esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 2 Basic Principles  
### Lambert-Beer Law  
The Lambert-Beer law is the basis of quantitative analysis of water quality parameters using UV-Vis spectroscopy. The measurement principle of the Lambert-Beer law relies on a beam of monochromatic parallel light that irradiates the surface of the tested medium [12]. After passing through the medium with a certain thickness, the medium absorbs part of the light energy, weakening the intensity of the transmitted light passing through the medium. The absorbance of the absorption medium is directly proportional to its thickness (Figure 3, Equation (1)).  
Mathematical expression of the Lambert Beer law is as follows:  
\[A=log(1/T)=Kal. \tag{1}\]  
In Equation (1): A is the absorbance; T is the transmittance that the ratio of the intensity of the outgoing light (I) to the intensity of the incident light (I\({}_{0}\)); K is the molar absorption coefficient related to  
Figure 3: Schematic diagram of thr Lambert-Beer law.  
Figure 2: Steps for determining water quality parameters based on UV-Vis spectroscopy (Note: The polluted water from left to right is eutrophication water body, factory sewage, domestic wastewater and marine pollution. The instrument used to collect spectral data is UV-2450. Savitzky–Golay smooth filter (SG), Wavelet transform (WT), Standard normal variate (SNV), Multiple scattering correction (MSC), Principal component analysis (PCA), Successive projections algorithm (SPA), Genetic algorithm (GA), Competitive adaptive reweighted sampling (CARS), Particle swarm optimization (PSO), Random frog (RF) algorithm, Partial least squares regression (PLSR), Support vector machine regression (SVR), Extreme learning machine (ELM) model, Principal component regression (PCR), and Stepwise regression (SR).  
the nature of the absorbing substance and the wavelength \(\lambda\) of the incident light; a is the concentration of the absorbing substance in mol/L; and l is the thickness of the absorbing layer in cm.
# A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors
## 3 The Proposed Multi-Physics Modelling-Based Method  
The operation of an SRM is a process of interaction of electro-magnetic-mechanical multiple physical fields. The performance parameters of multiple physical fields are constrained by each other. The electromagnetic characteristics of traditional motors are usually linear or quasi linear. For SRM, it has poles both on stator and rotator, and the windings are bounded on the stator, as shown in Figure 1. On the other hand, due to the saturation characteristics of ferromagnetic material, the flux linkage begins to saturate when the current reaches certain values. During the motor running process, the SRM's electromagnetic properties versus position and phase current exhibit nonlinear characteristics and thus are difficult to model accurately compared with traditional motors. Due to its nonlinear electromagnetic characteristics and the special action mode of electromagnetic force, the multi-physics interaction behavior of SRMs is more complicated than that of traditional motors. In addition, the components of SRM, including stator, rotor, shell winding, end cover and bearing, have influence on the system modal. Therefore, for the performance optimization and control method design of SRM, it is of vital importance to model and analyze from the perspective of multi-physics field, and fully take into account the influence of more components on the system modal. This paper presents a numerical simulation method that fully considers the interaction relationship of multi-physical fields. It includes three segments: digital controller and drive circuit module, magnetic field module, and mechanical module. To obtain an excitation current, the gate signals of the controller module are employed to drive the power amplifier. The winding of SRM is energized by the generating current, and an induced magnetic field will be formed. Thereafter, the electromagnetic forces can be acquired. Finally, the vibration of SRM is predicted by combining the electromagnetic forces and the system structure modal.
