esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 3 Lab-in-a-Box (LiB) Architecture
### Modular System Architecture
#### 3.1.1 Power Supply  
In any application, choosing an appropriate power source is essential for energy efficiency and the overall feasibility of the system. It is necessary to account for the overall power consumption and the required backup time. If the IoT system is to be placed on the human body or mounted in a remote area, it will require a battery or an energy harvester that can provide the portability. For wearable devices, batteries should not contact skin directly as the heat dissipation could cause burn [45]. Using energy harvester devices as an alternative to batteries for powering wearable devices is an emerging field of research, but such devices are still in their infancy and cannot deliver much energy [46; 47]. The milliamp hours (mAh) rating is indicative of how long a battery can power a system given the current consumption rate. Although it is not enough to calculate the runtime of a system, it provides an approximation for developers to start designing the power system. The source voltage should be close to the system voltage, as there would be a waste at the conversion element if the battery voltage is higher. Linear regulators are good for stepping down voltages to system voltage and provide smooth and ripple free output at the desired voltage level, but they dissipate excess energy as heat. The drop out voltage of the linear regulator is also another parameter designers must consider while selecting components as it can be as high as two volts.  
If the system voltage is too close to the battery or source voltage and the drop out voltage of the regulator is high, the regulator will not be able to provide the desired output voltage level. There are low dropout (LDO) voltage regulators that have very small dropout voltages (140 mV) and can work seamlessly with source voltages that are close to the system voltage. Linear regulators can only be used in cases where the battery or the source voltage is higher than the system voltage, and Direct Current (DC) DC-DC converters are used where the battery or the source voltage is lower than the system voltage. If a system requires linear regulators and the regulator is expected to drop significant voltage at a good amount of current, a heatsink should be mounted to dissipate the generated heat.
###### Abstract  
The fully polarimetric aperture synthesis radiometer (FPASR) is capable of acquiring the fully polarimetric brightness temperature (BT), which has become increasingly significant in remote sensing. Antenna pattern errors can introduce significant errors to the reconstructed image of the FPASR. Analyzing and correcting the antenna pattern errors is crucial for obtaining high-quality BT images. In this paper, the antenna pattern errors are analyzed and classified into additive and multiplicative errors. A two-step correction method is proposed to reduce the influence of antenna pattern errors on the reconstructed BT. An end-to-end simulator for FPASR has been developed to assess both the antenna pattern errors and the effectiveness of the correction method. The simulation results show that the two-step correction method can reduce the brightness temperature error caused by the antenna pattern errors by over 70%. The successful image of the flight experiment validates the correction method as well.  
Keywords:fully polarimetric aperture synthesis radiometer; antenna pattern errors; visibility; G-matrix Article
## 4 Materials and Methods
### SDS/PAGE, Circular Dichroism and LC/ESI Mass Spectrometry
#### 4.5.3 LC/ESI Mass Spectrometry  
Liquid Chromatography Electrospray Ionization Mass Spectrometry (LC/ESI-MS) analyses were performed on a 6210 LC-TOF spectrometer coupled to a HPLC system (Agilent Technologies).  
All solvents used were HPLC grade (Chromasolv, Sigma-Aldrich), trifluoroacetic acid (TFA) was from Acros Organics. Solvent A was 0.03% TFA in water, solvent B was 95% acetonitrile, 5% water, 0.03% TFA. Before analysis, protein samples were diluted in acidic denaturing conditions to a final concentration of 10 \(\upmu\)M with solvent A. Protein samples were firstly desalted on a reverse phase-CS cartridge (Zorbax 300 SB-C8, 5 mm, 300 A 5\({}^{\circ}\)0.3 mm, Agilent Technologies) and then eluted with 95% solvent B.  
MS spectra were recorded in the positive ion mode in the 300-3200 m/z range and the data processed with MassHunter workstation software (v. B.02.00, Agilent Technologies).
