esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Metallic access networks utilizing homogeneous symmetric lines as a transmission medium are still in the center of interest of network operators. The utilization of metallic homogeneous lines in future access networks is expected, with necessary advances in different signal processing techniques in DSL technologies for improving their service provision in heavy environmental conditions. For this aim, appropriate simulation models of metallic transmission media can be effectively exploited. Moreover, measurements on metallic homogeneous symmetric lines, together with the evaluation of measured test parameter values, play a significant role also in current days. In mutual cooperation, the analysis of availability on actual homogeneous symmetric lines for DSL service provision can be realized.  
In this contribution, we present a created mobile application for evaluating MELT test parameters and for analyzing homogeneous symmetric lines for DSL service provision. The evaluation of MELT test parameters consists of comparing the measured values of the requested parameters of a metallic transmission medium with the standardized values. The  
Figure 6: Evaluation of MELT test parameter values of the test line #2 in the mobile application.  
analysis of quantities consists of determining whether a given quantity meets a specified allowed range, is within the permitted tolerance or is outside an expected range. A novelty of this work is presented by the innovative mobile application, realized in the environment of an Android operating system, that can evaluate MELT test parameters, diagnose connection failures, solve potential causes and analyze appropriate homogeneous symmetric lines for DSL service provision. Technicians of network operators can utilize this mobile application as a profitable tool for advanced measurements and diagnoses on actual operative homogeneous lines in metallic access networks.  
In future works, we can expand this mobile application for evaluating the MELT test parameters with functionalities related to other tests (e.g., SELT and DELT) and/or for evaluating and testing of specifically provided services (IPTV, VoIP, etc.).  
Conceptualization, M.L. and R.R.; methodology, R.R.; software, M.L.; validation, M.L.; formal analysis, M.L.; investigation, M.L.; resources, R.R.; data curation, M.L.; writing--original draft preparation, M.L.; writing--review and editing, R.R.; visualization, M.L.; supervision, R.R.; project administration, R.R.; funding acquisition, R.R. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
## 3 Materials and Methods
### Visually Secure Image Encryption Scheme
\[c_{m_{k_{i}j}}\rightarrow\ HH_{k_{i}j+1},\ k_{i}j+2 \tag{6}\]  
where \(\rightarrow\) defines the embedding operation, \(i\) and \(j\) are the row and column indexes of \(k\), respectively. As a result of the embedding process, the new \(HH_{p}\) sub-band has been achieved. \(HH_{p}\) contains the original values of \(HH\) with the values of the \(c_{m}\) permuted vector in the position set by \(k\). In this way a chaotic encryption has been realized to embed the vector \(s\) and the fingerprint feature vector in a safe manner. Moreover, \(k\) is encrypted through AES and ECC cryptography algorithms, as described in the next section, to increase the proposed scheme's security. The 2D-Inverse Discrete Wavelet Transform (2D-IDWT) is applied to reconstruct the reference image after the watermarking process, according to Equation (7). \[I^{\prime}=IDWT2\Big{(}LL,U,V^{H},HH_{p},\ w\Big{)} \tag{7}\]  
where \(I^{\prime}\) is the visually meaningful encrypted image and \(IDWT\) is 2D-IDWT function. \(I^{\prime}\) is visually very similar to \(I\) because the low frequency sub-band \(LL\) has not been altered  
Figure 3: 2D-DWT process on reference image. and the other new high frequency sub-bans (\(U,V^{H},HH_{p}\)) do not contribute to the visual alteration of the image. Figure 4 shows the entire watermarking process applied to the reference image. Finally, EHR is embedded into a visually meaningful encrypted image through the LSB algorithm. LSB consists of changing the least significant bit of each pixel in the image with each bit of text data. By changing the least significant bit of each pixel, the single pixel is not significantly altered, and the content of the image is preserved despite the manipulation. For example, given \(p=\left[b_{1}^{p}\text{, }b_{2}^{p}\text{, }\dots\text{, }b_{l}^{p}\right]\) which represents the bit values of a pixel of \(I^{\prime}\), \(e=\left[e_{1}\text{, }e_{2}\text{, }\dots\text{, }e_{n}\right]\) which represents the byte of EHR, and \(e_{1}=\left[b_{1}^{e_{1}}\text{, }b_{2}^{e_{1}}\text{, }\dots\text{, }b_{m}^{e_{1}}\right]\) which represents the bit values of the first byte of EHR, the LSB is applied according to Equation (8) for each \(p\) in the image \(I^{\prime}\). \[b_{l}^{p}\gets b_{i}^{e_{j}}\text{ }with\text{ }i=1,\text{ }\dots\text{, }m\text{ and }j=1,\text{ }\dots\text{, }n \tag{8}\]
## 5 Results Using Test Data Obtained with FEM Model
### Test of Four-, Five-, and Six-Layered Soil Models  
Although the three-layered model is easier for FEM modeling, we also conducted tests with four-, five-, and six-layered models to determine whether these models were better. For all nine subareas, four-, five-, and six-layered models were used for determining soil parameters. The calculated best OF (B), worst OF (W), mean OF (M), and Standard Deviation (SD) are presented in Table 17, where the results of the three-layered model copied from Tables 5-13 are also presented, which offers an easier comparison of the results. For easier evaluation of the differences between the soil models, the mean OF values for all subareas and all soil models are presented in Figure 12.  
