esa-sceva/satcom-chunk-collection huggingface dataset:
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 4 Experimental Testing and Analysis  
The experimental process is shown in Figure 8. Firstly, the Mask R-CNN algorithm is used to complete the identification of the catenary insulators. Secondly, the target frame is cropped. Thirdly, the insulator image is preprocessed. Finally, whether the insulator is damaged is detected through Zernike moments correlation calculation.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 1 Introduction
The contributions of this paper are threefold. First, we provide a systematic comparison of several PS architectures for PAS framework. Second, using rate loss as well as information rates for finite-length shapers as the performance metrics, we claim that shaping strategies which aim to construct energy-efficient signal sets are more effective than the techniques which focus on matching distributions for the AWGN channel. For the analyzed schemes, this means that MPDM and SpSh, are more efficient for short blocklengths than CCDM whose sole objective is to obtain the capacity-achieving distribution. Our claim is then verified via frame error rates (FERs) that are obtained in end-to-end decoding simulations of the PAS system employing long and short systematic LDPC codes from [77; 78] respectively. The improvements in power efficiency that we obtained during end-to-end decoding simulations are consistent with the predictions made by finite-length information rates. The third contribution of this paper is to provide a discussion on the required storage, computational complexity, and latency of different DM and SpSh algorithms. The paper is organized as follows. The first part is tutorial-like. In Section 2, background information on amplitude shaping is provided. In Section 3, uniform and shaped signaling schemes are described. Section 4 reviews DM and SpSh schemes from shaping architecture and algorithmic implementation perspectives. The second part of the paper is reserved for the comparison of four amplitude shaping architectures. Rate losses, information rates, and end-to-end decoding performance of PAS are studied in Section 5. Section 6 is devoted to a high-level discussion on latency and complexity of the schemes under consideration. Finally, conclusions are given in Section 7.
According to the IPC-610 [4] standard, voids may not exceed 30% of the total weld surface and, as agreed with the manufacturer, an additional criterion is the admission of a maximum of 8% of the individual void space under the soldering surface of the tested transistors. The problem of the influence of the size of the voids on the thermal properties of power transistors or LEDs is considered, among others, in the papers [25; 26]. In this paper, the results of the investigations illustrating an influence of selected factors, e.g., void size, on the thermal resistance of RF power transistors operating in the Doherty system are presented and discussed. In the performed investigations, some of the tools offered for conducting statistical analyses using the 6 Sigma methodology and the Minitab application [7] are used. In order to define all the baseline values of the tested system and the components it contains, tools such as the Quality of House using the QFD (Quality Function Deployment) method are presented. The obtained results allow for the determining of the existing potential dependences between the individual defined quantities. In the analyses, the results of the measurements presented in [27] are used as the input data. The objects of the investigations are RF power transistors BLC9G22 LS-160 VT [28] and BLC10G22 LS-240 PVT [29] operating in the RF electronic module, where they play the role of amplifiers in the Doherty system [27]. The results of the analyses indicate whether there is a correlation between the size of the voids appearing in the soldering area and the value of the junction temperature of these devices at the determined values of the power P dissipated in them. It is also verified whether, under selected power conditions, the location of the transistors at certain points on the PCB has any significance and affects the efficiency of the heat dissipation generated in these devices. The following sections will discuss: the statistical methods used to improve production processes (Section 2), the results of measurements and analyses carried out with the use of the Minitab application in relation to the assessment of the impact of voids in the soldering area of the transistors on their thermal parameters (Section 3) and conclusions from the performed experiments (Section 4).
