esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Accuracy Assessment  
Here, the reported overall accuracies were assessed and compared to comprehensively investigate the RS-based wetland studies. Figure 19a shows the histogram of the overall accuracies reported in wetland classification studies. Note that the papers (\(n=67\) out of 344) that did not report the overall accuracy of their classification methods have not been counted in this chart. According to Figure 19b, about 69% of the studies have an overall accuracy of greater than 85%. Additionally, studies of very small-scale areas yielded the highest overall accuracy, followed by investigations involving local and provincial study areas. The studies on a national scale, however, were found to have the lowest median overall accuracy. In Figure 19c, the overall accuracies based on the input data have been presented. Accordingly, the integration of optical and SAR datasets presents high classification accuracies in the case of wetland studies. Figure 19d depicts the reported overall accuracies from the methodological viewpoint. As can be seen, DL, ANN, and ensemble learning algorithms yielded the best classification results in the reviewed literature. Supervised and object-based methodologies reported high median overall accuracies compared to unsupervised and pixel-based techniques, respectively (see Figure 19e,f). Finally, Figure 19g represents the median overall accuracy reported in reviewed papers using high, medium, and coarse spatial resolution imagery. A median overall accuracy of more than 84% was achieved for all spatial resolution imagery. Studies using medium-resolution datasets achieved the highest median overall accuracies in wetland mapping, followed closely by those using high-resolution data.
## 3 Results
These findings imply that while minor discrepancies in W\({}^{\prime}\) estimates exist, the exponential functions provide an accurate approximation for CP at 105 s for MONO and 90 s for BI. Figure 6: Regression plot comparing W\({}^{\prime}\) calculated from 3 MT with exponential functions: MONO \(CP_{135}\) (**A**) and BI \(CP_{90}\) (**B**). The dashed line represents the identity (\(y\) = x), and the solid line represents the model slope tested against 1 (MONO: \(p\) = 0.28, BI: \(p\) = 0.01). Figure 7: Regression plot comparing CP calculated from the 3 MT with exponential functions: MONO \(CP_{105}\) (**A**) and BI \(CP_{90}\) (**B**). The dashed line represents the identity line (\(y\) = \(x\)), and the solid line represents the regression slope tested against 1 (MONO: \(p\) = 0.53, BI: \(p\) = 0.50). The mean difference between CP and W\({}^{\prime}\) derived from the 3 MT and those from the MONO method were 5.51% and 13.86%, respectively, when compared to \(CP_{3 MT}\) and \(W^{\prime}_{3 MT}\). Specifically, CP values derived from the 3 MT ranged from 128 to 297 W, while W\({}^{\prime}\) values spanned 3.40 to 21.90 KJ. In comparison, the values estimated using the mono-exponential function across a 120 s duration were similar, with \(CP_{120}\) values ranging from 119 to 293 W, and \(W^{\prime}_{120}\) ranging from 3.49 to 20.51 KJ.
# EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
## 1 Introduction
Moreover, an autoscaling function allocates more resources to an application service when it exceeds the available resource threshold. Therefore, the autoscaling function provides adaptiveness of application resources according to the dynamic demands on the application services. To address the limitations of EdgeX Foundry, we propose _EdgeX over Kubernetes_ (EoK) in this study. EoK combines EdgeX Foundry and the representative container orchestration platform Kubernetes. EoK supports remote deployment of multiple services to edge nodes in a short duration. Moreover, by utilizing the Horizontal Pod Autoscaler (HPA) feature of Kubernetes, EoK can dynamically increase the number of replicas for each service based on its real-time demand. The objective of EoK is to enable container orchestration on EdgeX Foundry to facilitate the use of EdgeX Foundry in practical edge computing infrastructure. The main contributions of this study are summarized as follows. * We propose EoK as a practical implementation of EdgeX Foundry to improve manageability through the remote deployment of services. Moreover, EoK provides resource autoscaling capabilities, which are crucial in edge computing systems. The implementation of EoK is described in detail to ensure the reproducibility of this work. * We performed experimental evaluations to demonstrate the feasibility and advantages of the EoK system. The results obtained demonstrate that services of EdgeX Foundry can be easily deployed from the Kubernetes master node. Moreover, the HPA feature in Kubernetes can result in improved throughput and latency of services in the EoK system compared with the original EdgeX Foundry platform. The remainder of this paper is organized as follows. Section 2. surveys related work. Section 3 gives preliminary explanations of Kubernetes and EdgeX Foundry. In Section 4, we describe the implementation of EdgeX over Kubernetes. Performance evaluations are presented in Section 5. Finally, we conclude the paper in Section 6.