Based on Figure 12, it can be seen that there were no significant differences between mean OF values using models with different layers. In the case of AD and CD, the lowest mean OF was obtained using the three-layered model. Based on this, it can be concluded that the three-layered model is appropriate, especially if we consider that FEM modeling is easier using a lower number of soil layers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Subarea**} & **ABC** & **ABC +** & **Deviation of \(t\)** & \multirow{2}{*}{**Duplications**} & **ABC +** & **Deviation of \(t\)** & \multirow{2}{*}{**Duplications**} \\  & **_t_(s)** & **STMA** & **Using STMA** & **STMA** & **LTMA** & **t_(s)** & **(\%)** \\ \hline AD & 33.25 & 28.01 & 84.2 & 1557 & 30.39 & 91.4 & 2655 \\ AE & 39.12 & 27.33 & 69.9 & 1707 & 25.92 & 66.3 & 2551 \\ AF & 41.07 & 28.77 & 70.1 & 1641 & 29.80 & 72.6 & 2615 \\ BD & 35.66 & 31.18 & 87.4 & 1670 & 30.68 & 86.0 & 2738 \\ BE & 33.40 & 26.84 & 80.4 & 1677 & 25.93 & 77.6 & 2484 \\ BF & 36.35 & 28.74 & 79.1 & 1549 & 26.82 & 73.8 & 2454 \\ CD & 32.99 & 27.55 & 83.5 & 1930 & 29.31 & 88.8 & 2852 \\ CE & 31.43 & 28.33 & 90.1 & 1736 & 27.36 & 87.1 & 2650 \\ CF & 34.34 & 23.13 & 67.4 & 1675 & 23.33 & 67.9 & 2784 \\ \hline Mean & 35.29 & 27.76 & 79.1\% & 1682 & 27.73 & 79.0\% & 2643 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Mean calculation times, deviations of calculation times, and duplications for 50 independent runs. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Maximum Difference** \\ \hline \(\rho_{1}\), \(\rho_{2}\), \(\rho_{3}\) & 0.1 m\(\Omega\)m \\ \(h_{1}\), \(h_{2}\) & 0.1 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Precisions of the parameters that determine the identification of duplications.
## 5 Applications of BSNs
### State-of-the-Art Research on BSN Applications
#### 5.1.3 Sports  
In the field of sports, BSN is mainly used for motion recognition and physiological status detection, which can help athletes with scientific training, posture correcting and skill improvement. A multimodal remote sensing platform called TennisSense is introduced in Reference [4]. The platform applies wireless inertia monitor units placed on arm, aided by network digital cameras and a set of UBISENSE 3D tracking sensors placed around the tennis court. The sensors can capture and recognize athletes' actions, and then evaluate and correct them. For the same purpose, Reference [5] proposed a system for kinematic analysis of swimming strokes. The system utilizes an inertial sensor mounted on the swimmer's goggles for acceleration measurement, which can calculate parameters such as the pitch and roll angles from recorded acceleration. Reference [6] develops a framework for the use of inertial sensors as a low-cost putting coach for golf. The framework offers great help to golf players on the promotion of movement precision when swinging clubs. In the field of sports, in addition to behavioral recognition, other BSN techniques can also be used to improve the training quality. For example, in Reference [145], researchers design a sweat pH monitoring system which can measure the pH of sweat in real time by placing barcodes made of novel ionic liquid polymer on the surface of body, which achieves non-invasive detection of the physiological state at the time of moving. Reference [146] presents an alternative approach for heart rate measurement which uses sound signals received from a microphone instead of an ECG sensor method. It can estimate the athlete's physical status from the heart rate, and help athletes train scientifically and avoid injuries caused by electrodes in direct contact with the skin.
