esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 4 Results
### Optimization of the Heat Demand
#### 4.2.1 Minimum Variance Optimization for 480 h
A negative number corresponds to reduction in heat demand and a positive number to increase in heat demand. Although the total heat demand was a few percent lower in all the simulations, in some individual buildings the heat demand actually increased. This occurred in 27 buildings with all the applied indoor temperature models and in 110 buildings with some of the applied indoor temperature models. The median increase was 2.1% and at most the increase was 16.8%. An increase of a few percent in heat demand may seem small, but considering that the studied buildings have high heat demands, it could mean a significant increase in heating costs. The increase can be explained by the error in the predicted heat demand. When considering the optimization results that produced the minimum heat demand in each building, the maximum total heat demand was decreased 11% and the total heat demand during the 480-h optimization period was 4.4% lower than the original measured total heat demand. The applied optimization method does not fully utilize the heat storage in buildings, as can be seen in Figure 5, when considering the highest heat demand of the period during the day nine. It can be seen that the heat demand is decreased also in the early morning hours before the peak, which would affect the amount of the peak load cut during the high demand. This is due to the fact that the formulation of the optimization problem is not directly intended for peak load cutting although daily peak loads are cut as seen in Figure 5.
## 2 Materials and Experiment Procedure  
Sample parts in the size of 300 mm \(\times\) 200 mm were taken from the subframe, as shown in Figure 1. The sample sheets were first cut from the subframe and then milled to the geometry specified in the test standard. The milling process was performed with carbide tools at the appropriate speed and feed rate. The final process was a finishing process to ensure that the roughness of the milled surface was less than Ra 3.2 \(\upmu\)m. After milling, the surface of the gauge section was ground with 2000# SiC paper and then slightly polished. The machined specimens were measured by a Coordinate Measuring Machine to ensure dimensional consistency. A fresh sheet of SiC paper was used for each sample, and the polished surfaces were measured with the surface roughness measuring instrument to ensure that the roughness was below Ra 1.6 \(\upmu\)m.  
In this study, the A356 aluminum alloy was experimented on after being milled to standard dimensions. The chemical composition of the A356 material used in this study is shown in Table 1. The standards referenced for the experiments are shown in Table 2, and the thickness of all specimens was 5 mm. Quasi-static and dynamic experiments were conducted on the CMT5205 electronic test system and the Zwick-HTM-6020 high-speed test system, respectively. Three replicate tests were performed on the specimens in each set of experiments to ensure the reliability of the experiment results. Digital Image Correlation (DIC) technology was applied in all experiments to record and analyze the strain during the experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Material** & **Test** & **Reference Standard [25; 26]** & **Repeat Time** & **Strain Rate** \\ \hline \multirow{6}{*}{A356} & Static tensile & GB/T 228.1-2010 & 3 & 0.001/s \\  & Dynamic tensile & GB/T 30069.2-2016 & 3 & 100/s, 500/s \\ \cline{1-1}  & R5 notch & GB/T 228.1-2010 & 3 & 0.001/s \\ \cline{1-1}  & R20 notch & GB/T 228.1-2010 & 3 & 0.001/s \\ \cline{1-1}  & D10 center hole & GB/T 228.1-2010 & 3 & 0.001/s \\ \cline{1-1}  & 45\({}^{\circ}\) shear & GB/T 228.1-2010 & 3 & 0.001/s \\ \hline \end{tabular}
\end{table}
Table 2: The experiment standard referenced in this study.  
Figure 1: Specimens were taken from the subframe.  
To investigate the relationship between the stress triaxiality and plastic strain at failure, we designed specimens with different geometric shapes: static tensile, dynamic tensile, 45\({}^{\circ}\) shear, R5 notch, R20 notch, and D10 center hole specimens, as shown in Figure 2. During the loading process, the triaxiality of each specimen was maintained until the material failure behavior occurred. The stress states were different for each specimen, allowing us to perform a more comprehensive analysis.
