esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
###### Abstract  
This research focuses on developing a low-cost automated demand response controller (DRC) with OpenADR 2.0a capability to enable existing infrared-controlled (IR-controlled) air conditioners (ACs) in homes and buildings to participate in automated demand response programs (ADRPs). The DRC consists of four modules: a smart socket module, an infrared module, a temperature sensor, and a voltage/current module. It can receive, analyze, and respond to demand response (DR) events and perform necessary demand and energy control strategies via IR. Power line communication (PLC) is used for communication without additional wiring. The system is tested under two conditions: participating in ADRPs via OpenADR and not participating in ADRPs. An 8.8% load reduction is observed with different temperature settings when not participating in ADRPs, and energy reductions of 21% to 46% are achieved using various cooling/fanning duty cycles in ADRPs. The proposed system can be integrated with any DR algorithm to meet demand management requirements under the OpenADR program, contributing to significant demand reductions.  
automated demand response; demand response controller; home energy management system  
[PERSON], [PERSON]; [PERSON]; [PERSON]. Development of a Low-Cost Automated Demand Response Controller for Home Energy Management. _Appl. Sci._**3024**, 14, 11434. [[https://doi.org/10.3390/app142311434](https://doi.org/10.3390/app142311434)]([https://doi.org/10.3390/app142311434](https://doi.org/10.3390/app142311434))
## 8 Conclusions and Research Challenges
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Proposals-Contributions** & **Routing** & **Multi-replication** & **Storage** \\ \hline
**GHT** & GPSR & single home node & - \\ \hline
**GHT with multiple replicas** & GPSR & Grid-based (\(4^{d}\)) & - \\ \hline
**RendezVous Regions** & Region-oriented & single home node & - \\ \hline
**HVGR** & Landmarks + & single home node & - \\  & Region-oriented & & \\ \hline
**pathDCS** & Landmarks + & single home node & - \\  & Path-oriented & & \\ \hline
**Dynamic DCS** & GPSR & single home node & Change home node \\  & & & over the time \\ \hline
**Grid-Based Dynamic** & Geographic Routing + & single home node & Change home node \\  & local decision & & when saturated \\ \hline
**Resilient DCS** & GPSR & Grid-based & - \\  & & different type of queries & \\ \hline
**ToW** & GPSR & Grid-based (\(4^{d}\)) & \\  & + & optimal d & - \\  & Combing routing & & \\ \hline
**Double Rulings** & Circumference Routing & Circumference replication & - \\ \hline
**QAR** & GPSR & Grid-based (\(d^{2}\)) & \\  & + & optimal number of replicas & - \\  & Combing routing & & \\ \hline \end{tabular}
\end{table} 
Table 3: DCS proposals summary. We have mentioned in this paper the autonomous Wireless Sensor and Actor Networks, where the network is self-operated without the necessity of a sink that connects it to the external world. Most of the research papers presented in this survey are focused on standard WSNs and assume that queries are generated from a single point, the sink. We believe that the interest on DCS will increase when autonomous WSANs become more relevant. In such a case, many different event types will be used in the network in different planes: control, management, data, etc. Therefore, DCS seems to be a very efficient storage solution for them. Finally, DCS has been a very interesting topic for the research community and now it is time to start focusing in real implementations for DCS in order to demonstrate that it is a feasible system, able to support many applications running in parallel on top of it. This work was partially supported the Spanish government through the Project T2C2 TIN2008-06739-C04-01 and by the EU through the FP7 project MyUI (\" MyUI: Mainstreaming Accessibility through Synergistic User Modeling and Adaptability \") under grant agreement number 248606. Finally, we would like to thank professor [PERSON] for its great support and help in our DCS research line.
# An Approach to Analyze Vulnerability of Information Flow in Software Architecture
## 3 Proposed Approach
### Construction Approach of Service Invocation Diagram Based on Software Architecture
#### 3.4.2 Definition and Construction Approach for Service Invocation Diagram  
From the research above, the information about service invocation can be obtained. Then by using graph theory, a service invocation diagram can be constructed, which can be used as basis for later vulnerability analysis of information flow in architecture.  
