esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Apart from other technical possibilities, the HS-SPME is currently the most widely used referenced approach for BVOCs analysis, since it allows for the microextraction and concentration of analytes in a coating fiber in one single step prior to GC-MS analysis, and it is a solvent-free approach in line with the GAC principles. More recently, bar adsorptive microextraction (BA\(\mu\)E), a novel passive sorption-based technique, has been proposed for the trace analysis of target compounds with a wide range of polarity through the direct sampling mode in the liquid phase (e.g., wastewater, urine, etc.). Due to the very high selectivity and sensitivity of the different sorbent phases available, including some types of polymers (Ps) and activated carbons (ACs), this modern analytical technique has shown an effective performance for monitoring a large number of priority and emerging organic compounds, including herbicides, fungicides, pharmaceutical and personal care products and many other classes [4; 5]. Furthermore, BA\(\mu\)E technology presents very interesting features, as it is a user- and eco-friendly approach as well as cost-effective, that make it unique and a real alternative compared to other common dedicated microextraction techniques. Meanwhile, BA\(\mu\)E was never proposed to be applied in the HS sampling mode for the analysis of volatile or semi-volatile organic compounds in order to demonstrate the great enrichment capacity and scope of this analytical tool. In the present contribution, HS-BA\(\mu\)E is proposed for the first time as an alternative passive microextraction technique for BVOCs analysis and is compared with the well-established HS-SPME technique. To evaluate the performance of both methodologies, the five major monoterpencids (\(\alpha\)-pinene, \(\beta\)-pinene, myrcene, limonene and 1,8-cineole; Figure 1) emitted by the leaves of the _Eucalyptus globulus_ Labill. and _Pinus pinaster_ Aiton tree species will be evaluated in combination with GC-MS analysis. The development, optimization, validation, and application to real matrices are also addressed, including the advantages and limitations regarding the analytical performance of both methodologies. The importance of monitoring BVOCs in natural forest environments to anticipate if the amounts founded may have an influence whenever forest fires occur under extreme conditions is also discussed.
# Earth Observations for Sustainable Development Goals
###### Abstract  
The Earth Observation for computing SDG indicators is the capability to define the spatial distribution below national scale. This is particularly important when studying air quality, as some pollutant distributions are very dependenton human activities, more present in industrial areas and cities. The SDG 11.6.2 indicator \"Annual mean levels of fine particulate matter (e.g., PM\({}_{2.5}\) and PM\({}_{10}\)) in cities (population weighted)\" has huge implications on human health and climate. Current official reporting systems, based on in-situ monitoring networks (i.e., United Nations, Eurostat), are not able to represent the actual diversity of urban conditions and are not covering smaller cities. One paper proposes to supplement official reporting with Earth Observation, to enable calculating more representative and holistic values for the indicator, based on population density, as opposed to differing city definitions [2]. Another important aspect is that Earth Observation studies, similar to this one, not only support the monitoring of the SDG indicator, but also attempt to tackle the actual problem related to the SDG targets: in this particular case, to reduce city concentrations of particulate matter and, subsequently, the exposure of citizens to harmful substances.  
While the previous cited paper has the focus in individual cities, remote sensing allows elaborating air quality analysis at the scale of a country, even if the country is as big as China [3]. Indeed, heavy air pollution caused by particulate matter (PM\({}_{2.5}\)) and nitrogen dioxide (NO\({}_{2}\)) can be analyzed by remote sensing. This data, when combined with census data from the Organization for Economic Co-operation and Development (OECD), can be used to estimate the SDG indicator 3.9.1: \"Mortality rate attributed to household and ambient air pollution\". Moreover, the beneficial effects of the lockdowns imposed by the authorities on the population to reduce the propagation of the COVID-19 are demonstrated as concentrations of PM\({}_{2.5}\) decreased by an average of 17% and of NO\({}_{2}\) by an average of 57%, reducing the number of premature deaths in almost 100,000 people.  
To be able to calculate these and other SDGs indicators, such as SDG 11.3.1 and SDG 11.2.1 at intra-urban scale, better data on population distribution and density, and population internal migrations at city scale, are needed. Previous data about population numbers and population density were improved by adding building height information extracted from LIDAR and using height for site-specific weight values for population density correction [4]. By doing so, results presented an 8.6% decrease in populations of population density in the region of Bari in southern Italy.
