esa-sceva/satcom-chunk-collection huggingface dataset:
# MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
## 3 Proposed Design
### Index Model
#### 3.2.2 Algorithms  
In this part, we propose two algorithms, the ISA (Indexed Store Algorithm) and the IRA (Indexed Retrieve Algorithm) to overcome problems caused by native HBase features that can only perform a full table scan by filters to retrieve exact rows when querying on columns other than rowkey. Although adding indexes will improve random read performance, we still need to examine and minimize the potential impact on write operation.
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
## 2 Proposed Architecture for SDN-DON Emulation
### Advanced Automatic Deployment of Emulated SDN DON
#### 2.2.1 General Architecture  
Figure 2a showcases the general architecture of our proposal for an advanced automatic deployment of an emulated SDN DON framework. As it can be seen in the figure, the main difference with the legacy work is to leverage Kubernetes functionalities to manage and automatize the creation of agents, thus, minimizing the dependency of the API gateway. The other key contribution is the novel structure of the optical agent, shown in Figure 2b, facilitating the scalability and the emulation of a wide catalogue of YANG-based optical models. Further details of the structure of the agent are elaborated in next subsections.  
This architecture is targeted to maximize the automation and the scalability of the emulation framework trying to exploit the benefits of a dynamic management and orchestration of the containerized optical agents. Moreover, two architectural decisions in our implementation deserve further description. First, it is worthwhile mentioning that we choose to employ docker containers because of they are more lightweight in terms of resources than VMs. We leverage on the fact that containers share the operating systems  
Figure 1: Architectural details of the previous work [36] (**a**) General overview of the architecture for automatic creation of agents in a software-defined optical networks (SDONs) environment; (**b**) Detailed view of the components in an optical network agent.  
whereas VMs aim at emulating virtual hardware. Given that docker containers share the operating systems, docker applications consume a portion of the resources compared to a VM. Second, our architecture considers one docker container per agent for a realistic emulation of a carrier-grade deployed scenario. In particular, telecom operators commonly deploy control architectures that handle distinct geo-located optical nodes with dedicated computing resources for service deployment (e.g., in the edge computing paradigm) while also devoting part of those resources for the control of the network infrastructure. In this context, optical resources are managed and operated with a per-node computing element that receives and sends commands from/to the SDN controller while interfacing the hardware at the data plane. Hence, our proposal aims at emulating such conditions with an independent and isolated entity, i.e., docker container, per software agent as in the case of distinct geo-located optical nodes.
## 4 Numerical Simulations
### Reconstructed Images  
We used a finer mesh for the forward problem to avoid any inverse crime. The accurate conductivity in this section is represented in this finer mesh, while the reconstructed images are in a coarser mesh. The mesh used for the forward problem contained 2304 triangular elements, while the mesh used for the inverse problem contained 576 triangular elements. From Figure 3, we know that the reconstructions in training and test sets are good when they are of the same noise level. So, we did two different reconstruction tests. First, we tested across noise levels by training the network on data with a particular noise level and trying it on data with a different noise level. The second test set consisted of triple inclusion examples with a noise level different from the training set. The training set only consisted  
Figure 3: Coefficient of determination \(R^{2}\) vs. number of RBFs in hidden layer for different RBF network models. (**a**) RBF network with no regularization and variable Gaussian width. (**b**) RBF network with no regularization and fixed Gaussian width. (**c**) RBF network with Tikhonov regularization and variable Gaussian width. The regularization parameter \(\lambda\) is \(10^{-5}\). (**d**) RBF network with Tikhonov regularization and fixed Gaussian width. The regularization parameter \(\lambda\) is \(10^{-5}\).  
of single and double inclusion examples. Therefore, failure to reconstruct triple inclusion examples would mean the model is not general enough.  
Figure 4 shows the results of the first test. Figure 4a shows the accurate conductivity used for the forward problem. Since the forward problem was performed in a finer mesh than the inverse problem, it is in a finer mesh than the other figures for Figure 4b-i; the training data contained 4% Gaussian noise, while the boundary voltage data for reconstruction had 6% Gaussian noise. From the figures, we conclude that using variable Gaussian widths using Equation (14) does not give a good model that is general enough, despite having similar performance within the same noise level, as implied in Figure 3. Variable widths do not perform well across different noise levels, as depicted in Figure 4. Therefore, we do not use variable width Gaussian rbf networks for our subsequent analyses.  
