esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
### Markov Decision Process  
Traditional RL describes the target task as an MDP [1] with elements \((\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R},\gamma)\). \(\mathcal{S}=\{\mathbf{s}_{1},\mathbf{s}_{2},...,\mathbf{s}_{n}\}\) and \(\mathcal{A}=\{\mathbf{a}_{1},\mathbf{a}_{2},...,\mathbf{a}_{n}\}\) denote the finite sets of states and actions. The transition from one state \(\mathbf{s}\) to another one \(\mathbf{s}^{\prime}\) under a certain action \(\mathbf{a}\) follows unknown dynamics \(\mathcal{P}^{\mathbf{a}}_{\mathbf{s}^{\prime}}=p(\mathbf{s}^{\prime}|\mathbf{s},\mathbf{a})\), and the corresponding task-related reward function is defined as \(\mathcal{R}^{\mathbf{a}}_{\mathbf{s}^{\prime}}\). The RL agent aims to search an optimal policy \(\pi(\mathbf{a}|\mathbf{s})\) to maximize the reward from the current state \(\mathbf{s}\):  
\[r_{\mathbf{s}_{t}}=\sum_{\begin{subarray}{c}\mathbf{a}\in\mathcal{A}\\ \mathbf{s}_{t+1}\in\mathcal{S}\end{subarray}}\pi(\mathbf{a}|\mathbf{s}_{t})\mathcal{P}^{ \mathbf{a}}_{\mathbf{s}_{t+1}}\mathcal{R}^{\mathbf{a}}_{\mathbf{s}_{t}\mathbf{s}_{t+1}}. \tag{1}\]  
Define a discount factor \(\gamma\in(0,1)\); the value function is defined as the expected discounted total reward from \(\mathbf{s}\):  
\[V_{\pi}(\mathbf{s})=\mathbb{E}_{\pi}\Bigg{[}\sum_{t=0}^{\infty}\gamma^{t}r_{\mathbf{s} _{t}}|\mathbf{s}_{0}=\mathbf{s}\Bigg{]}. \tag{2}\]  
The maximized value function satisfies a Bellman equation:  
\[V^{*}(\mathbf{s})=\max_{\pi}\sum_{\begin{subarray}{c}\mathbf{a}\in\mathcal{A}\\ \mathbf{s}^{\prime}\in\mathcal{S}\end{subarray}}\pi(\mathbf{a}|\mathbf{s})\mathcal{P}^{ \mathbf{a}}_{\mathbf{s}^{\prime}}\big{(}\mathcal{R}^{\mathbf{a}}_{\mathbf{s}^{\prime}}+\gamma V ^{*}(\mathbf{s}^{\prime})\big{)}. \tag{3}\]  
It can be translated to a function of state-action pairs \(Q(\cdot)\) that is more widely used:  
\[Q^{*}(\mathbf{s},\mathbf{a})=\max_{\pi}\sum_{\mathbf{s}^{\prime}\in\mathcal{S}}\mathcal{P} ^{\mathbf{a}}_{\mathbf{s}^{\prime}}\Bigg{(}\mathcal{R}^{\mathbf{a}}_{\mathbf{s}^{\prime}}+ \gamma\sum_{\mathbf{a}^{\prime}\in\mathcal{A}}\pi\big{(}\mathbf{a}^{\prime}|\mathbf{s}^{ \prime}\big{)}Q^{*}\big{(}\mathbf{s}^{\prime},\mathbf{a}^{\prime}\big{)}\Bigg{)}. \tag{4}\]
## 1 Introduction
To date, approaches based on CNNs, such as object detection models, are the most efficient models for weed detection [24]. The most advanced CNN-based object detection models can be divided into two categories: (a) a two-stage approach; and (b) a one-stage approach [24]. A one-stage approach, as compared to a two-stage approach, only needs one pass through the neural network to predict all the bounding boxes [25]. This is much faster and more suitable for real-time applications [25]. You only look once (YOLO) is a one-stage object detection system that generates bounding boxes and class predictions after only one image evaluation [26]. Another one-stage objection detection model that has been used in the literature is the single-shot multibox detector residual network (SSD-Resnet) [27]. A DL-based lightweight weed detection system is presented to address the practical challenges of detecting weeds in real-time within crops.The system will detect and classify weeds in fields to assist an autonomous laser-based weed-killing vehicle. The main contributions of the work are as follows:  
* A lightweight deep learning model is developed for a commercial autonomous laser weeding robot to kill weeds in real time. * A large dataset was created by visiting several local farming fields in the area of Gadap town in Karachi, with the goal of collecting data of the most common weeds and the crops of Gadap town. As a result, a model trained on this data is more robust for this selected field. * Single-shot object detection models, YOLOv5 and SSD-RestNet, are used to detect and classify crops and weeds. The YOLO model's high performance in terms of its inference time in frame extraction and detection makes it an ideal model for weed detection systems. * The model is implemented on a Nvidia Xavier AGX embedded device [28] to make it a high-performance and low-power standalone detection system.
