esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview
### Polarizer and Guide Field  
In order to provide polarized neutrons with an optimal degree of polarization over the complete wavelength band SKADI will feature two polarizers optimized for the low (\(\lambda\) = 3-8 A) and high (\(\lambda\) = 8-13 A) wavelength band. This is achieved by two cavities with a single slanted supermirror that provides the polarization. Figure 7 presents the respective polarization and transmission. One cavity is a double coated Fe-Si supermirror with m = 2.5 in V-geometry using the corresponding McStas component [9]. Here, one additional polarizer position is left unoccupied for future upgrades.  
Figure 7: (**a**) Relative transmission of the two polarizer cavities, (**b**) polarization at sample position for the respective polarizer cavities. Simulation was conducted for a 20 m collimation with 1 cm \(\times\) 1 cm beam size and in pulse skipping mode (i.e., 3–13 Å wavelength band). In order to optimize both, transmission and polarization, the longer cavity is used for the short wavelength band, while the short cavity is used for the long wavelength band. Thus, the poor polarization at low wavelength is avoided for the shorter cavity, and still the higher transmission in the high wavelength band can be used. The vertical line is a fit of the relative intensity between the two cavities for the wavelength between 8 and 13 Å and it indicates a transmission of 90.8% for the 80 cm cavity when compared to the 40 cm cavity. The decreasing polarization at the boundaries of the used wavelength band are binning effects due to zero intensity outside the shown wavelength band.  
In order to maintain the polarization throughout the collimation, it is necessary to keep a homogeneous magnetic field after the polarizer until the sample position. While the field strength may vary, the direction needs to be consistent and should never flip. Calculations for permanent magnets that are aligned along the collimation show that a field strength between 5 and 13 mT can be maintained throughout the collimation, including all components, such as choppers. The chosen field direction is perpendicular to the beam direction. To also allow for different spin orientations, a spin flip is realized by a radio-frequency spin flipper directly after the polarizer [12]. The foreseen probability for a successful spin flip is \(\geq\)99%.
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 1 Introduction
Recently, various environmentally sustainable and highly effective alternatives have been suggested for the management of phytopathogenic fungi. These alternatives encompass the use of biological control methods [20], essential oils [21], plant extracts [22], and engineered nanomaterials [23]. The utilization of engineered nanomaterials has become increasingly significant in the management of phytopathogenic fungi due to their distinct physicochemical characteristics in comparison to their larger-scale counterparts [24]. As a result, several nanomaterials have demonstrated a superior efficacy compared to traditional agrochemicals in the management of plant diseases [25]. Various types of nanomaterials have been investigated as potential substitutes for the management of phytopathogenic fungi. These include carbon nanomaterials [26], nanopolymers [27], and metal nanoparticles [28; 29; 30]. Metal oxide nanoparticles have been widely recognized as a viable and environmentally sustainable option for managing phytopathogenic fungi in the field of agriculture [31; 32]. Iron oxide nanoparticles (IONPs) have been recently utilized in various biotechnological applications. These include the immobilization of enzymes on multifunctional magnetic nanomaterials [33], the development of low-cost biosensors for the detection of pathogenic bacteria using graphene-magnetic@chitosan nanosheets [34; 35], and biomedical applications [36]. In this context, IONPs were reported to possess antifungal effectiveness against fungal phytopathogens [37; 38]. A previous study indicated that the biogenic iron oxide nanoparticles of _Euphoria helioscopia_ leaf extract were spherical in shape, where the particle size ranged from 7 to 10 nm and possessed antifungal efficiency against the _Cladosporium herbarum_ strain [39]. The prevalence of fungal resistance has been widely observed and is primarily attributed to the excessive utilization of chemical fungicides. In addition to their detrimental effects on the environment, the urgent need for alternative fungicides that can be safely applied has become apparent. Therefore, the present study was conducted to greenly synthesize IONPs utilizing aqueous _Laurus nobilis_ leaf extract. Additionally, the biosynthesized IONPs were characterized via the application of physicochemical tech niques. Furthermore, the efficacy of the IONPs was assessed in terms of their antifungal properties. This evaluation was conducted using a food poisoning technique, specifically targeting the pathogenic _A. alternata_ strain known to cause significant damage to matured and harvested vegetables and fruits. Furthermore, a comparison was made between the antifungal effectiveness of the phytosynthesized IONPs and a commercially available fungicide known as metalaxyl + macozeb fungicide to determine the possible applicability of the biogenic IONPs in controlling phytopathogenic fungi.
## 5 NB-IoT Cell Search  
In this section, the proposed cell search method is presented, which is accomplished in both the time domain and frequency domain. The time domain process includes NPSS detection and time domain synchronization, and the cell ID identification is completed in the frequency domain.
