esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Compression Block  
Base64 is an encoding and decoding technique for converting binary data to the ASCII text format and vice versa [29]. It sends data across a media that only supports ASCII forms, such as email messages and extensible markup language (XML) data. Base64 divides every three bits of binary data into six-bit units. The newly generated data are encoded in a 64-radix number system and seven-bit ASCII text. Because each bit is split into two bits, the translated data are one-third, or 33 percent, more significant than the original data.  
Figure 16: Circuit for the bitwise OR generated through Verilog.  
Figure 17: Waveform results of the bitwise OR module.  
Files are typically formed up of 8-bit characters. However, for network communication via routers and switches, 7-bit length values are preferable. As a result, delivering the files in an 8-bit format causes file distortion. Base64 encoding is used to overcome this problem. The string is transformed to an ASCII value for a given random string input. These ASCII bits are concatenated together and written in binary 8-bit format. This concatenated number of bits is divided into 6-bit chunks. Each of the 6 bits in the Base64 table is decoded to values in a 7-bit ASCII-friendly format. The compression block consists of a 16-bit Base64 encoder, and this operation takes place when a selection of '111' is given into the ALCU as control bits. Figure 19 shows the compression procedure.  
Figures 20 and 21 show our implementation in Verilog. As we can notice in the figures, this block set has two parts; the first part converts the 8-bit byte data into a 7-bit ASCII encoding, but for transmission convenience, we need 6-bits for BASE 64, and the next part deals with the conversion of this 7-bit data stream to 6-bit stream. In Figure 20, we notice that the circuitry is complicated, but could be simplified if the output bit length and input length are considered. As mentioned before, the bit length is 16, which means we have two input ports of 16-bit size. The top part of the schematic has 32 ports for this reason. The output of a BASE 64 is only 6-bits in length. Hence, we can notice the ending or the bottom part of a shorter length in terms of ports. Our particular module is designed only to read one input port; hence, the logic levels are only arranged under the right part of 16 bits. In Figure 21, an ASCII converter is attached at the end of the BASE 64. Many computers from external mediums follow this standard and are easily understood by a computer of any generation. This module is full of registers, similar to a memory unit. It has various register blocks; as we notice, the output is only 7 bits in length in those rectangular blocks. The input comes at 6-bit lengths each; hence, it must store the additional bits before transmission.  
Figure 19: Compression procedure.  
Figure 21: Circuit for the ASCII encoder (Part of Base64 Encoder) generated through Verilog.  
Figure 20: Circuit for the Base64 encoder generated through Verilog.
## 4 Solving the Vibration Response of Circuit Unit
### Decomposition of the Vibration Response
#### 4.1.2 Steady-State Response  
The periodic excitation \(F(t)\) can be decomposed into the sum of infinitely many harmonic components using a Fourier expansion. That is  
\[F(t)=\frac{a_{0}}{2}+\sum_{j=1}^{\infty}c_{j}sin(j\omega t+\phi_{j}) \tag{11}\]  
where \(c_{j}=\sqrt{{a_{j}}^{2}+{b_{j}}^{2}}\), \(\phi_{j}=arctan(\frac{a_{j}}{b_{j}})\).  
