esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 3 Research Method
### Exclusion Criteria
#### 3.2.2 Identification of Citing Studies
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref.** & **Topic** & **Paper’s Keywords** \\ \hline
[78] & Lighting automation. & Smart home, ML, automation, transformer \\  & & Smart village micro-utilities, Embedded \\
[79] & Voice-controlled LS. & ML, Edge Impulse, Voice-activated \\  & & LED lighting \\
[76] & Detection of occupancy sensor signal & Occupancy sensors, Connected lighting, \\  & anomalies. & RF classifier. \\
[87] & Smart home control. & Smart home, IoT, ML, SR, \\  & & Pervasive Computing \\
[85] & Human activity recognition for smart & IoT, AI, NN, MCU, Kalman filter \\  & & IoT, interactive ML, intrusion detection, \\
[81] & Data-centric anomaly-based detection & anomaly detection, IoT security, Poisoning \\  & system. & attack, virtual sensors \\  & & ANN, automation, Cloud computing, facial \\
[88] & Monitoring system for domestic appliances. & recognition, image processing, IoT, ML, \\  & & microcontroller, NN, sensors \\
[90] & Automatic LS control based on human & Smart lighting, classification model, \\  & presence. & movement data, user comfort \\
[91] & Edge-computing architecture for smart & Edge-computing, smart lighting, model \\  & lighting control. & compression, knowledge distillation, kNN \\
[92] & Lighting control system tailored on home & Personalized lighting, intelligent lighting, \\  & residents. & prediction control strategy, \\  & & back-propagation neural network \\
[93] & Quantization method for Edge Computing & Smart lighting, Edge Computing, \\  & in smart LSs. & NodeMCU, kNN, model compression, \\
[94] & Comparison of RNN architectures. & IoT, smart lighting, binary classification, \\  & & DT, RNNs, LSTM, time-series \\
[95] & Occupants’ privacy-preserving in smart & Smart lighting, general data protection \\  & LSs. & regulation, privacy, camera, \\
[96] & Smart lighting control of offices. & image perturbation \\ \hline \hline \end{tabular}
\end{table} 
Table 29: Data synthesis from primary studies (2). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref.** & **ML Methods** & **Computing Paradigm** & **Employed IoT Devices** \\ \hline  & & & CO2 sensors, infrared \\  & & & sensors, motion sensors, \\
[70] & DL & Cloud-based & sound sensors, and \\  & & & temperature sensors are \\  & & & available for \\  & & & occupancy detection. \\  & & & Motion sensor, \\
[82] & DL & EdgeML & light-dependent resistor \\  & & & sensor, light dimmer, and a \\  & & & long-range RF module. \\
[71] & RL & Cloud-based & Ambient light sensors and \\  & & & PIR sensors. \\
[72] & Multi-linear regression, RF, & & Sony IMX219 image \\  & NN, and DT & Cloud-based & sensors, multi-channel \\  & & & spectral light sensors \\  & & & AS-7341. \\  & RF, Gradient Boosting, & & \\
[73] & and Extreme Gradient & Cloud-based & None. \\  & Boosting Classifier. & & \\
[77] & KNN, Naive Bayes, & & Light sensor and \\  & and HHMM & Cloud-based & PIR sensor. \\ \hline \hline \end{tabular}
\end{table} 
Table 28: _Cont._
## 4 Discussion
### Characterization of Root Canal sealers through SEM  
The surface morphology and particle sizes of the sealer materials were analyzed with the help of images obtained from SEM. Surface regularity is essential in determining whether a substance is biocompatible since it affects the ability of cells to adhere to the surface of the material [22]. According to the findings of our research, all the sealers we tested produced irregular micro-sized particles that aggregated in clusters with particle sizes ranging from 0.5 \(\upmu\)m to 200 \(\upmu\)m. However, CeraSeal produced smaller round-shaped particles in clusters that were discernible in the matrix with varying sizes ranging from 0.3 \(\upmu\)m to 68 \(\upmu\)m. The smaller particles were zirconium adhering to larger calcium-based particles. Several studies support the idea that using smaller sealer particles can produce a thinner film thickness and increase dental tubule penetration [16; 23; 24; 25]. However, it was claimed that bioceramic-based root canal sealers produced more sealer tags because of their smaller particle size, increased fluidity, and hydrophilicity upon contact with the root dentinal