esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil
###### Abstract  
The type and concentration of dissolved gases in transformer insulating oil are used to assess transformer conditions. In this paper, an online detection setup for measuring the concentration of multicomponent gases dissolved in transformer insulating oil is developed, which consists of an oil-gas separation system and an optical system for acquiring the transformer status in real time. The oil-gas separation system uses low pressure, constant temperature, and low-frequency stirring as working conditions for degassing large-volume oil samples based on modified headspace degassing. The optical system uses tunable diode laser absorption spectroscopy (TDLAS) to determine the gas concentration. Six target gases (methane, ethylene, ethane, acetylene, carbon monoxide, and carbon dioxide) were detected by three near-infrared lasers (1569, 1684, and 1532 nm). The stability of the optical system was improved by the common optical path formed by time-division multiplexing (TDM) technology. The calibration experiments show that the second harmonics and the concentrations of the six gases are linear. A comparison experiment with gas chromatography (GC) demonstrates that the error of acetylene reaches the nL/L level, while the other gases reach the nL/L level. The data conforms to the power industry testing standards, and the state of the transformer is analyzed by the detected six characteristic gases. The setup provides an effective basis for the online detection of dissolved gas in transformer insulating oil.  
near-infrared; multicomponent gas detection; TDLAS; oil-gas separation; transformer
## 7 Feasibility, Scalability, and Performance Evaluation  
As discussed in the previous sections, the mPOC system relies on a pair of devices, namely a smartphone and a Bio-Watch, with the mPOC application running on the smartphone. The system operates through three distinct modes of information data handling:  
1. Capturing Clinical and Physiological Presentations (CPPs) data: This mode involves collecting relevant clinical and physiological information. The Bio-Watch and smartphone are linked using a Wireless Body Area Network (WBAN), typically utilizing technologies such as Bluetooth.
2. Bidirectional communication with the Outbreak Tracking Center (OTC): In this mode, the mPOC system gathers public health data and maintains continuous communication with the OTC, ensuring timely updates on outbreaks and relevant information.
3. Establishing Device-to-Device (D2D) links with other users within wireless range: The third mode enables the mPOC system to establish D2D connections with nearby smartphones, facilitating the exchange of vital details. These connections rely on widely available mobile networks, such as 4G LTE/5G, which are both scalable and capable of handling the necessary bandwidth. The mPOC system primarily transmits short text-based messages, resulting in low bandwidth requirements and eliminating the need for multimedia data exchange.  
The computational analysis for the system can be easily performed on the smartphone itself, and the classification and characterization of health information can be accomplished using fog/edge computing techniques.  
For DRI-related evaluations of the performance of the mPOC system in relation to COVID-19, reference [46] considers six clusters that indicate various infection complications. These clusters are as follows:  
Cluster 1--flu-like without fever: Symptoms include headache, muscle and chest pains, dry cough, and sore throat.  
Cluster 2--flu-like with fever: Similar to Cluster 1, but with the addition of fever and voice hoarseness.  
Cluster 3--gastrointestinal: Symptoms consist of headache and chest pain, loss of smell and appetite, diarrhea, and sore throat without cough.  
Cluster 4--severity 1, fatigue: This cluster includes headache, fatigue, loss of smell, dry cough, fever, voice hoarseness, and chest pain.  
Cluster 5--severity 2, confusion: Symptoms comprise headache, loss of smell and appetite, dry cough, fever, voice hoarseness, sore throat, confusion, fatigue, and chest/ muscle pain.  
Cluster 6--severity 3, abdominal/respiratory: Similar to Cluster 5, but with the addition of shortness of breath, diarrhea, and abdominal pain.  
These clusters indicate an increased likelihood of patients requiring supplemental oxygen or ventilators, ranging from 1.5% (Cluster 1) to 19.8% (Cluster 6). It's worth noting that most of these CPPs, except for loss of smell and appetite (which is less prevalent in the recent COVID-19 subvariants), are applicable to our mPOC system with a relatively high degree of accuracy (gold standard of 90% and above). Tracking the loss of smell/taste can be incorporated through a simple question-and-answer query issued to the user by the mPOC application.
