esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrochemical Properties of NdCl3 and CeCl3 in Molten LiCl-KCl Eutectic Salt
###### Abstract  
NdCl3 and CeCl3 are characterized by the very close proximity of their standard reduction potentials. Thus, they represent the most challenging salt mixtures with respect to decoupling electrochemical responses and calculating concentrations. The goals of this study include the determination of the apparent standard potentials of both Nd3+/Nd2+ and Ce3+/Ce in LiCl-KCl-CeCl3 or NdCl3 mixtures. Matrix and concentration effects on fundamental electrochemical properties are of particular interest, as these thermodynamic properties will impact the ability to use voltammetry to measure concentrations in unknown samples. Several LiCl-KCl-CeCl3 or NdCl3 mixtures with NdCl3/CeCl3 concentrations ranging from 4 to 10 wt% at 753 to 793 K have been explored. For apparent standard potential values of CeCl3, a good agreement was obtained with the literature data, except above 773 K.  
electrorefining; molten salt; cyclic voltammetry; chronopotentionmetry +
Footnote †: journal: Applied Sciences
## 4 Discussion
### BNNA Performance Evaluation for GPP Estimation  
In recent years, machine learning methods have been widely used in GPP estimation for its flexibility in application. In this work, it was found that machine learning algorithm (BNNA) performed well in alpine meadow GPP prediction. The machine learning algorithm (BNNA) was more accurate than the LUE models. The advantages of using machine learning method to fuse multisource data to estimate GPP were that machine learning methods can easily fuse data from different sources together and can combine the advantages of different data for GPP estimation. However, the main shortages were the lack of mechanism process, the need for a large number of training samples, and the results of training in different regions cannot be applied to other regions. The implication of this study was to provide a new idea to improve the GPP estimation accuracy on the Tibetan Plateau. With the large number of phenological cameras, the method will have good application potential on the Tibetan Plateau, which is greatly affected by clouds and snow.
# Large Scale Implementations for Twitter Sentiment Classification
###### Abstract  
Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark's Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.  
Apache Spark; Big Data; Bloom Filters; Hadoop; MapReduce; Twitter +
Footnote †: journal:  
Article
# Effective Techniques for Protecting the Privacy of Web Users
## 3 Background
### Privacy Protection Tools (PP-Tools)
It works by sending the Do Not Track header with every page request, and assessing whether the user is still being tracked. Upon determining that the probability is too high, the algorithm automatically denies the request for a third-party domain. * NoScript(NS): [23] A white list-based tool allows content that has been explicitly authorized by the user, so the default behavior is to block all website content. The rules are expressed as regular expressions, similar to AdBlock Plus [19], and It works by blocking all executable content on a webpage that may threaten security including Java programs, Flash, Silverlight, JavaScript programs, and more. However, this may result in usability problems and requires considerable user interaction.
## 3 Different Aspects of Colour-Based Method
### Long Distance and Multiple Persons Simultaneously  
Most of the previous methods discussed above have some limitations. Firstly, most of the works considered very short ranges, the highest distance considered was only 3 m. Secondly, other than [PERSON] et al. (three subjects) and [PERSON] et al. (two subjects), all other methods measured vital signs for one subject at a time. In [117], a robust contactless method was proposed to calculate HR and RR using both iPPG and head motion from the videos taken by both a digital camera and a hovering UAV, considering long ranges of up to 60 m, multiple subjects in groups of up to six people simultaneously under both stationary and non-stationary circumstances using a video magnification technique. To eliminate noise artefacts caused by movement of subjects and camera, variations of illumination and variation of skin tone, they used a noise elimination technique using both CEEMDAN and CCA. The FFT was used for spectral analysis and two Butterworth bandpass filters were used for filtering. Finally, HR and RR were extracted by calculating the number of peaks using the MATLAB built-in function _'findpeaks'_. Moreover, they introduced a graphical user interface (GUI) that facilitates a user loading video data, selecting the magnification type, and executing the proposed system and configurations.  