walls [16]. The use of root canal sealers that provide regular surfaces should, as a result, be anticipated to yield superior results in terms of cell adhesion. Surface regularity should not be examined in isolation because other parameters, such as chemical composition, can also influence cell adhesion and biocompatibility [6]. The penetrating ability of root canal sealers is determined by factors such as particle size of the components and physicochemical properties of the root canal sealer [26]. It is ideal for the root canal sealers to provide a regular surface to penetrate into the dentine tubules because doing so will enhance the sealing efficiency and the stability of the material; moreover, it may entomb any bacteria that may be present, and the chemical constituents of the sealers may exhibit an antibacterial effect [27]. According to a few authors, particle size is crucial because it influences many material characteristics [28; 29]. A study by [PERSON] et al., 2016 [30], supports the idea that smaller particles may enter dentinal tubules more effectively. In this study, we assessed the physicochemical characteristics of five root canal sealers, namely BioRoot, IRoot, CeraSeal, OneFill, and DiaRoot. The CeraSeal sealer exhibited rounder particles that were smaller in size than those exhibited by other sealers. This can be attributed to the very small particle diameter sizes that the CeraSeal sealer possessed, which ranged from 0.3 \(\upmu\)m to 68 \(\upmu\)m. Because of their greater surface-to-volume ratio, smaller particles hydrate more quickly than larger ones, making them well suited for dental material and potentially enhancing the clinical performance of root canal fillings [31].
## 5 Results
### Optimization Algorithms (RQ2 &RQ3)  
Most of the reviewed studies used meta-heuristic optimization algorithms to solve the deployment problem. The meta-heuristic stands for the Greek words meta and _heuristic_, which means solving problems using an upper-level methodology. Meta-heuristic algorithms represent a part of optimization in computer science and applied mathematics concerning algorithms and computational complexity theory, which depend on inspiring their solutions from natural habits, such as particle swarms, annealing processes, and ant colonies. These types of algorithms are used in many fields, such as artificial intelligence, mathematical programming, soft computing, and operations research. These algorithms are general approximate algorithms that can be deployed to different types of optimization problems and can be updated to solve any hard problem, since they provide fast, flexible,  
Figure 11: Papers distribution.  
Figure 12: Papers ratio for each database.  
and robust solutions; besides, they are easy to design and implement. The disadvantage of these algorithms is that there is no guarantee that the approximated solution is closed to the optimal solution. Further, because of the no free lunch (NFL) theorem, there is no meta-heuristic algorithm that can be used generally for different optimization problems [103]. This means that an optimization algorithm can outperform well for a specific problem but not so well with another one. Hence, there is always a need for newly proposed optimization algorithms to find solutions for more complex problems. This can be done either by proposing new algorithms or updating existing ones or by a combination of two different types of optimization algorithms, such as a hybrid algorithm between classical and meta-heuristic algorithms or meta-heuristic and artificial intelligence algorithms [11].  
About 35% of the reviewed studies worked on an update to swarm intelligence optimization algorithms [40; 44; 46] such as particle swarm optimization (PSO) [42; 58; 66; 68; 69; 74; 77; 78; 81; 88; 97; 99], and colony optimization (ACO) [33], and bee colony optimization (BCO) [48; 65], due to their ability to solve complex problems and provide a satisfactory solution in a feasible time [90]. These algorithms are applied to enhance network performance by combining them with other approaches and then comparing the obtained results with other algorithms, such as the genetic, greedy, and multi-objective evolutionary algorithms. The ratio of participation for each algorithm is shown in Figure 13.
# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Fault Case Analysis
#### 4.1.2 Case 2 (Line Fault with the CB Rejection)  
Considering Figure 5 as an example, a fault occurs at L0318. Lp0318m and Lp1803m operate, and then CB0318 trips, but CB1803 fails to operate; thus, Lp1718s operates and CB1718 trips. The alarm messages are briefly described as \(\text{LP}_{1803}\text{m}(0)\), \(\text{LP}_{0318}\text{m}(2)\), \(\text{CB}_{0318}\text{(30)}\), \(\text{LP}_{1718}\text{s}(520)\), and \(\text{CB}_{1718}\text{(541)}\). The fault diagnosis process is as follows.  
1. Based on the alarm messages, \(COMS=\{\text{L}_{0318},\text{L}_{1718},\text{B}_{18}\}\) is obtained.
2. \(\text{L}_{0318}\) is selected for fault diagnosis. The \(\mathbf{a}^{0}_{p(11)}\), \(\mathbf{T}^{0}_{p(11)}\), \(\mathbf{a}^{0}_{p(12)}\), and \(\mathbf{T}^{0}_{p(12)}\) of the fault diagnosis model for the \(\text{CB}_{0318}\) and \(\text{CB}_{1803}\) subnets are as follows. \[\left\{\begin{array}{l}\mathbf{a}^{0}_{p(11)}=[0.9833,0.85,0.2,0.9913,0.2,0.2,0,0,0,0]^{\text{T}}\\ \mathbf{T}^{0}_{p(11)}=[[30,30],[30,30],\varnothing,[2,2],\varnothing,\varnothing, \varnothing,\varnothing,\varnothing,\varnothing]^{\text{T}}\end{array}\right.\] (32) \[\left\{\begin{array}{l}\mathbf{a}^{0}_{p(12)}=[0.2,0.2,0.75,0.9913,0.2,0.7,0,0,0]^{\text{T}}\\ \mathbf{T}^{0}_{p(12)}=[\varnothing,\varnothing,[541,541],[0,0],\varnothing,[520,5 20],\varnothing,\varnothing,\varnothing,\varnothing]^{\text{T}}\end{array}\right.\] (33)
3. The diagnosis results for the \(\text{CB}_{0318}\) subnet are \(\alpha_{p10(1)}=0.9873\) and \(T_{p10(11)}=[-38,-8]\). The diagnosis results of the \(\text{CB}_{1803}\) subnet are \(\alpha_{p10(12)}=0.725\) and \(T_{p10(12)}=[-20,10]\). The diagnosis results of \(\text{L}_{0318}\) are \(\alpha_{(1)}=0.8562\) and \(T_{(1)}=[-20,10]\), and its measured value is \(\eta_{(1)}=0.8993\), which is greater than \(0.6\). Thus, \(\text{L}_{0318}\) is a faulty component.
4. Similarly, the measured values of \(\text{L}_{1718}\) and \(\text{B}_{18}\) are \(0.3733\) and \(0.42\), respectively, and \(\text{L}_{1718}\) and \(\text{B}_{18}\) are non-faulty components.  
CB\({}_{1803}\) refused to operate in this case, and the suspected faulty component extended to \(\text{L}_{1718}\) and \(\text{B}_{18}\). However, the proposed method is not misdiagnosed, and its diagnosis results are accurate. This case proves that the method in this study can diagnose a faulty line with CB rejection and non-faulty components.
## 4 Results and Discussion
### Analysis of In Situ Stress Accumulation Levels and Seismic Risk  
Based on actual measurements and linear regression analysis, it has been determined that, in the study area, \(\sigma_{\rm H}>\sigma_{\rm h}>\sigma_{\rm v}\) within a depth range from the surface to 206 m corresponds to a reverse fault in situ stress state; conversely, below 206 m, \(\sigma_{\rm H}>\sigma_{\rm v}>\sigma_{\rm h}\) characterizes a strike-slip fault in situ stress state. Therefore, Equation (3) is employed in this study to analyze fault stability. For reverse faults, when the ground stress state is at depths greater than 206 m, \(\sigma_{1}=\sigma_{\rm H}\) and \(\sigma_{3}=\sigma_{\rm v}\); for depths less than 206 m, the ground stress state corresponds to that of a strike-slip fault, with \(\sigma_{1}=\sigma_{\rm H}\) and \(\sigma_{3}=\sigma_{\rm h}\). The values of \(\sigma_{3}\) at various depths were utilized to compute \(\sigma_{1}\) at friction coefficients of \(\mu=0.6\) and \(\mu=1.0\). Figure 5 presents the analysis results of the ground stress states in the study area. From Figure 5, it is evident that the maximum horizontal principal stress in several test sections exceeds the lower threshold of the fault slip critical value, whereas the remaining sections remain below this threshold. Therefore, the likelihood of fault slip in this region is minimal. The better linearity and lower \(\mu_{\rm m}\) values at the Xidatan may be related to the closer proximity to the fault and more complete stress release after the earthquake.  
