esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment Details  
### Properties of Workpieces  
As examples of crystalline ceramics and non-crystalline ceramics, alumina ceramics and fused silica ceramics were used in this investigation. In order to get the microstructures and grain size of the materials, the fracture surfaces were examined by scanning-electron microscopy, as shown in Figure 1. The specimens were processed into the shape of cuboid with a size of 20 \(\times\) 20 \(\times\) 10 mm\({}^{3}\) by grinding. Nine workpieces of each material were used.  
As seen in Figure 1a, the grain size is approximately 4 to 6 \(\upmu\)m and the material has compact structure and less pores. Figure 1b shows that there are many pores and no crystals in fused silica. The density and porosity were obtained by Archimedes method. The hardness and fracture toughness were tested by indentation method. HXD-1000 Vickers hardness tester was conducted to estimate the hardness and toughness. The specimen surface was progressively polished by abrasive paper before tested. Five indentations were made on the polished surfaces to obtain average value. Macro-Vickers hardness was determined with an applied load of 1000 gf. Palmqvist cracks were observed to obtain fracture toughness [15]. Properties of specimen materials are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Workpiece** & \begin{tabular}{c} **Density** \\ **/g\(\cdot\)cm\({}^{-3}\)** \\ \end{tabular} & **Porosity** & \begin{tabular}{c} **Hardness** \\ **/GPa** \\ \end{tabular} & \
\begin{tabular}{c} **Toughness** \\ **/MPa\(\cdot\)m\({}^{0.5}\)** \\ \end{tabular} &
\begin{tabular}{c} **Flexural Strength** \\ **/MPa** \\ \end{tabular} \\ \hline Alumina & 3.60 & 9.8\% & 19.38 & 3.5 & 500 [16] \\ Fused Silica & 2.11 & 17.58\% & 7.31 & 0.48 & 50 [17] \\ \hline \end{tabular}
\end{table}
Table 1: Properties of Al2O3 ceramics and fused silica ceramics.  
Figure 1: Micrographs of the two ceramics: (**a**) Microstructure of alumina and (**b**) microstructure of fused silica.
## 1 Introduction
In addition, based on data-driven modeling methods, scholars at home and abroad have modeled the input and output characteristics of nonlinear systems such as super magnetostrictive actuators [27], unmanned aerial vehicle systems [28], robot hydraulic actuators [29], two-wheeled robots with nonlinear motion [30], and five-degree-of-freedom oscillating systems [31], and accurately predicted the system output response under different input conditions, verifying the strong nonlinear modeling ability of data-driven methods. Aiming at the problem that the accuracy of the analytical model cannot meet the actual demand, this paper investigates the nonlinear relationship between friction, backlash, and transmission error, as well as the system state based on the data-driven theory, and establishes a high-precision dynamic model of the ISP servo device. Aiming at the practical problem that the mapping relationship between the transmission performance and the servo performance is not comprehensive on the basis of the established dynamic model, the mapping relationship between the transmission performance of the servo device, the external load disturbance, and the servo performance of the system are investigated through simulation, and it is concluded that friction and road disturbance torque are the biggest internal and external factors that affect the servo performance of the ISP. Taking a certain type of servo device as the object, an experimental platform including an electric load simulator is established, and the quantitative relationship between the influence of friction and road disturbance torque on the servo performance is obtained through experiments which verify the conclusion of the simulation. The research results can provide a reference for the design of ISP drive systems and servo performance predictions. The main contributions of this article are as follows:  
1. Based on the strong nonlinear fitting ability of an artificial neural network, the data models of friction, backlash, and transmission error are established, and the accuracy of the ISP servo system dynamic model is greatly improved. 2. The quantitative mapping relationship between the internal and external disturbance factors and the system servo performance is studied through simulation. It is con cluded that friction and road disturbance torque are the biggest factors affecting ISP servo performance. This article is organized as follows: Section 2 describes the ISP servo system and presents performance indicators; Section 3 establishes the dynamic model of the ISP; Section 4 establishes the simulation model of the elevation servo system; Section 5 evaluates and validates the servo performance through simulation and experiment; Section 6 concludes the research work.
