esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 1 Introduction  
The sense of smell is one of the oldest human senses, as living organisms throughout history have used it to detect odors and interact with the surrounding world. Since the introduction of the concept of electronic noses in the 1980s [1], this technology has witnessed significant development. Electronic noses now represent a standard tool that simulates the human sense of smell, detecting and analyzing odors and providing precise information, known as a 'fingerprint,' about mixed gases and scents [2]. Electronic noses (e-noses) typically consist of two main parts: a gas sensing system, which includes a set of gas sensors and gas transmission pathways, and an information processing system, which includes a precise processor, related devices, and pattern recognition algorithms [2; 3; 4; 5; 6]. Over the past thirty years and since the introduction of the electronic nose concept in the 1980s, we have seen significant advancement in this field. These technologies have transitioned from being large, costly, and energy-consuming devices to being small, cost-effective, and energy-efficient ones. Since it is a tool used daily in food analysis [7; 8], medical diagnosis [7; 9], environmental monitoring [9; 10], security, and various other fields, the response speed is a key driver for the development of the electronic nose. It is expected that the integrated electronic nose will be capable of detecting, identifying, and measuring volatile compounds in less than a few minutes, ideally in seconds [2]. Despite their broad applications and significant technological advancements, the most recent innovations and developments in recent years have not been thoroughly reviewed. This paper aims to bridge this gap by synthesizing cutting-edge research that leverages-newer sensor technologies and computational methods, thus expanding the application spectrum of e-noses. Our systematic review is distinct in its focus on the latest innovations that have emerged since traditional reviews. It presents a fresh perspective on the challenges, solutions, and future directions of e-nose technology. This timely contribution is crucial, as it reflects current trends and emerging needs within the scientific community, providing a pivotal reference point for researchers and industry professionals alike. In order to differentiate this review paper from existing articles, we conducted a keyword search within the Scopus database, employing terms such as 'electronic nose', 'integrated','mobile e-nose', 'wearable e-nose', and 'gas sensor array' within the relevant literature. We included the relevant review literature found, as shown in Table 1. Currently, the predominant focus in existing reviews revolves around sensor devices, diverse applications, and data processing algorithms related to electronic noses, with limited attention paid to the progress in achieving transportability and rapid response in integrated electronic nose devices.
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Water Treatment Methods Modifications  
Due to extensive water pollution, the precursors of DBPs in contaminated water sources typically consist of a mixture of autochthonous and allochthonous compounds. These include substances linked to microorganisms (such as algae and bacteria), organic matter from wastewater discharges, amino acids, and proteinaceous compounds. Generally, conventional water treatment methods encounter difficulties in effectively removing these DBP precursors. Pre-oxidation treatments like pre-ozonation and advanced post-treatments like ozone-biological activated carbon (O\({}_{3}\)-BAC)-integrated technologies are commonly incorporated into existing drinking water treatment plants. These technologies are developed to eliminate contaminants in polluted water sources, including ammonia nitrogen, organic matter, trace pollutants like algal metabolites, odor and taste compounds, pharmaceuticals, endocrine-disrupting chemicals, and persistent organic pollutants. Improved removal of organic precursors through pre-ozonation and advanced O\({}_{3}\)-BAC treatment significantly decreases the formation of regulated carbonaceous DBPs (e.g., chloroform) during subsequent chlorination processes. Additionally, pre-ozonation has been recognized as a practical approach for mitigating the iodinated disinfection of byproducts by converting iodide to iodate [7].  
There are several options available to control DBPs concentrations. These options include removing the precursor NOM before introducing chlorine, eliminating DBPs after formation, or using alternative disinfectants. Various cost-effective strategies are implemented, aiming to reduce the formation of THMs and HAAs during the chlorination of drinking water [6]:  
* Eliminating prechlorination or relocating the chlorination point: by avoiding chlorination at the initial stages of water treatment or changing the location where chlorine is introduced, the formation of THMs and HAAs can be reduced.  
* Implementing enhanced coagulation practices: enhanced coagulation techniques help to remove organic precursors before chlorination, thereby reducing the formation of DBPs.
* Optimizing chlorine dosing using disinfection benchmarking: water treatment plants can ensure effective disinfection while minimizing DBP formation by closely monitoring chlorine dosage and comparing it with established benchmarks.
