esa-sceva/satcom-chunk-collection huggingface dataset:
# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
## 2 Materials and Methods
### Temporal Trend Analysis
#### 2.3.2 Modified Mann-Kendall Test (mMK)  
Similarly to the MK test, the mMK follows the same approach to give the monotonic trend result. However, the difference is the addition of a variance correction approach in the mMK. Based on the approach of variance correction, it is considered that existence of the autocorrelation between some N data consists of the same information as with uncorrelated data [40]. Furthermore, based on sets of Monte Carlo simulations, no change in the convergent normality and mean of Mk test statistic (S) have been proven in the presence of serial correlation, while it changes the variance of the distribution of S.  
However, time series that are not random and influenced by autocorrelation makes trend test too liberal when none exists [40]. [PERSON] and [PERSON] [41] have proposed a modified version of the Mann-Kendall test, which is appropriate in the presence of autocorrelation. This version is based on the modified variance of S (Var*(S)) ([PERSON] and [PERSON], [41]). First, data are detrended. Then, the ranks of significant serial correlation coefficients are used to calculate the effective sample size of the dataset. These coefficients are then used to adjust the inflated/deflated variance of the statistical test.  
\[Var^{*}(S)=Var\ (S)\frac{n}{n_{s}^{*}} \tag{8}\]The correction factor \(\frac{n}{n_{\rm s}^{*}}\) is estimated as follows [40]:  
\[\frac{n}{n_{\rm s}^{*}}=1+\frac{2}{n*(n-1)*(n-2)}\sum_{i=1}^{n-1}(n-i)(n-i-1)(n-i- 2)\rho_{s}(i) \tag{9}\]  
where \(\rho_{s}(i)\) is the autocorrelation between the observation ranks.  
\(n_{\rm s}^{*}\) is the effective number of observations.  
The autocorrelation is computed after subtraction of the non-parametric trend estimator (e.g., [PERSON]'s slope estimator). Only significant values of the ranks \(\rho_{s}(i)\) are used to calculate the variance correction factor.
# Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks
## 2 Observed Data Analysis and Processing
### Unification of Traffic Variables' Dimensions  
Modern information technology (such as automatic vehicle identification technology and GPS positioning technology) drives the development of traffic detectors. Different periods and types of traffic data can be observed, such as link traffic flow, speed, density, travel time, etc. The inconsistencies of dimension between these observed variables affect further application of data. As noted above, there are analytical relations among link flow, speed, and travel time in congested networks. Therefore, traffic flow theory is adopted to transform the dimension of observed variables to unify observed multi-source link information. In this paper, the Green Shields traffic flow-speed-density relation function was used to unify multi-source observed variables in link travel time [19]. In other words,the inverse function of Equation (1) was used to transform the dimension of each traffic flow parameter relationship:  
\[v_{a}=\frac{l_{a}}{t_{a}}\Bigg{[}M_{j,a}\Bigg{(}1-\frac{l_{a}}{t_{a}S_{f,a}}\Bigg{)} \Bigg{]},\forall a\in A \tag{1}\]  
where \(A\) represents set of links, \(a\in A\); \(v_{a}\) represents traffic volume of link \(a\); \(t_{a}\) represents estimated travel time of link \(a\); \(l_{a}\) represents length of link \(a\); \(M_{j,a}\) represents congestion density of link \(a\); and \(S_{f,a}\) represents maximum driving speed of link \(a\). Link travel time can be derived from link length and vehicle speed.