## 4 Results and Discussions
### Effect of Falling Height under Different Submergence Conditions
#### 4.1.2 Cases of Partial Subaerial Landslides  
The falling heights (h) are set as \(-0.05\), \(-0.1\), \(-0.15\), \(-0.2\), \(-0.25\) and \(-0.3\), respectively, and the submergence conditions of the landslides are partial subaerial landslides. When the falling heights (h) are \(-0.05\), \(-0.15\) and \(-0.25\) m, the temporal evolution of the water elevation in G\({}_{1}\) and G\({}_{2}\) point are shown in Figure 7. Compared with Figure 6, the overall law of the tsunami wave train caused by subaerial landslide does not vary with the falling height, while that caused by the partial subaerial landslide will change with the decrease in the falling height. Specifically, from h = \(-0.05\) m to h = \(-0.25\) m, the initial submerged range of the slider becomes larger, and the gap between the first and second peak of the wave train decreases until the second peak exceeds the first peak and becomes the new highest peak in the wave train. However, in fact, in terms of value, only the first crest of the wave train decreases while the second crest does not change significantly, which leads to the phenomenon that the gap between the two is reduced, or even that the latter is reversed. This indicates that when the slider is initially submerged, the squeezing and pushing effect of the initial slider motion on the water decreases and decreases more with the increase in the initial submerged area. It can be seen from Figure 7 that the effect of the slider movement on the first trough and the second peak are weakened during the propagation process with the increases in the initial submersion range of the slider. Specifically, the amplitude of the second peak and the first trough propagating to G\({}_{2}\) decreases as the initial submersion range of the slider increases.  
Figure 6: The temporal evolution of the water elevation in G\({}_{1}\) and G\({}_{2}\) with the subaerial landslide: (a) h = 0.25 m; (b) h = 0.15 m; and (c) h = 0.05 m.  
Figure 7: _Cont._
## 1 Introduction  
According to the World Economic Forum, the estimated net benefits of the oil and gas industry, which is due to digital transformation, will be USD 945 billion over one decade (cumulative value 2016-2025). In addition, digital transformation is not realistic without artificial intelligence (AI) and data science, especially in industries such as oil and gas, finance, and the internet [1]. Conversely, due to the current oil and gas crash, digitalization is a good opportunity that will add value to all stakeholders. These opportunities show that there is a need to continue in the digitalization of the oil and gas sector to achieve the expected or more of the stated value by the year 2025. Digitalization of production-based industries is driven by enabling technologies which include machine learning (ML) and big data analytics [2, 3]. This drives the oil and gas sector to be among the technological advancements and convergence of industry 4.0 in Malaysia [4; 5]. Thus, the application of AI and machine learning is necessary for any digitalization and automation to succeed in various sectors such as healthcare, finance, industry automation, transportation, and cybersecurity [6; 7].  
Machine learning (ML) is one of the crucial and key enabling tools that oil and gas industries are now focusing on to implement digital transformation. The application of AI and ML allows an industry to automate and enhance production and human capacity. For example, ML is used to understand and predict behaviour using an ice database for experiment with enhanced prediction for oil production and recovery [8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. The Metocean study, conversely, is used by oil and gas, particularly offshore industries, to estimate the environmental conditions for better and successful operations, which include prediction of gas-liquid pattern flow, groundwater anomaly detection, and pipe incident detection [18; 19; 20; 21]. However, the existing Metocean system generates data manually using sensors such as wave buoy, anemometer and acoustic doppler current profiler (ADCP). Additionally, improving the existing techniques for Metocean datasets has been provided [22]. The input of these data, which appear in ASCII format to the Metocean system is also manual and silos. There is no fundamental method, model, or algorithm that allows the system to reason and automate intelligently. This slows down provisioning, while the monitoring element of Metocean data path is partial. Therefore, a study and model that can integrate the Metocean data as well as provide better decision making and reduce manual tasks is needed.  
The main aim of this paper is to provide an in-depth review of the method for developing and integrating oil and gas data, using a particular case study on the Metocean data system using a machine learning algorithm.