Figure 5 shows the second test result, where the models were trained on single- and double-inclusion examples and tested on triple-inclusion data with 5% Gaussian noise. For Figure 5b-e, we use 4% Gaussian noise for the training input data. From these figures, we conclude that a normal rbf network with fixed Gaussian width performs well and generalizes well. We find that Tikhonov regularization with different noise levels may improve performance, whereas Lasso and Elastic Net regularization are not very effective. In the next section, we give a more detailed comparison of the models used for Figure 4.
## 2 The Theoretical Basis of the Algorithms Used in This Research
### Dnn
#### 2.2.3 Activation Function  
Generally, a non-linear operator or activation function is used in neural networks. The presence of this function makes the model more powerful than the linear model. It is well-known that applying the Rectified linear unit (_ReLU_) activation function in deep networks increases the training speed. _ReLU_ simply rounds up negative values to zero [41].  
\[ReLU(x)=\left\{\begin{array}{ll}x&if\ x\geq 0\\ 0&if\ x\leq 0\end{array}\right.=\max[0,x) \tag{13}\]  
It is bounded below by 0 and unbounded above. In many cases, _Leaky ReLU_ has outperformed _ReLU_. When the function is not active, it allows a small and non-zero gradient [42].  
\[Leaky\ ReLU(x)=\left\{\begin{array}{ll}x&if\ x\geq 0\\ \alpha x&if\ x\leq 0\end{array}\right. \tag{14}\]The value of alpha (\(\alpha\)) in the _Leaky ReLU_(\(x\)) activation function determines the slope of the function for negative input values. A common alpha value is 0.01, but it can vary depending on the specific problem and experiment. Typically, \(\alpha\) is constrained to be in the range of \(0<\alpha\leq 1\) because setting \(\alpha\) to a value outside this range may lead to overly aggressive or ineffective behavior of the _Leaky ReLU_ function. Adjusting the alpha value allows you to control how much negative inputs affect the function output. In this case, the range of _Leaky ReLU_ is \((-\infty,+\infty)\).  
Recently, the use of Exponential Linear Units (_ELU_s) has increased the speed and accuracy of training. _ELU_ accepts negative values, allowing it to make the average unit activation closer to zero, like batch normalization, but at a lower computational cost [43].  
\[ELU(x)=\begin{cases}x&\quad\text{if }x\geq 0\\ \alpha(e^{x}-1)&\quad\text{if }x<0\end{cases} \tag{15}\]  
In the above equation, \(\alpha\) is a hyperparameter to be tuned with the constraint \(\alpha\geq 0\), that controls the output of the function for negative inputs. Typically, the value of \(\alpha\) is set to \(-1\), which ensures that the function is smooth and continuous everywhere. However, like other hyperparameters, \(\alpha\) can be adjusted based on the specific problem and experimentation.  
The performance of the evolving network is also investigated in the presence of a Scaled Exponential Linear Unit (_SELU_) activation function. By adding a little involution to _ELU_, _SELU_ is created [44].  
\[SELU(x)=\lambda\begin{cases}x&\quad\text{if }x\geq 0\\ \alpha(e^{x}-1)&\quad\text{if }x<0\end{cases} \tag{16}\]  
in which, \(\alpha\) is a negative scaling parameter, and \(\lambda\) represents the involution function.
# Privacy Goals for the Data Lifecycle
## 2 Related Work
### Privacy Goal Modelling  
One method for looking at privacy risk is through privacy goal modelling. The idea of using goals to elicit requirements or objectives has been used in requirements engineering for over 30 years [30]. This was extended to include anti-goals for security threat modelling [31], and later, to also define _\"desired properties\"_ that required protection as security goals [32]. More recently, privacy was also introduced as an extension to this, with many of the existing privacy frameworks also reviewing privacy through privacy goals or properties. For example, the LINDDUN framework based their work on (STRIDE [32] and KAOS [31], two security goal modelling frameworks, and extended this to include privacy [33]. Essentially, privacy goals seek to ascertain the properties or aims that need protection against a particular action (i.e., 'a threat' in security terms). LINDDUN takes each privacy threat types and maps these to system elements to derive privacy requirements [34].  
Similarly, IRIS (_\"Integrating Requirements and Information Security\"_) and CAIRIS (_\"Computer Aided Integration of Requirements and Information Security\"_) [35] seek to gather and visualise security requirements, vulnerabilities and threats using goal modelling, includingprivacy [21]. Effectively what privacy goals seek to establish are aims or properties that need protection against a particular action (in security terms 'a threat').  
