esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Impact of the Dip Angle of Muddy Interlayers
The relationship between the slope safety factor and the dip angle \(\theta\) of the mezzanine is given in Figure 12, which shows the following five features: (1) \(\theta\) increased from \(0^{\circ}\) to \(90^{\circ}\), the safety coefficients of the four calculation schemes showed the same pattern of change, i.e., first decreasing, then increasing, then decreasing; (2) \(\theta\) for the minimal value of safety factor was \(20^{\circ}\) when \(\beta=30^{\circ}\), \(\theta\) for the minimal value of safety factor was \(30^{\circ}\) when \(\beta=45^{\circ}\), and \(\theta\) for the minimal value of safety factor was \(40^{\circ}\) when \(\beta=60^{\circ}\) (\(30\%\) increase in strength); (3) The safety factor of upright bedding slope was slightly greater than that of horizontal bedding slope; (4) In general, the safety factor of steeply bedding rock slope was greater than that of gently bedding rock slope; and (5) The safety factor decreased as the slope angle \(\beta\) increased, and the \(\theta\) corresponding to the very small value of the safety factor increased. The above conclusions are consistent with [PERSON] et al. [12]. For rocky slopes containing a single layer of muddy interlayers, [PERSON] [13] concluded that \(\beta\) = 75\({}^{\circ}\) and \(\dot{\theta}\) increased from 25\({}^{\circ}\) to 60\({}^{\circ}\), the horizontal displacement increased and then decreased, and the safety factor decreased and then increased. The results of [PERSON] et al. [17] showed that \(\beta\) = 45\({}^{\circ}\), \(\theta\) increased from 30\({}^{\circ}\) to 39\({}^{\circ}\), and the horizontal displacement increased. According to [PERSON] [13], \(\theta\) for the extreme value of horizontal displacement was 45\({}^{\circ}\) and \(\theta\) for the very small value of safety factor was also 45\({}^{\circ}\), which was larger than \(\beta\) = 60\({}^{\circ}\) determined in this study when \(\theta\) was both 40\({}^{\circ}\). This was because the analytical model \(\beta\) = 75\({}^{\circ}\) of [PERSON] [13] was larger than this study. Although the above laws are obtained based on the bedding rock slope containing a single soft interlayer, they coincide with the bedding cataclastic rock slope containing multi-muddy interlayers studied in this chapter in the corresponding range of dip and slope angles. However, their study also reached different conclusions from this chapter, for example, [PERSON] et al. [17] showed that the safety factor decreased when \(\theta\) increased from 30\({}^{\circ}\) to 39\({}^{\circ}\).
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 2 Comparison of Wireless Performance in the 915 MHz and 2.45 GHz ISM Bands
### DC Power Consumption  
As for the power consumption aspect, a contextualization regarding requirements of the proposed application in terms of data rate, maximum communication range, and maximum transmitted power allowed is required, in order to determine an optimal wireless protocol to use for the application as well as analyzing the respective radio transceiver power consumption. In particular, it can be assumed that the communication of sensed parameter in the proposed system requires a desired Data Rate of 10 kbps as SpO2 measurement does not require a large data payload [11]. In addition, a communication range of 3-6 m is assumed in order to allow freedom of movement to the user during wireless communications. The maximum transmitted power allowed for a wearable application is specified as 0 dBm as outlined in [31]. In this analysis, the power consumption will be considered in terms of current consumption in sleep mode, transmitting mode and receiving mode.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 6 Our Approach: Proposed Model
### Layer 2: Proposed Routing Protocol
#### 6.2.1 Protocol Requirements  
The routing protocol requires the presence of the two tables shown in Figure 2 to be present in the memory of each mobile node.  
The \"Acked Messages table\" is used to save the IDs of all messages that are acknowledged, their acknowledgment time, and their time to live (TTL). The flowchart presented in Figure 3 illustrates the periodic process ensured by the table.  
