esa-sceva/satcom-chunk-collection huggingface dataset:
# HVdc Circuit Breakers: Prospects and Challenges
## 4 HVdc Circuit Breaker Topologies
### Solid State HVdc Breakers
#### 4.2.1 Solid State HVdc Breakers Employing Surge Arrestors (SA)  
Under the steady-state condition, the current flows from the dc source to the load through IGBTs (\(T\)) in SS-based HVdc breakers employing SA, as shown in Figure 5[95; 97]. However, the semiconductor switch turns-off when a fault is detected. This results in a rapid rise in voltage until the surge arrestor starts to conduct. The surge arrester is designed so that any voltage greater than the grid voltage is choked/blocked, leading to the de-energization of the line inductance [97]. Then the load current is commutated to the surge arrester. The IGBT voltage is quite limited to the surge arrester's clamping voltage and is assumed to be \(V_{\text{dc}}+V_{\text{margin}}\). The impedance at the fault point is also supposed to be small when IGBT turns-off at time t = 0.  
\(V_{\text{margin}}\) is applied across \(L_{\text{dc}}\) and the inductor current is calculated as [89]:  
\[i_{L}=I_{o}-\frac{V_{\text{margin}}}{L_{\text{dc}}}t \tag{3}\]  
where \(I_{o}\) is the amplitude of the fault current at t = 0. The time to turn off the fault current \(T_{\text{open}}\) is derived as follows:  
\[T_{\text{open}}=\frac{L_{\text{dc}}}{V_{\text{margin}}}I_{o} \tag{4}\]  
The energy absorbed by the surge arrestor can be given by:  
\[W_{R}=\Bigg{(}\frac{V_{\text{dc}}}{V_{\text{margin}}}+1\Bigg{)}\frac{1}{2}L_ {\text{dc}}I_{o}^{2} \tag{5}\]  
\(W_{R}\) is giving total energy being absorbed and suppressed by the surge arrester at the time of fault clearance, \(I_{o}^{2}\) shows the short circuit current, and LC signifies the system's  
Figure 5: Solid–state HVdc breakers with parallel surge arrestors [95].  
Figure 6: Solid–state HVdc breakers with free–wheeling diode [95].  
inductance. In a high voltage system, larger values of \(\mathrm{V_{dc}}\) than \(\mathrm{V_{margin}}\) are used to suppress the voltage across IGBT and reduce the conduction losses. In such a scenario, the term in the bracket of (5) becomes large which means \(W_{R}\) is enough higher than the stored energy in \(\mathrm{L_{dc}}\) at t = 0, \(\frac{1}{2}\mathrm{L_{dc}}\mathrm{L_{0}^{2}}\). Thus, surge arrester's high capacity is required to accommodate IGBT's more suppressed energy. The more is voltage suppressed across the IGBT, the higher the capacity surge arrestor required [97].
# Analysis of 3D Printed Dielectric Resonator Antenna Arrays for Millimeter-Wave 5G Applications
## 1 Introduction
By utilizing an easily accessible 3D printer and low-cost, flexible filaments, several DRA samples operating at microwave or mmWave were designed and manufactured. These samples were then analyzed using a portable microscope to examine the surface profile and provide detailed information about the fabrication process. Good agreement between the simulated and measured results, as well as surface profile figures, proves the feasibility of using FDM techniques to develop DRAs. The successful application of FDM in DRA manufacturing could revolutionize the production of antenna arrays, making them more accessible and adaptable to the rapid advancements in wireless communication technologies.
## 2 Internet of Vehicles and Smart Cities
### Definition  
With the growth of entities in VANETs over the years, new requirements appear that have to be fulfilled to appeal to the user's needs. In a vehicular network, vehicles frequently produce enormous amounts of data (from internal mechanical data to data related to the road or traffic state). The processing, analyzing, and evaluation of these large amounts of data is an arduous task that VANETs cannot handle due to the limited processing power of their devices. The limited applications of entrusted Internet services [10] and the connection/disconnection of vehicles due to getting in and out of the coverage area [11] are also noticeable constraints in VANETs, which drive the evolution towards the IoV. As such, vehicles in VANETs need to become \"smart\" objects that work cooperatively to ease these tasks' weight. This smart cooperation marks the line where VANETs start evolving into the IoV, as defined by the work of [PERSON] et al. [12].  
