esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Smart Community
### Smart Waste Management  
Efficient waste management can prevent environmental problems and reduce costs [71; 72]. Smart bins are the major research area in smart waste management, since they can reduce waste and improve the efficiency of waste management. For example, [PERSON] et al. [71] implemented an IoT-based smart garbage system in Seoul, which reduced the average amount of food waste by 33% over the course of a year.  
In general, the operation of smart bins includes recognizing a passerby and then opening the lid for them, using sensor weighing to detect bin capacity status, planning the best path for the garbage collection truck, and allowing citizens to view garbage can statuses in applications. [PERSON] et al. [71] used an RFID reader to identify the passerby and open the lid of the smart bin. In general, the smart bin is weighed by an ultrasonic sensor and will call the truck to collect the garbage when it reaches a certain weight [71; 73; 74]. Ultrasonicsensors usually determine the level of garbage by measuring the time interval between sending sound and receiving echo. Ultrasonic sensors are usually placed at the bottom of the smart bin to measure weight [71]. The ESP8266 NodeMCU is an example of a processor that can be used to install sensors. [74]. Waste sorting at the source can achieve efficient waste management and improved urban sustainability [75; 76]. [PERSON] et al. [74] detect metal with an inductive sensor. Their smart bin also collects data on shocks and vibrations. All the smart bins scattered around the city will be connected together through some IoT communication protocols/technology. For example, [PERSON] et al. [71] used CDMA2000 EVDO to connect smart bins. Then, we collected smart bin location data using GPS to plan the path of the garbage collection truck. For example, [PERSON] et al. [74] utilized the GPS NEO-6M model. The methods for vehicle route planning include Tabu search [73], Ant Colony [73], and Genetic Algorithm [73]. [PERSON] et al. [73] suggested considering sensor-generated fill levels of bins at customer premises, varying service durations depending on the customer, the time availability of each truck driver, the capacity of each truck, the availability of each customer, and the prioritization of collection services based on each customer's needs in collection vehicle problem planning. They found that Genetic Algorithms are better suited to solving time-windowed waste collection vehicle routing problems. Finally, the smart bin may have an application to provide information to users and administrators [71]. For example, [PERSON] et al. [74] proposed an IoT waste management system that integrates all phases, considering user attributes, health and environmental standards, and other factors. This system allows for the selection of the optimal route, real-time monitoring of container data, tracking of trucks, waste categorization, identification of peak waste time and risks, and generation of waste management statistics and reports.
# Resource Allocation for Reconfigurable Intelligent Surface Assisted Dual Connectivity
## 2 System Model  
We consider the OFDMA based cellular system. The overall system model of RIS-assisted DC is shown in the Figure 1. Let us denote the set of base stations (BSs) as \(\mathbf{J}=\{1,2,3,\ldots,J\}\) and the set of users as \(\mathbf{U}=\{1,2,3,\ldots,\mathrm{U}\}\). The set of subchannels in the system is denoted by \(\mathbf{C}=\{1,2,3,\ldots,\mathrm{C}\}\). The set of RIS in the system is denoted as \(\mathbf{R}=\{0,1,2,3,\ldots,R\}\). Whenever RIS is denoted as 0, it is considered as representing a line-of-sight (LOS) direct link between BS and the user, i.e., there is no RIS between BS \(j\) and user \(u\). We assume that the RIS has \(N\) elements in the panel to reflect and coherently combine and beamform the signal. The set of antenna elements in the RIS is denoted by \(\mathbf{N}=\{1,2,3,\ldots,\mathrm{N}\}\). Every antenna element has its own reflection coefficient and phase shift. The mathematical notations used in this paper are tabulated in Table 1. The physical channel modeling is presented in next subsection.  
Figure 1: System Model of RIS-assisted DC.