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
###### Abstract  
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing, to address the challenges of efficient AMR. Our Transformer network architecture is designed with the mindset of real-time edge computing on IoT devices. Four tokenization techniques are proposed and explored for creating proper embeddings of RF signals, specifically focusing on overcoming the limitations related to the model size often encountered in IoT scenarios. Extensive experiments reveal that our proposed method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. Notably, our model achieved an accuracy of 65.75 on the RML2016 and 65.80 on the CSPB.ML.2018+ dataset.  
automatic modulation recognition; deep learning; attention mechanism; Transformer network; IoT +
Footnote †: journal: _ToT_  
0024
## 3 Calculation Results and Discussion  
### Design Point Thermodynamic Performance Analysis  
At the design point (Cruise = 11 km, Mach = 0.8, AS-ratio = [0.00475(reformer):0.84532 (cathode):0.15(combustor)]), Table 2 presents crucial performance indicators for each constituent within the hybrid system. The SOFC's performance at this specific AS-ratio successfully produces power meeting the flight point's demands without compromising the thrust generation capabilities of the GT. The performance at design point shows that this AS-ratio has a bearing on optimal performance of the hybrid system.  
During cruise, the power requirement stands at 102 kW, a demand that is met by the SOFC. At various other flight points, differing power demands lead to distinct outcomes, each associated with its unique electric efficiency, voltage, and power density. Table 3 shows the system node characteristics for the hybrid system from the inlet to outlet at  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline CPOx & & \\ O/C ratio & 0.85 & - \\ Operational pressure & 300 & kPa \\ Fuel Inlet Temperature & 643.15 & K \\ Air Inlet Temperature & 850.22 & K \\ \hline SOFC [42] & & \\ Operational pressure & 0.4 & MPa \\ Average Current density & 5000 & Am\({}^{-2}\) \\ Active cell area & 0.09 & m\({}^{2}\) \\ Number of cells in one stack & 300 & - \\ Weight of one cell & 0.6 & g/cm \\ Anode thickness & \(0.09\times 10^{-2}\) & m \\ Cathode thickness & \(0.005\times 10^{-2}\) & m \\ Electrolyte thickness & \(0.001\times 10^{-2}\) & m \\ Air channel height & 0.002 & m \\ Conductivity-anode & \(80\times 10^{3}\) & (\(\Omega^{-1}\).m\({}^{-1}\)) \\ Conductivity-cathode & \(8.4\times 10^{3}\) & (\(\Omega^{-1}\).m\({}^{-1}\)) \\ Electrolyte Ionic conductivity & \((33.4\times 10^{3})\)e\({}^{(-10.3\times 103\)/T)}\) & (\(\Omega^{-1}\).m\({}^{-1}\)) \\ Anode diffusivity coefficient & \(0.2\times 10^{-4}\) & m\({}^{2}\)s\({}^{-1}\) \\ Cathode diffusivity coefficient & \(0.05\times 10^{-4}\) & m\({}^{2}\)s\({}^{-1}\) \\ \(k_{anode}\) & \(6.54\times 10^{-11}\) & A/m\({}^{2}\) \\ \(k_{cathode}\) & \(1.38\times 10^{-5}\) & A/m\({}^{2}\) \\ Fuel Utilization & 0.75 & - \\ \hline GT & & \\ Specification & Turbofan & - \\ Aircraft engine & NASA E3 & - \\ Type & 2 shaft high bypass & - \\ Dry weight & 900 & kg \\ Combustor Exit temperature & 1758 & K \\ Turbine Inlet Turbine & 1557 & K \\ Air mass flow (cruise) & 90 & kg/s \\ Thrust cruise & 8 & kN \\ Bypass ratio & 6.9 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: A-SOFC/GT hybrid system specifications.  
this flight point. The node markings are similar to those in the configuration Figure 1 and provide thermodynamic data and flow rates at that point.