## 2 Physical Model
### Establishment of the Model  
In order to analyze whether composite components could be formed from SiC ceramic and metal material under the influence of the EU wave, the SiC ceramic and TC4 alloy were chosen as the substrates. The size of TC4 alloy was 60 \(\times\) 3 mm, and the size of SiC ceramic was 30 \(\times\) 3 mm. The surface roughness of both was 10 \(\upmu\)m. Because Al could form effective metallurgical bonding with SiC and TC4 alloys, and the presence of Si could inhibit the formation of Al\({}_{4}\)C\({}_{3}\) metal compounds at the Al/SiC interface and the chemical reaction between Ti/Al, therefore, Al-12 Si was selected as the liquid solder. The physical properties of the substrate and the liquid solder are shown in Tables 1 and 2, respectively. In this paper, a two-dimensional plane model of CF behavior was established using COMSOL Multiphysics software, as depicted in Figure 1.  
At the beginning, the SiC ceramic was placed on the top of the TC4 alloy with a gap of 0.2 mm, and the liquid solder Al-12 Si was placed at the edge of the substrate gap. Subsequently, the coil and the permanent magnet were placed on the top of the liquid solder. The alternating current flowing into the coil can be given by the following:  
\[I=A\times\sin(2\pi ft) \tag{4}\]  
where A is the peak current,\(f\) is the current frequency. The model's initial parameters are as shown in Table 3. According to previous studies, the peak current and the static MF strength had a great influence on the LF generated in the liquid solder; therefore, they play a key role in the flow behavior of the liquid solder. To achieve the coupling of the magnetic and flow fields, the analysis modules \"MF\", \"MF, no current\", and \"two-phase flow, level set\" were used. Finally, the model was divided into 138,862 free triangular meshes, and the transient solver was used to solve the flow process of the liquid solder.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Substrate** & **Elastic Modulus (GPa)** & **Poisson Ratio** & **Density (g/cm\({}^{3}\))** \\ \hline SiC & 450 & 0.142 & 3.1 \\ TC4 & 110 & 0.31 & 4.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of substrates [25; 26; 27].  
Figure 1: Schematic diagram of CF model.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 2 Datasets and Methods  
### Interferometric Synthetic Aperture Radar (InSAR)  
InSAR provides high-resolution geodetic measurements of surface deformations. A typical resolution for a phase measurement is about one-fifteenth of a phase cycle [24]. The interferometric stacking technique, Small Baseline Subset (SBAS), was used in this study to calculate surface deformation. The SBAS technique is based on a combination of differential interferograms produced by data pairs to derive surface deformation velocities by inversion of temporal phase profiles, effectively reducing atmospheric effects, topographic uncertainties, and orbital errors [25; 26]. Images were processed using ENVI's SARscape modules from EXELIS VIS Information Solutions.  
Fifteen ascending Phased Array L-band Synthetic Aperture Radar (PALSAR) images acquired by an Advanced Land Observing Satellite (ALOS) with a ~23.6 cm wavelength were obtained from the Alaska Satellite Facility (ASF) and the Remote Sensing Technology Center of Japan (RESTEC);  
Figure 2: Water level decline from 1987–2006 in alluvial and carbonate aquifers of Quetta Valley [21]. The hydrographs of these wells clearly indicate a constant decline in their water levels. The water level decline rate was lower at ~1 m/year in alluvial aquifers and higher at ~5 m/year in carbonate aquifers. Locations of water wells in alluvium and carbonate are indicated by blue and red dots, respectively, in Figure 1.  
first inversion step. The atmospheric corrections were applied during second inversion step. An atmospheric filter was applied to smooth the signature of temporal displacement by using a low-pass spatial filter with a 1.2 km by 1.2 km window, which was followed by a high-pass temporal filter of 365 days [9; 29]. GPS data were used to convert InSAR relative deformation to GPS-derived absolute deformation. GPS data with an actual measured deformation rate can be used as control points or reference points to convert InSAR relative deformation to GPS-like absolute deformation based on the offset and bias determined at GPS locations.