## 2 Basic Theory
### Convolutional Neural Network (CNN)
#### 2.4.1 Convolution Layer  
The core of CNN is the convolutional layer. Adjusting the padding of the convolution kernel avoids image distortion and controls the stride size, reducing the number of input parameters and calculations. Different features are extracted through the sliding window of the convolution kernel to obtain a feature map, which can be expressed as the following equation [24]:  
\[\mathrm{X_{j}^{L}}=\mathrm{S}\left(\sum_{i\in\mathrm{M_{j}}}\mathrm{X_{i}^{L-1 }}\cdot w_{\mathrm{ij}}^{L}+b_{\mathrm{j}}\right) \tag{8}\]  
S is an activation function and uses the ReLU function as an activation function to enhance the nonlinear characteristics of the neural network and improve training speed. \(\mathrm{M_{j}}\) is the input feature set. \(\mathrm{w_{ij}^{L}}\) is the weight matrix. \(b_{\mathrm{j}}\) is the bias value of the convolutional layer.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 3 Deep Learning
### Transformer-Based Models  
Transformer is a deep learning model architecture based on self-attention mechanisms proposed in 2017 [69]. Since its introduction, Transformer has been incorporated into numerous papers, yielding innovative results. In the field of deep learning, many tasks (such as text generation and image segmentation) can be implemented using the Transformer architecture (Figure 4). In 2020, ref. [73] proposed the Vision Transformer (ViT), a visual Transformer designed for image classification. This research significantly advanced the development of Transformers, leading to the emergence of data-efficient image transformers, shunted transformers, and cross-former architectures [74]. These studies have played an important role in the agricultural field.  
Ref. [75] focused on crop disease issues in agricultural fields, where it is challenging to focus on diseased areas in complex backgrounds. By embedding the Transformer Encoder into an improved lightweight convolutional neural network, long-range feature dependencies were established to extract global features from diseased images. This approach achieved excellent performance across multiple datasets. Similarly, addressing agricultural disease issues, ref. [76] aimed to resolve the instability of existing models across spectral ranges (visible light and near-infrared). The paper employs various Vision Transformer (ViT) architectures and their hybrid variants, combined with multispectral imaging technology, to achieve high-precision early detection of plant diseases in complex environments.  
Figure 4: Transformer—model [69].
# Stability Analysis of Isolated Roof in Overlapping Goal Based on Strength Reduction
## 4 Effect of Width Ratio and Dislocated Width on the Stability of the Isolated Roof
### Characterization of the Variation in the Safety Factor for Different Width Ratios
On the contrary, the greater the dislocated width, the more obvious the bearing characteristics of the roof, and the denser the failure units. In addition, the distribution of the plastic zone shows very clear characteristics. If the center line of the upper and lower pillars is set as the axis, the failure units are concentrated on both sides of the axis and basically symmetrically distributed. 2. When the upper and lower ore pillars are completely aligned, the strength of the roof is continuously reduced until it is damaged, then it can be found that the roof has tensile damage in the process of reduction, and the plastic zone is shaped like an I-beam. This phenomenon of course has a reasonable explanation: when the dislocated width is 0, the upper and lower pillars can be regarded as a whole structure, and the pressure from the overlying strata can be smoothly transmitted to the floor through this whole structure. The roof is not subjected to shear, so there are few shear failure units in the  
Figure 8: Safety factor in relation to the width ratio. (**a**) Tensile safety factor; (**b**) Shear safety factor. roof. However, due to its own gravity, when the tensile strength is constantly reduced, a large number of tensile damage units will eventually be generated, resulting in damage to the roof. 3. When the upper and lower pillars are misaligned, the pressure from the overlying strata must be transmitted downwards through the partition roof, which plays a \"connecting link\" role in the support system. At this point, the top plate of the interlayer is subjected to both tensile stress and shear, resulting in tensile and shear zones. But the two occur at different locations, with tensile failure mainly occurring in the opposite area where the interlayer roof contacts the pillars, and shear failure mainly occurring in the area between the upper and lower pillars. 