esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Perception of IoT Devices in Smart Homes
###### Abstract  
IoT devices are used frequently in smart homes. To better understand how users perceive the security of IoT devices in their smart homes, a model was developed and tested with multiple linear regression. A total of 306 participants participated in the survey with measurement items, out of which 121 had already been using IoT devices in their smart homes. The results show that users' awareness of data breaches, ransomware attacks, personal information access breaches, and device vulnerabilities have an effect on IoT security importance. On the other hand, users often do not check their security settings and feel safe while using IoT devices. This paper provides an overview of users' perception of security while using IoT devices, and can help developers build better devices and help raise awareness of security among users.  
IoT; smart homes; security; privacy +
Footnote †: journal: Cybersecurity and Privacy  
Automatic A/C or heating systems (appliances can be switched on or off remotely, controlling energy consumption and thus providing the possibility to optimize and reduce costs);
* Smart assistants (e.g., Google Home or Alexa, which are voice-controlled and allow users to search, find information, answer phone calls, and set different timers and reminders);
* Smart lighting (the lights in the home can be turned on or off remotely);
* Smart locks (can approve or reject visitors and have the possibility of unlocking the door automatically when the owner is nearby);
* Smart alarms and sensors (inform users about dangerous situations such as gas leaks, house/apartment break-ins, etc., and help to take quick action in case of problems);
* IP cameras (video from the camera is transmitted to registered devices and can be viewed remotely);
* Smart washing machines and dishwashers (machines that dispense cleaning agents automatically and adjust the quantities according to different factors (e.g., dirtiness, weight) and allow remote switching on or off).  
The motivation to start this study was a rapid increase in smart home device use in recent years, and we wanted to explore how much the users of such devices are aware of security and privacy.  
The contribution of this study is the result of the statistical analysis, which can be used for further development of IoT devices for smart homes. It is important that users become more aware of the security and privacy of such devices.
## 5 Simulations and Performance Evaluations
### Different Numbers of MTs' Tasks  
As shown in Figure 5, we measured the total time consumption by the system in completing all tasks under the 3 strategies with different numbers of MTs' Tasks. In simulations, we increase \(R\) from 1 to 8, while other settings are listed in Table 1, except fixed values \(M=60\), \(K=7\), \(\phi_{k}=2.4\times 10^{6}\) MBPS, \(s_{k}^{\text{C2 NBS}}=80\) MB/s, \(s_{k}^{\text{NBS2C}}=30\) MB/s, \(s_{k}^{\text{B82 NBS}}=s_{k}^{\text{NBS2 BS}}=30\) MB/s, \(\forall k\in\mathcal{K}\). When \(R\) increases, the total time for the system to complete the tasks increases correspondingly, these 3 strategies are in accordance with the law. At the same time, the total time consumed by OMEC and OCMEC is always lower than NMEC, and OCMEC is always lower than OMEC. So, computing offloading and data caching can reduce the system delay.  
With the increase of \(R\), the total load of the system increases. When \(R\in\{1,2,3\}\), because of the low task load, the gain effect of OCMEC on reducing the delay is relatively low; when \(R\in\{3,4,5,6\}\), the task load increases continuously, and the total time-consuming gap between OCMEC and NMEC is increasing, and the gain effect of reducing the delay is getting better and better. When \(R\in\{6,7,8\}\), the total load exceeds the capacity of MEC-BS and MEC-NBS, and they cannot fully meet the calculation requirements of the tasks, so the total time gap gradually narrows, and the gain effect for reducing the delay gradually decreases.  
Therefore, the strategy proposed in this paper can perform better when the number of users is larger, and the proposed base stations data caching and computational offloading strategies can effectively reduce the end-user service response latency.
## 7 Conclusions  
UAVs are complex electromechanical systems, rapidly evolving, and applicable in various fields. They represent a closed-loop interactive platform with five major subsystems (as concluded by this paper). For a stable UAV mission, each of the reviewed subsystems must be carefully engineered to fit at best compatibility with other subsystems. The overall findings can be compressed into one sequential mechanism: the control subsystem is the main decision-maker concerning the flight path (i.e., navigation), flight safety (i.e., collision avoidance), and flight mission (i.e., payload integration). The data input for the control subsystem is fed through the UAV-installed sensors (i.e., environmental, vision, position), and processed via the computing subsystem (i.e., SBCs, SoM). When needed, data can be externally communicated via single/multi communication modules (i.e., LoRa, BLE, Wi-Fi, LTE-M). The power subsystem eventually supplies the required energy of each of the subsystems. The choice of physical devices in each of the subsystems is a real challenge, which is mainly dependent on a UAV's flight purpose.  
