esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology  
The approach presented in this research is intended to propose a collaborative Holt-Winters model for probable exclusion of the effect of stationary means and to make forecasting for seasonal demand more accurate. In this context, the values of appropriate smoothing constants are attained by simulating the historical demand data through an encoded program in MATLAB (R2009b, MathWorks, Natick, MA, USA) to be termed the smoothing constant simulator. Meanwhile, the initial values of the level and growth rate are estimated by the least square method. The initial values of the seasonal components are estimated by de-trending the time-series data, calculating the mean of the de-trended data for each period of chosen seasonal length, and then normalizing the obtained mean values to unity by using the correction factor [27].  
All these values are used in determining the time-series parameters such as the level (\(L_{T}\)), growth rate (\(b_{T}\)) and seasonality (\(SN_{T}\)) as well as the forecast (\(Y_{T}\)) by the conventional multiplicative Holt-Winters method. Moreover, the determined values of three parameters would also help to estimate the error (\(E_{L}\)) that is perceived to be magnified by the stationary nature of the initial values. In the fuzzy logic-based level prediction model the parameters \(L_{T}\), \(b_{T}\), \(SN_{T}\) and the actual demand \(Y_{T}\) are taken as input variables, while the values of the predicted level (\(L_{Tpred}\)) is considered as the output. A Mamdani fuzzy inference system is applied to contemplate the judgmental knowledge and the relationships established between the variables by means of IF-THEN rules. A de-fuzzification method is employed to obtain the non-fuzzy levels (\(L_{Tpred}\)) which are embedded into the multiplicative Holt-Winters method to obtain the collaborative multiplicative Holt-Winters (Coll-MHW) model-based forecasted demand (\(Y_{T}\)). Finally, the accuracy of our proposed collaborative model is assessed by comparing the _MAD_, \(MSE\) and the coefficient of determination for real-life industrial data and two case studies extracted from literature. A brief summary of the research methodology is depicted in Figure 1 by a flow diagram.  
Figure 1: Flow chart for proposed collaborative multiplicative Holt-Winters (Coll-MHW) forecasting approach.
## 2 Materials and Methods
As shown in Figure 1, H corresponds to the horizontal area-length ratio in the fiber cross section. This value is what controls the volume of the fabric while maintaining its shape. In the 3D model used in this study, the final volume fraction of the fabric is controlled by the horizontal area-length ratio of the woven fibers. Consequently, the volume fraction reported in this study was calculated by varying the H values of the fiber. As shown in Figure 2, the H value of the yarn was varied and modeled. The Monte Carlo method was applied to realize a composite model in which the UHMWPE weave is evenly distributed inside the PP matrix. The Monte Carlo method is an algorithm that mathematically approximates a function in modeling through random sampling to implement an iterative model. The Monte Carlo method allows the UHMWPE weave to exhibit the same probability distribution for each location in the PP matrix and allows the model to be infinitely symmetric. Figure 2 shows the modeled shape based on the FEA mode. \begin{table}
\begin{tabular}{c c c} \hline \hline  & PP & UHMWPE \\ \hline Modulus of elasticity (MPa) & 1325 & 25,000 \\ Shear modulus (MPa) & 432.29 & 10,417 \\ \hline Poisson’s ratio & 0.43 & 0.20 \\ \hline Bulk modulus of elasticity (MPa) & 3154.8 & 13,889.0 \\ \hline Density (kg/m\({}^{3}\)) & 904 & 950 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of PP and UHMWPE.
# Decoupled Monte Carlo Tree Search for Cooperative Multi-Agent Planning
## 4 Background
### Action Selection Policies for Monte Carlo Tree Search
#### 4.2.1 Deterministic Policies  
Deterministic action selection policies choose the same action given the same statistics. The nodes keep the number of visits, the number of selections for each action, and the reward taken as a result of the action.  
