esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.  
home energy management system; demand-side management; time-of-use; fuzzy logic; user comfort +
Footnote †: journal: Sensors
## 3 Experimental Setup
### LSM Configuration  
In this study, an experiment was performed in which the LSM was applied to induction thermography, and Figure 5 shows the experimental configuration. The material of the excitation coil for heating the specimen was copper and was manufactured in a 'U' type. For the measurement of the surface of the specimen, where heat is generated due to the induction coil, a long-wavelength FLIR SC645 model (un-cooled type, 640 \(\times\) 480 pixels, 7.5-13 \(\upmu\)m) IR camera was used. The IR camera was placed vertically at a distance of 600 mm from the specimen. The maximum output of the power supply was 15 kW. The excitation current was set to 135 A, the frequency of the alternating current was set at 40 kHz, and the speed was set to 5 mm/s, 10 mm/s, 15 mm/s, and 20 mm/s. As the speed of the specimen moving along the slide guide was slow, the amount of heat source supplied to the specimen increased, which is a very important factor because it determines the thermal contrast of the 2D thermal image. The image frame rate and lift-off were set to 50 Hz and 2 mm, respectively, where lift-off means the distance between the specimen and the copper coil. The cooling device in Figure 6 played a role in cooling the high temperature of the copper coil while the current was excited, and the excitation current was controlled by a controller and a power generator. The images acquired with the IR camera were post-processed through a PC.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 5 Discussion
### Limitations of Deep Learning for Medical Image Cryptography and Future Prospects
#### 5.2.1 Limited Generalization  
Deep learning algorithms that have been trained on a particular dataset may have trouble adapting adequately to new or varied medical image data. In cryptography activities, this may result in decreased performance and compromised security. The development of models that more accurately generalize across various medical imaging modalities, illness types, and patient groups may be the main emphasis of future study.
## 5 Simulation and Analysis  
In order to observe the rectification effect of the dual-loop decoupled control and verify the effectiveness of the proposed control strategy to suppress ZSCC, the parallel converter system shown in Figure 4 was performed on the MATLAB/Simulink R2016b platform, and three sets of simulation experiments were performed. The sampling period of the simulation platform was \(10^{-6}\) s, and other simulation parameters of the parallel system are shown in Table 1.  
From Table 1, it can be seen that in order to ensure that the AC-side line parameter difference is the only variable to study the circulating system, the other control parameters of the two converters  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Symbol** & **Value** & **Unit** \\ \hline Effective voltage of AC side & \(u_{\mathrm{a}}\,u_{\mathrm{b}}\,u_{\mathrm{c}}\) & 141 & V \\ Voltage frequency of AC side & \(f\) & 50 & Hz \\ Filter inductance 1 & \(L_{1}\) & 3 & mH \\ Filter inductance 2 & \(L_{2}\) & 3/7 & mH \\ Equivalent resistance & \(R_{1}\,R_{2}\) & 0.1 & \(\Omega\) \\ Voltage of DC side & \(u_{\mathrm{dc}}\) & 450 & V \\ Capacitor of DC side & \(C_{\mathrm{dc}}\) & 4000 & \(\mu\)F \\ Load resistance of DC side & \(R_{\mathrm{L}}\) & 30 & \(\Omega\) \\ Switching period & \(T_{\mathrm{s}}\) & 0.0001 & s \\ Proportional coefficient of current loop & \(k_{\mathrm{ip}}\) & 5.5 & - \\ Integral coefficient of current loop & \(k_{\mathrm{ii}}\) & 20.5 & - \\ Proportional coefficient of voltage loop & \(k_{\mathrm{up}}\) & 1.45 & - \\ Integral coefficient of voltage loop & \(k_{\mathrm{ui}}\) & 8.65 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for simulation of paralleled system.  
Figure 8: Control system block diagram of paralleled converters.  
in the system were kept completely equal, including the proportional and integral coefficient of the voltage-current loop, the switching period of the SVPWM controller, and so on.
# Optimizing the Scheduling of Teaching Activities in a Faculty
## 3 Model of the Automatic Timetable Generation System
### Database Considerations  
The storage of pertinent information, including course details, student inclinations, faculty accessibility, and timetable arrangements, mandates the utilization of a database. The recommended database solution for the proposed system architecture, which consists of an Angular-based frontend and a Java-based backend powered by Spring Boot, is a Relational Database Management System (RDBMS). An option that is suitable and used in the implementation of this scenario is PostgreSQL [50].  
PostgreSQL is a resilient and feature-rich open-source relational database management system (RDBMS). The system exhibits robust data integrity and concurrency control mechanisms, rendering it a fitting solution for managing concurrent requests originating from both the Angular frontend and Java backend.  