Another way IoV demarks itself from VANETs is in the sense that it aims at large coverage areas (city-scale or even global) and by aiming the integration of two technological visions: vehicle's networking and vehicle's intelligence, as proposed by [PERSON] et al. [13], with a focus on integrating objects (i.e., humans, vehicles, units) and environments as to build an intelligent network. The coalition of smart vehicular systems and cyber-physical systems brings the possibility of developing a global network that offers services and gives quality-of-life improvements to drivers and service providers, helping to reduce traffic congestion, pollution, and accidents.  
Moreover, IoV can be seen as an application of an IoT technology in an Intelligent Transportation System (ITS) technology, which is also the result of merging three different networks: the inter-vehicular network (from the vehicle to other vehicles), the intravehicular network (inside the vehicle-cyber-physical system) and the vehicular mobile Internet (from the vehicle to other objects on the network) [14]. It is an enormous distributed system for wireless communication and data exchange on a _vehicle-to-everything_ (V2X) mode with defined protocols and data interaction standards, like IEEE 802.11p. These modes are represented in Figure 1.
## 6 Pitfall Trap Deployment
### Tenet Application for Pitfall Trap Monitoring  
Our goal was to design a triggered data collection system, where each node frequently captures an image, but only sends it to the base station when an animal is likely to be present in the trap. This is necessary to reduce the energy consumption of the battery-powered sensor nodes and to conserve network bandwidth, since each image is 16 kB and data communication is the most significant part of the energy consumption. However, the object detection algorithm we use can exhibit false negatives. If a false negative detection occurs, no image will be delivered at that instant. Even if there actually were an animal in that trap, if it does not move, no image transfer will be triggered at subsequent sampling times, as well. This might result in a missed and eventually dead animal. To avoid this, we designed our application to take an image every 2 min and to transfer the image only if an object is detected, but also to transfer at least one image every 30 min regardless of the detection. Here is a Tenet task that realizes this relatively complicated logic.  
Repeat(120000 ms)  -> ImageDetect(TAKE_NEW, FLASH_ON, BW, 128, 128, x)  -> Count(y, 0, 1) -> Mod(y, y, '15') -> Eq(y, y, '0')  -> Or(x, x, y) -> Store(z, x) -> DeleteAllAttributeIf(x)  -> Send(E2E_ACK)  -> Retrieve(z) -> Not(z, z) -> DeleteActiveTaskIf(z)  -> DeleteAttribute(z) -> ImageDetect(RESET)  -> ImageFetch(LAST_IMAGE, 40, BW, 128, 128, out)  -> SendStr(1);  
In this task, ImageDetect() takes a new image and invokes a background-subtraction-based algorithm to detect noticeable differences between the new and previous image. Then, ImageFetch() retrieves the last image stored in the memory. Thus, the preceding task takes an image every 2 min and transfers it only if some change has been detected in the image or every 15 th run (every 30 min). If an object is not detected and it is not the 15 th run, the task sends a small message (\(x\equiv 0\)) using Send(E2E_ACK), as a keep-alive. Otherwise, it transfers the last image using SendStr() and resets the detection background. Each image is a 128-by-128 grey-scale image, whose size is 16 KB, and each packet can contain up to 40 bytes of image fragment data; so, each image requires 410 packets.  
These Image-style tasklets are different from other tasklets in that they are executed on the cameras themselves. Image processing requires memory and MCU cycles beyond the capabilities of the current generation of motes, and the Cyclops board was designed to provide specialized image processing tasks. For this reason, these Image-related operations are implemented within the camera themselves. This design allows any resource-intensive operation to be performed off-mote and can be applied to other specialized external sensors with on-board processing. Finally, the Cyclops was powered off between image captures to save energy. In this case, it remained powered off most of the time and was powered on for only a short duration every 2 min.