# State-of-the-Art Internet of Things in Protected Agriculture
## 1 Introduction
The IoT fundamental structure in protected agriculture is introduced in detail based on agricultural sensors, wireless sensor networks, cloud computing, edge clouding and machine learning technologies. The application of IoT in protected agriculture and how it makes contributions is displayed. Furthermore, the open issues, challenges, future opportunities and development trends are discussed. In this article, a comprehensive and profound review of IoT in protected agriculture has been executed based on a survey of real-world deployments and published papers over the past 10 years. Firstly, a keyword-based search for conference papers and articles was performed mainly from the scientific databases IEEE Xplore, Science Direct and ACM Digital as well as from Google Scholar. For search keywords, we used the following query: \"Internet of Things\" and either \"greenhouse\" or \"livestock\" or \"aquaculture\" or \"agriculture\". Then, we filtered out papers referring to IoT but not applied to the protected agricultural domain. Finally, we checked these papers selected from the previous step and picked out the final papers according to the problem they addressed, the solution proposed, impact achieved (if measurable), tools, systems and algorithms used. The rest of the paper is organized as follows. Section 2 presents the structure of IoT in protected agriculture. The key technologies of IoT in protected agriculture are introduced in Section 3. The three important applications in protected agriculture are summed up in Section 4. We further discussed the challenges to be faced and the problems that need to be solved for IoT in protected agriculture as well as a few future development directions in Section 5. Finally, Section 6 outlines the conclusions of this paper.
## 3 Results and Discussion  
### Synthesis and Characterization of ILs and MNPs  
The magnetic nanoparticles coated with crosslinked zwitterionic poly(ionic liquid)s were prepared by following the general scheme presented in Figure 1. The schematic structures of the MNPs are presented in Figure 2.  
Figure 1: General scheme of synthesis of magnetic nanoparticles coated with crosslinked zwitterionic poly(ionic liquid)s.  
Previous studies performed in our group [22] showed promising results of the application of zwitterionic, imidazolium-based crosslinked poly(ionic liquid)s in the extraction of OGNs. Thus, we decided to test three MNPs covered with different zwitterionic coatings. The first one (MNP-Ac) is analogical to this, which gave the best results in our previous paper [22]. In the MNP-Sul, a carboxyl group was replaced by a sulfonic group, often present in the structures in the ligands of zwitterionic stationary phases. The last material, MNP-Mal, has a coating with the malonic ligand. An additional carboxyl group was intended to increase the electrostatic repulsion in the elution stage.
# Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology
## 4 Performance Evaluation
### Influence of Optimally Charging Scheduling  
According to Algorithm 2 the vehicle density is managed and treated at BSS according to its SOC to minimize waiting time. The depleted batteries are sorted according to the charging discipline (discussed in Section 3) and run the operation loop for recharging the battery until all batteries are not fully charging. As shown in Figure 7 the waiting time at BSS of the vehicles is reduced more than 90% under the battery switching scenario. Intuitively, the more arriving density of vehicles seems to be less affected as compared to the benchmark result. This is because of managing different types of batteries variety (battery heterogeneity) at BSS concern in this work.  
As observed, average waiting time to switch (AWTS ) is slightly increased when the bulk of \(R_{V}\) is sending a reservation request to GC for battery switching. This is because of specific battery type under BS scenario. In traditional trends under plug-in charging mode arriving the bulk of EV seems to be less affected because the \(R_{V}\) is charged up regardless. While under heterogeneous BS technology the vehicle needs a particular type of battery and sometimes it is not readily available or runs out from BSS due to high demand.  
As shown in Figure 8 the performance of the proposed work is achieved good results because of the centralized mode. Since \(R_{V}\) communicate frequently with GC and clear all queries about switching reservation and reported GC for the selection of BSS. While GC also takes an update from mobile EV about mobility uncertainty and calculates AWTS. The result of Figures 7 and 8 implies that the desired performance is achieved under heterogeneous battery switching. This is mainly the benefit from reservation making and scheduling techniques for depleted battery recharging.  
Figure 7: Total Time.
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 3 Materials and Methods
### Detection Scheme  
Detectors may face challenges in accurately identifying weapons in complex scenarios, primarily stemming from the detector's diminished confidence in detecting specific weapons, despite its capability to identify a diverse range of weapons in various positions and sizes. Consequently, certain weapons may be erroneously filtered out. Another challenge arises from the prevalence of false positives in detecting weapons amidst everyday objects like mobile phones, keyboards, calculators, cups, and portable hard drives. Thus, a two-step detection scheme that integrates a Secondary Classifier (Section 3.4.1) module and a Temporal Window (Section 3.4.2) module into the baseline detector is implemented, as depicted in Figure 2.  
