esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results
### Approach Discussion  
The core objective of this study is to propose a sentiment-based DSS for supporting Jordanian Government work. The study was conducted during the period of the Covid-19 pandemic, in which the whole world witnessed dramatic governmental decisions and a massive amount of posts talking about them. Yet, after conducting experiments as previously explained this section discusses limitations, advantages, and disadvantages of the proposed approach, WOA-SVM, in a comparative manner with the rest of the tested methods: GA-SVM, PSO-SVM, and MVO-SVM.  
The proposed approach shows an excellent performance due to combined the SVM and WOA together for solving two major issues simultaneously: tuning the SVM parameters to enhance the sentiment prediction performance as well as weighting the features for identifying the most important information. Furthermore, compared with other approaches, such as deep learning and rule-based techniques, both approaches usually take time to perform due to the complexity of their models. Furthermore, the rule-based techniques required a lot of manual work as well as a deep knowledge of the domain, whereas deep learning cannot perform effectively on small datasets. On the other hand, the WOA-SVM can execute efficiently on small data and does not take long to perform. However, the proposed approach sometimes suffers from selecting the correct meta-heuristic algorithms for the problem, considering there is no optimal meta-heuristic algorithm that solves all problems.  
Figure 6: WOA-SVM weights for the features of Data 4.  
Figure 7: WOA-SVM weights for the features of Data 10.
## 5 Results and Discussions  
In the case of SSO, the implementation code and its workflow should be in mathematical notation form. Mathematical notations are used to calculate the cost in terms of computational behavior. We have used SSO with multifactor authentication to achieve AAAA authentication, authorization, accountability, and availability. The whole code has been divided into three parts: (1) running once per day, (2) loop for a specific time, and (3) use for built-in functions and libraries.  
The placed orders from the abc.com federated domain process execution and metadata processing occur once per day; hence, the algorithm cost for these constant functions will be O(1). At the same time, the tasks that are running once will be \(\Omega(1)\). Here the value is constant or running once, so the total cost will remain the same during the whole algorithm for these functions.  
In the case of exporting data to CSV, the format loop will run as per the number of orders in a whole day; hence, the algorithm cost will change to O(n), representing the constant evaluation of the required functional loops. For the built-in library function, inner function, and Shibboleth functionality, the algorithm's cost depends on o(n) and w(n). As much as the size of input or required function increases, it will be increased in parallel.  
The following diagram presents the AAAA comparison as per the number of requests between SSO and MFA. In our presented scenario, the number of federated orders via SSO varied per day in 15,000-20,000. Internal user access to the system via MFA was about 5000 per day. We present the AAAA for both the SSO and MFA, respectively. Figure 5 shows the plotting of the highest range rule to avoid complexity and to make it easy to understand.  
The Figure 5 shows that the percentage of accountability and availability create a massive difference between SSO and MFA deployment in prosed work. Results were obtained by implementing a test-running environment in the various need-based scenarios. We obtained quite similar results in the case of authentication and authorization. Hence, it can be concluded that in our design model, MFA proves better AAAA as compared to SSO.
## 3 Results and Discussion
### Membrane Characterization
#### 3.1.3 Membrane Thickness and Contact Angle  
Figure 4 shows the results of membrane contact angle (Figure 4a) and thickness (Figure 4b) measurements with and without the addition of the Aliquat-336 carrier.  
According to Figure 4a, the contact angle of the membranes decreased by increasing the content of Aliquat-336 in the polymeric solution. This could be explained by the presence of the ammonium groups in Aliquat-336 that confer a hydrophilic character. Similar results were found by [PERSON] et al. [39] and [PERSON] et al. [42]. Figure 5 shows a photo of the water contact angle WCA measurement for the membrane at 1/0.75 PVC/Aliquat-336 ratio. The addition of Aliquat-336 did not significantly affect the thickness value, and an increase was observed only for a PVC/Aliquat-336 ratio of 1/1 (Figure 4b).  
Figure 3: Infrared spectrum of the prepared membranes: PVC; PVC/Aliquat: 1/0.5; PVC/Aliquat: 1/0.75; and PVC/Aliquat: 1/1.  
This could be attributed to the swelling of the polymer matrix at high Aliquat-336 contents that finally affected the thickness of membranes.
