esa-sceva/satcom-chunk-collection huggingface dataset:
# Supercapacitor-Based Energy Storage in Elevators to Improve Energy Efficiency of Buildings
## 1 Introduction
The proposed method was verified in MATLAB, focusing on an indirect field-oriented control strategy for the drive. However, the elevator traffic and characteristic parameters of the elevator speed profile were not considered. In [26], an energy-saving approach was proposed for a group of elevators based on a DC microgrid. An energy-efficient device based on supercapacitors was developed for the elevator group, and experimental tests showed that the energy savings ranged from 15.9 to 23.1% and 24.1 to 54.5% depending on the experiments conducted. However, details of the energy-efficient device and experiments were not provided. Therefore, the importance of energy-efficient elevators in improving a building's energy efficiency is gaining increasing attention. However, a comprehensive study of energy savings is still pending. This paper proposes a supercapacitor-based energy storage system for elevator applications, consisting of a six-phase interleaved DC/DC converter and a supercapacitor bank. The objective of the paper is to evaluate the effectiveness of the proposed system and to estimate, as accurately as possible, the energy savings achieved. A comprehensive study of the energy savings obtained was performed using PLECS software. Since the proposed energy storage system was being prototyped, the simulation model of the energy storage system was built using its actual parameters and considering all physical constraints. In addition, the existing faculty elevator system was modeled, and the simulation model was verified using field measurements. Using the verified model of the existing elevator system, and considering all practical constraints in modeling the energy storage system, a complex simulation model was built to estimate the energy savings. The performance of the energy storage system and the achievable energy savings were analyzed under different operating conditions. Selected experimental results were evaluated to confirm the effectiveness of the proposed solution. The energy savings during a reference cycle, two consecutive regenerative trips, and considering actual elevator traffic data obtained from field measurements, were recorded and analyzed. Using the actual elevator traffic data, an estimate of the energy savings was found to be very close to the actual measurements. The energy consumption of the elevator system, with and without the energy storage system,was compared. The results showed that the proposed energy storage system was able to achieve significant energy savings. The paper is organized as follows: Section 2 describes the elevator system, and Section 3 describes the modeling and verification of the elevator system. The proposed energy storage system is presented in Section 4. The simulation results are presented in Section 5 and the discussion in Section 6. The conclusions are provided in Section 7.
## 4 Discussion
### Limitations and Future Directions  
This study is not without its limitations. First, and as noted earlier, the Vertec is not considered a \"gold standard\" tool for measuring jump height (such as in-floor force plates or camera systems) and has consistently been observed to underestimate jump height when compared to these criterion measures [15; 17]. However, as an alternative field-accessible tool, the Vertec can be regarded as both valid and reliable, which was further supported by our data [21]. Future studies could consider such \"gold standard\" comparisons; however, as our results indicate that the Jumpster app is an invalid and unreliable tool when compared to a commonly employed field tool with known standard results, such criterion measure assessments would be questionably reasonable or necessary. Another potential limitation for this study is the use of a harness and canvas pouch to support the phone rather than simply using the user's pocket as suggested by the app designer. However, as noted earlier, the pocket proved to be a worse option in pilot testing and thus a more secure method was used. One common limitation for studies such as this is the question of learning across trials. However, recent research indicates that reliability may not be affected by short-term learning effects in similar populations [42]. Additionally, our methods were largely agnostic to within-subject differences across trials as device-matched trials were primarily compared. Another possible limitation was observable in our plots, which suggest that data clustered around both high and low jump heights may have impacted our calculated relationships and CIs. Similarly, as other researchers have noted, the effects of age, muscle mass, and training (among other factors) on jump height, the fairly uniform age, unknown anthropometrics, and unknown training status of our participants could have influenced our results [43; 44; 45; 46]. Future studies may consider examining whether the Jumpster app could produce more reliable or valid results for individuals with specifically high or low jump heights due to various causes to address these limitations.
## 2 Measurements  
Our first set of high-resolution measurements (during the years 2009 until 2011) was performed using piezoelectric pressure sensors from PCB Piezotronics (Figure 1) that were connected to an imc Meissystem GmbH CS-1208 data logger. The diameter of the sensing element was 15 mm. The minimal pressure resolution was 0.13 Pa, and the possible temporal resolution ranged from 2.5 Hz to 80,000 Hz.  