The experimental setup and data acquisition of the proposed system is presented in Figure 10 where they considered three groups of people consisting of 15, 20 and 10 subjects with different skin tones, respectively, under three assumption: noise artefacts, multiple detection and long distance. Figures 11 and 12 demonstrate that the proposed method achieved better performance than ICA and PCA for both stationary and nonstationary scenarios with multiple subject detection and long distance, respectively. From these figures, it can also be noted that colour-based methods gave better performance than motion-based methods. However, the system used limited ROI, observing only the face and will be affected by unclear ROI.  
Figure 10: The experimental setup and data acquisition of the noise artefact removal, multiple person and long detection system [117].
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 3 Shake Table Test of Six-Story Coupled Wall System with Damper
### Input Waves  
As illustrated in Figure 15, the input waves utilized in the experiments include the white noise, sinusoidal, and Kokuji waves. The white noise, characterized by a broad frequency spectrum, was applied to identify the structure's natural frequencies and damping properties. The sinusoidal wave, with its incremental amplitude and frequency, was used to assess resonance effects, enabling the observation of amplified vibrations at critical frequencies. Additionally, the Kokuji wave, which simulates actual earthquake ground shaking, was implemented to evaluate structural performance under seismic loading conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Story** & **Stiffness (N/mm)** & **Weight (N)** & **Height (mm)** \\ \hline
6 & 245.5 & 4021.6 & 300 \\ \hline
5 & 237.6 & 2594.0 & 300 \\ \hline
4 & 194.2 & 2545.2 & 300 \\ \hline
3 & 226.7 & 2839.6 & 300 \\ \hline
2 & 188.1 & 2545.2 & 300 \\ \hline
1 & 186.4 & 2173.2 & 300 \\ \hline
0 & - & - & - \\ \hline Total & - & 16,718.8 & 1800 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Properties of the 6-story frame.  
Figure 15: Input waves for shake test: (**a**) white noise, (**b**) sinusoidal, (**c**) Kokuji.
## 2 Environmental Cues and Co-Cultivation
### Challenging Microorganisms with External Cues  
In addition to the cultivation parameters, external cues have been used to deviate cellular metabolism towards secondary metabolite production. For example, [PERSON] and co-workers were able to isolate three new secondary metabolites, chaetoglobin-510, -540, and -542, from cultures of the marine derivate fungus _Phomopsis asparagi_ challenged with the F-actin inhibitor jasplakinolide [33]. Another example is the production of the potent antibiotic jadomycin by the bacterium _Streptomyces vanzuelae_. When cultured under physiologically favorable conditions in the absence of cellular stress, low amounts of jadomycin are produced. However, when cells are subjected to heat- or ethanol-shock, production of the compound is greatly increased [34]. Similarly, heat- or ethanol-stressed _S. hygroscopicus_ cells produce a significantly higher amount of the antibiotic validamycin, with concentrations up to 13 g/L harvested [35,36]. More specific chemical cues have also been recently used. Another interesting line of research concerns the use of antibiotics, or molecules strictly related to them, to elicit the production of secondary metabolites. Studies in _Burkholderia thailandensis_ showed that sub-lethal concentrations of trimethoprim served as a global activator of secondary metabolism by inducing at least five biosynthetic pathways, when present at sub-inhibitory concentrations [37]. Similarly, a series of molecules related to the synthetic antibiotic triclosan, were able to enhance the yield of secondary metabolites produced by _S. coelicolor_[38]. In a broader sense, these cues or chemicals have been shown to alter the secondary metabolite production in a wide range of streptomycetes strains, leading also to the identification of several novel compounds [39]. Together with the successful chemical elicitation of other novel secondary metabolites including lunalides A and B, oxylipins, cladochromes F and G, nygerone A, sphaerolone, dihydrosphaerolone, mutolide and pestalone [40], these examples clearly show how such stresses greatly influence the production of secondary metabolites, suggesting easy approaches to assess the biosynthetic ability in less explored microorganisms.
## 6 Propagation Time for a Signal, Emitted and Intercepted by Satellites on a Space-Oriented Orbit
### Three-Dimensional Orbit Parametrization and the General Formulae for the Orbit Parametrization
Further, this analytical method will not be used because a transformation will be proposed, enabling to find analytical expressions for zero-order elliptic integrals in the Weierstrass or in the Legendre form.