## 2 Materials and Methods
### Analysis of Metabolites  
The sample (50 \(\pm\)1 mg) was mixed with 450 \(\upmu\)L of pre-cooled methanol: dH\({}_{2}\)O (3:1, v:v) and 5 \(\upmu\)L of internal standard (L-2-chlorophenylyalanine, 1 mg/mL stock). The mixture was vortexed for 30 s, homogenized using a ball mill at 35 Hz for 4 min, and then ultrasonicated in ice water bath for 5 min. After centrifugation at 8000\(\times\)\(g\) at 4 \({}^{\circ}\)C for 15 min, 150 \(\upmu\)L of the supernatant was transferred to a fresh tube, evaporated in a vacuum concentrator, added with 100 \(\upmu\)L of methoxygenation hydrochloride (20 mg/mL in pyridine), incubated at 80 \({}^{\circ}\)C for 30 min, and then derivatized with 120 \(\upmu\)L of bis(trimethylsilyl)trifluoroacetamide (containing 1% Trimethylchlorosilane, _v/v_) at 70 \({}^{\circ}\)C for 1.5 h [20].  
All samples were subsequently analyzed by a gas chromatograph coupled with a time-of-flight mass spectrometer (GC-TOF-MS, Agilent 7890) using a Rtx-5 ms capillary column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m; SHIMADZU, Kyoto, Japan). The sample (1 \(\upmu\)L) was injected in splitless mode. Helium was used as the carrier gas, the front inlet purge flow rate was 3 mL/min, and the gas flow rate through the column was 1 mL/min. The initial temperature was kept at 50 \({}^{\circ}\)C for 1 min, then raised at 10 \({}^{\circ}\)C/min to 310 \({}^{\circ}\)C, and held for 8 min. The injection, transfer line, and ion source temperatures were 280, 280, and 250 \({}^{\circ}\)C, respectively. The ionization was in electron impact mode at an energy of \(-\)70 eV. The mass spectrometric data were acquired in full-scan mode at an _m/z_ range of 50-500 amu at a rate of 12.5 spectra per second after a solvent delay of 6.33 min.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Chemical Vapor Deposition (CVD)
#### 3.1.4 Electroless Deposition  
Electroless is an elegant and multipurpose process of conductive metal deposition on any surface. Apart from other deposition processes, electroless is an autocatalytic chemical reaction and is done without electrical energy. It can deposit both thickly and thinly coated films quickly and effectively. It can deposit metals such as silver [201], aluminum, copper [202], nickel, and iron, which are uniformly and smoothly deposited on the surface of the textile substrate [51,203]. The main principle uses a reducing agent in a solution for the chemical reduction of metal ions and deposits them on the substrate's surface, which imparts electrical conductivity to the textile substrate. The chemical reaction involved in this process is as follows [204]:  
\[\mathrm{S=S^{n+}+ne^{-}}\]  
\[\mathrm{M^{n+}+ne^{-}=M}\]  
Electroless-deposition-produced conductive textiles, such as conductive woven cellulose fabric [205], PET fabric [206], and others, have a wide range of applications. Silver is deposited with the use of ultrasonics to create a shielding-effective PET fabric [207] treatment with silver nitrate and sodium hydroxide at 130 \({}^{\circ}\)C for 1 h, which increases the surface activity and eliminates the need for a catalyst that produces ethylene as a reducing agent, while ammonia treatment stabilizes the conductive layer [208]. Stretchable conductors are prepared by depositing silver through electrolessing plating on polyurethane filaments [209]. PVC is added to thermoplastic urethane during electrolessing plating, making the silver layer more adhesive and higher in conductivity.  
The electroless deposition of Cu imparts a variety of features to the textile substrates. Cu is seeded with Ag and coated on cellulosic fabric to make it conductive for the manufacture of a light-emitting diode [205]. [PERSON] et al. made ultra-stretchable conductors that deposit Cu on polydopamine-coated cotton fabric with a Pd\({}^{2+}\) catalyst [210]. [PERSON] et al. employed AgNO\({}_{3}\) instead of PdCl\({}_{2}\) to deposit Cu on PET textiles, resulting in a more uniform metal distribution and denser layer [211], and hydrazine monohydrate (80 wt\(\%\)) treatment at 60 \({}^{\circ}\)C improved the interaction between PET and the Cu layer [212]. The addition of 3-mercaptopropyltriethoxysilane to PET textiles during a deposition improves the washing performance [213]. [PERSON] et al. described a three-step copper deposition on cotton using Pd or Ag as a catalyst [214]. Cotton fabric can also be activated by Ag and Cu nanoparticle deposition before the electroless plating of Cu [215]. Cu has been electroless deposited on cotton fabric using (NH\({}_{4}\))\({}_{2}\)PdCl\({}_{4}\) as a catalyst to create a highly conductive fabric [216]. Laser treatment on cotton fabric improves abrasion resistance [217].