Specifically concerning TL monitoring applications (e.g., the DNeD project), with an electrical sensor as the payload, the SBC is at best NVIDIA Jetson series-based (i.e., powerful GPU for TL identification/detection). LoRa-based communication modules, on the other hand (e.g., RN2483), would provide a good signal transmission with regard to electrical/magnetic noise emitted around the TL. An optimized Li-I would represent an optimum  
Figure 12: DNeD’s project overview [314].  
trade-off between safety and flight endurance. The sensor box itself is the required sensing equipment for TL monitoring/non-invasive measurements.  
Eventually, the presented concrete lists of reviewed UAV's avionics (i.e., ARM-based SoM, ARM/Samsung-based SBCs, LoRa modules (SX1278, HOPERF RFM95W-86852, etc.), BLE modules (nRF54H20, CC2650, etc.), sensors, power supplies, etc., make this review a handy tool for the hardware design of any other UAV mission purposes.  
Writing--original draft and conceptualization, K.O.; review, editing, and supervision, D.S. All authors have read and agreed to the published version of the manuscript.  
This research paper is part of the project DNeD (\"Digitalisierte, rechtssichere und emissionsarme flugmobile Inspektion und Netzdatenferassung mit automatisierten Drohnen\", engl. \"Digitalised, legally safe and low-emission airborne inspection and grid data acquisition using automated drones\") and was funded by dtec.bw--Digitalization and Technology Research Center of the Bundeswehr. dtec.bw is funded by the European Union--NextGenerationEU.  
Not applicable.  
The data are unavailable due to privacy or ethical restrictions.  
The authors declare no conflicts of interest.
# Research on a Reductive Deep Chlorine Removal Process for Breaking Through the Solid Film Barrier
## 2 Materials and Methods
### Calculations Related to Thermodynamic Parameters
#### 2.4.2 Copper Slag Dechlorination  
The core mechanism of the copper slag dechlorination method involves the reduction of Cu\({}^{2+}\) in the solution by elemental copper in the copper slag, resulting in the formation of Cu\({}^{+}\) and the removal of chloride ions. The Cl\({}^{-}\) in the chlorinated solution ultimately precipitates as cuprouschloride (CuCl) and remains in the slag. The reaction mechanism for copper slag dechlorination is shown in Equation (6).  
\[\mathrm{Cu^{2+}+Cu+2 Cl^{-}=2 CuCl}\;\downarrow \tag{6}\]  
Copper slag dechlorination contains two half-cell reactions: one in which Cu\({}^{2+}\) gains electrons to be reduced to Cu\({}^{+}\) and combined with Cl\({}^{-}\) to form CuCl, and the other in which Cu\({}^{0}\) loses electrons to be oxidized to Cu\({}^{+}\) and combined with Cl\({}^{-}\) to form CuCl. The electric potential \(\Delta\mathrm{E}\), the standard Gibbs free energy \(\Delta\mathrm{G}^{\theta}\), and the standard equilibrium constant K of the reaction were calculated from the data in Table 2 using Equations (7)-(9).  
