esa-sceva/satcom-chunk-collection huggingface dataset:
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 4 Evaluation Use Case
### Fog-Cloud Infrastructure Dimensioning  
Nowadays, low power wireless technologies and fog computing architectures have gained tremendous emphasis due to the massive growth of connected devices in the network. The need for connecting simple IoT devices, such as sensors and actuators, is increasing rapidly. Variables used in our fog-cloud infrastructure dimensioning are shown in Table 5.  
In fog computing architectures, fog nodes are usually located within one hop from the IoT sensors. The variable \(C\) is used to indicate the communication range in meters between a fog node and an IoT sensor. Two variables, \(U_{LPWAN}\) and \(D_{LPWAN}\), are used to indicate the upload and the download data rate of the LPWAN technology, respectively. Then, the number of bits in each data sample is given by \(N\). The number of data samples to be transmitted is given by \(R\). This way, the upload and the download transmission time of a packet between an IoT sensor and a fog node can be expressed as shown in (1) and in (2), respectively.  
\[T(U_{LPWAN})=\frac{N\times R}{U} \tag{1}\]  
\[T(D_{LPWAN})=\frac{N\times R}{D} \tag{2}\]  
Moreover, the communication between a fog node and the cloud node is via the Internet, where the upload speed is given by \(U_{Fog}\) and the download speed is given by \(D_{Fog}\).
## 2 Model and Test Setup  
### Scaled Model for Piled-Raft Foundation (PRF)  
A square-patterned PRF with a side length of 46 m was modeled and scaled at a ratio of 1:50, as shown in Figure 1b,c. The scaled PRF model was manufactured with high-density polyethylene (HDPE). The HDPE has a density of 930-970 kg/m\({}^{3}\), tensile strength of \(\sim\)14 MPa, Young's modulus of \(\sim\)290 MPa [22; 23; 24]. We measured the P-wave velocity \(V_{P}\) of the HDPE material as 2450 m/s and the rod wave velocity \(V_{Rad}\) as 1600 m/s. Accordingly, the other elastic wave velocities can be also calculated; the shear wave velocity \(V_{S}\) = 950 m/s, the Rayleigh surface wave velocity \(V_{Rayleigh}\) = 780 m/s, and the dynamic Poisson's ratio \(\
u_{dynamic}\) = 0.42. Owing to the symmetric characteristic of the modeled PRF, one-quarter plane of the PRF was fabricated, as shown in Figure 2a. Thereby, the model PRF included 25 piles on which caps were threaded and connected to a raft on them. The spatial arrangement of the pile was asymmetrically patterned in the quarter plane, as it accounts for structural load distribution from the super-structure. The overall dimension of the model PRF was 460 mm \(\times\) 460 mm \(\times\) 80 mm in length, width, and height. The piles had a cylindrical shape with a diameter of 40 mm and a height of 500 mm. Figure 2b details the locations of the piles and accelerometers in the model PRF. In addition, the raft was divided into 25 tessellated zones by using the Voronoi diagram, which allocates the zones to the closest pile.
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 4 Methodology
### Scenarios Definition  
We proposed three different scenarios for serving backhaul to the drones. The first scenario (I) considers connecting all the UABS to the CN through LTE-advanced technology using a 3.5 GHz channel with a 20 MHz bandwidth. A first in first out (FIFO) based algorithm for resource block (RB) allocation is implemented where user resources are assigned simultaneously based on the backhaul constraints [36]. The RB is the tiniest component of resources that can be allocated to devices in LTE. Each RB has 12 subcarriers and seven symbols equivalent to a one time slot in the LTE frame. The amount of RB is dependent on the carrier bandwidth; for 20 MHz 100 RB are used [37]. We also assumed no interference between the backhaul network (UABS to CN) and the access network (users to UABS). For the second scenario (II), carrier aggregation (CA) was included, aggregating five carrier components (CC) to complement a total of 100 MHz equivalent to 500 RBs. The third scenario (III),  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Area size & 6.85 km\({}^{2}\) (Ghent, suburban) \\ Number of users/devices & 260 users \\ User distribution & Uniform \\ Traffic demand & 1 Mbps data/64 kbps voice \\ Facility size & 1500 drones \\ Intervention time & 1 h \\ \hline \end{tabular}
\end{table}
Table 1: Scenario simulation parameters.  
