esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bayesian Functional Mapping
### Structured Antedependence Model  
According to [PERSON] [33], the error at the current time depends on those at the previous times and the innovative error generated at the current time. Such a structured antedependence (SAD) model can be described by antedependence coefficients and innovation variances. The first-order SAD or SAD(1) model only contains one antedependence coefficient (\(\phi\)). Assuming a constant innovation variance (\(\
u^{2}\)), [PERSON] _et al._[34] derived a SAD(1)-structured covariance as:  
\[\begin{split}&\mathrm{var}(t)=\frac{1-\phi^{2t}}{1-\phi^{2}}\
u^{2 },\quad\forall\ \ 1\leq t\leq T\\ &\mathrm{corr}(t_{1},t_{2})=\phi^{t_{1}-t_{2}}\frac{1-\phi^{2t_{2} }}{1-\phi^{2}}\
u^{2},\quad\forall\ \ 1\leq t_{1}\
eq t_{2}\leq T.\end{split} \tag{21}\]  
It can be seen that neither the variance nor the correlation function is stationary for the SAD(1) model, i.e. the variance can change with time, and the correlation does not depend only on lag time \(|t_{1}-t_{2}|\).  
We pose an inverse-gamma prior on innovation variance \(\
u^{2}\) and a normal prior on antedependence coefficient \(\phi\). In a real data analysis, the priors are selected as \(\pi(\
u^{2})=IG(\alpha,\beta)=IG(1,1)\) and \(\pi(\phi)=N(\mu_{\phi},\eta_{\phi})=N(0,10)\). The explicit expressions that are proportional to the full conditional posteriors of these two parameters can be derived as:  
\[\pi(\
u^{2}|\mathbf{y},\lambda,\mathbf{Q},\mathbf{\Theta},\phi) \propto \pi(\mathbf{y}|\lambda,\mathbf{Q},\mathbf{\Theta},\
u^{2},\phi) \cdot\pi(\
u^{2}) \tag{22}\] \[\propto |\mathbf{\Sigma}(\
u^{2},\phi)|^{-\frac{n}{2}}\cdot\frac{\beta^{ \alpha}}{\Gamma(\alpha)}\cdot\
u^{2(-\alpha-1)}\] (23) \[\cdot \exp\{-\frac{\beta}{\
u^{2}}-\frac{1}{2}\sum_{j=0}^{2}\sum_{ \Omega_{i}=1}^{n_{j}}(\mathbf{y}_{ij}-\mathbf{f}(\mathbf{\Theta}_{j}))\mathbf{ \Sigma}^{-1}(\
u^{2},\phi)(\mathbf{y}_{ij}-\mathbf{f}(\mathbf{\Theta}_{j}))\} \tag{24}\]  
for \(\
u^{2}\), and:  
\[\pi(\phi\mid\mathbf{y},\lambda,\mathbf{Q},\mathbf{\Theta},\
u^{ 2}) \propto \pi(\mathbf{y}\mid\lambda,\mathbf{Q},\mathbf{\Theta},\
u^{2}, \phi)\cdot\pi(\phi) \tag{25}\] \[= \frac{1}{2}\cdot|\mathbf{\Sigma}(\
u^{2},\phi)|^{-\frac{n}{2}}\] (26) \[\cdot \exp\{-\frac{1}{2\eta_{\phi}}(\phi-\mu_{\phi})^{2}-\frac{1}{2} \sum_{j=0}^{2}\sum_{\Omega_{i}=1}^{n_{j}}(\mathbf{y}_{ij}-\mathbf{f}(\mathbf{ \Theta}_{j}))^{\prime}\mathbf{\Sigma}^{-1}(\
u^{2},\phi)(\mathbf{y}_{ij}- \mathbf{f}(\mathbf{\Theta}_{j})) \tag{27}\]  
for \(\phi\).  
The detailed M-H steps for updating \(\
u^{2}\) and \(\phi\) are given in Appendix C.
