esa-sceva/satcom-chunk-collection huggingface dataset:
# Copy Number Variation: Methods and Clinical Applications
## 2 Methods of CNV Detection
### Cytogenetic Techniques and Their Most Common Modifications  
Although chromosomes in plant and animal cells were first observed in the 19 th century [11], and CNVs were microscopically detected in _Drosophila_ in the early 20 th century [12], it took the first half of the 20 th century to assess the human diploid karyotype [13].  
Figure 1: Hallmarks in copy number variant (CNV) history. The 20 th century saw a steady development of methods, which finally allowed genome-wide, high-resolution CNV detection around the beginning of the 21 st century.  
This was finally allowed by several methodological improvements in karyotyping, leading to an establishment of conventional cytogenetic techniques which are still in general use. These include the use of cells cultured from the tested tissue, arresting of dividing cells in metaphase by colchicine, treatment by a hypotonic solution to spread the chromosomes, fixation of chromosomes on a glass slide for examination under a light microscope, and subsequent counting and grouping of chromosomes according to their morphological features [14]. A revolutionary step in human cytogenetics came with the introduction of different chromosome banding techniques revealing specific chromosomal patterns, including fluorescence-based Quinacrine banding (Q-banding) [15] and Giemsa staining (G-banding) [16], which have become the most widely used banding methods. Following the advent of cytogenetic and banding techniques, discoveries were quickly made with regard to CNVs associated with human pathologies. However, karyotyping techniques available in the 1960s only allowed detection of gross numerical and morphological abnormalities, because the resolution of light microscopes was limited to imbalances larger than 5 Mb.  
Conventional cytogenetic techniques combined with molecular techniques such as hybridisation led to the emergence of molecular cytogenetics, the main methods being fluorescence in situ hybridization (FISH) [17] and comparative genomic hybridization (CGH), both still requiring fluorescent microscopy [18]. FISH is based on hybridisation of sequence specific fluorescently labelled probes with subsequent microscopic detection of a given fluorescent signal that indicates the presence or absence of specific target DNA sequences [17]. This technique has undergone several modifications, from single-event-specific tests up to chromosome painting, making it possible to detect individual loci as small as 10 kb [19]. Certain limitations of conventional cytogenetics and FISH, mainly those of resolution, led to the development of CGH for CNV detection [18]. By the comparison of fluorescent signals generated from DNA of the tested and control samples, along the chromosomes to which they were hybridised, CGH is capable of identifying increased or decreased copy numbers of sequences at least \(\sim\)3-10 Mb in size [20].
# Edge Data Center Organization and Optimization by Using Cage Graphs
## 2 Motivation
Hence, a possible innovation is undertaking packet forwarding through the arithmetic operations involved in the algorithms proposed, where the hardware implementation plays a key role in assessing this. As a final innovation, an experimental analysis was carried out by calculating the average number of hops and the average number of links per node for some topologies employed in small data centers. In this way, the arithmetic values obtained for those topologies represent a performance measurement in the first case, as a smaller number of hops means a faster interconnection network, whereas those in the second case represent the ease of operation and maintenance, as a smaller number of links indicates a network topology that is easier to deal with and more easily forwards traffic. A concrete example of IoT/Edge architecture related to this study is an edge domain where IoT devices may be moving around the coverage area of the domain. IoT devices need to have their computing resources as close as possible due to their constraints; hence, it would be convenient to deploy the computing nodes distributed throughout the edge domain and being linked together according to a cage graph design, supposing that the coverage area is somewhat circular, such that the whole domain is divided into influence areas dominated by a single node. In this way, when an IoT device becomes closer to the area of a new node, a migration of computing resources is carried out between the node having the resources and the new node being closer to the device. Cage graphs have the advantage of allowing movement from one node to another in a fixed upper bound of hops, such as two hops away for Petersen graphs or three hops away for Heawood or Robertson graphs. Therefore, using cage graphs as the network topology to interconnect the nodes in a distributed data center offers the certainty of undertaking the migration of computing resources in very few hops, while offering diverse redundant paths to in the case of any link failure. Regarding some examples of a real-life implementation, the ideal situation is where users may be somewhat distributed within a wide area. In this context, smart agriculture may be a good scenario, as diverse crops can be disseminated throughout the field. Moreover, smart cities are another convenient scenario, as different points of service along the city can be interconnected. Additionally, IoT is a further scenario, as all devices within a smart factory may be linked together. In any of these cases, the interconnection among nodes may be achieved through one of the cage graph topologies proposed in order to arrive at the destination in two or three hops, such that a device moving through could obtain its computing resources from the closest location possible. For instance, in smart agriculture, it would possible for a fumigation robot to move along the field and migrate its computing resources to the closest node at any point.
# Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm
## 4 Experiments and Results
### Experiments with Cost-Sensitive Loss Function  
Before using cost-sensitive loss, the average R2 score was 0.5523 as shown in Table 4. After using cost-sensitive loss, the result of R2 score is improved slightly to 0.5546. From the simulations, we observed that the result of R2 score did not improve much by using cost-sensitive loss function. However, the cost-sensitive loss will increase the R2 score of the subset of data in the range where the number of data is small. From Figure 8 and Table 7, we can observe that in the range where there is fewer data, the error decreases, and the range where there is more data, the error increases. As accurately predicting in the region of high creatinine is more important due to higher risk of CKD. Thus, the cost-sensitive loss function achieved a positive goal.  
By using sampling-4 strategy of undersampling for data balancing, and cost-sensitive loss function, the impact of data imbalance problem is partially mitigated. The result is shown in Figure 9, where the \(y\)-axis represents the ground truth value and the \(x\)-axis is the predicted value. The left part is the result before dealing with imbalance problem; the right part is the result after using undersampling and cost-sensitive loss function. We can observe that the model is able to predict with better accuracy over the whole range of data value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Decrease in Gini Index** \\ \hline Sex & 0.49 \\ \hline Age & 0.11 \\ \hline HMG & 0.10 \\ \hline OLIG\_PROTE\_CD & 0.08 \\ \hline Waist & 0.04 \\ \hline SMOKE\_STATE & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Factors important in predicting Creatinine value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Set** & **Random Forest** & **XGBoost** & **ResNet** \\ \hline
1 & 0.5392 & 0.5581 & 0.5322 \\ \hline
2 & 0.5369 & 0.5562 & 0.5266 \\ \hline
3 & 0.5289 & 0.5455 & 0.5265 \\ \hline
4 & 0.5362 & 0.5574 & 0.5338 \\ \hline
5 & 0.5337 & 0.5526 & 0.5357 \\ \hline
6 & 0.5289 & 0.5453 & 0.5254 \\ \hline
7 & 0.5387 & 0.5545 & 0.5353 \\ \hline
8 & 0.5267 & 0.5417 & 0.5238 \\ \hline
9 & 0.5389 & 0.5561 & 0.5288 \\ \hline
10 & 0.5353 & 0.5499 & 0.5289 \\ \hline Average & 0.5343 & **0.5523** & 0.5297 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experiments with different regression algorithms.
## 5 Security Analysis
### Data Confidentiality
#### 5.1.1 Security Analysis of Fhe  
This paper uses a FHE scheme to achieve end-to-end data confidentiality. Data are encrypted in source nodes and decrypted in BS using the FHE scheme. During the data forwarding and aggregation processes, the intermediate nodes cannot decrypt the encrypted data packets without knowing the key, so it is hard for an adversary to breach all encryption keys or find the plaintext if it knows only the ciphertext. Therefore, our scheme is secure to ciphertext-only attacks. Even if the aggregation data is disclosed, the adversary can only get the aggregation result but not sensor data. Similarly, it is also secure to plaintext-only attacks.
## 1 Introduction  
Enamel matrix derivative (EMD) can induce the formation of hard tissue, such as alveolar bone and cement tissue [1; 2]. EMD is applied for periodontal therapy and bone regeneration. We previously showed that subcutaneous injections of EMD can induce the growth of cartilage tissue and eosinophilic round bodies (ERBs) [3]. We further analyzed these ERBs by using MALDI-TOF, and found fragments of exon 5 of amelogenin.  