## 3 Design of Neural Network and Production of Training Data
### Design of Classification and Regression Neural Network
#### 3.1.1 Structure of CPD-Net  
The structure of CPD-Net is shown in Figure 6. In order to extract the phase value of the time slot synchronous clock more quickly and accurately, we designed a shallow time slot synchronous clock recognition network. This network has a small amount of calculation and high recognition accuracy. The network consists of two convolutional layers and eight fully connected layers, and each layer is followed by a ReLU activation function. The first two convolutional layers are used for phase feature extraction, the last eight fully connected layers are used for phase classification, and finally, the phase value with the highest probability is output in the output layer. To sum up, the model of CPD-Net can be expressed as:  
\[\hat{Q}_{l}=f_{t}(f_{t-1}(\dots f_{1}(I_{n}))) \tag{1}\]  
where \(I_{n}\) represents the input data, and \(f_{t}\)(\(t\) = 1,2,...,10) represents the mathematical operation of each layer of the network.  
Figure 5: Timing diagram of starting position determination.  
In general, the purpose of training a neural network is to reduce the loss function by optimizing the parameters of the network. Since CPD-Net is a typed network, the cross-entropy error is used here as the loss function, which can be expressed as:  
\[l_{cpd}=-\sum_{i=1}^{M}Q_{i}*\log(P_{i}) \tag{2}\]  
where \(Q_{i}\) is the expected output value, and \(P_{i}\) is the predicted probability of network output.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
###### Abstract  
The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term.  
Attention Mechanism; Convolutional Neural Network; deep learning; Generative Adversarial Network; hybrid model; solar irradiance forecasting; Long Short-Term Memory
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 3 Logic of Events
### Axiom System
#### 3.3.2 Causal Axiom  
The Causality Axiom comprises three axioms: \(AxiomR\), \(AxiomV\), and \(AxiomD\). It establishes the causal correspondence between events in the \(Rcv\) class, \(Verify\) class, and \(Decrypt\) class with their respective events in the \(Send\) class, \(Sign\) class, and, \(Encrypt\) class. In other words, before any receiving or verification event, there must be a corresponding sending or signing event with the relevant information. This is specifically expressed as follows:  
\[\begin{array}{l}AxiomR:\forall e:E(Rcv)\exists e^{{}^{\prime}}:E(Send).(e^ {{}^{\prime}}<e)\wedge Rcv(e)=Send(e^{{}^{\prime}})\\ \left(\begin{array}{l}AxiomV:\forall e:E(Verify):e^{{}^{\prime}}:E(Sign).(e^ {{}^{\prime}}<e)\wedge Verify(e)=Sign(e^{{}^{\prime}})\\ AxiiomD:\forall e:E(Decrypt)\exists e^{{}^{\prime}}:E(Encrypt).(e^{{}^{ \prime}}<e)\wedge DEMatch(e,e^{{}^{\prime}})\\ DEMatch(e,e^{{}^{\prime}})\equiv{}_{def}plaintext{text}(e)=plaintext{text}(e ^{{}^{\prime}})\wedge\\ ciphertext(e)=ciphertext(e^{{}^{\prime}})\wedge MatchingKeys(key(e),key(e^{{} ^{\prime}}))\end{array}\right)\end{array} \tag{2}\]
## 2 Materials and Methods  
We studied magnetic properties and GMI effect in Fe\({}_{75}\)B\({}_{9}\)Si\({}_{12}\)C\({}_{4}\) (metallic nucleus diameter, d = 15.2 \(\upmu\)m, total diameter, D = 17.2 \(\upmu\)m) and Co\({}_{69.2}\)Fe\({}_{41.1}\)B\({}_{11.8}\)Si\({}_{13.8}\)C\({}_{1.1}\) (d = 25.6 \(\upmu\)m; D = 30.2 \(\upmu\)m) microwires prepared by using the Taylor-Ulitovsky technique described elsewhere [11; 12]. Fe-rich composition presents positive and high magnetostriction coefficient, \(\uplambda\)s (about \(40\times 10^{-6}\)) [22; 23], while Co-rich wires have low negative and vanishing magnetostriction values (about \(-3\times 10^{-7}\)) [40; 41].  
