esa-sceva/satcom-chunk-collection huggingface dataset:
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 2 Materials and Methods
### Data Processing
#### 2.2.4 Combination of the Methodologies in GIS Environment  
After the data processing was completed, all information mentioned above associated with the building characteristics, PS mean velocity, displacements, and leveling measurements were imported into ESRI's ArcGIS Pro software for further analysis.  
Firstly, concerning the buildings as a unit, a database was created in order to assemble all possible geographical and descriptive information about each one. Specifically, the database includes the geolocation of the building exported by Google Earth Pro. In addition to this, important information about the height and the floors of the structure was added to the database. Equally important was the starting date of the construction, as well as the ending date, in order to understand the result values of PS displacements. Apart from that, recent photos of each structure are included in the database, as well as the exact coordinates in WGS 1984, of each construction. As mentioned before, the results of this study are preliminary; hence, a well-structured and detailed database will be able to provide information for future studies over time.  
As is well known, the PSI technique provides spatially scattered measurements of mm- to cm-level-accuracy displacements with spatial density depending on the resolution of each sensor. Taking into consideration the high density of PS on the infrastructures and, therefore, the validity of the PSI results, a more detailed analysis, isolating and analyzing PS separately, was carried out on the structural components. The total number of PSs extracted by StaMPS and SNAP in Limassol equals 57,916. Ergo, after investigating PSs around/on each construction, a buffer zone of 200 m that includes PSs and structure information around every building was created. The number of 200 m was used as an assumption to demonstrate the land subsidence in each building and its surrounding area through PS analysis. No interpolation method (e.g., Kriging, IDW, etc.) was used because the study focused on the structures and not the region in general.
## 2 Enhancement Methods for High-Resolution SAR Ship Wake Processing  
### Typical Wake Images in Spaceborne SAR  
When maritime vessels move through the ocean, wake ripples are generated on the sea surface due to variations in water density layers, altering the roughness of the ocean surface. The sea surface roughness significantly influences the backscattering characteristics of electromagnetic waves, capturing the micro-dynamic phenomena caused by vessel movement [19]. SAR, as a highly sensitive small-scale wave detector, can measure subtle variations in sea surface roughness, enabling wake detection. Under complex marine conditions, the scattering echoes received by SAR mainly originate from smaller waves with wavelengths close to the transmission wavelength, while larger ocean waves introduce some degree of tilt modulation. Due to the inherent complexity of the sea surface, which is essentially characterized by random and continuous variations, the scattering observed by SAR radar exhibits similar characteristics. Studies have identified various characteristics of ship wakes in SAR images, with these differences being related to radar parameters, imaging angles, ocean conditions, wind speeds, vessel parameters, and motion parameters [20]. Based on the different image characteristics of ship wakes, they can be classified into the following three categories: surface waves (Kelvin wake or narrow V-shaped wake), turbulent wakes, and internal wave wakes.  
In the above Figure 1, (a) depicts the Kelvin wake phenomenon, characterized by bright line features, such as the leftmost bright line, typically located within the range of 16\({}^{\circ}\) to 19.5\({}^{\circ}\) on both sides of the vessel's track. (b) shows a typical narrow V-shaped wake of surface waves, also characterized by bright line features, forming bright line features in the wake of the vessel's movement. Bright lines with an angle of less than 10\({}^{\circ}\) from the turbulent wake are usually classified as narrow V-shaped wakes. (c) represents a turbulent wake, characterized by dark line features. In Figure 1a,b, the vessel target itself is not clearly visible and is almost undetectable. This is because the vessel is in motion along the satellite flight direction, causing changes in the azimuth frequency in SAR imaging processing, leading to the defocusing of the target image and a decrease in the target echo amplitude [21]. In Figure 1c, it can be clearly seen that the radial movement of the vessel causes a change in the Doppler frequency in the imaging processing, resulting in the vessel target image deviating from its actual azimuth coordinate position, but the wake remains in the correct position. Based on the above image analysis, since wakes in SAR images often appear as continuous bright or dark lines of a certain length, using Radon transform can convert the linear features in the image into peaks or valleys in the Radon transform domain, thus transforming the linear wake detection problem in SAR images into a peak/valley extraction problem.