For each harmonic component, a non-homogeneous special solution is obtained. According to the principle of the superposition of linear systems, the steady-state characteristics of the system can be obtained. The second-order non-homogeneous differential equation of arbitrary harmonics is written as follows:  
\[\ddot{x}(t)+2\zeta\omega_{n}\dot{x}(t)+{\omega_{n}}^{2}x(t)={\omega_{n}}^{2}A cos(\omega t) \tag{12}\]  
Let the special solution of the above equation be \(x(t)=Xcos(\omega t-\varphi)\), and substituting it into the differential equation:  
\[X\Big{[}\Big{(}{\omega_{n}}^{2}-\omega^{2}\Big{)}cos\varphi+2\zeta\omega_{n} \omega sin\varphi\Big{]}cos(\omega t)+X\Big{[}\Big{(}{\omega_{n}}^{2}-\omega^ {2}\Big{)}sin\varphi+2\zeta\omega_{n}\omega cos\varphi\Big{]}sin(\omega t)={ \omega_{n}}^{2}Acos(\omega t) \tag{13}\]  
The above equation can hold for any time, so the coefficients of the terms \(cos(\omega t)\) and \(sin(\omega t)\) on both sides of the equal sign must be equal. That is  
\[\begin{cases}X\Big{[}({\omega_{n}}^{2}-\omega^{2})cos\varphi+2\zeta\omega_{n} \omega sin\varphi\Big{]}={\omega_{n}}^{2}A\\ X\Big{[}({\omega_{n}}^{2}-\omega^{2})sin\varphi+2\zeta\omega_{n}\omega cos \varphi\Big{]}=0\end{cases} \tag{14}\]  
The solution is \(X=A|H(\omega)|\), \(\varphi=arctan\Big{[}\frac{2\zeta\omega/\omega_{n}}{1-(\omega/\omega_{n})^{2} }\Big{]}\), where \(|H(\omega)|\) is the amplification factor:  
\[|H(\omega)|=\frac{1}{\sqrt{[1-(\omega/\omega_{n})^{2}]^{2}+(2\zeta\omega/ \omega_{n})^{2}}} \tag{15}\]  
Equation (14) is transformed into an expression of the stiffness and damping coefficients. Denoted as \(\beta(k,c)\):  
\[\beta(k,c)=\frac{k}{\sqrt{(k-m\omega)^{2}+c^{2}\omega^{2}}} \tag{16}\]  
\(\beta(k,c)\) indicates the magnification of the static displacement amplitude under dynamic vibration. The smaller the amplification factor, the better the damping effect. Furthermore, only when the amplification factor is greater than 0 but less than 1 will the amplitude of the vibration will be attenuated. Therefore, in the parameter design of the vibration-suppression system for the MWD, the amplification factor should be ensured to be small enough. That is  
\[0<\beta<1 \tag{17}\]  
At the same time, the vibration isolation effect of the damping system is achieved when the frequency ratio is greater than \(\sqrt{2}\). However, to ensure the stability and reliability of the system, it is usually no greater than 5. That is  
\[\sqrt{2}<\frac{\omega}{\omega_{n}}<5 \tag{18}\]
## 3 Results
### Pomario Test Site
#### 3.2.1 Pre-Event Landslide Monitoring  
The Pomario landslide, delimited by the purple line on the enlarged maps in Figure 8a,b, occurred between 24 and 29 January 2019 after a long rainfall event; it is a retrogressive instability phenomenon that started with the fissures of the cemented cover layer and continued with a destructive evolution [18], which caused the collapse of a portion of the village (Figure 8c,d).  
The mean displacement rate map sketched in Figure 8a shows moving targets located close to the landslide area. Figure 8b highlights a few targets selected through the NLTA procedure according to (2) and showing an approximating polynomial degree greater than 1 (nonlinear trend). Two targets labeled as P1 and P2 in the figure are selected as representative of the behavior of the two clusters of targets identified by the NLTA. Their displacement time series are represented by black dots in the plots in Figure 8e,f and Figure 8f, respectively. The dotted red lines in the same panels represent the polynomial curves selected by NLTA for modeling the displacement trends. In both cases, the temporal coherence increases with respect to the initial linear model trend, due to the improved kinematic models.  
Figure 7: Mean LOS displacement rate maps over Pomático village: (**a**) Sentinel-1 ascending dataset; (**b**) Sentinel-1 descending dataset; (**c**) COSMO-SkyMed ascending dataset.  
The selected P1 and P2 targets show displacement time series with opposite behaviors, which are representative of the two clusters of targets located, respectively, inside and outside the landslide: the former shows a linear trend with a rate of about \(-4.5\) mm/y until the end of 2016, then it shows an increased downward linear rate of about \(-9.6\) mm/y; the latter a linear trend with a rate higher than target P1, of about \(-6.5\) mm until the beginning of 2017, then it shows a decrease in the absolute displacement rate, which reaches about \(-3.1\) mm/y.  