## 3 Dataset  
Although there are some publicly available datasets with cervical cell annotations, such as Herlev [31], SIPaKMeD [32], Cervix93 [33], and ISBI Challenges [34; 35], they are not adequate for the tasks of nucleus and lesion detection. On the one hand, Herlev only contains isolated images of cells, with annotations by abnormality of the cell. Some datasets, such as the Cervix93, the ISBI Challenges, and the SipakMeD databases, comprise images of microscopic fields with information regarding the nucleus regions but do not provide annotations regarding the cervical lesions in those fields. In contrast, the more recent CRIC [36] dataset includes images of microscopic fields with annotated cervical lesions, yet with no information concerning the nuclei structures.  
In view of the shortcomings of the existing public datasets, recent works presented two additional datasets acquired with the uSmartScope device: (i) the Adequacy Assessment Dataset [5], which consists of 41 samples with 42,387 manually annotated nuclei in terms of cell type, and (ii) the Region-Based Cervical Lesion Dataset [6], which consists on 21 samples with 927 manually annotated regions in terms of cervical lesions (single cells and cell aggregates). Given that this work aims to develop a nucleus-based approach for cervical lesion detection, none of these datasets entirely fulfilled that purpose. Thus, a new dataset was created, hereinafter referred to as the Nucleus-Based Cervical Lesion Dataset.
## 3 Methodology
### TF Filtering Using IF and Covariance Matrix Estimation  
Once the IF of a signal emitted by a source is estimated, a de-chirping based TF filter is applied to extract the source [30]. The de-chirping process using the IF \(\hat{f}_{v}(t)\) for \(v=1,2,\ldots,V\) is implemented as follow:  
First, the phase of the signal, i.e., \(\hat{\phi}_{v}(t)\) is estimated as:  
\[\hat{\phi}_{v}(t) = 2\pi\int_{0}^{t}\hat{f}_{v}(\tau)d\tau.\quad v=1,2,\ldots,V \tag{14}\]Using \(\hat{\phi}_{v}(t)\), we de-chirp \(x_{m}(t)\) (signal received at \(m\)-th element of the antenna array) [12; 30]:  
\[\begin{split} y_{v,m}(t)&=x_{m}(t)e^{-j\hat{\phi}_{v} (t)}\\ &=\left(\sum_{i=1}^{V}e^{-2\pi\left(\frac{(m-1)d}{\lambda}\right) \cos(\theta_{i})}a_{i}(t)e^{j\hat{\phi}_{v}(t)}\right)e^{-j\hat{\phi}_{v}(t)} \\ &=e^{-j2\pi\left(\frac{(m-1)d}{\lambda}\right)\cos(\theta_{u})}a_ {v}(t)e^{j\left(\phi_{v}(t)-\hat{\phi}_{v}(t)\right)}\\ &+\left(\sum_{i=1}^{K}e^{-2\pi\left(\frac{(m-1)d}{\lambda}\right) \cos(\theta_{i})}a_{i}(t)e^{j\hat{\phi}_{v}(t)}\right)e^{-j\hat{\phi}_{v}(t)} \end{split} \tag{15}\]  
Taking into account \(\phi_{v}(t)-\hat{\phi}_{v}(t)\approx 0\), we have  
\[\begin{split} y_{v,m}(t)=e^{-j2\pi\left(\frac{(m-1)d}{\lambda} \right)\cos(\theta_{v})}\hat{a}_{v}(t)\\ +\left(\sum_{i=1}^{K}e^{-j2\pi\left(\frac{(m-1)d}{\lambda} \right)\cos(\theta_{i})}a_{i}(t)e^{j\hat{\phi}_{v}(t)}\right)e^{-j\hat{\phi}_{ v}(t)}\end{split} \tag{16}\]  
We can extract instantaneous amplitude and phase information, i.e., \(e^{-j2\pi\left(\frac{(m-1)d}{\lambda}\right)\cos(\theta_{v})}\hat{a}_{v}(t)\), from \(y_{v,m}(t)\) by applying a low pass filter [12]. The source signal is obtained as:  
\[\hat{x}_{v,m}(t) = e^{-j2\pi\left(\frac{(m-1)d}{\lambda}\right)\cos(\theta_{h})} \hat{a}_{v}(t)e^{i\hat{\phi}_{v}(t)} \tag{17}\]  
The aforementioned procedure is utilized to extract signals received at all \(m\) sensors for the '\(v\)-th source. The extracted signals are then stacked as:  
\[\mathbf{x}_{v}(t)=[\hat{x}_{v,1}(t),\hat{x}_{v,2}(t),\dots,\hat{x}_{v,M}(t)]^ {T}. \tag{18}\]  
From \(\mathbf{x}_{v}(t)\), the covariance matrix is estimated:  
\[\mathbf{D}_{v}=\frac{1}{T}\int_{0}^{T}\mathbf{x}_{v}(\tau)\mathbf{x}_{v}^{H}( \tau)d\tau,\quad v=1,2,\dots,V. \tag{19}\]
## 1 Introduction  
Visiting a real construction site is not always possible due to site restrictions, the limited number of students permitted to enter a site, and, more recently, due to COVID-19. Virtual modules can be used for online education in architecture, engineering, and construction (AEC). They can also be applied to formative learning, flipped classroom [1], blended experimental teaching [2], and online teaching modes [3]. Previous studies investigated the feasibility of using virtual technology in education in different contexts, such as healthcare [4]. Recent studies intend to use virtual technology for education in construction and architecture such as [PERSON] et al. [5], [PERSON] et