Reference [16] discusses how the **length**, which is usually more than 100 times larger than the diameter of a hot-wire, influences the spatially resolution.  
In our measurements, the pressure data was sampled at 50 kHz with a total duration of 100 s. The wind speed and temperature were sampled with a 1 Hz temporal resolution.  
The piezoelectric pressure sensor was calibrated against a hot wire anemometer in a wind tunnel at the University of Oldenburg. The turbulent wind speed from the hot wire anemometer and the variation in turbulent pressure showed the same statistical properties up to 3 kHz (see [17]).  
Onshore measurements were conducted at the Kaiser-Wilhelm-Koog test site of Germanischer Lloyd/Garrad Hassan (see Figure 2). A lattice tower of 60 m height provided booms to mount the pressure sensor on and other measurement equipment. The pressure sensor was mounted at a height of 55 m. The wind speed and wind direction were recorded at a height of 55 m by calibrated cup anemometers and wind vanes, respectively. The temperature was recorded at height of 53 m by a resistor-type thermometer.  
Figure 1: Pressure sensor after six months of offshore service (diameter = 12 mm).  
FEZ Kiel's platform FINO3 was the location for the offshore measurements. About 80 km west of the island of Sylt (see Figure 2) the platform was constructed close to (at that time not) operational wind farms like DanTysk and Sandbank 24. The tower is a lattice tower type with booms of sufficient length for undisturbed measurements. Two pressure sensors (Figure 3) were mounted at a height of about 100 m above the mean sea level for parallel operation. The data acquisition equipment for meteorological signals was similar to that used onshore. The wind speed and wind direction were measured at a height of 100 m, and the temperature was measured at a height of 95 m above the mean sea level.  
Figure 3: Installation of two parallel pressure sensors (T1 and T2) at the FINO3 platform.  
Figure 2: Locations of onshore (Kaiser–Wilhelm–Koog) and offshore (FINO3 platform) test sites, 80 km west of the island of Sylt. ©FEZ FH Kiel GmbH, Graphics: [PERSON].
# Reactivity Assessment of Modified Ferro Silicate Slag by R\({}^{3}\) Method
## 3 Results and Discussion
### Reactivity Assessment of \(R^{3}\) Model Paste
#### 3.2.3 Influence of Zn\({}^{2+}\) in the Hydration Mechanism of MFS Slag  
Several authors have reported the influence of Zn\({}^{2+}\) in PC and alkali activated materials [39; 40; 41; 42; 43; 44]. For instance, [PERSON] et al. [44] studied the hydration of PC in the presence of ZnO and found that amorphous Zn(OH)\({}_{2}\) could be formed in the reaction with H\({}_{2}\)O, which could potentially transform into Ca(Zn(OH)\({}_{3}\))\({}_{2}\).2H\({}_{2}\)O, provoking the heat release. In addition, [PERSON] et al. [42] studied the hydration products of the PC in presence of Zn\({}^{2+}\) ions with infrared (IR) spectroscopy showing the existence of Ca(Zn(OH)\({}_{3}\))\({}_{2}\).2H\({}_{2}\)O species. The authors also concluded that Zn in presence of H\({}_{2}\)O can react to form Zn(OH)\({}_{2}\) which has additional hydroxyl ions producing a complex oxyanion which may precipitate as an insoluble Ca(Zn(OH)\({}_{3}\))\({}_{2}\).2H\({}_{2}\)O salt. Moreover, a recent study [43] showed the mechanism of ZnO in GGBFS based alkali activated materials through X-ray pair distribution function. The proposed mechanism by the authors states that ZnO dissolves in the highly alkaline solution, reacting with the dissolved Ca\({}^{2+}\) ions from the slag to form Ca(Zn(OH)\({}_{3}\))\({}_{2}\).2H\({}_{2}\)O. This mechanism continues as long as there is availability of free Zn in the pore solution. This proposed mechanism can be applicable to the MFS slag due to the presence of Zn\({}^{2+}\) ions in the glassy phase. Upon addition of H\({}_{2}\)O in the R\({}^{3}\) mix containing MFS slag slag as SCM, Zn\({}^{2+}\) (from MFS slag) and Ca\({}^{2+}\) (from Ca(OH)\({}_{2}\), CaCO\({}_{3}\), MFS) slowly tend to dissolve into the solution and eventually form Ca(Zn(OH)\({}_{3}\))\({}_{2}\).2H\({}_{2}\)O, which results in an exothermic reaction and heat release. This mechanism is expected to continue until all free ZnO present in the glass is consumed.
