esa-sceva/satcom-chunk-collection huggingface dataset:
# Organic Crystals for THz Photonics
## 5 Applications of Organic THz Sources
### THz Nonlinear Photonics
The bottom red trace is the square of the applied THz electric field and the blue trace below the tetrahydrofuran data shows the empty cuvette background response. Reprinted from Ref. [187] with permission from AIP Publishing. [PERSON] et al. reported on extreme laser spectral broadening and a change in optical transmission in GaP induced by a strong THz field [194]. DSTMS crystals were used in the experiment with thin aluminum and nickel films, where damage produced by a single and multiple THz pulses was investigated [171; 195]. The observed damage resulted in ablation and through holes as well as delaminations and cracking near the damage threshold. The real-time room-temperature THz imaging developed by [PERSON] et al. [196] was demonstrated on an aluminum foil by nonlinear frequency up-conversion in a DAST crystal. The results showed higher sensitivity in comparison to a commercial THz camera. Furthermore, analytical and numerical models [197] showed that the rapid heating of aluminum film with thicknesses of several tens of nanometers by the high-energy THz pulse causes a significant rise of effective electron collision frequency. Consequently, the increase in nonlinear transmission of the thin film as well as noticeable changes in reflectivity can also be observed. All of these recent examples demonstrate a very high potential of intense organic THz sources for nonlinear THz photonics.
## 3 Results
### Arrhenius Constitutive Model
#### 3.6.1 Calculation of the Arrhenius Constitutive Model Constants  
The material constants were determined by utilizing the true stress and true strain data. The calculating approach was demonstrated using a true strain of 0.4 (50%) as a pilot route. Equations (10) and (11) were derived through the natural logarithm of Equations (7) and (8), respectively.  
\[\ln\dot{\epsilon}=\ln A_{1}+n_{1}\ln\sigma-\frac{Q_{1}}{RT}\Rightarrow n_{1} =\left[\frac{\partial\ln\dot{\epsilon}}{\partial\ln\sigma}\right]_{T} \tag{8}\]  
\[\ln\dot{\epsilon}=\ln A_{2}+\beta\sigma-\frac{Q_{2}}{RT}\Rightarrow\beta= \left[\frac{\partial\ln\dot{\epsilon}}{\partial\sigma}\right]_{T} \tag{9}\]  
By applying a logarithm to Equation (9), Equation (12) can be derived, which was subsequently utilized for the computation of \(Q_{3}\) and \(n_{2}\). Equations (13) and (14) were derived by differentiating Equation (12).  
\[\ln\dot{\epsilon}=\ln A_{3}+n_{2}\ln\sinh(\alpha\sigma)-\frac{Q_{3}}{RT} \tag{10}\]  
\[n_{2}=\left[\frac{\partial\ln\dot{\epsilon}}{\partial\ln[\sinh(\alpha\sigma) ]}\right]_{T} \tag{11}\]  
\[Q_{3}=R\times\left[\frac{\partial\ln\dot{\epsilon}}{\partial\ln[\sinh(\alpha \sigma)]}\right]_{T}\times\left[\frac{\partial\ln[\sinh(\alpha\sigma)]}{ \partial\left(\frac{1}{T}\right)}\right]_{\dot{\epsilon}} \tag{12}\]  
The determined constants corresponding to a strain level of 0.4 (50%) are compiled in Table 3.  
The pattern between \(T,\dot{\epsilon},\)and\(\sigma\) was expressed as follows (Equation (15)):  
\[\sigma=\frac{1}{\alpha}\ln\left\{\left(\frac{z}{A_{3}}\right)^{\frac{1}{n_{2} }}+\left[\left(\frac{z}{A_{3}}\right)^{\frac{2}{n_{2}}}+1\right]^{\frac{1}{2 }}\right\} \tag{13}\]  
where \(Z=\dot{\epsilon}exp^{\frac{Q_{3}}{RT}}\)(Equation (2)).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline In(\(A_{1}\)) & \(n_{1}\) & \(Q_{1}\) [KJ/mol] & In(\(A_{2}\)) & \(\beta\) & \(Q_{2}\) & \(\alpha\) & In(\(A_{3}\)) & \(n_{2}\) & \(Q_{3}\) [KJ/mol] \\ \hline
23 & 3.5 & 51 \(\pm\) 5 & 17 & 0.22 & 50 \(\pm\) 5 & 0.062 & 14 & 2.67 & 51 \(\pm\) 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The computed values of \(A_{1}\), \(A_{2}\), \(A_{3}\), \(n_{1}\), \(n_{2}\), \(\beta\), \(Q_{1,2,3}\) and \(\alpha\) constants at strain level of 0.4 (50%).