\[\Delta\mathrm{E_{2}}\;=\mathrm{E_{Cu^{2+}/CuCl}}-\mathrm{E_{CuCl/Cu}}\;=0.57-0.171=0.399\;\mathrm{V} \tag{7}\]  
\[\Delta\mathrm{G_{2}}^{\theta}=-\mathrm{z_{2}FE}^{\theta}=-1\;\times\;96,484.5 \;\times\;0.399=-38,497.3\;\mathrm{J/mol}=-38.5\;\mathrm{kJ/mol} \tag{8}\]  
\[\mathrm{lgK_{2}}=\frac{\mathrm{z_{2}FE_{2}}^{\theta}}{2.303\mathrm{RT}}=\frac{ 38,497.3}{2.303\;\times\;8.314\;\times\;298}=6.747 \tag{9}\]  
\[\mathrm{K_{2}}\;=5.58\;\times\;10^{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Redox Electric Pair** & **Reduction Half-Reaction Equation** & **E\({}^{\theta}\) (V)** \\ \hline Cu\({}^{2+}\)/CuCl & Cu\({}^{2+}\)+ e\({}^{-}\) + Cl\({}^{-}\)\(\rightarrow\)CuCl & 0.57 \\ C\({}_{6}\)H\({}_{8}\)O\({}_{6}\)/C\({}_{6}\)H\({}_{6}\)O\({}_{6}\) & C\({}_{6}\)H\({}_{8}\)O\({}_{6}\) - 2e\({}^{-}\)\(\rightarrow\)C\({}_{6}\)H\({}_{6}\)O\({}_{6}\) + 2H\({}^{+}\) & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard electrode potentials of ascorbic acid dechlorination electrochemical pairs at 25 \({}^{\circ}\)C and under standard conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Redox Electric Pair** & **Reduction Half-Reaction Equation** & **E\({}^{\theta}\) (V)** \\ \hline Cu\({}^{2+}\)/CuCl & Cu\({}^{2+}\)+ e\({}^{-}\) + Cl\({}^{-}\)\(\rightarrow\)CuCl & 0.57 \\ CuCl/Cu & Cu\({}^{0}\) - e\({}^{-}\) + Cl\({}^{-}\)\(\rightarrow\)CuCl & 0.171 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Standard electrode potentials of copper slag dechlorination electrochemical pairs at 25 \({}^{\circ}\)C and under standard conditions.
# Effectiveness of LRB in Curved Bridge Isolation:
## 1 Introduction
[PERSON] and [PERSON] [22; 23] discovered that the LRBs managed the relative displacement between the beams and the bearings, hence reducing the probability of disassembly; however, they cannot suppress the seismic demands of the column. In contrast, when vibrating foundations were used, the dynamic performance of the columns was greatly improved, and it is anticipated that the columns will remain elastic through high seismic excitations. The LRBs could only minimize the seismic inertial force of the superstructure. In contrast, the oscillating base could also isolate the seismic inertial force created by the column, which is crucial for tall column bridges. [PERSON] and [PERSON] [24] noted that the torsion of LRBs during biaxial motion does not significantly affect the limit states. Figure 1: Lead Rubber Bearing [1]. Curved highway bridges are common at urban crossroads and areas with limited space. Due to their irregular geometry, such bridges are often implicated in various vibrational modes, making them more susceptible to vertical components of ground motion. The damage sustained by the Baihua Bridge during the 2008 Wenchuan earthquake in China is a prime example of such bridge damage. This bridge deck comprised many curved (\(\beta=1130\) and R = 66 m) and straight parts. The curved segments failed due to the substantial in-plane motion of the deck, whereas the straight segments remained undamaged despite a 60 cm lateral displacement at some of their lateral sliding bearings ([PERSON] and [PERSON], [25]). Some evidence shows that due to the insufficient seat length, the seismic bounding between the deck segments at in-span joints resulted unseating of the curved segments from their supports. ([PERSON] et al. [26]). [PERSON] [27] determined that derailment of trains running on bridges with LRBs and the base shear of bridges with LRBs was significantly lower than those without LRBs. The numerical results of [PERSON] et al. [28] revealed that long-duration ground vibration dominated both component failure probability and bridge system failure probability which are more than near-field and far-field ground motions. In the present study, the seismic performance of a continuous curved bridge isolated with LRBs under different seismic loading conditions is investigated. Three different ground motions, viz. near-field (with and without forward directivity) and far-field, have been considered. The effect of the incidence angle of the ground motions has also been investigated.
## 3 Methods and Materials
### Numerical Modeling  
Numerical modeling software, UDEC, i.e., universal distinct element code, was used for studying strata movement. The dimensions of the model were X \(\times\) Y = 300 \(\times\) 160 m, the X axis direction was the strike direction, and the Y axis direction was vertical to the bedding planes. All vertical boundaries were restrained horizontally while the bottom boundary was restrained vertically. The mesh size was small for some key strata, such as the coal seam and immediate roof.  
Only 27 roof strata were constructed, the other 150 m of overburden was simulated by applying 4 MPa vertical loading on the top of the model. Fifty meters of coal was left on the two sides of the model in order to eliminate the boundary effect. The model is shown in Figure 4.  
The Mohr-Colomb constitutive model was adopted for the failure criterion. According to the data provided by geological department of the mine, the physical and mechanical properties of the strata are presented in Table 3. The in situ stress measurement results provided by the department indicated that a premining stress with an approximate ratio of horizontal to vertical stresses (K) of value 1 both along and across the panels.  
