esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Design of a Minimalist Step Climbing Robot
## 3 Design and Modeling
### Kinematics of the Robot  
Kinematic analysis is performed for a robot to establish a relation between the relative motion of the links. The simplified kinematic model of the proposed robot is represented by rigid links as shown in Figure 6, where the front and rear wheel are represented by link AB and DE, respectively. Assuming the no-slip condition during the motion of the wheels at the edge of a step, the point of contact between the wheel and step is considered as a resolute joint for performing kinematic analysis. When the wheels are moving over the horizontal surface, the respective links connecting the center of the wheel and the contact point is considered perpendicular to the surface. In the following section, equations describing position, velocity and acceleration of the links are shown.  
Figure 5: Schematic for actuator length calculation while ascending.  
The position, velocity and acceleration of the links are illustrated with the help of Equations (6)-(8). The length of links AB, BC, CD and DE are system parameters. The location of point A and E are given by \((A_{x},A_{y})\) and \((E_{x},E_{y})\), respectively.  
\[l_{5}=\sqrt{(A_{x}-E_{x})^{2}+(A_{y}-E_{y})^{2}}\] \[\theta_{5}=\arctan\Big{(}\frac{A_{y}-E_{y}}{A_{x}-E_{x}}\Big{)}\] \[\beta=\pi-\theta_{5}\] \[\theta_{12}=\theta_{2}+(\pi-\theta_{1})\] \[\theta_{34}=\theta_{4}-\theta_{3}+\pi \tag{5}\]  
Since \(\theta_{12}\) and \(\theta_{34}\) are the input angles, \(\theta_{2}\) and \(\theta_{3}\) are derived by equating real and imaginary parts of the position equation of the kinematic chain.  
\[l_{1}e^{i\theta_{1}}+l_{2}e^{i\theta_{2}}+l_{3}e^{i\theta_{3}}+l_{4}e^{i \theta_{4}}+l_{5}e^{i\theta_{5}}=0 \tag{6}\]  
The corresponding velocity equations may be expressed as the following.  
\[l_{1}j\omega_{1}e^{i\theta_{1}}+l_{2}j\omega_{2}e^{i\theta_{2}}+l_{3}j\omega_ {3}e^{i\theta_{3}}+l_{4}j\omega_{4}e^{i\theta_{4}}+l_{5}j\omega_{5}e^{i\theta_ {5}}=0 \tag{7}\]  
The corresponding acceleration equation may be expressed as the following.  
\[\begin{array}{l}l_{1}(j\kappa_{1}-\omega_{1}{}^{2})e^{i\theta_{1}}+l_{2}(j \kappa_{2}-\omega_{2}{}^{2})e^{i\theta_{2}}+l_{3}(j\kappa_{3}-\omega_{3}{}^{2} )e^{i\theta_{3}}+l_{4}(j\kappa_{4}\\ -\omega_{4}{}^{2})e^{i\theta_{4}}+l_{5}(j\kappa_{5}-\omega_{5}{}^{2})e^{i \theta_{5}}=0\end{array} \tag{8}\]  
The unknowns \(\omega_{2}\), \(\omega_{3}\), \(\kappa_{2}\) and \(\kappa_{3}\) are determined by solving Equations (7) and (8), which may be used in dynamic analysis for deriving the forces at joints.
# Research on the Organic Binders in Archaeological Wall Paintings
## 4 Exploring Research Results
### Invasive Techniques
In recent years, proteomics procedures have become increasingly popular for the characterization of proteinaceous materials in ancient samples of several cultural heritage objects. A simplified protocol of in-situ protein digestion was proposed. They demonstrated the possibility of using a very small piece of hydrophilic gel loaded with trypsin to digest proteins directly in-situ on works of art with good efficacy. The strategy was developed on fresh and aged paint replicas, allowing the recognition of the usually employed organic binder based on egg, collagen, and casein [94]. A proteomic approach, spectrometry using liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to study the degradation of proteins present in the wall paintings of the Monumental Cemetery in Pisa, Italy. The authors noted that the deamidation of some amino acids present in the proteinaceous binder causes a process of deterioration in works of art, including wall paintings [95]. [PERSON] et al. (2020) has proposed a quasi-non-invasive analysis of paint medium, based on the in situ digestion of the proteins, using a hydrogel previously loaded with trypsin, and applied onto the objects surface. The digested peptides were examined by MALDI-ToF-MS and/or LC-ESI-MS/MS [96]. In the article, the authors report the results of the investigations, through ToF-SIMS and HPLC, of samples taken from the wall painting \"Madonna and Child enthroned with saints\" (1335-1338) by [PERSON]. The results of this study show that the ToF-SIMS technique is able to identify protein-based binding supports in the samples considered. In the secco-applied layers, markers for the components of egg (cholesterol and phosphocholine) were observed in the ToF-SIMS spectra obtained [43]. [PERSON] et al. (2009) have developed a procedure for identifying protein binders for molecular characterization of samples from the mural painting of St. Dimitar Cathedral in Vidin, Bulgaria, using LC-ESI/Q-q-TOF tandem mass spectrometry. The use of the proteomic survey made it possible to identify both egg yolk proteins and egg white proteins [97]. Always linked to the decay of the organic binder in ancient wall paintings, a recent article addressed the problem of blurring of the paint layer. Based on the experimental results obtained, the authors believe the decay of the organic binder can cause the blurring of wall paintings while keeping the pigment unchanged. Studies have been conducted on simulated wall paintings and on a fragment of the blurred ancient tomb mural painting Zang Huailiang Tomb, AD 730, Tang Dynasty, Sanyuan, Shaanxi, Chin [98].