* Transitioning to chloramines for secondary disinfection: chloramines, compounds formed by combining chlorine with ammonia, are less reactive than free chlorine and can help decrease the formation of THMs and HAAs.
* Exploring alternative DBP minimization strategies: this includes investigating novel treatment methods or combinations of treatments aimed at reducing the formation of DBPs during water chlorination.
###### Abstract  
The installation of subsea equipment is a critical step in offshore oil and gas development. A dynamic model to evaluate the lowering process is proposed. The cable-payload system is discretized as a series of spring dampers with the lumped-mass method. For the first time, not only the lowering velocity but also the rope's structural damping and the nonlinear loads, such as drag force and snap load, are considered. The lowering velocity of the cable is considered through a variable-domain technique. Snap loads are considered by setting the internal forces in the elements to be zero when the cable slacks. A series of simulations reveals that the lowering velocity has great effects on the dynamic force in the cable. However, the structural damping of the cable has little effect on the system response. The snap load may occur in the cable when subjected to rapid downward heave motion, and decreases with the lowered depth increasing. The cable stiffness affects the system's resonance depth, but has little effect on the peak dynamic force. The present work should be a valuable reference for future subsea equipment installation analysis.  
Keywords:subsea equipment installation; lumped-mass method; variable-domain method; lowering velocity; structural damping; cable stiffness; snap load +
Footnote †: journal: Applied Sciences
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 2 Materials and Methods
### Accuracy Evaluation Index  
Three indicators were selected for the comprehensive evaluation of different models. They are the deterministic coefficient (R\({}^{2}\)), root mean square error (RMSE), and relative error (RE). Among the three indicators, the closer the R\({}^{2}\) value of the coefficient of determination is to 1, the smaller the RMSE and RE are, which indicates the higher accuracy of the model and the more consistent the prediction effect is with the actual situation. The calculation formula is as follows:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(y_{i }-\overline{y})^{2}} \tag{1}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(\hat{y}_{i}-\overline{y})^{2}}} \tag{2}\]  
\[\text{RE}=\frac{1}{n}{\sum_{i=1}^{n}\frac{|\hat{y}_{i}-y_{i}|}{y_{i}}} \tag{3}\]  
where \(y_{i}\) represents the observed value of the test sample; \(\hat{y}_{i}\) represents the estimate of the test sample; \(\overline{y}\) represents the average of the observations of the test sample; \(n\) sample number.
## 3 Latest Applications in Distribution Systems
### Power Flow Analysis  
Power flow analysis is fundamental to power system planning, monitoring, and control. Approaches that utilize numerical optimizers, such as the Newton-Raphson (NR) method, are conventionally implemented as the solution. Power flow requires meeting load demands while not violating the physical constraints of the system. The problem of achieving the optimal steady-state operating point of the system that minimizes the generation cost while being reliable and safe is referred to as the optimal power flow (OPF) problem. The OPF problem is NP-hard [64], and some numerical methods are computationally expensive and do not guarantee a global optimum [65]. Furthermore, they lack sufficient adaptability to changes happening in the system, e.g., DER output, and remodeling might be required, exacerbating computational costs [29].  
The effectiveness of GNNs in dealing with varying operating conditions, by exploiting underlying correlations between nodes learned implicitly from modeling the power system network as a graph, is verified for the OPF problem in [29]. Meanwhile, such effectiveness of GNNs is extended in [30] by utilizing local marginal prices (LMPs) and voltage magnitudes, which are topology-dependent, to significantly reduce the model complexity. In parallel with centralized GNN-based methods, by framing the power flow balancing as a supervised vertex regression task, [33] develops a decentralized GNN to enable communication between vertices on the graph with no need for centralized operators, in which the deep GNN is equipped with convolutional auto-regressive moving average (ARMA) layers [66]. Results indicate that the decentralized GNN outperforms conventional multi-layer perceptron (MLP) approaches for fixed and varying topologies.  
On the other hand, the deep attention mechanism is integrated into GL for deterministic and probabilistic power flow [31; 32]. A physics-informed graph attention network is adopted for power flow analysis in [31], and the inductive learning ability of the attention-based model enhances computational efficiency. By aggregating neighboring information selectively through the graph attention-enabled convolution layer, [32] captures the complex correlation and stochasticity of wind and solar power generation, realizing improved performance in probability power flow calculation.  