###### Abstract  
Most of the processes with various dynamic characteristics can be reduced to the Second Order Plus Time Delay (SOPTD) model by using the model reduction method. We propose a novel hybrid approach that combines Long Short-Term Memory (LSTM)-based real-time model identification with Genetic Algorithms to enhance the Smith predictor control structure. This method compensates for the delay time of the SOPTD model while minimizing the Integral Time Absolute Error performance index. Our approach integrates an optimally adaptive Proportional-Integral-Derivative (PID) controller design algorithm that estimates the coefficients of the SOPTD model in the Smith Predictor control structure and adjusts the PID controller parameters dynamically. The method is improved through a combination of numerical calculation, Genetic Algorithms, and LSTM networks, showing approximately 15% better performance compared to conventional methods. The system demonstrates significant improvements in both performance metrics and resource utilization, including a 40% reduction in execution time and enhanced resource efficiency. Simulation results show that the proposed scheme exhibits improved adaptability to disturbances and process variations, with faster response times and reduced overshoots compared to traditional methods. The steady-state response of the higher-order model and the reduced model shows perfect matching for the unit feedback input.  
model reduction; adaptive control; smith predictor; PID control; LSTM networks; hybrid control; real-time optimization; genetic algorithms Article
## 2 System Kinematics Analysis
\begin{array}{l}\dot{\mathbf{r}}_{0}=\big{(}\dot{x}_{0},\dot{y}_{0}\big{)} ^{\text{T}}\\ \dot{\mathbf{r}}_{1}=\dot{\mathbf{r}}_{0}+\dot{x}_{\text{b}}\mathbf{e}_{0}+(l_{0}+x_{\text{ b}})\dot{\mathbf{e}}_{0}+x_{1}\dot{\mathbf{e}}_{1}+\dot{v}_{1}\mathbf{e}_{1}^{\prime}+v_{1}\dot{ \mathbf{e}}_{1}^{\prime}\\ \dot{\mathbf{r}}_{2}=\dot{\mathbf{r}}_{0}+\dot{x}_{\text{b}}\mathbf{e}_{0}+(l_{0}+x_{\text{ b}})\dot{\mathbf{e}}_{0}+l_{1}\dot{\mathbf{e}}_{1}+x_{2}\dot{\mathbf{e}}_{2}+\dot{v}_{2}\mathbf{e}_{2}^{ \prime}+v_{2}\dot{\mathbf{e}}_{2}^{\prime}\\ \dot{\mathbf{r}}_{\text{p}}=\dot{\mathbf{r}}_{0}+\dot{x}_{\text{b}}\mathbf{e}_{0}+(l_{0}+x_{ \text{b}})\dot{\mathbf{e}}_{0}+l_{1}\dot{\mathbf{e}}_{1}+l_{2}\dot{\mathbf{e}}_{2}\\ \dot{\mathbf{r}}_{3}=\big{(}\dot{x}_{3},\dot{y}_{3}\big{)}^{\text{T}}\\ \dot{\mathbf{r}}_{\text{p}^{\prime}}=\dot{\mathbf{r}}_{3}+l_{3}\dot{\mathbf{e}}_{3}\end{array} \right. \tag{2}\]  
According to the 4 th and 6 th equations of Equation (2), the kinematic expressions of the space robot and satellite are as follows:  
\[\left\{ \begin{array}{l}\mathbf{V}_{\text{p}}=\dot{\mathbf{J}}\dot{\mathbf{r}}_{\text{z}} \\ \mathbf{V}_{\text{p}^{\prime}}=\dot{J}_{3}\dot{\mathbf{q}}_{3}\end{array} \right. \tag{3}\]  
Figure 1: Structure of the flexible-base, flexible-joint and flexible-link space robot and the satellite. where \(\mathbf{V}_{\rm P}=\left[\dot{x}_{\rm P},\dot{y}_{\rm P^{\prime}},\omega_{\rm P}\right]^ {\rm T}\), \(\omega_{\rm P}=\dot{\theta}_{0}+\dot{\theta}_{1}+\dot{\theta}_{2}\), and \(\mathbf{J}\in\mathbb{R}^{3\times 12}\) is the Jacobian matrix of the flexible space robot. \(\dot{\mathbf{q}}_{\rm z}=\left[\dot{\mathbf{q}}_{\rm b^{\prime}},\dot{x}_{\rm b},\dot{ \mathbf{q}},\dot{\mathbf{\delta}},\dot{\mathbf{q}}_{\rm m}\right]^{\rm T}\), \(\mathbf{q}_{\rm b}=[x_{0},y_{0}]^{\rm T}\), \(\mathbf{q}=[\theta_{0},\theta_{1},\theta_{2}]^{\rm T}\), \(\delta=[\delta_{11},\delta_{12},\delta_{21},\delta_{22}]^{\rm T}\)  
