esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory
### Theory of an ANFIS  
An ANFIS usually contains five layers. [PERSON] introduced the method in 1997 [63]. A common ANFIS strategy is training using optimization methods. ANFIS employs the capabilities of fuzzy logic and neural network methods. Algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) can be employed in the ANFIS to determine the optimal model [64; 65; 66; 67].  
The structure of an ANFIS is shown in Figure 1, where two inputs (\(x\),\(y\)) and one output (\(f_{out}\)) are shown. Accordingly, the first layer can be defined for node \(i\) as [63]:  
\[O_{i}^{l}=\mu_{A_{i}}(x)\ \ \text{for}\ \ i=1,2\ \text{or}\ O_{i}^{l}=\mu_{B_{i-1}}(y) \ \ \text{for}\ \ i=3,4. \tag{4}\]  
By using a membership function (with a range covering the interval (0,1)), all nodes will be parameterized. We used the Gaussian function (given below) as a membership function in the ANFIS approach [63; 68]:  
\[\mu_{A}(X)=e^{-\frac{(x-c)^{2}}{2\sigma_{i}^{2}}}, \tag{5}\]  
where \(\sigma\) and \(C\) are the parameters of the Gaussian function.  
Some weighted terms and constant nodes are in the second layer [69]:  
\[O_{i}^{2}=\omega_{i}=O_{i}^{l}=\mu_{A_{i}}(x)\mu_{B_{i}}(y)\ \text{for}\ i=1,2. \tag{6}\]  
The average values of the weights are calculated using the following formula in the third layer:  
\[\sigma_{i}^{3}=\overline{\omega_{i}}=\frac{\omega_{i}}{\omega_{1}+\omega_{2}} \ \text{for}\ i=1,2. \tag{7}\]  
Each average value of the weight is multiplied by its associated function in the fourth layer:  
\[\sigma_{i}^{4}=\overline{\omega_{i}}f_{i}=\overline{\omega_{i}}(p_{i}x+q_{i}y +r_{i})\ \text{for}\ i=1,2, \tag{8}\]  
where \(p_{i}\), \(r_{i}\), and \(q_{i}\) are the resulting parameters.  
Eventually, by summing the previous outputs in the last layer, the output is calculated as follows:  
\[\sigma_{i}^{5}=\sum_{i}\varpi_{i}f_{i}=\frac{\sum_{i}\omega_{i}f_{i}}{\sum_{i} \omega_{i}}. \tag{9}\]  
According to the ten clusters proposed in the ANFIS, 120 membership function (MF) parameters should be optimally determined, where the number 120 reached by multiplying the number of clusters (in this work, 10), the number of parameters of the membership function (in this work, 2), and the sum of the input and output parameters (in this work, 6). The PSO was used to optimize the ANFIS model.
## 4 Tests and Experiments  
We evaluated the two proposed algorithms using a simulation, assuming the following parameters:  
* initial interval before first transmission \(X=15,552,000[s]\) (180 days),
* standard deviation of time drift \(Y=0.000005\) (which equals 5 parts per million (ppm)), consists of two parts: a constant drift randomly selected from the normal distribution, with a standard deviation of 2.5 ppm, and an additional skew also with a standard deviation of 2.5 ppm randomized for each interval.
* uniform packet loss model, with loss probability set to 5, 10, 20 and 30%. The research was conducted on two separate simulation models, covering both problems discussed in the paper:
* Reconnection of the device disconnected from the system for a longer period of time.
* Follow-up clock synchronization and optimization of listening window's size. The research was conducted on two separate simulation models, covering both problems discussed in the paper:
* Reconnection of the device disconnected from the system was performed on simulation made in Microsoft Excel. We created two sheets, one with 140 thousand rows and the second with one million rows (each row represents one device), performing thorough research covering a multitude of independent time skew possibilities. Each row consists of time skew values for 3 consecutive transmission intervals calculated according to a normal distribution (see Section 3.2); qualification formulas to determine if the time burdened with transmitter crystal skew is inside a window range; energy consumption formulas to calculate overall cost for transmission reception.