Another example that extended the idea of security goal modelling into privacy is the _\"Privacy Safeguard\"_ (PRiS) framework, a privacy requirements gathering method [36] that considers 8 privacy goals (authentication, authorisation, identification, data protection, anonymity, pseudonymity, unlinkability, and unobservability), using these as organisational goals to incorporate privacy into systems and processes [37]. These goals can then be used to analyse and assess the effect and impact each of privacy goals may have on the system, thereby incorporating privacy into system design [37].
# A Visual Mining Approach to Improved Multiple-Instance Learning
## 3 Background and Related Concepts
### Multiple Instance Learning  
In supervised learning, we define classification as follows: given an instance space \(\mathcal{X}\) (also called input space) composed by individual instances--each one is represented by a feature vector--and a label space \(\mathcal{Y}\) (output space) composed by classes that can be assigned to each sample, the task is to build a classifier, i.e., a map \(f:\mathcal{X}\rightarrow\mathcal{Y}\). The classifier is often obtained by using a training set of instances as input with their corresponding true labels [1].  
In MIL, an object is represented by different parts, although the object itself has its own label, each part may have, in principle, different labels. This causes the classical supervised learning definition to perform poorly in multi-instance scenarios.  
Formally, a multiple-instance learning algorithm learns a classifier \(f_{MIL}:2^{\mathcal{X}}\rightarrow\{-1,+1\}\), taking as input a dataset composed by bags \(B_{i}\), with \(i=1,\cdots,n\). Each bag contains a set of instances; the \(j^{th}\) instance inside a given bag is denoted as \(B_{ij}\), and \(j=1,\cdots,n_{i}\), in which \(n_{i}\) is the number of instances inside the bag \(B_{i}\). Considering a binary classification scenario, positive and negative bags are denoted, respectively, by \(B_{i}^{+}\) and \(B_{i}^{-}\). For the sake of simplicity, we will denote a bag as \(B\) when it represents either positive or negative bags.  
One of the crucial steps in this process is how to assign a label to the bag given the labels of its instances. The first MIL study [3], related to drug activity, assigned a positive label to a bag that has at least one positive instance. This approach makes sense for that application but has not been successful for other datasets [2]. The state-of-the-art strategies often select a _prototype_ instance in order to represent the bag, and there are many different heuristics that can be used to perform this task.  
In the following sections, we will give further details about two strategies for instance selection employed in multiple-instance learning. Moreover, we discuss ideas for visualization and interaction to improve prototype instance selection.
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
## 1 Introduction
[21] developed a one-dimensional mathematical model using NASA's Chemical Equilibrium with Applications interface to predict the performance of DCR. In fact, the performance is not only related to the type of engine, but is also closely related to the fuel type, combustion efficiency, total pressure loss, and engine structure. Although the overall performance of DCR and scramjet can be compared by using the one-dimensional mathematical model method, it is difficult to consider the actual fuel combustion efficiency and the total pressure loss of the air flow into the calculation model, so its applicability inevitably has some limitations. Using the computational fluid dynamics (CFD) numerical simulation to evaluate the engine performance is a more perfect method. For example, [PERSON] et al. [6; 22] used experiments and CFD simulation to study the performance of DCR. The results showed that the maximum combustion efficiency was 0.91 and 0.89 and the maximum specific impulse was 13,300 m/s and 7960 m/s under Ma 4 and Ma 6 conditions, respectively. Although the combustion efficiency was considered, the study was limited to DCR. The lack of research on the scramjet made it difficult to quantitatively compare the performances of the two engines. In summary, the comparison of the overall performance of DCR and scramjet engines in the above references was mainly based on a one-dimensional mathematical model, which ignored combustion efficiency and shock coupling effect and did not yield accurate engine performance results. Therefore, based on the CFD numerical simulation technology and considering the fuel combustion efficiency and shock coupling, this paper makes a comparative study of the performance of DCR and scramjet under the same operating condition,thus providing guidance for engine designers to choose the better engine. Kerosene C\({}_{12}\)H\({}_{23}\) was employed as the fuel, and a six-species and four-step chemical reaction mechanism was adopted.