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Classification Accuracy Evaluation  
In this study, overall accuracy (OA) and kappa coefficients (_Kappa_) are employed to quantitatively evaluate the classification accuracy. They can be derived from the confusion matrix of the testing data samples, where the rows represent the classified results and columns represent the referenced data. In the confusion matrix, the last row is the sum of all previous rows, and the last column is the sum of all previous columns. The OA is calculated by summing the number of pixels classified correctly divided by the total number of pixels, and the kappa coefficient measures the accuracy of the classification in another way; the definitions of both of them are shown below:  
\[OA=\frac{\sum\limits_{i=1}^{n-1}X_{ii}}{X_{nn}} \tag{32}\]  
\[Kappa=\frac{X_{nn}\sum\limits_{i=1}^{n-1}X_{ii}-\sum\limits_{i=1}^{n-1}\left( X_{in}X_{ni}\right)}{X_{nn}^{2}-\sum\limits_{i=1}^{n-1}\left(X_{in}X_{ni}\right)} \tag{33}\]  
where \(X=\left\{x_{ij}\right\}_{n\times n}\) (\(i\), \(j\) = 1, 2, 3,..., \(n\)) is the confusion matrix and \(X_{in}\) stands for the number of samples that belongs to the \(i\)-th class and classified as \(n\)-th class.
## 1 Introduction
### Polymer Rheology  
The rheological behavior of polymer is a key factor not only for the design of the EOR technique but also for its economics and performance. Polymer injection is often designed based on a target viscosity (e.g., oil viscosity at reservoir conditions) [3; 4; 5; 6], and surface rheology is an excellent tool to predict and support this process. Polymer solutions are non-Newtonian, meaning the viscosity of polymer solutions is shear-rate dependent [7; 8; 9]. A polymer solution can exhibit two types of viscosity--shear-thinning and shear-thickening viscosity. Shear thinning is observed with a decrease in shear viscosity with an increase in the shear rate, while shear thickening is observed with a proportional increase in shear viscosity with an increase in the shear rate [10; 11; 12]. Two types of polymers, namely biopolymers and copolymers of acrylamide, are used in the petroleum industry. Biopolymers exhibit only shear-thinning response due to a rod-like molecular structure; however, hydrolyzed polyacrylamide can exhibit both responses depending on the molecular weight and salinity of the brine. Note that the shear-thickening response is commonly associated with viscoelasticity, which describes fluids (e.g., polymer diluted solutions) that exhibit elongational or extensional behavior under flow in porous media [7; 13; 14]. Most polymers with high molecular weight used for EOR have been reported to have good viscoelastic response, with debatable acceptance [7; 13; 15; 16; 17].  
Rheological measurements performed in the laboratory (using a rheometer) can predict the shear-thinning response of polymer solutions. However, these measurements cannot completely describe the shear-thickening response. One of the main reasons for this is a lack of representation of the pore structure, mainly the change in flow geometries causing interstitial velocity, which causes shear-thickening behavior. The converging-diverging geometry of porous media produces the contraction and extension of the polymer molecular structure and results in the shear-thickening response. Several authors have reported different ways to define and predict the rheological shear-thickening behavior of polymer used for EOR, using either small-amplitude oscillatory shear (SAOS), first normal stress difference (N\({}_{1}\)), in situ rheology, or elongational/extensional viscosity [18; 19; 20; 21; 22; 23].  
Elongational or extensional viscosity is often associated with shear-thickening behavior during flow through porous media, hence helping with the further understanding of the flow dynamics of polymer solutions through porous media. These measurements include single-phase polymer flooding through core plugs/sand-packs, extensional measurements (e.g., e-VROC(r)), and filament stretching devices [10; 24; 25].