## 5 Barriers to Internet Access for Women in Developing Countries
### Financial and/or Institutional Constraints  
[PERSON] _et al._[10] (p. 380) note that, \"due to its role as a means of information gathering and sharing, use of ICT corresponds to having increased power and control within society. The digital divide draws attention to how disemppowered groups with limited economic resources have reduced access to ICTs\". There are still 1.4 billion people living on less than US$1.25 a day, and at least 70% of them are in rural areas [20]. Moreover, women often do not control finances or have sufficient personal income to purchase products or pay Internet service providers for monthly access [17]. As such, due to financial and institutional barriers, women lack the means to use, rent or purchase established and new technologies that could help them advance economically. The combination of laws, policies and social customs in many developing countries prohibit women from owning property and obtaining loans for technology purchases. A vicious cycle is thus perpetuated in which \"women cannot develop their skills, which prevents them from earning higher incomes, so they cannot afford the technologies that might boost them to the next rung on the economic ladder\" [20] (p. 7).  
To summarise, there appear to be a number of barriers that prevent women in developing countries from accessing and using the Internet: \"For many women, ICTs remain inaccessible due to affordability issues associated with poverty, lack of basic technological skills, low levels of literacy and numeracy,geographic isolation, and poor technology infrastructure...as well as the cultural expectations, norms and more that influence the ability of women to own and/or access ICTs in public places\" [18] (p. 135). The reason why fewer women access and use ICTs is a direct result of their unfavourable conditions with respect to education, employment and income. When these variables are controlled, women are generally more active users of digital technologies than men [31].
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 3 Design  
In this section, we describe the architecture design of the 2 LCCM, define the heavy and light congestion used to distinguish the two-level mechanism, and detail the switching strategy of the 2 LCCM.
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.4 User Anonymity  
User anonymity mainly comprises user identity protection, which prevents the adversary from obtaining the user's identity, and user un-traceability, which guarantees that the adversary cannot decide upon who the communicating user is, nor does it allow them to distinguish whether two instances of data interaction are from the same communicating user.  
For the first form of identity protection, on the one hand, during the registration phase, \(U_{i}\) only submits \(A_{0}\) to \(GWN\); thus, it cannot directly extract identity information for the adversary even if \(GWN\) could be destroyed. On the other hand, \(PID_{i}\) cannot be used to deduce the identity of the user during the authentication phase; thus, the adversary cannot capture the user's identity, \(ID_{i}\). As for the un-traceability of another user, the randomness of \(PID_{i}\) breaks the statistical property, which effectively confuses the adversary in determining whether two sessions are from the same communicating user.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 6 Future Trends in Fault-Type Classification  
The discussed transmission line fault classification studies mainly selected mature machine learning approaches such as FSI, ANN, SVM or DT, etc. However, huge advancements and new trends have been seen in the field of data mining and machine learning. [PERSON] et al. proposed an approach using restricted Boltzmann machine learning (RBML) to extract feature characteristics [PERSON] et al. (2016), the groundwork for deep learning (DL). Deep learning structure resembles multi-layer FNN, varying by the aspect that the unsupervised feature learning from a large amount of unlabeled input data saves the model from overfitting and falling into local optima. The fault classification capability of DL has increased recently [PERSON] et al. (2016) and its application on the power system is encouraging. Convolutional neural networks (CNNs) are recommended to handle multi-channel sequence recognition problems in [PERSON] et al. (2016), a promising idea for fault classification job.
## 4 Simulations and Results
### D2 HCP_ versus _the Thoppian and Prakash Protocol_  
Figure 15 shows a comparison of the latency of D2 HCP _versus_ the Thoppian and Prakash Protocol. It is noted that in the first case the latency is lower than the second and it is very regular too (in Thoppian and Prakash the latency grows exponentially when the number of nodes is high) allowing us to conclude that D2 HCP improves the results of its predecessor.