## 4 Findings and Results
### Descriptive Statistics
This reflects changing societal norms and progressive sociopolitical environments. Examining geographical divisions, the study of \"Region\" reveals diverse distribution patterns, with Marodijeh standing out as the region with the largest percentage of households (31.92%). These differences highlight variances in population density, economic activity, and infrastructure development across different regions. This calls for policy interventions that focus on the specific needs and requirements of each region. Similarly, the analysis of \"District\" highlights the specific demographic patterns in different areas, revealing that Hargeisa has the highest concentration of households, accounting for 31.94% of the total. This highlights the crucial importance of Hargeisa as a central location for population and economic activity in the examined area, necessitating specific actions to meet related infrastructure and socioeconomic needs. Furthermore, the classification based on \"Location Type\" revealed a preference for urban living, as 66.01% of households were in urban regions. These findings highlight the impact of urbanization on resource allocation, infrastructure development, and socioeconomic inequalities. This emphasizes the need for strong urban planning methods that provide fair access to resources and facilities. An analysis of the \"Occupation of the Household Head\" reveals a wide range of livelihood pursuits, including agricultural activities, government jobs, and self-employment operations. The presence of various types of jobs within families highlights the diversified character of economic activity. This diversity is the result of different skills, resources, and market conditions. In addition, the categorization of \"Type of Housing\" provides insight into the predominant housing circumstances, with \"Daar no fence\" home being the most common form, accounting for 39.31% of the total. This indicates a high occurrence of informal or temporary housing, highlighting the necessity for changes in housing infrastructure and urban development projects to address housing deficiencies and improve residential living conditions. An examination of \"School Type\" revealed common educational enrollment trends, with most pupils (72.16%) attending public schools. This highlights the dependence on educational institutions sponsored by the government and emphasizes the need to strengthen public education systems to guarantee fair access to high-quality education and promote inclusive socio-educational development paths. To summarize, the table provides important information on the demographic and socioeconomic trends that influence educational accessibility and socioeconomic disparities in the investigated region. These detailed observations are essential for guiding policy decisions founded on evidence, actions that are focused, and academic investigations that aim to promote fair socio-educational development paths and reduce systemic inequalities.
## Materials and Methods
### Obtaining the Gaze Direction \(\varphi_{g}\)
#### 2.2.1 Obtaining Gaze Point in the World Camera Coordinate System \((x_{g}^{e},y_{g}^{e})\)  
First, coordinates of the gaze point in the world camera coordinate system are acquired by the eye tracker. The eye tracker obtains coordinates of the gaze point by image-processing the position of the pupil by using left- and right-eye images captured by the eyeball camera. At this time, the precision with which the pupil may be detected differs depending on how the pupil appears. The pupil detection accuracy \(S_{pupil}\) is represented by a value between 0 to 1. In this study, through use of only the gaze point information \(S_{pupil}>0.6\), reliable gaze information was obtained. The above threshold value was determined through preliminary verification. This value \(S_{pupil}\) is one of two values affecting the gaze detection accuracy.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 5 Results and Discussion
### Rotordynamic Results Comparison Between Two numerical Methods
#### 5.1.2 Rotordynamic Coefficients  
When the time-dependent fluid-induced forces are obtained, the frequency-dependent rotordynamic coefficients can be determined according to Equation (6). Figure 14 shows the comparisons of rotordynamic coefficients determined with two methods. The two methods capture the identical frequency dependence of rotordynamic coefficients. The average differences of cross-coupled stiffness, direct stiffness, cross-coupled damping, and direct damping are 2.06%, 2.83%, 6.27%, and 2.67%, respectively. These two methods have the similar numerical accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multicolumn{2}{c}{**Initial Phase of \(F_{x}\) (rad)**} & \multicolumn{2}{c}{**Initial Phase of \(F_{y}\) (rad)**} \\ \cline{2-5}  & **Transient CFD** & **Orbit Decomposition** & **Transient CFD** & **Orbit Decomposition** \\  & **Method** & **Method** & **Method** & **Method** \\ \hline
30 Hz & \(-\)0.410\(\pi\) & \(-\)0.407\(\pi\) & \(-\)0.834\(\pi\) & \(-\)0.827\(\pi\) \\
60 Hz & \(-\)0.370\(\pi\) & \(-\)0.365\(\pi\) & \(-\)0.819\(\pi\) & \(-\)0.812\(\pi\) \\
90 Hz & \(-\)0.343\(\pi\) & \(-\)0.336\(\pi\) & \(-\)0.811\(\pi\) & \(-\)0.805\(\pi\) \\
120 Hz & \(-\)0.321\(\pi\) & \(-\)0.319\(\pi\) & \(-\)0.807\(\pi\) & \(-\)0.804\(\pi\) \\
150 Hz & \(-\)0.313\(\pi\) & \(-\)0.308\(\pi\) & \(-\)0.810\(\pi\) & \(-\)0.805\(\pi\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial phases of time-dependent fluid-induced forces at different whirl frequencies.  