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 2 Literature Review
### Modeling Activity Durations
The first two go beyond traditional parametric hazard-based models by using a Cox semi-parametric approach, where the hazard function is not constrained to follow a specific statistical distribution (e.g., Weibull). Instead, it varies based on individual characteristics from survey responses. [PERSON]'s work also provides a fresh perspective by addressing unobserved heterogeneity, a factor not directly captured by survey or diary variables but rather linked to hidden behavioral patterns that influence duration. Additionally, for the generation of synthetic travel activity patterns, we drew insights from various studies, particularly those employing activity-based models, such as [PERSON] et al. [42] and [PERSON] et al. [43]. This aligns with the methodological approach taken in our paper. The contributions of the current research work are stated as follows:  
* Development of an activity-based method integrating hazard-based models to enhance the accuracy of synthetic activity-travel pattern generation,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
Evaluation and comparison of Cox-based modeling approaches, including models with and without unobserved heterogeneity, to improve activity duration estimation. * Assessment of the realism of the generated synthetic datasets through statistical testing against real-world data from Athens, demonstrating improved behavioral fidelity.
## 4 Numerical Simulation Analysis of the Hole-by-Hole blasting and Three-Hole Simultaneous Blasting  
To further evaluate and analyze the effectiveness of the three-hole simultaneous blasting technology, a step blasting model was established employing the ANSYS/LS-DYNA (R14.0) numerical simulation software [24; 25]. The evolution range and severity of rock damage in the rear row were comparatively analyzed considering the three-hole simultaneous blasting and the hole-by-hole blasting cases. By selecting the units at typical locations in the model and analyzing the effective stress and the changes in displacement in the observation point units, the characteristics of the hole-by-hole blasting and the three-hole simultaneous blasting methods were determined and evaluated.
# Area-Time Efficient Hardware Architecture for CRYSTALS-Kyber
### Proposed Kyber Encryption Architecture  
The proposed encryption architecture constructs the ciphertext \(c=(c_{1},c_{2})\) from input message \(m\) using public key \(pk\). The main modules in the proposed encryption architecture include samplers, DDF-based NTT, DDF-based INTTs (INTT1 and INTT2), decoder, encoder, compression, point-wise multiplication, and addition modules, as shown in Figure 4.  
The value of \(\rho\) is obtained using the formula \(\rho=pk+12\cdot k\cdot n/8\). The rejection sampler generates \(\hat{\mathbf{A}}^{T}\) from the seed \(\rho\), while the binomial sampler generates error vectors \(\mathbf{e}_{1}\) and \(e_{2}\) from a random coin \(r\). \(\hat{\mathbf{r}}\) is obtained by performing NTT(\(r\)) using the DDF-based NTT module in Figure 4. A part of ciphertext, \(c\), \(c_{1}\), is constructed by encoding the compressed  
Figure 3: Proposed DDF NTT-based key generation architecture. (\(\mathbf{a}\)) Top level of key generation architecture, (\(\mathbf{b}\)) PRNG core architecture, and (\(\mathbf{c}\)) rejection and binomial samplers.  
message **u**, where \(\textbf{u}=\text{INTT}(\hat{\textbf{A}}^{T}\circ\hat{\textbf{r}})+\textbf{e}_{1}\). INTT\((\hat{\textbf{A}}^{T}\circ\hat{\textbf{r}})\) is performed by the proposed DDF-based INTT1 module in Figure 4.  
The architecture design of the compression block is presented in Figure 5. The compression elements (CE) in Figure 5a are detailed in Figure 5b.  