# A Novel Kinematic Directional Index for Industrial Serial Manipulators
## 4 Performance Investigation
### Performance Investigation in the Workspace  
As mentioned above, the performance was evaluated in a horizontal plane. For each robot, a proper set of points was defined, and a direction of interest was chosen. The SCARA set of points was given by nine points along any radial direction of the workspace with an angular step of ten degrees.  
Figure 4: KDI computed for the SCARA robot in point P (350, 150) along any horizontal direction.  
Figure 5: KDI computed for the articulated robot in point P (250, 300, 350) along any direction.  
Since the articulated robot was also investigated in a horizontal plane, the plane with the highest reach distance was chosen (i.e., where the wrist was at the same height as the shoulder). For this test, the set was given by seven points along any radial direction with an angular step of ten degrees. The points that are outside the workspace were considered. In Figure 6, the two sets of points are depicted together with the workspace limits.  
For the SCARA robot, the KDI index was computed along the direction of the y-axis (i.e., with a rotation of 90 degrees about the z-axis by means of the matrix \(R\)). The KDI was computed for the articulated robot with a rotation of 45 degrees about the z-axis. The KDI was normalized and plotted in Figure 7 for both robots. The yellow areas indicate the regions where the robots achieved their best performance, while the blue areas show where the robots were slower.
# FarpScun: Fully Anonymous Routing Protocol with Self-Healing Capability in Unstable Sensor Networks
## 2 Preliminary
### Cryptographic System  
As an asymmetric cryptographic system, \(SysGlobal\) consists of three basic operations: encryption \(E()\), decryption \(D()\), and \(m\)-bounded homomorphic decomposition, which includes three important functions: \(Merge()\), \(Sep1()\), and \(Sep2()\).  
Here, we introduce the basic principle of bounded homomorphic decomposition operation. Let \(\mathbf{m}\) be an integer. An encryption scheme \((E,D)\) is an \(m\)-bounded homomorphism decomposition scheme, if there exists an \(m\)-bounded homomorphic decomposition operation composed of \(Merge()\), \(Sep1()\) and \(Sep2()\) in order to assure the following conclusion hold.  
For any \(x,y\in M\) s.t. \(x<m\) and \(y<m\), taking secret and public key pair \((sk,pk)\) as input, the following equation holds: \(Sep1\left(D\left(Merge\left(E\left(x,pk\right),E\left(y,pk\right)\right),sk \right)\right)=D\left(E\left(x,pk\right),sk\right)\) and \(Sep2\left(D\left(Merge\left(E\left(x,pk\right),E\left(y,pk\right)\right),sk \right)\right)=D\left(E\left(y,pk\right),sk\right)\)).  
The Paillier Cryptosystem has a \(m\)-bounded homomorphic decomposition. In the Paillier Cryptosystem, the functions of \(Merge()\), \(Sep1()\) and \(Sep2()\) are defined, as follows.  
For any \(m<sqrt(n)\):  
\[Merge(x,y)=E(x)^{m}*E(y)(\bmod n^{2})=c. \tag{4}\]  
\[Sep1(D(c))=D(c)(div\ m)=x. \tag{5}\]\[Sep2(D(c))=D(c)(\operatorname{mod}m)=y. \tag{6}\]  
where \(E(x)\) is the encryption of the message \(x\) and \(E(y)\) is the encryption of the message \(y\). \(m\) is an index and \(c\) is encrypted ciphertext. When the ciphertext \(c\) is decrypted, the original messages can be obtained after the division operation of \(Sep1\) and the modulus operation of \(Sep2\).  
The proof is conducted, as follows:  
Based on the inequations of \(x<m,y<m\), and \(m<sqrt(n)\) and the two homomorphic properties of the Pailliar schemes described in equations (1) and (3) as below,  
\(E(x)*E(y)(\operatorname{mod}n^{2})=E(x+y(\operatorname{mod}n))\).  
\(E(x)^{y}(\operatorname{mod}n^{2})=E(x*y(\operatorname{mod}n))\).  