## 2 Materials and Methods
### IR and FT-IR Characterization and Analysis  
A PerkinElmer Spectrum One FTIR Spectrometer (Waltham, MA, USA) was employed to capture samples' Fourier-transform infrared (FTIR) spectra before and after DBD plasma treatment. FTIR measurements were conducted using attenuated total reflection (ATR), covering the spectral range from 650 cm\({}^{-1}\) to 3650 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\). Before and after polymerization by plasma, the samples were scanned by the FTIR spectrometer, having the silicon wafers placed sample side down onto the diamond of the spectrometer and then scanned.
## 3 Experiment Validation and Analysis
### Experiment and Analysis of Glare Suppression for Orientation Information
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Incidence Angle (\({}^{\circ}\))** & **Glare Polarization Angle (\({}^{\circ}\))** & **Glare Polarization Angle (\({}^{\circ}\))** & **Incidence Angle (\({}^{\circ}\))** & **Glare Polarization Angle (\({}^{\circ}\))** & **Glare Polarization Angle (\({}^{\circ}\))** \\
2 & **(Compensated)** & **(Uncompensated)** & **Angle (Uncompensated)** & **(Compensated)** & **(Uncompensated)** \\ \hline
0 & 87.97 & 93.53 & 50 & 84.12 & 89.89 \\
5 & 86.94 & 92.52 & 55 & 84.53 & 89.59 \\
10 & 86.04 & 91.68 & 60 & 85.08 & 89.95 \\
15 & 85.30 & 91.33 & 65 & 85.78 & 90.22 \\
20 & 84.69 & 90.99 & 70 & 86.63 & 90.65 \\
25 & 84.24 & 90.49 & 75 & 87.62 & 91.24 \\
30 & 83.90 & 90.47 & 80 & 88.75 & 91.72 \\
35 & 83.75 & 90.10 & 85 & 90.03 & 92.83 \\
40 & 83.73 & 89.86 & 90 & 91.46 & 93.96 \\
45 & 83.85 & 89.84 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Real-time orientation calculation glare suppression experiment results. Figure 11: Experimental apparatus of real-time orientation calculation of glare suppression. Figure 12: Relationship between incidence angle and polarization angle before and after orientation information compensation. a clear incident-polarization correlation in the glare suppression method using real-time orientation information compensation. This method not only effectively suppresses glare, but also reduces the error in the polarization angle of the glare, making the suppression more precise and effective.
## 4 Simulation Results
### Tunnel Horizontal Displacement  
Figure 8 presents the horizontal displacement distribution of the existing tunnel vault during the foundation pit excavation, showing that all tunnels move toward the pit. A significant increase in the rate of horizontal displacement is observed within a specific range of the foundation pit, indicating a zone of considerable disturbance. The depth of soil excavation significantly impacts the horizontal displacement of the tunnel, with deeper excavations causing greater displacement. This effect is due to the deep soil excavation pushing the lower and middle parts of the enclosure structure into the pit, thereby moving the tunnel further toward the pit. Given the tunnel's proximity to the pit, enhancing the horizontal displacement monitoring and early warning systems during the middle and late stages of pit construction is essential.
## 3 Literature Review and Comparison of Interferometric Coherence and Intensity Correlation
### Post-Event Methods for SAR Data Based Damage Assessment  
Up to today, the multi-temporal data approach for damage assessment can only be applied for the specific case of the availability of suitable pre-event SAR data (_cf._ Section 2), which according to [PERSON] _et al._[108] is not often the case. Therefore, several authors concentrated their research on the development of SAR damage assessment methods based on only post-event data. However, damage detection using only post-disaster imagery is much more complicated and its accuracy is much lower compared to accuracy achieved by multi-temporal data approaches [3]. As this article concentrates on the multi-temporal SAR data--interferometric coherence and SAR intensity correlation--approaches for damage assessment, this section only shows selected examples for post-disaster damage detection techniques.  