The procedure unfolds as follows. First, a frame is read and passed to the detector, which produces predictions. For each prediction, the confidence of the prediction is scrutinised and compared to a threshold. If the prediction falls below this threshold, the image is cropped around the area of the prediction and forwarded to the secondary classifier. If not, it proceeds to a temporal window. The secondary classifier generates a new confidence score for the cropped image, which is compared to another threshold distinct from the first one. If the confidence does not surpass this threshold, the prediction is discarded. Conversely, if it does, the initial confidence is updated with the new value, and the prediction undergoes a temporal window that filters the predictions. Details about both the secondary classifier and the temporal window are explained in the subsequent sections.  
It is important to note that this scheme is applicable to all types of detectors; as both modules are completely independent, one can be used without the other and vice versa, and the output of the previous component can be used as input, but this study concentrates on its implementation with YOLO detectors. Section 4.3.1 and will present objective experimentation to assess the effectiveness of these modules in the context of YOLO detectors and identify the optimal combination.
## 2 Structure and Simulation  
The 3D finite-difference time-domain (FDTD) method was used to study the phenomenon of this optical system. During the simulation with FDTD, the mesh accuracy was set to \(\lambda\)/34 to ensure the convergence of the simulated results. The schematic of the GMR system with a double-bar dielectric grating structure is shown in Figure 1a. In terms of boundary conditions, X and Y were set to periodic, and Z was the perfectly matched layer (PML). The mesh accuracy in GWL\({}_{1}\) and GWL\({}_{2}\) were \(\Delta\)x = \(\Delta\)y = 5 nm and \(\Delta\)z = 10 nm, respectively. The inset of Figure 1b shows the real part of the refractive index value for SiO\({}_{2}\)[20] and Si [21] as a function of the wavelength. The background index of the system was n\({}_{s}\) = 1.  
Light polarized in the X direction (TM polarization) incidents vertically above the system in the near-infrared spectral band. The period P of grating was 500 nm. A SiO\({}_{2}\) layer with a thickness of 1885 nm was designed on the top of the GMR structure in order to reduce the reflection of light. The distance (\(d_{2}\)) between the two GWLs was designed to be 2137 nm. Gratings G\({}_{11}\) and G\({}_{12}\) had the same depths of D = 55 nm, and different widths of \(W_{1}\) = 100 nm and \(W_{2}\) = 230 nm, respectively. The separation between two Si gratings was \(g\) = 30 nm. The Si waveguide thickness was t = 435 nm. Meanwhile, structural parameters of GWL\({}_{1}\) and GWL\({}_{2}\) were consistent.
## 2 Fundamental Theories
### Adaptive Parameter Optimization
#### 3.2.2 Adaptive Sine Cosine Algorithm  
As mentioned in SCA, \(r_{1}\) is a crucial parameter connecting with the sine and cosine functions \(r_{1}\)sin(\(r_{2}\)) or \(r_{1}\)cos(\(r_{2}\)). On the basis of the design principle of SCA, the algorithm firstly explores different regions and then exploits promising regions. Accordingly, the value of \(r_{1}\) should be monotone decreasing during iterations. However, the updating strategy of \(r_{1}\) as shown in Equation (26) is a linear decrease with equal-step, which will restrict the convergence and accuracy. For this purpose, an improved strategy for updating \(r_{1}\) which is called adaptive sine cosine algorithm (ASCA) is proposed in this paper.  