# Full Information Numerical Simulation of Two-Dimensional Steady Ground Temperature Field
## 7 Convergence Analysis  
Using the same model configuration as that used in the accuracy validation, with the background thermal conductivity set to \(\lambda_{b}\), this study investigates the influence of anomalous thermal conductivity on the convergence behavior of the iterative algorithm under varying numerical parameterizations. Specifically, high-conductivity anomalies (\(\lambda_{s}=5\lambda_{b},10\lambda_{b},50\lambda_{b},100\lambda_{b},500\lambda _{b}\)) and low-conductivity anomalies (\(\lambda_{b}=5\lambda_{s},10\lambda_{s},50\lambda_{s}\), \(100\lambda_{s},500\lambda_{s}\)) are defined to evaluate the iteration count required to achieve the preset computational tolerance (\(\varepsilon=0.005\)).  
Analysis of the data in Tables 2 and 3 indicates that the iteration count required to achieve the preset computational tolerance increases with the contrast between anomalous and background thermal conductivity, while stable convergence is consistently achieved. Furthermore, low-conductivity anomalies require fewer iterations and exhibit faster convergence compared to high-conductivity anomalies. The defined thermal conductivity contrasts cover the majority of scenarios encountered in practical geological settings, demonstrating the algorithm's broad applicability in numerical simulations of geothermal fields.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Split Nodes**} & \multirow{2}{*}{**Total Nodes**} & \multicolumn{2}{c}{**Total Times (s)**} & \multicolumn{2}{c}{**Memory Occupied (GB)**} \\ \cline{3-6}  & & **Algorithm of** & **COMSOL** & **Algorithm of his** & **COMSOL** \\  & & **This Article** & **Multiphysics** & **Article** & **Multiphysics** \\ \hline \(21\times 21\) & 441 & 0.008 & 5 & 0.005 & 1.23 \\ \(51\times 51\) & 2601 & 0.04 & 6 & 0.015 & 1.33 \\ \(101\times 101\) & 10201 & 0.062 & 7 & 0.08 & 1.38 \\ \(201\times 201\) & 40401 & 1.18 & 9 & 0.18 & 1.78 \\ \(401\times 401\) & 160801 & 2.48 & 11 & 0.68 & 2.8 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of computational efficiency for different grid sizes.  
Figure 5: Comparison of calculation time and memory usage between the algorithm in this paper and COMSOL Multiphysics software in different grids (**a**) Calculation time change curve (**b**) Calculation memory usage change curve.
## 2 Influenced Zone of Deep Excavation on Adjacent Tunnel
### Numerical Simulation of the Effect of Deep Exavation on Adjacent Tunnel
#### 2.1.3 Simulation Conditions  
In the numerical simulation, the factors considered include the excavation depth (\(H_{\mathrm{e}}\)), the maximum deflection of the retaining wall (\(\delta_{\mathrm{hm}}\)), the deformation mode of the retaining wall, and the positions of the tunnel (\(H_{\mathrm{t}}\), \(L_{\mathrm{t}}\)).  
