esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Measurement Experiment of the Stator and Rotor Deformation  
After the above impact test, the four TRUMs are disassembled, and the plastic deformation of the stator and rotor are measured. The web of the stator or rotor where the maximum stress and strain occur is chosen as the main measurement site to predict the whole plastic deformation trend. The initial state is considered to be very flat and non-plastically deformed, but allows machining tolerances of 10 \(\upmu\)m. A digital dial gauge with the accuracy of \(1\times 10^{-6}\) m manufactured by Mitutoyo Corporation is used. Taking the rotor web deformation measurement for example, the rotor is mounted on a special tooling and placed on a micrometer stand, as shown in Figure 13. The rotor web is divided into 12 equal parts in the circumferential direction, and each aliquot is divided into 10 points in the radial direction as shown in the figure. The maximum height difference among the 10 points is extracted separately, which is regarded as the deformation in the radial direction. A total of 12 radial maximum differences can be obtained, and the maximum value among them is selected as the approximate deformation of the rotor web. Plastic deformation of the stator web is measured in the same way. Table 3 demonstrates the plastic deformation values of the stator and rotor webs after the high-impact test.  
Figure 13: Deformation measurement of the rotor web.  
It is found that the stator plastic deformation of the motors (TRUM-2 and TRUM-4) with the disc spring are slightly larger than those (TRUM-1 and TRUM-3) without the disc spring, but the differences are extremely small with the order of magnitude of 10\({}^{-6}\) m. However, the differences in the rotors are obvious. It can be seen that the rotors' plastic deformation of TRUM-1 and TRUM-3 are 296 \(\upmu\)m and 232 \(\upmu\)m respectively which are much larger than those in TRUM-2 (13 \(\upmu\)m) and TRUM-4 (10 \(\upmu\)m). That is to say the rotors' plastic deformation of the motors without the disc spring are larger than those of the motors with the disc spring. This trend is consistent in principle with the simulation results that the maximum stresses of rotors of the motors without the disc spring are much larger than those of the motors with the disc spring.
## 1 Introduction  
Despite the potential application of urban sound recognition in Wireless Acoustic Sensor Networks (WASN), there exists a lack of evaluation of existing solutions. The achievable classifier's accuracy or the demanded time to perform the sound classification are just a couple of parameters, which must be taken into account when considering WASN applications demanding sound recognition. Current embedded systems provide enough computational power to perform urban noise classification, enabling edge-computing solutions. Nevertheless, we believe that such embedded devices are currently underused for such type of sound classification.  
The presented work evaluates the most popular Machine Learning (ML) techniques for Environment Sound Recognition (ESR), in particular for urban sound classification. Existing open source libraries for audio analysis and datasets for ESR are used to evaluate the achievable accuracy of classical sound classifiers. The best performing classifiers are evaluated on an embedded system in order to determine the achievable accuracy and the execution time that could be expected on such constrained devices. Several datasets for ESR, which partially include urban sounds are combined here to create larger datasets targeting urban sound classification. In addition, a scalable approach is proposed to not only exploit some characteristics of embedded devices but also to enable the combination of different sound classifiers to prioritize accuracy or response time. The steps followed to construct a hierarchical dataset, the selection of the audio features extracted to fetch the sound classifiers and the parameter's exploration for the optimization of the classifiers are presented in detail.  
Contributions towards urban sound recognition on embedded devices:  
* A cascade approach for scalable dataset customization.
* Evaluation of classical ML techniques for ESR on an embedded system.  
This paper is organized as follows. Section 2 presents related work. The methodology used for the evaluation of the ML techniques is detailed in Section 3. In Section 4, the proposed cascade approach is presented. The experimental results are detailed in Section 5. Finally, our conclusions are presented in Section 6.
## 2 Cluster Head Selection
### Multi-Factor Evaluation Schemes  
CH selection phase is the pivotal stage in cluster-based algorithms, which tangibly affects the performance of the network in the data transmission phase. To ensure a reasonable degree of network lifetime and cluster performance, the most desirable nodes in accordance with the type of application, topology of the network and capabilities of sensor nodes should be selected as CHs. In this respect, CH selection should be regarded as a multi-variable-decision issue with complex inter-relation between factors. The proposed solutions for the complicated process of CH selection in multi-factor evaluation systems are Analytical Hierarchy Process (AHP) and Fuzzy Logic Controller (FLC).