al. [6], [PERSON] and [PERSON] [7], and [PERSON] et al. [8]. However, the application of virtual technologies to show real physical practices in an immersive environment without using headsets for AEC education purposes has not been thoroughly investigated. There are complicated processes in construction, such as drilling and boring underground, which students have not experienced before, and traditional learning methods such as textbooks cannot easily deliver the knowledge. In contrast to text-based learning materials,there is a possibility of practicing in a simulated environment that allows students for correction and repetition to improve their skills with non-risk failures [9]. The purpose of this study is to present novel tools and online virtual applications to present the complicated processes of drilling, piling, and boring and an excavator digital twin to AEC students. The digital twin refers to the digital replica of a physical entity utilising the internet of things enabling two-way communications between them. The excavator digital twin and other education apps also address the deficiencies of traditional approaches in terms of promoting the engaging capabilities that allow students to be fully immersed in virtual space [6; 10; 11]. The purpose of these virtual apps is to enable large scale site visits, that will enable students to enter a virtual environment and learn a building case study or heavy equipment. The AEC courses may use project-based learning (PBL) approaches [12], so students are required to enhance their cooperation and collaboration skills [13]. Also, group project learning is recommended to all other educational disciplines at universities [14]. However, several severe challenges of PBL have not been adequately addressed for large classrooms. For example, the way instructors can measure each group member's contribution to their group assignment and give them immediate feedback in large classes will be much more difficult when it comes to the implementation of flipped learning methods or formative learning approaches.  
The main research questions are:  
1. How can mixed reality and digital twins be applied in construction education?
2. What virtual and augmented reality modules can provide a collaborative environment, and what factors may enhance students' engagement in construction education?
3. What are the key values and advantages of the selected technologies in construction?
# The Effect of LEDs on Biomass and Phycobiliproteins Production in Thermotolerant _Oscillatoria_ sp.
## 1 Introduction
White LEDs [63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 53; 58; 59; 60; 62; 64] is the most common light wavelength used on the production of several cyanobacterial strains; However, \"white\" light is a mixture of different wavelengths trying to simulate natural daylight, which can increase the overall biomass production. Still, it cannot increase the synthesis of specific photosensitive molecules such as phycobiliproteins. According to [PERSON] et al. [53], green (\(\Lambda\)max = 525 nm) and red (\(\Lambda\)max = 660 nm) improved both biomass and C-phycocyanin concentration (green color). Other researchers such as [PERSON] et al. [23], [PERSON] et al. [30], and [PERSON] et al. [54] have found similar results where red LEDs with a maximum wavelength of 660 nm improved the concentration of C-phycocyanin (C-PC). However, not every study evaluates the effect of wavelength on the synthesis of different phycobiliproteins present in the chromophore; other phycobiliproteins, such as Allophycocyanin (APC) and phycoerythrin (PE), can be found in lower concentrations in comparison to C-PC in most of the species studied [65]. On the other hand, most studies used single wavelengths, and the effect of multiple specific wavelengths (bluered, or others) is highly underrepresented in scientific literature as an effective tool to improve both biomass and phycobiliproteins in cyanobacterial strains. The present study aimed to evaluate the effects of light intensity and wavelength using LEDs on the growth rate and phycobiliprotein composition in a thermotolerant _Oscillatoria_ sp.
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 3 System Model and Problem Formulation
### Problem Formulation  
From the previous concepts and definitions, we formally state the problem of computing optimized wireless backhaul network topologies composed of MBs and SCs as follows.  