## 2 Materials and Methods
### Data
#### 2.2.2 MODIS Product Data  
The vegetation indices used in this paper are NDVI, NIRV, and kNDVI. We obtained MODIS-derived NDVI (MCD43A4_006_NDVI) from Google Earth Engine for the years 2001 to 2020. In addition, the computed NIRV and kNDVI were calculated for MCD43A4 Nadir BRDF-Adjusted Reflectance Daily 500 m product hosted on Google Earth Engine. The NIRV computation consists of two steps, first obtaining the median values of the red (620-670 nm) and NIR (841-876 nm) bands for each image-month scale in the study area and then calculating the NDVI from Equation (1), the NIRV was calculated from Equation (2). Before calculating the NIRV, 0.08 was subtracted from all NDVI values to account for the NDVI of partially bare soil. kNDVI was calculated from Equation (3).  
\[NDVI=\frac{NIR-Red}{NIR+Red} \tag{1}\]  
\[NIR_{V}=(NDVI-0.08)\times NIR \tag{2}\]  
\[kNDVI=tanh((\frac{NIR-Red}{2\sigma})^{2}) \tag{3}\]  
where \(\sigma\) is an adjustable length scale parameter, here set to 0.5 (_NIR + Red_), thus simplifying the equation to \(kNDVI=tanh((NDVI)^{2})\).  
GPP was obtained via Google Earth Engine using a 500 m resolution, 8-day cumulative synthetic product (MOD17A2H). In order to be consistent with the spatial and temporal resolution of the acquired SIF data, we computed all of GPP, NDVI, NIRV, and kNDVI as monthly scale data and resampled to 0.05\({}^{\circ}\).
## 3 Results and Discussion
### Properties of the Asphalt Binders Used in This Study  
Three binders were characterized in this part of the work: two commercially available binders selected for this study (base bitumen 50/70 and modified binder PMB 45/80-60) and the PEX modified binder obtained according to the process presented in Section 2.4. The basic properties (penetration and softening point or R&B temperature) of those binders are presented in Figure 6.  
Although PEX particles are only able to melt partially and they have been interacting with bitumen for only 2 min, it becomes clear that PEX waste certainly mod  
Figure 5: Differential scanning calorimetry (DSC) results of PEX waste.  
Figure 6: Basic properties of the asphalt binders used in this study, including the PEX modified binder.  
properties. In fact, the use of PEX reduced the penetration value and increased the softening point result in comparison with the base bitumen 50/70, which should also increase the stiffness modulus and permanent deformation resistance of the new mixtures incorporating PEX.  
The effect of PEX is more evident in the penetration test than in the softening point test, which is usual when modifying binders with polyethylene-based polymers [18]. Actually, the penetration of PEX modified binder is lower than that of PMB 45/80-60, while its softening temperature is far from being similar to the commercially available PMB.  
The viscosity of the studied binders could give valuable information about the performance at very high temperatures, being helpful to define the mixing temperatures to be used for production of asphalt mixtures. Figure 7 shows the evolution of the dynamic viscosity of the studied binders at different temperatures.  
It can be observed that the interaction between PEX waste and 50/70 bitumen resulted in a modified binder with increased viscosity, even though it is clearly lower than that of PMB 45/80-60. Thus, the modification of 50/70 bitumen by PEX waste changes its behavior in the whole range of temperatures of asphalt mixtures in service or during paving works.  
Moreover, by evaluating the mixing equiviscosity (fine and dotted line at 0.3 Pa\(\cdot\)s) temperatures of all binders used in this study, it was concluded that the asphalt mixtures with 50/70 binder, PEX modified binder and PMB 45/80-60 should be produced, respectively, at the temperatures of 150 \({}^{\circ}\)C, 160 \({}^{\circ}\)C and 180 \({}^{\circ}\)C. However, in order to increase the interaction between the PEX and the asphalt binders, and also by aiming at using the same production conditions for all mixtures, a single mixing temperature of 180 \({}^{\circ}\)C was selected to produce all mixtures, independently of the used binder.