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### ABE Scheme Based on SM9 IBBE  
We slightly modify the SM9 IBE scheme into an IBBE (Identity-based Broadcast Encryption) [34; 35] scheme to generalize our scheme, so that we can transform it into an ABE scheme that supports the generic access structure. The setup and key generation algorithms of IBBE are the same as the original SM9 IBE scheme. We propose the IBBE encrypt and decrypt algorithms based on the SM9's KEM-DEM-Encrypt and KEM-DEM-Decrypt algorithms. Taking the master public key \(M_{pk}\), a set of identities \(S=\{ID_{1},ID_{2},\ldots,ID_{n}\}\), and the message \(M\) as input, the IBBEncrypt algorithm proceeds as presented in Algorithm 9, and it outputs a set of ciphertexts \(C=\{CT_{1},CT_{2},\ldots,CT_{n}\}\).  
```
1 Let \(ID_{i}\) be the i-th \(ID\) of \(S\), and \(C\) be a null set.
2 For \(i=~{}1\) to \(|S|\) Do
3\(CT_{i}\gets SM9.\textbf{KEM-DEM-Encrypt}(M_{pk},ID_{i},M)\)
4\(C\gets C\cup\{CT_{i}\}\)
5 END For
6 output \(C\)
```  
**Algorithm 9**IBBEncrypt\((M_{pk},S,M)\)  
Taking the master public key \(M_{pk}\), the user identity \(ID\), the associated private key \(SK_{ID}\), and a set of ciphertexts \(C=\{CT_{1},CT_{2},\ldots,CT_{n}\}\) as input, we present the IBBEDecrypt algorithm. The pseudocode is presented in Algorithm 10.  
```
1 Let \(CT_{i}\) be the i-th ciphertext of \(C\)
2 For \(i=~{}1\) to \(|C|\) Do
3\(M\gets SM9.\textbf{KEM-DEM-Decrypt}(M_{pk},ID,SK_{ID},CT_{i})\)
4 IF \(M\
eq\bot\)
5 output \(M\) and terminate
6 ELSE
7 continue
8 END For
9 output \(\bot\)
```  
**Algorithm 10**IBBEDecrypt\((M_{pk},ID,SK_{ID},C)\)  
If the user identity \(ID\in S\) (of IBBEEncrypt algorithm) and \(SK_{ID}\gets SM9.\textit{Private-Key-Extract}\left(M_{pk},M_{sk},ID\right)\), then we can correctly decrypt the ciphertext with the IBBEDecrypt algorithm.  
We construct the ABE scheme that supports the generic access structure based on the IBBE scheme, with the following four parts:
# Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System
## 6 Related Work
The works of [28; 29] try to figure out the best locations for bike stations from candidate sites. The work of [30] proposes a mixed model to minimize fixed construction costs and variable operational costs. Research combining probability and simulation such as in [31] develops a probabilistic model to infer future demand, and the work of [32] adopts Monte Carlo to predict the over-demand probability in each bike station cluster. On the other hand, the works of [8; 33; 34; 35] focus on bike imbalance and rebalancing problems, proposing methods to transfer bikes between stations. **Bike demand analysis and prediction**. In all bike-related problems, the most widely studied is bike demand or traffic flow prediction. The studies of [22; 36] have identified the importance of natural environmental factors such as temperature, precipitation, and humidity on cycling activities across different cities. At the feature level, studies [5; 37] consider a single factor instead of multiple aspects features and thus may neglect representative elements. Other works collect historical data such as public transportation pattern records [38], crowd flow [39], meteorology data [7; 8; 40], and so on. Clustering methods applied to bike stations are more and more common in recent works since bike stations share partially similar regional characteristics and will reduce the variance and improve prediction accuracy. The difference between these works is what the cluster is based on. The works of [7; 9; 32; 41] cluster stations according to bike transition pattern records, geographical locations, bike usage, etc. The study of [42] employs SimRank to calculate the similarities between stations and then adopts the density clustering algorithm OPTICS. However, the works above are not applicable for our scenario since they rely on the historical mobility data and therefore are unavailable for batch prediction in newly established stations in expansion areas. Furthermore, they mostly aim to predict demand in a relatively short period from hourly [11; 43], rush hours [9], to weekends and holidays [32], and thus cannot be applied to our long-term prediction.
# Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks
## 4 Results
### Effect of DROB in Class B Gateway Buffer  
When analysing the results of packets sent by the GW, it is clear there was a big difference depending on the DR used. This is due to the effect of having longer or shorter airtimes needed to transmit a packet. The impact of the airtime was greater in the downlink stage, as the GW needed to transmit to many nodes in short periods of time, therefore encountering strong limitations due to the duty cycle, but other events can also happen in the output buffer of the GW when operating in class B, as explained in previous sections. This includes finding the pingslot scheduled still busy (again, due to longer airtimes), or even nodes having the same slot assigned (which can happen when the number of nodes is higher than available slots). In that regard, as can be seen in Figure 14, the configuration that achieves best results was DR5, which needs the shortest airtime, while the proposed DROB remains in an intermediate position as the GW used the entire range of available DRs during the time of simulation. Focusing on raw numbers, it may seem that keeping DR fixed in DR5 is the best option. Nevertheless, the number of nodes (relevant to scalability), the distance between nodes and the GW, the distance between nodes (density), and application requirements can impact the network operation greatly. The mechanism proposed can be further refined to adapt differently to channel changes, but with the results  
Figure 13: Number of DR changes introduced by the DROB mechanism during 24 h (**a**) and during the 1 h of high frequency interference (**b**).  
for this scenario it is valid to work under a certain set of parameters that attempt to stress the system and force the DR change trigger more often, where its benefits can be even higher. The main goal of this study was to provide a tool that enables some degree of adaptability to varying network conditions without a priori knowledge and pre-planning.
## 3 Results and Discussion
### Adsorbent Characterisation
#### 3.1.2 Scanning Electron Microscopy SEM/EDS  
The SEM analysis aims to illustrate the porosity created by the chemical activation. Figure 3 shows the SEM images of the ACSCGs at different magnifications. Numerous pores were developed on the surface of the activated carbon. This shows that potassium hydroxide (KOH) is effective in creating new pores and developing those naturally existing in the spent coffee grounds, leading to an activated carbon with a large surface area and a porous structure.  
Activation by KOH can be interpreted by the following two reactions [67].  
\[2\text{ KOH}\rightarrow\text{K}_{2}\text{O}+\text{H}_{2}\text{O}\]  
\[\text{C}+\text{H}_{2}\text{O}\rightarrow\text{CO}+\text{H}_{2}\]  
Figure 3: SEM micrographs of ACSCGs at different magnifications.  
Figure 2: XRD spectra of SCGs, ACSCGs and CAC.  
KOH is transformed initially into potassium oxide and steam; the latter diffuses into the carbon structure to react with the carbon atoms, generating CO and H\({}_{2}\) and a solid with a highly developed pore structure. According to [PERSON] et al. [68], the activation process reorganises the carbon structure, producing a more ordered structural skeleton. Pore development occurs in four stages: (i) opening of initially inaccessible pores, (ii) creation of new pores, (iii) enlargement of existing pores, and (iv) fusion of existing pores due to the rupture of the wall between them. The observed pores (\(\upmu\)m range) act as channels for the microporous network.  
The results obtained by energy dispersive X-ray microanalysis (EDS) of ACSCGs confirm the presence of carbon-rich compounds, as well as the presence of heteroatoms such as Mg, O, P, S, and Ca, as shown in Table 2. The absence of K shows that the washing process used at the end of the preparation process is efficient.
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 4 FPGA Implementation  
The FPGA code was developed in very high-speed integrated circuits Hardware Description Language (VHDL) in Quartus Prime 20.1 (Intel Corp, Santa Clara, CA, USA). The code includes the transmitter and the receiver, detailed in the following sections.