[PERSON] _et al._[110] mapped damaged bridges and dams after the 2008 Wenchuan Earthquake, China, using post-disaster high spatial resolution TerraSAR-X and COSMO-SkyMed imagery and SAR simulation approaches. [PERSON] and [PERSON] [112] used the decrease of the corner reflections of damaged buildings to distinguish between damaged and undamaged buildings. However, this approach was only useful to detect strong damages. Slight damages were very difficult to detect.  
[PERSON] _et al._[108] developed an approach based on post-disaster SAR data of TerraSAR-X and COSMO-SkyMed and the comparison of them with SAR simulations based on very high spatial resolution optical pre-disaster data. In their procedure, the simulated SAR signature of undamagedbuildings is compared with the real (post-event) SAR imagery to detect undamaged (good match of simulation and real SAR image) and damaged areas (strong differences between both). The reported overall accuracy of this procedure is _ca._ 90%.  
[PERSON] _et al._[120] describe a comparison of post-event high spatial resolution X-band airborne SAR data with pre-event optical imagery based on manual interpretation. This approach was successfully applied at the Magnitude 7.1 Yushu County Earthquake, China, which happened in April 2010.  
[PERSON] _et al._[115] used COSMO-SkyMed SAR data to study the 2009 L'Aquila, Italy, Earthquake and focused on changes in texture statistics and SAR reflectivity for damage assessment at the block level (to compensate the speckle effect; _cf._ also Section 3.4). In [117], [PERSON] _et al._ improved their aforementioned SAR texture based damage assessment technique by including information of the physical vulnerability of buildings in their approach. The achieved overall accuracy was _ca._ 56%.  
Moreover, [PERSON] _et al._[116] investigated the relationship between textural features and the degree of damage by using SAR imagery with different spatial resolution.
# A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
## 3 The Proposed HCD Algorithm  
In this section, we introduce a hybrid unequal multi-hop clustering based on density (HCD) to improve network lifetime and throughput. HCD is performed by the distributed method. In this method, clustering is performed as a hybrid of static and dynamic methods. We assumed that nodes can detect their own distance by received signal strength indicator (RSSI) and considered CH relation according to the energy level and nodes situation in the network. Since the CH node consumes more energy, it is prevented to select the nodes having no desirable energy status as a CH and, therefore, the network stability increases. Also, the energy-aware multi-hop method was used for routing between CH.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Cluster Size** & **Intracom.** & **Intercom.** & **Method** & **CH Election** & **Dynamics** \\ \hline LEACH [3] & equal & 1-hop & 1-hop & distributed & Random & dynamic \\ LEACH-C [3] & equal & 1-hop & 1-hop & centralized & deterministic by BS & dynamic \\ HEED [20] & equal & 1-hop & k-hop & distributed & hybrid, based on Attribute & dynamic \\ DDCHS [21] & equal & 1-hop & 1-hop & distributed & based on density \& distance & dynamic \\ VCCBC [12] & equal & 1-hop & k-hop & distributed & CH rotation & static \\ EEPSC [13] & equal & 1-hop & 1-hop & distributed & selection by previous CH & static \\ ECPF [22] & equal & 1-hop & k-hop & distributed & based on fuzzy logic & dynamic \\ BLAC [23] & equal & 1-hop & k-hop & distributed & or density & dynamic \\ ECDC [24] & equal & 1-hop & k-hop & distributed & based on energy \& coverage & dynamic \\ SHE [28] & equal & 1-hop & k-hop & centralized & deterministic by BS & dynamic \\ HUCL [15] & unequal & 1-hop & k-hop & distributed & hybrid, based on attribute & hybrid \\ GBRR [27] & equal & 1-hop & k-hop & distributed & based on grid structure & dynamic \\ DEERIC [29] & equal & k-hop & k-hop & distributed & based on energy & dynamic \\ EADUC-H [25] & unequal & 1-hop & k-hop & distributed & hybrid, based on attribute & hybrid \\ UDSC [30] & equal & 1-hop & k-hop & distributed & hybrid, based on Attribute & dynamic \\ EMAR [31] & equal & 1-hop & k-hop & distributed & at first random, then & static \\ proposed alg. & unequal & k-hop & k-hop & distributed & hybrid, based on density & hybrid \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of some of the clustering algorithms.
## 3 DePaaS: Architecture and Design
### Users, Usage Contexts and Use Cases
#### 3.1.3 Use Cases  
DePaaS provides services to both academic researchers and industry practitioners through its use cases. The initial design of DePaaS supports the following five use cases.  