###### Abstract  
Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time.  
Keywords:cost minimization; energy management system; multi-verse optimization algorithm; sine-cosine algorithm; smart universities; smart grid; scheduling +
Footnote †: journal: Applied Sciences
## 3 Methodology of TSDCA-BA Model
### Structural Design of the TSDCA-BA Model
#### 3.1.4 Squeeze-and-Excitation Networks  
The model incorporates a Squeeze-and-Excitation (SE) module to enable dynamic adjustment of feature channels. Figure 5 shows the structure of the SE module. Specifically, the input features undergo global average pooling along the temporal dimension to obtain a global descriptor for each channel. This is followed by a bottleneck fully connected layer that reduces the dimensionality, employing a ReLU activation function to enhance nonlinear representation. Subsequently, another fully connected layer restores the channel dimension to its original size, with a Sigmoid activation function constraining the output values between 0 and 1, thereby generating channel-wise attention weights. Finally, these weights are multiplied channel-wise with the original features to achieve adaptive recalibration of each channel. This mechanism effectively emphasizes important channels while suppressing irrelevant or redundant information, thus improving the model's representational capacity and overall performance.  
Figure 4: The overall architecture of the proposed BI-GRU-Conv Module.
## 4 Control Design  
The advanced control methodology MPC is based on a receding horizon strategy in which the control input is calculated by solving repetitive optimal control problems (OCP) over a finite time horizon taking the system dynamics and possible constraints for states and inputs directly into account. When dealing with highly nonlinear system dynamics as in the considered case, a nonlinear MPC strategy is necessary which, in general, suffers from the need of solving efficiently a nonlinear optimization problem at each time step in real-time. In the presence of an adequate feed-forward controller, the computational burden can be circumvented by using a linear time-varying (LTV) model approximation of the nonlinear system dynamics with respect to the desired trajectory and feed-forward control signal. Then, a so-called LTV MPC can be designed and only a linear-quadratic optimization problem has to be solved at each time step.
## 5 Limitations of the Current Study and Future Directions  
One limitation of the present study stems from the fact that a different method was used to select the SNR for the VD test between the children and adults. Specifically, VD was conducted using the same SNR across the adult participants but was individually adapted for the children based on their SRTn. We believe that this has not influenced the main outcomes of this study because, despite receiving the voice cues at a better SNR than the adults, the children nonetheless showed degraded formant perception compared to quiet conditions. This, however, should be confirmed in future studies where a similar testing method is employed for all participants. In addition, in the present study, the effect of noise on VD was examined using a steady-state speech-shaped noise and not temporally modulated or babble noises in order to avoid intervening top-down processes such as glimpsing and/or linguistic skills that differ between children and adults. Future studies may want to examine the effect of variable noise types to broaden the ecological implications of the current study. To extend the applicability of the current findings, it is important to assess the effect of noise on F0 and formant VD in populations who struggle to understand speech in noisy conditions, such as older adults, very young children, individuals with hearing loss, and those with language impairment.
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results
### Analysis of Error Variation with Epicenter Distance, SNR, Depth, and Magnitude  
As a \"black box\" technology, the deep learning method has an internal working mechanism that is not transparent and difficult to visualize, which is the most important factor in developing deep learning algorithms.  
These features have a significant impact on the estimation results. In this study, we did not directly integrate station information and other features with waveform data but expected the model to learn relevant information. Given this, we selected the DCRNNAmp, which performed well in the above comparative analysis, and deeply analyzed the relationship between its predicted magnitude and key factors, such as epicentral distance, signal-to-noise ratio (SNR), and depth, in order to determine whether the model has learned the relevant information that has a significant impact on the estimation results.  
It should be noted that the error in this paper is defined as the predicted value minus the actual value, with positive values indicating overestimation and negative values indicating underestimation. In the practical application of earthquake early warning (EEW), the thresholds of ground shaking parameters are mainly used to determine whether the ground shaking is destructive. Therefore, for the practical application of EEW, it is imperative to evaluate the effectiveness of deep learning methods in recognizing destructive earthquakes. In the analysis, we compared the large earthquakes with a magnitude greater than 6 with all of the data.
# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 4 Methods
### Modeling Hierarchical Dependencies
#### 4.3.1 Hierarchical Models  
Hierarchical relationships can be shaped directly into the architecture of any of the described models above. The ICD-9 taxonomy can be modeled as a tree with a general ICD root and 4 levels of depth, as already described in Section 1. This leads to a hierarchical variant of any of the models. In this variant, not 1 but 4 identical models will be trained, one for each of the different layers in the ICD hierarchy (corresponding to the length of the codes).  
Such an approach is presented in [34] and is adapted to the target domain of ICD categories. An overview of the approach is given in Figure 7.  
The input for each layer is partially dependent on an intermediary representation from the previous layer as well as the original input through concatenation of both. Layers are stacked from most to least specific or from leaf to root node in the taxonomy. Models corresponding to different layers will then rely on different features, or characteristics, to classify the input vectors. This way the deepest, most advanced representations, can be used for classifying the most abstract and broad categories. On the other hand, for the most specific categories, word level features can directly be used to make detailed decisions between classes that are very similar.
# Mechanical Modeling and Dynamic Characteristics of a Three-Directional Vibration Absorber
## 3 Results and Discussion
### Influence of Nonlinear Damping on Dynamic Performance
#### 3.4.2 Influence of Nonlinear Damping on Vibration Isolation Efficiency  
Similarly to the previous analysis, the vibration isolation performance of the three-directional vibration isolator under harmonic force excitation is evaluated using force transmissibility. Following the same approach as in the linear damping case, the expression for force transmissibility is derived, as presented in Equation (41).  
\[T_{t}=\frac{\sqrt{{f_{0}}^{2}+a^{2}\Omega^{4}+2a\Omega^{2}\Big{(}-a\Omega^{2}+ \varphi_{1}a+\frac{3}{4}\varphi_{3}a^{3}+\frac{10}{16}\varphi_{5}a^{5}\Big{)}}} {f_{0}} \tag{41}\]  
As shown in Figure 15, the force transmissibility near the resonance frequency is minimally affected by Coulomb friction damping. Within the frequency range from zero to the resonance frequency, the force transmissibility of the isolator decreases as the equivalent Coulomb friction factor \(\xi_{c}\) increases. Conversely, in the high-frequency region above the resonance frequency, the force transmissibility increases with an increase in \(\xi_{c}\).  
Furthermore, as \(\xi_{c}\) increases, the force transmissibility curve shifts downward and to the right, leading to an increase in the system's resonance frequency and a reduction in the effective isolation range of the vibration isolator.  
Figure 14: Variation in dynamic response amplitude of the vibration isolator with excitation frequency ratio \(\Omega\) under different equivalent Coulomb friction damping factors \(\xi_{c}\).
## Materials and Methods
### Forced Degradation Conditions  
The main stock solutions of PMT prepared at 25 ugmL\({}^{-1}\) was protected from light exposure and stored at 4 \({}^{\circ}\)C. The forced degradation conditions include hydrolytic conditions of 0.1 M sodium hydroxide (NaOH), acidic 0.1 M hydrochloric acid (HCl), and oxidative conditions of 3% _v/v_ hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) based on ICH recommendations [12; 13; 28; 34]. The basic and acidic degradation of PMT were performed by mixing 1 mL of PMT stock solution with 2 mL 0.1 M NaOH and 0.1 M HCl at room temperature for 6, 12, 24, 30 and 48 h. For the oxidative stress, 1 mL of PMT stock solution was treated with 2 mL of 3% (v/v) H\({}_{2}\)O\({}_{2}\) at room temperature for the same exposure times in hydrolytic degradation. Each sample measurement was repeated three times, and from each droplet, 30 spectra were collected and averaged.
## 4 Simulation Results
### Discussion
* For images where snow is dominant, image appears overly white, which also decreases the detection accuracy of state-of-the-art object detection methods. In this case, image contrast correction might produce the desirable results. In many cases, the occlusions on the road also pose a threat to the detector, which ultimately results in false detections. In such cases, an occlusion handling method could also be used to reliably detect any object. Figure 11: Sample images of low vehicle detection from compared datasets  
* For all of the aforementioned discussion, Figure 11 shows a few of the sample images where our developed method struggles. In images shown in Figure 11, our method either yields a very low vehicle detection rate or produces false detections. Therefore, future research could also focus on few of the cases as shown in Figure 11.
## 3 Methodology
### System Model
#### 3.1.2 Data Processing  
This stage denotes the processing of the data, including how the actual data will be processed and originated from the sender and how they will be successfully delivered to the destination without any loss, packet loss, delay, or consumption of energy. This means that the protocol will have to consume less energy. The base station processes the aggregated data for further analysis and application. The NBEER protocol uses this system paradigm to increase data transmission efficiency and reliability in UWSNs while consuming less energy and extending network lifetime.