* Follow-up clock synchronization and optimization of listening window's size was performed on a simulation model made in the Objective Modular Network Testbed in C++ (OMNeT++), a modular, component-based C++ simulation library and framework. We programmed a complex behavior of a receiver module listening to the transmitter  
Figure 4: Comparison of window length change parameters.  
module packets sent accordingly to a transmission scenario in times burdened with transmitter crystal skew error.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 5 Evaluation
### Key Generation Time Observation  
Whilst not the goal of this paper, a plot of compute time was additionally created and studied, purely for academic interest. This is illustrated in Figure 5. The compute time required per 1000 key generations exhibited a broadly similar shape to the energy consumption graph (Figure 4), with RSA algorithms generally requiring significantly longer processing times, particularly at higher NIST Security Levels. PQC again performed well against the ECC category methods, outperforming them as the security level increased in comparison.  
However, the relative performance in compute time between RSA and the two other categories differed. It appears that RSA consumed energy at a slower rate than the ECC and PQC techniques, resulting in a larger time multiple for RSA versus the other techniques.  
A tentative conclusion is that RSA may not be fully optimised in OpenSSL3.5 on the Pi, or that it is less able to be parallelised with more serialisation portions, exhibiting Amdahl's Law [47]. This finding is included as an observation, but is not explored further in this paper, as it is not the main purpose of the study. However, the author believes this to be an interesting observation, still worthy of note.
## 2 Mechanism of Microbial-Induced Calcite Precipitation (MICP)
### Reaction Mechanism of Urea Hydrolysis MICP  
[PERSON] et al. [40] found that 17-30% of bacteria collected from Australia can hydrolyze urea rapidly. The Bacillus pasteurii (BP) has a high urease activity within soil and has been widely used in the MICP treatment [41]. During its metabolism, its cell secretes a large amount of urease to produce adenosine triphosphate (ATP), which promotes the catalytic hydrolysis of urea to produce ammonium and carbonate ions. Meanwhile, the PH value in the system increases in this process. Due to the presence of calcium ions, carbonate ions and calcium ions gradually modulate to form CaCO\({}_{3}\) precipitation. The reaction equation of urea hydrolysis is shown in Equations (1)-(5) [42].  
\[\text{CO(NH}_{2})_{2}+\text{H}_{2}\text{O}\overset{\text{ urease}}{\rightarrow}\text{NH}_{2}\text{COOH}+\text{NH}_{3} \tag{1}\]  
\[\text{NH}_{2}\text{COOH}+\text{H}_{2}\text{O}\rightarrow\text{NH}_{3}+\text {H}_{2}\text{CO}_{3} \tag{2}\]  
\[\text{H}_{2}\text{CO}_{3}\rightarrow\text{2H}^{+}+\text{CO}_{3}{}^{2-} \tag{3}\]  
\[\text{NH}_{3}+\text{H}_{2}\text{O}\rightarrow\text{NH}_{4}{}^{+}+\text{OH}^{-} \tag{4}\]  
\[\text{Ca}^{2+}+\text{CO}_{3}{}^{2-}\leftrightarrow\text{CaCO}_{3}\downarrow \tag{5}\]  
The details of the urea hydrolysis of CaCO\({}_{3}\) precipitated by BP are shown in Figure 1. When BP metabolizes to produce urease, it will secrete a metabolite called bail polymer. Due to the existence of the double electric layer structure of the extracellular polymer and its microorganism, the microorganism tends to adsorb on the surface of the sand particles. Because of the negatively charged functional groups such as hydroxyl, amino, amido, and carboxyl, the surface of the microbial cell wall is also negatively charged and constantly attracts calcium ions in the environment, meaning that a large number of calcium ions gather on the cell surface. The carbonate formed after hydrolysis of urea will form CaCO\({}_{3}\) precipitation with these calcium ions and envelop the bacteria [43]. From the whole reaction process, it can be found that bacteria mainly play two roles: the core of which is to provide urease, and the other is to provide crystal nuclei for the formation of CaCO3 crystals [44]. The reaction equation of urease bacteria is displayed in Equations (6)-(10) [45].  