To prevent the table from becoming too large, a periodic check on the table tests whether the time since the message was acknowledged is greater or equal to the TTL; if true, the message will be dropped from the table. This means that all the copies of the message are dropped from the network due to their TTL expiration. The second table, \"Hosts table\", saves the ID of each node encountered by the current node and the last time those nodes met. Another requirement for the algorithm is the addition of a new integer header, \"Number of Copies\", and a new entry, \"Time Received\", in each message. The \"Number of Copies\" header is used to control the number of messages spread in the network to reduce the overhead and increase the delivery probability. Figure 4 shows the message overhead where the dashed parts represent the new added fields.
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
###### Abstract  
The utilization of enzyme-induced calcium carbonate precipitation (EICP) to consolidate aeolian sand has received significant attention in recent years. However, urease activity is directly affected by temperature, which varies greatly from day to night, especially in desert areas. To investigate the effect of alternating temperature on aeolian sand cementation by EICP, three experimental groups were designed to simulate the sunrise-to-sunset cycle in a natural desert environment: T1 (a process from heating to constant temperature to cooling), T2 (a process from cooling to constant temperature to heating), and T3 (a process of constant temperature throughout) as a control group. The differences in calcium carbonate content, precipitation rate of calcium carbonate, permeability coefficients, and shear wave velocity were compared and analyzed. Meanwhile, scanning electron microscopy (SEM) was conducted to observe the external cementation states by mineralization. The results showed that T2 had the highest calcium carbonate content, followed by T3 and, finally, T1, which were also confirmed by permeability coefficient and shear wave velocity tests. In addition, different alternating temperature processes would affect the survival time of the urease, and T2 showed the longest reaction time as the urease stayed active for the longest time in this process. The results provide a scientific reference for the selection of construction periods in which EICP can be optimally applied for the on-site aeolian sand cementation.  
EICP; aeolian sand; alternating temperature; sand consolidation; mixing method Article
## 3 Results
### Diurnal Variations in Canopy Spectral Observations  
#### 3.3.1 Diurnal Variations in Remote Sensing Observations Related to the Canopy Structure  
Figure 7 shows significant diurnal variation differences among the NDVI, NIRv, and FCVI under varying degrees of water stress. Considering the potential interference caused by changes in the solar zenith angle, such as the variation in the ratio of direct to diffuse light, observational data from 8:00 to 16:00 were selected to mitigate such effects. Under clear-sky conditions, the PAR values exhibited an arch-shaped pattern with minimal fluctuations (Figure 7a).  
As shown in Figure 7b, the diurnal variation in the NDVI was significantly influenced by water stress changes. Under non-water-stressed conditions, the NDVI values exhibited minimal variation throughout the day (irr1: DOYs 226 and 227; irr2: DOY 222). From DOYs 222 to 231, as water stress increased in the irr2 plot, the morning NDVI values remained unchanged and high, whereas the noon trough gradually decreased. After rewatering during the night of DOY 231, the trough values notably increased. The diurnal variation in the NDVI can reflect rapid changes in the vegetation structure induced by water stress.  
The diurnal variations in the FCVI and NIRv also reflect changes in vegetation water stress, but their trends differed from those of the NDVI. As shown in Figure 7c,d, the diurnal change trends of the NIRv and FCVI were bowl shaped and smoother than that of the NDVI. The NIRv and FCVI values continuously decreased from morning until they reached their lowest values at midday and then gradually increased. From DOYs 222 to 231, as water stress increased, the midday nadir values of the NIRv and FCVI in the irr2 plot steadily decreased, with a greater decline than that in the NDVI. After rewatering, the midday nadir values of both NIRv and FCVI, even under sufficient water conditions, remained significantly lower than their morning values, indicating a distinct diurnal variation trend compared with that of the NDVI after rewatering (irr1: DOYs 226 and 234; irr2: DOY 234). Compared with that of the FCVI, the NIRv exhibited a lower midday minimum value under severe water stress conditions.