4. The evolution of the failure scope and the failure form of the isolated roof show different laws respectively. The evolution law of the failure scope is that the plastic zone is initially born around the pillar, and with the increase of the dislocation, the failure unit gradually expands outwards and finally penetrates through the roof. Furthermore, the evolution of plastic zone morphology is as follows: the initial failure zone is limited to the periphery of the pillar, the tensile zone is like a folded line, and the shear zone is a diagonal strip. With the increase of the dislocation, the damage units continue to gather, and the plastic zone begins to expand outward, which leads to the continuous expansion of the boundaries of the plastic zone. Ultimately, the tensile zone evolves from a single folded line into a large wave, and the shear zone, which looks like a diagonal strip, evolves into a square strip. Figure 9: Evolution of failure units in the roof with increasing dislocated width by reducing tensile strength.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 5 Experimental Results  
In this section, the proposed decision-control approach is validated by the real experiments where the three Qbot robots are used to locate the signal source.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 4 Two-Stage Detection
### Stage-1: Utilizing OFDM Structure
Hence, the DLI contained in the pilot subcarriers can be cancelled out, that is  
\[\mathbf{z}_{k}=\bar{\mathbf{r}}_{k}-\boldsymbol{\psi}_{k,0}\in\mathbb{C}^{L_{ \text{PI}}\times 1}. \tag{31}\]  
It is remarkable that there are \(L_{\text{PI}}\) pilot subcarriers per OFDM symbol, and there are \(\tilde{K}=KL_{\text{PI}}\) pilot signals in the entire signal. Therefore, we should expand the factor graph \(\tilde{\mathbf{G}}_{0}\) and the codebook \(\{\mathbf{B}_{0}(m)\}_{m\in\{1,\cdots,M\}}\), which can be achieved by  
\[\tilde{\mathbf{G}}=\tilde{\mathbf{G}}_{0}\otimes\mathbf{1}_{L_{\text{PI}}}\in \mathbb{C}^{\tilde{K}\times 1},\quad\mathbf{B}(m)=\mathbf{B}_{0}(m)\otimes \mathbf{1}_{L_{\text{PI}}}\in\mathbb{C}^{\tilde{K}\times N},\quad m=1, \cdots,M. \tag{32}\]  
Equivalently, the expanded factor graph has \(\tilde{K}\) factor nodes (FNs) and \(N\) variable nodes (VNs) with degree \(K_{1}\). In the following subsection, we describe the MPA with the number of projection points \(\tilde{M}=2\) to support high-rate and long-range AmBC.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Radio Access Network
#### 3.2.2 Handover  
5G networks are designed to be highly flexible and heterogeneous with many different interfaces being supported. For instance, 5G allows several different technologies to interface with its network architecture besides its own New Radio (NR), some of which are Long-Term Evolution (LTE), IoT, Vehicular Ad Hoc Network (VANET), Wireless Local Area Networks (WLANs), and several types of Wi-Fi [53]. This Heterogeneous Network (HetNet) architecture is crucial for the evolution of 5G, as more and more devices connect to the network, highlighting the importance of available and reliable radio access methods. 3 GPP outlines two main forms of handover within 3 GPP networks in 5G: intra-handover and inter-handover.  
Inter-handover is necessary for keeping LTE compatibility within the network, as there are still around 5.2 billion LTE subscribers around the world [54]. Inter-handover allows for communication between the gNB and eNB within 4G networks along with the 5G CN and LTE ePC (Evolved Packet Core).  
Intra-system handover is a simpler operation as it does not involve communication with legacy systems. Intra-system handover takes place when a 5G UE is disconnected from one RAN and connected to another RAN. 3 GPP outlines this within TS 23.502 [55].  
As stated earlier, 5G comprises many different supplementary radio access networks, creating a HetNet. These technologies are not all defined by 3 GPP standards and have been considered since 3G technologies. When 3G was the main technology, 3 GPP interfaced with the Universal Mobile Telecommunications System (UMTS) and non-3 GPP networks to devise services that are robust in order to support user mobility [56; 57]. Support for more technologies grew over the years with the usage of 4G LTE and the introduction of 5G NR (New Radio). As these non-3 GPP standards grew and matured, researchers have lookedinto the handover process from a security and performance perspective [58]. This survey outlines the current research and finds that no comprehensive approach exists that handles all use cases of the 5G handover and that the trade-off between performance and security creates several open research areas.