We synthesized a 7-amino acid (WYQNMR) peptide based on these fragments and tested whether the synthetic peptide (SP) would behave similarly to EMD [4]. We found that the SP could induce bone-like tissue formation in artificial periodontal defects in rats [5; 6]. Moreover, we found that SP could enhance the cell proliferation of periodontal ligament (PDL) cells [7] and enhance osteoblastic differentiation in mesenchymal stem cells (MSCs) [8; 9; 10]. These findings also could help to clarify the biological functions of amelogenin exon 5.  
EMD induces anti-EMD antibodies [11]. Only peptides of greater than approximately ten residues (or >5 kDa) can function as antigens [12; 13]. SP is 7 amino acids long, with a mass of 1118 Da. Therefore, SP has no effect with respect to inducing an immunological response.  
Dental caries, tooth fractures, and other types of dental trauma induce tooth loss. Direct pulp treatment requires materials that protect the pulp tissue but induce hard tissue formation, in order to repair and maintain dental pulp tissues [14; 15; 16; 17; 18; 19]. This requirement has led to the design and introduction of new, bioactive agents for dental pulp tissue engineering materials.  
To investigate dental pulp tissue regeneration, a rat odontoblast-like cell line, KN-3, was created by Prof. [PERSON] and Prof. [PERSON] [20]. KN-3 cells showed high levels of odontogenic expression and the ability to form calcium nodules [21]. KN-3 cells have also been used as an authentic control to study the differentiation of induced pluripotent stem (iPS) cells and embryonic stem (ES) cells into odontoblast-like cells [22; 23].  
The biological response to SP regarding odontogenic differentiation has not yet been investigated. In the present study, we evaluated the odontogenic effects of SP in KN-3 cells.
## 9 Conclusions  
As interest in new solutions for improved traffic safety is increasing, the use of communication-based vehicle safety applications appears to be highly promising. In this context, this article addressed some of the issues associated with the exploitation of the VLC technology in V2V applications, focusing on an experimental-centered approach. To this goal, a vehicle rear-lighting-based VLC emitter was designed, implemented and tested, together with a VLC receiver, in order to attain the best possible results for this scenario. The V2V VLC prototype has been intensively tested in outdoor surroundings, in night conditions under the exposure of fluorescent lights and in daylight conditions as well. The experimental tests have been focused on two main concerns: (i) determining the maximum communication distances attainable with the proposed prototype and (ii) analyzing the effect of vehicle misalignment. The experimental results demonstrated a record communication range of 75 m with a relatively low-power VLC emitter based on vehicle rear lights, with BERs as low as \(10^{-7}\). It has been also demonstrated that with the proposed signal processing plan, the VLC receiver is able to compensate the SNR decrease, maintaining a decent BER for an extended range. This article has also reconfirmed the benefits of an adequate optical collecting system. Thus, optical filters and a narrow FOV obtained with optical lenses can significantly optimize a VLC receiver. Based on these results, this article has provided important evidence concerning the ability of VLC technology to establish reliable V2V links.  
Future work on this topic will be orientated toward the evaluation of the VLC system in real driving situations. Nevertheless, these experiments are highly complex in terms of connectivity, safety and measurement repeatability. In order to achieve this goal, a test track for intelligent cars is under construction as a necessary step for future developments.  
Conceptualization, C.B. and A.-M.C.; methodology, A.-M.C.; software, S.-A.A.; validation, A.-M.C., M.D. and V.P.; formal analysis, C.B.; investigation, S.-A.A. and C.B.; resources, M.D. and V.P.; data curation, C.B. and A.-M.C.; writing--original draft preparation, C.B. and A.-M.C.; writing--review and editing, S.-A.A., C.B. and A.-M.C.; visualization, S.-A.A. and C.B.; supervision, A.-M.C., M.D. and V.P.; project administration, M.D. and V.P.; funding acquisition, M.D. and V.P. All authors have read and agreed to the published version of the manuscript.  
This paper has been financially supported within the project entitled \"DECIDE--Development through entrepreneurial education and innovative doctoral and postdoctoral research, project code POCU/380/6/13/125031, project co-financed from the European Social Fund through the 2014-2020 Human Capital Operational Program.  
Not applicable.  
The authors declare no conflict 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.
