esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Modifying the structure of surface plasmon resonance based sensors by adding 2D materials has been proven to considerably enhance the sensor's sensitivity in comparison to a traditional three layer configuration. Moreover, a thin semiconductor film placed on top of the metallic layer and stacked together with 2D materials enhances even more sensitivity, but at the cost of worsening the plasmonic couple strength at resonance (minimum level of reflectivity) and broadening the response. With each supplementary layer added, the complexity of optimizing the performance increases due to the extended parameter space of the sensor. This study focused on overcoming these difficulties in the design process of sensors by employing a multi-objective genetic algorithm (NSGA II) alongside a transfer matrix method (TMM) and, at the same time, optimizing the sensitivity to full width at half maximum (FWHM), and the reflectivity level at a resonance for a four layer sensor structure. Firstly, the thin semiconductor's refractive index was optimized to obtain the maximum achievable sensitivity with a narrow FWHM and a reflectivity level at a resonance of almost zero. Secondly, it was shown that refractive indices of barium titanate (BaTiO\({}_{3}\)) and silicon (Si) are the closest to the optimal indices for the silver--graphene/WS\({}_{2}\) and MoS\({}_{2}\) modified structures, respectively. Sensitivities up to 302 deg/RIU were achieved by Ag-BaTiO\({}_{3}\)-graphene/WS\({}_{2}\) configurations with an FWHM smaller than 8 deg and a reflectivity level less than 0.5% at resonance.  
SPR based sensors; NSGA II optimization; sensitivity enhancement +
Footnote †: journal: Applied  
Article
## 4 System Under Examination  
Figure 1 depicts the configuration of a 150/20 kV substation. The length of the overhead transmission line is regarded equal to 15 km and the span between the metallic towers is 300 m. In addition, the resistance of the substation grounding system is regarded constant, equal to 1 \(\Omega\). Furthermore, a safety factor of 1.15 has been adopted (safety factor = Basic Insulation Level/protective level), according to [22]. Table 1 presents the electrical characteristics of the basic components of the substation under study.  
The transmission lines and the cable are modeled according to the [PERSON]'s traveling wave method [27]. Figure 2 depicts the geometry of a typical tower of a 150 kV transmission line. The towers are modeled according to equation [28]:  
\[Z_{r}=60\ln\biggl{(}\cot\biggl{(}\frac{1}{2}\tan^{-1}\Bigl{(}\frac{r}{h} \Bigr{)}\biggr{)}\biggr{)}, \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Surge Arresters**} \\ \hline U\({}_{\text{c}}\) & 86 kV \\ U\({}_{\text{r}}\) & 108 kV \\  & 5 kA & 242 kV \\ U\({}_{\text{res}}\) & 10 kA & 254 kV \\  & 20 kA & 280 kV \\  & 40 kA & 313 kV \\ Energy capability & Class 3 \\ \hline \multicolumn{2}{c}{**Conductors of the Transmission Line**} \\ \hline Type & ACSR \\ Cross section & 636 MCM \\ Nominal current & 795 A \\ \hline \multicolumn{2}{c}{**Cable**} \\ \hline Insulation & XLPE \\ External layer & PVC \\ Conductor cross-section & 800 mm\({}^{2}\) \\ Nominal current & 840 \\ BIL & 750 kV \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical characteristics of the equipment.  
Figure 1: Configuration of the substation under study (E: entrance of the substation, I: intermediate position, T: transformer).  
where  
* \(r\), the radius of the tower base (m), and
* \(h\), the height of the tower (m).  
The development of an overvoltage greater than the dielectric strength of the insulators of the line results in a flashover. The flashover strength \(V_{FO}\) (in kV) is given as follows [17; 29]:  
\[V_{FO}=\left(400+\frac{710}{t^{0.75}}\right)\cdot D \tag{6}\]  
where  
* \(D\), the length of the insulator string (m), and
* \(t\), the elapsed time after lightning stroke (\(\upmu\)s).  
Surge arresters are modeled according to the Pinceti-Giannettoni model (Figure 3) [30; 31], considering their electrical characteristics for the computation of the parameters of the equivalent circuit.  
Finally, the grounding resistance is modeled according to the following equations, considering the soil ionization due to lightning current [18]:  
\[R(I)=\frac{R_{o}}{\sqrt{1+\frac{I}{I_{g}}}} \tag{7}\]  
Figure 3: The Pinceti–Giannettoni Model [30; 31].  