One of the well-known deterministic action selection policies is Upper Confidence Bounds (UCBI) [32; 36]. It is shown that the action selection at each node can be handled as an independent multi-armed bandit problem and has a good performance for large MDP problems [32]. When the algorithm reaches the action selection part, it gives priority to the actions of the state which were not previously explored. This is so as to let each action have statistics before comparing them using UCB1. When all the actions of the state are taken at least once, the algorithm calculates the UCB1 value of each action according to the Equation (1):  
\[Q(n,a)=\frac{\sum_{i}^{n_{a}}R_{i}}{n_{a}}+C\sqrt{\frac{\ln N}{n_{a}}} \tag{1}\]  
The function \(Q\) represents the evaluation of expected cumulative rewards for each search node and action. The \(R_{i}\) denotes the cumulative reward taken in the simulation \(i\) by following the action \(a\) from node \(n\). \(C\) is a constant which is used to scale the exploration term. \(N\) stands for the number of visits to the node \(n\). \(n_{a}\) stands for the number of action \(a\) taken in node \(n\). The action with the highest UCB1 value is selected.
# Departure from Flux-Gradient Relation in the Planetary Boundary Layer
## 2 Methodology
### Idealized WRF-Les  
Previous LESs under neutral, stable, and neutral PBLs performed with a surface condition typical of an onshore site showed \(\beta=0^{\circ}\)[7]. We therefore performed two idealized LESs of a neutral PBL over a flat surface; one over water, i.e., a roughness length of 0.0002 m, and the other over land, i.e., a roughness length of 0.65 m, which is the value found over the urban sector at the Hamburg tower [19]. The LESs were performed using the WRF model (version 4.1.2) with a single domain of 7500 m \(\times\) 7500 m \(\times\) 2000 m in the two horizontal and vertical directions, respectively, with a horizontal resolution of 15 m and a Coriolis parameter correspondent to the latitude of the FINO3 site and the Hamburg tower for the offshore and onshore simulations, respectively. The vertical grid spacing of \(\approx\)5 m was kept constant up to 250 m and stretched out thereafter. We used a time-step of 0.2 s and the subgrid-scale model of Deardorff [20].  
The simulations were performed for a dry atmosphere. The initial potential temperature was kept constant (289.5 K) up to 700 m, where we imposed an inversion of 10 K km\({}^{-1}\) upwards. At all vertical levels, the initial \(u\) and \(v\) velocities were set to 11 and 0 m s\({}^{-1}\), respectively, at FINO3 and to 10 and 0 m s\({}^{-1}\), respectively, at Hamburg to try to match the highest observed values at both sites. MOST was applied at the surface via the WRF-in-built surface-layer scheme. The LES was run for 12 h, a heat flux of 0 K m\({}^{-1}\) was imposed at the surface, and periodic boundary conditions were applied.  
The relevant parameters within the whole domain were output every 10 s within the period 10.5-11.5 h because we found the highest velocity for both simulations within this hour. The statistics were computed over this one-hour period (such as means, variances, and covariances) and then spatially averaged over the whole domain.  
The total momentum fluxes were computed by adding the subgrid to the resolved parts. Note that the coordinate system, in this case, is relative to the wind direction at the PBL top.
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 2 Soft X-ray Contact Microscopy
### Exposure Environment  
Most of the early SXCM experiments were performed by placing dehydrated specimens directly inside the vacuum chamber of the microscope, in which the soft X-rays propagated from the source to the specimen. With a resist such as PMMA as the recording medium, a relief structure of the sample was obtained and digitized with AFM. Because the primary goal of SXCM is to acquire high-resolution images of living specimens, the environment of the biological sample must be designed to maintain it in a hydrated state during exposure. For observation of living cells, soft X-rays are typically transmitted through silicon nitride membrane with thickness of 100-200 nm, covering the biological specimens cultivated directly onto the top surface of the photoresist. The whole assembly, consisting of the membrane, specimen in the culture medium, and the photoresist, are placed inside the vacuum chamber and sealed, employing specially designed mechanical capsules [40; 41] to prevent evaporation and specimen drying.  
Another approach is to place the assembly inside the pressurized environment chamber filled with helium [42] separated from the source chamber's vacuum environment to prevent specimen drying. In this case, no particular effort is required to perform sealing, and the dismounting of the sample is much easier.