Three measuring lines were laid out to collect strata movement data. Line 1 was located within the main roof, line 2 lay 30 m above the coal seam, and line 3 was situated 88 m above the coal seam.  
Simulation steps were to first run the model to reach stress equilibrium before mining, and then extract the coal seam from left to right, step by step. For each step, 5 m of coal web was extracted. Roof movement, deformation, and cave-in were recorded.  
Figure 4: Universal distinct element code (UDEC) model constructed for the case study mine.  
Figure 3: Physical model and measuring point layout.  
Please note that the shields that have little influence on development of the strata movement were not simulated in this numerical modeling study.
## 3 Setting and Theoretical Prediction  
### Setting of Monitoring Points  
In order to obtain the unsteady fluctuation distribution and propagation characteristics in the full flow passage, a number of monitoring points were selected at different locations of each component. Figure 4 shows 12 monitoring points on the intermediate surface of the draft tube. The points at the inlet of the draft tube are denoted as DT01-03, the points at the inlet of the straight cone tube are denoted as DT04-06, the points at the elbow tube are denoted as DT07-09, and the points at the inlet of the draft tube are denoted as DT10-12. Figure 5 shows the 20 monitoring points on the middle flow surface of the runner. The points from the outlet to the inlet near the pressure surface of the long blade are denoted as RN01-05, the points from the outlet to the inlet near the suction surface of the short blade are denoted as RN06-10, the points from the outlet to the inlet near the short blade pressure surface are denoted as RN11-15, and the points from the outlet to the inlet near the suction surface of the long blade are denoted as RN16-20. Figure 6 shows 160 monitoring points in different sections(span0.1 for the top, span0.5 for the middle, and span0.9 for the bottom) of the double-row cascade flow passage, and 48 \(\times\) 3 points were distributed in the circumferential direction of the bladeless region, which are denoted as GR001-GR048 (span0.5), GR101-GR148 (span0.1), and GR001-GR048 (span0.9) in a counterclockwise direction. Four monitoring points were arranged in each span0.5 section of four different guide vane passages, totaling 16 points. According to the serial number of the passage, they are GV001-GV004 (guide vane passage), SG001-SG004 (mid-span between the guide vanes and stay vanes), SV001-SV004 (stay vane passage), and SS001-SS004 (stay vane exit). Figure 7 shows 12 monitoring points arranged on the middle surface of the volute flow passage according to the flow passage profile, which are denoted as SC01-SC12 from the tongue to the outlet.
## 4 Results and Discussion
### Performance of the Ensemble Streamflow Forecasts
Figure 9: Comparison of time series of precipitation obtained from basin-averaged raw ECMWF (blue color) and NCEP (pink color) at 1, 2, 3, and 4 lead days, with observed precipitation. The shaded area represents the 5 th–95 th percentile values obtained from 1000 postprocessed ensembles. Figure 11a1-a4 show that the peak occurrence time predicted by raw NCEP closely matches the observed streamflow, just as the raw ECMWF does. As for the magnitude, the predictions at 1 lead day show 7.31% higher for the first peak event and 14.77% lower for the second peak event compared with the observations. There are considerable overestimations for the first peak event and underestimations for the second peak event at 2, 3, and 4 lead days. By using the GPP method, the NCEP forecasts at 1 and 2 lead days are improved in terms of representing the hydrograph. The absolute differences are lower than 13.41% and 14.89% for the first and second peak events, respectively. By using the BMA method, the magnitude of predicted streamflow is much smaller than observations. Similarly, the performance of the NCEP forecasts decreases as lead days increase. These results of Figures 10 and 11 demonstrate that the ECMWF performs better than NCEP in flood prediction; the GPP method performs better than the BMA method in improving the prediction skill. The skillful forecast lead time can be 3 days/2 days for GPP-ECMWF/GPP-NCEP flood predictions, with differences in magnitude of less than 15%. It is shorter than the 5 lead days of NCEP for flood season streamflow predictions pointed out by [PERSON], [PERSON] [25]. This is due to difficulties in estimating precipitation in the mountain river basin in our evaluation. Figure 10: Hydrographs of ensemble streamflow simulated by XAJ model using raw and postprocessed EPFs from ECMWF as inputs at 1, 2, 3, and 4 lead days, compared with observed streamflow. The shaded area represents the 5 th–95 th percentile values driving by 1000 postprocessed ensembles.