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 8 IoT Security in 5G
### Lessons Learned  
5G offers various benefits for IoT and IIoT systems, including low latency, high speed, and many others, but it also involves its own security issues. These are mainly related to the nature of communication itself and the SDN/VNF-based network elements on the control plane. 5G-supported IoT is a hot topic now, therefore the number of proposed solutions are emerging, which enables 5G to be an optimal communication platform for IoT. Traditional security concerns in IoT networks and 5G are very common, thus they are addressed during the standardization of 5G; however, specific ones, such as in the case of low powered nodes and sensors, are not. The greatest advantage of 5G regarding IoT security is the flexibility of the system, thus efficient, use-case specific solutions can be applied. Regarding low-powered nodes, which are huge drivers for IoT, using PLS solutions and lightweight cryptography techniques is an effective way to improve security. The unified scheme for security of 5G based IoT networks is shown in Figure 9.
# Laser Cooling and Trapping of Rare-Earth-Doped Particles
## 3 Optical Refrigeration of Rare-Earth-Doped Particles  
In the majority of cases, bulk, \(cm-\) and \(mm-\) sized RE-doped low-phonon samples were laser-cooled. Optical refrigeration of RE-doped samples with different shapes and sizes was investigated in [27].  
Three different approaches can be used to calculate the optical force applied to a particle. These approaches depend on the size of the particles compared to the wavelength of the laser beam. If the radius \(r\) of the particle is much larger than the light wavelength \(\lambda\) (\(r>20\lambda\)), diffractive effects can be neglected. In this case, geometrical optics (ray optics) can be used for the optical force calculation. If the particle radius is comparable to the light wavelength (\(r\ \sim\ \lambda\)), the wave's scattering should be considered. If the particle radius is much smaller than the light wavelength (\(r\ll\lambda\)), dipole approximation can be used.
## 2 Materials and Methods  
PBZT ceramics with the composition Ba/Zr/Ti = 25/70/30, both in pure form and doped with 1-4 at.% lanthanum, were prepared from simple oxides and carbonates via solid-state synthesis. The reagents, including PbO (Avantor, POCH, 99.0%, Gliwice, Poland), BaCO\({}_{3}\) (99.99%, Sigma-Aldrich, St. Louis, MO, USA), TiO\({}_{2}\) (Avantor, 99.9%, Gliwice, Poland), ZrO\({}_{2}\) (Avantor, 99.0%, Gliwice, Poland), and La\({}_{2}\)O\({}_{3}\) (99.99%, Sigma-Aldrich, St. Louis, MO, USA), were weighed in stoichiometric amounts according to the formula (Pb\({}_{0.75}\)Ba\({}_{0.25}\))\({}_{1-3x}\)/La\({}_{3}\)(Zr\({}_{0.70}\)Ti\({}_{0.30}\))O\({}_{3}\). The weighed and mixed powders were formed into compacts in steel dies using a hydraulic press and then synthesized in crucibles. Synthesis was carried out at \(T\) = 925 \({}^{\circ}\)C for \(t\) = 2 h. After synthesis, the compacts were crushed, ground, and then re-pressed into disks and sintered at \(T\) = 1250 \({}^{\circ}\)C for \(t\) = 4 h. This procedure was repeated before final sintering at \(T\) = 1300 \({}^{\circ}\)C for \(t\) = 7 h. The sintering and synthesis process was carried out in a double crucible using a PbO and ZrO\({}_{2}\) backfill to eliminate PbO sublimation. The flowchart of the complete fabrication process is shown in Figure 1.  
The microstructure and chemical composition of the obtained ceramics were investigated using a JEOL JSM-7100 FTTL LV scanning electron microscope (Akishima, Japan). This microscope is equipped with a JEOL-EDXS energy dispersive X-ray spectrometer (EDS) offering an energy resolution of 138 eV.  