# Nanoparticles Based-Plant Protein Containing
## 1 Introduction
The methods of producing nanoparticles depend on the material inside the core, which includes organic, aqueous or inorganic phase, and also on the shape of the product. Zein is the primary storage protein of _Zea mays_, which makes up about 45-50% of _Zea mays_ protein [27]. Due to their unique properties (e.g., biodegradability, biocompatibility, non-toxicity, and economic justification), this type of protein has a special place in the production of biopolymers, and biodegradable films. Much research is being done to optimize the fabrication, purification and processing of zein and, most importantly, its extraction. Zein plays a functional role in some processes, due to several unique properties including biodegradability and biocompatibility, it can act as strong barrier against oxygen and a heat resistant biopolymer [28]. Therefore, much effort is being made to build it commercially at a low cost [29; 30; 31; 32; 33]. Zein can be converted into various structures and shapes as microsphere, film, micelles, NPs, fibers, and gels [22; 27; 33]. Therefore, zein can be an alternative in capsule carriers for food and drug. To previous studies, NPs based on biopolymers (e.g., zein) have more hydrophilic groups, smaller size, and solubility [22; 27; 34; 35; 36; 37]. Encapsulating these compounds (EO) reduces their reaction with environmental factors (water, oxygen, light) and reduces evaporation, increases transmission power, covers taste and causes uniform distribution of the final product [38]. There are many reports that the bioavailability of the compounds encapsulated in nanoparticle (nanomeric dimensions) is more than normal size [39]. Several studies have been carried out on the encapsulation of plant essential oils inside zein NPs [22; 28; 40; 41]. The use of zein for the encapsulation of thymol, citric acid and nisin has been carried out by [PERSON] et al. [41]. In another study, thyme essential oil was incorporated into zein nanoparticles [41]. Also, [PERSON] et al. have used rosemary essential oil using the zein electrolysis method [28]. Here, the aim was loading the rosemary essential oil into zein nanoparticles using a modified method. Essential oil loaded in zein nanoparticles were evaluated in terms of morphology, physical condition, thermal properties, encapsulation efficiency and essential oil release from zein.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
###### Abstract  
This paper proposes a scheme that adds XOR bit operations into the encoding and decoding process of the conventional non-orthogonal multiple access (NOMA) system to alleviate performance degradation caused by the power distribution of the original signal. Because the conventional NOMA combines and sends multiple data within limited resources, it has a higher data rate than orthogonal multiple access (OMA), at the expense of error performance. However, by using the proposed scheme, both error performance and sum rate can be improved. In the proposed scheme, the transmitter sends the original data and the redundancy data in which the exclusive OR (XOR) values of the data are compressed using the superposition coding (SC) technique. After this process, the data rate of users decreases due to redundancy data, but since the original data are sent without power allocation, the data rate of users with poor channel conditions increases compared to the conventional NOMA. As a result, the error performance and sum rate of the proposed scheme are better than those of the conventional NOMA. Additionally, we derive an exact closed-form bit error rate (BER) expression for the proposed downlink NOMA design over Rayleigh fading channels.  
non-orthogonal multiple access (NOMA); exclusive OR (XOR); bit error rate (BER); sum rate; exact BER analysis  
Msc: 94-10
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### Deep Learning-Based Anomaly Detection  
One of the major causes of anomalous behavior in cellular networks is cell outage. Cell outage management is part of the self-healing process in SON. A possible solution for cell outage detection could be achieved with the deployment of an autoencoder. This could potentially lead to the development of the deep learning method for SONs given that the huge amount of data available with the realization of 5G networks. Here, we present a brief introduction of an autoencoder that is actually an artificial neural network model and explain the idea behind the possible use of autoencoders for cell outage management. The data required to implement autoencoder cell outage management could be cell level data, where the KPIs are represented, for example, by receiver response reference signal (RSRP) values and receiver response reference quality (RSRQ) values, and, in addition, the neighboring cell RSRPs and RSRQs. However, in 5G networks, due to densification, the representation KPIs could consist of a large vector of measurements which could be more suitable for deep structured autoencoders.
