esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper investigates an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to cope with the problem of spectrum scarcity and poor wireless environment. The main goal of the proposed framework in this work is to optimize the overall performance of the system, including sensing, communication, and computational offloading. We aim to achieve the trade-off between system performance and overhead by optimizing spectrum and computing resource allocation. On the one hand, the joint design of transmit beamforming and phase shift matrices can enhance the radar sensing quality and increase the communication data rate. On the other hand, task offloading and computation resource allocation optimize energy consumption and delay. Due to the coupled and high dimension optimization variables, the optimization problem is non-convex and NP-hard. Meanwhile, given the dynamic wireless channel condition, we formulate the optimization design as a Markov decision process. To tackle this complex optimization problem, we proposed two innovative deep reinforcement learning (DRL)-based schemes. Specifically, a deep deterministic policy gradient (DDPG) method is proposed to address the continuous high-dimensional action space, and the prioritized experience replay is adopted to speed up the convergence process. Then, a twin delayed DDPG algorithm is designed based on this DRL framework. Numerical results confirm the effectiveness of proposed schemes compared with the benchmark methods.  
integrated sensing and communication; intelligent reflecting surface; deep reinforcement learning; resource allocation 2023239896103390/s23249896  
2
## 2 Profile Generation and Modification
### Profile Modification  
The errors occur during the manufacture of the components of a cycloidal gearbox, and if manufacturing errors are not taken into account, problems may arise in assembly. Therefore, in order to meet the needs of engineering applications and to compensate for these tolerances, the manufacture of cycloid gears was corrected for the cycloid tooth profile.  
As can be seen from Equation (6), the cycloid disc profile equation has six parameters (\(n_{c},n_{p},e,R_{p},R_{rp},\varphi_{p}\)). If the reduction ratio is determined when designing the cycloid gear, \(n_{c}\) and \(n_{p}\) cannot be changed, while \(\varphi_{p}\) is the input parameter for generating the cycloid profile; therefore, a total of three parameters, such as \(e\), \(R_{p}\), and \(R_{rp}\), were used as profile correction parameters. The parameters that may affect the profile are \(R_{p}\) and \(R_{rp}\) sincethe eccentricity \(e\) is modest and its error may be accounted for in \(R_{p}\) and \(R_{rp}\). In order to understand the actual effect of these parameters on the gearing accuracy, the profile modification was quantitatively studied based on single and composite conditions.
###### Abstract  
In the precise displacement measurement based on laser interferometry, the measurement technology for the refractive index of air is widely used to improve the measurement accuracy. However, the existing measurement method of the refractive index of air based on direct measurement is not easy to realize in practical work because of its complex measurement principle and the huge volume of the measurement device; while the measurement accuracy and speed based on the indirect method cannot adapt to the real-time, fast and accurate measurement requirements of industrially changing environments, resulting in distortion of the results. In this study, a measurement method of the refractive index of air based on a special material etalon is proposed. The method enables rapid and direct measurement of the air refractive index when the environment changes and it is given the realization process. Finally, the experimental results show that the deviation between this method and the modified Edlen formula is about 2.5 \(\times\) 10\({}^{-7}\), and that this method can quickly reflect the changes of the environment, which prove the correctness of this method and its ability manage rapid environmental responses. This method is worth popularizing in industrial measurement.  
metrology; precision measurement; air refractive index; wavelength correction 1
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## Conclusions and Perspectives