## Abbreviations  
* A Ambient photosynthetic rate of vascular plants
* Ab Ambient photosynthetic rate of bryophytes
* Amax,b Maximum photosynthetic rate of bryophytes
* Ac Rubisco-limited CO2 fixation rates of vascular plants
* Aj Electron transport-limited CO2 fixation rates of vascular plants
* a Slope (linear regression)
* API Application programming interface
* b Intercept (linear regression)
* C Carbon
* CO2 Carbon dioxide
* Ci \(\rm{CO_{2}}\) concentration in intercellular spaces at carboxylation sites of Rubisco activity of vascular plants
* Ratio between CO2 concentration in intercellular spaces at carboxylation sites of Rubisco activity and atmospheric CO2 concentration
* CPU Central Processing Unit
* C3 3--carbon atom carbon fixation pathway (85% of plants on Earth)
* DOY Day of year
* DVP Atmospheric vapor pressure deficit
* d Willmott's index of agreement
* dSL Depth of the entire soil profile
* dWT Water table depth
* _DIMONA_ **D**ynamic **I**ntegrated **M**odel for **Organism--**e**N**vironment **A**nalysis
* EC Eddy covariance technique
* ER Ecosystem Respiration
* fP Effect of soil water availability on the photosynthesis of vascular plants
* fR Effect of soil air availability and/or air availability with adaptations to wetlands
* on root/rhizoid respiration
* fU,i Effect of air availability to roots/rhizoids due to soil water and/or adaptations to wetlands in soil layer i on N and P nutrient uptake
* f0 Effect of soil water and air availability on plant growth
* f0A Effect of air availability on plant growth, root respiration, and nutrient uptake
* f0A derived from adaptations to wetland soil conditions
* f0P Ambient effect of soil water availability on photosynthesis
* f0R Soil air availability to roots/rhizoids
* f0R,i Air availability to roots/rhizoids in soil layer i
* f0W Soil water availability to plants
* GIS Geographic information system
* GPP Gross primary productivity
* GUI Graphical user interface
* gb Leaf boundary layer conductance
* gs Stomatal conductance of vascular plants
* Iswrr Incoming solar short-wave radiation at 2 m above the canopy
* i Index
* IoT Internet of Things
* Jm Ambient electron transport rate of vascular plants
* ML Machine learning
## 3 Results and Discussion
### Atomic Force Microscopy Analysis of Al-BiSn Composites  
Atomic force microscopy (AFM) was carried out to examine the surface morphology of the extruded Al-BiSn composites. The AFM images of the extruded Al-BiSn composites containing different BiSn contents are represented in Figure 6a-d. The root-mean-square (RMS) roughness has been used to describe the surface roughness of the developed composites. The extruded pure Al sample,  
Figure 5: FE-SEM images of **(a)** pure Al and **(b)** Al-(5 wt.% BiSn), **(c)** Al-(10 wt.% BiSn), and **(d)** Al-(15 wt.% BiSn) extruded composites. **(e,f)** EDX analysis of Al-BiSn composites with 10 and 15 wt.% of BiSn.  
having a single homogeneous grain of Al, has resulted in a very smooth surface (RMS = 16.68 nm). However, the surface roughness of Al-BiSn composites increases with the increasing amount of BiSn content, reaching 20.26 nm at the optimum concentration (15 wt.% of BiSn). This increase in surface roughness can be attributed to (i) the incremental increase in the micron-sized BiSn particles in the Al matrix, (ii) the new interface of the reinforcement, and (iii) the high clustering/agglomeration of the BiSn particles, which was also noted in the FE-SEM images (Figure 5b-d).
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 4 Discussion  
### Comparison to Spatiotemporal Analysis in other Environments  
In monsoonal environments, pervasive cloud cover seasonally obscures Earth-leaving radiance. This results in a consistent, recurrent transition in the information content that is present in optical satellite image data. When clouds are absent, satellite imaging observes the land surface; when clouds are present, the atmosphere is observed. This seasonality, combined with the relationship between the timing and extent of the monsoon cloud cover and phenology, creates a novel special case of the general spatiotemporal analysis problem. This special case may be of particular interest to scientific and lay audiences alike, given the number of monsoonal landscapes with particularly important environmental and/or socioeconomic properties.  
The spatiotemporal analysis approach presented here leverages the bivariate (atmosphere + biosphere) properties of monsoon environments using time series of a single land surface parameter (NDVI). However, in other cases, it could be reasonable to approach the problem from the perspective of parallel analysis of two related land surface parameters. For example, this second approach has been taken by the recent study of [45] for the purposes of mapping and monitoring rice agriculture. The two variables of interest in the rice study were vegetation abundance and land surface temperature at the spatial and temporal resolution of the Landsat 8 optical and thermal sensors. In contrast to the results presented here, the finer spatial resolution of the Landsat sensors oversampled the fields in the agricultural study area, but the coarser temporal resolution undersampled day-to-day phenological variations in the timing of planting and harvest. When viewed together, these two studies provide complementary cases illustrating the results of applying this spatiotemporal analysis methodology to datasets with variable spatial and temporal resolutions. This extends previous work from univariate studies, such as those that are presented in [43; 46], as well as the examples discussed in [23]. In addition, we note that the methodology proposed here can complement rather than replace time series methods such as those proposed by [47; 48; 49] and in situ observations, such as those proposed by [50; 51]. For example, the EOF characterization of temporal endmembers can inform the selection of functional forms to be used in fitting time series, as well as the identification of sites of interest for focused field investigation.