PostgreSQL's ACID [51] compliance ensures transactional integrity, which is crucial for maintaining reliable and accurate data in a timetabling system. Its focus on data integrity [51] guarantees that information remains accurate and consistent, even when dealing with complex datasets and relationships. Furthermore, PostgreSQL's efficient concurrency control mechanisms [52] allow for the smooth handling of multiple users interacting with the database simultaneously, contributing to a seamless and reliable user experience.
# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 4 Discussion
### Limitations  
Despite the proven effectiveness of the proposed model, it is worth highlighting some limitations that could affect its applicability. Firstly, the contextualization of economic data: the costs used for the economic evaluation of the interventions are derived from regional sources, particularly the Lazio region's price list. While these data are representative of the analyzed context, they may not reflect the economic, regulatory, and market conditions of other Italian regions or European countries. Consequently, the accuracy of the ROI and payback estimates may vary depending on the local context. Regarding the absence of incentives and fiscal instruments: the economic model does not consider the impact of government incentives, tax breaks (such as the 110% Superbonus or the Conto Termico), or local subsidies, which could have a significant effect on the economic viability of the measures. The absence of these variables in the calculation results in a conservative estimate, which may underestimate the financial benefits for end users. The simulations depend on current data and are based on static conditions linked to building, climate, technological, and economic data for the two-year period 2022-2023. However, future developments in these variables, such as rising energy prices, climate change, technological advances, or new European regulations, could significantly impact the energy and economic performance of the assessed measures. The model therefore needs to be updated periodically to maintain its predictive validity. Regarding the lack of validation in real conditions: although the model has achieved high predictive performance (R\({}^{2}\) = 0.91), it has not yet been tested in real retrofit cases. Field validation, through the concrete application of the model to actual interventions, is a necessary step to verify its operational effectiveness. In summary, despite some limitations relating to the contextual nature of the data and the exclusion of dynamic and incentive variables, the model provides a solid methodological basis for supporting informed decisions in energy retrofitting.
## 2 Methods and Simulations
### Distortion of Misaligned Synthetic Signal
In addition to the fundamental frequency spectrum of 100 MHz, there are also three other spectra in the directly synthesized signal: 5.1 GHz, 5.3 GHz, and 10.3 GHz in Figure 3b. From the labeled spectrum energy in Figure 3b, it can be seen that the power of each spectrum is relatively high, indicating that there is significant distortion in the directly synthesized signal.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 4 Analysis Results
### Stripline with Differential Signal  
#### 4.2.1 Design Structure  
The stripline is also one typical transmission line employed to avoid electromagnetic interference (EMI). The basic structure consists of one signal layer and two power layers. The signal layer is centered between two large conducting ground planes, and the dielectric fills up the regions between ground planes. In this design, the four-level pulse amplitude modulation (PAM4) is employed to enhance the data transmission rate. In addition, differential signaling eliminates the crosstalk between adjacent signals. The channel width W and the distance between two lines in one differential pair \(D_{S}\) are the main parameters specifying the structure characteristic. The ratio (\(K_{S}\)) between the space and width has a significant impact on the differential impedance. The distance between two differential pairs \(D_{p}\) is set to be equal to \(D_{S}\) to preserve the differential signal properties. Hence, the \(D_{P}\) is identical to \(D_{S}\) in this design to minimize the area. The structure geometry is described in Figure 9.  
Figure 8: Eye diagram for W = 25 \(\upmu\)m, \(K_{D}\) = 2 at \(V_{TX}\) = 0.75 V.  
Figure 9: Stripline structure with differential signal.
# Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
## 3 Overall Design of Tooth Rail Wind Shaft Inspection Robot
### Design of Other Modules  
#### 3.4.1 Design of Communication Module  
Communication has always been a research difficulty in underground information transmission. Wired communication has high data transmission efficiency, but high economic cost and poor flexibility; meanwhile, wireless communication has high flexibility, low cost and easy expansion, but the data transmission rate is greatly affected by the environment. Combined with the structural characteristics of the wind well inspection robot, wired communication and wireless communication are adopted. As shown in Figure 14, generally, between the main shaft and the wind shaft, optical fiber is embedded in the well and underground for communication between the ground and the bottom. In order to realize reliable communication between the well and the underground, this paper takes the embedded optical fiber as the communication carrier. Firstly, the intrinsically safe camera and other sensors are arranged on the detection device, which are connected to the converter through the network cable and then connected to the switch installed in the lower mobile robot through the optical fiber. It is further transmitted to the nearest  
Figure 13: Curve of tooth rail diameter and installation spacing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Outside Diameter/m** & **Inner Diameter/m** & **Thickness/mm** \\ \hline Ring gear & 4.0 & 3.0 & 100 \\ Upper orbit & 4.1 & 2.8 & 50 \\ Lower orbit & 4.1 & 2.8 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design parameters of gear rail.  
underground base station by wireless means. Finally, it communicates with the ground core switch through switches and embedded optical fibers to transmit data so as to realize communication with the ground control terminal and complete the communication of the overall inspection system.