## 2 Related Work
investigated a D2D collaborative computing offloading design for two users dynamically exchanging computational loads over a D2D link. The design achieved minimax optimization for a given finite time horizon [24]. [PERSON] al. studied the multi-user collaborative partial offloading and computational resource allocation problem, using a DQN algorithm to maximize the number of collaborative devices to maximize the computational resources under the maximum latency constraint of the application and limited computational resources [25]. [PERSON] et al. investigated a computational offloading scheme in a multi-user UAV system, where a UAV with a computational task can offload part of the task to a nearby assistant to satisfy a latency constraint [26]. [PERSON] et al. used the Lagrange multiplier method to solve the problem of computing resource allocation and adopted a greedy strategy to select D2D users, substantially reducing the average execution delay of the task [27]. They all increase the computing power by increasing the number of devices rather than fully utilizing the per-device computing resources. D2D communication, a key component in enhancing MEC, has also received significant attention. Its ability to facilitate high-speed, short-range connections makes it a valuable asset in offloading tasks. However, research in this area has often focused on increasing the computational power through additional devices rather than optimizing per-device resource utilization. Our study contributes to this field by proposing a PPO-based D2D-assisted computation offloading and resource allocation scheme. This scheme not only addresses the dynamic nature of MEC networks but also optimizes resource utilization at a per-device level, filling a critical gap in the current research landscape.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 6 Performance Evaluation
### Comparison with Other Rain Rate Estimation Models: Brief Description  
The proposed model is compared with other methods currently available in the literature, which are briefly described hereinafter below.
## 1 Introduction
[PERSON] et al. [13] used the least squares method for circle fitting after preprocessing the fastener image, and based on the texture feature differences between the rail spike area and the non-spike area, they used a voting algorithm to determine the rail spike area, thereby locating the center of the rail spike. However, areas with texture features similar to the fastener can lead to mispositioning, dramatically affecting the algorithm's final accuracy. Interference in railway complex environments for visual positioning can be divided into three types: (1) Abnormal conditions such as nut water stains, rust, and oil stains cause edge blurring, which interferes with positioning; (2) changes in on-site lighting interfere with positioning; (3) cluttered backgrounds such as ballast interfere with positioning. The current method for locating nuts is to locate the center of the nut after the fastener has been identified. It does not fully consider the interference of the complex environment of the railway, so the positioning accuracy is poor when the method is applied to the robot disassembly operation. In response to this issue, this paper achieves accurate positioning in railway environments by improving the Canny algorithm and combining it with determining the shape characteristics of fasteners and nuts. In the assembly relationship of the fastening system, the centers of the nut, spiral rail spike, and spring clip are on the same axis. Compared to the nut, spring clip, and gauge plate, the spiral rail spike has a distinctive circular edge feature that is relatively stable, which helps to improve positioning accuracy. Therefore, by directly positioning the spiral rail spike's center, the robot's working position for assembling or disassembling the nut can be determined. The structure of this paper is as follows: Section 2 introduces the working principle of the fastener-nut disassembly and assembly robot and highlights the importance of visual positioning for the robot's automated operation. Section 3 elaborates on the principle and implementation process of the proposed spiral spike positioning method. Section 4 completes the collection and classification of experimental samples and verifies the positioning effect of the proposed method through comparative experiments and relevant evaluation indicators. Finally, Section 5 summarizes the entire research and proposes prospects.
# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
## 2 Modelling and Simulation of Flashover Phenomenon
### Flashover Modelling  
Physical and chemical causes determine the appearance of this phenomenon. In this line, research have been carried out to know what is happening inside the enclosure. For this reason is interesting to list previous most relevant work in this area to introduce the reader. Starting with [PERSON]'s research in 1928 [17] many studies in enclosure fires have been carried out in the last decades on compartment fires. Table 4 shows relevant studies about enclosure fires since 1928 to the present. Specially those related to flashover phenomenon. Quantitative studies of flashover were first reported in the mid-1960s [18]; since then, more studies have attempted to formulate guidelines for the occurrence of flashover based on different simplifications [20]. Due to the complexity of this phenomenon and considering the