# Photon Counting Statistics of a Microwave Cavity Coupled with Double Quantum Dots
## 2 Model and Theoretical Formalism
### Hamiltonian Model
The fourth term, \(H_{T}\), describes the tunneling coupling between the DQDs and electrode leads, so  
\[H_{T}=\sum_{k}\Bigl{(}\Omega_{lk}d_{1}^{\dagger}c_{lk}+\Omega_{\kappa}d_{2}^{ \dagger}c_{\kappa k}+H.C\Bigr{)}, \tag{5}\]  
where \(\Omega_{lk}\) (\(\Omega_{\kappa k}\)) denotes the coupling strength between the left (right) lead and the left (right) QD. The interaction Hamiltonian \(H_{I}\) between the left QD and the microwave cavity field is  
\[H_{I}=-\lambda d_{1}^{\dagger}d_{1}\Bigl{(}a^{\dagger}+a\Bigr{)}, \tag{6}\]  
where \(\lambda\) is the coupling between the cavity field and the electron in the left dot. \(\lambda=\eta\omega_{m}\), \(\eta\) is the scale of QD-cavity coupling strength. By performing a standard canonical transformation, \(\overline{H}=e^{s}He^{-s}\) with \(S=-\eta\Bigl{(}a^{\dagger}-a\Bigr{)}d_{1}^{\dagger}d_{1}\), we obtain the transformed total Hamiltonian as  
\[ \begin{array}{l}\overline{H}=e^{s}He^{-s}\\ =\sum\limits_{\alpha k}E_{\alpha k}c_{\alpha k}^{\dagger}c_{\alpha k}+\frac{ \varepsilon}{2}\sigma_{z}+[\Omega+\Omega_{0}\cos(\omega_{d}t)]\Bigl{(}\sigma _{+}X^{\dagger}+H.C\Bigr{)}\\ +\omega_{\rm m}a^{\dagger}a+\sum\limits_{k}\Bigl{(}\Omega_{lk}d_{1}^{\dagger} c_{lk}X+\Omega_{\kappa k}d_{2}^{\dagger}c_{\kappa k}+H.C\Bigr{)}\end{array}  \tag{7}\]  
where \(X=\exp\Bigl{[}-\eta\Bigl{(}a^{\dagger}-a\Bigr{)}\Bigr{]}\), \(\sigma_{+}=d_{2}^{\dagger}d_{1}\), \(\sigma_{-}=d_{1}^{\dagger}d_{2}\). Here, we introduce a unitary transform \(U=\exp\Bigl{[}-i\frac{\omega_{d}}{2}\sigma_{z}t\Bigr{]}\) to eliminate the driving term, and, under the rotating-wave approximation (\(\omega_{d}\gg\Omega\)), the final total Hamiltonian becomes  
\[H_{f}=H_{\rm sys}+\sum_{\alpha k}E_{\alpha k}c_{\alpha k}^{\dagger}c_{\alpha k }+\sum_{k}\Bigl{(}\Omega_{lk}d_{1}^{\dagger}c_{lk}X{\rm e}^{-i\frac{\omega_{ d}}{2}t}+\Omega_{\kappa k}d_{2}^{\dagger}c_{\kappa k}{\rm e}^{i\frac{\omega_{ d}}{2}t}+H.C\Bigr{)} \tag{8}\]  
with  
\[H_{\rm sys}=-\frac{\Delta}{2}\sigma_{z}+\omega_{\rm m}a^{\dagger}a+\frac{ \Omega_{0}}{2}\Bigl{(}\sigma_{+}X^{\dagger}+H.C\Bigr{)}, \tag{9}\]  
where \(\Delta=\omega_{d}-\varepsilon\) is the detuning between the microwave driving frequency and the energy-splitting of the DQDs.
## 5 Discussion
### Model Limitations  
Due to data availability, our Two-stage linear model was only validated with maize data from five flux sites. Its applicability to other sites relies on the quality of their own flux data. Also, our Two-stage linear model is derived from maize sites, and its applicability to other vegetation types requires further validation. While other vegetation types have also demonstrated a close correlation between V\({}_{\text{m25}}\) and leaf age [8; 63; 64; 58], extending this method to other vegetation types, such as perennial plants, presents additional challenges. Perennials often have canopies that consist of leaves at various stages of development, ranging from young and fully functional to older and senescent leaves [63]. This variation necessitates a more nuanced approach to modeling V\({}_{\text{m25}}\) in perennials, one that can account for the complex age structure of their canopies [58].