# Mesoscale Modelling of the Mechanical Behavior of Metaconcretes
## 5 Discussion
The following equation is proposed as a function of the compressive strength of the concrete without resonators and the volumetric content of the resonators based on the results of the simulations conducted in this work. This equation approximates the compressive strength of the MC:  
\[f_{c}=\frac{1}{1+\gamma\rho}f_{c0}, \tag{3}\]  
where \(f_{c0}\) is the compressive strength of the concrete without resonators, and \(\gamma\) is a constant obtained via a least-squares fit. In our analysis, \(\gamma\) was determined to be 0.114, with a coefficient of determination, \(R^{2}\) = 0.826. Figure 9 presents the fitted function alongside the numerical results. While increasing the resonator content broadens the low-frequency band gap and enhances wave attenuation, it simultaneously diminishes the quasi-static load-bearing capacity. The present results indicate that limiting the resonator volume ratio to \(\rho\approx\) 3-5% preserves at least 55% of the reference compressive strength--an acceptable threshold for lightly loaded vibration-control elements, such as isolation slabs or track beds. This strength window will serve as the basis for future work, wherein the current mesoscale framework will be coupled with transient wave-propagation simulations to quantify how the observed reduction in \(f_{c}\) affects attenuation, bandwidth, and efficiency. The simulation results suggest the potential for a novel function that estimates Young's modulus from the compressive strength of MC. Given the dispersion of the data in both the lower and upper ranges of compressive strength, one flexible approach is to assume that \(E\) behaves as a power function of \(f_{c}\), following the format of Eurocode 2 [58]. In other words, the aim is to identify the constants \(\alpha\) and \(\beta\) as follows:  
\[E=\alpha(f_{c})^{\beta} \tag{4}\]  
Through a least-squares fitting procedure, constants are appropriately determined to obtain \(\alpha=\)1.3123 and \(\beta=\) 0.7646. This fit yields a coefficient of determination \(R^{2}=\)0.9358. Consequently, Figure 10 displays the simulation results alongside Equation (4). Figure 10: Relationship between the compressive strength (\(f_{c}\)) and the modulus of elasticity (\(E\)). Source: own elaboration. Figure 9: Relationship between the compressive strength (\(f_{c}\)) and volumetric resonator ratio (\(\rho\)). Source: own elaboration. While these relationships offer a valuable preliminary framework for structural design, their validity is constrained to the parameter ranges that have been investigated. Further experimental validations would be instrumental in extending the scope and reliability of these relationships.
## 3 Results
### Systematic Evaluation of the Identified Literature
#### 3.4.5 Decentralized Applications
also optimize energy consumption and reduce waiting times but shorten production time by using DTs. A miniature physical model of a production line with three hardware boards as workstations, a conveyor belt for transport, and optical sensors for color detection, was created. At this stage, it has been demonstrated that the VM accurately reflects the physical unit. The DT in this work is limited to presenting a concept for linking the physical entity and VMs, and, thus, no optimizations are presented [34]. A DT for the final stage of a truck assembly line also shows a successful linkage between the physical entity and the VM. The assembly process is performed by eight workstations on the main conveyor belt. Every second, the state (\"works\", \"awaiting components\", \"waiting for a frame\", \"blocked\") of the workstations are updated. The model enables near-real-time monitoring of the physical system and potential detection of problems within the system before they occur. If problems are detected, the model will report and provide information about them. Nevertheless, no optimization within the DT and no closed-loop control are realized [40]. Moreover, in the included publications, application programming interfaces (APIs) and database approaches [36; 41] as well as OPC UA [22; 46] are described as methods for linking physical and virtual space. Furthermore, ISO also proposes the use of MTConnect for physical entities in production systems in its draft Digital Twin framework for manufacturing. For IoT devices various protocols such as OCF, LwM2M, oneM2M, and OPC-UA are proposed. MQTT is mentioned as well, as it supports the \"publish\" method in the transaction protocol. The layer two protocol TSN is also referred to for the transmission of time-critical data [50].