## 5 Conclusions  
As the world seeks alternatives for fossil fuel, renewable energy in general, and solar PV energy especially, presents a bright, promising future. An STS is one of the technologies that increase the PV system production via minimizing the reflected sun rays from a PV module surface and maximizing the transmitted sun rays through a PV module surface. The current work represented an experimental investigation for azimuth-based SAST and sensor-based SAST to study the feasibility of different STSs. In this study, the design for the different STSs was presented, the actual fabrication was achieved, the control schema for each STS was applied, and the actual experimental investigation was carried out for the different STSs simultaneously. The results showed that the sensor-based STS generated 14.8% more net power than the azimuth-based STS, where the sensor-based STS produced 7.8% more power than the azimuth-based STS, and the azimuth-based STS consumed 192% more power than the sensor-based STS. Additionally, the electrical energy balance revealed that using the sensor-based STS would increase the net energy production by 12.68% compared to azimuth-based SAST, where the energy produced by the sensor-based STS was 7.7% more than the azimuth-based STS energy production. In addition, the energy consumed by the azimuth-based STS was 65% more than the consumption of the sensor-based STS. The study concluded that using the STS is still convenient in the studied area, which implies that the proposed solar tracking system can be implemented in solar system applications in Dammam. Furthermore:  
* The sensor-based STS is more efficient than the azimuth-based STS, due to its low consumption and high tracking accuracy since it follows the actual rays of the sun.
* On the contrary, the azimuth-based STS is less efficient than the sensor-based STS, due to its colossal consumption and low tracing accuracy since it follows the theoretical location of the sun only.
* This study suggests examining the degradation rate of each STS, in addition to investigating the impact of different weather conditions on the STS. Moreover, the effect of designing a hybrid STS (that combines both STSs) should be carried out to understand the feasibility of using this combined STS.  
Conceptualization, N.G.H., M.A.A. and S.A.; methodology, N.G.H., I.S.O., I.K.A. and A.M.A.; software, I.S.O., I.K.A. and A.M.A.; validation, N.G.H., M.A.A. and S.A.; formal analysis, I.S.O., I.K.A. and A.M.A.; investigation,  
Data Availability StatementThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
The authors are grateful to the Deanship of Scientific Research (DSR) at Imam Abdulrahman Bin Faisal University, IAU, Kingdom of Saudi Arabia, for the continued support and guidance.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Field Measurements
#### 2.2.1 Aerosol Concentration Measurements  
The dust analyzer Atmas is designed for express and inspection measurements and continuous monitoring of the mass concentration of dust with various origins and chemical compositions while monitoring the maximum permissible concentrations in the atmospheric air.  
One of the main quality indices of atmospheric air is the amount of suspended particles in the air. Suspended particles (particulate matter, PM) refer to atmospheric pollutants, which are often analyzed in terms of their particle mass concentrations. Special attention should be devoted to the concentration of species in the air with particle diameters less than 2.5 (PM2.5) and 10 \(\upmu\)m (PM10) [28]. When measuring atmospheric air, the metrological characteristics of Atmas only determine the maximum 1-time concentration of aerosols, which is determined both for the total suspended matter and separately for PM10 and PM2.5. The concentration of impurities in the atmosphere is determined by a sample taken over a 20-30-min time interval.  
At the same time, the volume of the sample is not regulated. The high sensitivity of the piezo-balance method does not require large volumes of samples to be taken to measure the mass concentrations of dust at the level of 0.1 mg/m\({}^{3}\) and higher (specific air flow is 1 L/min). Dust sampling is carried out by a built-in blower through the impactor, which is installed on the inlet flange. The impactor has two interchangeable nozzles and a removable manifold. A dust sensor is installed in the center of the output flange of the measuring chamber, the signal from which is fed to the frequency measurement unit. The measuring nozzle PM10 has a calibrated hole, which filters out particles with an aerodynamic diameter of more than 10 \(\upmu\)m from the air flow, which are deposited on the impactor collector. In still air, particles with an aerodynamic diameter of more than 10 \(\upmu\)m are practically absent since their gravitational settling rate is high. Suspended atmospheric particles are present as a mixture, whose physicochemical characteristics vary as a function of their origin, the different meteorological factors influencing their transport, and particle interactions. Ultrafine particles with diameters less than 0.1 \(\upmu\)m, which are often called fine suspended particles, are also classified as PM2.5. PM2.5 particles account for 50-70% of PM10 over most of the territory of Europe [28].