Figure 2: The city center of Ghent with 260 ground users in red circles.  
was a direct link scenario using mmWave connectivity. In this scenario, UABSs were connected to the CN using a 60 GHz link. The radio and link budget parameters for the simulation are presented in Table 2. The 3.5 GHz link used LTE-advanced orthogonal frequency division multiple access (OFDMA) configuration.
## 2 Mathematical Modeling  
The structure of various polymeric graphene-based nanocomposites must ensure the reflection loss (RL) optimum value, preferably within the \(-12\) dB, for which microwave is incident on the surface. Figure 1 presents the multi-layered structure in which x is the layer order, and a perfect electric conductor (PEC) backs the structure.  
In the proposed multi-layered structure, the EM properties of various single-layered structures measured using the waveguide set-up have been used, and further, the RL is calculated using Equation (1).  
\[\text{RL}_{\text{dB}}=20\log_{10}\biggl{(}\frac{Z_{\text{ix}}-Z_{0}}{Z_{\text{ix} }+Z_{0}}\biggr{)}\text{ dB} \tag{1}\]  
The nomenclature of the above equation is as follows,  
\(Z_{0}\): free space impedance = 377 \(\Omega\) and  
\(Z_{ix}\): input impedance. It is calculated using Equation (2) for each layer '\(x\)'.  
\[Z_{\text{ix}}=\eta_{x}\frac{Z_{i(x-1)}\cos(k_{x}t_{x})+\text{j}\eta_{x}\sin(k_{ x}t_{x})}{\eta_{x}\cos(k_{x}t_{x})+\text{j}Z_{i(x-1)}\sin(k_{x}t_{x})}\text{ }\Omega \tag{2}\]  
This equation is general; if \(Z_{i(x-1)}=1\), it applies to a single-layered structure. The wave number \(k_{x}\) is calculated using Equation (3).  
\[k_{x}^{2}=(2\pi\text{f})^{2}\mu_{0}\varepsilon_{0}\mu_{x}\varepsilon_{x}=(2 \pi\text{f})^{2}\mu_{0}\varepsilon_{0}\mu_{x}(\epsilon^{\prime}{}_{x}-\text{j }\epsilon^{\prime\prime}{}_{x})\text{ m}^{-2} \tag{3}\]  
where the permittivity and permeability of the xth layer are substituted in Equation (4) to estimate the \(\eta_{x}\)  
\[\eta_{x}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}}\sqrt{\frac{\mu_{x}}{ \varepsilon_{x}}}\text{ }\Omega \tag{4}\]  
where free space permeability and permittivity are taken as \(\mu_{0}\) = 4\(\pi\)\(\times\)\(10^{-7}\) H/m and \(\varepsilon_{0}\) = 8.85 \(\times\)\(10^{-12}\) F/m, respectively.
## 2 Methods
### Experiments  
#### 2.3.1 Datasets  
We validate the effectiveness of _Cq_SL on a widely adopted cardiac image segmentation challenge dataset by conducting several comparisons to other baseline models. We use the STACOM 2017 _Automated Cardiac Diagnosis Challenge (ACDC)_ dataset ([[https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html](https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html)]([https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html](https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html)), accessed on 2 October 2021), consisting of short-axis cardiac cine-MR images acquired for 100 patients (1920 labeled and 23,530 unlabeled images) divided into 5 subgroups: normal (NOR), myocardial infarction (MINF), dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and abnormal right ventricle (ARV), available through the 2017 MICCAI-ACDC STACOM challenge [48]. The images were acquired over a 6 year period using two MRI scanners of different magnetic strengths (1.5 T and 3.0 T). The images were acquired using the SSFP sequence with spatial resolution 1.37 to 1.68 mm\({}^{2}\)/pixel and 28 to 40 frames per cardiac cycle. We split the dataset into three sets--training (70), validation (15), and test (15).