, and \(\mathbf{q}_{\rm m}=[\theta_{\rm m1},\theta_{\rm m2}]^{\rm T}\). \(\mathbf{J}_{3}\in\mathbb{R}^{3\times 3}\) is the Jacobian matrix of the satellite. \(\mathbf{V}_{\rm P^{\prime}}=\left[\dot{x}_{\rm P^{\prime}},\dot{y}_{\rm P^{\prime} },\omega_{\rm P^{\prime}}\right]^{\rm T}\) and \(\omega_{\rm P^{\prime}}=\dot{\theta}_{3}\), \(\dot{\mathbf{q}}_{3}=\left[\dot{x}_{3},\dot{y}_{3},\dot{\theta}_{3}\right]^{\rm T}\).
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 2 Security Challenges in 5G Network
### Communication Security
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Attack Segment**} & \multirow{2}{*}{**Challenge/Threat**} & \multicolumn{4}{c}{**Effected Technology**} & \multirow{2}{*}{**Privacy**} \\ \cline{3-4}  & & **SDN** & & **NFV** & **Channels** & **Cloud** \\ \hline \multirow{6}{*}{Device Threats} & Bots & * & & & \\  & DDoS Attacks & * & * & * & * \\  & MitM Attacks & * & & & * \\  & Firmware Hacks & & & & * \\  & Device Tampering & & & & * \\  & Malware & & & & * \\  & Sensor Susceptibility & & & * & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Air \\ Interface Threats \\ \end{tabular} } & Jamming & * & & * & & \\  & MitM Attacks & * & & * & * \\  & Eavesdropping & & * & & * \\ \hline \multirow{6}{*}{\begin{tabular}{c} Edge \\ Network Threats \\ \end{tabular} } & MEC Server Vulnerabilities & * & * & & * \\  & Rouge Nodes & * & * & * & * \\  & Authentication Issues & & & & * \\  & Side Channel Attacks & * & & & * \\  & Improper Access Control & * & * & * & * \\ \hline \multirow{6}{*}{\begin{tabular}{c} Backhaul Threats \\ \end{tabular} } & DDoS Attacks & & * & * & \\  & Control and User Plane & & * & * & * \\ \cline{1-1}  & Sniffing & & * & * & \\ \cline{1-1}  & MEC Backhaul Sniffing & & * & & \\ \cline{1-1}  & Flow Modification Attacks & & & * & \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} 5G Core Network \\ Threat \\ \end{tabular} } & Software Issues & & & * & \\  & API Vulnerabilities & * & & & \\  & Network Slicing Issues & * & * & & \\ \cline{1-1}  & DoS and DDoS Attacks & * & * & * & * \\ \cline{1-1}  & Improper Access Control & & & & * \\ \cline{1-1}  & Virtualization Issues & & * & * & \\ \hline \multirow{6}{*}{
\begin{tabular}{c} External Network \\ Threat \\ \end{tabular} } & Application Server & & & & * \\  & Vulnerabilities & & & & * \\ \cline{1-1}  & Cloud Service & & & & * \\ \cline{1-1}  & Vulnerabilities & & & & * \\ \cline{1-1}  & BoTs and other IP-based & * & & & * \\ \cline{1-1}  & Attacks & & & & * \\ \cline{1-1}  & Application Vulnerabilities & & & & * \\ \cline{1-1}  & API Vulnerabilities & * & & & * \\ \cline{1-1}  & Roaming Partner & * & * & * & \\ \cline{1-1}  & Vulnerabilities & & & & * \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Security issues of 5G communication. new flexible and resilient security systems for 5G network are needed since it is impossible to change the existing system to prevent potential adversaries' attacks.