The phase composition of the prepared samples was analyzed using X-ray diffraction (XRD) with a Panalytical X'pert PRO MPD diffractometer (Eindhoven, The Netherlands) in the standard \(\Theta\)-2\(\Theta\) configuration. This diffractometer was equipped with a PW 3050 goniometer, a PIXcel 1D detector, and a copper (Cu) source (CuK\(\alpha\) = 1.54178 A). For phase and structural analysis, the HighScore Plus v. 3.0e software, along with the PDF-2 DL database (2022 edition) provided by JCPDS-ICDD, was utilized.  
The impedance spectroscopy testing were performed using a computerized automatic system based on an Agilent E4980A LRC meter (Agilent, Santa Clara, CA, USA). Measurements were taken across a temperature range of \(T\) = (500-823) K, with increments of \(T\) = 10 K, and a frequency range spanning from \(f\) = 20 Hz to \(f\) = 2 MHz. Prior to data analysis, a preliminary test was conducted each time to ensure data consistency by employing Kramers-Kronig relations.  
Temperature measurements of DC electrical conductivity were made using a computerized system, which included a Keithley 6485 picoammeter (Keithley Instruments, Solon, OH, USA) as an integral part. A low-intensity DC field of 10 V/cm was applied to the sample to measure the electrical conductivity.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Site**} & \multicolumn{2}{c}{**Season**} & \multicolumn{2}{c}{**Year**} & \multicolumn{2}{c}{**Month**} & \multicolumn{2}{c}{**Day**} & \multicolumn{2}{c}{**Site**} & \multicolumn{2}{c}{**Season**} & \multicolumn{2}{c}{**Year**} & \multicolumn{2}{c}{**Month**} & \multicolumn{2}{c}{**Day**} \\ \hline \multirow{4}{*}{ARANJUEZ} & Spring & 1994 & 03 & 20 & & Spring & 2003 & 04 & 20 \\  & Summer & 1997 & 06 & 20 & ORUSCO DE & Summer & 1995 & 06 & 19 \\  & Autumn & 1993 & 11 & 06 & TAJUNA & Autumn & 1996 & 11 & 20 \\  & Winter & 1996 & 12 & 18 & & Winter & 1992 & 12 & 18 \\ \hline \multirow{4}{*}{BATRES} & Spring & 1993 & 05 & 02 & & Spring & 1993 & 05 & 02 \\  & Summer & 1991 & 06 & 19 & PEZUELA DE & Summer & 1997 & 06 & 15 \\  & Autumn & 1996 & 10 & 19 & LAS TORRES & Autumn & 1996 & 09 & 08 \\  & Winter & 1996 & 12 & 14 & & Winter & 1994 & 12 & 16 \\ \hline \multirow{4}{*}{COLMENAREJO} & Spring & 1998 & 04 & 09 & & Spring & 2002 & 04 & 09 \\  & Summer & 1994 & 06 & 16 & & Summer & 1994 & 06 & 17 \\  & Autumn & 1996 & 09 & 20 & & Autumn & 1994 & 10 & 13 \\  & Winter & 1996 & 12 & 18 & & Winter & 2000 & 12 & 18 \\ \hline \multirow{4}{*}{CONJUNTA DE} & Spring & 2003 & 04 & 19 & & Spring & 1995 & 04 & 03 \\  & Summer & 1991 & 06 & 19 & & Summer & 1991 & 06 & 19 \\  & Autumn & 1996 & 11 & 21 & & Autumn & 1996 & 09 & 20 \\  & Winter & 1995 & 12 & 14 & & Winter & 1996 & 12 & 15 \\ \hline \multirow{4}{*}{ESTREMERA} & Spring & 1993 & 03 & 17 & & Spring & 1996 & 03 & 27 \\  & Summer & 1994 & 06 & 19 & ROZAS DE & Summer & 1998 & 06 & 19 \\  & Autumn & 1996 & 11 & 20 & PUERTO REAL & Autumn & 2001 & 09 & 27 \\  & Winter & 1996 & 12 & 16 & & Winter & 1996 & 12 & 15 \\ \hline \multirow{4}{*}{FRESNO-RIBATEJADA} & Spring & 2002 & 04 & 11 & & Spring & 2001 & 05 & 18 \\  & Summer & 1991 & 06 & 19 & SAN MARTIN & Summer & 2000 & 06 & 15 \\  & Autumn & 1996 & 09 & 08 & NORESTE & Autumn & 1999 & 10 & 11 \\  & Winter & 1995 & 12 & 14 & & Winter & 1996 & 12 & 15 \\ \hline \multirow{4}{*}{FUENTIDUENA} & Spring & 2003 & 04 & 20 & & Spring & 1998 & 05 & 20 \\  & Summer & 1995 & 06 & 19 & SAN M\({}^{\rm a}\) DE & Summer & 1994 & 06 & 16 \\ \cline{1-1}  & Autumn & 1998 & 09 & 21 & LA ALAMEDA & Autumn & 1996 & 09 & 20 \\ \cline{1-1}  & Winter & 1996 & 12 & 16 & & Winter & 1996 & 12 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Long-term data statistics for four sites.