## 1 Introduction
### Global Context of Production of Green Hydrogen
Like gray hydrogen, the methane gas reforming production process is used, but it is combined with carbon capture and sequestration technologies to mitigate environmental impacts. The process begins whenthe methane is heated with water vapor at high temperatures ranging from 700 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C. The chemical reaction between methane and steam generates hydrogen (H\({}_{2}\)) and carbon monoxide (CO), CO then reacts with more steam to produce H\({}_{2}\) and carbon dioxide (CO\({}_{2}\)). This results in high yields of H\({}_{2}\)[11]. After hydrogen production, the generated CO\({}_{2}\) is captured, compressed, and permanently stored in geological formations or used in industrial processes. This significantly reduces GHG emissions, resulting in low-carbon hydrogen production. The carbon footprint of blue hydrogen production is estimated to range from 3.97 to 6.87 kg of CO\({}_{2}\) per kg of H\({}_{2}\) produced, depending on the efficiency of CO\({}_{2}\) capture from the atmosphere. With capture rates close to 90%, the associated carbon footprint will be lower. While blue hydrogen represents an alternative solution on the path to decarbonization because it has lower emissions compared to gray hydrogen (12-13.5 kg of CO\({}_{2}\) per kg of H\({}_{2}\) produced with natural gas), its dependence on fossil fuels is problematic [12]. Green hydrogen is defined as that produced from the electrolysis of water, a process whereby the molecule is divided into H\({}_{2}\) and oxygen. To achieve this, electricity from renewable sources (wave, tidal, hydroelectric, geothermal, biomass, solar, and wind) are used. Since it does not require fossil fuels, it does not generate direct CO\({}_{2}\) emissions. This represents the main benefit of green hydrogen, as it does not rely on fossil fuels, nor does it exacerbate the problem of global warming through GHG emissions [13; 14].
## 5 Applications
### Characterization of Absorbers/Tissue Constituents
The quantification of tissue constituents using typical chromophores (lipid, water, collagen, oxy, and de-oxy hemoglobin) resulted in an overestimation of some tissue constituents. This led to the hypothesis of missing constituents that are unique to thyroid tissue. Figure 12 shows the characterization of thyroid-specific tissue constituents: thyroglobulin, tyrosine, and iodine [119]. Thyroglobulin, the most abundant protein in thyroid tissue, has a monotonous decreasing absorption spectrum with a shoulder and a peak, respectively, at 1000 and 1190 nm. Multiple features are found in the tyrosine spectrum with 6 peaks (875, 915, 1090, 1145, 1185, 1290 nm) spread across the broadband 600-1350 nm range. Iodine was found to have an exponential decaying absorption spectrum with minimum to no absorption at long NIR wavelengths. The inset in Figure 12 describes the anatomy of the human thyroid and the breakdown of tissue constituents. Though the potential of the in vivo characterization of the thyroid so far has only been limitedly explored, a few examples are presented in the next section. Figure 11: Absorption spectrum of lipid (pork fat): (**a**) in the near infrared (NIR) [125] and (**b**) in the shortwave near infrared (SWIR) [132].
# Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot
## 2 Materials and Methods
### Evaluation  
The evaluation was carried out in two ways. First, the accuracy score and loss value of both models were compared. The accuracy is a measure of how accurate the model's predictions are compared to the true data. The loss value is the sum of errors made for each example in training or validation sets. The models were trained with 80% of the total images that comprise the dataset and were tested through the 20% remaining.  
Then a comparison of PeTra's performance was carried out using both the CNN fitted with data labelled by the KIO device, and the CNN refitted by bootstrapping. A new specific dataset was gathered for this purpose, as described in Section 2.5. Ground-truth data to evaluate the performance of both CNNs were obtained by locating people in the scenes in known positions, see Figure 3b.  
To evaluate the precision, each Rosbag file is played twice: the first one by using PeTra with the CNN fitted with data labelled by the KIO device; and the second one by using PeTra with the CNN refitted by bootstrapping. For each running, we need to know whether or not all the people in the scene have been recognized properly. Specifically, we need to know how many people are in the scene, the number of people detected by PeTra, the number of people correctly detected, the number of people not detected by PeTra, and the number of people wrongly detected. Such data allow us to obtain the confusion matrix that allows visualization of the performance of our algorithm.  
Moreover, to evaluate the overall performance of both CNNs the following Key Performance Indicators (KPIs) obtained through the confusion matrix are considered: Sensitivity (_Sen_), Specificity (_Spe_), Precision (_Pre_), Accuracy (_Acc_), and Matthews Correlation Coefficient (MCC). The _Sen_ score--see Equation (1)--shows the rate of positive cases that were correctly identified by the algorithm. The _Spe_--Equation (2)--measures the proportion of negative cases that were correctly identified by the algorithm. The _Pre_ score--see Equation (3)--shows the fraction of relevant instances among the retrieved instances. The _Acc_--see Equation (4)--measures the proportion of correct predictions both positives and negatives cases among the total number of cases examined. Finally, the _MCC_--see Equation (5)--is used as a measure of the quality, considering both true and false positives and negatives cases. It takes into account true and false positives and negative cases.  