## 3 The Effect of RF Exposure on _in Vitro_ Models: Finding the Pathways
### To Adapt to Survive  
One of the most interesting findings about RF is that exposure to a relatively low intensity of RF-EMF (adaptive dose) can prompt a surviving response in cells when subsequently undergoing potentially lethal insults. This preservative effort is known as adaptive response (AR). It has been described in human lymphocytes [47,48] where an adaptive dose of 1950 MHz RF UMTS (universal mobile telecommunication system) signal was delivered for 20 h with a SAR of 1.25 W/kg. The following treatment with mitomycin C (48 h with 100 ng/mL) significantly reduced genotoxicity with respect to sole mitomycin controls. [PERSON] _et al_. [49] showed that AR effect was found only in cells that were in S phase at the time of the first adaptive dose exposure, while no effect was found in G0- and G1-phase cells, suggesting a different genotoxicity susceptibility in relation to active cell cycle phases. Similar results were described when lymphocytes pre-exposed to RF exhibited resistance to the genetic damage induced by subsequent exposure to X-rays (XR), an ionizing physical mutagen that induces predominantly strand breaks in DNA [47]. In human promyelocytic leukemia HL-60 cells pre-exposed to 900 MHz RF (0.25 \(\mu\)W/kg average SAR) and subsequently treated with doxorubicin, [50] [PERSON] described a reduction in apoptotic rate with alteration in mitochondrial membrane potential and intracellular calcium and magnesium homeostasis. Although the intracellular mechanism that sustains adaptive response is not fully unveiled the beneficial exposure to RF are worth further investigations given the everyday combined exposures to different EMF sources, as well as the simultaneous exposure to EMF and other factors such as chemicals, noise, stress, _etc._ that could interfere with cell function.  
We have reviewed above some of the recent relevant studies on the mechanism involved in DNA damage and repair _in vitro_. Limitation on interpretation of the findings still remains mostly related to experimental conditions and data managing [51]. To date, it appears that the puzzle of intracellular mechanisms involved in EMF-RF exposure cell response is not in any way completed. _In vivo_ studies, however, might provide a deeper view of the functional effect of RF exposure to predict the degree of risk for population health.
# A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
## 5 Bubble Departure Frequency
### Bubble Departure Frequency
Figure 13: Comparison between different theories and experimental results [80]: **(a)** comparison of the three models of [PERSON] et al. with experimental results; and **(b)** comparison of [PERSON]’s model with experimental results. [PERSON] et al. [80] analyzed the heat-transfer process in the bottom region of the boiling cycle, and proposed that the heat transfer leading to bubble re-growth consists of two parts, i.e., rewetting of the surrounding superheated liquid and heat conduction between the solid and the gas, which they model as transient heat conduction at a semi-infinite wall with convective boundary conditions, and they obtained the correlation Equation (49), where \(C_{w}=Nu\times Ra^{-0.25}\), and \(Ra=\frac{\kappa\Delta pD^{3}}{\mu_{i}\mu_{i}}\). Figure 13b shows the comparison between the predicted straight line of Equation (49) and the experimental value. The straight line is obtained by different physical properties of the wall and the liquid, and the experimental and predicted straight lines for the same conditions have the same color. \[D^{0.5}f=\left(C_{w}^{2}\left(\frac{k_{l}}{k_{s}}\right)^{2}(v_{l}\alpha_{l}) ^{-\frac{1}{2}}\alpha_{s}\right)g^{\frac{1}{2}} \tag{49}\]  
The above relational equations show that there are different types of relationships between bubble departure frequency and bubble detachment diameter, and the accurate prediction of the detachment diameter makes the prediction of the bubble departure frequency more difficult to obtain. In addition to the bubble detachment diameter, the bubble departure frequency is also affected by various surface properties and liquid properties. A better correlation formula may be obtained by a dimensionless analysis of parameters such as wall roughness, cavity dimensions, pressure, and wall-superheating conditions.