For the excavation depth, \(H_{\mathrm{e}}\) = 18 m is adopted. For the deformation magnitude of the retaining wall, the guideline CCES 03-2016 [29] provides the 3-level control standards of the retaining wall deflection in soft soil, i.e., 0.18\(\%\)\(H_{\mathrm{e}}\), 0.3\(\%\)\(H_{\mathrm{e}}\) and 0.7\(\%\)\(H_{\mathrm{e}}\). Considering that the deformation of braced deep excavation is usually controlled to a good level, so \(\delta_{\mathrm{hm}}\) = 0.18\(\%\)\(H_{\mathrm{e}}\) is adopted. For the deformation mode of the retaining wall, the convex type is adopted. The tunnel position is set using the following rules: in the horizontal direction, the spacing between each tunnel center is 3 m when \(L_{\mathrm{t}}\leq H_{\mathrm{e}}\), and that is 6 m  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**No.** & **Parameters** & **Values** & **No.** & **Parameters** & **Values** \\ \hline
1 & \(\gamma\) & 19.8 kN/m\({}^{3}\) & 9 & \(m\) & 0.8 \\
2 & \(e\) & 0.6 & 10 & \(v_{\mathrm{ur}}\) & 0.2 \\
3 & \(c^{\prime}\) & 14.0 kPa & 11 & \(p^{\mathrm{ref}}\) & 100 kPa \\
4 & \(g^{\prime}\) & 25.7\({}^{\circ}\) & 12 & \(R_{\mathrm{f}}\) & 0.9 \\
5 & \(\psi\) & 0 & 13 & \(K_{0}\) & 0.57 \\
6 & \(E_{\mathrm{30}}^{\mathrm{ref}}\) & 7.2 MPa & 14 & \(G_{\mathrm{0}}^{\mathrm{ref}}\) & 99.3 MPa \\
7 & \(E_{\mathrm{oed}}^{\mathrm{ref}}\) & 5.1 MPa & 15 & \(\gamma_{0.7}\) & 0.20 \(\times\) 10\({}^{-3}\) \\
8 & \(E_{\mathrm{ur}}^{\mathrm{ref}}\) & 36.8 MPa & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of HSS model for the soil [11; 12].  
when \(L_{\text{t}}>H_{\text{e}}\), and the maximum value of \(L_{\text{t}}=3.0H_{\text{e}}\); in the vertical direction, the spacing of each tunnel center is 3 m when \(H_{\text{t}}\leq H_{\text{e}}\), and that is 6 m when \(H_{\text{t}}>H_{\text{e}}\), and the maximum value of \(H_{\text{t}}=3.0H_{\text{e}}\). According to the statistics of 42 case histories by [PERSON] et al. (2022) [23], the adopted \(L_{\text{t}}\) and \(H_{\text{t}}\) cover the majority of tunnel positions in actual engineering and are widely representative. Because no construction activities are allowed within 3 m of the edge of the tunnel, and the buried depth of the tunnel crown should not be less than 1.0 time the diameter of the tunnel, the value of \(L_{\text{t}}\) starts from 6 m, and the value of \(H_{\text{t}}\) starts from 9 m. After considering the above factors, a total of 110 numerical models are established, as listed in Table 2.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 6 State-of-the-Art NB-IoT WUR
### NWUS Demodulation and Detection  
As discussed in Section 4.1, the NB-IoT NWUS is a 132-length Zadoff-Chu correlation sequence encoded into 11 OFDM symbols at a symbol period of 66.7 \(\upmu\)S. The NWUS occupies 12 subcarriers with 15 kHz spacing for a total bandwidth of 180 kHz. To decode the NWUS, an FFT is required to convert the Zadoff-Chu sequence to the frequency domain, which is then correlated with a stored NWUS on-chip template. To reduce the high power overhead of the FFT engine seen in other OFDM systems, for this chip, a custom digital baseband utilizing a 12-point FFT is designed and optimized for the NWUS.  