## 2 Methodology  
### Dynamics of the Inverted Pendulum System  
Modeling of the inverted pendulum on a cart can be computed considering its free body diagram, which is shown in Figure 1,  
Where the states of the system are  
\[x =\text{cart position }[\text{m}]\] \[\dot{x} =\text{cart velocity }[\text{m/s}]\] \[\theta =\text{angular position of the pendulum }[\text{rad}]\] \[\dot{\theta} =\text{angular velocity of the pendulum }[\text{rad/s}]\]  
which can be collected in the vector \(x=\left[\begin{array}{ccc}\theta&\dot{\theta}&x&\dot{x}\end{array}\right]^{T}\). On the other hand, \(M\) [kg] is the mass of the cart, \(m\) [kg] is the mass of the pendulum, \(l\) [m] is the length to the center of mass of the pendulum, \(u\) [N] is the control input and \(g\left[\frac{\text{m}}{g^{2}}\right]\) is the gravity acceleration. Then, the dynamic model of this system can be computed from motion equations of Lagrange as suggested in [18], and it can be presented in a nonlinear state space equation as  
\[\frac{d}{dt}\left[\begin{array}{c}\theta\\ \dot{\theta}\\ x\\ \dot{x}\end{array}\right]=\left[\begin{array}{c}\dot{\theta}\\ f_{1}(x)+g_{1}(x)u\\ \dot{x}\\ f_{2}(x)+g_{2}(x)u\end{array}\right] \tag{1}\]  
with  
\[f_{1}(x) =\Delta(-\gamma^{2}\dot{\theta}^{2}\sin\theta\cos\theta-\alpha \delta\sin\theta)\] \[f_{2}(x) =\Delta(\gamma\beta\dot{\theta}^{2}\sin\theta+\gamma\delta\sin \theta\cos\theta)\] \[g_{1}(x) =-\Delta\gamma\cos\theta\] \[g_{2}(x) =\Delta\beta\]  
Figure 1: Schematics of the inverted pendulum on a cart.  
where \(\alpha=m+M\), \(\beta=ml^{2}+I\), \(\gamma=ml\), \(\delta=-mgl\), \(\Delta=1/(\alpha\beta-\gamma^{2}\cos^{2}\theta)\) have been used to simplify the representation, and \(I\) is the moment of inertia.  
Now, by using the approach given in [3], the fourth-order system (1) is put in function of the actuated state and the unactuated state by selecting a suitable output \(y\) as  
\[y=L\theta+x \tag{2}\]  
where \(L\) is a design constant. The new states in function of the actuated and unactuated outputs are given by  
\[\frac{d}{dt}\left[\begin{array}{c}y\\ \dot{y}\end{array}\right]=\left[\begin{array}{c}\dot{y}\\ F(\mathbf{x})+G(\mathbf{x})u\end{array}\right] \tag{3}\]  
where  
\[F(\mathbf{x}) =Lf_{1}(\mathbf{x})+f_{2}(\mathbf{x}) \tag{4}\] \[G(\mathbf{x}) =Lg_{1}(\mathbf{x})+g_{2}(\mathbf{x}) \tag{5}\]  
A system modeled by (3) is to be used to represent the dynamics of the inverted pendulum on a cart and it is the objective plant to control in the following sections. It is important to note that this mathematical model is only required for simulations and not for the real-time implementation.