## 5 Obstacles and Challenges in Evolving V2X Communication
### Architecture Obstacles and Challenges
#### 5.6.2 Cellular Communication (Uu Interface)--Security Architecture and Compliance  
In the V2X domain, trust among the vehicles is essential. This is ensured by using public key cryptography. The keys used to sign messages have to be well protected due to the safety aspects of V2X communication. Thus, private keys must be stored in hardware security modules in order to ensure that they are not leaked out. The virtualized environment used in the 5G systems, especially the MEC servers, provides numerous benefits for the applications, but the deployment of such hardware security modules and compliance with the strict security standards might be challenging. Another aspect of V2X communication is that, due to the distributed and localized broadcast properties of the communication, encryption is not facilitated. This approach is logical in the case of such a communication method, but it might not be sufficient in the case of centralized communication [14; 17; 25].  
The evolution of cellular vehicle-to-everything (V2X) communication technology has encountered several obstacles and challenges that need to be addressed for its successful implementation. Ref. [58] emphasizes the need for continued research and development, as well as effective regulation, to ensure the successful implementation of these technologies.
## 3 Results and Discussions
The EVM values of the data system (64-QAM) and Brillouin gain spectrum of the sensing system are measured simultaneously and the results are illustrated in Figure 5a,b. The measured EVM values are fitted with a linear curve and the obtained slope is \(0.024\pm 0.0025\%/^{\circ}\)C. At room temperature (\(\sim\)25 \({}^{\circ}\)C), the obtained BFS is 10.89 GHz and the slope (temperature sensitivity) is \(1.07\pm 0.013\) MHz/\({}^{\circ}\)C.  
Figure 4: Measured EVM for various data system powers at a fixed sensing power, (**a**) 16-QAM (**b**) 64-QAM with 10 MHz bandwidth. Figure 3: Measured EVM of QPSK modulation with and without band-pass filter (BPF) for various data system powers and fixed sensing power of 18 dBm. At room temperature (\(\sim\)25 \({}^{\circ}\)C) and at a fixed data system power (10 dBm) and a sensing power (18 dBm), the three-dimensional Brillouin spectrum is obtained and shown in Figure 6. Sweeping the Brillouin frequencies from 10.85 GHz to 10.95 GHz and a frequency step of 1 MHz, the three-dimensional spectrum was mapped. Using the Lorentz curve fitting, the peak Brillouin frequency over the 25 km fiber is illustrated in Figure 6 inset. Finally, in order to evaluate the sensing system performance and spatial resolution in the presence of active data transmission, a 20 m fiber length at end of the fiber of the 25 km fiber is placed in the temperature-controlled oven and the rest of the fiber remains at the room temperature of 25 \({}^{\circ}\)C. The temperature was set at 50 \({}^{\circ}\)C in the oven. The peak Brillouin frequency over the 25 km optical fiber is illustrated in Figure 7 inset. The spatial resolution of 5 m is attained as shown in Figure 7, which confirms the 50 ns pulse width used in the BOTDR sensor system. For a 25 \({}^{\circ}\)C temperature change on 20 m long fiber at the fiber end, the estimated BFS change is 26.75 MHz, as the sensing fiber sensitivity is 1.07 MHz/\({}^{\circ}\)C. From Figure 7, the measured BFS change is 27.28 MHz for a temperature change of 25 \({}^{\circ}\)C. Therefore, the BFS error is 0.53 MHz, which corresponds to a temperature error of \(\pm\)0.49 \({}^{\circ}\)C. The results validate a precise temperature measurement without any effects originated from the data communication system. Figure 5: The temperature effects on (**a**) data system EVM of 64-QAM (**b**) sensing system Brillouin gain spectrum (BGS) response. Figure 6: Three-dimensional Brillouin gain spectrum along the 25 km fiber distance. Inset: BFS distribution along the fiber distance.