Traditionally, data padding is used to convert the FFT into a more traditional Cooley-Tukey radix-2 16-point FFT. However, this work introduces a 12-point FFT (Figure 17) that is more power- and area-efficient for decoding the NWUS.  
Four key design considerations are taken to reduce area and power. First, as a single WUS symbol contains 12 subcarriers, the FFT is chosen to be 12-point which allows for no  
Figure 16: Block diagram of fabricated WUR with integrated digital backend.  
Figure 17: The optimized 12-point FFT. Through exploiting even–odd symmetry and inverting operations, the number of multipliers can be reduced.  
data padding, eliminating unnecessary multipliers. Second, the 12-point FFT is performed on a single clock cycle with a fixed-point precision of 16 bits to reduce area overhead from pipeline registers. This is possible given the relatively slow symbol rate of the NWUS, allowing for a slower clock speed. Third, even-odd symmetry mapping is applied to the twiddle factors, reducing the number of multipliers needed per FFT output from 12 to 4. Fourth, inversion mapping is applied to the FFT calculations, which exploits the fact that many calculations differ by only the sign of the operations. This allows for simple data inverters to be used rather than additional multipliers. In total, the total number of complex multipliers needed is 48 per FFT, 25% less than that needed for a 16-point FFT. Table 1 compares the performance of different FFT engine architectures. The optimized 12-point FFT including twiddle factor LUTs occupies only 72 \(\upmu\)m\({}^{2}\) of area and consumes 4 \(\upmu\)W while clocked at 1.4 MHz.  
After the FFT, the data are correlated against an on-chip programmable NWUS template and trigger a wake-up request if a correlation threshold is reached. The correlator has a length of 12 using 16-point precision and accumulates each FFT output over 11 symbols to correlate the entire 132-length Zadoff-Chu NWUS sequence. The accumulation threshold for the NWUS is adjustable given channel conditions to ensure robust tolerance to false detections. The accumulator and threshold detector also support data repetitions to increase processing gain to achieve higher sensitivity as allowed by the NB-IoT standard. However, repetition-less data are reported for this work.
# Numerical and Experimental Investigation of Recycled Brick Coarse Aggregate Concrete
###### Abstract  
This paper investigates the mechanical and micro-interfacial properties of recycled concrete. Concrete specimens with recycled brick coarse aggregate (RBCA) admixtures of 0%, 25%, 50%, 75%, and 100% were prepared. Apparent density, slump, and mechanical tests were carried out to evaluate the properties and behavior of the recycled concrete. The evolution laws of parameters such as compressive strength, peak strain, and elastic modulus of the recycled concrete specimens were tested and analyzed. Combined with numerical analysis, the stress distribution and damage propagation process of recycled concrete specimens with different RBCA contents were revealed. Furthermore, the RBCA content parameters and the thickness of the interface transition zone were analyzed to predict the mechanical behavior of RBCA concrete. The results show that the slump of fresh recycled concrete is less than 50 mm when the content of RBCA exceeds 50%. The apparent density of concrete is 15.55% lower than that of ordinary concrete when the content of RBCA is 100%. The cube compressive strength of RBCA concrete decreases with increased RBCA content. With increasing RBCA substitution rate, the internal cracks change from discrete to concentrated. The substitution rate of RBCA and thickness of the interfacial transition zone are negatively correlated with compressive strength and positively correlated with elastic modulus and peak stress.  
m achenical performance; random aggregate model; damage mechanism; mesoscopic mechanics; recycled aggregate concrete Article
## 5 Discussion
### Discuss the Scan-to-Scan and Scan-to-Map  
From the above, single-frame point cloud registration using scan-to-scan registration and continuous-frame registration utilizing scan-to-map registration. The scan-to-scan is the registration of two adjacent frames. The advantage is that the calculation is small, and the disadvantage is that the error accumulation is large, especially for long distances. Scan-to-map is the current frame and keyframe subgraph matching. The advantage is high precision; error accumulation is small; the disadvantage is that the calculation is large. ASV requires accurate positioning and six degrees of freedom state estimation when performing tasks. Considering the number of LiDAR lines, this paper chooses the scan-to-map registration method.
# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
## 2 Theory and Methodology
### Theory of CRRTM  
In contrast to traditional RTM imaging, CRRTM requires a different approach, as illustrated in Figure 1. In this method, the seismic data obtained at the receivers are shifted to the corresponding source locations before back-propagation is performed. Conversely, the seismic wavelet must be forward-propagated from the receiver locations. Finally, the wavefields obtained from these two propagations are used to compute the imaging results under specific imaging conditions.  
To demonstrate the rationality of the CRRTM, we use the model shown in Figure 2 and the zero-lag cross-correlation imaging condition as an example. The model includes a horizontal reflective layer, with the observation well represented by a green rectangle containing a single receiver R1 depicted by a triangle. Given that the propagation speeds of P-waves (primary waves) and S-waves (secondary waves) differ significantly in most cases, we simplify our discussion by considering only the propagation of P-waves. The case of S-waves is addressed in subsequent sections.  
In traditional RTM imaging, the seismic wavelet propagates from the source to the reflection point at time \(t_{1}\) and then from the reflection point to the receiver at time \(t_{2}\)  
Figure 1: Workflow of traditonal RTM and CRRTM.  
Figure 2: Schematic of the propagation path for seismic waves in VSP survey.  
This process is illustrated in Figure 2 by red and blue lines, respectively. If the maximum recording time at the receiver is \(T_{max}\), the part of the seismic data recording the reflection point information is located at \(t_{1}+t_{2}\). During reverse-propagation, these data are excited by the receiver at time \(T_{max}-(t_{1}+t_{2})\). After propagation for time \(t_{2}\), it reaches the reflection point at time \(T_{max}-t_{1}\). Therefore, at the reflection point, the source and receiver wavefields satisfy traditional imaging conditions.  
In contrast, for CRRTM imaging, the seismic data recorded at the receiver are moved to the source location for back-propagation. The time required for the reverse-propagated seismic data to return to the reflection point also differs. According to [PERSON]'s principle, a seismic signal reverse-propagated from the source travels along the same path as the initial seismic wavelet from the source to the reflection point. Time \(t_{1}\) is required to reach the reflection point. Thus, the seismic data arrive at the reflection point at time \(T_{max}-t_{2}\). Simultaneously, the seismic wavelet propagating from the receiver also requires time \(t_{2}\) to reach the reflection point according to [PERSON]'s principle. By ensuring that the wavefields met the imaging conditions at the reflection point, CRRTM was validated for the accurate imaging of subsurface structures.  
Therefore, at the reflection point, the wavefields satisfied the imaging conditions for both methods. This demonstrates that the common-receiver point imaging method can accurately image subsurface structures.
# Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses
## 2 Materials and Methods  
### Study Site, Plants and Bees  
The experiments were conducted from 26 September in 2020 to 25 February in 2021 in three solar greenhouses at Zhangye, Gansu, China, situated at 38.83\({}^{\circ}\) N 100.43\({}^{\circ}\) E, with an elevation of 1521 m. The experimental site was located in the Gobi Desert of the middle part of the Hexi Corridor in Northwest China and belongs to the arid mid-temperate zone. An average 2975 h annual sunshine duration, 6100 MJ m\({}^{-2}\) annual solar radiation and 140-174 annual frost-free days provide an efficient solar energy for protected cultivation. Solar-powered plastic greenhouse is the most popular cultivation system, which relies entirely on solar energy for heating, cooling and crop production.  