\[\text{CO(NH}_{2})_{2}+2\text{H}_{2}\text{O}\rightarrow\text{H}_{2}\text{CO}_ {3}+2\text{NH}_{3}(\text{g}) \tag{6}\]  
\[\text{H}_{2}\text{CO}_{3}+2\text{NH}_{3}\leftrightarrow 2\text{NH}_{4}{}^{+}+2 \text{OH}^{-} \tag{7}\]  
\[\text{H}_{2}\text{CO}_{3}\rightarrow\text{H}^{+}+\text{HCO}_{3}{}^{-} \tag{8}\]  
\[\text{HCO}_{3}{}^{-}+\text{H}^{+}+2\text{OH}^{-}\leftrightarrow\text{CO}_{3}{} ^{2-}+2\text{H}_{2}\text{O} \tag{9}\]  
\[\text{Ca}^{2+}+\text{CO}_{3}{}^{2-}\leftrightarrow\text{CaCO}_{3}\downarrow \tag{10}\]
## 3 Results and Discussion
### Multi-Location On-Body RF EMF Exposure Evaluation (Physical Measurements)
Figure 10: The KSL ratio, presenting the discrepancy between the unperturbed E-field and the results of recordings of the E-field influenced by the proximity of the body during the physical test measurements, to model multi-channel (narrowband) wearable equipment at various locations of single probes on the body, where particular PEMs were located: H—head, CF—chest font, CB—chest back): (**a**) 1 st exposure scenario with quasi-horizontal EMF propagation; (**b**) 2 nd exposure scenario with quasi-vertical EMF propagation; the distribution of results at a particular frequency band are characterized by MMR—the range from minimum to maximum of recorded values (bar), and the mean value (dot). The obtained data (Figure 11) confirm the conclusions from virtual measurements, whereby increasing the number of on-body measurement points (up to three points H + CF + CB case) reduces the difference between exposure evaluation results in exposure scenarios (a) and (b) of different EMF propagation and reduces the uncertainty of assessing workers' RF EMF exposure (comparing to twin-points measurements) to the extend depending on the frequency and on-body location of particular measurement points up to 40%.
## 5 The Cryptographic Suite  
Supporting secure communication in an underwater acoustic network is challenging due to the severe limitations of the networking environment in terms of very high message propagation delay, very low bandwidth, and high energy consumption for communication [21].  
Cryptography is the primary means to support confidentiality and integrity. Cryptographic primitives tend to cause the _ciphertext expansion_ phenomenon. Actually, the message padding as well as modification identification codes (MICs) and message authentication codes (MACs) make the message length to increase after applying cryptography [24]. It follows that message expansion becomes responsible of a longer transmission delay and an increased energy consumption. Depending on the cryptographic primitive, such an additional communication overhead due to security may be comparable to, if not even greater than, the overhead related to transmitting the payload itself. It follows that a properly designed cryptographic suite for an underwater acoustic network has to avoid, or at least mitigate, the message expansion phenomenon.  
Furthermore, underwater nodes are generally deployed in unattended, possible hostile, environments [25]. Generally, cost reasons prevent us to deploy tamper-resistant devices. For this reason, we cannot exclude that an adversary may be able to compromise an underwater node and alter its behaviour. Detecting compromised nodes much depends on detecting anomalies in expected communication and movement patterns [26], which are highly application-dependent. However, reactive measures to remove the compromised node from the system once detected should be considered by design. Concerning this, the cryptographic suite provides rekeying as a reactive measure to remove, at least logically a compromised device from the network. Actually, by revoking the keys held by a compromised device we revoke its ability to send and receive messages. However, in some situations revoking keys can be insufficient. Indeed the intruder could still be able to flood or jam the network. In these cases, physical removal becomes essential. However, even in situations where physical revocation of a compromised node is necessary, the ability of rekeying the system is indispensable. Actually,although the misbehaving device will be physically removed, it has been under the adversary control for a while and thus any secret therein contained (e.g., passwords and cryptographic keys) cannot be trusted anymore and have to be considered compromised.  
In Sections 5.1 and 5.2 we describe our solutions aimed at avoiding message expansion in secure message communication. In Section 5.3 we describe an efficient and scalable rekeying service.