## 3 Materials and Methods
### Sample Extraction and Cleanup Procedure  
About 2.0 g of comminuted fruit samples were weighed and added into a 50 mL polypropylene centrifuge tube. Then 10.0 uL of 100 ug/L internal standards (IS) solution was added to the tube. Subsequently, 5.0 mL of acetonitrile as extraction solvent was employed, and the sample solution was vortexed for 10 min after adding 1.0 g of sodium chloride and 1.0 g of magnesium sulfate. The sample was then centrifuged at 8000 r/min for 2 min. Afterwards the supernatant was transferred to another polypropylene centrifuge tube. The residues were repeat extracted with 5.0 mL of acetonitrile again. Subsequently, the two sample extracts were merged, 10 mL pure water was sneaked into the extract, the sample solution was adjusted to pH 7.0, and then 5.0 mg DETA-MPs-GO adsorbent was added for purification. The above mixture was further vortexed for 10 min. The magnetic adsorbent could be quickly collected under a magnet, and the supernatant was discarded. Subsequently, 1.0 mL of elution solvent was added to the elute target analytes by shaking for 5 min. The elution solution was similarly isolated in a magnetic field. Furthermore, a hydrochloric acid solution was used to adjust the elution solution to neutral, and then it was transferred to a 2.0 mL vial and evaporated to dryness under a N\({}_{2}\) stream at 40 \({}^{\circ}\)C. Finally, the residue was re-dissolved with 0.5 mL acetonitrile and measured on UPLC-HRMS.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Centralized and On-Premises VBDA (2010-2012)  
The initial phase of VBDA was defined by centralized, on-premises architectures, where video acquisition, storage, and processing were handled entirely on local servers. These systems employed RAID or SAN/NAS storage and used relational databases such as MySQL v5.5 and PostgreSQL v9.0, with occasional support from early NoSQL platforms like MongoDB v2.4 for metadata handling [17; 19]. With no support for distributed processing or elastic scalability, they were ill-suited for high-throughput or real-time analytics. Video understanding was based on classical machine learning techniques using handcrafted features. Among these, Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) were the most widely adopted for object detection and recognition due to their robustness to scale and orientation changes [19]. Additionally, LBP and Haar-like features were frequently used in face detection pipelines and embedded systems, given their computational efficiency [17; 19]. Although tools like OpenCV 2.0 (2010) introduced basic GPU acceleration through CUDA, performance was still constrained by hardware limitations globally, including in developed countries such as the United States, Germany, and Japan. Despite early adoption of GPU acceleration via CUDA, these regions faced practical deployment challenges due to memory bandwidth, power constraints, and the high cost of large-scale real-time video processing. A major shift occurred in 2012 with the introduction of AlexNet, which demonstrated the power of deep learning for visual  
Figure 1: Evolution of the VBDA platform.  
recognition and revealed the shortcomings of rule-based methods. This breakthrough established the foundation for transitioning to scalable, AI-enhanced cloud architectures, which characterized the subsequent batch-processing era [22; 23].
## 2 Materials and Methods
### Optimum Value Computing
#### 2.2.2 Optimal Control Process  
The calculation process is depicted in Figure 7. The following sequence of steps was performed:  
* Initialization of the ROV in the destination heading in degrees and the setting of a semicircular region of radius \(R\) centered at the ROV point.
* Execution of obstacle avoidance when an obstacle was detected.
* Computation of the lowest cost function value and its heading degree.
* Use of the fuzzy logic controller to make the ROV change its current heading degree to the cost function heading (degree) to avoid the obstacle.
* Updating of the ROV's position.  
Figure 6: Optimum value computing of the obstacle avoidance approach: the destination \(C_{Dos}\), the obstacles \(C_{Obs}\), the point \(P\) on the radius of the scanning sonar, the distance \(D_{i}\) between point \(P\) and the destination, and the distance \(d_{(i,j)}\) between point \(P\) and the obstacle.  
In Figure 8a, the destination and the obstacle are aligned, whereas in Figure 8b, they are not. In total, seven points (\(n=7\)) exist on the radius of the scanning sonar, and \(\theta\) is 120\({}^{\circ}\). For each of the points, the value of the cost function was calculated. In Figure 8a, point \"1\" and point \"7\" have the lowest cost values; therefore, the ROV would move forward in the direction of either point \"1\" or point \"7\". In Figure 8b, point \"1\" has the lowest cost value; the ROV should turn toward point \"1\" when the destination and the obstacle are not aligned. In the following experiments, the destination is assumed to be a virtual point ahead of the ROV (5 m, maximum range of the scanning sonar) in the target heading direction.