# Lightweight Tennis Ball Detection Algorithm Based on Robomaster EP
## 4 Experimental Results
### Comparative Experimental Results and Analysis  
To verify the effectiveness of the improved algorithm, commonly used target detection algorithms were selected for comparative analysis, and the same training strategy and parameters were used for each group of experiments. The experimental results are shown in Table 5.  
From the results of the comparison experiments, it can be seen that the algorithm proposed in this study had the most comprehensive performance, taking into account the needs of lightweight models and detection accuracy, with solid generalization ability, the highest mean accuracy and recall, and slightly more parameters, computation, and model size than the lightweight neural networks Mobilenet and Shufflenet, but Mobilenet and Shufflenet had lower mean precision and recall. Compared to the original YOLOv5s  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]** & **Recall** & **Parameters** & **GFLOPs** & **Weight/MB** \\ \hline YOLOv5s & 0.957 & 0.911 & 7,022,326 & 15.8 & 14.5 \\ SSD & 0.839 & 0.443 & 26,285,486 & 63 & 91 \\ Faster R-CNN & 0.93 & 0.935 & 28,536,850 & 181 & 109 \\ YOLOv5s+Shufflenetv2 & 0.939 & 0.915 & 3,792,950 & 7.9 & 8.1 \\ YOLOv5s+Mobilenetv3 & 0.947 & 0.895 & **3,542,756** & **6.3** & **7.5** \\ YOLOv3 & 0.964 & 0.944 & 61,523,734 & 154.9 & 123.6 \\ YOLOv3-tiny & 0.945 & 0.884 & 8,669,876 & 12.9 & 17.4 \\ YOLOv4 & 0.909 & 0.885 & 64,363,101 & 60.5 & 244 \\ YOLOv4-tiny & 0.868 & 0.854 & 6,056,606 & 7.0 & 22.4 \\ YOLOv3-s & 0.966 & 0.966 & 8,968,255 & 26.9 & 34.3 \\ YOLOv3-tiny & 0.971 & 0.969 & 5,055,855 & 15.4 & 19.4 \\ YOLOv7 & 0.962 & 0.97 & 37,194,710 & 104.9 & 71.3 \\ YOLOv7-tiny & 0.968 & **0.98** & 6,014,038 & 13.1 & 11.7 \\ Ours & **0.978** & **0.98** & 4,100,759 & 8.8 & 8.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different lightweight models for tennis ball detection and localization.  
algorithm, the number of parameters and computation were reduced by 42% and 44%, respectively, the model size was reduced by 39%, and the average precision improved by 2%, verifying the effectiveness of the improved algorithm. Compared with the classical target detection algorithms SSD and Faster R-CNN, the overall performance was much better, with a significant increase in mean accuracy and recall and a significant reduction in the number of parameters, computation, and model size. The performance of the YOLO family of target detection algorithms, YOLOv3, YOLOv4, YOLOX, and the lightweight model, was still much better. Even when compared to the current best-performing YOLOv7 algorithm, the proposed algorithm has better overall performance, reducing the number of parameters and computation by 32% and 33%, respectively, and the model size by 25% compared to YOLOv7-tiny, validating the effectiveness of the improved algorithm and the lightness of the model.