We measured the impedance, Z, and its magnetic field dependence over a wide range of frequencies (10-900 MHz), using a vector network analyzer. As-prepared and stress-annealed samples were placed in a micro-strip sample holder, and impedance was obtained from the reflection coefficient S11, as described elsewhere [42]. From Z-values obtained for different magnetic fields, H, we evaluated the magnetic field dependences of the GMI ratio, \(\uplambda\)Z/Z, which is defined as follows:  
\[\uplambda\)Z/Z = [Z (H) - Z (H\({}_{\text{max}}\))]/Z (H\({}_{\text{max}}\)),  
where H\({}_{\text{max}}\) is the maximum applied DC magnetic field. In order to compare the frequency dependence on the GMI effect, we plotted frequency, \(f\), dependence of a maximum GMI ratio, \(\uplambda\)Z/Z\({}_{\text{m}}\), defined as a maximum \(\uplambda\)Z/Z obtained at a given frequency.  
Hysteresis loops of as-prepared and stress-annealed samples were measured by the flux metric methods described elsewhere [12; 38]. For a better comparison of samples annealed under different conditions, we represent the hysteresis loops as the normalized magnetization, M/M\({}_{0}\) versus the applied magnetic field, H, where Ms is the magnetic moment at the maximum amplitude of magnetic fields, H\({}_{\text{max}}\).  
Annealing treatments were conducted in a conventional furnace, with and without stress, at temperatures, T\({}_{\text{ann}}\), below the crystallization typically observed above 450-500 \({}^{\circ}\)C. The stress-annealing process was performed under the attachment of different mechanical loads, to one end of the microwire. Different values of mechanical loads allowed us to apply tensile stresses during the annealing, \(\sigma_{a}\), up to 900 MPa. The stress value during the annealing, \(\sigma_{\rm a}\), within the metallic nucleus and glass shell was evaluated, as described elsewhere [38; 39].
## 2 Materials and Methods
### Quality Assurance and Quality Control (QA/QC)  
Meteorological measurements and diagnostics data were retrieved weekly from the air monitoring station via a satellite modem. The continuous meteorological measurements from the RM Young and Vaisala WXT 520 instruments were reviewed and compared to identify any anomalies which were invalidated accordingly. Analytical results for the PM\({}_{2.5}\) mass and elements came with flags/comments associated with individual samples which were used to validate the analytical results. Twenty-nine travel blanks were collected to track potential contamination associated with the pre-treatment of the air filters, transportation between the lab and the air monitoring station, field sampling, and laboratory analyses. The blanks were treated as regular air samples for analytical purposes. Method detection limits (MDLs) for the entire process (including the sampling and lab analyses) were calculated from the mean of the blanks plus three times the standard deviation by applying a nominal air volume of 24 m\({}^{3}\). Prior to the calculation, outliers were identified by the [PERSON]'s criterion statistical test [25]. All analytical data were compared against individual MDLs (Table S1). Data below the MDLs were replaced with \(\frac{1}{2}\) MDLs.
## 3 The Proposed MPPT Algorithm
### Chosen Values  
The chosen values for the startup time and for the limits of Equations (6), (9), and (13) are summarized in Table 1, together with the chosen values of the filter inductance and capacitance and the requested positive and negative power slope.  
Finally, it has to be noted that the sizing procedure has been illustrated with reference to the standard irradiation (1 kW/m\({}^{2}\)) and by fixing strong constraints. If referred to partial shading conditions; the same procedure would have led to different values of chosen parameters. Thus, in the real case of variable irradiation, the fixed limits may not be fulfilled by the chosen parameters. Nevertheless, as it will be seen in SS6, the chosen values will continue to assure satisfying results, even for a deep shading condition.  