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
## 4 Supervised Learning Model  
To predict encounters between satellite pairs without relying on orbit propagation techniques, we employ an SL approach. This method is applied to learn the relationship between the input data consisting of pairs of satellite initial states and the output data characterizing the communication opportunities over the following two days. Although the training stage may involve significant computational costs depending on the complexity of the architecture and the number of training samples, the SL demonstrates remarkable cost efficiency in the inference part, which takes place after the training process to predict the output associated with new and unseen data. Additionally, post-training optimization techniques can be applied to further reduce the inference time in exchange for a minimal compromise in model performance, which is especially attractive for edge devices and resource-constrained systems. A computational cost analysis is presented in Section 5.4.  
Given the high level of performance demonstrated by the fully-connected neural network used in [16], this model architecture is reused but adapted to accommodate the two additional features of the input vector regarding the drag term. Additionally, the incorporation of two extra hidden layers has been shown to result in a notable increase in performance. In that sense, the final architecture comprises 14 neurons for the input layer; 576 neurons for the output layer; and 4 pairs of hidden layers with 32, 64, 128, and 256 neurons, respectively. This configuration entails a total of 279,488 parameters to be learned to minimize the loss function.  
All parameters related to the activation function, batch size, and training epochs remain consistent with prior works. However, a weighted loss function is implemented to address the significant imbalance that characterizes the output dataset.
# Wireless Sensor Networks for Big Data Systems
## 4 Open Issues
### Real-Time Communications on Fog Computing  
Because of the delay-sensitive property of sensed data in WSN, real-time communication and processing are highly preferred for big data systems. However, severe constraints on nodes in WSNs make it difficult to support real-time communications. Moreover, a big data system on cloud computing architecture is not suitable to perform real-time tasks. In order to support real-time tasks, fog computing [66] was recently proposed to extend the cloud services to the edge of the network by computation, communication and storage closer to the edge devices and end users, which aims to enhance low latency, mobility, network bandwidth, security and privacy. Therefore, a big data system on fog computing can be a feasible solution to support real-time processing. However, there is no outstanding solution for real-time communications [67] in WSN yet. Figure 6 shows the conceptual architecture for fog computing, which works as an intermediate layer between the cloud and the users to provide real-time communications.
## 2 Materials and Methods
### Analytical Method Validation for Quality Assurance  
All quantifications were performed using external calibration curves. For PAH quantification, a series of solutions was prepared with the EPA 610 PAH Mix (Sigma-Aldrich, Germany). The accuracy of the analyses was verified using the internal standard method with standard solutions of deuterated aromatic hydrocarbons (Semivolatile Internal Standard Mix) [30].  
The accuracy of the PCB analyses was verified by adding an internal standard using Pesticides Surrogate Spike, which is an acetone solution of decachloroiphenyl and 2,4,5,6-tetrachloro-m-xylene. To correctly identify the PCB congeners tested, some of the samples were fortified with standard solutions of seven indicator congeners (NEN 0813) and eight toxic congeners (PCB Mix-8). A 300 \(\mu\)L mixture of both standard solutions, in which the concentration of each congener was 160 \(\mu\)g\(\cdot\)dm\({}^{-3}\), was added to the samples.  
Standard solutions of PAHs and PCBs were also fractionated. Each of the columns prepared for methods 3 and 4 was loaded with 1 cm\({}^{3}\) of the solutions, containing 16 PAH at 40 \(\mu\)g\(\cdot\)dm\({}^{-3}\) each and 15 PCB at 16 \(\mu\)g\(\cdot\)dm\({}^{-3}\) each.  