## 4 Performance Evaluation of Post-Installed Anchors
### Static Loading Test
#### 4.1.3 Pull-Out Performance Evaluation  
For the pull-out tests on the M10 set anchor, a steel anchor failure finally occurred in both the cracked and non-cracked concrete, as shown in Figure 10(a). The maximum pull-out load in the cracked concrete specimens was smaller than that in the non-cracked concrete specimens, owing to a reduction in adhesive force between the concrete and anchor. For the M12 set anchor, a concrete cone failure by the adhesive force occurred only under the slightly increased anchor diameter, as shown in Figure 10(b). As with M10, the maximum pull-out load in the cracked concrete specimens was smaller than that in the non-cracked concrete specimens, owing to a reduction in the adhesive force. Figure 11 shows the differences in performance depending on the presence of a concrete crack, and Table 5 shows the average maximum load for each test.
## 4 Experiments
### Results on MNIST Dataset
#### 4.2.1 Robustness to Initial Learning Rate  
We set learning rate (LR) from \(10^{-5}\) to \(15\), and used fine granularity in the interval \([0.01,1]\) but loose granularity outside it. Figure 1 shows the results of test accuracy after \(10\) epochs when LR lies in \([0.01,1]\). It is observed that only Adadelta and Adacomp work when LR is greater than one. Additionally, we illustrate results when LR lies in \([1,15]\) and the effective intervals of all methods are shown in Figure 2. The training batch size was fixed as \(64\) and the initial seed was set as \(1\) in all cases.  
Two conclusions are observed from Figures 1 and 2.  
First, Adacomp is much more robust than the other seven iterative methods. From Figure 1, Adacomp achieves test accuracy greater than \(98\%\)_for all settings_ of learning rates. In contrast, RMSprop, Adam, and Adamax are sensitive to learning rate and only work well for very small learning rates. The other remaining methods, except Adadelta, have an intermediate robustness between Adamax and Adacomp. However, when the learning rate is greater than one, only Adadelta and Adacomp still work. Furthermore, from the top of Figure 2, it is observed that Adadelta works well until learning rate increases up to \(9\) while Adacomp still works even when learning rate is up to \(15\). The bottom of Figure 2 (logarithmic scale of horizontal axis) illustrates that each method has its own effective interval. From the top RMSprop to the penultimate Adadelta, the effective interval gradually slides from left to right. However, Adacomp (ours) has a much wider interval, which means one can successfully train the model almost without tuning learning rate. The reason for strong robustness is that Adacomp adjusts learning rate to a high value in the first few epochs before shrinking to a small value, no matter what the initial setting.  
Figure 1: Test accuracy with respect to learning rate after \(10\) epochs. NaN denotes test accuracy that is less than \(15\%\). Dataset is MNIST.  
Second, the adaptive strategy may not always outperform SGD, which uses the fixed learning rate. As shown in Figure 1, adaptive methods, Momentum, Adagrad, RMSprop, Adam, and Adamax, are more sensitive to learning rate than SGD. This means that when using adaptive strategies, proper setting of the initial learning rate is necessary. However, we also observe that adaptive methods, Adadelta and Adacomp, are much more robust to initial learning rate than SGD.
## 4 Demodulation Method
### ED Method for MIMO Wi-Fi Backscatter  
The ED method used in the existing Wi-Fi backscatter system is incompatible with the proposed system because the tag sensor uses a single antenna. Therefore, we modified the ED method to fit the proposed system. Demodulation of symbols modulated with channel diversity utilized the Euclidian distance between the CSI data of Wi-Fi packets affected by preamble and data backscatter patterns (\(p_{l}\) and \(d_{m}\)) of the tag sensor. The reader stored \(L\) CSI patterns of Wi-Fi packets first received during the preamble duration (\(p_{0}\) to \(p_{L-1}\)) of a Wi-Fi backscatter frame structure for each antenna. When N antennas were employed in the receiver, \(L\times N\) CSI patterns were saved, and then for the \(i\)-th data backscatter symbol (\(d_{i}\)), the reader calculated \(L\) distance between the prestored CSI data and the CSI of the currently received Wi-Fi packet for each antenna. The \(L\) distance obtained in each antenna was combined by the preamble backscatter pattern to obtain the total Euclidean distance. That is, when there were \(L\) preamble backscatter patterns, \(L\) Euclidean distance was calculated for a data backscatter symbol. The Euclidean distance between the \(m\)-th data backscatter symbol (CSI pattern) and the \(l\)-th preamble backscatter symbol (CSI pattern) is given by  
