esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Array Design
### Analytical Techniques
#### 2.1.3 Bandwidth  
As is well known from array theory [17; 19], the array bandwidth is severely limited by the phase shifters unless one resorts to true time-delay devices. The latter usually require expensive and complex architectures that can only be justified when the array needs to ensures an excellent performance over a considerably large bandwidth. Conversely, for relatively small fractional bandwiths as those considered here, phase shifters are usually preferred. As a result, the beam is exactly steered at a point in space (\(\theta_{0},\phi_{0}\)) only at thecenter frequency \(f_{0}\), i.e., when the \(u\) and \(v\) terms appearing in (1) are evaluated at \(k_{0}=k(f_{0})\). As a result, for \(f\
eq f_{0}\), the beam is _squinted_ by an angular deviation \(\Delta\theta\) from the theoretical pointing angle \(\theta_{0}\), given by the formula  
\[\Delta\theta=|\arcsin(\sin\theta_{0}f_{0}/f)-\theta_{0}|. \tag{10}\]  
The main consequence of the beam squint is that the \(-3\) dB fractional bandwidth \(\Delta f/f_{0}\) is affected. In particular, for a linear array of length \(L\) it is determined by the expression [19]  
\[\frac{\Delta f}{f_{0}}=0.886s_{\mathrm{f}}\frac{\lambda_{0}}{L\sin\theta_{0}} \simeq s_{\mathrm{f}}\Theta_{0\mathrm{h}}\csc\theta_{0} \tag{11}\]  
with \(\lambda_{0}\) being the center wavelength. Again, the last expression is obtained in the limit of large arrays using the right-hand side of (5).
## 4 Discussion
### Influence MMS and Altitude of Sensor
Whereas, if the altitude is 60 cm, the system would acquire 768 raw images with storage of 9.45 GB, which is more expensive and not feasible when size of the area is increased. Based on several calculation and discussions with final users, it was decided that (90-120 cm) altitude, 0.5 cm MMS and 10.5 cm/s speed is highly recommended for practical usage of hyperspectral imaging system.
## 4 Experiments and Analysis of Results  
### Datasets and Pre-Processing  
Three real-world public datasets, Cosmetics, Diginetica-buy, and Amazon, were used to evaluate the proposed method and baseline model. Cosmetics is a kaggle competitive dataset consisting of user behaviors from medium-sized online cosmetic shops. Diginetica-buy is an e-commerce dataset consisting of user purchasing behaviors on the website. Amazon is a recommendation baseline dataset consisting of user purchasing behaviors on Amazon.com. For a fair comparison, the three benchmark datasets were preprocessed according to the experimental setup in [PERSON] et al. Table 1 contains detailed statistical information about these three datasets.
## 3 Results and Discussion
### pH and Thermal Stabilities of the Biofoculant  
Table 3 illustrates the pH stability of the flocculant. The biopolymer was active within an extensive pH range (3-12), giving flocculating efficiencies above or equal to 89% (Table 3). The pH of solutions tends to alter the charge status of biopolymers and the structural profiles of colloids, consequently affecting flocculation rate [52]. Therefore, the profound efficacy of the biofoculant within the wide pH range further affims its economic friendliness, as it could cut costs of adjusting pH during its application. [PERSON] et al. [53] also recorded that the biofoculant from _Halomonas_ sp. V3a attained maximum activity at pH 7. The characteristics of the biofoculant under heat treatment are illustrated in Table 3. More than 85% residual flocculation was retained after exposure to heat at varied temperatures. It should be noted that there were no significant differences (\(p>0.05\)) in  
Figure 3: Pyrolysis profile of the biofoculant from _B. megaterium_ BMBF.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dosage** & **\%FE \(\pm\) SD** & **Metal Ions** & **\%FE \(\pm\) SD** & **pH** & **\%FE \(\pm\) SD** & **Temperature** & **\%FE \(\pm\) SD** \\
**(mg/mL)** & 95 \(\pm\) 2.0 a & Na\({}^{+}\) & 59 \(\pm\) 2.3 a & 3 & 89 \(\pm\) 1.3 a & 50 & 98 \(\pm\) 0.0 a \\
0.4 & 93 \(\pm\) 1.5 a & K\({}^{+}\) & 75 \(\pm\) 3.4 b & 4 & 89 \(\pm\) 3.1 a & 60 & 98 \(\pm\) 1.0 a \\