## 3 ML for SatCom RRM: Architecture and Techniques
### Reinforcement Learning for RRM
#### 3.3.2 Deep Q Learning  
DQL uses a neural network to approximate the function of the \(Q_{b}\) value, thus avoiding the use of a table to represent it (Figure 8b). At the input of the neural network, there will be the state of the \(b\)th agent, and at the output, there will be the \(Q_{b}\) for each of the possible actions; the \(b\)th memory, \(M_{b}\), stores all the experiences. The loss function of the neural network is the mean square error of the predicted \(Q_{b}\) value and the target \(Q^{\prime}_{b}\) value:  
\[L_{b}(s^{b}_{t},a^{b}_{t},\omega_{t})=(Q^{\prime}_{b}-Q_{b}(s^{b}_{t},a^{b}_{t },\omega_{t}))^{2} \tag{13}\]  
\[Q^{\prime}_{b}=r_{t}(s^{b}_{t},a^{b}_{t},\omega_{t-1})+\gamma\max_{a^{b}_{t}} Q^{\prime}_{b}(s^{b}_{t+1},a^{b}_{t},\omega_{t-1}) \tag{14}\]  
where \(Q^{\prime}_{b}\) is the temporal difference target, and \(Q^{\prime}_{b}-Q_{b}\) is the temporal difference error; \(\omega_{t}\) is the current neural network parameters and \(\omega_{t-1}\) includes the previous parameters.
## Relevant Contributions Related to Sensor Placement
### Relevant Contributions Related to Security
#### Secure Multiuser Communications  
As has been previously discussed, the spreading use of WSNs requires the increase of security measures and protection/mechanisms to reject attacks or information leakage. This situation is more evident when dealing with multiuser communications. In Reference [6], the authors depict a typical scenario where a multiple antenna base station acts as the router for multiple nodes in the WSN. This router node is a more complex node, which in turn means a higher cost, facilitating the interaction of the WSN nodes with a standard network transmission (e.g., Internet IPv4). The local operation may be tuned as desired. Thus, to secure the multiuser communications in the deployed WSN, a cooperative jammer node has been used. The use of a router and jammer nodes in a WSN deployment may be considered very general and extensible for many applications of current WSNs. The architecture tries to extend the battery lifetime of WSN nodes while providing good secrecy performance. The correct selection of the jammer node increases the performance on secrecy for the WSN.  
The proposed secure mechanism in Reference [6] is based on a physical layer security technique, the switch-and-stay combining scheduling scheme. The algorithm requires that the jammer node has the information from a global channel state of both the legitimate channel and the eavesdropper's channel. Besides, a legitimate channel may be considered as an eavesdropper for a given communication. Two different situations have been analyzed. First, a zero-forcing beamforming scheme is considered to represent a maximum interference from the eavesdropper while avoiding the interruption to the selected legitimate user. Second, the emission of a null-space artificial noise is considered, assuming that the link to the eavesdropper is not known. For both schemas, the achievable secrecy rate has been obtained in closed-form expressions, providing the secrecy outage probability and the effective secrecy throughput, so it is possible to configure the best parameter values for a particular WSN deployment.
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
## 6 Conclusions  
The effects of EMI coupled to a communication channel by narrowband resonant mechanisms have been investigated in terms of an equivalent channel capacity loss. In this framework, the effects of non-modulated and random SS-modulated EMI under different resonant frequency and quality factors of the EMI coupling mechanism have been compared, thus extending the analysis presented in [37], which was limited to the case of wideband EMI coupling. The analysis has revealed a higher EMI-induced capacity loss for SS-modulated compared to non modulated EMI under practical values of the quality factor \(Q\), while a modest improvement in the worst case capacity loss is observed only for impractical values of \(Q\) exceeding 10,000. Simulations on a 4-QAM digital link featuring Turbo coding under EMI resonant coupling have also revealed that SS-modulated EMI gives rise to higher BER at lower EMI power compared WITH non-modulated EMI also in the presence of resonant coupling with practical values, thus confirming a worse interfering potential of SS-modulated EMI.  
Figure 14: Bit error rate of a communication channel corrupted by SS-modulated EMI under strongly resonant coupling (\(Q=100\)) vs. different modulation depth \(\delta\) at different EMI normalized r.m.s. amplitude values.  