## 2 Materials and Methods  
### Structure of the Triple Inverted Pendulum  
For a better description of the frequencies acting in the frequency spectrum, the well-known approach of the triple inverted pendulum (TIP, see Figure 1) with a control of the individual joints is developed. The model is developed with the model language Modelica in the Dymola environment. The Modelica standard libraries \"Mechanics/Multibody\" and \"Modelica_LinearSystems2\" are used. For the parameterization, a human being with a size of 180 cm and a mass of 75 kg is assumed. The weight distribution on the individual elements was calculated based on the percentage weight distribution in the body according to [PERSON] et al. [40]. For mechanical stabilization and approximation of the muscular physiology, spring-damper systems act on the joints. The design of the damping systems in the joints and muscles was adapted from the publication by [PERSON] et al. [28]. The essential change, compared to the models of [PERSON] et al., is the extension of the inverted pendulum by a control structure that actively influences the joint movement. This also offers the possibility to let the gravity vector run in the normal direction. The model can stand upright against gravity. The positioning forces of the muscles are simulated by moments acting on the joints. The starting position of the joints is selected so that the force vector of the center of mass CoM passes through the hip joint behind the knee and in front of the ankle joint [41]. This results in an angle of 150\({}^{\circ}\) between the lower and upper thigh. Furthermore, the upper body is inclined by 3\({}^{\circ}\) in \(x\) direction. Thus, the plumb line in the model of the CoM runs as theoretically described for humans.  
The model (see Figure 2) is controlled by a multi-variable controller with 4 input variables and 3 output variables. The input variables are the joint angles measured in the model, which are physiologically equivalent to golgi receptors. In addition, the visual influence on the control is realized by means of a distance measurement to a fixed point in space. The aim of this regulator is that the hip flexion should provide an upright and calm position of the head. For this purpose, a master controller is designed for the control of the hip angle based on the distance measurement, which calculates a setpoint for the hip angle control. The knee and ankle joint controls are designed as simple angle controls. The outputs of the 3 controllers are each connected to the other controllers as disturbance variables. Furthermore, two disorders attack the upper body rod in \(x\) direction, oscillating as a disturbance of 7 N with 0.3 Hz which represents respiration and is expressed in z direction as a disturbance of 1 N with 1.3 Hz, which symbolizes the running pulse wave. For comparison and evaluation of the results the TIP model was simplified to the double inverted pendulum model (DIP). For this purpose, the knee joint was removed from the model and the regulation was adjusted accordingly. For the examination a power density spectrum derived from the acting moment in the ankle joint.
## 3 Case Study
### Prediction Model of SMA Parameters  
#### 3.3.1 Parameters Selection  
The core of achieving the SMA prediction is to grasp the key tunneling parameters that influence the SMA accurately. Parameters surely unrelated to the SMA, such as item number and oil temperature, were excluded. The practical experience and previous studies indicated that the rotation speed (\(N\)), advance rate (\(V\)), and rotation speed of the screw machine (\(N_{s}\)) are the main operating parameters of the machine operators. Cutterhead thrust (\(F\)) and torque (\(T\)) indirectly reflect the interaction between the EPB machine and the excavation face, and torque of the screw machine (\(T_{s}\)) reflects the conditions of slag discharge [58; 59]. Chamber pressure (\(C_{p}\)) can be used to evaluate the stability of the excavation face, and it is the representative parameter of EPB shield machine. Propelling pressure (\(PA\)-\(PF\)) is the main execution parameter of SMA correction for EPB operators [60]. Some of the above parameters were also used in the prediction models of SMA in [PERSON] et al. [18] and [PERSON] et al. [19].  
Based on the above analysis, these 13 tunneling parameters were adopted as the input options of SMA prediction model. In addition, the boring time (_Time_) of a drilling cycle was also added as an input feature, which determines the boring length and indirectly affects the final SMA state. Specifically, the actual SMA parameters of the past record were also selected as the input feature to calculate the future SMA [17]. The description of these parameters is listed in Table 2.
## 3 Results and Discussions  
Figure 3 demonstrates the steps of feature extraction and shortlisting. For the stress-predict dataset, the tsfresh library calculates 1578 trends, seasonality, periodicity, and volatility-based features for heart rate (789) and respiratory rate (789) signals, combined. The hypothesis test (\(p\)-value) is performed within the library to check the independence between each feature and label (target variable) and selects 314 features out of 1578 features. For further dimensionality reduction, PCA using singular value decomposition (SVD) was performed. For comparison, PCA resulted in 37 features when implemented on a full feature set (1578), while selecting only 19 features with the feature set obtained after [PERSON]'s hypothesis test (314). As the selected features might still have correlated features, a correlation analysis was performed using Pearson, Kendall, and Spearman  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Actual Labels**} \\ \hline  & & **Positive** & **Negative** \\ \cline{2-4}
**Predicted Labels** & **Positive** & True Positive & False Positive \\ \cline{2-4}  & **Negative** & False Negative & True Negative \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion Matrix.  
methods to determine the most specific features of heart rate and respiratory rate signals to accurately distinguish stress conditions.