0.6 & 93 \(\pm\) 2.5 a & Li\({}^{+}\) & 91 \(\pm\) 0.6 c & 5 & 89 \(\pm\) 2.5 a & 70 & 96 \(\pm\) 1.5 a \\
0.8 & 92 \(\pm\) 1.3 a & Mg\({}^{2+}\) & 72 \(\pm\) 3.4 b & 6 & 90 \(\pm\) 2.0 a & 80 & 96 \(\pm\) 2.0 a \\  & & Mn\({}^{2+}\) & 69 \(\pm\) 0.6 b & 7 & 99 \(\pm\) 0.5 a & 90 & 93 \(\pm\) 0.2 a \\  & & Ba\({}^{2+}\) & 95 \(\pm\) 1.5 c & 8 & 95 \(\pm\) 1.0 a & 100 & 89 \(\pm\) 0.5 a \\  & & Ca\({}^{2+}\) & 96 \(\pm\) 6.1 c & 9 & 93 \(\pm\) 0.5 a & & \\  & & Fe\({}^{3+}\) & 82 \(\pm\) 2.8 b & 10 & 92 \(\pm\) 0.5 a & & \\  & & Control & 71 \(\pm\) 1.0 b & 11 & 92 \(\pm\) 2.1 a & & \\ \hline \hline \end{tabular} FE illustrates flocculating efficiency and SD represents standard deviation. The letters represent statistical differences at \(p<0.05\).  
\end{table}
Table 3: Impact of dosage, metal ions, pH and temperature on the flocculation.  
flocculation at all tested temperatures. Therefore, the biofleculant was as classified as being heat-stable. The thermal stability was credited to the high content of polysaccharides. Generally, biofleculants rich in polysaccharides are known to be more heat-stable in comparison to the protein-rich ones. The results were in agreement with those of the biofleculant from _Arcuadendron_ sp., which showed thermal stability under exposure at different temperatures [54].
## 2 Materials and Methods
### Methodology  
#### 2.3.1 Wind Profile  
The distribution of wind speed with height is known as the wind profile. In typical cases, friction on the ground reduces the wind speed in the lower atmosphere. In order to study the variation in the wind field in time series, power law wind profile theory is used to assess the variation in the wind speed at different heights [39], as shown in Equation (1):  
\[\frac{v(z)}{v\Big{(}z_{ref}\Big{)}}=\left(\frac{z}{z_{ref}}\right)^{\alpha} \tag{1}\]  
where \(v(z)\) is the wind speed at height \(z\); \(v(z_{ref})\) is the wind speed at the reference height \(z_{ref}\); and \(\alpha\) is the wind shear index, which depends on the surface roughness and atmospheric stability.
# Attosecond Pulse Shaping by Multilayer Mirrors
## 4 Achieved Results and Examples of Multilayer Mirrors for Attosecond Pulses
### Above the Silicon L-Edge in the 100-150 eV Photon Range  
Various multilayer mirrors have been realized for attosecond pulses above 100 eV [56; 57], going from the EUV spectral range to the soft X-ray range, for addressing more core states of numerous elements. The next section describes, as examples, attosecond dispersion control by multilayer mirrors based on [PERSON] et al. [PERSON] et al. (2008), as well as a chromium/scandium (Cr/Sc) mirror designed for a central energy of around 145 eV and reflecting attosecond pulses on the order of 600 as. The latter experiment was realized at a central energy which was at that time 27 eV higher than the recent energy limit in table-top attosecond pump studies [34]. Attosecond dispersion control is used as a synonym  
Figure 2: Panel (**a**) shows the simulated (black line) and measured (green crosses) reflectivity of a 32 eV (full-width-at-half-maximum, FWHM) bandwidth flat phase (red dash-dotted line) multilayer mirror (design plotted in panel (**b**)), revealing a measured 3.6% reflectivity centered at 70 eV. Together with a 300 nm thick Zr filter (yellow dotted line), it functions as a 25 eV (FWHM) broad bandpass and compressor (blue dashed line) for the generation of 80 as EUV pulses. Panel (**c**) shows the simulated 64 as long pulse (black line) and the temporal phase (red circles) of a pulse supported by the mirror upon reflection as an outcome of Fourier transformation of the mirror’s spectrum. The blue dashed line shows the temporal structure of the bandpass (mirror \(+\) filter), resulting in a 90 as long pulse, compared with its Fourier-limit (light green dash-dotted line) of 80 as.  
for controlling the second derivative of the spectral phase of an attosecond pulse, its group delay dispersion. This control offers the possibility for pulse compression down to the Fourier-limit.