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 1 Introduction  
Growth and division of a eukaryotic cell are complex processes coupled to various stimuli, the availability of nutrients, and additional external and internal cues, all of which are tightly regulated at many levels, especially when challenged by suboptimal conditions and other stresses. When it was found that the antibiotic rapamycin was a potent inhibitor of the proliferation of virtually every eukaryotic cell type examined (e.g., fungi, T cells, and tumor cells) [1; 2; 3], it became clear that the molecular target of rapamycin must be highly conserved and its function critical for cell survival. Indeed, ever since the authentic target of rapamycin (TOR) was first discovered using an elegant genetic approach in budding yeast (_Saccharomyces cerevisiae_) [4; 5], TOR has emerged as a universal, centrally important sensor, integrator, and controller of eukaryotic cell growth [6; 7].  
As first demonstrated in yeast [8; 9; 10; 11], TOR is found in all eukaryotic cells in two distinct macromolecular complexes, TOR complex 1 (TORC1) and TOR complex 2 (TORC2). TORC1 is sensitive to inhibition by rapamycin, whereas TORC2 is normally insensitive [12; 13; 14; 15]. The catalytic subunit in both TORC1 and TORC2 is the very large TOR polypeptide; metazoans possess a single TOR-encodinggene (human mTOR, 2549 residues), whereas budding yeast [4], fission yeast [16], and other fungi [17] encode two TOR proteins, Tor1 and Tor2 (2470 and 2474 residues, respectively, in _S. cerevisiae_).
###### Abstract  
Channelrhodopsins (ChRs) belong to the unique class of light-gated ion channels. The structure of channelrhodopsin-2 from _Chlamydomonas reinhardtii_ (CrChR2) has been resolved, but the mechanistic link between light-induced isomerization of the chromophore retinal and channel gating remains elusive. Replacements of residues C128 and D156 (DC gate) resulted in drastic effects in channel closure. T127 is localized close to the retinal Schiff base and links the DC gate to the Schiff base. The homologous residue in bacteriorhodopsin (T89) has been shown to be crucial for the visible absorption maximum and dark-light adaptation, suggesting an interaction with the retinylidene chromophore, but the replacement had little effect on photocycle kinetics and proton pumping activity. Here, we show that the T127A and T127S variants of _Cr_ChR2 leave the visible absorption maximum unaffected. We inferred from hybrid quantum mechanics/molecular mechanics (QM/MM) calculations and resonance Raman spectroscopy that the hydroxylic side chain of T127 is hydrogen-bonded to E123 and the latter is hydrogen-bonded to the retinal Schiff base. The C=N-H vibration of the Schiff base in the T127A variant was 1674 cm\({}^{-1}\), the highest among all rhodopsins reported to date. We also found heterogeneity in the Schiff base ground state vibrational properties due to different rotamer conformations of E123. The photoreaction of T127A is characterized by a long-lived P\({}_{2}\)[380] state during which the Schiff base is deprotonated. The conservative replacement of T127S hardly affected the photocycle kinetics. Thus, we inferred that the hydroxyl group at position 127 is part of the proton transfer pathway from D156 to the Schiff base during rise of the P\({}_{3}\)[380] intermediate. This finding provides molecular reasons for the evolutionary conservation of the chemically homologous residues threonine, serine, and cysteine at this position in all channelrhodopsins known so far.  
Keywords:channelrhodopsin; resonance Raman; flash photolysis; hybrid QM/MM simulation; electrophysiology +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Activity Tracking Feature Extraction  
We extracted seven activity tracking features from the time-series data obtained using the Fitbit devices. The features related to the 24-h activity rhythms and sleep patterns include inter-daily stability (IS), intra-daily variability (IV) and dominant rest phase onset (DRO). The sleep-related features include the total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO) and sleep quality [31]. These features were inspired from the results of a study conducted by [PERSON] et al., which demonstrated the association of these features with depression and anxiety in older adults [10].  
IS represents the stability of an activity rhythm of a daily life pattern [32], given by  
\[IS=\frac{n\sum\limits_{h=1}^{p}(\bar{x}_{h}-\bar{x})^{2}}{p\sum\limits_{i=1}^{ n}(x_{i}-\bar{x})^{2}} \tag{1}\]  
where \(i\) denotes each hourly point, \(n\) denotes the total number of data, \(x\) denotes the number of movements induced by steps, \(x_{i}\) denotes the individual hourly data, \(\bar{x}\) denotes the mean of all data, \(p\) denotes the number of hourly data per day and \(\bar{x}_{h}\) denotes the hourly means for the same hour between days.  