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 1 Introduction
### Definitions of WAMS
Most of the literature studies the role of PMUs and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S/N** & **WAMS Technology Explored** & **Areas of Power System** & **Technology Considered** & **Reference** \\ \hline
1. & **SMT** & Monitoring & PMUs+SCADA & [1; 3; 6] \\ \hline
2. & **Wireless Communication** & & & \\
**Technologies** & & Monitoring, smart grid & PMUs+SCADA & [10] \\ \hline
3. & **Wide-Area Power Control and** & & & \\
**Monitoring** & **(WAPCAM)** & & & \\  & & & & \\  & & & & \\ \hline
4. & **Wide-Area Measurement and** & & & \\  & **Control Systems (WAMCS)** & & & \\  & & & & \\ \hline
5. & **Wide-Area Monitoring,** & & & \\  & **Protection, and Control** & & & \\  & **(WAMPAC)** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table showing some areas of WAMS currently explored and the extensive academic interest in WAMS. WAMS infrastructure as isolated technologies with limited objective goals, which are assessed to test the ability of the infrastructure to optimize the power system network [21; 24; 25; 28]. While significant research has been conducted on WAMS technologies and smart grid innovations, limited studies have focused on the deployment of WAMS technologies in traditional power system networks, which remain predominant in developing economies and particularly in sub-Saharan Africa. This gap has resulted in a lack of comprehensive data necessary for conducting sensitivity analyses of wide-area power systems (WAPS). Such analyses are crucial for enabling sub-Saharan African countries to develop robust fault detection schemes and establish pathways for integrating renewable energy into conventional grids. This review aims to highlight the progression of research in WAMS and identify key areas where these technologies could be effectively applied in traditional and aging power system networks within low- and middle-income economies. Additionally, it explores the potential role of AI in optimizing WAMS operations and examines the development of cyber-physical power system (CPPS) networks. According to the Smart Grid Architecture Model (SGAM), the CPPS represents a foundational framework for advancing smart grid development within conventional power grids, offering a structured approach to enhancing grid resilience, efficiency, and adaptability in emerging economies. This review discusses WAMS operation and dynamic fault detection, benefits that AI can provide with WAMS, cyber-physical power systems (CPPS), and smart grid development, and concludes with recommendations for future research.
# Exploring the Extension of Virtual Reality beyond the Five Senses: A Study on Human Aura Detection
## 1 Introduction  
The realm of virtual reality, primarily dominated by visual experiences, stands at the cusp of a paradigm shift. As we advance in our understanding of human perception, there arises a pertinent question: Should VR be limited only to the five senses? Or is there a need to explore beyond, into potential senses that have remained uncharted, such as the human aura? This energy field, often visualized as a luminous body of light surrounding the person, is said to be a manifestation of the life force or energy that permeates the universe. In recent years, the study of the human aura has seen a resurgence of interest, particularly in the fields of alternative medicine and holistic health. One of the key technologies that have facilitated this progress is the development of high-speed photodetectors. These devices have been used to detect and analyze the subtle changes in the energy field around the human body.
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
### AI in Earthquake Engineering
Based on the time series \(k\)-means (TSkmeans) algorithm, different acceleration records and corresponding structural responses (obtained by a numerical method) are divided into different label data clusters. The responses of nonlinear and linear structures under different seismic waves are predicted by trained models. The method can be used to monitor the health of structures with different structural vibration control techniques (such as isolation, energy dissipation, and dynamic vibration absorption technologies), which has great engineering application prospects.
## 3 Results
### Bulk Density Results  
According to the results in Figure 3, increasing SSP content leads to a clear rise in composite density due to the higher intrinsic density of the filler compared to the resin matrix. By contrast, EPA and EPB have minimal impact on density, acting mainly as matrix-forming agents rather than bulk fillers. The quadratic model for bulk density prediction is shown in Equation (5).  
\[\begin{split}\text{Bulk Density}=1325.87649+0.873764\times\text{ SSP}-6.92027\times\text{EPA}-11.31147\times\text{EPB}-6.09676\times 10^{-14}\\ \times\text{SSP}\times\text{EPA}-1.21493\times 10^{-13}\times\text{SSP} \times\text{EPB}-1.46318\times 10^{-13}\times\text{EPA}\times\text{EPB}\\ +1.27793\times\text{(SSP)}^{2}+0.103680\times\text{(EPA)}^{2}+0.407423 \times\text{(EPB)}^{2}\end{split} \tag{5}\]  
Table 4 shows that the contents of SSP, EPA, and EPB have statistically significant effects on bulk density (\(p<0.0001\)). SSP is the dominant variable, increasing density due to its high-density filler nature. EPA (F = 25.88, \(p=0.0014\)) and EPB (F = 10.87, \(p=0.0132\)) exhibit comparatively limited effects. The two-way interactions (AB, AC, and BC) were not significant (\(p=1.0000\)), indicating that the independent effects of each variable are more prominent. The quadratic effect of SSP (A\({}^{2}\)) was significant (\(p=0.0001\)), suggesting a non-linear influence, whereas the quadratic effects of EPA and EPB were not statistically significant. Although the model generally fits the data, a significant lack of fit is observed (\(p=0.0259\)). Overall, SSP has the strongest impact on density, and careful optimization of its content is essential for achieving the desired material performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline Model & 392.09 & 43.57 & 157.58 & \textless{0.0001} \\ A-SSP (g) & 305.49 & 305.49 & 1104.95 & \textless{0.0001} \\ B-EPA (g) & 15.88 & 15.88 & 57.44 & 0.0001 \\ C-EPB (g) & 3.97 & 3.97 & 14.37 & 0.0068 \\ AB & 0.0000 & 0.0000 & 0.0000 & 1.0000 \\ AC & 0.0000 & 0.0000 & 0.0000 & 1.0000 \\ BC & 0.0000 & 0.0000 & 0.0000 & 1.0000 \\ A\({}^{2}\) & 56.07 & 56.07 & 202.79 & \textless{0.0001} \\ B\({}^{2}\) & 0.0484 & 0.0484 & 0.1749 & 0.6883 \\ C\({}^{2}\) & 0.0467 & 0.0467 & 0.1688 & 0.6935 \\ Residual & 1.94 & 0.2765 & & \\ Lack of Fit & 1.44 & 0.2871 & 1.15 & 0.5263 \\ Pure Error & 0.5000 & 0.2500 & & \\ Cor Total & 394.03 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANOVA analysis for activation energy of the bio-composites.  