While GNNs are implemented to adapt to varying topologies, several efforts in DRL, for example, proximal policy optimization (PPO) in [34] and soft actor-critic (SAC) in [35] are made to improve the adaptability of the OPF problem to the varying operating conditions. The well-trained agent is able to achieve near-optimal status efficiently while dealing with uncertainties, such as topology changes and uncertainties of DERs in [34].
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 4 Modeling the Query Flow Within the Mini-Datacenter
### Modeling Coarse-Grain Forwarding  
In the coarse-grain approach, the gateway selection is modeled based on the availability of gateways and buffer occupancies, as shown in Equation (1). If there is only one gateway available, it is selected by default. However, if there are \(l\) multiple gateways with heavily loaded buffers \((B_{i}\approx B_{max})\); then, the selection of a gateway may be the one with the least occupancy or a random one in case of similar occupancies. With the availability of multiple gateways, we have defined a score \(S_{S_{l}}=\left(1-B_{i}(t)/\,B_{max}\right)\) to each gateway based on its buffer occupancy, ranging from 1 (empty buffer) to 0 (completely filled buffer), as shown in Equation (1); the \(g_{i}\), which has a maximum score, will be selected from the available gateways.  
\[g_{i}^{selected}=\begin{cases}g_{i=}G_{avail}&if\mid G_{avail}\mid =1\\ P(S_{selected}=g_{i})=\frac{1}{l}&if\mid G_{avail}\mid>1\;and\;\left(S_{S_{1}}= S_{S_{2}}=\ldots=S_{S_{l}}\right)\\ \left\{g_{l}\mid S=max(S_{S_{l}},S_{S_{2}},\ldots,S_{S_{l}})\right\}&if\mid G_ {avail}\mid>1\;and\;\left(S_{S_{l}}\
eq S_{S_{2}}\
eq\ldots\
eq S_{S_{l}}\right) \end{cases} \tag{1}\]  
Figure 2: Query forwarding approaches: (**a**) Course-grain and (**b**) Fine-grain.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 2 USV Thruster Fault Feature  
### USV Thruster Fault Condition  
In this study, a fault was defined as the occurrence of at least one deviation from the expected behavior of the system that is not generally allowed. Fault detection is defined as the recognition of fault occurrence by the system or the user through symptoms using various methods. Fault diagnosis is defined as the process of detailed classification of fault types, such as the size of a fault, the location of its occurrence and the time taken for fault detection, from inputs on symptoms of the fault [26].  
Figure 1 presents a schematic for the process of general fault diagnosis. First, fault features are extracted from acquired data. The fault features are examined to perform fault detection and recognize symptoms. Then, based on the identified symptoms, faults are classified for diagnosis. When the fault types are identified, the hazard of the fault is classified to facilitate the decision making of the user, such as the continuation of the mission, the repair of the fault or the return of the USV.  
Among possible fault conditions caused by external factors of the system, the breakage of the thruster blade and entanglement of floating objects were selected as the faults to be detected and analyzed in this study.  
Figure 2 outlines the illustration of the materials used for the reproduction of real-world faults in the study. A thin rope (\(\sim\)06 mm), a thick rope (\(\sim\)012 mm) and a net were used to reproduce conditions of entanglement of floating objects on the thruster blade. For reproduction of the breakage condition of the thruster blade, fault conditions were generated by breakage of one side of the two-leaf thruster blade by 1 cm, 2 cm and 3 cm, respectively. In this way, we aimed to study not only the diagnosis of the type of fault but also the severity of the fault by applying different sizes of the fault.  
Before performing a fault diagnosis, appropriate selection of fault features for the acquired data is necessary. In particular, in the data-driven method, a fault is diagnosed based on the analysis of data patterns, which makes it crucial to select appropriate fault features according to the type of fault.
# Heat Sink Equivalent Thermal Test Method and Its Application in Low-Orbit Satellites
## 1 Introduction
A new method without test cutting called the heat sink equivalent thermal test method, which uses the heat sink of vacuum test equipment to complete the heat flow simulation, achieves the goal of reducing costs and shortening the thermal test duration under the premise of ensuring the reliability of the satellite. Without using a heat flow simulation device, the time and cost of the design and manufacture of the infrared heating cage can be avoided, and the safety of the thermal test is also improved. Nitrogen gas is used to control the temperature of the heat sink, which is energy-saving, environmentally friendly, and economical. And the heat sink temperature can be adjusted quickly. At present, the test method has been successfully applied to the Jilin\(-\)1 GF02F and GF03 series satellites, and the test results are remarkable. The TCS (thermal control system) operates stably after the satellite is in orbit. This article will briefly introduce the theoretical basis of the heat sink equivalent test method and take the GF02F satellite as an example to introduce the application of the method.