## 2 Principles and Methods
### Principles of SDN-Based ISTNs
Satellite network designers may use the semi-physical platform to analyze and evaluate system performance, optimize network parameters, and significantly decrease the cost of maintaining the network. Semi-physical simulation, also known as hardware-in-the-loop simulation (HILS), is a type of simulation technology that incorporates some real hardware implementations into the simulation loop to better emulate the whole system. The method is regarded as one of the most important tools in the development of space systems in the aerospace field that can enhance the confidence level of the simulation results and address the system modeling challenges.
## 3 Materials and Methods
### Overall Approach to Evaluate the Models  
The method of evaluation of the performance of the models was identical for all the datasets. Let us consider a given dataset \(\{\mathbf{X},\mathbf{y}\}\) of sample size \(n\). We randomly split the dataset into two parts:  
* A number of \(n_{\text{train}}\) observations \(\{\mathbf{X}_{\text{train}},\mathbf{y}_{\text{train}}\}\) are used as a training set to calibrate a given model, say \(f\). This learning step is detailed in Section 3.3;
* A number of \(n_{\text{test}}\) observations \(\{\mathbf{X}_{\text{test}},\mathbf{y}_{\text{test}}\}\) (with \(n=n_{\text{train}}+n_{\text{test}}\)) are used to compute the performance of model \(f\) learned on \(\{\mathbf{X}_{\text{train}},\mathbf{y}_{\text{train}}\}\). The model performance was defined by the RMSEP computed on the \(n_{\text{test}}\) predictions (RMSEP\({}_{\text{test}}\)). The split of \(\{\mathbf{X},\mathbf{y}\}\) between \(\{\mathbf{X}_{\text{train}},\mathbf{y}_{\text{train}}\}\) and \(\{\mathbf{X}_{\text{test}},\mathbf{y}_{\text{test}}\}\) was randomly repeated 100 times to consider, in the results, the sampling variability. The final performance was measured by an average (over the 100 above repetitions) relative error rate. This average relative error rate was defined by the ratio of the mean RMSEP\({}_{\text{test}}\) (over the 100 replications) to the mean of the response \(\mathbf{y}\). This standardization by the mean (as for a coefficient of variation) allowed for summarizing the results of the nine variables together. Agronomic data, such as forages, have large intrinsic variability due to the heterogeneity of their biological material. If this variability is under-represented in the test set \(\{\mathbf{X}_{\text{test}},\mathbf{y}_{\text{test}}\}\), this can generate overfitting, optimistic estimates of the performance of the models, and finally misleading conclusions in methods' comparisons [21]. For preventing such effects, we decided to implement a \"severe\" split between the training and the test: in each of the 100 above repetitions, the \(\{\mathbf{X}_{\text{train}},\mathbf{y}_{\text{train}}\}\) and \(\{\mathbf{X}_{\text{test}},\mathbf{y}_{\text{test}}\}\) represented each half of the dataset (i.e., \(n_{\text{train}}/n=n_{\text{test}}/n=1/2\)), while often a softer choice in machine leaning studies is \(n_{\text{train}}/n=2/3\) and \(n_{\text{test}}/n=1/3\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Unit** & **Description** \\ \hline ADF & \%DM \({}^{1}\) & Acid detergent fiber [29] \\ ADL & \%DM & Acid detergent lignin [29] \\ ASH & \%DM & Ashes \\ CF & \%DM & Crude fiber [30] \\ CP & \%DM & Crude protein [30] \\ DM & \% & Dry matter, 103 degrees Celsius, 24 h \\ DMDCELL & \%DM & Pepsine-cellinase dry matter digestibility [31] \\ NDF & \%DM & Neutral detergent fiber [32] \\ OMDCELL & \%OM \({}^{2}\) & Pepsine-cellinase organic matter digestibility [31] \\ \hline \hline \end{tabular} \({}^{\dagger}\) Dry matter; \({}^{2}\) Organic matter.  
\end{table} 
Table 2: Components of the chemical compositions (response variables \(\mathbf{y}\)) predicted by the models.