## 2 Materials and Methods
### Statistical Parameters  
From the confusion matrix, several metrics were extracted to quantify the goodness of the correlation. First of all, the normalized values of each category in the confusion table are computed according to the expressions in Equation (3)  
\[\text{TPR}=\frac{\text{TP}}{\text{P}};\quad\text{FPR}=\frac{\text{FP}}{\text{ N}};\quad\text{FNR}=\frac{\text{FN}}{\text{P}};\quad\text{TNR}=\frac{\text{TN}}{\text{N}} \tag{3}\]  
The simpler qualifier that can be extracted is the accuracy (ACC), which measures the ratio of correct predictions (TP and TN) among the total cases (TOT) according to Equation (4):  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}} \tag{4}\]  
The F\({}_{1}\) score is the harmonic mean between the precision \(=\frac{\text{TP}}{\text{TP}+\text{FP}}\), and the recall or sensitivity \(=\frac{\text{TP}}{\text{TP}+\text{FN}}\). F\({}_{1}\) is therefore calculated with Equation (5):  
\[\text{F}_{1}=\frac{\text{2 TP}}{\text{2 TP}+\text{FP}+\text{FN}} \tag{5}\]  
Another correlation parameter that performs better for unbalanced classifications, in which the number of positives and negatives is very different, is the Matthews Correlation Coefficient (MCC), also called phi-coefficient (\(\phi\)), and it is given by the expression in Equation (6):  
\[\text{MCC}=\frac{\text{TP}\times\text{TN}-\text{FP}\times\text{FN}}{\sqrt{( \text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+ \text{FN})}} \tag{6}\]  
Furthermore, finally, the Diagnostic Odds Ratio (DOR) is a measure of the effectiveness of a diagnostic test, and it is given by Equation (7). DOR has positive values larger than one when the test is useful and increases as the performance is better.  
\[\text{DOR}=\frac{\text{TP}/\text{FN}}{\text{FP}/\text{TN}}=\frac{\text{TP} \cdot\text{TN}}{\text{FP}\cdot\text{FN}} \tag{7}\]  
Additionally, the Receiver Operating Characteristic (ROC) has been calculated. This is a method historically introduced by military radar receivers in the 1950s to diagnose the ability of a system to detect true positives with respect to false alarms. The ROC is the plot of the TPR vs. the FPR when moving a sensitivity parameter. In our case, the parameter is the _s4_th_, and the curve is studied for the rest of parameters indicated in Table 2.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 2 Methods
### Feature Extraction  
STFT essentially uses a sliding window symmetric about the center to observe the signal inside the window and obtains the spectrum of that time slot by performing Fourier transform on the signal inside the window. Finally, the time-frequency image of the target signal is obtained by stitching. The operation steps on the received signal \(r(t)\) are as follows: First, the window function is moved to the initial position of the signal. The center of the window function is \(\tau_{0}\), and the signal in the window is represented as  
\[\mathrm{y}(t)=r(t)w(t-\tau_{0}) \tag{3}\]  
where \(w(t)\) is the window function and \(r(t)\) is the source signal \(y(t)\). Then the Fourier transform is performed, and the spectral distribution of the first segmented sequence \(R(w)\) is obtained as  
\[R(w)=F(y(t))=\int_{-\infty}^{+\infty}r(t)w(t-\tau_{0})e^{-jwt}dt \tag{4}\]  
The window function is moved to \(\tau\) after the first Fourier Transform (FFT) operation, where the distance it is moved is called the bounce size. During the shift, a certain overlap between the two windows should be ensured, i.e., the width of the window should be larger than the distance moved, which is called \"overlap\". The STFT of the signal is obtained by continuously sliding the window and repeating the above operation.  
\[S\mathrm{TFT}(w,\tau)=F(r(t)w(t-\tau))=\int_{-\infty}^{+\infty}r(t)w(t-\tau) e^{-jwt}dt \tag{5}\]  
In this paper, according to our experience. We set the window length to 1024. The window type is Hamming window. Each step length is 1.  
In addition, in order to avoid the bias of feature quantities because of the difference in input signal power, the magnitude of the short-time Fourier time-frequency image is normalized.  