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 1 Introduction
### Potential of Halophytes
In practice, the development of a phytomanagement strategy should require the definition of where metals will concentrate according to the field specificities and how to manage the remediated metals during or after the phytoremediation process. From this point of view, most of the publications evaluating the potential of halophyte species in metal phytoremediation distinguish the location(s) where metals are mostly accumulated in the plant, as this generally determines the indication for phytoextraction or phytostabilization [18]. [PERSON] et al. [34] mentioned another faculty of some salt excretors to proceed with phytoexcretion in supplement of phytoextraction, e.g., as with _Tamarix smynensis_, but this faculty should be further investigated. [PERSON] et al. [36] presented a classification of the different halophyte species owing to the removal strategy that are frequently recommended for the phytoremediation of metals, especially in India. For instance, species such as _Atriplex halimus_ can be indicated for phytoextraction of Cd, Pb, Mg, and Zn, while _Arthrocennium macrostachyum_ can be applied to Cd phytostabilization.
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 3 FPGA Implementation
### Precision and Quantization  
In the training process of neural networks in a personal computer (PC), we chose the double precision floating-point type as the data type of neural network parameters, in order to obtain a network with high precision. In the FPGA implementation process, however, this kind of high-precision data type is neither feasible nor necessary. Taking this into account, the  
According to this characteristic, this paper chooses an integer power of 2 as the quantization coefficient, such that division operations can be replaced by bit shift operations. The actual product value can be expressed as  
\[R=I_{1}I_{2}\gg(log_{2}n),n=2^{k}(k\in N), \tag{6}\]  
Bit shift operation is very suitable for FPGA structure, occupying few hardware resources. Such a quantitative method greatly reduced the resource usage of multiplication. In the experiment, \(n\) was set to 2048. Examination of the results shows that no overflow happened during the operation process.
# Removal of Methylene Blue from Aqueous Solution by Bone Char
## 2 Experimental
### Removal of Methylene Blue by Bone Char  
Methylene blue (CAS: 122965-43-9, ECP) solutions were placed in closed brown glass bottles and agitated in an incubator shaker at a speed of 150 rpm. The effects of pH, initial dye concentration, and adsorbent dosage have been investigated in our previous research [21]. In this study, the initial pH was set as 10.4 because methylene blue can be adsorbed on the bone char surface mainly by strong electrostatic attraction between negative bone char surface adsorption sites after deprotonation and positive methylene blue ions without free chloride ions when the pH value is higher than the point of zero charge (pH\({}_{\text{PZC}}\) = 8.0) of bone char. Another reason for this initial pH is that ZnO, the photocatalyst that will be loaded on the bone char surface in later research, is not stable at pH \(\leq\) 5 or \(\geq\)11 during photocatalysis [22]. The initial dye concentration was 4.48 mg L\({}^{-1}\), and the adsorbent dosage was 4 g L\({}^{-1}\). At certain time intervals, the concentration of methylene blue solution was identified by a UV-VIS spectrometer (Perkin Elmer, Lambda 35, Waltham, MA, USA) at 664 nm. When the adsorption process was finished, bone char samples were washed with hot deionized water several times for thermal desorption, followed by drying for the next run. The removal rate (\(R\)) and equilibrium adsorption quantity (\(q_{e}\), mg g\({}^{-1}\)) of methylene blue dye were obtained as follows [23]:  
\[R\left(\%\right)=(c_{0}-c)\times 100/c_{0} \tag{1}\]  
\[q_{e}=(c_{0}-c_{e})V/m \tag{2}\]  
where \(c\), \(c_{0}\), and \(c_{e}\) are the dye concentrations detected at the certain, initial, and equilibrium time (mg L\({}^{-1}\)), respectively [17; 24; 25].
## 3 Experimental Methods
### The Application of Fuzzy Control
#### 3.2.2 Fuzzication Rules and Logical Design  
Fuzzy logic theory can utilize experts' knowledge and experience to formulate control rules of the controlled system. IF-THEN language is usually used to describe fuzzy rules. Fuzzy rules describe the relation between the input and output of the system. In general, fuzzy control rules can be described as follows:  
IF \(x\) is \(A\) THEN \(y\) is \(B\)  
Figure 8: Membership functions of \(Lux\).  
Figure 9: Membership functions of \(D_{n}\).  
In Formula (6), \(A\) and \(B\) are linguistic variables in membership function, \(x\) is the input variable and \(y\) is the output variable; their corresponding membership functions are defined by \(\mu_{A}\) and \(\mu_{B}\).  