Figure 14: _Cont._  
Figure 13: Comparison of fluid-induced force amplitudes.
## 4 Experimental Validation of the Geometrical Efficiency Calculation  
### Metal-Based Cage Armour Systems  
#### 4.1.1 Geometrical Analysis  
Determining the efficiency of a cage armour system is, for all metal-based systems fairly straightforward, at least approximatively. It theoretically suffices to determine the probability that the piezoelectric element will fall in between the components of the cage armour, while the projectile body comes into sufficient contact with these components to cause the required short-circuit in the detonation chain. Although small variations will exist depending on the exact interaction between the projectile and the cage armour system, a fairly accurate estimate can be based solely on a geometrical analysis. This approach has been followed by other research groups [10], yet without experimental validation. The latter will be provided in the next subsection of this paper.  
The probability of correct functioning of the cage armour will vary as a function of vertical (elevation) and horizontal (azimuth) impact angle and with the specific threat (projectile) that is considered, as the dimensions of the projectile subcomponents change depending on the projectile type. Unfortunately, the perceived geometry of especially a net solution from the standpoint of the projectile will change considerably whenever the trajectory of the projectile leads to a compound (i.e., azimuth and elevation different from zero degrees) impact angle. For instance, a square mesh will transform into a parallelogram mesh when viewed from a compound angle. Determining rigorous analytical solutions for compound impact angles, is hence not as straightforward as one would initially think.  
Figure 11: Examples of chain/ball armour applied to a Swedish StrV 102 (**a**) and an Israeli Merkava (**b**).  
Nevertheless, first-order analytical approximations can be relatively easily developed by assuming the influence of azimuth and elevation to be independent. These approximations are relatively accurate up to 60\({}^{\circ}\) compound impact angles. Although for higher impact angles more important deviations may appear, the more complex cage armour systems, e.g., based on nets, have an efficiency close to zero anyway due to the too-small remaining presented free spacing between the wires. Approximate mathematical expressions for the different considered cage armour systems are given in Appendix A.  
It is important to notice that the determined expressions to calculate the cage armour efficiencies also do not take into account the possible presence of mountings or attachments needed to fix the cage armour system to the vehicle. Any presence of an item that might trigger the warhead before it can be short-circuited will lower the actual efficiency compared to the theoretically derived efficiency.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 4 Catalytic Applications of Mesoporous Silica Materials
### Organic Synthesis
#### 4.2.3 Synthesis of Dimethylcarbonate  
[PERSON] et al. reported flowerlike MgO in the synthesis of dimethylcarbonate (DMC) by a meso-SiO\({}_{2}\), which was coated on Fe\({}_{2}\)O\({}_{3}\), MgO, an SnO nanosphere, Co\({}_{3}\)O\({}_{4}\), and nanowire through a simple solution method [120]. In the paper, low-cost synthesis strategies for both nano- and meso-SiO\({}_{2}\) using mesoporous SiO\({}_{2}\) were reported. They found that the meso-SiO\({}_{2}\) coating with its tetrahedral geometry can form a layer to bind and cover the nano building blocks. The silica coating dramatically improved the structural stabilities of several types of nanostructures, including flowerlike nanomaterials (Fe\({}_{2}\)O\({}_{3}\), MgO), hollow spheres (SnO), nanowires, and nanosheets (Co\({}_{3}\)O\({}_{4}\)). The results showed that a coating of meso-SiO\({}_{2}\) is an ideal method for showing that mechanic stability leads to the remarkable catalytic stability of fragile nanomaterials.  
The MgO@m meso-SiO\({}_{2}\) catalyst converted 85% of the ethylenedonate, and there were no morphology changes in 10 sequential runs, whereas uncoated, flowerlike MgO broke into pieces after merely one run (Figure 8). This approach created a worthwhile route for making highly active, yet fragile nanostructured catalysts practical for scale-up applications [120].  