## 2 Methodology of Research, Radio Planning, and Design of 5G Network
### Link Budget Dimensioning and Planning  
The maximum allowable path loss (MAPL) is the maximum attenuation received between the mobile antenna from UE and the antennas from gnodeB for uplink and downlink links throughout the air interface. The calculation of the coverage takes into account the losses between UE and gnodeB, and the required data for the calculation of the link budget, displaying the parameters used by the UE terminal and gnodeB entities, are shown in Tables 2 and 3. First, the value of the thermal noise is calculated using (1), and the number of subcarriers is computed using (2), so [12]  
\[\text{N}_{\text{thermal}}\ =10\times\log(\text{K}\times\text{T}\times\text{B}), \tag{1}\]  
where  
Figure 2: Flowchart of the research for 5G NR network planning [15]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Parameters for Calculate (MAPL)**} \\ \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Notation**} & **Downlink** & **Uplink** & **Downlink** & **Uplink** \\ \cline{3-6}  & & \multicolumn{4}{c}{**C-Band: 3.5 GHz**} & **mmwave Band: 28 GHz** \\ \hline gnodeB transmitter power (dBm) & a & 43 & - & 36.5 & - \\ \hline SCS (KHz)/B (MHz) & - & 30/100 & 30/100 & 60/200 & 60/200 \\ \hline Resource Block & RB & 273 & 273 & 264 & 264 \\ \hline Subcarrier quantity & b & 3276 & 3276 & 3168 & 3168 \\ \hline gnodeB antenna gain(dBi) & c & 25 & 25 & 33.5 & 33.5 \\ \hline gnodeB cable loss (dBi) & d & 0 & 0 & 0 & 0 \\ \hline Penetration loss (dB) & e & 28 & 28 & 19 & 19 \\ \hline Folliage loss (dB) & f & 10 & 10 & 10 & 10 \\ \hline Body block loss (dB) & g & 3 & 3 & 7 & 7 \\ \hline Interference margin (dB) & h & 6 & 2 & 1 & 1 \\ \hline Rain/lce margin (dB) & i & 0 & 0 & 3 & 3 \\ \hline Slow fading margin (dB) & j & 7 & 7 & 7 & 7 \\ \hline UE antenna gain (dB) & k & 0 & 0 & 0 & 0 \\ \hline Noise Bandwidth (SCS) (MHz) & B & 0.03 & 0.03 & 0.06 & 0.06 \\ \hline Boltzmann Constant & K & \(1.38\times 10^{-23}\) & \(1.38\times 10^{-23}\) & \(1.38\times 10^{-23}\) \\ \hline Temperature (\({}^{\circ}\)K) & T & 293 & 293 & 293 \\ \hline Thermal Noise Power (dBm) & l & \(-129.1592799\) & \(-129.1592799\) & \(-126.14898\) & \(-126.14898\) \\ \hline UE noise figure (dB) & m & 9 & 9 & 9 & 9 \\ \hline Demodulation threshold SNR (dBm) & n & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\ \hline Planning Area (Km\({}^{2}\)) & A & 6.72 & 6.72 & 6.72 & 6.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Link budget simplified From 5G New Radio planning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Bandwidth**} & \multicolumn{2}{c}{**SCS (KHz)—3.5 GHz**} & \multirow{2}{*}{**Bandwidth**} & **SCS (KHz)—28 GHz** \\ \cline{2-5}  & **15 (KHz)** & **30 (KHz)** & **60 (KHz)** & **60 (KHz)** & **120 (KHz)** \\ \hline
20 (MHz) & 106 & 51 & 24 & 50 (MHz) & 66 & 32 \\ \hline
50 (MHz) & 270 & 133 & 65 & 100 (MHz) & 132 & 66 \\ \hline
100 (MHz) & N/A & 273 & 135 & 200 (MHz) & 264 & 132 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Resource Block For medium/high-freq. band (3 GPP_TS 38.101-1,38.101-2 v17.6).