# Research on Real-Time Model of Turboshaft Engine with Surge Process
## 3 Real-Time Surge Model of Turboshaft Engine  
### Classic MG Model  
In 1985, based on their theoretical research, [PERSON] and [PERSON] collaborated to complete a two-dimensional unsteady incompressible nonlinear model, referred to as the MG model, that described the dynamic process and characteristics of the rotating stall and surge of the compression system as shown in Figure 2. This model is described by the following three mutually coupled differential equations:  
\[\frac{d\Psi}{d\xi}=\frac{1}{4B^{2}l_{c}}(\Phi-\Phi_{T}(\Psi)) \tag{8}\]  
\[\frac{d\Phi}{d\xi}=\frac{1}{l_{c}}\bigg{(}\Psi c(\Phi)-\Psi-\frac{3H}{4}\bigg{(} \frac{\Phi}{W}-1\bigg{)}J\bigg{)} \tag{9}\]  
\[\frac{dJ}{d\xi}=J\bigg{[}1-\bigg{(}\frac{\Phi}{W}-1\bigg{)}^{2}-\frac{J}{4} \bigg{]}\delta \tag{10}\]  
where \(\Phi\) is the flow coefficient in the compressor; \(\Psi\) is the pressure-rise coefficient; \(J\) is the square of the amplitude of angular disturbance of flow coefficient; \(\xi\) is the dimensionless time; \(H\) and \(W\) are respectively the semi-height and semi-width of cubic axisymmetric characteristic in Figure 3; \(l_{c}\) specifies the length of compressor and duct; \(\delta\) is a constant coefficient that reflects the flow hysteresis in the compression system. \(B\) is the well-known Greitzer-\(B\) parameter defined as:  
\[B=\frac{U}{2a_{S}}\sqrt{\frac{V_{p}}{A_{C}l_{C}}} \tag{11}\]  
where \(U\) is the tangential speed of the rotor. \(a_{S}\) is the local sound speed; \(V_{p}\) is the plenum volume and \(A_{C}\) is the cross-section of the compressor.  
Figure 3: The characteristic curves of compressor and throttle.  
Figure 2: Schematic diagram of the compression system.  
\(\Psi_{C}(\Phi)\) is the steady-state characteristic curve of the compressor expressed by the following cubic curve function:  
\[\Psi_{C}(\Phi)=\Psi_{C0}+H\Bigg{[}1+\frac{3}{2}\bigg{(}\frac{\Phi}{W}-1\bigg{)}- \frac{1}{2}\bigg{(}\frac{\Phi}{W}-1\bigg{)}^{3}\Bigg{]} \tag{12}\]  
\(\Phi_{T}(\Psi)\) is the throttle valve characteristic which can be expressed by the following equation:  
\[\Phi_{T}(\Psi)=\gamma_{T}\sqrt{\Psi} \tag{13}\]  
Figure 3 shows the compressor characteristic curve and the throttle valve characteristic line at different throttle valve openings, where points A and B are in the stable state, point C is the surge operating point of the compressor, and points D and E are in an unstable state. When the compressor operating point is located in the positive slope region of its characteristic line, the compressor is in an unstable working state.  
Let \(J=0\) in Equation (10), which represents the disappearance of the airflow disturbance wave at the inlet of the compressor, and the MG model of the second-order pure surge is obtained:  
\[\frac{d\Phi}{d\xi}=\frac{1}{l_{c}}(\Psi c(\Phi)-\Psi) \tag{14}\]  
\[\frac{d\Psi}{d\xi}=\frac{1}{4B^{2}l_{c}}(\Phi-\Phi_{T}(\Psi)) \tag{15}\]
# Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4
## 4 Experimental Results and Analysis
### Influence Factors and Result Analysis  
#### 4.2.1 Detection Results under Different Improvement Methods  
The basic YOLOv4 object detection algorithm was improved for lightweight modelling by using the lightweight convolutional neural network MobileNetV3 as the feature extraction network, increasing the scales of feature extraction, and optimizing the SPP and PANet networks by using depthwise separable convolution and attention mechanisms. To demonstrate the effectiveness of these improvements, ablation experiments were performed without gamma correction preprocessing; the results are shown in Table 2 and Figure 9, with \(\surd\) representing the method used to improve the model.  