flashover phenomenon at the beginning of post-flashover stage, a simplified approach is preferred (it is convenient to consider a specific model developed for this case) rather than a general one. A model for the estimation of room flashover potential was suggested by [PERSON] et al. [23] by determining experimentally the occurrence of this phenomenon when the upper gas layer temperature reaches 550-650 \({}^{\circ}\)C (823-923 K). The procedure suggests the simplest expedient for measuring mass loss rates, namely under free-burn conditions. It was concluded that the relationship between heat release rate and flashover potential is significant. Not only is the temperature of the gas layer related to the flashover occurrence but also heat flux at floor level is another factor to be considered. In research conducted by [PERSON] et al. [26] the experimental basis for working definitions of flashover was evaluated. Comparisons of available calculation procedures, ranging from simple correlations to computer-based fire models that can be used to estimate flashover were presented. It was concluded that flashover can occur when upper gas temperature is \(\geq\)600 \({}^{\circ}\)C or heat flux at floor level is \(\geq\)20 kW/m\({}^{2}\) depending
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Mach-Zehnder Modulators
#### 4.3.1 MZM: Total-Ionizing Dose Effects  
It was first noticed that relatively high doses (\(\sim\)1.3 MGy) of radiation can completely render an MZM useless (i.e., an inability to modulate light) [84]. The reason for this was later attributed to a \"pinch-off\" effect that begins to form on the p-side of the modulator [85]. Shown in Figure 12a,b are cross-sections of the modulator pre- and post-250 kGy of radiation, respectively [63]. Note that the carrier concentration on the p-side of the modulator is reduced by several orders of magnitude. This reduction is produced when TID positively charges the oxide and pushes the free carriers out of the arm, losing the electrical connection between the contact and the intrinsic modulator. Without this connection, the modulator can no longer function as intended.  
To try and mitigate the pinch-off effect, [PERSON] et al. showed that a shallow etch, and/or higher contact doping in the modulator can make it more tolerant to TID [63]. A shallow etch (i.e., thicker SOI), means that a larger dose would be required to deplete the carriers and fully disconnect the p-side contact. Larger doping on the p-side arm makes it harder to fully deplete the carriers, which also results in a more radiation-tolerant modulator.
## 1 Introduction  
Smoking tobacco is considered a risk factor in implantoprosthetic treatment, as the failure rate of implants among smokers is almost 2-fold higher than in non-smokers [1]. Recent meta-analysis shows that the effect of smoking tobacco is dose-dependent, and the implant failure risk is 4-fold higher in patients who smoke more than 20 cigarettes a day [2]. The multidirectional impact of cigarettes on wound healing in the head and neck region, its influence on the process of osseointegration, and on the composition of the oral microbiome, all affect the outcome of implant therapy [3; 4; 5]. The stability of implants is a key factor in the process of osseointegration. Primary implant stability, acquired through mechanical forces between the surface of the implant and the bone, is dependent mostly on the implant design and dimensions, bone architecture and the insertion protocol. Secondary implant stability is gained after the osseointegration of the implant and is constituted by a biological connection between the bone tissue and the implant surface [6]. Implant stability is a prerequisite for proper osseointegration, as excessive micromobility of the implant may disrupt the formation of bone in contact with the titanium screw, and could lead to fibrous encapsulation of the implant [7].  
Literature on dental implant stability among smokers is limited. Most previous studies report that smoking tobacco does not have a significant impact on implant stability after full osseointegration [8; 9; 10; 11; 12]. A recent study revealed lower stability among smokers [13], while another indicated that primary stability may be higher in smokers versus non-smokers [14]. Some researchers found that the process of osseointegration may be altered by tobacco use and that the process of acquiring secondary stability may be slower in smokers [11; 12]. Existing evidence related to implant stability among smokers is scarce. Heterogenous results appear to be contradictory.  
All of the above-mentioned studies evaluated the implants inserted in a conventional way, in the healed alveolar bone crest. Nowadays, there are many new techniques for placing implants in simultaneously regenerated bone, zygomatic implants or implants placed directly into fresh sockets after tooth extraction [15; 16; 17]. To date there is no research evaluating the stability of immediate implants inserted into fresh sockets, following extraction, in smokers. Moreover, most of the existing studies were performed according to a one-step protocol employing an open model of implant healing. The comparison between posterior and anterior area of implant insertion is lacking [8; 9; 10; 11; 13; 14]. The current research is focused on immediate implants inserted in the aesthetic, as well as posterior area of the maxilla, where all of the implants underwent a closed healing process.  