# Investigating the Effect of Motion Capture Suits on the Test-Retest Reliability of Gait Parameters
## 3 Results  
### Ankle Angle Variables (Standard PiG Foot Definition)  
ICCs of the ankle angle were lower at HS, TO and Min in the suited trials compared to the non-suited. ICC values were highly varied in both non-suited (ranging between 0.254 and 0.743, for HS and TO, respectively) and suited trials (ranging between 0.238 and 0.575 for HS and Max, respectively). There was moderate reliability in non-suited trials with an average ICC across all four events (0.531) larger than that for the suited (0.435), which showed poor reliability (PDifference = 0.32) (Table 1) (Figure 2). It appears that, when using the PiG model, the HS measure was consistently bad across both suited and non-suited conditions. However, there were not enough measures to discern a robust association. This should be noted by those who are planning on using PiG for analysis of ankle measures during heel-strike events. CGM2 foot definition: ICC values in the CGM2 definition were significantly higher than the PiG definition (PDifference non-suited = 0.02; PDifference Suited < 0.01). The ICCs of the ankle angle were marginally higher in the non-suited condition at HS, TO and Max when compared to the suited condition, but not at Min (PDifference = 0.77) (Table 2). There was excellent reliability across all timepoints in the non-suited condition and excellent reliability at TO and Min in the suited condition, with HS and Max being good (ICC = 0.872; ICC = 0.878, respectively).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Joint** & \multicolumn{3}{c}{**ICC**} & \multicolumn{3}{c}{**MDC**} \\ \hline  & **Non-Suited** & **Suited** & _p-Value_ & **Non-Suited** & **Suited** & _p-Value_ \\ \hline Ankle & 0.531 & 0.435 & 0.32 & 2.32 & 18.90 & 0.02 \\ Knee & 0.832 & 0.707 & 0.13 & 2.06 & 3.98 & 0.06 \\ Hip & 0.899 & 0.945 & 0.19 & 1.88 & 1.71 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average ICC and MDC in Non-Suited and Suited trials at each joint across all events (PiG).  
Figure 2: Mean and 95% Confidence intervals (CI) of ICC values for each joint (PiG ankle, alternative, ankle, knee, hip and CGM2 ankle, knee and hip) and for both suited and non-suited conditions.  
The MDC in the PiG definition was significantly lower in the non-suited (average 2.33\({}^{\circ}\)) compared to the suited (average 18.90\({}^{\circ}\)) trials (\(p\) = 0.02; Table 1). The MDC of the ankle angle in the non-suited trials was over 8-fold smaller than the MDC of the ankle angle in the suited trials (Figure 3). MDC values in the CGM2 were again significantly lower than the MDC values in the PiG (PDifference non-suited \(<\) 0.01; PDifference suited = 0.03) (Figure 3). There was, however, no difference in the MDC between suited (MDC = 1.21\({}^{\circ}\)) and non-suited (MDC = 0.63\({}^{\circ}\)) trials when looking solely at the CGM2 definition (PDifference = 0.08).
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
## 5 Discussion
### Simple Model for Waning Mass Flux
We used the initial flux \(TADR_{0}=10.4\) m\({}^{3}\)/s (Section 4.3), magma density \(\rho=2000\) kg/m\({}^{3}\), tube diameter \(a=1.5\) m, viscosity \(\eta=10^{4}\) Pa s, and the area of the reservoir \(A_{0}=3\times 10^{4}\) m\({}^{2}\). Except for the length of the tube \(L\) and the lower flux \(TADR_{L}\), all parameters were taken from [PERSON] _et al._[8]. \(L\) was taken to be smaller, for during the 2014 eruptive period the vent was located at 650 m a.s.l., while during the 2007 effusive period it was located at 400 m a.s.l. Three values for the tube length \(L\) feeding the vent were used (see Figure 9). For these three models, the initial height \(h_{0}\) was estimated to 48 m (model 1, L = 20 m), 86 m (model 2, L = 35 m), and 125 m (model 3, L =50 m), respectively. Model 2 fits the true elevation difference between the crater terrace and the effusive vent better than the other two. Models 1 and 2 fit the initial decrease of the effusion rate quite well but all models fail to capture the slightly higher effusion rates between 21 August and 4 September.
