tunnel of a certain section of Changsha Metro Line 6 as the engineering background, this paper establishes a numerical model of a small radius curve and straight tunnel, analyzes the law of surface deformation caused by the shield excavation of the small radius curve and straight tunnel, and studies the construction parameters influence on surface settlement, surrounding rock deformation, and segment stress. The research conclusions are as follows:  
1. Compared with straight tunnels, shield excavation of small radius curved tunnels will aggravate the surface settlement, and the maximum surface settlement will appear on the inner side of the curve section, and as the radius of the curve decreases, the surface subsidence increases, and the distance between the peak subsidence point and the center of the tunnel increases.
2. When the curve radius of the tunnel is smaller, the lateral displacement of the ground surface moves farther to the inner side, and the range of soil mass with lateral displacement in the inner side is wider.
3. Increasing the heading face pressure and grouting pressure can reduce surface settlement, but the heading face pressure should not exceed 350 kPa, and the grouting pressure should not exceed 250 kPa.
4. When the curve radius is smaller, the deformation of the surrounding rock and the segment stress are larger.  
Conceptualization, methodology, supervision, review and editing, P.L.; software, validation and original draft preparation, Y.L.; resources, funding acquisition, H.X., Z.Z. and S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Project of Science and Technology Plan in Yuhua District, Changsha City (YHKJ2019-ZG-02).  
Not applicable.  
The authors declare no conflict of interest.