Indeed, the only parameter that should be fine-tuned across the whole range of the operating conditions is the positive reference power slope. In fact, in contrast to the ideal case of Figure 3--carried in the standard condition--the value of the hysteresis threshold is linked to the positive power slope and the PV voltage derivative in correspondence to the MPP:  
\[th_{dvc}=2\frac{p_{slope}}{v_{mpp}}\left.\frac{\text{d}v_{p}}{\text{d}i_{p}} \right|_{MPP}, \tag{14}\]  
From Equation (14), it can be seen that \(th_{dvc}\) should be changed as a function of radiation on which \(\text{d}v_{p}/\text{d}i_{p}\big{|}_{MPP}\) depends. However, a variation of \(th_{dvc}\) would lead to variable behavior of the PV  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\delta_{i}\) & \(\delta_{v}\) & \(\zeta_{\text{min}}\) & \(\zeta_{\text{r,min}}\) & \(\delta_{\Delta xc}\) & \(t_{StartUp}\) & \(P_{slope}\) & \(n_{slope}\) & \(L\) & \(C\) \\ \hline
0.1\% & 0.1\% & 50 & 5000 & 0.002\% & 20 s & 10 kW/s & 200 kW/s & 400 \(\mu\)H & 14 mF \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chosen values of the Maximum Power Point Tracking sizing procedure.  
power ripple across different operating conditions. Instead of fixing the \(th_{dvc}\) constant, an adaptive law of \(p_{slope}\) is proposed, which is as follows:  
\[p_{slope}=\frac{v_{mpp}th_{dvc}}{2\left.\mathrm{d}v_{p}/\mathrm{d}i\right|_{MPP}}, \tag{15}\]  
In Equation (15), the voltage derivative depends on the actual PV developable maximum power. If any instantaneous assigned reference power value \(P^{*}\) is considered to be equal to the PV developable maximum power, \(p_{slope}\)will depend on the actual assigned power via the PV characteristic. Thus, the power slope is a time-dependent function:  
\[p_{slope}\left(t\right)=\frac{v_{mpp}\cdot th_{dvc}}{2\cdot f\left(P^{*}\left( t\right)\right)}, \tag{16}\]  
The power slope will be assigned depending on the actual power value in the further numerical analysis (SS6), where the ideal generator is replaced by the real inverter and shading conditions are investigated. This will result in an increase in the startup time. However, the system will reach its rated power in a reasonable time value of about 110 s.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 3 Materials and Methods
### Determining Places to Hide Data  
The watermark is embedded in selected vertices of the 3D model, in the components of the selected attribute. It is crucial that attackers cannot identify the locations of data attachment and that the modifications made result in the least noticeable changes. The solution to both these problems is the developed algorithm for dispersing changes throughout the model. The process begins with the creation of an arithmetic sequence, the expressions of which are the indices of the vertices.  
\[a_{n}=n,\quad n\in\mathbb{N}_{0}\bigcap[0,K-1], \tag{1}\]  
where \(K\) is the number of all vertices.  
The pseudorandom number generator is then initialised by introducing a seed derived from the steganographic key. This generator is used to shuffle the array in which the \(a_{n}\) is placed. The last step is to use only the \(M=\lceil(K/L)\rceil\) values from the array, where \(L\) is the number of least significant bits used to append data in a single coordinate. The symbol \(\lceil\cdot\rceil\) indicates rounding up a number. In this way, a sequence of unique numbers, which are the indices of the vertices, was obtained.  
A steganographic key is required to re-identify the corresponding vertices and attributes. By using the seed of a pseudorandom number generator, the information of previously prepared vertices can be reconstructed efficiently and quickly.
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 2 Methodology
### Electrical Resistivity Tomography  
ERT investigates the horizontal and vertical distribution of the subsurface resistivity and is widely used for near-surface applications, geotechnical investigations, and lithological variations [58; 59; 60]. It can adumbrate the existence of subsurface discontinuities, fractures, or even voids. It is generally accepted that the ERT technique is more sensitive to variations in the subsurface soil properties than other geophysical techniques. ERT is well known for its high-resolution subsurface investigation, which is basically defined by the geometry of the electrodes used for the data acquisition. The processing results are a 2D image of the subsurface resistivity distribution.  
In the present study, the Wenner array was applied, using a minimum electrode spacing of 1 m, for the determination of the near-surface resistivity distribution in the chosen seismological stations. The depth of investigation was adequate for the purpose of the study. The ABEM geoelectrical system was used.  