We can have:  
\(D(c)=D(E(x)^{m}*E(y))=D(E(x*m)*E(y))=D(E(x*m+y))=x*m+y\).  
\(SysGlobal\) is used in the RREQ process. The asymmetry in the system \(SysGlobal\) is that the public key of the destination node is used in order to encrypt the messages and the private key of the destination node is used to decrypt message.  
The other symmetric cryptographic system \(SysLocal\) with polynomial time encryption \(E_{fast}()\) and polynomial time decryption \(D_{fast}()\) is considered.  
\(SysLocal\) is used in the RREP process when a reply message is sent from the destination node to the source node. It's a symmetric encryption algorithm. During the RREQ process, each node stores a key locally and then sends it to the destination node. Subsequently, the destination node can use the received symmetric keys to encrypt the RREP message layer by layer. At last, during the RREP process, each relay node decrypts a layer with a symmetric key stored locally.
## 3 Results
### Comparative Experiments
#### 3.2.1 Dehazing Effectiveness
In contrast, the proposed WRPM demonstrates exceptional performance in both detail recovery and color restoration. The generated images exhibit enhanced clarity in texture representation and more natural color reproduction, achieving visual quality that substantially outperforms both the DCP and AOD-Net algorithms. The restored results closely approximate real scenes, indicating the superior capability of WRPM in maintaining photorealistic fidelity during the dehazing process. The selected hazy image in Figure 7 represents a typical scenario with nonuniform haze density distribution between distant and nearby areas. While the distant regions suffer from extremely dense haze, the foreground areas exhibit relatively light fog contamination. As shown in Figure 7, EMRA-Net demonstrates moderately effective haze removal in nearby regions but shows significant limitations in addressing dense haze within distant areas, manifesting substantial residual haze artifacts. GCANet, conversely, overly concentrates on distant haze elimination, resulting in suboptimal restoration of foreground regions where considerable haze persists, particularly evident in the building area marked by the red rectangle. In comparison, the proposed WRPM achieves more balanced dehazing performance. Our method not only demonstrates remarkable haze removal capability in distant heavy-haze regions (as indicated by the blue frame), it also maintains superior restoration quality in near-view areas. The foreground building within the red frame exhibits minimal haze residue with clear detail restoration and natural color reproduction, closely resembling haze-free reference images. This comparative analysis indicates that the WRPM effectively addresses the challenging task of nonuniform haze distribution by adaptively balancing regional restoration priorities, thereby achieving comprehensive haze removal across both distant and nearby regions.
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 4 Case Study
### Results and Discussion  
The evaluation of the compression algorithms shows that not all configurations lead to better performance. Figure 2 shows the configurations where the combination of PCA filtering with the DWT compression improved on both individual compressions. The lines drawn indicate the combination of PCA and DWT configurations directly translating into a PCA + DWT configuration. The increase in compression rate shown also exhibits an increase in the NRE, which leads to a trade-off between lower data storage and maintaining data accuracy. PCA filtering with 5 and 10 components appear in the configurations with higher compression rates. This means that the filtering is more aggressive than PCA with a higher count of principal components. Higher principal component configurations also achieved high compression when combined with DWT but with a decrease in compression rate. This leads us to conclude that the filtering in these cases hindered the DWT decomposition instead of contributing to it.  
Figures 3 and 4 show the NFRE for the frequency ranges of 0-20 Hz and 15-65 Hz, respectively. Seismic survey data analysis puts greater emphasis on low frequency signal where the seismic response to the surveying process is more dominant. Therefore, we consider the NRE at the ranges of 0-20 Hz and 15-65 Hz. Figures 2-4 also show the comparison of the compression algorithm with the ZFP compression algorithm. ZFP has configurations for fixed-precision, fixed-rate, and fixed-tolerance, which were all individually varied. The figures show that, independent of the frequency range, ZFP can maintain a lower NRE, but at the cost of much lower compression rates than the proposed algorithm. The  
relevant data have been lost. However, in the frequency domain, it is harder to identify a pattern, and the difference has an appearance closer to noise.  