## 3 Techniques for CNN-Based BUS Classification Framework
### CNN-Based Network Architecture  
CNN-based deep learning approaches for BUS classifications have been studied widely in recent times. However, our study primarily addressed the use of multiple parametric images generated from single RF data to improve classification performance. Therefore, we selected four popular and well-known CNN models as the backbone network in our experiment: VGG-16, ResNet-50, DenseNet-201, and EfficientNetV2-L, which have already verified their prediction performances using the ImageNet dataset.  
VGG-16 [38] is a variant of the VGG series network with 16 layers. It stacks 3 \(\times\) 3 convolution layers to increase the nonlinearity and decrease the number of parameters. The increased nonlinearity improves the performance of the decision function. ResNet-50 [39] is a variant of the ResNet series network with 50 layers. Because there is a vanishing problem of previous layer information in deeper networks, ResNet has a special block called a residual block, which transfers shortcuts from the previous layer to the second layer. With residual blocks, the problem of vanishing gradients for deeper networks has been solved. DenseNet-201 [40] is a variant of DenseNet series networks, and its layers are connected to all previous layers. Using the dense connection to all layers, DenseNet can improve the feature propagation and reduce the number of parameters while solving the vanishing gradient problem. EfficientNetV2-L [41] is a variant of the EfficientNetV2 series network. It replaces depth-wise convolutions in the early stage to remove bottlenecks and improve the training speed. These networks achieved top-1 accuracies of 74.4%, 77.15%, 77.42%, and 85.7% when classifying the ImageNet dataset.  
The traditional CNN-based deep learning network and proposed ensemble architecture with multiple parametric images are shown in Figure 5. For the traditional network using B-mode images, shown in Figure 5a, one prediction layer was added to the original CNN backbone network for the binary decision between benign and malignant. It is possible to modify the last fully connected (FC) layer of the backbone network to 1 \(\times\) 2 for binary prediction. However, this could mean losing the advantages of the FC layer owing to the removal of neurons and weights.  
The proposed end-to-end architecture of the ensemble network with four parametric images is illustrated in Figure 5. Each parametric image had its own CNN backbone network and was trained separately using only one type of parametric image. Each linear layer produced a feature vector of size 1 \(\times\) 1000, and all four feature vectors were combined in the decision layer to predict the final result. The decision layer used two voting algorithms: uniform and weighted soft voting. While uniform soft voting simply averaged all confidences of individual backbones (or parametric images), weighted soft voting performed a weighted average according to the prediction results in the training process. The weight of each backbone was calculated using the Bayesian optimization method [42] for the training set of data.
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 3 Development of Finite Element Model for G40 Concrete and UHPFRC Using Non-Interface Element
### Property Assignment
#### 3.1.2 Properties of CFRP  
Composite materials such as Carbon-Fibre-Reinforced Polymer (CFRP), consisting of fibres embedded in a resin matrix, are typically treated as orthotropic due to their directional mechanical properties, notably the Young's modulus. Accurate analysis requires data that can predict stress, forces, torque, and potential damage within the CFRP material. In the current model, however, the elastic properties of CFRP were assumed isotropic for simplification, meaning they exhibit uniform behaviour in all directions, which is a deviation from the orthotropic nature typically found in these composites. For the Three-Dimensional (3D) modelling of the CFRP rod in this study, \"engineering constants\" were employed to define these isotropic properties, with the relevant data presented in Table 5.