## 4 Simulation Results
### Sensitivity Analysis
Concretely, Spearman's correlation coefficient is a more suitable tool to use for sensitivity analysis because the obtained TCIs differ a lot more, giving a definite ranking as compared to Pearson's TCIs, which mainly differ on the second and third decimal. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Case** & \multicolumn{2}{c}{**Weight Vectors**} \\ \hline Uniform Weighting & \(w_{0}=\) & [1/6 1/6 1/6 1/6 1/6 1/6 ] \\ Importance on NS & \(w_{1}=\) & [2/6 4/30 4/30 4/30 4/30 4/30] \\ Importance on L1 RMS & \(w_{2}=\) & [4/30 2/6 4/30 4/30 4/30] \\ Importance on Hz std & \(w_{3}=\) & [4/30 4/30 2/6 4/30 4/30 4/30] \\ Importance on V std & \(w_{4}=\) & [4/30 4/30 4/30 2/6 4/30 4/30] \\ Importance on Amb Stat & \(w_{5}=\) & [4/30 4/30 4/30 4/30 2/6 4/30] \\ Importance on HDOP & \(w_{6}=\) & [4/30 4/30 4/30 4/30 4/30 2/6] \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Weight matrices examined. In Tables 14 and 15, the criteria from the most to least sensitive are sorted where the numbers 1-6 are the criteria IDs. L1 RMS (no. 2) is the most sensitive, followed by HDOP (no. 6).
## 3 Concept
### Influence Factor Analysis  
Based on the research objective and previous research, a systematic analysis of the factors influencing the production logistics performance of assembly systems was carried out as a function of organisation structure and system configuration. For this purpose, the strengths and weaknesses of pure organisation structures were investigated using extended literature research (Section 2) and in coordination with industrial partners. According to [PERSON] [31], the assembly planning process is then applied [31]. This process begins with a requirements analysis that records the total production quantity and the number of product variants. This is followed by a product analysis in which, among other things, the quantities and the joining directions and processes are specified. The subsequent assembly operations analysis describes the assembly operations and their sequences in detail, considering the restrictions imposed by the product or the assembly devices or assembly operating equipment used. Finally, the functional analysis links the individual assembly operations with defined time specifications to enable them to be distributed across the workstations and to determine the cycle time.  
Adopting a hybrid organisation structure represents a long-term strategic decision for producing companies. In this context, assembly planning is divided into strategic dimensioning and operational design, although this research project does not consider the latter. The factors influencing the dimensioning of flow-oriented, hybrid organisation structures were determined deductively from the presented approach to assembly planning.  
* Product portfolio: How many different product variants should be considered in the hybrid organisation structure?
* Forecast production programme: In which quantities should the product variants be produced for a specific forecast period?
* Customer demand rate: What is the average period after a customer purchases a product?
* Assembly operations: Which assembly operations comprise the product portfolio's product variants?
* Assembly precedence relationships: What precedence-related restrictions exist between the assembly operations of the products in the product portfolio?
* Duration of assembly operations: How long do the assembly operations take according to the flow and job shop principle?
## 2 Materials and Methods
### Evaluation of TSNP Application in Biosensing and Diagnostics
#### 2.4.3 Polyethylene Glycol Coating of AuTSNP  
To secure nanoplate extra protection against the physiological environment for biosensing assays, AuTSNPs were coated with polyethylene glycol (PEG). One of the most researched polymers for coating or stabilizing nanoparticles is polyethylene glycol. Public health agencies granted approval for the use of this biocompatible, neutral, and hydrophilic polymer in biomedical and pharmaceutical applications [37]. The AuTSNP solution was centrifuged at 16,000 rpm at 4 \({}^{\circ}\)C for 40 min and the clear supernatant was discarded. The blue pellet of the nanoplates was resuspended with a 0.01% solution of PEG 20,000 at a 1:1 AuTSNP-PEG ratio.