## 2 Materials and Methods
### Experimental Design  
To better understand the effect of important parameters such as pH, the initial concentration, and the amount of adsorbent, a Box-Behnken design was applied to the removal of MB, here selected as a dye model. In order to obtain the most important independent and influential variables with the minimum number of runs, the response surface methodology (RSM) proceeds by a two-in-one technique, i.e., both mathematical and statistical techniques are used together to obtain a relationship between a response, here defined by the yield removal (Yield (%)) as the dependent variable, and a number of independent variables defined by \(\mathrm{X_{1}}\), \(\mathrm{X_{2}}\),..., \(\mathrm{X_{n}}\). Usually, RSM is combined with factorial design methods such as the Box-Behnken design (BBD), i.e., a second-order multivariate design based on a three-level incomplete factorial design with no axial points [29].  
In our case, fifteen experiments were performed with three replicates at the center point to estimate the pure error. Minitab(r) 17 software was used to apply the BBD model. Experimental data were fitted to a quadratic model using a second-order polynomial model as follows:  
\[\mathrm{Yield}(\%)=\beta_{0}+\sum_{i=1}^{n}\beta_{i}.\mathrm{X_{i}}+\sum_{i=1} ^{n}\beta_{ii}.\mathrm{X_{i}^{2}}+\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\beta_{ij}. \mathrm{X_{i}}.\mathrm{X_{j}}+\varepsilon \tag{3}\]  
where n, \(\beta_{0}\), \(\beta_{i}\), \(X_{i}\) and \(\mathrm{X_{j}}\), \(\beta_{ii}\), \(\beta_{ij}\) and \(\varepsilon\) represent the number of variables, a constant term, the coefficients of the linear parameters, the variables, the coefficients of the quadratic parameters, the coefficients of the interaction parameters, and the residual associated with the experiments, respectively. A total of 15 experiments were needed to estimate the removal of MB on 4 AZW.  
The predicted values for the yield removal of MB were then obtained by applying the quadratic model. The accuracy and fitness of the model were evaluated by an analysis of variance (ANOVA): Various descriptive statistic parameters were used such as the \(p\)-value, an F-test, degrees of freedom (df), the determination coefficient (R\({}^{2}\)), and the adjusted determination coefficient (R\({}^{2}\)adj) [30,31]. Moreover, 3D-surface plots also allowed to describe the effect on the desired response of interaction between two-by-two factors [32].
## 3 Results and Discussion
### Precision  
To study the intraday precision of the proposed method, six replicates of the same hydrolysate, obtained after using the optimal hydrolysis conditions with the same HPLC conditions, were injected and evaluated. The precision was expressed as the mean relative standard deviation (RSD%),  
Figure 3: Log C\({}_{\mathrm{f}}\) versus Log X for linearity presentation. C\({}_{\mathrm{f}}\) (mg/mL) is the concentration of fructose, and X (mv.s) is the peak area of HPLC-ELSD analysis.  
Figure 2: Effect of trifluoracetic acid (TFA) concentration (C\({}_{\mathrm{TFA}}\)), hydrolysis time (t) and hydrolysis temperature (T) on fructose concentration in hydrolysates.  
being 1.11% (Table 1), thus indicating a good precision of the proposed method. Considering the literature, RSD% values for precision lower than 5% are acceptable.
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 3 Experiments and Analysis of Results  
### Dataset Overview  
Experiments were conducted on Dataset A, a public birdsong dataset provided by the Beijing Academy of Artificial Intelligence, containing 14,311 audio recordings of 20 bird species ([[https://data.baai.ac.cn/details/Birdsdata](https://data.baai.ac.cn/details/Birdsdata)]([https://data.baai.ac.cn/details/Birdsdata](https://data.baai.ac.cn/details/Birdsdata)), accessed on 9 February 2025). Each recording was segmented into 400-millisecond audio clips and resampled to 16 kHz. The dataset was divided into training (50,088 samples), validation (14,382 samples) and test (7085 samples) sets, in a 7:2:1 ratio. The distribution of audio segments across the categories is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline
**Classifier Types** & **Classifier Names** & **Description** \\ \hline \multirow{6}{*}{Ensemble methods} & \multirow{2}{*}{Random forest (RF)} & Combines multiple decision trees to enhance classification performance through a voting mechanism. \\ \cline{2-3}  & & A gradient boosting framework that builds an ensemble of weak classifiers, balancing efficiency and accuracy. \\ \cline{2-3}  & \multirow{2}{*}{Categorical boosting (CatBoost)} & Similarly to XGBoost, optimizes categorical feature handling for boosting decision trees. \\ \cline{2-3}  & & A deep learning model using nonlinear transformations across multiple layers for classification. \\ \hline \multirow{2}{*}{Neural network classifiers} & \multirow{2}{*}{Multilayer perceptron (MLP)} & Finds the optimal hyperplane for separating data; can handle both linear and nonlinear classification tasks. \\ \cline{2-3}  & & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 6: Dataset A statistics of 2-second audio segments for each bird species.  