The remaining part of ciphertext \(c\), \(c_{2}\) is constructed from the public key \(pk\), error polynomial \(e_{2}\), input coin \(r\), and input message \(m\). The output \(\hat{\textbf{r}}\) of the decode module  
Figure 4: Proposed encryption architecture.  
Figure 5: Compression block: (**a**) compression architecture, and (**b**) compression element.  
in Figure 4 is multiplied with the transform of sample \(\mathbf{r}\), \(\hat{\mathbf{r}}=\mathrm{NTT}(\mathbf{r})\) using point-wise multiplication. This result is transformed using the DDF-based INTT2 module in Figure 4, and then the error polynomial \(e_{2}\) is added. The result of this addition, \(\mathrm{INTT}(\hat{\mathbf{t}}^{T}\circ\hat{\mathbf{r}})+e_{2}\), is added with the decompression of input message \(m\) to generate \(v=\mathrm{INTT}(\hat{\mathbf{t}}^{T}\circ\hat{\mathbf{r}})+e_{2}\) + Decompress\((m)\). The architecture design of the decompression block is presented in Figure 6. The decompression elements (DE) in Figure 6 are detailed in Figure 6b. Ciphertext \(c_{2}\) is generated by encoding the compression of \(v\), \(c_{2}=\mathrm{Encode}(\mathrm{Compress}(v,d_{v}))\) using compression and encoder modules. Finally, ciphertext \(c=(c_{1},c_{2})\) is constructed.
# Credibility Analysis on Twitter Considering Topic Detection
## 6 Qualitative and Quantitative Evaluation
### Qualitative Analysis  
To evaluate human perception, we used the survey proposed in [65], where ten tweets were randomly selected from Twitter. This survey (the form is available at [[https://forms.gle/2](https://forms.gle/2) uZNYze2 YSmCT1v7]([https://forms.gle/2](https://forms.gle/2) uZNYze2 YSmCT1v7), accessed on 1 July 2022) contains opinions of 40 participants that have undergraduate and postgraduate degrees in different areas of study, using the following question _Q: How credible the following tweet is?_ Then, it ranks them in a scale 1 to 10, where 1 means not believable at all and 10 means totally believable. Further, the tweets are evaluated using the original credibility model, as well as the extended version. Table 6 shows the results obtained in this test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Tweet ID**} & \multirow{2}{*}{**Survey-Avg (\%)**} & **Original** & **Extended** & \\  & & **Credibility** & **Credibility** & **Affhstags** \\  & & **Model (\%)** & **Model (\%)** & **Model (\%)** & \\ \hline xxxxxxxxx9982542508038 & 70 & 68.51 & 68.51 & 00 \\ xxxxxxx6261988499456 & 45 & 44.16 & 44.16 & 00 \\ xxxxxxx454692450304 & 70 & 76.52 & 76.52 & 00 \\ xxxxxxx11923732992 & 15 & 49.74 & 49.74 & 00 \\ xxxxxxx4739103236099 & 65 & 69.78 & 72.33 & 01 \\ xxxxxxx4980596994048 & 30 & 28.99 & 28.99 & 00 \\ xxxxxxx0877124628487 & 50 & 37.67 & 37.67 & 00 \\ xxxxxxx6507817824261 & 45 & 27.86 & 27.86 & 00 \\ xxxxxxx3352350662666 & 65 & 44.05 & 44.05 & 00 \\ xxxxxxx6331631550472 & 40 & 38.69 & 38.69 & 00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of the extended model.  
Figure 10: Credibility values under an account’s timeline.  
The results show similar human perception values with respect to the original and extended credibility models (an average of 10.11% of difference), which validate the models. Since most of the tweets do not have hashtags, our extended credibility analysis remained almost the same as the original one, with the exception of tweet ID:xxxxxx4739103236099, which has a hashtag and where our model obtained 72.33%, while for the original model, it obtained 69.78%. To improve this evaluation, a new survey on tweets that contain hashtags is planned for future work.
## 2 Review of Issue Contents
### Plasma Sources  
[PERSON] et al. [1] designed a helical resonator for radio-frequency plasma generation starting from a theoretical model. The helical resonator is a concept for the production of high voltage at radio frequency, useful for electrical discharges in gases and plasma sustainment. When properly designed, the helical resonator enables the avoidance of the use of a matching network. In this work, researchers consider the treatment of the helical resonator, including a grounded shield, as a transmission line with a shorted end and an open one, with the latter connected to a capacitive load. The input voltage is applied to a tap point located near the shorted end. The authors derived formulas that allowed a prediction of the resonator performance, consequently enabling one to properly design a plasma source according to the parameters required for the plasma. What is important is that the discussion presented in the paper considers the resonator without plasma: the effects of a plasma formed inside the device is going to be the object of a forthcoming publication.  