## 3 Results
### Microbiology
were slightly less sensitive to substances **11**, **13** and **16** (MIC = 31.25-1000 \(\upmu\)g/mL), which indicated good and moderate activity of the substances or their mild effect on _E. faecalis_ ATCC 29212 (compound **16**). Other compounds, namely **6**, **12** and **14**, mainly showed bioactivity at MIC = 31.25-1000 \(\upmu\)g/mL and MBC = 250->1000 \(\upmu\)g/mL against these microorganisms, especially against _S. epidermidis_ ATCC 12228 (good activity at MIC = 31.25-62.5 \(\upmu\)g/mL). _S. aureus_ ATCC 43300, insensitive to substances **6** and **14**, was exceptional in this respect. In turn, other compounds had weaker (MIC = 125-1000 \(\upmu\)g/mL) or absent (MIC > 1000 \(\upmu\)g/mL) antibacterial effects. Moreover, all compounds except one, i.e., hydrazide-hydrazone 7, inhibited the growth of _Microoccus luteus_ ATCC 10240 in the MIC range from 31.25 to 1000 \(\upmu\)g/mL. Additionally, substance **7** showed no activity towards any cocci or bacilli (Tables 4 and 5). In the case of the reference Gram-negative bacteria, only compound **16** showed some bioactivity. The rods from the _Enterobacterales_ family and _Pseudomonas aeruginosa_ ATCC 9027 were sensitive to this substance at MIC = 31.25-500 \(\upmu\)g/mL and MBC = 31.25-1000 \(\upmu\)g/mL. The bioactivity of compound **16** with a bactericidal effect (MBC/MIC = 1-2) was good towards _Klebsiella pneumoniae_ ATCC 13883 or moderate against other rods. _Bordetella bronchiseptica_ ATCC 4617 was the least susceptible to this substance (MIC = 1000 \(\upmu\)g/mL and MBC > 1000 \(\upmu\)g/mL). All remaining compounds showed no activity towards Gram-negative microorganisms (Tables 4 and 5). Our data presented in Tables 4 and 5 also indicate potential antifungal activity of the tested compounds towards yeasts. Among them, substances **2**, **3**, **4**, **6**, **7**, **9** and **16** showed moderate and mild anticandidal effect or no bioactivity. The minimal concentrations of selected substances that inhibited the growth of _Candida_ spp. ranged from 500 to 1000 \(\upmu\)g/mL. The MBC values exceeded 1000 \(\upmu\)g/mL in each case. Two strains, _Candida albicans_ ATCC 10231 and _Candida parasilosis_ ATCC 22019, were most sensitive to these compounds. On the other hand, _Candida brucei_ ATCC 14243 was unsusceptible to all tested substances. The other compounds had no antifungal activity (Tables 4 and 5).
## 3 Research Design
### Proposed Algorithm
#### 3.1.3 Coordinate Transformation Steps  
As shown in Figure 1b, we need to calculate the distances between the fixation point and the left and top edges of the image, denoted by \(d_{1}\) and \(d_{2}\), respectively. These two distances can be computed using the aforementioned method and are applicable to four different reference point combinations: LTRTLB, RTLBRB, LTLBRB, and LTRTRB. Each combination consists of three key boundary points from the stimulus image.  
Figure 2 provides a practical demonstration of this transformation process, illustrating how fixation points are processed from raw eye-tracking data Figure 2a through the computational transformation Figure 2b to the final coordinate system representation Figure 2c.  
As an example, consider the LTRTLB (Left-Top, Right-Top, Left-Bottom) reference combination. Once the distances \(d_{1}\) and \(d_{2}\) have been determined, the fixation point can be mapped to the original image coordinate system using the following formula. To clarify these reference points, Table 1 defines their coordinates, and Figure 3 illustrates their positions.  
Figure 3: Schematic diagram illustrating the positions of LT, RT, and LB, and the distances used in Equations (1) and (2).  