\(\mu_{\rm m}\) represents the ratio of the maximum shear stress to the average stress. As illustrated in Table 1, measurements taken at various points within this region reveal that \(\mu_{\rm m}\) ranges from 0.16 to 0.63. With only a few measuring sections exhibiting \(\mu_{\rm m}\) values greater than 0.5, most sections show values near 0.3, resulting in an average \(\mu_{\rm m}\) value of approximately  
Figure 4: Rose diagram showing the orientations of maximum horizontal principal stress.  
0.33. Japanese scholar [PERSON] [34] discovered that the parameter \(\mu_{\rm m}\) can be utilized to assess the influence of original in situ stress on fault slip. It was observed that \(\mu_{\rm m}\) in a given area tends to decrease before and after an earthquake. The value of \(\mu_{\rm m}\) represents the level of stress accumulation, which is independent of the direction of stress. A larger \(\mu_{\rm m}\) indicates a higher level of stress accumulation, thereby increasing the likelihood of fault slip, and conversely for smaller values. Specifically, when \(\mu_{\rm m}\) falls within the range of 0.5-0.7, it suggests that crustal stress is approaching its limit state; a value close to 0.5 signifies high levels of stress accumulation, while values below 0.3 indicate low levels of stress accumulation [33; 34]. We believe that the level of stress accumulation in this area is relatively low and consequently suggests a diminished possibility of fault slip.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
###### Abstract  
This paper discusses an elliptical ultrawideband (UWB) antenna and a dual-band UWB notch antenna. To achieve a UWB bandwidth, two corner cuts are etched into the rectangular slot on the partial ground plane. An inverted-U-shaped and conductor-shaped resonator are utilized to achieve dual-band notch characteristics on a partial ground plane. The suggested antenna has an overall dimension of \(24\times 32\) mm\({}^{2}\). The suggested UWB antenna has a gain of 4.9 dB, a bandwidth of 2.5-11 GHz, a linear phase response, a group delay of less than 1 ns, and a steady radiation pattern. The suggested UWB notch rejects WLAN and ITU bands from 5.2-5.7 GHz and 7.2-8.5 GHz, respectively, with an impedance bandwidth of 2.5-11 GHz. The UWB notch antenna features a linear phase, a group delay of less than 1 ns, and a stable radiation pattern.  
ultrawideband (UWB); UWB antenna; parasitic resonator; dual-band-notched; monopole antenna +
Footnote †: journal: Applied Physics Letters
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 4 Recent Advancement on UAV Tracking Antenna
### UAV Tracking Techniques  
UAV tracking has been implemented based on a variety of techniques, notably using received signal strength indicator (RSSI), using time difference of arrival (TDOA), tracking using global positioning system (GPS), and using monopulse. In this section, we present the latest trend in UAV antenna signal tracking techniques.
# A Multicomponent Face Verification and Identification System
## 2 Materials and Methods
### Deep Learning Model for the Face Identification and Verification
#### 2.2.3 Pre-Trained Models  
Four cutting-edge pre-trained face recognition algorithms were evaluated on the curated ID vs. Spot dataset:  
1. FaceNet: A deep convolutional network devised by Google for face recognition [23].
2. ArcFace: A method employing additive angular margin loss for deep face recognition [29; 62].
3. Dlib (baseline): A toolkit encompassing various machine learning algorithms, including face recognition [63; 64]. Specifically the _dlib_face_recognition_resnet_model_v1_, a 29-layer ResNet trained on a cleaned subset of MS-Celeb-1M and producing 128-D embeddings, was employed [65; 66]. Dlib's ResNet offers a compact (12 MB) C++ inference engine with zero external dependencies, widely adopted on edge devices and often used in academic baselines. A lightweight CNN such as LeNet-5 does not reach acceptable accuracy on modern benchmarks; hence, Dlib is a more realistic \"classical\" baseline.