## 2 Experimental
### Sample Preparation  
Fly ash and GGBFS were independently mixed with the alkaline activator. NaOH solution was prepared by dissolving NaOH pellets in seawater to 10 mol/L, and then cooling to room temperature. Na\({}_{2}\)SiO\({}_{3}\) solution was mixed with NaOH solution with a weight ratio of 3:7 [29; 30]. The weight ratio of the activator (10 M NaOH in seawater + Na\({}_{2}\)SiO\({}_{3}\) solution) to the binder (fly ash or GGBFS) was fixed at 0.45. Table 3 summarizes the detailed mixture proportion of the samples. In the present study, the sample of alkali-activated fly ash with seawater (fly ash with seawater) will be denoted FSW and that of alkali-activated GGBFS (GGBFS with seawater) will be SSW in the following sections. After mixing, the pastes were cast into 50 \(\times\) 50 \(\times\) 50 mm cubic molds for strength and XRD tests, and \(\Phi\) 25.4 \(\times\) 25.4 mm cylinder molds for MIP. The samples were cured at a temperature of 60 \({}^{\circ}\)C and a relative humidity of 99%. The compressive strengths of the hardened samples were measured at the age of 7 and 28 days of curing. The loading rate was 0.4 mm/min. Fractured specimens after a compressive strength test were finely powdered for the XRD and chloride tests. At the age of 7 and 28 days of curing, cross sections with a thickness of 2 mm along the length of the hardened cylindrical samples were prepared for MIP analysis using a precision saw. The specimens were immersed in isopropanol to stop further hydration. After vacuum drying, the specimens were examined by MIP.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **FA** & & **GGBFS** \\ \hline
**Oxide** & **wt \%** & **Oxide** & **wt \%** \\ \hline SiO\({}_{2}\) & 51.29 & CaO & 47.42 \\ Al\({}_{2}\)O\({}_{3}\) & 22.16 & SiO\({}_{2}\) & 31.97 \\ Fe\({}_{2}\)O\({}_{3}\) & 9.71 & Al\({}_{2}\)O\({}_{3}\) & 12.51 \\ CaO & 6.44 & MgO & 3.21 \\ K\({}_{2}\)O & 1.87 & SO\({}_{3}\) & 2.51 \\ Na\({}_{2}\)O & 1.73 & TiO\({}_{2}\) & 0.66 \\ MgO & 1.72 & K\({}_{2}\)O & 0.53 \\ TiO\({}_{2}\) & 1.36 & Fe\({}_{2}\)O\({}_{3}\) & 0.41 \\ P\({}_{2}\)O\({}_{5}\) & 1.29 & MnO & 0.34 \\ SO\({}_{3}\) & 1.24 & Na\({}_{2}\)O & 0.21 \\ SrO & 0.37 & SrO & 0.08 \\ MoO\({}_{3}\) & 0.34 & BaO & 0.07 \\ BaO & 0.23 & ZrO\({}_{2}\) & 0.05 \\ MnO & 0.08 & C1 & 0.02 \\ V\({}_{2}\)O\({}_{5}\) & 0.05 & - & - \\ ZnO & 0.02 & - & - \\ Cr\({}_{2}\)O\({}_{3}\) & 0.02 & - & - \\ CuO & 0.02 & - & - \\ NiO & 0.02 & - & - \\ Cl & 0.01 & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of fly ash and ground-granulated blast furnace slag (GGBFS) used in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ca2\({}^{+}\)** & **K\({}^{+}\)** & **Mg2\({}^{+}\)** & **Na\({}^{+}\)** & **CI\({}^{-}\)** & **SO\({}_{4}\)\({}^{2-}\)** \\ \hline
480 & 300 & 1200 & 10,000 & 15,000 & 2600 \\ \hline \end{tabular}
\end{table}
Table 2: Ionic concentrations of seawater (ppm).
## 5 Materials and Methods
### GC-MS Data Pretreatment, Compound Identification, and Quantification  
The operation procedures for GC-MS data pretreatment were developed by our laboratory [52]. The acquired MS files from GC-TOFMS analysis were exported in NetCDF format by ChromaTOF software (v3.30, Leco Co., CA, USA). CDF files were extracted using custom scripts (revised MATLAB toolbox hierarchical multivariate curve resolution (HMCR), developed by [PERSON], et al. [53]) in MATLAB 7.0 (The MathWorks, Inc., MA, USA) for data pretreatment procedures such as baseline correction, de-noising, smoothing, alignment, time-window splitting, and multivariate curve resolution (based on the multivariate curve resolution algorithm). The optimized parameters were as follows: signal-to-noise (S/N) = 30, baseline offset = 0.5, and peak width = 2. The resulting three-dimensional dataset included sample information, peak retention time, and peak intensities. Retention index (RI) calibration and mass spectral deconvolution were performed with ChromaTOF(tm) software (v3.30, Leco Co., CA, USA). Commercial mass spectral databases such as Wiley 9, mainlib, and replib were fully integrated with the ChromaTOF(tm) deconvolution software and were chosen for mass spectral comparison. The selected VOCs and non-VOCs were positively identified by comparing their mass spectral data and RI values with those of authentic standard compounds. The RIs of VOCs were calculated using n-alkanes C4-C30 as external references. The RIs of non-VOCs were calculated using the FAME of C8, C9, C10, C12, C14, C16, C18, C20, C22, C24, C26, C28, and C30 linear chain lengths [54]. Mass spectral matching was manually supervised and matches were accepted with thresholds of match \(>\)800 (with maximum match equal to 1000) and reconfirmed by RI values (\(\pm\) 25) when available. All identified compounds met level 2 or above of the metabolite identification set by the ChemicalAnalysis Working Group (CAWG) Metabolomics Standards Initiative (MSI) [55]. Compounds were quantified using the method proposed and validated by [PERSON] et al. [56].