## 4 Solution Algorithms
### The Procedure of the Algorithm  
The selection of the operational methods and the values of the parameters are as follows:  
1. Initial population: the size of the population for each generation is Pop Size = 100.
2. Coding: in this paper, the method of real number coding is used; in this coding method, each gene of the chromosome is the real value of the decision variable.
3. Fitness function: to use the transformation method shown in Equation (17), the problem of minimizing the sum of the time cost of passenger travel, the operating cost of public transportation companies, and the cost of bus exhaust emissions is studied: \[fit(f(x))=\frac{1}{F(min)}\] (17)
4. Selection of the operator: a 1-to-1 substitution mechanism is adopted to ensure that the population size is diversified. The substitution phenomenon occurs only when the descendants appear stronger than their parents. In this paper, an elite retention strategy is used to select a specified number of best solutions to replace the worst solutions after each iteration, where the number and probability are calculated according to the iteration progress.
5. Crossover operator: the crossover probability in this paper takes the value of Pc = 0.9.
6. Variation operator: the existing research results show that the range of the value of the variation probability is generally between 0.001 and 0.1. In this paper, the value is taken as \(P_{m}\) = 0.01 during the algorithm design process.
7. Algorithm termination condition: the termination condition of the algorithm is the maximum number of iterations Max Gen = 300.  
The algorithm flowchart, shown in Figure 1, first initializes the population through the following steps: assigning model data, algorithm parameter setting, and encoding. Then, genetic operations are conducted on the population to decode the departure intervals and quantities for each time period using crossover and mutation. Based on the established model, the total waiting cost for passengers, operating cost of public transportation enterprises, and exhaust emission cost are calculated. Following the verification of model constraints, the fitness of chromosome individuals is determined and selection is made. This process is iterated continuously, with selection, crossover, and mutation operations, while assessing real-time fitness results of chromosome individuals. If optimal fitness results are achieved, the genetic algorithm terminates and outputs the results. However, if individual chromosome fitness results are not optimal, the algorithm continues selecting, crossing, and mutating chromosomes until the optimal result is obtained.  
Figure 1: Flowchart of the genetic algorithm.
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
###### Abstract  
Carbonate reservoirs are widely distributed and have great exploration potential. As a key indicator for reservoir characterization and evaluation, accurate and efficient porosity prediction is crucial for the exploration and development of oil and gas in carbonate reservoirs. To address the issues of low prediction accuracy and weak generalization ability in carbonate reservoir porosity prediction, a porosity prediction model (CNN-BiLSTM-Transformer) combining a convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and Transformer network is proposed. This model is applied to the Moxi gas field in the Sichuan Basin, using conventional logging curves as input feature variables for porosity prediction. Root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R\({}^{2}\)) are used as evaluation metrics for comprehensive analysis and comparison. The model extracts curve features through the CNN layer, captures both short- and long-term neighborhood information via the BiLSTM layer, and utilizes the Transformer layer with a self-attention mechanism to focus on temporal information and input features, effectively capturing global dependencies. The Adam optimization algorithm is employed to update the network's weights, and hyperparameters are adjusted based on feedback from network accuracy to achieve precise porosity prediction in highly heterogeneous carbonate reservoirs. Compared with traditional machine learning and deep learning models, the improved model better captures domain-specific information, resulting in an R\({}^{2}\) increase of 0.23 and reductions in RMSE and MAE by 0.016 and 0.014, respectively. Experimental results show that the porosity prediction model based on the CNN-BiLSTM-Transformer algorithm achieves lower average relative error and better prediction performance. Therefore, the CNN-BiLSTM-Transformer model can effectively predict the porosity of carbonate reservoirs and offers valuable insights for carbonate reservoir parameter prediction.  
Keywords:convolutional neural network (CNN); long short-term neural network (LSTM); Transformer neural network; carbonate reservoir; porosity prediction
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 6 Security Objectives of WSN-IoT
### Availability  
The nature of keeping the service accessible to clients is accessibility. The goal of accessibility is to provide clients with the ability to obtain services at any time and from any location. It is critical to keep assets regularly available to clients and the organization. Consequently, all clients must be confirmed to combat assaults and risks to the organization. Accessibility may help to avoid blockage circumstances such as framework conflicts and organizational blockages that disrupt the information flow.