### Is RAC1B Really the Bad Brother of RAC1 in Cancer?
Finally, several studies have used the different activities of RAC1B vs activating versions of RAC1 to argue that there are differences in the activities of the two splice variants. Usually, RAC1 mutants with defective intrinsic GTPase activity and constitutive activation were used, i.e., RAC1-Q61L. Given the greater similarity of RAC1B with another class of activated RAC1 mutants that are fast-cycling and have at least partially retained the intrinsic GTPase activity, for example RAC1-P29S (see Section 4.1), it would possibly be more conclusive to employ a member of this class rather than a GTP-locked RAC1 mutant for comparison with RAC1B. Future research should be devoted to the role of RAC1B in cancer, with a particular focus on distinguishing its role from that of RAC1. This is especially important for studies which involved the use of experimental approaches that did not allow for discrimination of the effects of both RAC1 isoforms, i.e., a study using genomic deletion of _Rac1_ from pancreatic progenitor cells in different mouse models of PDAC. Deletion of _Rac1_ in K-Ras-G12D-induced PDAC in mice reduced formation of acinar-ductal metaplasia (ADM), pancreatic intraepithelial neoplasia (PanIN) and tumors, and significantly prolonged survival, leading the authors to conclude that in mice, Rac1 is required for early metaplastic changes and neoplasia-associated actin rearrangements in development of pancreatic cancer [75]. However, since the genomic deletion knocked out both Rac1 and Rac1b protein, the observed effects could not be unambiguously attributed to Rac1 alone in case of the (likely) coexpression of Rac1b with Rac1 in pancreatic progenitor cells. It would be exciting to compare the effects on ADM, PanIN and tumor formation in these mouse models after selective knockout of either Rac1 or Rac1b.
## 3 Network Model
### Modulation Format Conversion
The number of occupied SBs and SLs in option 1 is larger than it is in the other three options, all three options improve network resource utilization to some extent. Since node 2 and node 3 are simultaneously set as CNs in option 4 and the most efficient MF is utilized on each SDM link, so the least amount of spectrum resources is occupied to carry A. In summary, choosing the appropriate MF and setting CNs to perform MFC can effectively reduce the spectrum resources and space resources occupied by traffic requests, thus improving the utilization of network resources.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Naive Algorithm (Naa)  
Each node in _NAA_[43] broadcasts a fake message periodically. The time duration of these periods should be long enough to avoid draining the battery quickly. If a node has a real message to transmit, it waits until the upcoming fake message is ready. However, instead of transmitting the fake packet, it is replaced by the real message. Once the real message is received by intermediate nodes, the process is repeated until the real message reaches its destination. The adversary cannot distinguish the difference between real and fake messages since they are sent using the same transmission rate. Nevertheless, the delay in this technique is high because it uses fixed long periods to transmit real messages.
## 2 Methodology
### Long Short-Term Memory Network  
LSTM is an improved model of recurrent neural networks (RNNs) that solves the problem of gradient disappearance of RNNs by introducing a gating mechanism and a memory unit. Thus, LSTM can better process long sequence data [31].  
As shown in Figure 2, LSTM is composed of three gate structures: a forget gate, an input gate, an output gate, and two unit structures (hidden unit \(h_{t}\) and memory unit \(c_{t}\)). LSTM uses a gating mechanism to select input information and update the state of the memory unit to complete the recording and transmission of long-term historical information.  
Figure 2: Architecture of RNNs and LSTM.
## 4 Distributions of Path Delay, Path Attenuation, Normalized Doppler Shift of the Scattering Path
### Simulations of CIR, TF, ACF, and Normalized Doppler PSD
#### 5.3.3 The Distribution of \(\gamma_{\text{DIF}}\)  
The PDFs of normalized Doppler shift \(\gamma_{\text{DIF}}\) is illustrated in Figures 14-16. In the Rising state, as shown in Figure 14 the PDF of \(\gamma_{\text{DIF}}\) becomes narrower with the increasing of observed moments, depicting non-stationary feature of the channel. In Figure 16, a reverse trend can be observed compared with the PDF of \(\gamma_{\text{DIF}}\) obtained in the rising state, which also depicts the non-stationary feature of the channel. While as shown in Figure 15 the PDFs of \(\gamma_{\text{DIF}}\) in the cruising state are very similar, reflecting quasi-stationary feature of the channel.  
The PDFs of \(\gamma_{\text{DIF}}\) in the Rising state \(t=400\) s, \(t=600\) s and \(t=800\) s, are fitted with a polynomial as Equation (67). The coefficients are as shown in Table 4. The PDFs of \(\gamma\) in the cruising and descending states can be fitted with the same formula (67), with different coeffcients.  
\[f\left(\gamma_{\text{DIF}}\right)=c_{1}\gamma_{\text{DIF}}^{4}+c_{2}\gamma_{ \text{DIF}}^{3}+c_{3}\gamma_{\text{DIF}}^{2}+c_{4}\gamma_{\text{DIF}}+c_{5} \tag{67}\]  
Figure 16: The PDFs of normalized Doppler shift (Descending).  