[PERSON] et al. [2] developed a practical method for controlling the switch been the symmetric and asymmetric modes of a dielectric barrier discharge at atmospheric pressure by changing the frequency of the applied voltage. In this work, through a qualitatively validated 1D fluid model, the discharge evolution, manipulating process and underlying mechanism are also presented. It must be noted that the work is a numerical one. The concept is discussed based on simulations. They showed that the effectiveness of the control strategy was determined by the seed electron level at the frequency-altered phase and that there was a critical range of the seed electron density. For example, under the original driving frequency of 14 kHz, the seed electron level approximately ranges from \(2\times 10^{13}\) m\({}^{-3}\) to \(8\times 10^{15}\) m\({}^{-3}\). Researchers found, numerically, that the discharges withan initial driving frequency of 14 kHz could always be converted to the symmetric mode when the control frequency was beyond 30 kHz.
## 2 Materials and Methods
### Total Phenolic Compounds (TPC) and Total Antioxidant Capacity (TAC)  
TPC and TAC analyses were conducted as described in [PERSON] et al. 2024 [PERSON] et al. (2024). Briefly, TPC was determined using the Folin-Ciocalteu method: 30 \(\mu\)L of deionized water was added to 10 \(\mu\)L of methanolic extract, 10 \(\mu\)L of Folin-Ciocalteu reagent, and 200 \(\mu\)L of 30% Na\({}_{2}\)CO\({}_{3}\). After 30 min at room temperature, the absorbance of the mixture was measured at 725 nm. TAC was evaluated using two methods: ferric reducing antioxidant power (FRAP) and 2,2\({}^{\prime}\)-azino-bis (3-ethyl- benzothiazoline-6-sulfonic acid (ABTS\({}^{\bullet+}\)) radical scavenging activity assays. For FRAP, 160 \(\mu\)L of FRAP assay solution (20 mM ferric chloride solution, 10 mM TPTZ solution, and 0.3 M acetate buffer) was mixed with 10 \(\mu\)L of the sample, standard, or blank and then dispensed into each well of a 96-well plate. Absorbance was measured at 595 nm at 37 \({}^{\circ}\)C after 30 min of incubation. ABTS\({}^{\bullet+}\) was assessed using the OxiSelect(tm) Trolox Equivalent Antioxidant Capacity (TEAC) Assay Kit (ABTS) (Cell Biolabs INC.) following the manufacturer's instructions. TPC results were expressed as mg of gallic acid equivalents (GAE) per gram of dry weight (DW) of the sample. TAC determined by FRAP was expressed as mmol Fe\({}^{2+}\) equivalents per gram of DW of the sample, while TAC measured by ABTS\({}^{\bullet+}\) was expressed as mmol of Trolox equivalents (TE) per gram of DW.
## 4 Conclusions  
A change in the pore size distribution of the soil was observed after soil contamination. Due to the soil-water-contaminant interaction, the matric suction of the contaminated samples decreased while its osmotic suction increased compared with the correspondent natural soil suctions. This indicates that the role of the osmotic suction should be considered when studying unsaturated soils that are contaminated.  
The SWRC of the arsenic-contaminated soil was influenced by the calibration curve used for the filter paper Whatman Grade 42. It was found that when using the calibration curve provided by the ASTM D5298, the suction values tended to be higher than the ones obtained using the calibration curve developed for the case of the arsenic-contaminated soils. Thus, to avoid misinterpretation of the unsaturated soil's behavior when contaminated, it should be recommended to obtain the calibration curve according to the type and the concentration of the contaminant present in the soil.  
Limitations of this research are mainly due to the filter paper method applied. Such limitations are because the matric suction measurements depend on the adequate contact between the soil and the filter paper. In contrast, such contact must be avoided for the total suction measurement. The equilibration time needed for soil suction measurements is a minimum of seven days and depends on the type of soil and the expected soil suction magnitude to be measured. Moreover, this method does not evaluate the soil suction at different overload pressures.  
After comparing the permeability coefficient of the contaminated soils through the falling head with the oedometric test, it is concluded that the equation used to predict the permeability coefficient of unsaturated soil under contaminated conditions does not represent the soil behavior. Hence, it is recommended to avoid its use for geotechnical analysis and design when working with contaminated soils.  