Figure 2: Tower of 150 kV transmission line.  
\[I_{g}=\frac{1}{2\pi}\cdot\frac{E_{o}\cdot\rho}{R_{o}^{2}} \tag{8}\]  
where  
* \(R(I)\), the grounding resistance,
* \(R_{o}\), the low current of grounding resistance,
* \(I_{g}\), the limiting current to initiate sufficient soil ionization, and
* \(\rho\), the soil resistivity.
###### Abstract
Graphene-polymer is another sophisticated composite that utilizes graphene's exceptional electrical conductivity and mechanical strength to improve high-frequency electromagnetic wave transmission in applications such as 5G technologies and communication systems [50]. These composites are essential for advancing technologies that depend on effectively controlling and manipulating electromagnetic waves. A confluence of technological, environmental, and strategic variables influences the quality of internet signals inside a structure. iPyroxy is a sophisticated material progressively regarded as a substrate for antennas because of its advantageous electrical, dielectric, and optical characteristics. The novelty and contributions of the proposed work are as follows:  
* The proposed study characterizes the iron pyrite-epoxy composite, which exhibits fine electromagnetic properties, revealing a transmission coefficient of 90% over a very wide range of frequency from 1 GHz to 15 GHz. Furthermore, for the application of substrate materials in antennas, the reflection losses were relatively small. This implies that clear and strong signal transmission is possible for electromagnetic applications. * This composite also presents a band gap tunable to 1.3 eV, thus enabling the selective absorption and emission of electromagnetic radiation. Characteristics like these increase its utility in diverse applications requiring a specific interaction of electromagnetic waves, such as UV-Vis spectroscopy and advanced antenna systems. * The epoxy resin binder ensured that the composite possessed mechanical strength and improved dielectric properties at the same time. This ensured that the transmission efficiency was enhanced and quite stable in high-performance electromagnetic systems. The product could therefore be relied on for highly complex applications such as antennas that demanded transmission with high-quality reliability and minimal attenuations of signal.
# An Internet of Things-Based Low-Power Integrated Beekeeping Safety and Conditions Monitoring System
## 2 Related Work on Internet of Things (IoT) Beehive Incident Response and Management Systems
### New Beehive Cells Supporting the IBSMC Architecture
Depending on the high or low angular shift, shift variation and/or shift duration, animal intrusion or theft intrusion alerts are issued to the central hive cell and therefore to the ISMCA. 3. A non-fixed rotating camera for taking photograph snapshots and instant snapshots: This camera is an additional/optional device, equipped with its own processing unit and an attached 3G transponder. It is powered up only from the center of the array (central hive). It is an external IBSMC system part connected to the central hive and it is power controlled by the central hive. Its main functionality is to be powered up by the central hive cell on a critical alert and take 4 photos rotating by 90\({}^{\circ}\) angles using a servo-rotating motor that allows the rotation of the camera by 360\({}^{\circ}\), which instantly transmits to the Information System (ISMCA), using HTTP POST uploads followed by metadata information of event time and direction. The critical alerts involve cases of at least 3 hives lid shifts or one hive turnover event. The central hive is responsible for the incidents coordination and data forwarding for all automated hives and therefore the beehive array's data transmissions to the closest LoRaWAN gateway. It has been named by the author as the central hive due to the resemblance of its battery pack with a beehive brood box. It also includes some extra parts, such as a galvanized pipe with embedded ground rod, which supports the PV panels, rotating camera and control unit box. Figure 2d presents the architecture of the central hive. The most important part of the central hive is its control box. The control box includes a 48 MHz 32 bit ARM M0+ CPU, that connects both to a UART-capable LoRaWAN class A RN2483-capable module (RHF76-052), responsible for the data transmission to the LoRaWAN gateway Software Serial connected to at least one RFM12B transponder, depending on the number of automated hives controlled. The rotating camera module of the central hive is connected to a power interrupt circuit controlled by the control unit box. The power interrupt circuit, apart from enabling and disabling camera power supply, also includes a control logic that forces the camera to rotate via its servo into five different set positions (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\)), to take snapshots, compress them and transmit them to the ISMCA (HTTP PUT method) using its internal 3G/4G USB doggle transponder.