## 2 Results and Discussion  
### Selection of SPME Fiber Coating and Extraction Time  
The total VOCs area (arbitrary units) obtained at different extraction times in combination with different SPME fibers is presented in Figure 1. The best absorption was obtained at 180 min. In particular, the two fibers DVB/CAR/PDMS and CAR/PDMS displayed the highest sorption capacity compared to PDMS/DVB and PA fibers. An increase also was observed in the VOC adsorption increasing extraction time until 180 min. Finally the DVB/CAR/PDMS fiber was chosen also because it was very sensitive to small molecules like xylenes, ethylbenzene, styrene present in different cheeses. Indeed, the CAR/PDMS/DVB fiber has been reported to possess greater affinity for aromatic and aliphatic hydrocarbons and alcohols than CAR/PDMS and PDMS [15, 16, 17, 18].  
Once optimized the SPME extraction conditions, the analytical parameters of the method (linearity, limit of detection (LOD), limit of quantification (LOQ), precision and accuracy) were determined to validate the SPME-GC/MS procedure for determination of selected VOCs migrated from cling-films to chees.
## 4 Discussion
### Reliability of Future Assessment Factors  
To validate the rationality of estimating future RMAX3 by using the projected precipitation dataset, we calculated the annual error between the RMAX3 extracted from the NEX-GDDP dataset and that from the historical dataset (CHIRPS) for the period of 2015-2021, as well as the average over these seven years (Figure 13).  
\[E_{i}=\left(RMAX3_{i}^{P}-RMAX3_{i}^{H}\right)/RMAX3_{i}^{H}*100 \tag{6}\]  
where \(E_{i}\) is the error of NEX-GDDP data; \(RMAX3_{i}^{P}\) is the projected RMAX3 extracted from NEX-GDDP data for year \(i\); and \(RMAX3_{i}^{H}\) is the historical RMAX3 from CHIRPS data for year \(i\). Except for 2018 and 2020, the mean and median errors of the projected RMAX3 did not deviate by more than 20% of the actual values for other years. In certain years with more stable rainfall conditions, such as 2015 and 2019, these two indicators were even within 5%. The poor projections in 2018 and 2020 can be attributed to weaker rainfall intensity in the former and an extremely large rainstorm in the latter. Regarding the overall mean results of the seven years used in our modeling, the mean and median errors were within 10% (Figure 13h,p), suggesting that the future precipitation dataset has good simulation performance close to the real situation. Additionally, the numerical distribution of errors in most scenarios was close to a normal distribution with a low frequency of error outliers (Figure S12), which also reflects the dataset's robustness. Notably, compared with the spatial map of RMAX3 for the baseline scenario (Figure 6a), it was observed that the area with high RMAX3 (northwestern PLEZ) corresponded to the negative error reported in Figure 13h,p. This systematic bias indicates that the NEX-GDDP dataset simulation for the PLEZ tended to underestimate extreme rainfall intensity in areas with high RMAX3. Considering the dominant role of RMAX3 in determining future flood risk, the actual future risk situation in some areas of the PLEZ may be more severe than the results presented here.  
Similar tests were run on the projected data for population and GDP (Figures S13 and S14), proving the same reliable capability to estimate the future situation. Additional attention needs to be paid to a possible higher vulnerability because of the underestimation of GDP.