# One-Dimensional Nanostructure Engineering of Conducting Polymers for Thermoelectric Applications
## 4 Conclusions and Outlook
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Polymer Composites** & **Preparation Method** & \(\sigma\) & **S** & **PFmax** & \(\kappa\) & **ZT** & **Ref** \\  & & **(S cm\({}^{-1}\))** & **(uV K\({}^{-1}\))** & **(uW m\({}^{-1}\) K\({}^{-2}\))** & **(W m\({}^{-1}\) K\({}^{-1}\))** & **ZF** & **Ref** \\ \hline PEDOT/CNTs & In situ polymerization & 586 & 18 & \(19.00\pm 1.43\) & – & – & [84] \\ PEDOT/CNTs & In situ polymerization & 15 & 12 & \(0.229\pm 0.014\) & – & – & [85] \\ PANI/Bi\({}_{2}\)Te nanowires & In situ polymerization & 12 & 35 & 1.57 & 0.1096 & 0.0043 & [86] \\ PANI/CNTs & In situ polymerization & 17 & 10 & 0.18 & – & – & [87] \\ PANI/SWCNTs & In situ polymerization & 125 & 40 & 20 & – & – & [88] \\ PANI/ZnO nanorods & In situ polymerization & 28.15 & 41.55 & 4.86 & 0.87 & 0.0017 & [95] \\ PPY/SVCNTs & In situ polymerization & 399 \(\pm\) 14 & \(22.2\pm 0.1\) & \(19.7\pm 0.8\) & – & – & [96] \\ PBHT/DWCNTs & Covalent grafting & 115 & 35 & 46 & 2.0 & 0.0069 & [100] \\ Te/PEDT nanowires & Self-assembly & 500 & 24 & 29.05 & – & – & [101] \\ PPY nanotubes/fCO & Self-assembly & 80 & 28 & 7.28 & – & – & [102] \\ PPY nanowires/CNTs & Self-assembly & 300 & 27 & 21.7 & – & – & [103] \\ PEDOT nanowires/PEDT & Physical mixing & 1270 & 59.3 & 446.6 & 0.26 & 0.44 & [104] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the thermoelectric properties of 1D conducting polymer nanocomposite materials. Future breakthroughs in this exciting new area may reside in the molecular design and preparation of novel monomers or polymers and the incorporation of other functional inorganic nanofillers. In spite of the significant performance enhancement achieved with these 1D materials, the in-depth mechanism understanding of the structure-property relationships and interfacial interactions in the composites are still needed for future design and engineering of high performance polymer-based thermoelectric materials and devices. Conceptualization, J.X. and K.W.S. ; writing--original draft preparation, S.-X.W. and D.X.Y.S. ; writing--review and editing, K.W.S. ; supervision, J.X. ; funding acquisition, K.W.S. and J.X. This research was funded by A*STAR, SERC, Thermoelectric Materials Programmer. Grant number: 1527200019. This work was also supported by National University of Singapore, Department of Building. The authors declare no conflict of interest.
# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs' learning capabilities to model the network's dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system's ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system's performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.  
wireless sensor network; artificial neural networks; backpropagation; routing attacks +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON] Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks. _Sensors_ 2024, 24, 1641. [[https://doi.org/10.3390/s24051641](https://doi.org/10.3390/s24051641)]([https://doi.org/10.3390/s24051641](https://doi.org/10.3390/s24051641))
## 4 LoRaWAN Downlink Routing Control Strategy  
### Bandwidth Occupancy of the Downlink Path  
The link bandwidth occupancy rate of the LoRaWAN network downlink communication at different time point is shown in Figure 10. The bandwidth occupancy rate of route \(R_{n}\) at time \(t\) is obtained according to the maximum value of the link bandwidth occupancy rate \(\mu_{j}(t)\) in the routing set, as shown in Equation (11):  
\[CB_{n}(t)=\max[\mu_{j}(t)] \tag{11}\]  
where \(CB_{n}(t)\) is the bandwidth occupancy rate of path \(R_{n}\) at time \(t\), \(\mu_{j}(t)\) is the bandwidth occupancy rate of link \(l_{j}\) at time \(t\), and \(l_{j}\) is the link included in path \(R_{n}\). In Figure 10 it is seen that from node \(v_{a}\) to \(v_{b}\), the maximum link bandwidth occupancy rate in route \(R_{1}\) at time \(t\) is 0.5. According to Equation (12), the bandwidth occupancy rate in route \(R_{1}\) at time \(t\) is 0.5. Similarly, the bandwidth occupancy rate \(CB_{2}\) in route \(R_{2}\) is 0.3, and the bandwidth occupancy rate \(CB_{3}\) in route \(R_{3}\) is 0.75.  