Figure 13: Bit error rate of a communication channel corrupted by SS-modulated EMI under weakly resonant coupling (\(Q=1\)) vs. different modulation depth \(\delta\) at different EMI normalized r.m.s. amplitude values.  
**Author Contributions:** Conceptualization, methodology, formal analysis: P.S.C.; validation, writing F.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 4 Experimental Results
### Experiment Results
Although VCS eliminates complex computation processes and key dependencies, there are still issues like pixel expansion that need to be addressed to meet visual decryption requirements. The size correlation of secret information affects the carrier image size, leading to excessive resource consumption throughout the information protection process, and large carrier images are hard to obtain. To tackle this problem, we propose a no-expansion VC scheme based on downsampling preprocessing. This method reduces the cost of the encrypted image size through downsampling, addressing the image expansion issue caused by each secret pixel being encrypted into four sub-pixels. Experimental results demonstrate that our approach effectively eliminates the size expansion issue of VC while maintaining good image quality during decryption.
###### Abstract  
Given the complex mechanism of surface settlement induced by shield tunneling, accurate prediction of surface settlement and rational design of tunneling parameters are critical for ensuring safe and efficient tunneling operations. To address the limitations of the Informer algorithm in predicting surface settlement during shield tunneling, the standard convolution was replaced with dilated causal convolution, and three measures were employed: soil layer classification and characterization, a moving prediction window, and special factor handling. An improved Informer algorithm was developed. The prediction time of the improved Informer algorithm was reduced by up to 30.64% compared to the original algorithm. The improved Informer algorithm exhibited significant enhancements in prediction accuracy, perception range across different timescales, and computational efficiency. Compared with random forest and long short-term memory algorithms, the improved Informer algorithm achieved superior performance, making it more suitable for predicting surface settlement induced by shield tunneling. By integrating the improved Informer algorithm with Shapley additive explanations theory, the contributions of shield tunneling parameters to surface settlement were analyzed. A multi-objective optimization algorithm was constructed for the shield tunneling parameters. Key parameters, including the shield thrust, cutterhead torque, and tunneling speed, were selected for optimization. The surface settlement after parameter optimization was reduced by 16.79%.  
shield tunnel; improved informer algorithm; dilated causal convolution; surface settlement prediction; SHAP analysis; parameter optimization Article
## 2 Design
### Mirrors and Sample Chambers  
The apparatus developed consists of two independent vacuum chambers--one for the mirrors and the other for the sample. The vacuum chambers are separated by a beryllium window, which protects the mirror surfaces from contaminants from the sample chamber, including particles produced via the ablation of sample materials. The beryllium window with a thickness of \(\sim\)75 \(\upmu\)m is made using a physical vapor deposition method. The mirror chamber was designed such that the mirrors can be operated under high vacuum conditions below 10\({}^{-5}\) Pa. In the mirror chamber, we installed stable, high-precision alignment mechanics. Greaseless driving mechanisms with stepper motors are used in all alignment stages of the mirrors, including a linear translational stage utilizing a solid lubricant. The rotating stages, which are composed of a precision linear actuator, with a precision of 10 nm/step  
Figure 1: Calculated reflectivity of designed mirrors. (**a**) Dependence of reflectivity on the incident photon energy. (**b**) Dependence of reflectivity on the surface position of the vertical focusing mirrors and (**c**) the horizontal focusing mirrors. Position 0 mm shows the upstream edge of the focusing mirror. The change in reflectivity is due to variation in the incident angle along the elliptical cylinder surface.  
(full-step drive) and a flexure hinge at the rotation center, are employed in the alignment of incident angles and perpendicularity between the mirrors. These stages are operated at a precision higher than 0.1 \(\upmu\)rad using a harmonic drive gear and a micro-step drive control, while the driving mechanics of the incident angles need to be stabilized at a level of 0.1 \(\upmu\)rad to obtain high pointing stability of the optical system.  