Figure 14: The PDFs of normalized Doppler shift (Rising).  
Figure 15: The PDFs of normalized Doppler shift (Cruising).
# Improving Eight-State Continuous Variable Quantum Key Distribution by Applying Photon Subtraction
## 4 Performance and Discussion  
In Figure 4, based on the improved eight-state CVQKD protocol, we show the relationship between the secret key rate and the transmission distance of the protocol. The left-to-right relationship curves correspond to the original eight-state protocol, three-photon subtraction, two-photon subtraction, and one-photon subtraction. We compare the original eight-state protocol and the modified protocol. Interestingly, the less photons we subtract, the better performance we can achieve. Therefore, we obtain optimal performance of the eight-state protocol by performing a one-photon subtraction operation.  
Note that in actual use, the noise \(\epsilon\) often exists in the quantum channel. Therefore, studying \(\epsilon\) is very important for the performance of our improved eight-state CVQKD protocol. The relationship between the secret key rate and channel loss is shown in Figure 5. The left-to-right relationship curves correspond to \(\epsilon=0.032\), \(\epsilon=0.025\), \(\epsilon=0.008\), and \(\epsilon=0.001\), respectively. We find that the use of a photon-subtraction operation can tolerate more channel loss.  
As the photon-subtraction operation is utilized on Alice's side, the intensity of the light field is improved and, thus, the entanglement in the EPR states generated by Alice can be well-enhanced, which ultimately contributes to the performance improvement (tolerating more channel loss) of the eight-state protocol, as illustrated in simulation results above.
## 8 Current Challenges and Future Prospects  
In 2015, the UN developed strategies that help attain sustainable agricultural development by 2030. However, the latest statistical data issued by the WHO do not look favorable for sustainability. This plan shows that more than 800 million people globally face food shortages, which is one out of every nine people in the world [185]. To address these issues, the overall grain production needs to be increased. Additionally, cash crops need to be increased to meet industrial requirements such as rubber and cotton. Most significantly, the demand for bioenergy, such as ethanol, will also increase by 2030.  
This review outlines the main issues that agriculture would face by 2050. This review essentially addresses two main questions. How do the advanced technologies help to: (1) feed about 10 billion people? and (2) increase agricultural production without occupying more agricultural land? Moreover, these issues bring several challenges, such as the reduction in agricultural manpower, the decreasing arable land area, the shortage of water resources, the impact of climate change, etc. As the world shifts towards urbanization, not only is the population of rural areas declining, but it is also rapidly aging. As a result, young farmers are required to step up to take liability. Population imbalances and generational transfers will have a serious impact not only on the rural agricultural manpower but also on-farm management. Besides, while arable land is diminishing, many lands are only appropriate for select crops due to certain geographical and environmental constraints. Besides, climate change has begun to affect almost all crops. These variations are anticipated to intensify many existing long-term ecological difficulties, such as turf, floods, soil degradation, groundwater depletion, and more.  
Compared with developed nations, more than 50% of the populations of developing nations are directly or indirectly involved in agriculture. Still, they are far behind developed countries in terms of quantity and quality. The population of developed countries, which is less than 2%, is performing much better. This difference is evident because Australia, the US, and most European countries have adopted improved tools and methods. Over the past fifty years, they have adopted improved instruments and techniques to increase crop production. These differences demonstrate that advanced technology and sophisticated approaches make the farm more productive and ecologically sound.  
It is expected that the future of agriculture will develop into a progressive sector when integrated systems appreciate artificial intelligence and big data capabilities. These integrated systems will combine several agricultural types of equipment, tools, and techniques applicable for various agricultural management from souring to yield forecasting. Advanced machines, such as robots of agriculture, cloud computing, artificial intelligence, and big data, can usher in a different era of super-convergence in the farming sector. Here are crucial tools and techniques that need to be applied for sustainable agriculture in the future.