Figure 10: The path bandwidth occupancy rate at time \(t\).  
Figure 9: The BIC heat map of different model parameters.  
Figure 11 shows the link bandwidth occupancy rate at time \(t+T\). The maximum link bandwidth occupancy rate score \(s_{j}(t+T)\) in the routing set determines the routing bandwidth occupancy rate score \(PS_{n}\) of the route \(R_{n}\) at time \(t+T\), as shown in Figure 11. \(s_{j}(t)\) is defined in Table 3, which is used to represent the congestion degree of link \(l_{j}\) at time \(t\). The bandwidth occupancy score of route \(R_{n}\) at time \(t+T\) can be described as follows:  
\[PS_{n}=\max[s_{j}(t+T)] \tag{12}\]  
In Figure 11, in the process from node \(v_{a}\) to \(v_{b}\), the maximum link bandwidth occupancy score \(s_{j}(t+T)\) in route \(R_{1}\) is 1. According to Equation (12) and Table 3, the bandwidth occupancy rate of route \(R_{1}\) is \(PS_{1}=1\) at time \(t+T\). Similarly, the bandwidth occupancy rate of route \(R_{2}\) is \(PS_{2}=1\), and the bandwidth occupancy rate of route \(R_{3}\) is \(PS_{3}=1\).
## 3 Example Case
### Equipment  
In this study, to collect three-dimensional data for photogrammetry, a drone (as a UAV) and a laser scanner were used. The drone used is a phantom 4 RTK from DJI Technology INC., see Figure 5. The Phantom 4 RTK drone has a location system, communication, and propulsion systems, as well as a flight controller, and a battery. The maximum flight speed is 49.9 m per second and the drone weighs 1391 g. The battery life is sufficient for a 30 min flight. Horizontal accuracy for the Phantom 4 RTK is three centimeters and it stores three-dimensional observational data, which will be used  
Figure 4: Lappeenranta-Lahti University campus area selected for the photogrammetry.  
Figure 3: Estimation of three-dimensional specifications of a vehicle by comparing two planar images.  
with the postprocessing software. The three centimeter horizontal accuracy is the relative accuracy. As pointed out, there is a ground reference check point where the drone started to fly from. Note that, the reference spot was inside the campus area. The drone carries a 20 megapixel camera with a CMOS sensor. A three-axis gimbal is attached to the drone to provide stability for the camera and enable a high resolution and clear images. The drone is also equipped with obstacle sensors to prevent crashes during flight. The remote flight controller uses the GS RTK app to generate a flight plan. The controller has a built-in 5.5 inch (13.97 cm) screen which shows the flight map of the drone, see Figure 6.  
A laser scanner, Faro S70, was used to obtain a high-quality and accurate three-dimensional environment, see Figure 7. The laser scanner collects points in the order of millions (points cloud) to converge planar images to a three-dimensional model.The laser scanner also captures the textures of surfaces of buildings and other objects. It can be used both indoor and outdoor and it is suitable for distances between 0.6 m to 70 m. It can recognize the point locations with an accuracy of \(\pm\)1 mm and can provide one million points per second.  
Figure 5: Phantom 4 RTK drone and controller.  
Figure 6: Flight maps of the chosen area for photogrammetry.  
For the post processing step, the FARO S70 laser scanner uses FARO SCENE software or the Autodesk Reality Capture software, (ReCap software).
## 6 Abuse Attack Techniques
### Compromised Accounts  
#### 6.5.1 CPLS as a Primary C&C Communication Channel  
ELISA [18] is an OSN-based botnet that enables the botmaster to communicate with their bots by leveraging unaware user interactions and concealing its messages within victims' posts. ELISA constructs an overlay network, which interacts with typical users to deliver messages across the entire botnet. To ensure confidentiality, the C&C communication is safeguarded by using encryption and signatures between the botmaster and their bots.  