Next, IV represents the variability of the activity rhythms throughout the day. In other words, IV indicates the frequency of transitions between rest and activity [32], which is given as  
\[IV=\frac{n\sum\limits_{i=2}^{n}(x_{i}-x_{i-1})^{2}}{(n-1)\sum\limits_{i=1}^{n} (x_{i}-\bar{x})^{2}}. \tag{2}\]  
TST represents the sum of all the time spent sleeping during the day. WASO is the total waking time during sleep hours. SOL represents the time required to fall asleep after  
Figure 1: Data acquisition and analysis system.  
going to bed. Sleep quality is defined as the proportion of real sleep during a detected sleep period.  
For TST, WASO and sleep quality, we used the values provided by the Fitbit cloud. For DRO and SOL, which are not provided by the Fitbit cloud, we calculated from the step data as follows. The DRO was measured as the start time of the 5-h period with the least activity within 24 h. SOL, the time it takes a subject to fall asleep, was measured from the start time provided by the Fitbit cloud to the time it took for the steps reach to zero.  
The characteristic of data and input features by symptoms in the data set are shown in Table 3. To calculate the features described earlier, only data satisfying the following conditions were used. First, we used subjects whose data were collected for one week or longer. Second, the collected data were used only if there were at least 48 h without any missing data. Third, in the case of missing more than 3 h in a row among 24-h data, all data for that date were excluded.
# Adsorption Separation of Various Polar Dyes in Water by Oil Sludge-Based Porous Carbon
## 1 Introduction
The textile, paper, and printing industries have made dyes in wastewater a significant contributor to water pollution [6; 29]. When released into the environment, organic dyes can have a range of harmful effects on both human health and aquatic organisms. Moreover, the biodegradation process of these dyes is slow and complex, leading to the release of potential carcinogens. [PERSON] et al. [30] successfully synthesized a layered MOF, [Cd(H\({}_{2}\)L)(BS)\({}_{2}\)]n\(\cdot\)2 nH\({}_{2}\)O (L-MOF-1, H\({}_{2}\)L = N1,N2-bis(pyridin-3-ylmethyl)ethane-1,2-diamine, BS = benzenesulfonate), which exhibited stability in both acidic and basic environments. Notably, the L-MOF-1 adsorbent demonstrated a remarkable adsorption capacity for CR, reaching approximately 12,000 mg/g within a mere 20 min. [PERSON] et al. [31] successfully synthesized organobentonite by replacing the exchangeable Na\({}^{+}\) ions in Na-bentonite (Na-Bt) with cetyltrimethylammonium bromide (CTAB). They then conducted a thorough investigation into its adsorption behavior as a highly effective adsorbent for dye removal. The results showed that the maximum adsorption capacities for RhB and Acid Red 1 were 173.5 mg/g and 157.4 mg/g, respectively. These values were achieved at a temperature of 30 \({}^{\circ}\)C, with pH levels of 9 and 8, and an initial concentration of 300 mg/L. [PERSON] et al. [32] developed a simple and eco-friendly one-pot method for synthesizing silver nanoparticles, utilizing bioactive components from Viburnum opulus fruit extracts as reducing and stabilizing agents. The researchers then tested the catalytic efficiency of these AgNPs for the degradation of three harmful organic dyes including carmoisine, tartrazine, and vivid blue FCF. All the dyes tested exhibited excellent effectiveness and remarkable catalytic activity in degrading vivid blue FCF. While various adsorbents have demonstrated successful dye removal from water, their practical applications are limited by high raw material costs and complex production processes, which limit their scalability and feasibility. This study involves the activation of OS using ZnCl\({}_{2}\), followed by the preparation of a series of OSC (OSC500, OSC700, and OSC900) through ZnCl\({}_{2}\) activation at high temperatures. The phase structures and morphologies of these materials were characterized, and their adsorption properties were investigated for polar dyes. The adsorption models and mechanisms were also proposed. The ultimate goal of this research is to develop a method for designing and optimizing novel adsorbent materials with enhanced selectivity and adsorption capacity.