## 3 Analysis of Results
### Density  
Figure 6 shows the average results obtained from the density tests on the different mixes of hardened UHPFRC. The results show a higher density for the mixes with GCW-GS and an increase in density as the percentage of substitution increases. This is due to the higher particle density of GCW-GS, i.e., 2856 kg/m\({}^{3}\), compared to micronized quartz, i.e., 2609 kg/m\({}^{3}\). In the case of UHPFRC with GCW-D, although a reduction in density was  
Figure 5: Slump of UHPFRC for both wastes (GCW-D [14] and GCW-GS).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & **Cement** & **GCW-GS** & **Fe\({}_{2}\)O\({}_{3}\) (14.6\%** & **CaO (4.5\%** & **\% Fe\({}_{2}\)O\({}_{3}\) over** & **\% CaO over** \\  & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **GCW-GS) (kg/m\({}^{3}\))** & **GCW-GS) (kg/m\({}^{3}\))** & **Cement** & **Cement** \\ \hline Control & 800 & - & - & - & - & - \\
35\% GCW-GS & 800 & 79 & 11.5 & 3.55 & 1.43\% & 0.44\% \\
70\% GCW-GS & 800 & 158 & 23.06 & 7.11 & 2.88\% & 0.88\% \\
100\% GCW-GS & 800 & 225 & 32.85 & 10.12 & 4.1\% & 1.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percentage of Fe\({}_{2}\)O\({}_{3}\) and CaO relative to the amount of cement in the mix.  
observed, the variation is less than 2% and is within the error bars (Figure 6). In summary, the incorporation of GCW-GS produced an increase in the density of the concrete while the incorporation of the residue GCW-D did not affect this property.
# Helicopter Takeoff and Landing Point Location in Cities for Emergency Services
## 4 Optimization of Selection Based on the Greedy Algorithm
### Location Optimization  
#### 4.3.1 Initial Location Optimization  
According to the previous research on location optimization using the greedy algorithm [5], the optimal rescue radius is usually selected first. The optimization purpose of this section is to make the rescue range of the take-off and landing point cover the urban area of Nanjing as much as possible.  
In order to further simplify the optimization simulation, this section selects the center point coordinates of the conforming area to replace the entire surface. The geographic data of the initial location is iteratively analyzed according to the above algorithm, and finally, the minimum facility distribution is obtained. After comparison, when the rescue radius is 30 km, five helicopter take-off and landing points need to be built, achieving 98.7% coverage, which basically meets the experimental expectations. The results are shown in Table 6.
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 2 Analysis of Key Technologies for the Transition from Open-Pit to Underground Mining  
### Analysis of Mine Development Systems in the Transition from Open-Pit to Underground Mining  
The mine development system serves as the core support structure for mining operations and plays a critical role in the transition from open-pit to underground mining, effectively bridging the two methods [10]. Its design directly affects the selection of mining methods, safety, and transportation efficiency while also imposing overarching constraints on slope stability control, resource recovery rates, and environmental management [11]. Therefore, the scientific selection of an appropriate development system is a fundamental prerequisite for ensuring safe and efficient mining operations [10]. Depending on how underground and open-pit development technologies are integrated, engineering practices commonly categorize the mine development systems for the transition from open-pit to underground mining into three types: independent development systems, partially integrated development systems, and fully integrated development systems [12]. To gain a deeper understanding of the applicability and advantages of these systems, the following sections will examine the characteristics, implementation conditions, and practical applications of each system in detail.
## 2 Theory
### Group Sparse Reconstruction  
GS reconstruction is an extension of CS that exploits the correlations among adjacent transform coefficients caused by their structured sparsity [18, 30, 31]. Structured sparsity, or block sparsity as it is also known, is the tendency of large transform coefficients to be adjacent to each other and form clusters rather than be evenly distributed across the transform domain. In GS reconstruction, adjacent transform coefficients are reconstructed together in groups rather than individually, as is done in CS. By reconstructing groups of coefficients, the GS signal model correlates individual transform coefficients with their neighbors, allowing them to influence each other during reconstruction. Because of the structured sparsity relationships among transform coefficients, it has been shown the GS signal model provides results equal to or better than CS in an RMSE sense, which does not exploit these relationships [30, 32].  