As outlined in Sections 6.3.1, the Turla malware [69] leverages the victim's already opened session to access the default mailbox profile. Consequently, communication between the compromised Outlook email and the botmaster's email takes place through either an encrypted malicious instruction or encrypted PDF attachments.
## 3 Results and Discussion
### Validation of Machine Learning Models  
An additional measure to test the validity of the machine learning models is cross-validation. During the evaluation, the splitting of data into training and test datasets already validated the models in terms of how they react to unseen data. However, this validation method can lead to sampling bias if one subset includes only some of the existing instance groups. Therefore, cross-validation served as another method to ensure the validity of the created models. K-fold cross-validation ensures more reliability in terms of accuracy and parameter determination. It is a technique that is commonly used to measure the performance and generalizability of a model and helps to reduce the chances of overfitting [67]. For the presented models, a five-fold cross-validation was applied to measure the overall performance (Table 10).  
Furthermore, in order to validate the model, two new datasets were created with literature data and randomly created test instances. In this way, the machine learning models were validated with previously unseen data. To properly validate the results of the design phase, CP Optimizer and OR-Tools both solved the given instances to evaluate the prediction by the machine learning models. The comparison of the selector predictions with the actual results of the two solvers showed satisfying results. For the JSSP, the decision tree achieved an accuracy of 78.5%, the random forest model achieved an accuracy of 70.1%, and the neural network achieved an accuracy of 67.1%. For the FJSSP validation dataset, the accuracy scores of the correct prediction were even higher (Table 11).  
The testing of the individual machine learning models on the new datasets emphasized the interdependency between datasets and algorithm performance. Overall, the results showed high performance metrics and consequently validated the concept of the algorithm selection approach for AGV scheduling problems.
###### Abstract  
This paper proposes a new group-sparsity-inducing regularizer to approximate \(\ell_{2,0}\) pseudo-norm. The regularizer is nonconvex, which can be seen as a linearly involved generalized Moreau enhancement of \(\ell_{2,1}\)-norm. Moreover, the overall convexity of the corresponding group-sparsity-regularized least squares problem can be achieved. The model can handle general group configurations such as weighted group sparse problems, and can be solved through a proximal splitting algorithm. Among the applications, considering that the bias of convex regularizer may lead to incorrect classification results especially for unbalanced training sets, we apply the proposed model to the (weighted) group sparse classification problem. The proposed classifier can use the label, similarity and locality information of samples. It also suppresses the bias of convex regularizer-based classifiers. Experimental results demonstrate that the proposed classifier improves the performance of convex \(\ell_{2,1}\) regularizer-based methods, especially when the training data set is unbalanced. This paper enhances the potential applicability and effectiveness of using nonconvex regularizers in the frame of convex optimization.  
Keywords:convex optimization; proximal splitting algorithm; generalized Moreau enhancement; group sparsity; weighted \(\ell_{2,1}\)-norm; sparse representation-based classification +
Footnote †: journal:  
_Article_  
A Linearly Involved Generalized Moreau Enhancement of \(\ell_{2,1}\)-Norm with Application to Weighted Group Sparse Classification
# Optimization of Intelligent-Reflecting-Surface-Enabled Covert Communication in Multi-Sensor Systems
###### Abstract  
This study investigates covert communication in multi-sensor systems employing Intelligent Reflecting Surfaces (IRSs). Different from previous works, we focus on optimizing transmission amplitudes and phase angles for a 2-BPSK codebook in the presence of asymmetric noise over complex Gaussian channels. We adopt KL divergence as a covertness constraint and mutual information as a metric for transmission rate. We employ Taylor series expansion to approximate KL divergence and mutual information. Leveraging these approximations, we derive optimal phase angles through a proposed gradient descent algorithm. The numerical simulations validate the effectiveness and precision of our Taylor approximation method. Through validation in different scenarios, our algorithm demonstrates robust convergence, deriving all optimal phase angles. Comparing initial phase angles from prior works to those obtained via our algorithm, we observe a higher covert transmission rate.  
c 2024 14 1727. [[https://doi.org/10.3390/app14051727](https://doi.org/10.3390/app14051727)]([https://doi.org/10.3390/app14051727](https://doi.org/10.3390/app14051727))