Considering that the fitness values of all individuals would change among the iterations, a hierarchical strategy based on the fitness values of individuals is introduced to divide all individuals into three subgroups [44]. More specifically, let \(f_{i}\) be the fitness value of individual \(i,f_{a}\) be the average fitness value of all individuals, and \(f_{e}\) represents the average fitness value of individuals that fitness value better than \(f_{a}\). If \(f_{i}\) is better than \(f_{e}\), individual \(i\) is defined as elite individual; if \(f_{i}\) is better than \(f_{a}\) but worse than \(f_{e}\), individual \(i\) is defined as ordinary individual; if \(f_{i}\) is worse than \(f_{a}\), then define individual \(i\) as inferior individual. The hierarchical strategy is illustrated in Figure 3a. After the above process, the number of individuals in different subgroups can be adaptively adjusted according to the fitness values during iterations, and different subgroups implement different adaptive operations. The updating strategy for \(r_{1}\) is defined as follows and changes of \(r_{1}\) for individuals of different subgroups is shown in Figure 3b:  
(1) Elite individuals: Are the best individuals among all ones, and close to the optimal value. A smaller \(r_{1}\) is given with a cubic function to enhance local searching:  
\[r_{1}=(r_{\max}-r_{\min})(\frac{T-t}{T})^{3}+r_{\min} \tag{27}\]  
where \(r_{max}\) and \(r_{min}\) are the maximum and minimum value of \(r_{1}\) respectively, while \(T\) and \(t\) are the maximum and current number of iterations respectively.  
(2) Ordinary individuals: Possess good global and local searching ability. \(r_{1}\) maintains the original linear decreasing trend:  
\[r_{1}=a-t\frac{a}{T} \tag{28}\]  
(3) Inferior individuals: Are far from the optimal value. A bigger \(r_{1}\) is needed to enhance global searching in early iteration, and to jump out the local optimum later:  
\[r_{1}=(r_{\min}-r_{\max})e^{-b\frac{t}{T}}+r_{\max} \tag{29}\]  
where \(b\) is a positive constant to adjust exponential curve.
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
###### Abstract  
In the study of aircraft wake target detection, as the wake evolves and develops, the detection area of the LiDAR often shows the presence of two distinct vortices, one on each side. Sometimes, only a single wake vortex may be present. This can lead to a reduction in the accuracy of wake detection and an increased likelihood of missed detections, which may have a significant impact on the flight safety. Hence, we propose an algorithm based on the YOLOv8n-CBAM-EfficientNetV2 model for wake detection. The algorithm incorporates the lightweight network of EfficientNetV2 and the Convolutional Block Attention Module (CBAM) based on the YOLOv8n model, which achieves the lightweight improvement in the YOLOv8n algorithm and the improvement in detection accuracy. First, this study classifies the wake vortices in the wake greyscale images obtained at Hong Kong International Airport, based on the Range-Height Indicator (RHI) scanning characteristics of the LiDAR and the symmetry of the wake vortex pairs. The classification is used to detect left and right vortices for more accurate wake detection in wind field images, which thereby improves the precision rate of target detection. Subsequently, experiments are conducted using a YOLOv8n-CBAM-EfficientNetV2 model for aircraft wake detection. Finally, the performance of the YOLOv8n-CBAM-EfficientNetV2 model is analysed. The results show that the algorithm proposed in this study can achieve a 96.35% precision rate, 93.58% recall rate, 95.06% F1-score, and 250 frames/s. The results show that the method proposed in this study can be effectively applied in aircraft wake detection.  
a 2024 14 7754 1 [1] College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China; [EMAIL_ADDRESS] (X.T.); [EMAIL_ADDRESS] (Y.S.)  
Hong Kong Observatory, Hong Kong 999077, China; [EMAIL_ADDRESS]  
2  
ircraft wake; LiDAR; lightweight; attention mechanism; target detection
# Immuno-PET for Glioma Imaging: An Update
## 3 Immuno-PET: Basic Principles and Technical Features
Among the various PET-derived parameters, maximum and mean standardized uptake values (SUVmax and SUVmean, respectively) have been shown to have a meaningful prognostic impact in oncology, as well as the more recently introduced volumetric data, such as metabolic tumor volume (MTV) or total lesion glycolysis (TLG) [32]. These quantitative data are crucial for monitoring disease progression, evaluating treatment responses, and guiding treatment decisions. More recently, some technological innovations have further boosted the applications of PET in immuno-oncology, such as the implementation of silicon photomultiplier (SiMP)-based detection, referred to as the \"digital PET\", and the introduction of long axial field-of-view (LAFOV) PET/CT scanners, capable of covering the entire scan length in a single or a few bed positions, and characterized by exquisite sensitivity [33].