The limit of detection (LOD) for each compound was determined as the concentration in the extract that produced an instrumental response at two different ions to be monitored, with a signal to noise ratio of 3:1 for the less sensitive signal [34]. A blank method was included for every 10 samples. The LOQ was estimated as 10 times the standard deviation of 10 independent blank measurements (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**PAH** & **LOD** & **LOQ** & **PCB** & **LOD** & **LOQ** \\ \hline NA & 0.30 & 0.91 & PCB 28 & 0.031 & 0.094 \\ ACL & 0.19 & 0.59 & PCB 52 & 0.029 & 0.087 \\ AC & 0.23 & 0.70 & PCB 101 & 0.044 & 0.132 \\ FA & 0.11 & 0.34 & PCB 81 & 0.035 & 0.110 \\ PHE & 0.08 & 0.26 & PCB 77 & 0.030 & 0.089 \\ AN & 0.11 & 0.32 & PCB 118 & 0.018 & 0.056 \\ FL & 0.05 & 0.15 & PCB 114 & 0.066 & 0.197 \\ PY & 0.05 & 0.16 & PCB 153 & 0.062 & 0.183 \\ BaA & 0.05 & 0.16 & PCB 105 & 0.060 & 0.179 \\ CHR & 0.06 & 0.18 & PCB 138 & 0.059 & 0.174 \\ BbFA & 0.08 & 0.24 & PCB 126 & 0.053 & 0.158 \\ BkFA & 0.07 & 0.22 & PCB 156 & 0.030 & 0.090 \\ BaP & 0.06 & 0.17 & PCB 157 & 0.030 & 0.091 \\ IP & 0.09 & 0.28 & PCB 180 & 0.009 & 0.028 \\ DbahA & 0.08 & 0.25 & PCB 169 & 0.040 & 0.120 \\ BghiP & 0.10 & 0.32 & & & \\ \hline \end{tabular}
\end{table}
Table 3: LOD [ug\(\cdot\)kg\({}^{-1}\)] and LOQ [ug\(\cdot\)kg\({}^{-1}\)] values for the compounds tested.  
Statistical analysis was performed with Statistica 13.3. The results are presented as arithmetic means. One-way ANOVA (Tukey's test; \(p<0.05\)) was used to analyze the significance of the differences.
## 3 Results and Discussion  
### Bacterial Inactivation Effect and Kinetics Model Analysis  
The inactivation effect on _S. typhimurium_ and _S. aureus_ by DBD-NTP are presented in Figures 1 and 2, respectively. All survival curves consist of three phases: shoulder or tailing phase, a log-linear phase, and a transition phase between the shoulder and log-linear phases. As shown in Figure 1a and Table 1, survival curve of _S. typhimurium_ (total population) on the surface of glass showed a log-linear phase (from 8.05 \(\pm\) 0.07 to 4.91 \(\pm\) 0.61 log (CFU/cm\({}^{2}\))) followed by a long tail. As shown in Figure 1b and Table 1, survival curve of _S. typhimurium_ (total population) in 0.9% NaCl solution showed a shoulder phase (treated time from 0 to 73.3 \(\pm\) 4.08 s) followed by a log-linear phase (treated time after 73.3 \(\pm\) 4.08 s). In this study, the inactivation efficiency of DBD plasma was evaluated 5 different parameters. In addition, a log-linear shoulder or tailing model was also used to analyze the plasma-induced inactivation. The analysis was \(R^{2}\) (0.9761-0.9993) and RMSE (0.0393-0.7184), which determined the goodness of fit of the models.  
Figure 1: Survival curves of _S. typhimurium_ after exposure to dielectric barrier discharge (DBD) plasma. Cells were inactivated on solid(like) surface of glass and gelatin (**a**) and in liquid carrier of NaCl (**b**), phosphate buffered saline (PBS) (**c**) and bovine serum albumin (BSA) (**d**). All points were actual values and all lines were fitted values of the model. BHI: Brain Heart Infusion Agar; SS: Salmonella Shigella Agar.  
Figure 2: Survival curves of _S. aureus_ during DBD-NTP (non-thermal plasma treatment). Cells were inactivated on solid surface of glass and gelatin (**a**) or in a liquid suspension of NaCl (**b**), PBS (**c**) or BSA (**d**). All points were actual values and all lines were fitted values of the model.
## 4 Experimental Sensing Setup
### Dual Path Correlation Spectroscopy  
Once the target laser wavelength has been reached, gas detection can be carried out directly by passing the laser emission through the cell and monitoring it with an optical detector. However, laser intensity fluctuations can affect the measurement readings. Therefore, in our sensing setup the laser output was divided into two channels by utilizing a 90/10 fiber coupler (Figure 4). The beam with 90% illuminates the measurement channel, while the 10% beam illuminates the reference channel. At the end of the optical channels, two identical indium gallium arsenide (InGaAs) detectors are placed to measure the overall light intensity of each channel. Both signals generated by detectors were conditioned and low pass filtered by using simple analog circuits. Furthermore, these signals are recovered with a data acquisition board and processed to calculate the depth of modulation. Here, it is important to point out that within the same PID control program the required functions were added to record the channel signals and to perform the depth of modulation calculations. At this point, we would like to point out that basically all the electronic and processing stages have the potential to be implemented within a simple microcontroller based acquisition board.