## 2 Experimental Study
### Experimental Result Analyses  
#### 2.6.1 Loading-Displacement Curve  
The counterforce fed back of loading points of the lateral loading device in the loading process can be obtained through MTS. The relationship between the counterforce and the displacement was plotted to obtain the hysteretic loops at the loading point of the steel plate shear wall with PEC columns, as shown in Figure 8.  
In the initial stage of the test loading, the counter-force and displacement were basically linear, the area of hysteresis loop was small, the stiffness of specimen was larger, and the energy consumption was less. At this time, the test piece was in the elastic stage, and the infill steel plate worked well with the fringe frame. As the test continued to load, some parts on the two main diagonal lines of the infill steel plate yielded, and the counter-force and displacement appeared non-linear. At this time, the specimen entered the elastoplastic stage, and the stiffness of the specimen decreased. The area of the hysteresis loop increased and was relatively full, and the energy dissipation performance of the specimen gradually increased. Specim entered the yield stage after loading continually. The residual  
Figure 8: Loading-displacement curve.  
strain of the infill steel plate kept increasing under the action of main tensile stress. The plasticity had been developed to a greater extent leading to significant stiffness degradation of the specimen. The hysteresis loop appeared obvious \"pinching\" phenomenon, and its shape changed to an S shape. However, the area of hysteresis loop was larger, and the specimen still had good energy dissipation performance at this time. The phenomenon of \"pinching\" is an important characteristic of the hysteretic curve of beam-column joints [79], which results in the reduction of energy dissipation and bearing capacity. Many scholars have proposed different \"pinching\" models to simulate the mechanical response of structures under cyclic loading [80]. For example, the Pinching4 model in OpenSees is a user-defined model code developed by [PERSON] [81]. The mechanical curve defined by multiple line segments can take into account the gradual increase of stiffness and strength and pinching effect in hysteretic loading.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries
### Embeddings
#### 3.1.1 OpenAI's Text Embedding  
OpenAI's text embeddings serve as a potent tool for converting text into numerical representations. It works by encoding each word or token into its corresponding embedding high-dimensional vectors in a continuous space. These vectors capture semantic and syntactic relationships between words or pieces of text. The embeddings are trained using large amounts of text data, where the model learns to predict the context of a word based on its surrounding words. In our work, we utilized the embedding model Text-embeddingada-002, released by OpenAI in December 2022, due to its superior performance compared to traditional embedding models in tasks such as sentence similarity, text search, and code search [30]. Additionally, it has demonstrated competitive results in text classification tasks [30].
## 7 Applications of the Typical Medium DCA to Systems with Disorder
### Results of First-Principles Studies of Localization  
The combined method EDHM+TMDCA (described in Section 6) has so far been applied to study localization from first principles in two types of functional materials: superconductors [12] and diluted magnetic semiconductors [172]. Due to its ability to access systems with multiple orbitals and complicated disorder potentials, it provides a powerful approach to study localization caused by the impurities in these functional materials in an unbiased and material-specific way.
# The Role of Cryptogams in Soil Property Regulation and Vascular Plant Regeneration: A Review
## 2 Effects of Cryptogam on Soil Properties
### Effects of Cryptogam on Soil Fertility and Nutrients Availability  
#### 2.2.1 Effects of Cryptogam on Soil Organic Matter and Carbon Turnover  
The primary sources of organic matter and carbon in soils are the secretions and dead residues of living plants [51]. In some harsh environments, such as deserts [15; 16; 25], sandy land [14], drylands [52], karst [20; 21], waste tailings [53], and high-latitude [54] and high-altitude [55], most vascular plants are unable to survive. With cryptogam secretions, and dead residues as almost the only sources of soil C [2], cryptogams tend to increase soil C content in these areas. However, the rate of decomposition of moss crusts is very slow under the conditions of high temperature and low humidity in the desert [27]. Moreover, the effect of moss on soil C content is greater than that of lichen [23; 26; 55]. In alpine and high-latitude regions, the removal of moss crust directly leads to a decrease in TOC, DOC, and MBC content in soil, and thus a decrease in soil CO\({}_{2}\) flux [24]. In boreal forests, the appearance of a moss layer provides an additional source of organic matter for the ecosystem. Some studies even show that, in the case of poor soil, the litter of vascular plants is more easily decomposed and consumed, while the organic matter of moss is not easily decomposed and instead accumulates in the soil. Therefore, the significance of moss layers is greater for soil C accumulation [56]. However, it has also been found that in boreal forests, lichen layers can lead to a decrease in soil organic matter content [28]. In addition, the moss layer also has an impact on the litter decomposition of vascular plants. Litter distributed below the moss layer will accelerate the decomposition, while litter distributed above the moss layer will slow down the decomposition [57]. In other words, the moss layer can inhibit the decomposition of vascular plants by intercepting their litter, thus leading to the accumulation of soil organic matter. Furthermore, after the occurrence of wildfires, the ground cover is largely destroyed; if there is no other ground cover, there will be strong erosion, leading to the loss of organic matter, while moss will rapidly form a biological crust, increasing the surface runoff but significantly reducing the loss of sediment and organic matter [58]. The drying and rehydration of moss due to drought and precipitation will lead to the loss of some C in moss tissues, which will be leached into the soil by precipitation and enter the soil C cycle, increasing the soil C content [59].