Figure 7: The in-situ generation of ultra-small Pt nanoparticles on mesoporous silica rods in high-activity mode in the oxidation of \(n\)-hexane.
## 3 Results and Discussion
### Stability and Recyclability of Bi@Fe/CN-3  
In order to study stability and recyclability of Bi@Fe/CN-3, the SMX degradation was investigated by four-cycle experiments under the same condition. As shown in Figure 8, the SMX removal rates were 99.7%, 97.9%, 94.7% and 93.3%, respectively. The SMX degradation efficiencies declined slightly after four cycles. It was concluded that Bi@Fe/CN-3 showed excellent catalytic stability and recyclability.  
Figure 8: Cycle experiments of Bi@Fe/CN-3 in Bi@Fe/CN-3/PMS system. Reaction conditions: [catalyst] = 0.10 g/L, [PMS] = 0.60 mM, [SMX]\({}_{0}\) = 15.0 mg/L, initial pH = 5.18.  
The SEM images of used Bi@Fe/CN-3 were shown in Figure S2. Similar to the fresh Bi@Fe/CN-3, the used Bi@Fe/CN-3 were irregular block granules with rough surfaces too. Besides, the FTIR spectrum of used Bi@Fe/CN-3 was presented in Figure S2c. Obviously, the FTIR spectrum of used Bi@Fe/CN-3 was very similar to that of fresh Bi@Fe/CN-3. This indicates that the composition of surface functional groups did not change with the degradation of SMX in Bi@Fe/CN-3 system.  
In addition, the surface chemistry and element content of used Bi@Fe/CN-3 were further clarified by XPS analysis and listed in Table S1. Compared with fresh Bi@Fe/CN-3, there was little change in the content of each element in used Bi@Fe/CN-3, further confirming the good stability of Bi@Fe/CN-3.
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
###### Abstract  
Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today's quantum computers. Machine learning has the potential to improve the performance of QPE algorithms, especially in the presence of noise. In this work, QPE circuits were simulated with varying levels of depolarizing noise to generate datasets of QPE output. In each case, the phase being estimated was generated with a phase gate, and each circuit modeled was defined by a randomly selected phase. The model accuracy, prediction speed, overfitting level and variation in accuracy with noise level was determined for 5 machine learning algorithms. These attributes were compared to the traditional method of post-processing and a 6x-36 improvement in model performance was noted, depending on the dataset. No algorithm was a clear winner when considering these 4 criteria, as the lowest-error model (neural network) was also the slowest predictor; the algorithm with the lowest overfitting and fastest prediction time (linear regression) had the highest error level and a high degree of variation of error with noise. The XGBoost ensemble algorithm was judged to be the best tradeoff between these criteria due to its error level, prediction time and low variation of error with noise. For the first time, a machine learning model was validated using a 2-qubit datapoint obtained from an IBMQ quantum computer. The best 2-qubit model predicted within 2% of the actual phase, while the traditional method possessed a 25% error.  
Keywords:quantum computing; ensemble models; neural network; quantum phase estimation; post-processing; measurement interpretation +
Footnote †: journal: _Algorithms_
# Precipitation Polymerization: A Powerful Tool for Preparation of Uniform Polymer Particles
## 2 Categories of Precipitation Polymerization  
During the development of PP, several different kinds of approaches based on the conventional free radical polymerization mechanism have been developed for the preparation of uniform crosslinked spherical polymer particles. Except for traditional thermo-induced precipitation polymerization (TRPP) [16; 17; 26; 27; 28], new methods such as distillation precipitation polymerization (DPP) [29], reflux precipitation polymerization (RPP) [30; 31], photoinduced precipitation polymerization (PPP) [32; 33; 34], solvothermal precipitation polymerization (SPP) [35], controlled/\"living\" radical precipitation polymerization (CRPP) [36; 37] and self-stabilized precipitation polymerization (2 SPP) [38; 39] have also been successively developed (Figure 3). So far, in addition to DVB, many other multifunctional monomers have been applied to prepare crosslinked polymeric microspheres, including methacrylates, acrylamide, styrene, acrylonitrile, and \(p\)-chloromethylstyrene. Functionalization of polymer particles is of great importance for their application, and microspheres have unreacted double bonds on their surface, allowing their post functionalization and the synthesis of microspheres with core-shell structures [40; 41].  