# Low Complexity Deep Learning Framework for Greek Orthodox Church Hymns Classification
## 3 Experiments and Results
### Dl Models  
The model's weights update is performed using an Adam (adaptive moment estimation) algorithm [45]. The training process exploits the capabilities of an NVIDIA RTX 3080 GPU. Each base model is trained for 100 epochs with a mini-batch size of 4. The learning rate is set to \(10^{-4}\), since the experiments with different learning rate formats underperformed. To study the generalization capability of our CNNs, five-fold cross-validation is utilized. The dataset is split in such a way that the training set contains 70% of the initial data, and the validation and the test sets consist of 15% of the initial dataset each. Sincefive-fold cross-validation is used, each training fold consists of 2892 images. Finally, the same folds are used for all the experiments.  
In Table 2, the computational cost of each custom model is presented. Each model is studied regarding the space it occupies in terms of Megabytes, the number of parameters that need to be updated after each training epoch, and finally the number of multiplications and additions that need to be performed in a single forward pass. In Tables 3-5, the performance of the proposed approaches is presented, while the training and validation loss and accuracy are shown in Figures 4-9. As five-fold cross-validation is carried out, the presented values are average values.  
Figure 5: Shallow CNN performance: Loss.  
Figure 6: Deep CNN performance: Accuracy.  
Figure 7: Deep CNN performance: Loss.  
The results presented above show that Micro VGG outperforms the other approaches in terms of performance, at a smaller computational cost.
# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 4 Materials and Methods
### Methods for Distributed Action Rule Generation
#### 4.1.2 Random Partitioning  
We compare the random vertical partitioning method to our initial proposed method (vertical partitioning based on attribute correlation). For random partitioning, instead of splitting based on correlation, it is fully random. This method was proposed by authors of [35], where they split the datasets vertically, by attributes, and extract action rulesfrom multiple partitions. Similar to our method discussed previously, \"once all parallel processes are complete, action rules from each partition are combined to yield the final recommendation of action rules\" [35].  
The process is illustrated in Figure 2 and the steps include:  
1. Generate multiple sets of groupings for flexible attributes, varying the number of groupings (N) from two to the total number of attributes (exclusive). In each set, shuffle the attributes randomly and divide them into k groups, where k ranges from 0 to N\(-\)1, creating diverse arrangements of the attributes. Store each set of groupings in a partition list.
2. Iterate through each partition in the partitions list obtained in Step 1. Again, each partition is our set of flexible attributes randomly grouped. With each iteration, extract action rules for each group of flexible attributes. Then, create all possible combinations, taking one action rule from each group and checking its support.
3. Determine the best partition of the dataset by comparing the F-scores of the sets of action rules discovered in each partition.  
Again, for this method, splitting is done randomly. In our proposed method, splits are determined by correlation and clustering, not just randomly.
# Solid-Phase Microextraction Techniques and Application in Food and Horticultural Crops
###### Abstract  
Solid-phase microextraction (SPME) is a sample preparation technique which utilizes small amounts of an extraction phase for the extraction of target analytes from investigated sample matrices. Its simplicity of use, relatively short sample processing time, and fiber reusability have made SPME an attractive choice for many analytical applications. SPME has been widely applied to the sampling and analysis of environmental, food, aromatic, metallic, forensic, and pharmaceutical samples. Solid phase microextraction is used in horticultural crops, for example, to determine water and soil contaminants (pesticides, alcohols, phenols, amines, herbicides, etc.). SPME is also used in the food industry to separate biologically active substances in food products for various purposes, for example, disease prevention, determining the smell of food products, and analyzing tastes. SPME has been applied to forensic analysis to determine the alcohol concentration in blood and that of sugar in urine. This method has also been widely used in pharmaceutical analysis. It is a solvent-free sample preparation technique that integrates sampling, isolation, and concentration. This review focuses on recent work on the use of SPME techniques in the analysis of food and horticultural crops.  
foods; head space extraction; gas chromatography-mass spectroscopy; pesticides; foods; solid phase microextraction; volatile compounds +
Footnote †: journal: molecules
## 2 Principles of DAS Fiber Optic  
DAS can be acquired using single-mode optical fiber as its fiber optic material. The most common type of fiber optic material is silica, which is a type of glass. Silica is an excellent material for fiber optic cables because it has very low optical attenuation (loss of light intensity) and can be drawn into very thin fibers with high tensile strength [24].  