## 4 Applications on Electroencephalogram (EEG) Prediction
### Organization of the ANN-ALO
In addition to that, different adjustments (parameter values) that were used to form five different ALO based training approaches are presented in Table 4. It is notable that ALO has just two parameters that can be adjusted for optimization. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **ANN-BPA** \\ **Model No.** \\ \end{tabular} & \begin{tabular}{c} **Values of ANN** \\ **Input Neurons** \\ \end{tabular} & \begin{tabular}{c} **Value of ANN** \\ **Output Neuron** \\ \end{tabular} & \begin{tabular}{c} **Total Hidden** \\ **Layers** \\ \end{tabular} & \
\begin{tabular}{c} **Total Neurons in** \\ **Each Hidden Layer** \\ \end{tabular} &
\begin{tabular}{c} **Average True** \\ **Prediction Rate** \\ \end{tabular} \\ \hline \multirow{4}{*}{\(1\)} & \(x(time)\); & & & & \\  & \(x(time-3)\); & & & & \\  & \(x(time-6)\) & & & & \\  & [10] & & & & \\ \hline \multirow{4}{*}{\(2\)} & \(x(time-2)\); & & & & \\  & \(x(time-2)\); & & & & \\  & \(x(time-3)\); & & \(x(time+2)\) & 3 & 6 & 90.63\% \\  & \(x(time-4)\); & & & & \\  & [65] & & & & \\ \hline \multirow{4}{*}{\(3\)} & \(x(time)\); & & & & \\  & \(x(time-3)\); & & & & \\  & \(x(time-6)\); & & & & \\  & \(x(time-9)\) & & & & \\ \hline \multirow{4}{*}{\(4\)} & \(x(time)\); & & & & \\  & \(x(time-2)\); & & \(x(time+2)\) & 4 & 7 & 79.84\% \\  & \(x(time-4)\) & & & & \\ \hline \multirow{4}{*}{\(5\)} & \(x(time)\); & & & & \\  & \(x(time-3)\); & & \(x(time+3)\) & 4 & 6 & 84.19\% \\ \cline{1-1}  & \(x(time-6)\); & & & & \\ \cline{1-1}  & \(x(time-9)\) & & & & \\ \hline \hline \end{tabular} Total data rows for each EEG time series: 4000/total data rows for test: 2000. The best model is in bold style.  
\end{table} 
Table 2: Average true prediction rate values of different back propagation algorithm (BPA)-trained ANN models for the EEG time series (Figure 5) in the study. Hybrid systems employing the same ANN and five different ALO adjustments were applied to the chaotic EEG data. In detail, each EEG data (applications: A1,..., A10) contained a total of 3990 rows corresponding to \(x(time+3)\): \(x(time)\), \(x(time-3)\), \(x(time-6)\), and \(x(time-9)\), and 70% of the data for each time series (2793 rows) was employed for the 'training' processes, and generally, predictions were observed over the whole time period by including all the points whether they were associated (70% of the data) or not associated (remaining; 30% of the data) with the training process. In this study, the data was called training and test data, respectively, rather than the terms in-sample and out-of-sample (i.e., training, selection, and out-of-sample) suggested by [106] to provide a simple approach to perform the evaluation around. The general findings obtained with the performed applications are provided in the following section.