As can be seen in Table 2, when only the CSPDarkNet53 backbone feature extraction network is replaced with MobileNetV3, the mAP is only able to reach 78.49%. When the three attention mechanisms (SENet, CBAM, and ECA) were used to improve the SPP and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**MobileNetV3**} & \multirow{2}{*}{**Multi-Scales**} & \multicolumn{4}{c}{**SPP and PANet**} & \multirow{2}{*}{**mAP (\%)**} & \multirow{2}{*}{**FPS (Pictures/s)**} \\ \cline{5-6}  & & & & **SENet** & & & **CBA** & \\ \hline
1 & \(\surd\) & & & & & 78.49 & 40.73 \\
2 & \(\surd\) & & & & 80.30 & 39.25 \\
3 & \(\surd\) & & & \(\surd\) & & 79.72 & 40.02 \\
4 & \(\surd\) & & & & \(\surd\) & 80.98 & 39.69 \\
5 & \(\surd\) & \(\surd\) & & & \(\surd\) & 81.44 & 38.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection results using different improvement methods.  
Figure 9: The AP of each class using different improvement methods.  
PANet networks, the mAP could be improved by more than 1%, with the introduction of ECA having the highest mAP of 80.98%. Moreover, when the features of three scales were increased to four scales, the mAP could reach 81.44% after feature fusion and enhancement with ECA-SPP and ECA-PANet. At the same time, the optimal improvement method (Group 5) significantly improved the AP of each category compared to Group 1, while the mAP of the model increased by 2.95% and the detection speed (FPS) was only reduced by 2.67, meaning that the detection speed for a single picture was only reduced by 1.72 milliseconds.
## 3 Physical Description of the On-Chip, Optical Wireless Link
### Physical Optical Interconnects  
The source of the optical power at the transmitting link end can be an external off-chip laser coupled to the optical bus [11; 32], emitting a nominal power \(\mathrm{P_{TX}}\) on the bus, steady in time according to the expected working temperature of commercial GPU/CPU architectures. Then, a dedicated micro ring resonator (MRR) can be used to provide both electrical-to-optical conversion [33] and On-Off Keying (OOK) Non-Return-to-Zero (NRZ) amplitude modulation [17] with a bandwidth \(\mathrm{B_{opt}}\) over the optical carrier. The modulated signal is finally routed towards the optical antenna (see Section 3.2) and wirelessly spread within the chip. As shown in the block scheme in Figure 2, an integrated optical amplifier may be present before the transmitting antenna, in order to cope with the large path loss coming from the optical wireless channel and to push the received signal strength to the level required to drive the digital logic in the lower, electrical layer [6]. Beyond the wireless channel, the optical incoming field is first gathered by the optical receiving antenna, then directed into the optical bus and finally filtered off by another MRR. A photodiode (PD) with responsivity \(\mathrm{\eta_{PD}}\) reconstitutes the OOK signal in the electrical domain, ready to be demodulated.  
As long as the analysis is limited to a single link, symbols detection errors are due to the propagation impairments (briefly discussed in Section 3.3) and to the noise effects experienced by the optical communication system. Since the thermal noise is often acknowledged as the dominant noise contribution [34], other noise sources like the PD shot noise and the relative intensity noise of the laser have been neglected herein. Conversely, in a multi-link scenario mutual interference among the different, simultaneous communications may arise, to an extent greatly dependent on antennas radiation properties, spacing and position. These issues will be discussed more in detail in the following Sections 4 and 5. Although an effective implementation of O(Wi)NoC could benefit from WDM [11; 35; 36], this work involves no multiplexing scheme (e.g., simulations are run with only one WDM channel), in order to focus the investigations on the influence of antennas and core distances, excluding any issues above the physical layer.  
Figure 2: Block scheme of the on-chip, optical wireless link.