Figure 8: (**a**) Enlarged mean LOS displacement rate map over the Pomarico landslide derived by processing the COSMO-SkyMed ascending dataset; (**b**) map of targets selected though the NLTA according to (2) and showing a polynomial degree greater than 1; (**c,d**) pictures of the urban area affected by the landslide and bordered by the white rectangle in (**a**); (**e,f**) displacement time series corresponding to targets labeled, respectively, P1 and P2 in panel (**a,b**): black dots and the red dotted lines represent, respectively, the displacement values and the polynomial fit curves derived though the NLTA. The PAI (Piano di Assetto Idrogeologico) landslide risk map by the Basilicata section of the Southern Appennine District Basin Authority [27; 28] is plotted with colored lines delimiting the risk classes areas, according to the colormap in the figure.
## 4 Integration of the PCS with the ASSIST-IoT Reference Architecture  
### ASSIST-IoT Smart Worker Safety System  
The results of the laboratory tests provided a basis for further work related to the integration of the PCS within the broader Cloud-Edge-IoT ecosystem, using the ASSIST-IoT reference architecture [24]. Figure 11 presents an overview of the proposed system in the context of the ASSIST-IoT Smart Safety of Workers pilot study. The PCS communicates with Gateway Edge Nodes (GWENs) located on the construction site using the UWB technology. In the virtualized (Kubernetes) environment provided by the GWENs, several ASSIST-IoT enablers and other components are deployed. The enablers are easily reusable software components, each providing a set of generic functionalities. The streams of data to and from the PCS are routed efficiently on the edge using the Edge Data Broker enabler. The measurements from the weather station and the smartwatch take a similar route--the smartwatch is paired via Bluetooth Low Energy to a UWB location tag that then relays the heart-rate measurements. The Location Tracking enabler interfaces with the UWB location tag and sends the measurements (including the user's heart rate) to the Edge Data Broker enabler. The data from all sensors (including the PCS) are annotated semantically using the Semantic Annotation enabler to enable interoperability between devices. The Semantic Repository enabler plays a supporting role by managing the needed data models, such as domain ontologies, or the annotation configuration for the Semantic Annotation enabler.  
The collected measurements are streamed to the Workplace safety controller, which is responsible for determining the adequate cooling power at a given moment considering the current conditions, using AI methods. Moreover, it remembers user preferences and takes them into account in future decisions. The AI can issue commands to the PCS (to increase or decrease the cooling power) and send alerts to the user, which are displayed on the smartwatch. The commands and the alerts are similarly routed via the Edge Data Broker enabler.  
Figure 10: Participants’ assessment of thermal sensations using the scale from Table 2.
# Performance Analysis of Physical Layer-Based Multiple-Input Multiple-Output on WiMAX (MIMO-WiMAX) +
## 2 WiMAX Physical Layer Model
### Communication Channel  
WiMAX uses wireless channels to handle the communication that takes place between the transmitter and receiver sides of the system. Within the scope of this work, the Rayleigh fading channel is taken into consideration [12].
## 2 Background  
### Mammographic Segmentation  
Though a great deal of work has been conducted in breast profile and ROI segmentation, both remain open challenges. While some model-driven supervised approaches exist [2,8], the majority of segmentation algorithms utilize semisupervised or unsupervised approaches, particularly region growing [8,9], contour detection [8,10], polygon fitting [10], and image clustering [1,2,3,8]. Our work falls into the clustering category and represents the first application of WaveCluster to the mammographic domain. Image morphology is commonly used as a preprocessing step to remove small regions of noise and homogenize local intensity variations, and is also used to supply connected component detection to many segmentation algorithms. A Hough transform is often used to remove the pectoral muscle from the image prior to analysis [11].  
In all cases, the objective of segmentation is extraction of \"interesting\" regions of an image from the surrounding background, either for display or as a precursor to further region-restricted analysis.