Fuzzy inference: through calculation of the system's fuzzy logic, combined with the fuzzy inference engine of all IF-THEN rules in fuzzy rule base, a reasonable system's fuzzy output value is decided. There are many common fuzzy inference methods, including Product Inference Engine and Minimum Inference Engine. The fuzzy inference engine in this paper is the minimum inference engine, and its definition is as follows:  
\[\mu_{B^{\prime}}(y)=\max_{x}[\min(\mu_{A^{\prime}}(x),\,\mu_{A\to B}(x,y))] \tag{7}\]  
Depending on the variance in ambient light intensity (\(Lux\)) and adopting adequate digital variable resistance (\(D_{n}\)), we defined five general rules in the fuzzication database. The rules are as follows:  
1. IF(\(Lux\) is VS) THEN(\(D_{n}\) is VL)
2. IF(\(Lux\) is S) THEN(\(D_{n}\) is L)
3. IF(\(Lux\) is M) THEN(\(D_{n}\) is M)
4. IF(\(Lux\) is L) THEN(\(D_{n}\) is S)
5. IF(\(Lux\) is VL) THEN(\(D_{n}\) is VS)
## 5 Discussion
Regarding scalability, the CWSM can handle large datasets because it uses text-level processing faster than pixel-to-pixel comparative analyses. Adjusting the _WCCS_ weights modifies the model's performance based on the scenario and the diversity of the dataset. The scientific value of the RBin prototype lies in the impact that cloud technologies, along with ML methods, have on everyday life, an aspect demonstrated through their application in the field of waste classification. The demonstration was possible by developing a hybrid model, combining automatic object labeling via GVAS and its classification using ML_NET. The model performing the actual classification is based on LbfgsMaximumEntropyMulti. The functionality of the RBin prototype demonstrates the superior capabilities offered by custom-made solutions, which also integrate pre-trained technologies with applicability in recognizing categories of waste (cardboard, glass, metal, plastic). The proposed model generates the _WCCS_ that enhances the reliability of classification decisions. This score manages situations where multiple categories may be assigned to the same object, reducing the risk of incorrect classification. The impact on the field of automated recycling comes from the improvement in classification accuracy, reaching 99.75% after applying the _WCCS_.
# AVSS: Airborne Video Surveillance System
## 5 AVSS Features
### Data Collection and Presentation  
Each UAV has a web server to enable outside access to its information. The web server has a video stream, streamed videos, and an image. The image is the camera's view which is updated every second. The server collects the still images and shows it to its own web server. The UAVs are only connected to a private network, so GCS downloads the image and shows the image from the local storage from UAVs to allow any user that has access to the GCS can see the pictures. With the web page, the server shows the position of the UAVs and picture taken by the UAVs. Figure 4 shows the prototype of ground control station. In addition, Figure 5a,b shows the mobile view of video server and the streamed video from the surveillance UAV respectively. With Figure 4, we can recognize the location of UAVs, pictures taken by UAVs, and video provided by a selected UAV. The number represented at the left-bottom shows the index of UAV which provides the live video streaming. Also, with the command bar, which represented red text box at the left-bottom, we could send movement command to UAV.