###### Abstract  
Carotenoids, polyphenols, and ascorbic acid are valuable and important biologically active compounds that contribute to the health benefits of different foods, and rosehips are known for their high biologically active substance contents. The aim of this research is to identify and compare the contents of carotenoids, polyphenols, and ascorbic acid in the flesh and seeds of different rosehip species grown organically. A field experiment was conducted during the period 2017-2018 on an organic farm in Lithuania. Rose fruits were collected at full ripening in September. The quantitative and qualitative determinations of polyphenols and carotenoids in rosehips flesh and seeds were conducted by HPLC methods. The following polyphenic compounds were identified: five different flavonoids (rutin, kaempfer-3-O-glucose, luteolin, quercetin, and quercetin-3-O-glucose) and five phenolic acids (gallic, chlorogenic, caffe, \(p\)-coumaric, and ferulic) from different rose fruit fleshes and seeds species. In addition, six carotenoids (\(\beta\)-carotene, \(\alpha\)-carotene, lutein, zeaxanthin, _cis_-lycopene, and _trans_-lycopene) in rosehip flesh and five different carotenoids in rosehip seeds (\(\alpha\)-carotene, lutein, zeaxanthin, _cis_-lycopene, and _trans_-lycopene) were identified. Overall, the results of this study demonstrate that the highest content of total phenolic acid is in rosehip seeds, while higher concentrations of carotenoids, flavonoids, and ascorbic acid are present in rosehip flesh.  
Keywords:polyphenols; carotenoid; seeds; flesh; rosehip +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
Optimized low loss with broadening bandwidth was obtained as shown in Figure 3. The antenna's reflection coefficient, demonstrates resonance at a frequency of 27.85 GHz, corresponding to a reflection coefficient level of \(-42\) dB and a bandwidth extending from 27.2 to 29.2 GHz, which is corresponding to a bandwidth of 2 GHz. These simulated results play a crucial role in forecasting and evaluating the antenna's performance before the construction of a prototype of the proposed antenna structure. These results were confirmed using measurement methods of the fabricated prototype of the proposed antenna. Figure 1: Single-element antenna: (**a**) front view, (**b**) back view, and (**c**) patch slot, inset, and stub dimensions. Figure 2: Parametric sweep of (**a**) L\({}_{\rm inset}\) and (**a**) L\({}_{\rm stub}\) of proposed antenna design. Figure 4 illustrates the computed peak gain of the proposed antenna versus frequency. The 1 \(\times\) 8 series-fed antenna exhibits a peak gain of approximately 15 dBi at the resonance frequency of 28 GHz. This indicates the antenna's ability to concentrate the radiating EM energy in a specific direction at the target frequency band, underscoring its suitability for N261 band applications. The peak gain value serves as a testament to the antenna's ability to transmit and receive signals with heightened effectiveness and precision within the designated frequency range.
## 2 System Description
### Receiving Part  
The receiving part is a single-channel system, including the RF front end, the mixer, the A/D converter, and the digital down conversion (DDC). The RF front end is designed with superheterodyne architecture and consists of an RF switch, suboctave filters, and amplifier. The suboctave filter group is used to avoid out-of-band noise and multiple-frequency signal interference. In the receiving channel, a single intermediate frequency (IF) of 41.4 MHz and band-pass sampling techniques are applied to simplify the hardware structure. The DDC module consists of a Hilbert transformer, a cascaded integrator comb filter (CIC), and a finite impulse response (FIR) digital filter, which is embedded in a FPGA [19].
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Monostatic Deployment
#### 2.1.1 Need of Full-Duplex Operations  
Within a monostatic system, there is a need for full-duplex operations [1] with SI cancellation techniques to facilitate simultaneous transmission and reception, which occurs when the strong signal received directly from the transmitting antenna overshadows the weak signals reflected by the objects. Traditional sensing systems solutions, such as short transmitted pulses and idle periods for receiving echoes, are unsuitable for modern mobile communications systems and connected vehicles, requiring high spectral efficiency, low latency, and continuous environmental awareness.