To construct a service invocation diagram, we propose mapping rules between service invocation information obtained above and elements in the diagram. First, services are mapped into vertices in the diagram. Second, the invocation relationship is mapped into the edges. Third, the operation type of service invocation is mapped as weights on the edges. The weight value mapping from the reading operation is 1, and the weight value from the writing operation is 2. Moreover, the direction of an edge is based on the direction of data flow between services. As a result, the service invocation diagram constructed in this paper is a weighted and directed graph.  
**Definition 11**.: _Service invocation diagram. Service invocation diagram is a data structure used to describe invocation information between services. It consists of a nonempty set of vertices \(V\) (composed of \(n\) vertices, \(n>0\)) and a set of edges \(E\) (relations between vertices), which can be denoted as_  
\[G=(V,E)\]  
After learning the service invocation information and defining the service invocation diagram, we can describe the service invocation diagram by use of an adjacency matrix.  
**Definition 12**.: _Adjacency matrix of service invocation diagram. For a service invocation diagram \(G\), a one-dimensional array with \(n\) elements \(L\) [0, \(n\)-1] can be given to describe the information of vertices. Based on this, a two-dimensional array \(A\) [0, \(n\)-1] [0, \(n\)-1] can be given, which is the adjacency matrix for this service invocation diagram. In matrix \(A\), the element \(A_{ij}\) stores the information relation between vertices \(i\) and \(j\). The matrix can be denoted as_  
\[\mathrm{A_{ij}}=\left\{\begin{array}{l}w_{ij}\\ 0\end{array}\right.\]  
When there exists a relationship between service \(i\) and \(j\), then there exists an edge between vertices \(i\) and \(j\), the weigh on the edge is \(w_{ij}\). As mentioned above, \(w_{ij}=1\)_or_ 2.  
When there is no relationship between services \(i\) and service \(j\), the value of \(w_{ij}\) is 0.  
Finally, by traversing all invocation relations between services, the adjacency matrix and service invocation diagram can be obtained.
# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
## 3 Results and Discussion
### Winter
#### 3.2.3 Model Performance  
Figure 6 shows NMB and NME results for average hourly O\({}_{3}\) concentration during the summer and winter months at the Grande Prairie, Beaverlodge, and Edson stations.  
Grande Prairie  
In summer, all three mechanisms meet the NMB and NME criteria, while none meet the goal. CB6R3 performed better followed by RACM2 and SAPRC07. In winter, the NMB criteria were exceeded, and the NME criteria were met by all three mechanisms. As was the case for the summer, CB6R3 performed slightly better for all criteria than the other mechanisms. The modest improvement in the performance of CB6R3 relative to the other mechanisms may be attributed to additional organic pathways to produce O\({}_{3}\).  
Beaverlodge  
O\({}_{3}\) predictions by all three mechanisms followed a similar pattern as Grande Prairie, with all mechanisms performing slightly better at this station. NMB and NME criteria were met in both seasons. Summer NMB values were better than winter, while NME values were similar in both seasons. Beaverlodge is a low NO\({}_{\text{X}}\) and moderately high VOC area, as depicted in the emissions heat map (Figure 2). CB6R3 appears to show better performance compared to the other mechanisms under these conditions.  
Edson  
During summer, both RACM2 and SAPRC07 meet the NMB goal but the CB6R3 NMB is considerably higher, so it does not meet the goal but does meet the criteria. In winter, all three mechanisms exceed the NME criteria. Edson is a both a moderately high NO\({}_{\text{X}}\) and VOC emission area, but the KZMIN value in winter at this site, as for the other sites, may not be appropriate and may be significantly biasing the models to be low. Under these conditions, CB6R3 seems to provide a very marginal improvement in performance.
# Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
## 2 Related Work
### Service Migration in Edge Computing (EC)  
The limited services of edge servers and the users' mobility bring more challenges to the EC paradigm. Service migration between edge series has attracted attention as a means of achieving low-latency and seamless computation. A service migration strategy based on user mobility was proposed [34]. As the user device moves, the service migrates to another edge server. The literature [35] developed an optimal iterative relaxation-and-rounding-based solution approach to minimize the migration cost. Considering the high mobility of vehicles, the authors transform the service migration problem into an uncertain decision optimization problem and designed a Latency-aware Service Migration method with Decision theory (LSMD) [23]. However, it is difficult to achieve fast algorithm convergence due to the traditional heuristic algorithm's high time complexity. The decision process of service migration is modeled as a one-dimensional MDP [36]. Therefore, it is appropriate to use the reinforcement learning algorithm for service migration decisions. Reinforcement learning is emerging as a promising approach to optimize service migration strategies in academics. The migration strategy was optimized based on reinforcement learning, with the service migration delay as the objective function [37]. In addition, some researchers proposed a novel service migration algorithm and architecture to support mobility tasks based on reinforcement learning, which can efficiently reduce the extra delay and energy costs of the migration process [38]. The literature [39] modeled the service migration problem as a complex optimization and implemented deep reinforcement learning to approximate the optimal policy.  