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 6 Digital Transmission
### Satellite Communications  
Satellite Communications enable a telecommunication link between a source transmitter and a receiver at different positions on planet Earth; satellites only retransmit and amplify the radio signals via a transponder (Table 8). Satellite transmission requires line of sight, therefore obstructed by the curvature of the planet. Satellite Communication for the Smart Infrastructure will be used to interconnect two remote locations where physical cabling is not possible, in locations such as deserts or islands. Satellite Communications are used for television, telephone, radio, internet, GPS and other security applications.
# Business Impact Analysis of AMM Data: A Case Study
## 3 Analysis of AMM Data  
The AMM meter is classified as a static meter like all meters that measure electrical energy using electronic components without mechanically moving parts. Each static meter determines the readings using Object Identification System (OBIS) codes. Previously, these were only a few readings, which included voltage, current, and active energy values. Atpresent, it is also possible to determine from the meter data whether there has been a security breach, for example, by removing the cover or possibly attempting to tamper with the data with a magnet, from information in the meter's error register, as shown in the OBIS codes shown in Table 1.
## 2 Problem Formulation
### The Treatment of Fluid Field  
The duct is filled with incompressible fluid, and sound waves also propagate in a fluid environment. Under low-speed cases, the fluid flow is governed by steady incompressible Navier-Stokes (NS) equations. It can be written in vector form as:  
\[\
abla\cdot\mathbf{U} =0 \tag{7}\] \[\frac{\partial\mathbf{U}}{\partial t}+\mathbf{U}\cdot\
abla \mathbf{U} =-\frac{1}{\rho}\
abla p+\
u\
abla^{2}\mathbf{U}\]  
where \(\mathbf{U}\) is the fluid velocity, \(t\) is the time, \(\rho\) is the fluid density, \(p\) is the pressure, and \(\
u\) is its dynamic viscosity coefficient [19].  
Inviscid flow analysis is often adopted to give a quick estimate of the flow pattern. In that case, viscous term \(\
u\
abla^{2}\mathbf{U}\) is neglected, and the corresponding remaining form is called the Euler equation.  
In order to numerically solve the fluid governing equation, Reynolds averaging is one of the most used methods for turbulence modeling. The fluid solution variables are decomposed into mean and fluctuating components. Thus, the governing equations can be written as the Reynolds-averaged Navier-Stokes (RANS) equations. Reynolds stresses are induced to present the effect of turbulence, and they must be modeled to enclose these equations. The Reynolds stress tensor is written as follows:  
\[\overline{u_{i}^{\prime}u_{j}^{\prime}}=\
u_{t}(\frac{\partial u_{i}}{\partial x _{j}}+\frac{\partial u_{j}}{\partial x_{i}})+\frac{2}{3}k\delta_{ij} \tag{8}\]  
where \(u_{i}\) is the mean component of velocity \(\mathbf{U}\) in the \(i\) direction, \(i\) represents the corresponding flow direction and \(i=1,2\) for the two-dimensional situation. \(u_{i}^{\prime}\) is the fluctuation velocity component in the \(i\) direction, \(\
u_{t}\) is the turbulent viscosity, \(k\) is the turbulent energy, and \(\delta_{ij}\) is the Kronecker delta.  
The Reynolds number is applied in many fields, from the flow of liquid in a duct to the air passages on the wings of aircraft. It can also be used to forecast a change from laminar to turbulent flow, and is used to scale the flow situation of different sizes. The Reynolds number is defined as follows:  
\[Re=\frac{\rho uD}{\mu}=\frac{uD}{\
u} \tag{9}\]  
where \(u\) is the flow velocity (m/s); \(\rho\) is the density of the flow (kg/m\({}^{3}\)); \(D\) is a characteristic length, which is the diameter of the tube in this case (m); \(\
u\) is the kinematic viscosity of the flow (m\({}^{2}\)/s); \(\mu\) is the dynamic viscosity of the flow (Pa \(\cdot\) s).  
The use of the model can be judged by the value of the Reynolds number. The k-epsilon (\(k-\epsilon\)) turbulence model is the most commonly used model in CFD calculation for turbulent flow. This model describes turbulence through two transport equations. The \(k-\epsilon\) model was originally designed to improve the mixing-length model and find an alternative method for specifying turbulence scales in medium-to-high-complexity flows.