# Design of a Laser Driver and Its Application in Gas Sensing
## 3 Application of the Laser Driver  
### Experimental Setup  
The layout of our apparatus, as shown schematically in Figure 7, is similar to that used in normal TDLAS-WMS-based trace gas detection, except for the adding of the aforementioned laser driver and a home-made circuit for logarithmic transformation and differential detection. As shown in this figure, the laser driver consists of a core board, a motherboard, a driving board and a touch screen. The motherboard is responsible for outputting the driving signal generated by the core board to the laser inserted on the driving board. The key to the operation of logarithmic transformation circuit is LOG114, which is a dedicated logarithmic amplifier for measuring low-level and wide dynamic range currents in communications, lasers, medical, and industrial systems. The referenced frequency of the lock-in amplifier is set to twice the modulating frequency, and the obtained second harmonic is digitized by a data acquisition (DAQ) card (USB-3210). The signal line draw from the laser driving board to the DAQ card is used to provide the time reference for the periodic scan. The other signal line connected to the lock-in amplifier is used to provide a referenced signal for the coherent detection. A PC programmed with LabVIEW software performs the averaging of the collected digital signals for 10 scan periods. For preparing CH\({}_{4}\)-N\({}_{2}\) mixture with desired mole fraction, the mixing ratio of two cylinders filled with high-purity N\({}_{2}\) and CH\({}_{4}\) is regulated by a bank of mass flow controllers (Teledyne Hastings, HFC-302 with THPS-400 controller), with the total pressure measured by a vacuum pressure gauge and maintained at approximately 1.01 \(\times\) 10\({}^{5}\) Pa.  
Figure 6: Screenshot of user-interface of the laser driver.
## 6 Discussion and Future Work  
Although the destination of V2C communication discussed in this paper is the cloud server, the proposed method is not limited to connections between vehicles and the cloud. We consider it possible for vehicles communicating with edge servers to apply the proposed  
Figure 6: Summary of performance results.  
method for reliability improvement by setting the connecting destination of vehicles to edge servers.  
The proposed method employs path selection and redundant path adjustment based on the end-to-end performance of packet delivery between a vehicle and the cloud server, instead of focusing solely on the performance of wireless communication for path management. This is because a V2C path does not only involve wireless links. Choosing the most appropriate path(s) is one of the main focuses of future work. Further, it is challenging to enable the integration of the proposed method with conventional redundancy management methods that focus on a single path, a wireless link, or resource deployment.
## 3 Flight Controller Design
### Proposed Analytic Form of the Proposed Position Integral Backstepping Controller
\begin{bmatrix}\mathbf{e}_{x}\\ z_{1}\end{bmatrix} \right\|\leq\varepsilon_{xx0}\right\} \tag{134}\]  
\(\square\)  
Equations (108) and (132) reveal that the asymptotic stability is guaranteed when the disturbances \(d_{x}\) and \(d_{z}\) vanish. There are several ways to improve the tracking performance, integral gain \(k_{ix}\), or considering larger \(F_{min}\). In practical scenarios, the measured states are always contaminated with the measurement noise. The advantage of the proposed AIBC is that it avoids differentiating the virtual controls \(\varphi_{1}\) and \(\varphi_{2}\) directly. An analytic form of \(\varphi_{2}\) is provided by (95), which consists of measurable states without any numerical differentiation. A smoother control signal is thus obtained. Meanwhile, the proposed AIBC is less prone to inducing control signal saturation since it does not amplify noise effects induced by numerical differentiation. In other words, the proposed AIBC allows higher control gain to suppress the error boundary \(\varepsilon_{xz}\). According to the actuator configuration, there exist upper bounds for the control inputs, which implies that the system states are bounded. Therefore, one can deduce that there exists a positive value \(M^{+}\) to satisfy (130) so that the state-disturbance coupled terms \(\Delta\phi_{2,0}\) in (126) are bounded. In the design of the IBC (54) and the proposed AIBC (94), the signum function is introduced to reject the matched perturbations. In fact, (54) and (94) can be further interpreted as the sliding mode controllers. The existence proofs of the sliding modes for the IBC and the AIBC are derived in Appendix B. **Remark 2**. : _The derivation procedures of the proposed AIBC for the extension to the 6-DoF drone are described as follows: (i) First of all, the 6-DoF quadrotor model will be divided into an underactuated subsystem and a fully actuated one, as shown in (19) and (20), respectively. Generally, the underactuated subsystem consists of the translation dynamics \((X,Y)\) and the roll and pitch dynamics \((\phi,\theta)\). As for the fully actuated subsystem, it contains the altitude dynamics \(Z\) and yaw dynamics \(\psi\). (ii) Next, apply the integral backstepping control algorithm to derive the position control law, similar to Sections 3.2 and 3.3. (iii) Finally, follow the procedures from Section 3.4 to introduce the system dynamics to obtain the analytic form of the position integral backstepping controller derived in the previous step._