As shown in Figure 3, the horizontal coordinate of the image on (a) is time, and the vertical coordinate is frequency; the horizontal coordinate of the image on (b) is the frequency, and the vertical coordinate is time. According to the different RGB values of each pixel, it can reflect the energy contained in the corresponding time and frequency of this pixel. Compared with previous images, the normalized image can make energy easier to locate and identify its features through vision. The time-frequency distribution image isresized to 875 \(\times\) 656 \(\times\) 3 and then passed to the subsequent model for target detection (875 \(\times\) 656 denotes the length \(\times\) width format of the image, and 3 denotes the number of RGB channels).
## 3 Results and Discussion
### AE Method Application for Bridge Monitoring
#### 3.2.4 Identification of AE Signals from the Hsu-Nielsen Source  
Simulation of AE signals by the Hsu-Nielsen source was carried out at various points of the lateral beam of the bridge structure inside one of the bridge sections. The distance from the sensor to the Hsu-Nielsen source was from 0.15 to 15 m. Totally about 100 pulses were excited (Figure 8).  
For detection of signals from the Hsu-Nielsen source, a planar location was constructed (Figure 9). It is revealed that the signals from Hsu-Nielsen source are with certainty located in their actual places and form compact clusters, in contrast to noise. This confirms the possibility of identifying signals from defects based on location data.  
Since the bridge is a structure of testing characterized by a high level of acoustic noise, a locational cluster corresponding to a defect can be masked by a large number of false locations of noise events. The complexity of the detection algorithm lies in the fact that the values of the AE parameters of the noise and useful signals vary in fairly wide and overlapping ranges. This is due to the variety of noise sources and different distances between the sensor and points where the defect or simulator is placed - from 0 to 15 m.  
The difference of parameters is best shown in the two-dimensional plots of characteristics, for example, on the scatter plot diagram \"Counts/Duration versus Duration\" (Figure 10a) and the scatter plot diagram \"Energy versus Counts/Duration\" (Figure 10b). As can be seen from the chart, the AE impulses emitted at the distance of 7 m from the sensor have the parameter distribution other than the noise distribution. When the distance increases over 7 m, the differences disappear, the AE impulse parameters become close to those of noise [13].  
Figure 8: Signals from Hsu-Nielsen source at 1 m distance from AE sensor.  
Figure 9: Planar location. Hsu-Nielsen source signals are marked by red, noise is marked by orange. Color of points indicates total number of events in this coordinates. Yellow points: 3–10 events, orange points: 11–25 events, red points: 26–100 events. Blue circle – location cluster of signals from Hsu-Nielsen source.
# Non-Newtonian Behaviors of Crumb Rubber-Modified Bituminous Binders
## 1 Introduction
Apart from the difference among the viscometers and rheometers, the non-Newtonian behavior of some binders (e.g., polymer modified bitumen) is a main reason for the observed measurement difficulties and this subsequently leads to issues in the interpretation and comparison of interlaboratory test results [12]. Therefore, it is necessary to investigate the non-Newtonian behaviors of highly modified bituminous binders and propose universal parameters (e.g., zero shear viscosity) [13] that are independent of applied shear loading instead of merely comparing the apparent viscosity values. Crumb rubber modified bitumen (CRMB) has been utilized in the asphalt paving industry for decades due to its various benefits. It not only provides an environmental friendly disposal solution to scrap tires, but also improves the overall pavement performance, e.g., higher resistance to rutting, ageing, fatigue and thermal cracking [14]. The increasing use of crumb rubber modifiers (CRMs) in bituminous pavements requires a better understanding of their effects on the chemical, physical and rheological properties of CRM binders so that their processing at the plant can be optimized, their production at the site can be controlled, and their performance in the field can be more accurately predicted [15; 16]. It was shown that the interactions of bitumen and rubber and their effects on the final properties of CRMB depend on the raw material parameters (e.g., bitumen composition, CRM type, morphology, particle size and dosage) and interaction conditions (e.g., mixing temperature, time and rate, energy type of the mechanical mixing exerted) [17; 18]. Many studies have demonstrated that CRMs improve the rheological properties of base bitumen with evident non-Newtonian behaviors [19; 20]. The addition of CRMs into base bitumen dramatically increases the viscosity of the binder, which on the one hand improves the rutting resistance, but on the other hand creates workability issues through pumping, mixing and compaction [21]. The technology of warm mix additives offers promising solutions to the above drawback which can significantly lower the viscosity of binders at mixing and construction temperatures [14; 22]. As mentioned before, the neat bitumen's viscosity can be measured at 60 \({}^{\circ}\)C or higher in the Newtonian region. This statement is of no validity for the CRMB binders. The addition of warm-mix additives further complicates the non-Newtonian behaviors of CRMB. Based on the above considerations, this study aims to investigate the fundamental non-Newtonian behaviors of CRMB with/without warm-mix additives from the prime parameter in rheology-viscosity. The shear rate dependency of viscosity and temperature effects were investigated with different rheological models.