# Analysis of Volatiles in Food Products
## 4 Sensory Characterization
### Electronic Nose (E-Nose)  
The electronic nose is not strictly used to prepare sensory characteristics of food products according to a definition it is a matter of sensory analysis with trained panelists. Therefore, it can be said that e-nose is an instrumental way to reveal some aroma discriminates of foods. The e-nose is a group of chemical sensors, associated with a pattern recognition system that is responsible for odors transport through it. Various aromas affect different responses in the sensor system, and these reactions provide a signal pattern characteristic to a particular aroma [123]. The computer system recognizes the pattern of signals and then is able to compare the character of the aroma of varied foods' extracts by pattern recognition system, e.g., artificial neural network. This artificial olfaction could be helpful in the characterization of hundreds of samples in a relatively short time [124]. For instance, a producer who uses coffees from around the world, can analyze coffee samples by the e-nose, and the coffees' odors can be putting together on a multidimensional response map (Figure 3. The analysis of numerous coffee samples, using the best conditions, can result in a group of points for each sample, that furthermore can be grouped. As the number of samples increases, the differences among the groups ought to also grow. Mapping the sensor responses of an unknown sample on the same scheme could allow its detection, by its proximity to one of the known samples.  
Electronic noses have been already applied in the food industry for quality control and detection of contaminants and off-odors. [PERSON] et al. [125] also described the advantages of portable electronic nose usage. However, the human nose is often more sensitive to odorous compounds than are instruments used to detect odors. The study of [PERSON] et al. [126] about the aroma of espresso coffee presented a higher correlation of sensory panel results with aroma characteristics obtained from on-line proton-transfer-reaction mass spectrometry (PTR-MS). The important innovation was using a combination of HS-SPME with a mass spectrometer detector and to determine the aroma of in-cup coffee [127]. The proposed method allows to receive the fingerprint of volatiles in tested food material and predict the sensory quality of other coffee beans; however, the limitations are a high number of samples that need to be analyzed to prepare a proper model and, in the next step, the insufficient access to data mining program.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 4 Solar Potential Assessment Using GISs
### Rooftop Extraction Tool  
A rooftop solar PV system is a PV system in which electricity-generating solar panels are mounted on the rooftop of a residential or commercial building or structure. With the ever-increasing population and unavailability of large-scale solar power plant installation in urban areas, interest in rooftop solar systems that can be installed on buildings in urban area is increasing. GISs have been utilized to extract rooftops to assess their solar potentials on the national and regional scales.  
[PERSON] and [PERSON] [67] extracted rooftops from Google Earth satellite imagery of the Karachi region of Pakistan using a feature extraction tool of ENVI EX software employing object-based image recognition. From the extracted rooftop data and their characteristics (i.e., building orientation, shading effect, and other roof uses), the PV energy and power output were assessed. The characteristics of previous GIS-based rooftop PV potential studies were also summarized.  
[PERSON] et al. [68] calculated roof surface area using vector-type roof data compiled throughout Spain. The installation of solar hot water systems (SHWSs) and PV systems was considered. With assumptions for SHWS demand coverage and payback evaluation, the results showed that SHWSs could contribute up to 1662 ktoe/year of primary energy. In addition, the PV potential of each rooftop was calculated by multiplying the area of the roof times simple constants derived from technical parameters, and it was found that PV systems would provide 10 TWh/year of electricity.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 4 Adjustment Approaches of IPCB Geometry Configuration
### Horizontal Adjustment of an IPB  
According to assumption (2) in this section, the horizontal velocity of an IPB is proportional to the local horizontal wind velocity [38; 39; 40]. Then, the horizontal kinematics of an IPB can be described by Equation (16).  
\[\begin{split}\dot{\lambda}&=\gamma\cdot w_{z}( \lambda,\phi,h,t)\\ \dot{\phi}&=\gamma\cdot w_{m}(\lambda,\phi,h,t)\end{split} \tag{16}\]  
In Equation (16), \(\lambda\) represents the longitude of IPB position; \(\dot{\lambda}\) represents the variation of \(\lambda;\phi\) represents the latitude of IPB position; \(\dot{\phi}\) represents the variation of \(\phi;w_{z}(\lambda,\phi,h,t)\) represents zonal wind velocity at position \((\lambda,\phi,h)\) and at time \(t\); \(w_{m}(\lambda,\phi,h,t)\) represents meridional wind velocity at position \((\lambda,\phi,h)\) and at time \(t\); \(\gamma\) represents a drag coefficient of IPB in the horizontal plane.  