# Robotics in Gastrointestinal Endoscopy
## 2 Review of Robot Assisted Endoscopic Devices (Robot Assisted ESD System)
### Robotic Endoscopic Multitasking Platform
#### 2.1.2 Endoluminal Assistant for Surgical Endoscopy (ICube Laboratory, Strasbourg, France)  
The ISIS-Scope/STRAS System ([PERSON], IRCAD, Tuttlingen, Germany) was developed as a remotely operated robotized platform of manual Anubiscope ([PERSON]) [16; 17]. The Endoluminal Assistant for Surgical Endoscopy (EASE) is the latest version of STRAS. EASE is a platform composed of the master and slave units (Figure 2) [17]. The slave unit consists of a mobile master console and a detachable endoscope. The 53.5-cm-long detachable endoscope has two 4.3 mm side working channels for the platform's unique robotic instruments and a 3.2 mm central working channel for conventional endoscopic instruments. This platform provides 10 degrees of freedom. The surgeon at the master console unit can control both the endoscope and instruments. The surgeon has two screens: a frontal view with an endoscopic view and a graphically reconstructed view with a user interface that maps the positions of the endoscope and instruments. The master controller consists of joysticks and four-way thumb switches to control endoscope movements. EASE is designed for use in lower gastrointestinal procedures and has reported 12 successful ESDs for large lesions in porcine colon models [16]. A recent small trial showed that the dissection speed gap between novices and experts was reduced when using EASE for ESD and showed a shorter learning curve for novices [17]. This platform currently has no CE or FDA approval.
## 5 Simulation Results and Water Tank Experiments
### Simulation Results
#### 5.1.1 Simulation of Joint Control of Yaw Rate and Surge Velocity  
In the yaw rate control mode, the initial surge velocity and yaw rate are both 0. The surge velocity is set to 2 m/s and the yaw rate is set to 15\({}^{\circ}\)/s at 10 s. Figure 10(a) shows the variation curves of surge velocity and thrust, while Figure 10(b) shows the variation curves of yaw rate and azimuth angle. The red and blue lines correspond to the absence of wind and wave interference and the presence of wind and wave interference, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter [Unit]** & **Value** & **Parameter [Unit]** & **Value** \\ \hline \(\lambda_{\text{n}}\) [rad \(\cdot\) s\({}^{-1}\)] & 1.4 & \(\omega_{\text{n}\text{p}}\) [rad \(\cdot\) s\({}^{-1}\)] & 0.7 \\ \(\zeta_{\text{n}}\) [1] & 1 & \(\zeta_{\text{r}\text{p}}\) [1] & 1 \\ \(\omega_{\text{n}\text{t}}\) [rad \(\cdot\) s\({}^{-1}\)] & 1.3 & \(\omega_{\text{n}\text{p}}\) [rad \(\cdot\) s\({}^{-1}\)] & 0.6 \\ \(\lambda_{r}\) [rad \(\cdot\) s\({}^{-1}\)] & 3.5 & \(R_{i+1}\) [m] & 25 \\ \(\zeta_{r}\) [1] & 1 & \(\Delta/\) m [1] & 30 \\ \(\omega_{\text{n}\text{r}}\) [rad \(\cdot\) s\({}^{-1}\)] & 3 & \(\kappa\) [1] & 0.001 \\ \(\zeta_{\text{p}}\) [1] & 1 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of reference models and controllers.  
Figure 11: The simulation result of yaw rate control. (**a**) Surge velocity and thrust variation curves. (**b**) Yaw rate and azimuth angle variation curves.  
As shown in the figure, regardless of the presence or absence of wind and waves, the surge velocity and yaw rate can track the given values without steady-state errors or overshoot. The surge velocity adjustment time is 3.8 s, and the yaw rate adjustment time is 1.9 s. During the acceleration process, the thrust steadily increases, and remains constant during direct navigation at a surge velocity of 2 m/s; when turning, the thrust increases to maintain a constant surge velocity of 2 m/s. The azimuth angle can quickly increase when the yaw rate increases, and maintain a fixed angle required to maintain stable rotation.