\[Sen=\frac{T_{p}}{T_{p}+F_{N}} \tag{1}\]  
\[Spe=\frac{T_{N}}{T_{N}+F_{P}} \tag{2}\]  
\[Pre=\frac{T_{p}}{T_{p}+F_{P}} \tag{3}\]  
\[Acc=\frac{T_{p}+T_{N}}{T_{p}+F_{N}+T_{N}+F_{P}} \tag{4}\]\[MCC=\frac{T_{P}\times T_{N}-F_{N}\times F_{P}}{\sqrt{(T_{P}+F_{N})\times(T_{P}+F_{P}) \times(T_{N}+F_{N})\times(T_{N}+F_{P})}} \tag{5}\]
## 5 Real-Time Adaptation  
Even though the RT-ETDE framework is designed to be lightweight, further techniques need to be applied to meet the real-time constraint. We apply three techniques including point skipping, language-dependent optimization, and multi-processor offloading. Since the number of points in the point cloud can be extremely large, using all of them incurs a high processing load on the system. Therefore, we apply uniform point skipping where only one out of every \(N_{P}\) points is selected for processing. Via preliminary experiments, we found that \(N_{P}=8\) achieves the balance between detection accuracy and processing time. On the other hand, language-dependent optimization techniques include minimizing the variable allocation, maximizing the use of low-level codes, and using high-speed registers for frequently used variables.  
We also propose an offloading scheme for multi-processor systems. Particularly, we apply this scheme to the Xilinx Zynq 7000 processing unit which is used in the real PIG. This system consists of a dual-core ARM Cortex-A9 processor operating at 800 MHz as a primary processor and two Microblaze processors operating at 125 MHz as secondary processors. The primary processor needs to perform all tasks consisting of data collection, processing, and control. The RT-ETDE framework may interfere with these tasks. Therefore, we need to partially offload the RT-ETDE framework to the secondary processors considering their limited processing power.  
Figure 13: T-junction branch radius estimation based on \(C\)-bands, farthest point, and circumcircle.  
The detection and detection operation for each feature are extracted to become the elbow detection and estimation (EDE) process and T-junction detection and estimation (TDE) process, respectively. The pose and straight pipe estimation and framework coordination are given to the ETDE process. The ETDE process is located on the master processor for interacting with the PIG main process while EDE and TDE processes are transferred to two secondary processors. Figure 14 shows the sequence diagram of the multi-processor offloading scheme. First, the PIG main processing process initializes the ETDE, EDE, and TDE processes. When the data is available from the 3D ToF camera, it is sent to the ETDE process for processing. The pose and straight pipe are first estimated. Then, points for the elbow and T-junction are extracted and sent to EDE and TDE processes, respectively. The ETDE process does not wait for the result but becomes ready directly for the new data. When the second data comes from the 3D ToF camera, the ETDE process also performs pose and straight pipe estimation as in the first data. However, after that, it directly requests the detection and estimation results of the first data from EDE and TDE processes. Finally, it extracts and sends the points to EDE and TDE processes for detection and estimation. The process of the second data is repeated for the following data. Through this mechanism, ETDE, EDE, and TDE processes run in parallel, therefore saving processing time.
## 3 Results
### Physiological Parameters  
Data regarding SPAD value showed that Cd-stress significantly decreased the SPAD value of _Brassica juncea_ (L.) plants (Table 2). Minimum SPAD value (38.94) was observed in the Cd-stressed control plants that was 27.2% less than the plants grown under unstressed conditions. Inoculation with Cd-tolerant, IAA-producing rhizobacterial strains effectively reduced the effect of Cd-stress on SPAD value of _Brassica juncea_ (L.) thus resulting in a significant improvement over the uninoculated Cd-stressed control plants. Under Cd-stress, the improvement in SPAD value due to inoculation with rhizobacterial strains ranged  
Figure 3: Effects of Cd-tolerant, IAA and EPS-producing rhizobacterial strains on Cd concentration in _Brassica juncea_ (L.) plant tissues under Cd-stress in pot trial; (\(n=3\)); Means with same alphabets do not differ statistically from one another (\(p\leq 0.05\)) (**A**) Cd tolerance index; (**B**) Cd in roots; (**C**) Cd in shoot.  
from 10.4% to 17.7% with the maximum improvement caused by the strain FN13. The strains FN14 and FN16 gave non-significant improvement in SPAD value when compared with the uninoculated Cd-stressed control plants. Under unstressed conditions, the maximum improvement (13%) in SPAD value of _Brassica juncea_ (L.) plants was observed due to inoculation with FN13 followed by FN14 and FN16, respectively.  