Our work also adopted deep reinforcement learning to achieve quick decision-making, and we dynamically combined the service migration with the task-offloading. Decisions regarding when and where to migrate the task's corresponding service were related to our dynamic computation offloading strategy.
# Measuring the Seismic Resilience of Housing Systems in Italy
## 3 Application to Housing Systems in Southern Italy
The hazard model has been developed according to the Italian reference model, known as MPS04 [52; 53] and incorporated in the current Italian seismic code [54], including the stratigraphic amplification effects. Thus, the ZS9 seismic source zone model of Italy [55] has been employed together with the seismicity rates of the Italian Parametric Earthquake Catalogue CPT104 [56]. To incorporate the stratigraphic amplification effects into the hazard model, the new generation of the Ground Motion Prediction Equation (GMPE) proposed by [PERSON] et al. [57] has been adopted, jointly with the shear-wave velocity to a 30 m depth (Vs30) provided by [PERSON] et al. [58]. More specifically, hazard curves in terms of PGA for multiple return periods have been computed at the exposure site model through an event-based analysis conducted with OpenQuake-engine software [59], version 3.13.0. Using the same software with a probabilistic event-based risk calculator and the aforementioned models, loss exceedance curves and probabilistic loss maps have been developed and an aggregate loss curve of assets with the same taxonomy classification has been calculated for each municipality taking into consideration a return period of 100, 250, and 500 years. Then, the procedure suggested by [PERSON] et al. [28] has been followed to estimate the robustness of each municipality for the different vulnerability models and return periods. In this way, all assets that experience a slight damage (loss ratios below 15%) and the 60 percent of the assets that experience a moderate damage (loss ratios equal to 15% but less than 50%) have been considered as usable buildings and their occupants have been computed for each municipality and taxonomy.The robustness of each municipality has been therefore evaluated for each vulnerability model and return period using Equation (3), which expresses in percentage terms the ratio between the total number of expected residents in usable buildings (taking into account all taxonomies) and the total number of the population. Finally, for each vulnerability model and return period, the restoration time \(T_{RB}\) has been estimated through Equation (4), and the seismic resilience of housing systems in the study area has been assessed by means of Equations (5), (6) and (8), investigating different control times. Figures 4-6 summarize the resilience measurements obtained considering a control time equal to 12, 24, 36 and 60 months and averaging the respective values of two vulnerability models for each return period. Figure 4: \(Cont.\)  
Figure 4: Resilience assessment for a return period of 100 years considering the following control time \(T_{LC}\): **(a)** 12 months; **(b)** 24 months; **(c)** 36 months; **(d)** 60 months. Figure 5: Resilience assessment for a return period of 250 years considering the following control time \(T_{LC}\): **(a)** 12 months; **(b)** 24 months; **(c)** 36 months; **(d)** 60 months.
## 3 Bayesian Estimation under the SEL, GEL, and QEL Functions  
The choice of loss function is an essential element of Bayesian methods. In this section, we first compute the Bayesian estimators under three commonly applied loss functions, including the squared error loss (SEL), general entropy loss (GEL), and Q-symmetric entropy loss (QEL) functions, in order to compare the effects of the estimators under the newly proposed loss function.  