## 3 Results and Considerations  
### CODCr Concentration Changes and Removal Efficiency Based on Mixing:Aeration Conditions  
The C:N:P ratio of raw water properties was fixed at 10:3:1, and the C/N proportion ratio was maintained above 2.5 for the smooth removal of nitrogen. Then, changes in CODCr concentration and removal efficiency based on changes in the setup time of mixing: aeration were analyzed.  
The CODCr concentration of the raw water, which is artificial wastewater, was 300 mg/L, and treated water concentration of mixing: aeration conditions (60-60 min, 70-50 min, 80-40 min and 90-60 min) were each 29 mg/L, 16 mg/L, 50 mg/L and 29 mg/L, and was the lowest at a mixing:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{2}{c}{**Units**} & \multicolumn{2}{c}{**Conditions**} \\ \hline \multirow{3}{*}{Anaerobic phase} & \multicolumn{2}{c}{Drain flow} & L/h & \multicolumn{2}{c}{Auto} \\  & \multicolumn{2}{c}{Anaerobic phase} & \multicolumn{2}{c}{O} \\  & \multicolumn{2}{c}{MLSS} & mg/L & \multicolumn{2}{c}{2000} \\ \hline \multirow{3}{*}{SBR reactor} & \multicolumn{2}{c}{Aeration} & min & 60 & 50 & 40 & 60 \\  & \multicolumn{2}{c}{DO} & mg/L & \multicolumn{2}{c}{3} \\ \cline{1-1}  & \multicolumn{2}{c}{Mixing} & min & 60 & 70 & 80 & 90 \\ \cline{1-1}  & \multicolumn{2}{c}{DO} & mg/L & \multicolumn{2}{c}{0.2} \\ \hline \multirow{3}{*}{
\begin{tabular}{c} MBR \\ ( aeration period) \\ \end{tabular} } & \multicolumn{2}{c}{Drain flow} & L/h & \multicolumn{2}{c}{2} \\  & \multicolumn{2}{c}{Drain (On/Off)} & min & \multicolumn{2}{c}{3/7} \\ \hline \multicolumn{2}{c}{HRT} & min & \multicolumn{2}{c}{360} \\ \hline \hline \end{tabular}
\end{table}
Table 2: The detailed operating conditions of the reactor.  
Figure 3: Schematic diagram of the Bench-scale shipboard STP.  
aeration of 70-50 min and 90-60 min. The removal efficiency for each condition of mixing: aeration (60-60 min, 70-50 min, 80-40 min and 90-60 min) were each 90.33%, 94.67%, 83.33% and 94.67%. Typically, the removal of organic matter takes place in the aeration stage, and is known to oxidize organic matter by microorganisms that use oxygen. This study did not conduct a separate analysis of BOD, but given that the removal efficiency of CODCr exceeded 85%, BOD was also likely removed. Also, removal efficiency was at its lowest when mixing: aeration was 80-40, and thus aeration time should also be taken into consideration during CODCr removal. Figure 4 shows raw water and treated water changes based on changes in mixing: aeration time. Figure 5 shows the changes in removal efficiency based on changes in mixing: aeration times.
###### Abstract  
The increasing number of satellites orbiting around Earth has led to an uncontrolled increase in objects within the orbital environment. Since the beginning of the space age on 4 October 1957 (launch of Sputnik I), there have been more than 4900 space launches, leading to over 18,000 satellites and ground-trackable objects currently orbiting the Earth. For each satellite launched, several other objects are also sent into orbit, including rocket upper stages, instrument covers, and so on. Having a reliable system for tracking objects and satellites and monitoring their attitude is at present a mandatory challenge in order to prevent dangerous collisions and an increase in space debris. In this paper, the evaluation of the reflection coefficient of different shaped objects has been carried out by means of the bi-static reflection method, also known as NRL arch measurement, in order to evaluate their visibility and attitude in a wide range of frequencies (12-18 GHz). The test campaign aims to correlate the experimental measures with the hypothetical reflection properties of orbiting systems.  
Keywords: attitude monitoring; NRL arch; reflection coefficient; EM characterization +
Footnote †: journal: Applied Sciences