## 5 Simulation Verification
### The Influence of Noise  
To evaluate our method's robustness against noise interference, the single-phase ground fault scenario from Section 5.1 on cable line \(l_{3}\) is used for testing. Moreover, 20 dB noise is added to both the line-mode and zero-mode traveling waves originally shown in Figures 7 and 8. IMF\({}_{m}\) component of noisy line-mode traveling wave is shown in Figure 13. Line-mode traveling wave characteristic signal with noise after morphological gradient processing is shown in Figure 14. An analysis of the entropy kurtosis ratios revealed IMF\({}_{1}\) as the optimal component for both noise-contaminated waves, leading to their selection  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(d_{a}\)/km & \(R\)/\(\Omega\) & \(t_{1}\)/ms & \(t_{0}\)/ms & \(d_{m}\)/km & \(\Delta\)/m \\ \hline \(l_{5}\)-0.4 km & 10 & 125.006 & 125.008 & 0.  
as characteristic traveling wave signals. IMF\({}_{m}\) component of noisy zero-mode traveling wave is shown in Figure 15. Noisy zero-mode traveling wave characteristic signal after morphological gradient processing is shown in Figure 16.  
Figure 14: Line-mode traveling wave characteristic signal with noise after morphological gradient processing.  
Figure 13: IMF\({}_{m}\) component of noisy line-mode traveling wave.  
Figure 15: IMF\({}_{m}\) component of noisy zero-mode traveling wave.  
An analysis of the noise-contaminated line-mode traveling wave depicted in Figures 13 and 15 reveals significant signal degradation, with the original wave pattern largely obscured by noise. However, the parameter-optimized MVMD method successfully extracts the characteristic traveling wave signal, preserving critical singularity features while effectively mitigating noise interference. The temporal difference between line-mode and zero-mode wave head arrivals is measured at 26 ms. The application of Formula (22) yields a fault distance of 5.388 km, resulting in an absolute error of 188 m.  
Further validation tests were conducted on cable line \(l_{3}\), with single-phase ground faults simulated at three distinct locations: 0.4 km, 3.2 km, and 5.2 km from section M. Test parameters included a fault initiation time of 0.135 s, 10 \(\Omega\) grounding resistance, and 270\({}^{\circ}\) initial fault phase angle. Both line-mode and zero-mode traveling waves were subjected to varying noise levels (20 dB, 30 dB, and 40 dB). The fault localization results, presented in Table 9, demonstrate the method's robust performance under diverse noise conditions, confirming its strong resilience to noise interference.
## 3 Case Study  
### Reference Building  
The present study constructed a reference model for typical existing residential buildings in Shenyang, China, which has a severely cold climate. The reference building represents a typical residential structure built in the region during the 1980s (Figure 2). It is a typical seven-floor construction with a 3519 m\({}^{2}\) total floor area and 63 apartments, each with a ceiling height of 2.7 m (Figure 3). A comprehensive investigation and collation of information on the envelope structures of existing residential buildings were conducted. The investigation indicated that a significant number of local residential buildings constructed in the 1980s were not equipped with insulation layers. Two layers of single-pane glass were used for the windows without broken bridge frames. Since 2022, the government has carried out large-scale renovations of existing residential buildings according to local residential building energy-saving design standards [51]. The renovation added 100 mm EPS to the exterior wall and 200 mm to the roof. The windows remained unchanged, consisting of two layers of single-pane glass. Table 3 presents the calculated heat transfer coefficients of the exterior walls, roofs, floors, and exterior windows, as well as the thermal performance parameters of the residential building envelope in this case.  
Table 4 presents the internal heat gain from artificial lighting, equipment, and three occupants per unit, along with the typical workday schedule for each residential unit. Despite the absence of shading overhangs in the reference building, their potential inclusion was explored in the subsequent parametric simulations. Specifically, overhangs above south-facing windows were modeled, with their depth being a key design parameter under optimization. Conversely, no external shading devices were considered for north- and east-facing windows.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Internal Heat Gain** & & **Weekday** & **Weekend** \\ \hline Occupant & 100 W/occupant & 0:00–8:00 and & 0:00–24:00 \\ Artificial lighting & 10 W/m\({}^{2}\) & - & - \\ Refrigerator & 120 W & Always on & - \\ Television & 55 W & 19:00–22:00 & 10:00–16:00 and 19:00–22:00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Internal heat-gain schedule.  
Figure 2: Image of the reference building.