Figure 3: **(a)** Effects of EPA and SSP contents and (b) effects of EPB and SSP contents on bulk density, and (c) effects of EPA and EPB contents on bulk density.
## 3 Results and Discussion
### Influence of Ionic Strength  
Standard solutions of pharmaceuticals at a natural pH 7 were prepared in the presence of 0.001 M, 0.01 M, and 0.1 M NaCl instead of MilliQ water to determine the sorption capacity by changing the concentration of the inorganic salt.  
A slight increasing trend of IMT sorption with higher ionic strength for all three sorbent samples (Figure 3) can be attributed to possible neutralization of the TiO\({}_{2}\) surface by positive ions, which allows non-electrostatic interactions between pharmaceutical and sorbent [53]. Increased sorption may also be the result of decreased solubility of the pharmaceutical due to addition of soluble salts in aqueous solution [48]. The higher concentration of Na\({}^{+}\) ions affecting the decrease in CRZ sorption on TiO\({}_{2}\) may be the result of decreased active sites due to competitive sorption between Na\({}^{+}\) and CRZ, and weak electrostatic repulsion interactions due to equal charges of sorbent and sorbate, including ambient pH [54]. Determining the effect of ionic strength provides important information on how much the removal of organic contaminants from water is affected by the presence or addition of different ions. These results may also indicate that the sorption of IMT and CRZ is also affected by different substances in the water matrix, which needs to be confirmed by additional experiments.
## 6 Conclusions  
In this paper, a distributed adaptive topology reorganization scheme that maintains connectivity in multi-hop UWSNs affected by shadow zones has been developed in the context of two Spanish-funded research projects. It alleviates the effect of energy limitations and is able to maintain network connectivity in multi-hop three-dimensional UWSNs in the presence of shadow zones solving a location optimization problem.  
Three different cases have been determined according to the number of links in the three-dimensional UWSN affected by the shadow zone. A mathematical model has been developed for each case to find the optimal placement for the sensor nodes, whose ongoing communications are being disturbed.  
The average transmission loss values are reduced significantly (especially for deep water + convergence zone) when the optimal locations are computed and the communication between sensor nodes is again reestablished outside the shadow zone. Shallow water shows lower transmission loss values in comparison with the other propagation phenomena. Deep water + convergence zone suffers lower transmission loss values than deep water + deep sound channel and is more appropriate for underwater communication. Only in deep water the transmission loss values are especially decreased with the shadow zone height for the frequency of 10 KHz and only very slightly decreased for the frequencies of 0.1 KHz and 1 KHz; the transmission loss values don't vary with the shadow zone height in shallow water.  
The average SNR values in the presence of shadow zones are negative, which indicates that the signal is below the noise level and transmission loss, and it can't be properly recovered. The average SNR values are increased significantly when the optimal locations are computed and the communication between underwater sensors is again reestablished outside the shadow zone. Furthermore, the improved SNR values are maintained for all frequencies. With shallow water the SNR values are better because in shallow water the transmission loss values are lower. The SNR values are higher with deep water + convergence zone than with deep water + deep sound channel. Nevertheless, in all cases the SNR loss values are increased outside the shadow zones.