**Problem Formulation 1.** Given a set of wireless backhaul entities \(V=M\cup S\) located at positions in the plane designated by function \(l\), and radio ranges \(R_{S}\) for SCs and \(R_{M}\) for MBSs, find a set \(E^{\prime}\subseteq V\times V\) of feasible communication links, according to Definition 1, such that the _Laplacian Spectral Gap_\(\lambda_{2}\) of \(G=(V,E^{\prime})\) is maximized.  
As we have already mentioned, the objective of maximizing the Laplacian Spectral Gap is to jointly optimize:  
* the Network Diameter \(D_{G}\) of \(G=(V,E^{\prime})\);
* the Network Robustness \(R_{G}\) of \(G=(V,E^{\prime})\);
* the Network Cost \(C_{G}\) of \(G=(V,E^{\prime})\) and;
* the Network Entropy \(S_{G}\) of \(G=(V,E^{\prime})\) as defined in Section 3.1.  
In this first formulation, the location of all stations is part of the input and is specified a priori by the function \(l\).  
Another interesting network design problem is to find suitable locations for the Macro Base Stations, i.e., to find a function \(l_{M}:M\rightarrow\mathbb{R}^{2}\) that assigns a location in the plane to the MBSs such that the network performance is optimized. In this case, only the number of MBSs, not their locations, is part of the input.  
**Problem Formulation 2.** Given a set of SCs \(S\) located at positions in the plane designated by the function \(l_{S}:S\rightarrow\mathbb{R}^{2}\), a number \(m\) of MBSs, and radio range \(R_{S}\) for SCs and \(R_{M}\) for MBSs; find a function \(l_{M}:M\rightarrow\mathbb{R}^{2}\) that assigns a location in the plane to the MBSsand a set \(E^{\prime}\subseteq V\times V\) of feasible communication links such that the _Laplacian Spectral Gap_\(\lambda_{2}\) of \(G=(M\cup S,E^{\prime})\) is maximized.  
Table 1 summarizes the notation used throughout the paper.
# Analysis of the Failure Area of the Slope Using the Slip Line Method
## 1 Introduction
presented new plasticity stability solutions for an unsupported conical rock slope through a finite element limit analysis framework [16]. [PERSON] et al. studied the seismic bearing capacity of the strip foundation on a cohesive soil slope, and various landfill depths were investigated through finite element limit analysis [17]. In contrast, studies using slip line focused on estimating ground-bearing capacity rather than searching for failure surfaces. [PERSON] developed a program to determine the ground-bearing capacity of foundations using the slip line method [18]. [PERSON] et al. (2015) studied slip line field solutions for the extreme bearing capacity of pipelines in drained soils [19]. [PERSON] and [PERSON] studied the shearing characteristics of slip zone soils and strain localization analysis of a landslide [20]. [PERSON] studied the seismic bearing capacity of strip foundations on rock formations using the Hoek-Brown failure criterion [21]. [PERSON] et al. conducted a slip-line-theory-based stability analysis on the effect of deep excavation on adjacent slopes [22]. [PERSON] studied the bearing capacity of shallow foundations located at various distances from a slope [23]. [PERSON] and [PERSON] studied the acupressure analysis of the tunnel ceiling according to the tunnel plastic zone [24] and [PERSON] studied the failure load calculation of the V-shaped rock notch using the slip line method [25]. In terms of solid mechanics, [PERSON] (1950) studied plasticity by numerical analysis using the slip line method [26]. In terms of soil mechanics, the study of failure surfaces using the slip line method was initiated by [PERSON] [27]. [PERSON] analyzed the cases where there is unit weight and internal friction angle (\(\gamma\
eq 0\) and \(\Phi\
eq 0\)), which are general geometric conditions in the slip line method, in a study to find the failure surface using the slip line [27]. However, they could not solve the equation; therefore, they analyzed it through a numerical method (finite difference method). Based on the studies of [PERSON], [PERSON] et al. applied the slip line method to single ground using Excel spreads to analyze the plasticity area [28]. In this study, to improve existing studies limited to single ground and methodological errors of the slip line method, we applied failure surface analysis using the slip line method to heterogeneous ground with reference to the research of [PERSON] et al. and [PERSON]. In addition, we conducted a finite element limit analysis (strength reduction method), similar to the slip line method, and a comparative analysis to emphasize the features of the slip line method. Based on these results, this paper presents a failure surface determination method using the slip line method.