Results regarding the effect of Cd-tolerant, IAA-producing rhizobacterial strains on relative water contents of _Brassica juncea_ (L.) under Cd-stressed conditions (Table 2) showed that Cd-stress has a significantly negative impact on RWC when compared with unstressed control plants. The negative effect of Cd-stress on RWC was diluted due to inoculation with Cd-tolerant, IAA-producing rhizobacterial strains. Under unstressed conditions, the minimum RWC was observed in the leaves of uninoculated plants while the maximum RWC was given by the plants inoculated with FN13 that was 5.6% higher than uninoculated unstressed control plants. All the strains were effective in enhancing the RWC of _Brassica juncea_ (L.) ranging from 3.0% to 5.6% but the improvement was not significantly different from control plants. The Cd-stressed plants showed 23.4% less RWC than uninoculated control plants and these results were statistically significant. Inoculation was effective but non-significant in improving RWC under Cd-stressed conditions where the increase in RWC ranged from 2.0% to 11.3% when compared with uninoculated control plants. Under Cd-stress, the maximum RWC were given by the inoculation with strain FN13 followed by FN16.
# A New Stochastic Process of Prestack Inversion for Rock Property Estimation
## 3 Stochastic Process of Inversion
### Cost function  
Seismic data in migrated time-domain can be expressed as follows  
\[S(\theta,t)=w(\theta,t)*R(\theta,t)+n(t) \tag{20}\]  
In most cases, we assume noise is randomly distributed in seismic data with zero expectation.  
The objective function has the same property as the Rastrigin function, where it has one global minimum and large number of local minima. The Rastrigin function is usually used for optimization as a test function. The Rastrigin function is expressed as follows  
\[f(x)=An+\sum_{i=1}^{n}\left[x_{i}^{2}-A\cos(2\pi x_{i})\right] \tag{21}\]  
Figure 2: The general workflow of stochastic inversion.  
And the objective function can be expressed as follows,  
\[F=W_{1}*\sum_{\theta=0}^{\theta_{1}}\abs{S(\theta,t)-D_{obs}(\theta,t)}^{2}+W_{2}* \sum_{j=1}^{m}\abs{X_{j}-M}^{2} \tag{22}\]  
where \(W_{1}\) and \(W_{2}\) are weighting factors. \(X_{j}\) is the _jth_ particle in a generation after the application of a low-frequency filter, and \(M\) is the low-frequency model. The objective function is composed by two terms, the first term is the difference between synthetic data and real data at a particular angle, the second term is the difference between the low-frequency impedance model and the inversion result of each particle. If we have P-S wave seismic data, the objective function can include all these pieces of information to improve the inversion result.  
Partial stack of seismic data can be applied to mute noise is acceptable during process. The AVO reflection coefficient from the Aki & Richards two-layer reflectivity equation can be efficient in the near and middle offset. As for the far offset or near critical angle, the Zoeppritz equation is more accurate than other equations without considering computation cost [31; 32]. The Aki & Richards equation can be expressed as follows,  
\[R_{pp}(\theta)=\frac{1}{2\cos^{2}\theta}\frac{\Delta\alpha}{\alpha}-4\frac{ \beta^{2}}{\alpha^{2}}\sin^{2}\theta\frac{\Delta\beta}{\beta}+\frac{1}{2}(1- 4\frac{\beta^{2}}{\alpha^{2}}\sin^{2}\theta)\frac{\Delta\rho}{\rho} \tag{23}\]  
where theta is the incident angle, alpha is the average p-wave velocity, beta is the S-wave velocity and rho is density.  
Set stop criterion or position-clipping criterion  
From empirical study, the model is trying to update in each time of iteration unless it is the true model. Considering cost efficiency, the stopping criterion should be accepted before inversion. When the value of \(F\) reaches a predefined minimum, or the time of iteration reaches its maximum, the inversion process will stop automatically, and consequently a global optimum will be the output. Usually, we set the maximum time of iteration, which is a trade-off between accuracy and computational cost.