We can compare further with another configuration of the PCA and DWT compression algorithm shown in Figure 7. The 40 principal components energy threshold = 0% quantization = 2% configuration achieved 10.07:1 compression with an NRE of 0.016. This compression is much less aggressive and appears much closer to the original data in the decompressed plot. Also important to note, the difference plot shows no visual artifacts from the original dataset. We can see in the relative difference that the noise presents a random spectral behavior that does not reproduce the patterns in the original signals. The randomness means that the noise is uncorrelated from the input signal. Therefore, the compressed signals are conserving the signal of interest.  
Figure 5: Reconstruction difference of PCA + DWT + LZW + quantization, five principal components energy threshold = 10% quantization = 8%.  
Figure 6: DWT + LZW + quantization, energy threshold = 10% quantization = 8%.
# Effects of Night Ventilation on Indoor Air Quality in Educational Buildings--A Field Study
## 2 Materials and Methods
### Air Distribution Methods  
Figure 2 shows the air distribution methods in the measured indoor environments. The ventilation systems were mainly mixing ventilation systems. In most cases, the air distribution was implemented from the corridor wall grilles, the ceiling diffusers, or the duct diffusers in the air distribution system. The provided ventilation airflow rates were according to guidelines that were ensured before the test periods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kindergarten 1** & **Ventilation (CAV)** & **Weekdays** & **Weekend** \\ \hline built: 2015 & pre-started & 03:00–17:00 & 04:00–17:00 \\ CAVmax: 3 L/s\(\cdot\)m\({}^{2}\) & continuous & 00–24 & 00–24 \\ floor: 37 m\({}^{2}\), occ: 10–20 & intermittent & 04–20 + 22–01 & 00–04 + 08–12 + 16–20 \\ \hline
**Kindergarten 2** & **Ventilation (CAV)** & **Weekdays** & **Weekend** \\ \hline built: 2003 & & & \\ CAVmax: 3.5 L/s\(\cdot\)m\({}^{2}\) & pre-started & & \\ floor: 30 m\({}^{2}\) & continuous & 00–24 & 00–24 \\ occ: 5–15 persons & intermittent & as in case 1 + 01–03 & 02–05 + 10–13 + 18–21 \\ \hline
**Kindergarten 3** & **Ventilation (CAV)** & **Weekdays** & **Weekend** \\ \hline built: 2012 & pre-started & 05–18 & - \\ CAVmax: 3 L/s\(\cdot\)m\({}^{2}\) & continuous & 00–24 & 00–24 \\ floor: 21 m\({}^{2}\), occ: 5–10 & intermittent & 05–18 + 20–22 + 01–03 & 02–05 + 10–13 + 18–21 \\ \hline
**Kindergarten 4** & **Ventilation (VAV)** & **Weekdays** & **Weekend** \\ \hline
2013, 34 m\({}^{2}\), 10–15 yrs & continuous & 00–24 & 00–24 \\ unoccupied: 1.5 L/s\(\cdot\)m\({}^{2}\) & continuous & 00–24 & 00–24 \\ \hline
**Kindergarten 5** & **Ventilation (VAV)** & **Weekdays** & **Weekend** \\ \hline
2014, 36 m\({}^{2}\), 5–10 yrs & continuous min & 00–24, min at night & 00–24, min \\ VAVmax: 2.8 L/s\(\cdot\)m\({}^{2}\) & continuous max & 00–24, max at night & 00–24, max \\ \hline \hline \end{tabular}
\end{table}
Table 2: The measured kindergartens: occ denotes the usual number of occupants in the room, floor refers to the floor area in the room, CAVmax is the maximum airflow rate per floor square in a constant air volume system, and VAVmax is the corresponding airflow in a variable air volume system.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 1 Introduction
In stereo, the positional relationship of the camera sets at the calibration time can deviate, leading to decreased accuracy. In tunnel excavation, a vibration of the drill creates such a situation. Thus, this method is considered to be effective for tunnel excavation. The contributions of this paper are summarized as follows:  
* Proposal of a catadioptric imaging system that combines an omnidirectional camera equipped with a fisheye lens and a single spherical mirror. * Proposal of a compact 3D information estimation method for the scene shown in Figure 1, which does not require prior knowledge by one-shot imaging.