In the study, the three greenhouses used were of the same type, horticulture management and area (15 m \(\times\) 100 m). The north, east, and west sides of the structure were built from rammed earth. The south side of the structure was a titled roof supported by a steel frame, and covered with two transparent polyethylene film layers (the light transmittance is 90%) with one of the layers could be opened and closed. During cold seasons (late autumn, winter and early spring), the roof was covered with straw mats at nights to reduce heat losses. An aisle, 0.5 m in width, was maintained along the north wall. The greenhouse was equipped with fly netting to prevent bees from flying out.  
Tomatoes (_Lycopersicum esculentum_ cv. 'Jingcai') were transplanted to the greenhouse on 4 September 2020. Tomatoes were planted in 67-68 rows of 26-30 plants each. All rows were perpendicular to the north wall, with a length of 14.5 m from north to south. Five inflorescences were retained on each plant to produce fruit; the inflorescences bloom sequentially from the bottom to the top of the plants. The peak blooming period of each inflorescence usually lasts for 6-8 days, and the blooming periods of adjacent inflorescences overlap slightly. The whole flowering period of the tomato plants lasted for ca. 75-80 days, beginning 25 September. During the flowering period, the temperature in the greenhouses was kept within 10-32 \({}^{\circ}\)C and the relative humidity was kept within 30-60%, conditions which were favorable for bee flight.  
Colonies of the bumblebee _Bombus lantschouensis_ used in the study were provided by the Institute of Apicultural Research, Chinese Academy of Agricultural Sciences. At the early bloom, a single bumblebee colony was placed in the middle of each greenhouse along the aisle in the north. The bumblebee colony consisted of one healthy queen, approximately 60 workers and with no males at the beginning of the pollination study. Two kilograms of sugar syrup (50% sugar content, _w/w_) was provided within the beehive.
## 3 Materials and Methods
### Classification  
To determine if a cluster \(cl^{j}\) is SVBo or VBo, three proportions \(P_{i}^{cl^{j}}\), where \(i=1,2,3\) and \(cl^{j}\) is a cluster, are computed using the descriptors previously defined. Each proportion \(P_{i}^{cl^{j}}\) is compared with its corresponding \(d_{i}\) threshold and the results from the comparisons are used to label that \(cl^{j}\) as SVBo or VBo. The first proportion, \(P_{1}^{cl^{j}}\) is computed as follows in Equation (11):  
\[P_{1}^{cl^{j}}=\frac{\#\text{SVBo}}{\#\text{beats in }cl^{j}} \tag{11}\]  
This proportion is the percentage of SVBo identified in the cluster by the one-class SVM algorithm. If the proportion surpasses the threshold \(d_{1}\), then the \(cl^{j}\) is labeled as being SVBo.  
The second proportion, \(P_{2}^{cl^{j}}\) is shown in the next Equation (12).  
\[P_{2}^{cl^{j}}=\frac{\#\text{SVBo}}{\#\text{beats in }cl^{j}} \tag{12}\]  
This proportion represents the error of the one-class SVM in labeling the SVBo or the error of SOM clustering SVBo with VBo. It is expected that clusters that are VBo only have mostly BTB and much less ATB. However, some of SVBo may be mixed in a cluster of VBo, and the one-class SVM may identify them as VBos. In such cases, comparing the rate between the number of SVBo in this cluster and the total number of BTB with its threshold \(d_{2}\) tells us what type of cluster it is. A majority of BTB will not let the proportion reach the threshold. If the cluster does not contain any BTB, this proportion is not computed. \(P_{3}^{cl^{j}}\) is shown in (13).  
\[P_{3}^{cl^{j}}=\frac{\#\text{ATB}}{\#\text{beats in }cl^{j}} \tag{13}\]  
Lastly, as mentioned before, because it is hypothesized that most of the ATBs are SVBo because a great number of beats are NB in many signals, this rate represents the relation between the ATB and the number of beats in this cluster. The values of the thresholds are determined experimentally, as will be explained. If any of these thresholds are reached, the cluster is labeled as SVBo; otherwise, it is represented as VB.