## 4 Opposite Normalized Trust-Region Reflective (ONTRR) Algorithm
### Solution Quality and Computational Efficiency
#### 5.2.3 Accuracy Difference and Model Degeneration  
Two points can be drawn from Tables 2 and 3. Firstly, the parameters extracted from the LWF-based objective function are more accurate than those extracted from the IEF-based objective function. The basic reason is that the implicit nature of Equation (6) increases the difficulty of parameter extraction, while Equation (7) is an approximate explicit expression. Secondly, the IEF-based objective function is prone to model degeneration. One can see from Table 2 that the TDM parameters satisfy \(n_{1}=n_{2}\), which can also be found for the LNMHGS [27] algorithm. In addition, the TDM parameters extracted by the HDE [22] algorithm meet \(n_{1}=n_{3}\), while the DPDE [21] algorithm yields \(I_{03}\approx 0\). These make the IEF-based TDM degenerate into that of DDM, as evidenced by the identical RMSE values in Table 2. It is clear from Figure 5 that, although both the IEF- and LWF-based simulated current data are in good agreement with the measured current data, there are still visible  
Figure 4: The consumed MaxNFEs of various algorithms with the IEF-based objective function.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cell/Module** & **R.T.C. France Cell** & **PWP201 Module** & \
\begin{tabular}{c} **STM6-40/36** \\ **Module** \\ \end{tabular} &
\begin{tabular}{c} **STP6-120/36** \\ **Module** \\ \end{tabular} \\ \hline
**Model** & **SDM** & **DDM** & **TDM** & **SDM** & **SDM** \\ \hline \(n\), \(n_{1}\), \(n_{m}\) & 1.47726779 & 1.31093877 & 1.01967259 & 47.59822350 & 1.52046669 & 1.24445622 \\ \(n_{2}\) & & 1.84426858 & 1.04486626 & & & \\ \(n_{3}\) & & 1.87876348 & & & & \\ \(I_{00}\), \(I_{plane}\) (A) & 0.76078797 & 0.76119223 & 0.76171411 & 1.03143382 & 1.66390345 & 7.47528407 \\ \(I_{0}\), \(I_{01}\), \(I_{0m}\) (A) & 0.31068461 & 0.01984383 & 3.08359786 \(\times\) 10\({}^{-6}\) & 2.6380767 & 1.74124582 & 1.93088845 \\ \(I_{02}\) (A) & & 1.0000000 & 7.72669701 \(\times\) 10\({}^{-5}\) & & & \\ \(I_{03}\) & & 1.00000000 & 7.76269701 \(\times\) 10\({}^{-5}\) & & & \\ \(R_{a}\), \(R_{\text{max}}\) (m/\(\Omega\)) & 36.54694512 & 65.31374450 & 111.21052185 & 1235.63416949 & 4.26778393 & 4.69217169 \\ \(R_{ab}\), \(R_{\text{max}}\) (\(\Omega\)) & 52.88979013 & 56.5273441 & 63.71629154 & 821.64129780 & 15.93149782 & 15.83882138 \\ \(R_{\text{RMS}}\) & 7.730063 \(\times\) 10\({}^{-4}\) & 6.745134 \(\times\) 10\({}^{-4}\) & 5.835373 \(\times\) 10\({}^{-4}\) & 2.052961 \(\times\) 10\({}^{-3}\) & 1.721922 \(\times\) 10\({}^{-3}\) & 1.425106 \(\times\) 10\({}^{-2}\) \\ MaxNFEs & 1000 & 5000 & 1000 & 1000 & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal results extracted from the LWF-based objective function by ONTRR algorithm.  
differences between them. For the IEF-based cases in Figure 5b,c, the simulated current data and the sum of individual absolute errors (IAE\({}_{\text{IEF}}\)) of TDM are identical to those of DDM. These results support the aforementioned statements about accuracy difference and model degeneration.
# Visualisation of Information Using Patient Journey Maps for a Mobile Health Application
## 6 Discussion
### Important Elements of the PJM UX  
The results of this study indicate that users prefer more detailed information regarding their health and highlight the importance of designing a UX that makes health information easy to understand, enabling mobile health app searching.  
In addition to organising information in a patient-centric manner, we used various visualisation techniques, which can be improved further in the future. First, the prototype used colours to differentiate between disease types, and this colour representation is familiar to most users. However, as colours may have certain meanings on their own (e.g., the colour red may be perceived as more serious or urgent than green), we need to be more careful when applying colours beyond the purpose of distinguishing diseases.  
Language is also important for conveying information to users more easily. Thus, we attempted to use user-friendly words to explain health data, although medical terms may be difficult to understand. This drawback can be mitigated using a UX that provides a detailed explanation of any term upon tapping. Users have been highly satisfied with this feature in previous studies [55].  
It is challenging to convey all of the detailed health information that patients wish to view in the form of visualisations because some information may be very specialised or, depending on the health condition of the patient, may need to be provided by a medical professional. For example, patients with multiple diseases may wish to know how the different medications they are taking affect one another or may wish to know more about the medication. In such cases, it is ideal to provide a channel for them to directly contact a healthcare professional. Some healthcare apps offer a feature for contacting medical professionals [25]. It is possible to provide more personalised, professional, and detailed information to patients and improve their communication with healthcare professionals using such a feature and information from PHRs.