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 3 Interference Evaluation Model  
While the aforementioned DME interference model characterized the interference source properties, its actual impact on B2a signal reception performance required further quantification through the receiver processing chain. This section analyzes how DME pulses affect the carrier-to-noise ratio and reception performance of the B2a signal through critical modules, including the pulse blanker. The derivation of the interference evaluation model was finally completed.  
The reception performance of the GNSS signals primarily encompasses acquisition, tracking, and demodulation, which mainly depends on the signal-to-noise-plus-interference-ratio (SNIR) at the output of the instant correlator in the receiver. However, due to the complexity of analyzing the SNIR, the \(C/N_{0}\) was frequently utilized to characterize the signal [21, 22]. The degradation in signal reception performance can be further calculated once the \(C/N_{0}\) degradation is known.
# Evaluation of Sustainable Development in European Union Countries
## 3 Analysis and Handling of Data
### Visual Filtering of the Data  
The visual inspection allows periods, alternatives, or subcriteria to be detected that have a systematic lack of data. The most common reasons for discarding elements in the visual analysis of the data are:  
* Subcriteria: lack of information for most countries and periods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & \
\begin{tabular}{c} **Initial** \\ **Subcriteria** \\ \end{tabular} &
\begin{tabular}{c} **Subcriteria Discarded** \\ **by Visual Filtering** \\ \end{tabular} & **Reduction** \\ \hline Socio-economic development (SED) & 19 & 14 & 26.32 \\ Sustainable consumption and production (SCP) & 25 & 20 & 20.00 \\ Social inclusion (SIN) & 23 & 15 & 34.78 \\ Demographic change (DCH) & 14 & 10 & 28.57 \\ Public health (PHE) & 12 & 4 & 66.67 \\ Climate change and energy (CCE) & 10 & 10 & 0.00 \\ Sustainable transport (STR) & 22 & 20 & 9.09 \\ Natural resources (NRE) & 11 & 2 & 81.82 \\ Global alliance for development (GAD) & 18 & 3 & 83.33 \\ Good policies (GPO) & 7 & 2 & 71.43 \\ Total & 161 & 94 & 41.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evolution of number of subcriteria by visual filtering.  
* Alternatives: difficulty in obtaining measurements, or incomplete information. In some cases, there are alternatives that have sufficient data in certain subcriteria, but in others the information is scarce, and thus is discarded.
* Periods: the starting date for gathering data on the subcriteria is very variable, and some elements are measured only in even or odd years. In addition, in some criteria, the information is not updated to the year 2016.  
This leads to a total of 67 subcriteria being identified whose information is insufficient, that is, 41.61% of all the subcriteria from the database. Table 1 summarizes the number of subcriteria discarded as a function of the higher-level criteria, underlining the references to natural resources, the global alliance for development and good policies, as, in all of these, over 70% of the subcriteria were eliminated.  
The visual inspection has identified four countries, Albania, Macedonia, Serbia, and Turkey, for which data are missing for most of the subcriteria, and they are therefore discarded, causing a reduction from 35 to 31 alternatives (11.43%). Furthermore, periods can be found in which sustainability can be evaluated, and a range was set from 2005 to 2015 (eleven years), and so this first filter has led to a reduction from 27 periods to 11 (a reduction of 59.26%).
# Fake Base Station Detection and Link Routing Defense +
## 5 Our Scheme  
Our design scheme aims to detect a fake base station and uses link routing upon detection to recover from the threats described in Section 4. We divide our defense scheme against fake base stations into two parts: fake base station detection for passive defense and link routing for switching to a known legitimate base station as an active defense. We deploy a passive defense to sense the abnormality in networking (detection) before applying the active defense (recovery). In passive defense, we detect the fake base station attacks on availability, and in active defense, link routing addresses the Sybil threat.
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 2 Theoretical Background of Wavelet Transforms
### Multi-Resolution Analysis (MRA)  
The MRA, also well known as a pyramid algorithm, is defined as a hierarchical representation of DWT [12]. It is based on decomposing the original signal into \(m\) level by successively translating and convolving the mother wavelet using low-pass (LP) and high-pass (HP) filters. These filters retain the detail (D) and approximation (A) components. The signal can be reconstructed by the sum of the last approximation component and all the detail components, as illustrated in Figure 2.