# Optimal Coordination of PV Active Power Curtailment and EVs Charging among Aggregators
## 3 Centralised Optimisation by the DSO for Multiple Aggregators  
In this stage, the DSO seeks to coordinate for each aggregator, its maximum PV generation and the charging power of all their EVs to maximise the operational performance of the network and fulfill the energy and power needs of the end users. Since the total PV power managed by an aggregator \(k\) could overload the DSO's assets due to the reverse power flow, it has to be limited to a certain power level, i.e., it should be applied an active power curtailment strategy (e.g., fixing a maximum point to active power [12; 13; 14; 15] or considering a reference signal as the voltage at the connection node [16; 17]) for the PV units under that aggregator. Both the export limit of PV power and the charging profile for each aggregator are centrally calculated based on a linear optimisation model which takes as inputs the requirements of each aggregator, its forecasted load profile and the thermal limits of the network's assets. The optimisation problem to be solved at every time step is formulated as shown in Equation (11).  
\[\textit{maximise}\ f=\sum_{k\in K}\sum_{t\in N_{\textit{labels}}}P_{k,t}^{PV} \cdot\zeta_{k,t}+\sum_{k\in K}\sum_{t\in N_{\textit{labels}}}P_{k,t}^{EV}- \omega\cdot\sum_{k\in K}\sum_{t\in N_{\textit{labels}}}OL_{k,t}-\gamma\cdot \sum_{k\in K}\sum_{t\in N_{\textit{labels}}}\Delta_{k,t}^{P} \tag{11}\]  
The first term of the objective function seeks to maximise the aggregated PV power generation by dynamically adjusting the export limit \(\zeta\) of all aggregators, which is between zero and one, in order to maintain the thermal limits of the distribution transformer and the main service cables at the LV side. The second term indicates the optimal charging power \((P_{k,t}^{EV})\) for the aggregator \(k\) at time \(t\). In the third term, the overloading condition due to an unexpected massive EV charging during peak demand on the feeders from the distribution transformer at each time interval is penalised employing a large positive weight \(\omega\). Note that the overloading is represented by the penalty deviation variable \(OL_{k,t}\) which helps to ensure the problem feasibility. The last term penalises significant variations in the charging rate \((\Delta_{k,t}^{P})\) of each aggregator using a large positive factor \(\gamma\).
## 2 Proposed Antenna Design
### Design and Configuration of Antenna
As shown in Figure 2b, to enhance the coupling, the edge of the circular patch is truncated into a straight shape with a chord length (\(L_{c}\)), forming a major segment shape, thereby increasing the length of the coupled section between the patch and the feedline. However, there is a trade-off between the increase in coupling and axial ratio when \(L_{c}\) increases. Figure 3: Simulated surface current on the proposed antenna at two different phases: (**a**) \(0^{\circ}\) and (**b**) \(90^{\circ}\). Figure 2: The proposed antenna design: (**a**) overall configuration; (**b**) patch element geometry and feedline-to-element gap; (**c**) photographs of the proposed antenna. The final design dimensions are \(W_{1}=30\) mm, \(W_{2}=1.3\) mm, \(W_{3}=0.7\) mm, \(W_{4}=0.25\) mm, \(L_{1}=50\) mm, \(L_{c}=2.1\) mm, \(R=2.35\) mm, \(l=6.03\) mm \(=0.63\lambda_{g}\), and\(d=8.59\) mm \(=0.68\lambda_{0}\), \(G=0.1\) mm.
## 5 Transmission Rate Control Scheme Based on Network Node Bandwidth Allocation  
This section introduces the transmission rate control model we designed based on the network node bandwidth allocation algorithm, which can obtain the maximum transmission rate that the current network can provide.
## 2 Current and Voltage Transformations of Delta-Connected Machine
### Current Transformations  
Figure 1 shows the basic winding diagram of the star- and delta-connected IM. If the machine is star-connected (Figure 1a), then the measured currents at the inverter output directly correspond to the phase current of the individual windings. The transformation of the measured currents to the phase current space-vector can be expressed as  
\[\dot{i}_{1}=K\Big{(}i_{\text{a}}+i_{\text{b}}\mathbf{a}+i_{\text{c}}\mathbf{a }^{2}\Big{)}. \tag{1}\]  
In the following text, if the space vectors are not written in an arbitrary form, then the transformation constant is considered equal to \(2/3\).  