The aim of this study was to objectively compare the primary and secondary stability (6 and 24 months after implant insertion) of immediate implants in the anterior and posterior area of maxilla in smoking and non-smoking patients by means of resonance frequency analysis (RFA) and damping capacity (PTV).
# Evaluation of Garlic Landraces from Foggia Province (Puglia Region; Italy)
## 3 Results and Discussion
### Organic Acids  
The organic acid concentrations of the studied garlic genotypes are presented in Table 3. 'Monteleone' along with the CG showed the highest total organic acid concentrations. In the former, a high content of citric, oxalic, and pyruvate acids was detected, while the CG distinctively showed the highest amount of citric acid and the lowest amount of malic and ascorbic acids. 'Cortigli', 'Peschici', 'Anzano', and 'PanniD' had the lowest total concentrations of organic acids, and 'Panni' had the lowest values of all the individual organic acids, except for malate.  
On average, the total organic acid contents in our landraces (25.0 g kg\({}^{-1}\) dw; 1.0 g 100 g\({}^{-1}\) fw) were lower than that in the CG (Table 2), and in other genotypes such as those from Greece [24] (2.79 g 100 g\({}^{-1}\) fw) and from Latvia (3.87 g 100 g\({}^{-1}\) dw) [30].  
In the examined landraces, the organic acid profile was represented by 43% citric, 21% malic, 18% oxalic, and 18% pyruvate acid (Table 2). The organic acid composition of our landraces varied greatly compared with other literature data. A wider composition of organic acids was found in garlic samples from Latvia by [PERSON] and [PERSON] [30] and in Italian garlic varieties by [PERSON] et al. [28], additionally reporting the presence of fumaric, formic, succininic, quinic, salicylic, and tartaric acids, probably due to a different assay implemented.  
In the collected landraces, and particularly in 'Monteleone', 'Peschici', and 'Anzano', ascorbic acid (the main biologically active form of vitamin C) was distinctively higher than in the CG (Table 2). However, the averaged values were lower than those reported for some Italian varieties by [PERSON] et al. [6] and those reported in the INRAN-CREA (5 mg 100 g\({}^{-1}\) fw) and in the USDA (31.2 mg 100 g\({}^{-1}\) fw) food composition databases.
# Respiration Monitoring for Premature Neonates in NICU
## 2 Related Work
Methods on remote sensing of respiration or other contactless physiological measures have been developed for adults and infants while our applications focus on premature infants in NICU.
## 3 The Proposed Multimodal Model
### Stage 1: Data Pre-Processing
#### 3.2.3 Image Pre-Processing  
Image pre-processing is a process that standardizes the image data to facilitate learning and improve the performance of the model. Our proposed model pre-processes images through two main steps. Figure 5 shows an example of the image pre-processing.  
1. Resize: The task involves resizing the image data to a consistent size. Since the collected images may have varying sizes, resizing them to a uniform size is necessary. We use the transforms module from Torchvision to resize all images to the size of \(258\times 258\).
2. Normalize: Next, the proposed model normalizes the image data to ensure a consistent distribution and scale of features. To achieve this, the model uses the transforms module to normalize the pixel values of the images so that they fall within the range of (\(-1\) to \(1\)).  
Figure 4: Example of code pre-processing.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 5 Results and Evaluation
### Analysis of Experimental Results  
To analyze the influence of different experimental parameters on the experimental results, we mainly studied the influence of the wall material on the experiment. From different wall thicknesses, different equipment spacing, different positions of personnel, and different test participants. These aspects were used to verify the reliability of the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Scene** & **Wall Materials** & **Accuracy (\%)** \\ \hline bedroom & Concrete wall & 93.6 \\ meeting room & Gypsum wall & 94.8 \\ meeting room & Door & 96.5 \\ hall & Glass wall & 97.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different wall materials.  
Figure 8: Experimental equipment: (**a**) Receiver device; (**b**) Transmitter device.  
Figure 9: Experimental scenes of walls with different materials: (**a**) Concrete wall; (**b**) Glass and wooden door material; (**c**) Gypsum wall material.