Single-mode fiber is a type of optical fiber that allows only a single mode of light to propagate, which results in lower signal attenuation and dispersion compared to multimode fiber [25]. This allows DAS to achieve high sensitivity and spatial resolution, making it a suitable technology for monitoring seismic activity over long distances. The other type of fiber is multimode fiber which allows multiple modes or rays of light to propagate through the fiber simultaneously. Unlike single-mode fiber, which only allows a single mode to propagate, multimode fiber has a larger core diameter and a higher numerical aperture, which allows for a larger range of incident angles and modes to be coupled into the fiber. Multimode fiber is also used in some sensing applications, such as distributed temperature sensing (DTS) and distributed strain sensing (DSS), which rely on the changes in the light signal caused by changes in temperature or strain. However, the use of multimode fiber for sensing is generally limited to short distances due to its higher attenuation and lower spatial resolution compared to single-mode fiber.  
The fundamental principle of DAS is the use of a single optical fiber as both a sensor and a means of transmitting data [26]. DAS systems use a laser light to illuminate a segment of optical fiber, and then detect the backscattered light from the fiber using a photodetector. The backscattered light is affected by the mechanical and temperature changes along the fiber, which can be used to detect and locate events, such as acoustic signals, vibration, and strain. By measuring the time of flight of the backscattered light along different segments of the fiber, it is possible to create a high-resolution image of the acoustic or vibration activity along the entire length of the fiber [27; 28].
# Bladeless Heart Pump Design: Modeling and Numerical Analysis
## 2 Theoretical Analysis of Heart Pump Parameters  
During the pump design, the following parameters had to be determined: the pump head \(\Delta p\), the pump flow \(Q\), the internal disk radius \(R_{1}\), the external disk radius \(R_{2}\), the disk distance \(h\), the number of disks \(n\), and the angular velocity \(\omega\). The most important criteria are the head pump and the flow. The parameters have to be within strictly defined limits to ensure normal blood circulation through the body. The parameters used for the calculation are: \(\Delta p=65\) mmHg, \(Q=5\) L/min [24]. Another important criterion is high hemocompatibility with minimal negative pump impact on the blood (minimal thrombosis and hemolysis). The acceptable amount of shear stress is in the range of 1-150 Pa [25; 26; 27; 28; 29]. Amounts of shear stress of less than 1 Pa have been linked with thrombosis and above 150 Pa have been associated with hemolysis. The third important criterion is to achieve the smallest possible pump volume. The diameter and number of disks are adjusted to achieve the minimum pump volume. The fourth criterion is high efficiency to allow the longest possible autonomy without charging the batteries. The rotational Reynolds number is defined with Equation (1)  
\[Re=\frac{\rho\cdot\Delta v\cdot h}{\mu} \tag{1}\]  
where \(\rho=1050\,\mathrm{kg/m^{3}}\) is the blood density, \(\mu=0.0035\)\(\mathrm{kg/(m\cdot s)}\) is the blood dynamic viscosity, \(h=1\) mm is the distance between two disks, and \(\Delta v\) is the relative fluid velocity. The maximum rotational Reynolds number is \(Re\approx 1600\), so the flow is considered as laminar [30]. The assumption of laminar fluid flow is not valid in the regions near to the inlet and outlet of the disk and to the flow separator. The area of laminar fluid flow is within the range of \(\theta=0\)-1.5 \(\pi\) (Figure 1) and consequently the flow may be determined using the Navier-Stokes equations. It is necessary to introduce restrictions to analyze the flow between two parallel rotating disks. The flow is stationary \(\left(\frac{\partial}{\partial t}=0\right)\) and uniform \(\left(\frac{\partial v_{z}}{\partial\theta}=0\right)\). There is no flow in the vertical direction (\(v_{z}=0\)) and the influence of mass forces is negligible (\(f_{i}=0\)). The continuity equation and the Navier-Stokes equations describe the fluid flow in a cylindrical coordinate system [31], Equations (2)-(5).