Figure 2: (a) Eye-tracker fixation visualization. (b) Computation process after transformation. (c) Static representation of fixation points.  
\[\text{org}\text{X} = \frac{d_{1}\cdot\text{orgWidth}}{|LT-RT|} \tag{1}\] \[\text{org}\text{Y} = \frac{d_{2}\cdot\text{orgHeight}}{|LT-LB|} \tag{2}\]  
where \(|LT-RT|\) and \(|LT-LB|\) represent the distances from the top-left corner of the stimulus image to the top-right and bottom-left corners, respectively.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 3 Surface Modification of Diamond Thin Films
### Electrochemical Reactions
#### 3.2.1 Reactions Involving Diazonium Salts  
The use of phenyl diazonium salts was first reported by [PERSON] et al. in 1999 [30] and quickly became an extensively employed method for the functionalisation of diamond surfaces with linker arms that serve as anchors for the covalent bonding of biomolecules such as antibodies [31] or DNA oligonucleotide chains [32]. The process starts with the synthesis of a diazonium salt, e.g., 4-nitrobenzenediamine tetrafluoroborate (Figure 4) [33], which is then used to treat H-terminated diamond thin films (40 mM) in the presence of a solution of 1% SDS (sodium dodecyl sulfate) to produce a surface terminated with 4-nitro-aryl groups. Subsequent application of the current reduces the nitro groups to primary amines.  
Primary amines formed on the surface of diamond by the phenyl diazonium salt method can be used as anchoring points for a variety of molecules. An example of this is the reaction with thiol-modified DNA oligonucleotides via SSMCC cross-linking [32] (details of the process for cross linking DNA with NH\({}_{2}\)-terminated diamond are described in Section 3.1).  
A drawback of this method is when the phenyl radical species react not only with the surface of the diamond film, but with the phenyl ring grafted on the surface, ultimately forming undesirable multilayers [34].
# Simulation of the Fracturing Process of Inclusions Embedded in Rock Matrix under Compression
## 2 Fracturing Behaviors of Single Inclusion Embedded in Rock Matrix
### Effect of Material Properties on the Fracturing Behaviors of Inclusion
#### 2.4.1 Effect of the Material Heterogeneity on Fracturing Behavior  
In this study, homogeneity indexes of 1.5, 2.0, 3.0, 4.0, 6.0, 8.0, 10.0, 15.0, and 20.0 were selected. The materials with higher m values represent more homogeneous materials, whereas those with lower m values are more heterogeneous. It can be seen in Figure 9 that the effect of heterogeneity on the stress inside inclusion is negligible. The absolute value of the stress ratio of \(\sigma_{\text{(min)}i}/\sigma_{\text{(max)}r}\) is close to 0.30, which agrees well with the analytical solution as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Inclusion** & **Matrix** \\ \hline Homogeneity index (\(m\)) & 1.5, 2, 3, 4, 6, 8, 10, 15, 20 & 1.5, 2, 3, 4, 6, 8, 10, 15, 20 \\ [PERSON]’s modulus (\(E\))/GPa & 5, 50, 100, 150 & 2, 5, 50, 150 \\ Uniaxial compressive strength (\(f_{c0}\))/MPa & 50 & 100 \\ Tensile strength (\(f_{t0}\))/MPa & 2.5 & 10 \\ Poisson’s ratio (\(v\)) & 0.25 & 0.45 \\ Friction angle (\(\varphi\))/\({}^{\circ}\) & 30 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physico-mechanical parameters employed in simulation for analysis of the effect of properties on the failure behavior of single inclusion.  
However, the impact of heterogeneity on fracturing mode is not negligible. Figure 10 shows the failure process of inclusion with a homogeneity index of 1.5, 4.0, and 10.0, respectively. For the heterogeneous inclusion (e.g., \(m=1.5\)), the location of initial failure is disordered and scattered. The final fracturing path is very flexuous and too many flaws are formed inside the inclusion. However, for the relatively homogeneous rock (e.g., \(m=10.0\)), the fractures initiated from one side of the circular interface are kept propagating along the direction of maximum principal stress. The final fracturing path is relatively smooth. The number of flaws and consecutive fractures in the inclusion is relatively small.  