The sample chamber has an effective space of over 100 mm in length from the beryllium window to the focal point, as shown in Figure 2. The typical vacuum pressure of this chamber is 0.1-1 Pa, and it can be evacuated using a turbo-molecular pump. The sample environment can also be operated under atmospheric pressure helium conditions depending on the sample requirements of the experiments. Various sample stages and sample supply systems, including a liquid jet injector, can be placed in the space around the focal point.  
The mirror manipulators and sample scanning stages are mounted on a granite surface plate. These stage system and the chambers with the vacuum pumps are connected through bellows and sustained independently on the floor. Therefore, the effect of vibration from the vacuum pumps on the stage systems is minimized.
# BIM-Based Method for the Verification of Building Code Compliance
## 5 Conclusions  
In this study, the workflow and the creation of automated routines using Autodesk Revit and Dynamo software were evaluated, exploring its potential to support designers in assessing their project's compliance with local and national regulations. With this method, a novel procedure was established for automatic verification of the Brazilian master plan and building code indexes. It provides real-time outputs during the project's early stages, allowing design innovation and project changes with few resources. To demonstrate the concept, two case studies were evaluated and it was proven that the use of BIM provides the required resources for a faster and more reliable data collection and analysis. Speed, consistency and efficiency in assessing building compliance were demonstrated. It has provided important data for decision-making during the design phase, but also for municipalities to quickly assess the building's compliance with the local regulations and reduce process bureaucracy.  
It was observed that Autodesk Revit has the potential to store the required multi-disciplinary information for the project evaluation, while Dynamo has the capacity to gather such data and perform the required calculations. Moreover, every BIM model characteristic can be assessed and processed by Dynamo, allowing for the creation of different types of quantitative and/or qualitative assessment routines. Despite the existence of some limitations, the method has proved to be quite reliable, offering a couple of options to overcome them. Overall, it has been concluded that the use of this software has the potential to become an essential tool both for the dissemination of BIM, as well as for the compliance assessment automation. It can significantly support design, as well as reduce the administrative processes and time of getting construction permits.  
Finally, it is noteworthy that the applied methodology can be directly replicated for other Brazilian buildings and locations, as well as for other countries by performing punctual adaptations to meet the standards of each location.  
**Author Contributions:** Conceptualisation, F.S.V. and J.P.C.; methodology, F.S.V.; software, F.S.V.; validation, J.P.C., F.S.V. and L.B.; formal analysis, F.S.V. and J.P.C.; investigation, J.P.C. and F.S.V.; writing--original draft preparation, F.S.V. and J.P.C.; writing--review and editing, F.S.V., J.P.C. and L.B.; visualisation, J.P.C.; supervision, L.B.; funding acquisition, J.P.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Portuguese Foundation for Science and Technology, through the Regional Operation Programme of North (Grant number SFRH/BD/145735/2019).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 3 Quality Assessment Using EMD
### The Overall Quality Score Based on Local and Global EMD  
The LEMD proposes a local metric about how much of the structure information is preserved in corresponding image patches, and the GEMD suggests a global method about how similarity of visual saliency features between reference and retargeted images. Therefore, we define an overall quality score (QS) based on local and global EMD as follows:  
\[\text{QS}=\text{LEMD}+\text{GEMD}=\frac{1}{M}\sum_{i=1}^{M}\text{EMD}(IH^{ ref}_{p,i},IH^{ret}_{p,i})+\frac{1}{N}\sum_{j=1}^{N}\text{EMD}(IH^{ref}_{f,j},IH^{ret}_{f,j}). \tag{6}\]  
According to the above EMD calculation process, we can find that the value of EMD is the distance between two image patches. The smaller value of QS, the closer the distance, and the more similarities between the two images.
# Efficient Detection Approach for DTMF Signal Detection
## 1 Introduction
Accordingly, a highly efficient DTMF detection algorithm, designated as the multi-frequency detecting (MFD) approach, is proposed in this study as an alternative to conventional single point detection approaches. It merely requires one-time detection computation to get a DTMF task done, accounting for a considerable computational load reduction. This detection complexity reduction is an important issue for applying a DTMF detection technique to embedded devices. The rest of this paper is outlined as follows. The related works are described in Section 2. Section 3 presents the proposed algorithm for DTMF tone detection. Experimental results are demonstrated and discussed in Section 4. This work is summarized at the end of this paper.