In the delta-connected IM (Figure 1b), the measured currents at the inverter output are related to the phase currents as  
\[i_{\text{La}} =i_{\text{a}}-i_{\text{c}}, \tag{2}\] \[i_{\text{Lb}} =i_{\text{b}}-i_{\text{a}},\] (3) \[i_{\text{Lc}} =i_{\text{c}}-i_{\text{b}}. \tag{4}\]  
The space vector of the line currents can be written as  
\[\dot{i}_{\text{LL}}=K\Big{(}i_{\text{La}}+i_{\text{Lb}}\mathbf{a}+i_{\text{Lc }}\mathbf{a}^{2}\Big{)}. \tag{5}\]  
Substituting (2)-(4) to (5) yields  
\[\dot{i}_{1}=\frac{1}{\sqrt{3}}\dot{i}_{\text{LL}}\mathbf{e}^{\frac{\pi}{6}}. \tag{6}\]  
Figure 1: Three-phase winding: (**a**) star connection; (**b**) delta connection.  
Equation (6) enables calculating the space vector of the phase currents out of the space vector of the line currents of a delta-connected machine. Furthermore, out of (2)-(4), it follows that  
\[i_{\text{La}}+i_{\text{Lb}}+i_{\text{Lc}}=0. \tag{7}\]  
However, (7) only holds for the line currents and not the phase currents, which can exhibit circular current components. Considering (7), separation of (6) to real and imaginary part, respectively, yields  
\[i_{\text{1a}}=\frac{1}{2}K(i_{\text{La}}-i_{\text{Lb}}), \tag{8}\]  
\[i_{\text{1}\beta}=\frac{\sqrt{3}}{2}K(i_{\text{La}}+i_{\text{Lb}}). \tag{9}\]  
For the most common choice of \(K=2/3\), (8) and (9) can be finally rewritten as  
\[i_{\text{1a}}=\frac{1}{3}(i_{\text{La}}-i_{\text{Lb}}), \tag{10}\]  
\[i_{\text{1}\beta}=\frac{1}{\sqrt{3}}(i_{\text{La}}+i_{\text{Lb}}). \tag{11}\]
# Boundary-Aware Concurrent Queue: A Fast and Scalable Concurrent FIFO Queue on GPU Environments
## 5 Experimental Results and Analysis
### Analysis Performance on Real World Application (Figure 19)  
Breadth-First Search (BFS) is one of the most prominent use cases for concurrent queues due to its heavy reliance on the FIFO property to traverse graph layers level by level. BFS processes nodes in the exact order they are discovered, ensuring that all nodes at a given depth are explored before progressing to the next. A concurrent FIFO queue is essential in maintaining this order even when multiple threads or processors simultaneously enqueue and dequeue nodes.  
Recognizing BFS as a real-world application, we evaluated the performance of BACQ against the state-of-the-art Broker queue (Figure 19). Our analysis spanned a comprehensive range of graph sizes, from 100 vertices to 1 million, with edge counts varying from 600 to 6 million. Edge weights were randomly assigned to simulate diverse graph scenarios. Performance was assessed based on elapsed time, and BACQ demonstrated a remarkable 2\(\times\) improvement in performance compared to the Broker queue.  
Figure 16: Performance of enqueue–dequeue mix (50:50) (X = # of requests, Y = BOPS (billion ops/s).  
Figure 17: Performance of enqueue–dequeue mix (70:30) (X = # of requests, Y = BOPS (billion ops/s).
## Materials and Methods
### Volatile Organic Compounds (VOCs) Profile  
The extraction of VOCs in dried sweet potato headspace was performed by headspace solid-phase microextraction (HS-SPME) Arrow 1.10 mm (Divinylbenzene/Carbon WR/Polydimethylsiloxane (DVB/C-WR/PDMS)) (CTC Analytics AG, Zwingen, Switzerland) and followed by GC-MS analysis with Shimadzu GCMS-QP2020 (Shimadzu Company, Kyoto, Japan) equipped with Zebron ZB-5 MSi (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column (Phenomenex, Torrance, CA, USA). Briefly, 0.200 \(\pm\) 0.01 g of fully grinded dried material was placed into a 20 mL headspace vial, along with 10 \(\upmu\)g of undecane-2-one (Sigma-Aldrich, Steinheim, Germany) as internal standard and preincubated for 5 min at 60 \({}^{\circ}\)C, 250 rpm. Thereafter, the samples were subjected to 30 min of VOC extraction with the same conditions. After extraction, VOCs were desorbed at 250 \({}^{\circ}\)C in a GC injector with split 10. For analytes separation, the following temperature program was used: 50 \({}^{\circ}\)C to 130 \({}^{\circ}\)at 4 \({}^{\circ}\)C/min, then to 180 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and, finally, to 280 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min. For mass spectrometry, the following conditions were used: ion source temperature 220 \({}^{\circ}\)C, interface temperature 250 \({}^{\circ}\)C, scanning mode from 40 to 400 \(m/z\).  