1. _User Registration_: All roles, except \"DePaaS Admin\", register themselves into DePaaS. The \"DePaaS Admin\" comes as a built-in feature of the DePaaS platform. With this use case, academic researcher and industry practitioner will get a unique identity within DePaaS using which DePaaS services could be accessed.
2. _Uploading SDP Models_: The \"SDP Model Provider\" uploads the SDP model by providing details such as model description, suitable usage contexts, suggested datasets, tunable parameters, model performance values, known issues, etc. The user also uploads the executable files of the SDP model. With the help of this use case, DePaaS would provide an opportunity for the SDP Model Provider to showcase the novel SDP model. Simultaneously, the use case meets the industry demand for novel and improved SDP models.
3. _Uploading Datasets_: The \"Dataset Provider\" uploads the defect dataset by providing details such as the description, information about the source of the dataset, available software features, suggested feature combinations, known issues with the dataset, etc.  
With the help of this use case, DePaaS meets the data-demand of the software research community and the software industry. As the volume and diversity of the dataset increases, better SDP models could be developed.
4. _Performing (or running) SDP and looking at results_: The \"SDP Runner\" runs the SDP workflow. The platform handholds the journey of the user through the SDP process.  
With the help of this use case, DePaaS serves the software industry with vital information about software defects. It also serves the author of the SDP model by providing feedback about the quality of the SDP model.
5. _Improving DePaaS_: At the end of each SDP run, the model performance is evaluated. The source dataset, feature sets, values of the tunable parameters, performance parameters are preserved for future analysis and improvements. \"SDP Researchers\" can analyse the historical SDP data and suggest new values for tunable parameters or suggest new combination of feature sets. They can also suggest ways to clean-up datasets and call for uploading/creation of novel SDP models. Such activities would improve the DePaaS platform, which serves the interests of both the academicians and the practitioners.
# Cleaner Processes for Making Laundry Soap from Vegetable Oils and an Essential Oil
## 2 Materials and Methods  
The laundry soaps are prepared from a mixture of 2 vegetable oils: olive pomace oil (INCI: Olea Europaea (Fruit) Oil) and coconut oil (INCI: cocos nucifera (coconut) oil). For comparison, the soaps are also manufactured from a single type of vegetable oil (olive pomace and coconut oil) by hot and cold processes.  
The vegetable oils were purchased from the company Dried Fruits (Bucharest, Romania). They were obtained in 2024 and have a 2-year shelf life.  
Neem (_Azadirachta indica_), Tea Tree (_Melaleuca alternifolia_) and Thyme (_Thymus vulgaris_ L.) are used as essential oils.  
To characterize the oils used in the saponification process, the substances alcoholic KOH, HCl, phenolphthalein, CCl4, Iodine/KI, Na\({}_{2}\)S\({}_{2}\)O\({}_{3}\) and soluble starch are used. For the comparative analysis of the formulated soaps, 2 laundry soaps (Popular (Sidoarjo, Indonesia) and ChanteClair (Seregno, Italy)) sold in Romania and 3 detergents such as Fairy (Procter & Gamble, Cincinnati, OH, USA) (specially for removing grease), a Reference detergent ICE Non-Phosphate (A) incorporating an optical brightener (abbreviation DA) and a Reference detergent (B), ECE phosphate, without optical brightening agent (DB), are used. The selection of the 2 laundry soaps for comparison is based on both their widespread use in Romania and their cleaning capacity. In the case of ChanteClair soap, another criterion is the similarity in formulation because it is obtained by saponifying olive oil, a vegetable oil that also predominates in the new laundry soaps formulated and tested in this article.  
The choice of the 3 detergents is based on their well-known ability to remove greasy impurities. In addition, the two detergents abbreviated as DA and DB are reference detergents and are commonly used in oily soil removal tests. However, since the used engine oil stain is very pronounced, the tests are carried out both at 60 \({}^{\circ}\)C (according to AATCC Test Method 130-2000) and at higher temperatures (95 \({}^{\circ}\)C and 100 \({}^{\circ}\)C) for good comparisons. It is known that the choice of washing temperature depends on the degree of soiling, the depth of the stains, and the nature of the soiled fabric.