GS reconstruction can be formed as a constrained convex optimization problem that uses the \(l_{2,1}\)-norm as the objective function instead of the \(l_{1}\)-norm used in CS. The \(l_{2,1}\)-norm over a vector \(u\in\mathbb{C}^{N}\) is defined as:  
\[\|u\|_{2,1}=\|u_{g1}\|_{2}+\|u_{g2}\|_{2}+...+\|u_{gL}\|_{2} \tag{8}\]  
where \(u_{gi}\in\mathbb{C}^{P}=\{u_{j},u_{k}...u_{n}\}\) and \(j,k,n\in\{S=1...N,P\leq N\}\) is one of the \(L\) groups of \(u\). Because the \(u_{gi}\) groups may overlap, \(\|u\|_{2,1}\) is non-convex and variable substitution must be used on \(u\) to ensure that GS reconstruction is a convex optimization problem:  
\[\begin{array}{rl}\min_{u,z}&\|z\|_{2,1}\\ \text{such that}&\|R\mathcal{F}u-f\|_{2}\leq\sigma\\ &z=Gu\end{array} \tag{9}\]where \(z\) is defined as \(z=Gu\) and \(G\in\mathbb{R}^{LP\times N}\) is the group matrix of \(1\)s and \(0\)s that determines which transform coefficients from \(u\) belong to each group in \(z\)[33]. \(G\) is defined as having a single \(1\) per row so no \(z_{gi}\) overlap but each \(z_{gi}\) may contain any number of coefficients from \(u\). The remaining terms are unchanged from Equation (1). Because each transform coefficient from \(u\) may be within more than one \(z_{gi}\) group and separate reconstructions must be created for each version of that coefficient, the set of transform coefficient groups, \(z\), may contain more points than \(u\) and is an over-complete reconstruction of \(u\).  
The GS problem reduces to the CS problem when each group contains one coefficient, thus making \(G=I\) and \(z=u\). As implemented for these experiments, the grouping pattern must offer complete coverage of the entire 4D MRSI volume so that each spatial, spectral-domain coefficient is contained in at least one group.
## Appendix A Theoretical Overview
### The Feed-Forward Neural Network (FFNN)  
The FFNN is used to map input and output to provide a better approximation of the function \(y=f(x,\theta)\), where \(\theta\in\{w,b\}\) and \(w\) and \(b\) are the weights and bias vectors, respectively. These values are adjusted via the learning process [71]. Typically, the FFNN consists of an input layer, one or more hidden layers, and an output layer. The inputs are taken from the input layer to the hidden layer, and then the calculated values are transferred to the output layer. At the output layer, a specific type of calculation is performed depending upon whether the task is a classification or regression task. This depends on the activation function and the data orientation. The simplified calculation for the FFNN is described by Equations (10)-(11) below.  
There are n input signals (or nodes) in the input layer, \(M\) hidden units in the hidden layer, and k output signals in the output layer. This allows the construction of \(M\) linear combinations of input variables \(x_{1}\),..., \(x_{n}\) as in Equations (10) and (11):  
\[a_{j}=\sum_{i=1}^{n}w_{ij}\cdot x_{i}+b_{j}, \tag{11}\]  
\[z_{j}(x,\theta)=Activation\big{(}a_{j}\big{)}, \tag{12}\]  
where \(j\) is the index and \(M\) the total number of linear combinations of input variables (i.e., \(j=1,\ldots,M\)), \(w_{ij}\) and \(b_{j}\) are the weights and biases, and \(a_{j}\) is the linear combination of the weighted sum. The value \(a_{j}\) is then passed to the activation function that applies a transformation (e.g., _tanh_, logistic sigmoid, rectified linear unit (Relu), etc.) to the output (\(z_{j}\)) of the hidden layer. This process is then repeated for the output layer, as in Equations (11) and (11):  
\[a_{k}=\sum_{j=1}^{M}w_{kj}\cdot z_{j}+b_{k}, \tag{13}\]  
\[y_{k}(x,\theta)=Activation(a_{k}), \tag{14}\]  
where \(k=1,\ldots,K\), (\(K\) being the total number of outputs), and \(w_{kj}\) and \(b_{k}\) are the weights and biases for the output layer from the hidden layer. The output unit activations are then finally transformed via an activation function to produce a set of network outputs \(y_{k}\).  
To learn the weights and bias values, the FFNN employs backpropagation algorithms to reduce the error between the predicted and actual output values in order to provide an accurate prediction.