Since it is assumed that wind velocities vary with altitude but not with horizontal location, and wind velocities are steady in IPCB service time, the relation between altitude and wind velocity is emphasized in this paper. In general, wind velocity increases as altitude increases in the troposphere, reaching a maximum of about 10-15 km. Then, wind velocity decreases, reaching a minimum in the lower portion of the stratosphere at about 18-25 km [43; 44]. For simplicity, a seventh-order polynomial is employed to fit the relation between altitude and wind velocity in this paper, as described below [45; 46; 47].  
\[\begin{split} w_{m}=c_{m0}+c_{m1}h_{std}+c_{m2}h_{std}{}^{2}+ \cdots+c_{m7}h_{std}{}^{7}\\ w_{z}=c_{z0}+c_{z1}h_{std}+c_{z2}h_{std}{}^{2}+\cdots+c_{z7}h_{std}{} ^{7}\end{split} \tag{17}\]  
In Equation (17), \(\mathbf{c_{m}}\) and \(\mathbf{c_{z}}\) represent meridional wind coefficients and zonal wind coefficients, respectively. \(h_{std}\) represents normalized altitude, which can be calculated by Equation (18).  
\[h_{std}=(h-\mu_{d})/\sigma_{d} \tag{18}\]  
In Equation (18), \(\mu_{d}\) and \(\sigma_{d}\) are both normalized parameters. Figure 4 illustrates wind fittings for a specific area in March, June, September, and December.  
Figure 4: Wind fittings from 1 km to 30 km for a specific region.  
From Figure 4, it can be seen that wind velocity changes remarkably with altitude, which provides opportunities for IPBs to adjust their horizontal trajectories utilizing different winds. In particular, a so-called \"quasi-zero wind layer\" existed at an altitude of about 21 km in June and September, as shown in Figure 4. At altitudes up and down the quasi-zero wind layer, the direction of zonal wind reverses, and the magnitude of meridional wind is small, which is beneficial to IPBs to lengthen their flight time in specific airspace [48; 49; 50; 51; 52; 53].
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 1 Introduction  
Aquaponics and aquaculture has received growing concern in modern times, and this reinforces its increasing impact on society as an innovative response to food security [1]. Technological advancements in agriculture have opened a path for modern agricultural practices such as aquaponics, which is an eco-friendly system for cultivating fish and plants without soil [2]. Aquaponics is a symbiotic system which combines aquaculture with hydroponics in fish and plant cultivation to save water and utilize dissolved nutrients more efficiently, without the use of agrochemicals [3]. This advancement has also opened a new era for the design and development of both fish and plant health monitoring systems [2].  
The time consumed in monitoring and resetting parameters to prerequisite levels is a major shortcoming in aquaponics systems [1]. Fish conserve flings, and it is tedious to replenish oxygen, maintain temperature, and water levels or fertilize plants frequently; hence, these metrics need to be frequently monitored to ensure fish and plant health or growth [3]. To overcome this complex and time-consuming process of manually monitoring plants and fish health in an aquaponic system, an automatic health monitoring sensor has been proposed. This study designs a smart health monitoring system which makes it easy for deployed sensors to accurately measure and transmit data on a variety of metrics such as pH, temperature, water level, and dissolved oxygen, etc., to remote farmhouse monitors in real-time.  
The competitiveness of several developed aquaponics monitoring systems depends on technological advances, the number of metrics the system can monitor, the flexibility of design, cost-effectiveness, and differences in geographic environments, which cannot be generalised [1]. A lot of studies have been conducted on the design of aquaponics monitoring systems which can measure and display parameters such as pH level, water level, humidity, temperature, etc., to the user [4, 5]. A few sensors, such as Libelium smart water ions, exist on the market. However, there is no cost-effective aquaponics monitoring system designed to detect selective ions in an industrially relevant aquaponics system.  
Existing designs are expensive and are mostly useful for laboratory use and not production or industrial use, and this has motivated the effort of this work.