4. MobileNetV2: A method employing large margin cosine loss for face recognition [67; 68; 62].  
Additional details for the parameters and giga floating point operations (GFLOPs) of the aforementioned models are provided. The Dlib model is a streamlined ResNet-34 with \(\sim\)6 million parameters, 0.4 GFLOPs, 29 conv layers, and half the filters per layer, hence its significantly lower parameter count and computation. MobileNetV2-AM refers to a MobileNetV2 backbone with an additive margin loss (ArcFace/CosFace); the full MobileNetV2 (width = 1) has \(\sim\)3.4 million parameters and \(\sim\)0.3 billion multiply-adds at 224 \(\times\) 224 input, which scales to roughly 0.15 GFLOPs at 160 \(\times\) 160. FaceNet-IR-v1 uses the Inception-ResNet v1 architecture and has \(\sim\)7.5 million parameters, and its pretrained model requires on the order of 1.6 GFLOPs for 160 \(\times\) 160 images. ArcFace-ResNet100 is a 100-layer ResNet model; it has \(\sim\)65 million parameters and, when evaluated on 112 \(\times\) 112 input, about 24.2 GFLOPs, which corresponds to roughly 49 GFLOPs at 160 \(\times\) 160. Further details can be found in [29, 63, 64, 67, 69].
## 4 CFD Modelling
### Modelling of the Atmospheric Boundary Layer (ABL)  
Stable atmospheric conditions are modelled by means of an additional buoyancy production (or dissipation) term \(f\cdot G_{b}\) which is added to the \(k\) and \(\epsilon\) equations, without solving an additional conservation equation for energy or temperature. This term takes the form:  
\[G_{b}=-\mu_{t}\left(\frac{\partial u}{\partial z}\right)^{2}\cdot\frac{Ri}{f_{ m}} \tag{9}\]  
\[Ri=\zeta\frac{0.74+4.7\zeta}{(1+4.7\zeta)^{2}} \tag{10}\]  
\[f_{m}=1+5\zeta \tag{11}\]  
\[\zeta=\frac{z}{L} \tag{12}\]  
where \(\mu_{t}\) is the turbulent viscosity, \(Ri\) is the Richardson number, and \(L\) the Monin-Obukhov length.  
As adopted in [15], \(f=1\) and \(f=0\) for \(k\) and \(\epsilon\) equations, respectively (the additional term is added only to the \(k\) equation).  
The inlet conditions for wind velocity, \(k\), and \(\epsilon\) are given by the Monin-Obukhov similarity profiles for stable atmospheric stratification [17; 18]:  
\[u_{0}(z)=\frac{u_{*}}{\kappa}\left[\ln\left(\frac{z}{z_{0}}\right)+5\frac{z}{L }\right] \tag{13}\]  
\[k_{0}(z)=\frac{u_{*}^{2}}{\sqrt{C_{\mu}}}\cdot\left(\frac{f_{\epsilon}}{f_{m} }\right)^{0.5} \tag{14}\]  
\[\epsilon_{0}(z)=\frac{u_{*}^{3}}{\kappa\cdot z}\cdot f_{\epsilon}\left(\frac{f _{\epsilon}}{f_{m}}\right)^{0.5} \tag{15}\]  
where \(f_{\epsilon}=1+4\frac{z}{L}\), \(u_{*}\) is the friction velocity, \(z\) is the vertical height, \(z_{0}\) the roughness height, and \(\kappa=0.41\), the von [PERSON]'s constant.  
The above profiles reduce to the well-known profiles for neutral atmospheric conditions [17; 18; 19; 20] as the Monin-Obukhov length \(L\) takes very large values (positive or negative).