Experiments were also conducted on Dataset B, which consists of audio recordings from 16 bird species sourced from [[https://xeno-canto.org/](https://xeno-canto.org/)]([https://xeno-canto.org/](https://xeno-canto.org/)). The dataset was manually curated, with endpoint detection and adaptive filtering for noise reduction conducted by the authors of [26]. Each recording was divided into 400-millisecond audio segments and resampled to 16 kHz. The dataset was partitioned into training (21,087 samples), validation (5970 samples) and test (2880 samples) sets in a 7:2:1 ratio. This dataset covers a wide range of frequencies and temporal dynamics, providing a comprehensive benchmark for evaluating classification performance. The category distribution of the audio segments is shown in Figure 7.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
## 3 Experimental Results  
### CAP Jet Features  
Under the discharge conditions listed in Section 2.2, the discharge power obtained from the Lissajous figure method was \(P_{\text{in}}\) = 0.8 W. As shown in Figure 3, the CAP jet issued from the generator contained various kinds of reactive species. Besides the abundant helium species, several kinds of RONS, i.e., nitrogen molecules, hydroxyl (OH), and oxygen atoms (O) [36], also had relatively high intensities, indicating that the CAP device was a suitable source for plasma medicine and was promising for investigating the plasma dosage. In addition, with an initial room temperature of 22 \({}^{\circ}\)C, the gas temperature of the CAP jet did not exceed 24 \({}^{\circ}\)C after the discharge was on for 10 min, proving that the CAP jet had good thermal stability and safety during the whole CAP treatment process.  
Here, there is one issue we hope to emphasize. As illustrated in Figure 2a, during the CAP treatment, the plasma jet directly interacted with the cell culture in the 96-well plate which worked as a liquid substrate. Although previous studies have shown that different substrates could influence the features of the impinged plasma jets, for example, the number densities of some important RONS such as hydroxyl (OH) and atomic oxygen (O) increased obviously due to the existence of water [37] compared with the plasma free jet, in our opinion, determination of the cell viabilities would, to some extent, not be affected since the cell culture used in all the experiments was completely the same in this study. Our goal in this paper is to determine the plasma dosage by avoiding the considerations of the extremely complicated interactions between the CAPs and the treated biological targets (Figure 1).
## 6 Discussion  
The reported application-level latency results illustrated in Section 5.3 show the effectiveness of interoperability in V2X communication between different vehicle OEMs and, even more, it shows that our experimental OBU is functionally correct and it can be used to evaluate standards-compliant heterogeneous V2X communication architectures being interoperable with commercial OBUs. Furthermore, the ACCA service exposes widely used client-server (UDP/HTTP) and client-broker (MQTT) communication architectures, with request-response and publish-subscribe models, respectively, enabling seamless interoperability between the ACCA architecture and other similar systems. In turn, it extends the OBU's capability of interworking with C-ITS systems outside ACCA, in which this has already confirmed by the reported latency results shown in Section 5.3.2. Overall,  
Figure 9: Application-level latency from the Traffic Management System (TMS) to the OBUs connected to the same edge cloud. OBU 1 receives DENM messages over UDP and OBU 2 receives over MQTT.  
the obtained latency results highlight the tangible performance benefits when leveraging the edge-based service provisioning for V2X services, like the ACCA.  
The data traffic in the ACCA service architecture is composed of small-sized messages transmission, and this does not impose strict bandwidth requirements on the communication links. Note that a single DENM message was in the order of 100 to 200 bytes, as there were no security-related components included. The frequency of DENM message generation in the tests was low when compared to CAM frequency. The message reception reliability measured at 100% without any packet drops in all test scenarios. Additionally, both of the radio links and wire-line between the edge cloud servers and the centralised cloud computing platform of the ACCA service deployment have not been congested during the tests from any other competing traffic in background.