Suppose \(\hat{\theta}\) is the estimate for the parameter \(\theta\) to be estimated. Then, the SEL, GEL, and QEL functions are defined as follows:  
\[L_{SEL}\big{(}\hat{\theta},\theta\big{)}=\big{(}\hat{\theta}-\theta\big{)}^{2}, \tag{15}\]\[L_{\text{GEL}}\big{(}\hat{\theta},\theta\big{)}=\left(\frac{\hat{\theta}}{\theta} \right)^{p}-p\ln\frac{\hat{\theta}}{\theta}-1,p\
eq 0, \tag{16}\]  
\[L_{\text{QEL}}\big{(}\hat{\theta},\theta\big{)}=\left(\frac{\hat{\theta}}{\theta }\right)^{q}+\left(\frac{\hat{\theta}}{\theta}\right)^{-q}-2,q\
eq 0. \tag{17}\]  
In order to evaluate the performance of different Bayesian estimators in multiple scenarios, we consider both informative and non-informative priors.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 2 Signal Models
### Correlator Estimation Model  
In the signal tracking stage, the received intermediate frequency signal is subjected to a series of operations, including carrier stripping and correlation integration, before being converted into a baseband signal comprising the data code alone. In the absence of a spoofing signal, the in-phase channel (I-channel) and quadrature channel (Q-channel) signals can be expressed as follows:  
\[\begin{cases}I_{d}=\sqrt{2P_{a}}D(t)R(\tau)\cos(\omega_{e}t+\theta_{e})+n_{i} (t)\\ Q_{d}=\sqrt{2P_{a}}D(t)R(\tau)\sin(\omega_{e}t+\theta_{e})+n_{q}(t)\end{cases} \tag{2}\]  
where \(I_{d}\) and \(Q_{d}\) are the signal output results of the I-channel and Q-channel, respectively; \(\omega_{e}\) and \(\theta_{e}\) are the carrier frequency difference and initial phase difference between the true signal and the receiver's copy, respectively; \(P_{a}\) is the power of the real satellite signal; \(\omega_{e}=2\pi(f_{a}-f_{0})\); \(\theta_{e}=\theta_{a}-\theta_{0}\); \(f_{0}\) and \(\theta_{0}\) are the frequency and initial phase of the copy carrier, respectively; \(R(\tau)\) is the normalized autocorrelation function; and the Gaussian noise terms \(n_{i}(t)\) and \(n_{q}(t)\) correspond to the I- and Q-channels, respectively.  
In the case of a spoofing attack, Equation (2) is rewritten as follows:  
\[\left\{\begin{aligned} & I_{d}=\sqrt{2P_{s}}D(t)R(\tau)\cos(\omega_{e}t+ \theta_{e})+\sqrt{2P_{s}}D(t)R(\tau+\Delta\tau_{s})\cos((\omega_{e}+2\pi\Delta f _{s})t+\theta_{e}+\Delta\theta_{s})+n_{i}(t)\\ & Q_{d}=\sqrt{2P_{s}}D(t)R(\tau)\sin(\omega_{e}t+\theta_{e})+ \sqrt{2P_{s}}D(t)R(\tau+\Delta\tau_{s})\sin((\omega_{e}+2\pi\Delta f_{s})t+ \theta_{e}+\Delta\theta_{s})+n_{q}(t)\\ \end{aligned}\right. \tag{3}\]  
where \(P_{s}\) is the power of the spoofed satellite signal. It can be observed that the values of \(I_{d}\) and \(Q_{d}\) are susceptible to alteration as a consequence of spoofing attacks. Any spoofing attack will result in a modification of \(I_{d}\) and \(Q_{d}\). These values can be utilized for the purpose of spoofing detection.
## Appendix
### Respiration (Rec) Estimation for the GPP Estimation  
GPP is calculated using the NEP data plus ecosystem respiration (Rec) as  
\[\text{GPP}=\text{NEP}+\text{Rec}(\text{T}_{\text{air}}), \tag{1}\]  
where Rec is the plant respiration plus soil respiration as a function of air temperature (T\({}_{\text{air}}\)).  
We determined the nighttime Rec [2], _i.e._, the nighttime NEE, as an exponential function of T\({}_{\text{air}}\) and applied the function to the daytime data to estimate daytime Rec using the following simple exponential function:  
\[\text{Rec}=A\text{ exp}(B\times\text{T}_{\text{air}}) \tag{2}\]  
where \(A\) and \(B\) are empirical constants to be determined by regression, \(B\) is related to the temperature coefficient, and \(A\) is Rec at 0 \({}^{\circ}\)C.  