For data processing, Shimadzu software GCMS Postrun Analysis (Shimadzu Company, Kyoto, Japan) was used. The identification of VOCs was based upon the comparison of experimentally obtained linear retention indices (LRIs) calculated against C\({}_{7}\)-C\({}_{40}\)\(n\)-alkanes mix (Sigma-Aldrich) analysis and experimentally obtained mass spectra with those available in Flavors and Fragrances of Natural and Synthetic Compounds GCMS Library (FFNSC 3) (Shimadzu Company, Kyoto, Japan). The LRIs filter was set at \(\pm\)15 points, and only compounds with a similarity score \(\geq\) 90% were considered as correct hits. Quantification was based on the peak area normalization against the internal standard peak area. The approximation of VOC concentration was calculated in relation to the amount of added internal standard.
###### Abstract  
The structural health monitoring (SHM) of safety relevant composite components is becoming increasingly relevant as it enables in-service diagnosis and data acquisition capabilities, contributing to the optimization and efficient operation of the overall system and ultimately saving costs and resources. In this field, machine learning (ML) techniques are attracting growing attention due to their capability to recognize complex patterns, making them very suitable for the identification of damages in operating mechanical structures. However, the acquisition of sufficiently large amounts of labeled and representative data from both pristine and damaged structures is very costly. To address this, a ML-based SHM approach is proposed that identifies structural damage using only physics-based synthetic strain data generated from the structure's numerical finite element model. It employs a semi-supervised anomaly detection approach, trained solely on synthetic pristine data, to identify deviations in experimental data indicating damage. The method is validated on an aircraft spoiler demonstrator made of a composite sandwich panel, instrumented with a strain gauge grid on its surface layer. The results show that the proposed SHM approach accurately classifies damaged and undamaged experimental data, independent of the prevailing load case, solely based on synthetic pristine strain data. It is also able to localize these damages in the form of a confidence area with respect to the sensor grid. This demonstrates the feasibility of using only synthetic pristine data for data-driven SHM of composite aerospace structures.  
structural health monitoring; synthetic strain data; anomaly detection; machine learning 2025
# Prediction and Comparison of In-Vehicle CO\({}_{2}\) Concentration Based on ARIMA and LSTM Models
## 3 Results
### Analysis of In-Vehicle CO\({}_{2}\) Concentration
Figure 6: Measured value of CO2 concentration: (**a**) test results of different fan speeds (T1–S1, T2–S2), (**b**) test results of different occupants (T3–1 person), (**c**) test results of different driving speeds (T4–suburban road, T5–expressway), (**d**) test results of different vehicle volumes (T6–Honda Fit). The T1 test result serves as the fundamental dataset for comparative analysis with other test results. The majority of indoor CO\({}_{2}\) concentration standards, as summarized in the preceding introduction section, are set at 1000 ppm. [PERSON] and [PERSON] (2011) indicated a concentration threshold of 2500 ppm for automotive CO\({}_{2}\) concentrations, above which negative effects can occur. Therefore, we used 1000 ppm and 2500 ppm as the evaluation criteria. From the test data, it is found that the rate of CO\({}_{2}\) increase in the vehicle is rapid when the RC ventilation mode is turned on. When there are three occupants in the vehicle, the rate of CO\({}_{2}\) increase for the T6-Honda Fit artificial condition is the fastest, reaching 1000 ppm in 160 s (about 2.5 min) and 2500 ppm in 470 s (about 8 min); the T3-1 artificial condition has the slowest rate of CO\({}_{2}\) increase, reaching 1000 ppm within 500 s (about 8 min). The CO\({}_{2}\) concentration reached 1000 ppm within a range of 120-245 s during the T1-S1, T2-S2, T4-suburban road, and T5-expressway tests. The average concentration within 20 min of the six groups of working conditions all exceeded 1000 ppm, and the T1-S1, T6-Honda Fit working conditions even exceeded 2500 ppm, with the proportion of time exceeding 2500 ppm in the test process exceeding 59%. CO\({}_{2}\) concentration is a key indicator for judging the degree of ventilation in a vehicle (2016). Therefore, when the windows are closed, and internal air circulation is activated, the concentration of CO\({}_{2}\) can easily exceed safe levels, posing a potential hazard to driving safety. A timely increase in ventilation is necessary to reduce the in-vehicle CO\({}_{2}\) concentration.