Figure 10: _Cont._  
Figure 9: Effect of heterogeneity on the stress inside inclusion.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 1 Introduction
This is especially useful when observing the ocean's surface topography due to its high variability. By 1 August 2021, one year of passes had been collected, so that there were enough data to compare the two altimeter systems [13]. Here, we focus on the open ocean, where [3] has assessed the feasibility of using ICESat-2's photon-counting lidar to determine wave and wind characteristics with a high spatial resolution and thereby model the individual surface waves. The ability to observe individual surface waves along a ground track that is also being monitored by a radar altimeter would provide additional knowledge about the ocean surface, such as peak wave heights and the distribution of wave heights.
## 3 ANFIS-Based Modeling
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Structure** & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R2**} \\ \hline
**MF Number** & **MF Type** & **Model I** & **Model II** & **Model I** & **Model II** \\ \hline \multirow{4}{*}{2-2-2} & Triangular & 0.1932 & 2.6039 & 0.8438 & 0.9394 \\  & Trapezoidal & 0.2107 & 4.4552 & 0.8141 & 0.8226 \\  & Gaussian & 0.1906 & 3.0474 & 0.8478 & 0.9170 \\  & Generalized bell-shaped & 0.1953 & 3.4949 & 0.8404 & 0.8909 \\ \hline \multirow{4}{*}{3-3-3} & Triangular & 0.0430 & 2.1223 & 0.9920 & 0.9598 \\  & Trapezoidal & 0.0962 & 2.9568 & 0.9583 & 0.9219 \\  & Gaussian & 0.0998 & 2.5242 & 0.9612 & 0.9431 \\  & Generalized bell-shaped & 0.1006 & 2.5679 & 0.9576 & 0.9411 \\ \hline \multirow{4}{*}{4-4-4} & Triangular & Too high & Too high & Too low & Too low \\  & Trapezoidal & Too high & Too high & Too low & Too low \\  & Gaussian & Too high & Too high & Too low & Too low \\  & Generalized bell-shaped & 0.2111 & Too high & 0.8134 & Too low \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different ANFIS structures with respect to the MF number and type. with \(O_{i}\) being the output for \(i\) rules, \(\mu\) the MF, \(x,y\), and \(z\) are the input parameters, and \(A\), \(B\), and \(C\) the fuzzy sets. \[O_{i}^{(1)}=\mu A_{i}(x),\ \ \ \mu B_{i}(y),\ \ \ \mu C_{i}(z) \tag{1}\]  
The second layer can be mathematically described as a set of two fuzzy rules, assuming that \(x=Vc\), \(y=f\), and \(z=ap\), with Equations (2) and (3) [43]. Where \(f\) is the first-order polynomial, and \(p_{i}\), \(q_{i}\), \(r_{i}\), and \(s_{i}\) are the consequent parameters. In this layer, each node multiplies the incoming signals to represent the firing strength (\(w_{i}\)) of a rule, which can be described by Equation (4). \[if\ x=A_{1}\ \ \ and\ \ \ y=B_{1}\ \ \ and\ \ \ z=C_{1}\ \ \ then\ \ \ f_{1}=p_{1}x+q_{1}y+r_{1}z+s_{1} \tag{2}\]  
\[if\ x=A_{2}\ \ \ and\ \ \ y=B_{2}\ \ \ and\ \ \ z=C_{2}\ \ \ then\ \ \ f_{2}=p_{2}x+q_{2}y+r_{2}z+s_{2} \tag{3}\]  
\[O_{i}^{(2)}=w_{i}=\mu A_{i}(x)\times\mu B_{i}(y)\times\mu C_{i}(z) \tag{4}\]  
Then, each node calculates the normalized firing strength at the third layer with Equation (5) for \(j\) inputs. Consequently, each node computes the output of each rule using the normalized firing strength and a linear output function with Equation (6). Finally, the single node computes the final output by summing all incoming signals with Equation (7). \[O_{i}^{(3)}=\overline{w_{i}}=\frac{w_{i}}{\sum\limits_{j}w_{j}} \tag{5}\]  
\[O_{i}^{(4)}=\overline{w_{i}}\times f_{i}=\overline{w_{i}}\times(p_{i}x+q_{i}y +r_{i}z+s_{i}) \tag{6}\]  
\[O_{i}^{(5)}=\sum\limits_{i}\overline{w_{i}}f_{i} \tag{7}\]