\[\text{q}_{l,m}=\ \sum\
olimits_{j=1}^{N}\sum\
olimits_{c=0}^{C-1}\bigl{|}p_{j,l}(c )-d_{j,m}(c)\bigr{|}^{2} \tag{11}\]  
where \(C\) is the number of subcarriers assigned to the CSI of Wi-Fi packet, \(p_{j,l}\) is the CSI of the Wi-Fi packet received at the \(j\)-th antenna in the reader during the period of the \(l\)-th preamble backscatter symbol, and \(d_{j,m}\) is the CSI of a Wi-Fi packet received at the \(j\)-th receive antenna in the reader during the period of the \(m\)-th data backscatter symbol. The \(m\)-th data backscatter symbol, \(r_{m}\), is detected with the shortest Euclidean distance as follows:  
\[r_{m}=\underset{l}{\text{arg}}\Bigl{(}\min\Bigl{(}q_{l,m}\Bigr{)}\Bigr{)}\ (1 =\ 0,\ 1,\ \ldots,\ \text{L}-1) \tag{12}\]  
where \(\min(*)\) is the function that outputs the smallest value. According to the symbol mapping, the bit-level data stream can be restored from the detected symbol, \(r_{m}\). For example, when there were \(L\) symbol patterns, \(\log_{2}L\) data were for each detected symbol. In Wi-Fi backscatter system, the reader used Euclidean distance to compare the similarity between the symbol candidate and the received channel. Determining the degree of similarity was the magnitude of the Euclidean distance, so square root in operation was unnecessary. The square root was omitted for all demodulation methods presented in this paper.
## 2 Materials and Methods
### Mathematical Formulation of the System  
FMCW radar is a type of radar that emits a sinusoidal signal over a certain period of time sweep \(T\). The sinusoidal signal is modulated by frequency modulation with a certain bandwidth \(B\). This signal is also known as a chirp signal. A chirp signal is a sinusoidal wave whose frequency is increased linearly with time [12]. Its initial frequency is \(f_{c}\).  
The transmitted FMCW signal \(s(t)\) is expressed as follows:  
\[s(t)=A\,\cos(2\pi f_{c}t+\pi\frac{B}{T}t^{2}). \tag{1}\]  
where \(A\) is the amplitude; \(f_{c}\) is the initial frequency; \(B\) is the bandwidth; and \(T\) is the period of time sweep.  
This \(s(t)\) signal is reflected back to the radar receiver by the target. The received signal \(r(t)\) is the delay time \(t_{d}\) version of the transmitted signal:  
\[r(t)=(A-\alpha)\,\cos(2\pi f_{c}(t-t_{d})+\pi\frac{B}{T}(t-t_{d})^{2}), \tag{2}\]  
where \(A\) is the amplitude; \(\alpha\) is the path loss; \(f_{c}\) is the initial frequency; \(t_{d}\) is the time delay; \(B\) is the bandwidth; and \(T\) is the period of time sweep.  
The received signal \(r(t)\) is then further processed by the radar receiver system to produce a signal called beat signal \(b(t)\). If there are four targets in the radar field of view, then the total beat signal is the sum of the four beat signals from each target. If known, \(R_{i}\) is the distance from the radar of the target \(i\), \(v_{i}\) is the velocity of the target \(i\), and \(\theta_{i}\) is the angle of arrival. Then, given \(t_{s}\), \(n\), and \(k\), so the beat signal is as follows:  
Figure 2: (**a**) The location of the heart in the rib cage inside the chest cavity [11]; (**b**) The location of the lungs inside the chest cavity [11].  
\[b(t_{s},n,k)\approx\frac{\alpha}{2}\sum_{l=1}^{4}\biggl{(}\cos\biggl{(}2\pi\biggl{(} \frac{B}{T}\frac{2R_{i}}{c}t_{s}+\frac{2f_{c}v_{i}}{c}nT+\frac{d\sin(\theta_{i}) }{\lambda}k\biggr{)}+\biggl{(}\frac{4\pi f_{c}R_{i}}{c}\biggr{)}\biggr{)}\biggr{)}, \tag{3}\]  
where \(0<t_{s}<T\) is the number of Range Bins; \(0<n<(N-1)\) is the number of Doppler Bins; and \(0<k<(K-1)\) is the number of Virtual Antennas.  
By obtaining the phases of the beat signals, the information about the relative distance between the target's chest and the radar can be calculated. The distance is relative because the chest is constantly moving according to the rate of breathing and the rate of heartbeat.