Equation (2) is an empirical formula expressing the relationship between the T\({}_{\text{air}}\) and Rec at sites measured by the EC method under nearly neutral atmospheric stability using U* filtering to avoid the flux underestimation on stable nights caused by friction velocity [1]. U* threshold values are site-specific values and are summarized in Table 2. The threshold values are 0.1 m\(\cdot\)s\({}^{-1}\) for JP-Mase [2]; 0.12 m\(\cdot\)s\({}^{-1}\) for JP-FJY [3]; 0.2 m\(\cdot\)s\({}^{-1}\) for CA-Let [76], US-Dk1 [4] and TH-SKR [89]; 0.3 m\(\cdot\)s\({}^{-1}\) for JP-TMK [5]; and 0.5 m\(\cdot\)s\({}^{-1}\) for JP-TKY [6]. For JP-TMK, the respiration curve used the soil temperature at 5 cm, instead of T\({}_{\text{air}}\)[5].
## 4 Results  
The proposed DT and RF models were trained using 85% of the collected database. The other 15%, on the other hand, was used for evaluating the reliability of the prediction models. It is worth explaining that specific optimal percentages have not been suggested for training and testing divisions thus far. It should be clarified that the larger the training database, the better the prediction models can learn the relationship between inputs and output. The testing database, on the other hand, should be sufficient for assessing the accuracy of the proposed models. Additionally, similar percentages have generally been used in similar studies of ML-based models, as they return acceptable results.  
The correlation between the predicted and actual values of ultimate strain is depicted in Figure 6 for both the models. The ideal line (predicted values = actual values) and the lines with 20% lower and higher values are shown by solid red and dashed blue lines, respectively. Figure 7 compares the predicted values to the corresponding real ultimate strains obtained in the results of experimental investigations.  
Figure 5: Choosing the optimal number of trees for the RF model.  
Figure 6: _Cont._  
The graphical comparison between the predicted and actual values illustrated in Figures 6 and 7 reflects the high ability of the DT and RF models for learning the relationship between the input variables and ultimate strain. The DT model exhibited slightly better accuracy compared to the RF model. The prediction models are evaluated deeper in the next section.  
Figure 6: Correlation between estimated and actual values: (**a**) DT: training, (**b**) DT: testing, (**c**) RF: training, and (**d**) RF: testing.  
Figure 7: Actual and estimated values: (**a**) DT: training, (**b**) DT: testing, (**c**) RF: training, and (**d**) RF: testing.
## 2 Related Work
### Key Contributions  
This paper introduces a complete model to reshape the existing ETC structure. It suggested that placement of two reader antenna at a particular height and proper angle is adequate to cover the toll gate area. This way it offers a cost effective, low latency and higher throughput system. It also identified the challenges such as possible reader collision, tag collision and provided required solution. A three-fold solution is proposed based on these objectives:  
* Two-ray model: The installation of the reader antenna makes sure the maximum signal strength to detect vehicle. The concrete road is accounted for the ground's reflection coefficient when determining the reader antenna's position. This method helps determine the optimal antenna height and downtilt angle for optimizing signal strength.
* Modified pulse protocol: A triangular distribution function for the contention window is implemented in the Modified Pulse Protocol algorithm to identify the reader collision challenges and enhance the efficiency of the conventional pulse protocol.
* M-ary Trimming Query tree: To resolve tag collisions for managing the framework that involved multiple tagged vehicles, the M-ary Trimming Query tree algorithm is introduced. An improved tree-splitting method is improvised with a clipping principle to reduce idle node times, emerge low tag identification times, and increase system efficiency.  
Using modified algorithms, we have developed a intrgated model for RFID antenna deployment that includes solutions for reader-tag collisions. In order to verify the analysis and confirm the model's efficacy, MATLAB version 20.0 is used.  
The rest of this paper is organized as follows. In Section 3, the optimal location of the reader antenna in the Two-ray model is comprehensively discussed. As discussed in Section 4, the Modified Pulse Protocol algorithm improves the distribution of contention window to mitigate reader collision challenges. By eliminating idle cycles in Section 5, we are able to construct an M-ary Query tree algorithm that reduces tag collisions and enhances system performance. Section 6 summarizes all executed results. Practical applicability of the proposed research is discuss in Section 7. Finally, Section 8 draws the conclusions.