The _Res2 DIntro_ Software by _Geotomo_ was used for the processing of the acquired resistivity data. During this processing, the software develops an initial model and tries to minimize the misfit error between this model and the raw data points. The inversion process continues until the minimum possible misfit error is achieved. At first, the quality of the acquired data was checked, by removing any possible bad points that would also affect the inversion process. Afterwards, the inversion was carried out based on the least-squares routine, since no extremely sharp boundaries were expected. In the inversion process, the topography of each section has also been embedded, since some of the stations had a smooth topographic relief.  
For the estimation of the coordinates of each electrode and the topography of each geoelectrical line, we used dual-frequency _TopCon HiperPro GPS_ antennas with Differential Global Positioning System (_dGPS_). The coordinate system used for this study was the local (Greek) EGSA'87 (Datum GGRS'80).
# Combination of Robust Algorithm and Head-Tracking for a Feedforward Active Headrest
###### Abstract  
Active headrest can reduce the low-frequency noise around ears based on the principle of active noise control. This paper presents a combination of robust algorithm and head-tracking for a feedforward active headrest to reduce the broadband noise for a sleeper on a high-speed train. A robust algorithm based on the feedforward active noise control is proposed to improve the noise control performance during head rotations. The head-tracking system with infrared rangefinders tracks the head position based on the Kalman filter to further improve system performance with head movements. Experiments were conducted on a model of a sleeper on a high-speed train. The experimental results show that the proposed active headrest system effectively controls broadband noise with head movements and rotations.  
active noise control; active headrest; robust algorithm; head tracking +
Footnote †: journal: Applied Sciences
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 3 PS-OCT Applications
### PS-OCT in Teeth  
PS-OCT has been used for imaging dental structures for almost 20 years [208; 209; 210]. In teeth, PS-OCT provides contrast for dentin, enamel, as well as curious lesions. Dentin is a calcified tissue and is a central component of teeth. On the crown, it is covered by enamel, a highly mineralized  
Figure 10: PS-OCT of cardiac tissue and vessels. (**a–c**) PS-OCT images of ex vivo chicken myocardium (adapted with permission from [14], Optical Society of America, 2001). (**a**) Intensity image; (**b**) Phase retardation image; (**c**) Image of fast axis distribution. (**d–f**) 3D visualization of a carotid arterial sample (adapted with permission from [70], Optical Society of America, 2015). (**d**) OCT intensity image; (**e**) Fiber orientation tractography; (**f**) Fiber alignment map in the vessel wall. Surface layers were partly removed in three sections to reveal OCT intensity and fiber orientation/alignment at three representative depths in the arterial wall. (**g–r**) PS-OCT images of fibrous plaques (adapted with permission from [205], Elsevier, 2007). (**g–i**) OCT intensity images; (**j,k**) Phase retardation images (scale range: 0-180\({}^{\circ}\)) showing high birefringence; (**i**) Retardation image of fibrous plaque showing black region corresponding to low birefringence below the luminal surface. (**m–o**) Picrosirius red stained histology section, showing orange-red fibers (thicker fibers), yellow-green (thinner fibers) and low collagen content in the plaque under polarized light microscopy, respectively; (**p,q**) Trichrome-stained histology images; (**r**) Corresponding histology section stained for \(\alpha\)-smooth muscle actin shows numerous smooth muscle cells within the fibrous plaque. Scale bars are 500 \(\upmu\)m.  
substance. The apatite crystals in dental enamel are highly ordered and produce negative birefringence. In contrast, collagen makes dentin positively birefringent. Processes such as demineralization lead to birefringence changes which can be observed by PS-OCT.  
PS-OCT was demonstrated for the assessment of early and advanced demineralization in dentin as well as in enamel (Figure 11d-e) [211; 212; 213; 214]. Ablation of demineralized tooth structures was monitored by PS-OCT [215]. Demineralization was also investigated in tooth roots [216]. PS-OCT of enamel treated by CO\({}_{2}\) laser irradiation confirmed inhibited demineralization [217]. In particular, caries--characterized by mineral breakdown of teeth due to bacterial activity--has been an interesting target for PS-OCT imaging. Caries lesions in various conditions were investigated and their progression was followed longitudinally [209; 210; 218; 219; 220]. Consequently, also remineralization processes in enamel and dentin were imaged based on their birefringence [211; 221]. Recently, an automated method for assessing remineralized lesions was developed based on PS-OCT [222].