# A Study on the Amplitude Comparison Monopulse Algorithm
## 7 Conclusions  
In this paper, two least-squares-based algorithms and their covariance prediction equations have been presented for amplitude comparison monopulse radar. The conventional monopulse problem is reinterpreted as a target location estimation problem in the track axis with four pseudorange equations in which geometrical distance is weighted by the antenna gain.  
The full measurement-based and sum-difference measurement-based algorithms are proposed to estimate the target location based on the iterated least-squares estimation method. While the full measurement-based algorithm utilizes all four receiving signals from the four antennas, the sum-difference measurement-based algorithm uses the same sum and difference signals as the conventional algorithm.  
By theoretical analysis with the same assumptions used in the conventional monopulse algorithm, it has been shown that the full measurement-based and sum-difference measurement-based algorithms are nominally the same, and the conventional monopulse algorithm is a special case of the proposed least-squares-based algorithms with the first iterative step. Furthermore, it turns out that the covariance prediction equation for the proposed least-squares-based algorithms can be utilized to determine the level of estimation errors even for the conventional algorithm.  
According to numerical simulation results with 100,000 Monte Carlo runs, the proposed least-squares-based algorithms show similar estimation performance as the conventional ones in low levels of noise, and better performance with high levels of noise and large deviation of an unknown target from the center of the track axis. The proposed covariance prediction equation provides very accurate RMS errors for both the conventional and least-squares-based algorithms, and thus it can be used to predict the performance of the monopulse algorithms before the algorithms are actually performed.  
Methodology and mathematical formulation, S.P.; software and simulation, M.K., D.H. and S.P.; formal analysis, S.P.; writing--original draft preparation, M.K., D.H. and S.P.; writing--review and editing, M.K., D.H. and S.P. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge support from the Electronic Warfare Research Center (EWRC) at Gwangju Institute of Science and Technology (GIST), originally funded by the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD).  
The authors declare no conflict of interest.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 4 Experiment Results
### Recognition Rate of Each Object  
The average recognition rates of the classifiers are an objective evaluation index of their performance. Simultaneously, further information is found by listing the recognition rate of each target here. The confusion matrix is a good reflection of the recognition rate of each object. However, not all confusion matrices in the feature spaces of the two classifiers are needed to be listed here, because the information they try to convey is similar. Table 4 lists the 10-class target confusion matrices of the two classifiers under the feature spaces of Down-sample\({}^{2}\), PCA\({}^{4}\) and PCA\({}^{2}\), which are explained above.  
Firstly, it is common that the recognition rates of BMP2 and T72 are very low. The reason is that there are many variants of the two targets, which are not included in the training set, in the test set. So the variant has a more important influence in classification than the depression angle in this experiment for only 2\({}^{\circ}\) difference in the depression angle. With an increasing difference in depression angle, the recognition rates indeed descend rapidly [9]. Then, the recognition rates of BRDM2 are obviously low in the feature spaces of PCA, because the dimensionality of PCA is too small to contain all the primary information. To illustrate it, Figure 7 is plotted. The figure shows the average recognition rate and the recognition rate per target of SRC under different PCA feature spaces. PCA\({}^{2}\) and PCA\({}^{4}\) are described above. PCA\({}^{5}\) and PCA\({}^{6}\) refer to reducing the dimensions of the cropped image to 900 and 1600 by PCA, respectively. From Figure 7, the recognition rate of BRDM2 indeed rises with the increasing dimensions of PCA. Similarly, BTR70 enjoys the same trend, which is more moderate than BRDM2. Contrarily, the recognition rates of other targets tend to descend with the increasing dimensions, and the shape of the average recognition rate in Figure 7 is a response to that. Therefore, it is not wise to improve the recognition accuracy of one object, sacrificing the whole performance. Last but not least, the classifiers both tend to classify other targets into 2S1 incorrectly, which is mainly caused by its structures. The structures, such as the barrel and upper surface, make it similar to tanks (T72, T62) and armored personnel carriers (BMP2, BTR60, BTR70 and BRDM2). Simultaneously, it is no wonder that the classifiers classify 2S1 into other targets.  
Figure 6: The recognition rates under different percentages of training samples. The horizontal coordinate stands for the percentage of training samples, and the vertical coordinate represents the recognition accuracy.