Conceptualization, C.E.H.-M.; methodology, C.E.H.-M. and I.V.-N.; investigation, I.V.-N. and C.E.H.-M.; data curation, I.V.-N.; data visualization: I.V.-N.; formal analysis, I.V.-N. and C.E.H.-M.; writing-original draft preparation: I.V.-N.; writing-review and editing: C.E.H.-M.; supervision, C.E.H.-M.; project administration, C.E.H.-M.; funding acquisition, C.E.H.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Consejo Nacional de Ciencia y Tecnologia (CONACYT), grant number CONACYT 270161, and by the Universidad Autonoma de Queretaro, grant number UAQ FIN-2020-03 (FONDEC-UAQ-2019).  
Not applicable.  
Data Availability Statement: Data presented is contained within the article.  
Acknowledgments:[PERSON] acknowledges the scholarship provided by CONACYT for his Ph.D. studies.
Tetraphenyl fulvenes with the acceptor groups -C(CN)\({}_{2}\) and -C(COOMe)\({}_{2}\) in position 6,6\({}^{\prime}\) display similar emission spectra in different solvents. They exhibit maxima at 435 nm, which should be generated from S\({}_{2}\) decay, similar to the other fulvene derivatives, which display the same emission peak in solution. To increase the dipolar moment of the tetraphenyl fulvenes derivatives, we thought to connect electron donor D groups to the phenyl rings 3 and 4, as shown in Figure 1c. We expected this substitution to inject electron charges to the fulvene unit and to induce a molecular dipolar moment. We do not expect the same by substitution on the 2,5 phenyl groups that are forced to lay perpendicular to the pentadienyl ring because of the steric hindrance of 6,6\({}^{\prime}\) large substituents. In this way, the compounds, upon UV-vis light irradiation, could exhibit charge separation, which could enhance and redshift the fluorescence. We synthesized 2D-\(\pi\)-2A chromophores, shown in Figure 2, where the \(\pi\)-Bridge was the tetraphenyl fulvene. As the p electron donor, we selected the 2-thiophene compounds **5 mT** and **5 nT**. For understanding the donor group role, we also synthesized the compounds **5 mF**, **5 nF**, **5 mP**, and **5 nP**, which exhibit 4-fluorophenyl as weak \(\pi\)-electron donor groupsand N-bonded phenothiazazine as the \(\pi\)-electron donor group with shorter \(\pi\)-conjugations. We analyzed the UV absorption and the emission in different polarity solvents to understand whether the compounds exhibit charge separation under light irradiation and how this charge separation affects the emission. Figure 1: (**a**) Molecular structure of fulvene with description of frontier orbitals involved in the S1 and S2 excited states (S\({}_{1}\): HOMO \(\rightarrow\) LUMO. S\({}_{2}\): HOMO-1 \(\rightarrow\) LUMO) reported in the literature [44; 45; 46; 47; 48]. (**b**) Tetraphenyl fulvene with electron acceptor groups linked to the carbon 6 of fulvene reported by [PERSON] et al. [49]. (**c**) Connection of TPF with electron donor on the phenyl rings 3 and 4. Figure 2: Structure of synthesized compounds. Minuscule letters indicate the acceptor functional groups linked to the carbon 6 of the fulvene (**m** is methylester, **n** is nitrile). Majuscules indicate the donor functional groups linked to the phenyl groups (**T** is thiophene, **F** is fluorophenyl, **P** is phenothiazazine).
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 2 Formulation of the Design Problem
### Passband Amplitude Error  
For the passband error, if we consider \(H_{d}(w_{1},w_{2})\) to be the desired frequency response of the filter and that \(c_{k}\) is the value of vector \(c\) at the start of the \(k_{th}\) iteration, a passband error function at frequencies \(w_{1}\) and \(w_{2}\) can be defined as  
\[e_{h}(x,w_{1},w_{2})\approx|H(c_{k},w_{1},w_{2})\,|-|H_{d}(w_{1},w_{2})|.\]  
Assuming that the desired amplitude response is unity in the passband, therefore, the passband error function becomes:  
\[e_{h}(x,w_{1},w_{2})\approx|H(c_{k},w_{1},w_{2})\,|-1,\forall(w_{1},w_{2})\in \Omega_{pb}. \tag{9}\]  
The expression above can be used as is in the case of the nonlinear approach.  
For the transition and stopband regions, the magnitude response is subject to prescribed levels of transition-band and stopband gains, as explained in Section 4.
## 2 Data Sources and Study Methods
### Evaluation Methods  
#### 2.2.1 Information Entropy Weight  
The information entropy weight method can use the statistical properties of the selected indicators to determine their respective weights, which can effectively avoid the subjectivity of the Analytic Hierarchy Process (AHP) or Delphi method in the weighting process [40; 41]. Therefore, this method is adopted to measure the level of the tourism industry and urbanization. For specific steps, refer to the relevant literature [42].