# Battery Management System Hardware Concepts: An Overview
## 4 Measurement
### Current Measurement
#### 4.1.2 Contactless Current Sensors  
Apart from current measurement using shunt resistors, which are directly integrated in the current path, also contactless current sensors are commercially available. These sensors use the magnetic field that a current produces and are e.g., known as magneto resistive sensors (e.g., Sensitec (Lahnau, Germany) CDS4150 [32]), fluxgate sensors (e.g., LEM CAB300 [33]), or Hall sensors, which will be examined in a little more detail in the following. As the name suggests, Hall sensors are based on the Hall effect and are able to sense DC and AC. If a current flows through the Hall sensor, the sensor generates a voltage that is proportional to the product of magnetic flux density and current [31; 34]. In contrast to the shunt-based method, the Hall sensor does not add any further resistance to the current path, which means that no additional conductive losses occur. In addition, the galvanic isolation this method provides is advantageous. No further optocouplers or digital insulators are required for signal conditioning when Hall sensors are used.  
Hall sensors can be bought as integrated circuits and need to be placed on the current path (such as Honeywell's (Morris Plains, NJ, USA) SS413A [35]). The current sensors that measure the field strength usually have an analog voltage or current output that needs filtering. In addition, complete modules that can be built into the application without signal treatment circuitry are available. These sensor blocks usually consist of a ferrite ring with an inserted Hall sensor. A busbar or current link is then put through this ring for the ferrite to catch most of the busbar's field. Galvanic isolation is given at all times here (see LEM's product family HAH1 BV as an example [36]).  
One of the main disadvantages of the Hall effect based sensors is their limited bandwidth--usually not higher than a few ten kHz, such as Allegro Microsystems' A136x product family (Worcester, MA, USA) [37] (120 kHz) or LEM's LTC200 series sensors (100 kHz) [38]. If the battery system developer needs an increased bandwidth, then the classic shunt resistor must be used, which in general is also considered less pricey, as higher costs and larger volume are associated with Hall sensors. A further drawback of this measuring method is that the output signal of a Hall effect sensor has a large temperature drift [10] that needs to be compensated in the sensor module or the signal processing chain.
## 3 Results
### Comparison between Instantaneous SIF and Daily SIF Across Space and Time
It results in a relatively lower increase in \(\mathrm{R^{2}}\) for the \(\mathrm{SIF_{ins}}\)-\(\mathrm{SIF_{daily}}\) overall correlation across all latitudes when using the cos-based method (increasing the value of \(\mathrm{R^{2}}\) from 0.789 to 0.842) than that when using the PAR-based method (increasing the value of \(\mathrm{R^{2}}\) from 0.789 to 0.942). Figure 6: Scatter diagrams between the measured \(\mathrm{SIF_{daily}}\) with \(\mathrm{SIF_{inst}}\) (**a**) and the upscaled \(\mathrm{SIF_{daily}}\) using the cos-based (**b**) and PAR-based (**c**) methods for sunny and cloudy days at two sites, as indicated by the colors. The black dotted line represents the 1:1 line. In view of the above results, latitude and weather conditions were two important factors affecting the variations in \(\mathrm{SIF}_{\mathrm{inst}}\)-\(\mathrm{SIF}_{\mathrm{daily}}\) correlations and, thus, the SIF-APAR and SIF-GPP relationships across space and time, respectively. The effects of latitude can be reduced by upscaling \(\mathrm{SIF}_{\mathrm{inst}}\) to a daily scale using the cos-based method, whereas the effects of weather conditions cannot be corrected by this method due to clouds or scattered lights. \(\mathrm{SIF}_{\mathrm{inst}}\) may not be a substitute for \(\mathrm{SIF}_{\mathrm{daily}}\) when assessing the SIF-GPP relationship across space and time unless considering both latitudinal and diurnal weather changes via SIF upscaling using the PAR-based method.
## 4 Simulation Results
Figure 11: Strictly positive secrecy capacity for DH with SLIPT-based EH FSO link with varying atmospheric turbulence and pointing error conditions. \(\alpha_{E}=2.296\), \(\beta_{E}=2\), \(\xi_{E}=1.1\), \(\tau=0.4\), and \(\mu_{E}=15\) dB.