Over the years, many research groups have become interested in further exploring the scope, versatility, and mechanisms of PP, including addressing the challenges that remain in the enhancement of productivity and solving the restriction of solvents in order to enlarge more types of monomers [42; 43; 44]. In addition to being a source of academic interest, those new systems and their resulting crosslinked polymeric microspheres have displayed genuine potential for industrial applications and have been systematically summarized [12]. Herein, we collect together some of the more recent results within the field, with the main  
Figure 2: The chemical structures of crosslinkers (**1–5**) and monomers (**6–17**) for the precipitation polymerization. (**1**) Divinylbenzene, (**2**) ethylene glycol dimethylacrylate, (**3**) trihydroxymethyl propane trimethyl acrylate, (**4**) \(N\),\(N^{\prime}\)-methylenebis(acrylamide), (**5**) \(N\),\(N^{\prime}\)-Bis(acryloyl)cystamine, (**6**) acrylic acid, (**7**) methacrylic acid, (**8**) 2-hydroxyethyl methacrylate, (**9**) glycolyl methacrylate, (**10**) vinyl acetate, (**11**) styrene, (**12**) maleic anhydride, (**13**) 1-vinyl-2-pyrrolidone, (**14**) 4-vinyl pyridine, (**15**) 1-vinylimidazole, (**16**) 2-(methacryloyloxy)ethyl)-dimethyl-(3-sulfopropyl) ammonium, (**17**) 2-methacryloyloxyethyl phosphorylcholine.  
emphasis being placed on the new types of precipitation polymerization, such as categories, origin, and mechanisms. We hope our collected findings will illustrate the value of the research and, furthermore, will encourage other researchers to become involved in the challenges ahead.
## 4 Implementation of the System
### Subsystem Implementation  
#### 4.2.1 Blockchain Logic Smart Contract  
The core of the blockchain-based system relies on a smart contract that makes use of two essential functions. The first one is the'store' function, which introduces the AR hash to an array. The second one is the 'check' function, which verifies whether the submitted hash is present on the array or not. Note that the critical operation in the smart contract is the 'check' function, which will be executed by third-party providers who want to verify specific merits. The execution of such an operation is cheap, energy efficient and very fast, because it is a simple read from the blockchain [88], which makes it suitable for contexts with a great number of read-type requests (as it will be illustrated in Section 5). Similarly, it can be easily inferred that the gas consumption of the'store' function will be minimal. Precisely, such a function just involves a single _SSTORE_ operation on the EVM to set the value of a hash to true. The efficiency of such a write operation will be demonstrated in Section 5 by deploying the contract to a public testnet.  
The technique used in the proposed smart contract provides a flexible verification mechanism applicable to many kinds of domains where data need to be monitored continuously. In fact, this technique is also used to track the history of small blockchains through their Merkle trees (i.e., their state). This architecture is called a dual-chain system [46], where a private network or sidechain could be devoted to store academic data (i.e., ARs hashes), and a public blockchain to register the Merkle trees of such a private blockchain.  
To verify the security of the developed smart contract, it was evaluated using two widely recognized tools for smart contract auditing: Slither [89] and Mythril [90]. The obtained results indicate that no additional security measures are required, since the current method of storing hashes is sufficiently robust against potential attacks. The security challenges may arise on other aspects of the application that need to be addressed for a production-ready decentralized platform. For example, the use of the same set of keys may increase the risk of physical linkability of a student to his/her address [91]. Moreover, cases of lost or stolen keys have to be considered in a production environment. Furthermore,  
Figure 11: Successful verification of an AR.  
Figure 10: After the updating process, students can see the added marks.  
if third parties do not manage the AR provided by a student correctly, there is a risk of data leak. This can be prevented as described in Section 2.6, where selective disclosure through ZKPs permits complying with the GDPR. The authorization control of the DApp should also be enforced, as only authorized accounts should be able to confirm the changes in the database. This demands a thorough control access that can be achieved by introducing a smart contract for role management [92] to comply with current security standards.