esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes a new implicit time integration method based on the GGW method. To control the numerical damping effectively, the time step is divided into two sub-steps. In the first step, the GGW method is used, and in the second step, a new parameter is introduced according to the idea of trapezoidal integration. The numerical analysis of numerical stability and accuracy shows that spectral radius, period extension rate, and amplitude attenuation rate of this method can all be controlled by changing the parameters \(\alpha\) and \(\beta\). Generally, we take \(\beta=1/2\) and \(\alpha\in[2/19,2/7]\), thus \(\rho_{\infty}\in[0.5,0.8]\). Several numerical examples are given to demonstrate the effectiveness of the proposed method.  
The first representative example is an undamped single-DOF problem. The results show that the proposed method has better accuracy, smaller period extension, and smaller amplitude attenuation compared with other well-known time integration methods. The second example is a large displacement nonlinear problem. When using the same time step, the Bathe method, trapezoidal rule, and Wilson method cannot compute reasonable results, while the results of the proposed method are very similar to the exact solutions, which proves that the method is suitable for nonlinear problems. The third example is a large-span planar structure problem. The results show that the proposed method can not only possess high dissipation in a high-frequency range, but also preserve good accuracy in a low-frequency range. Hence, a larger time step can be chosen to solve such problems.  
In summary, the proposed method has the following merits:  
1. Reasonable and controllable numerical damping. It possesses high dissipation in a high-frequency range and preserves good accuracy in a low-frequency range as well;
2. Applicability of both linear and nonlinear problems. This method can maintain high accuracy in large-displacement nonlinear problems;
3. Due to the advantages mentioned above, a stable and reasonable numerical result can still be obtained when a larger time step is selected, which results in the superior efficiency.  
The proposed method is expected to have a bright future in nonlinear vibration problems.  
Conceptualization, Q.X.; Methodology, Q.X., W.W. and Q.Y.; software, W.W. and Q.Y.; validation, Q.X. and W.W.; formal analysis, W.W.; investigation, Q.X. and W.W.; resources, Q.X.; data curation, Q.X. and W.W.; writing--original draft preparation, W.W.; writing--review and editing, Q.X. and W.W.; visualization, Q.X.; supervision, Q.X.; project administration, Q.X.; funding acquisition, Q.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 51508305). The authors are solely responsible for the content.  
Not applicable.  
The authors declare no conflict of interest.
# Progress of Capillary Flow-Related Hydrovoltaic Technology: Mechanisms and Device Applications
## 2 Current Understandings of Capillary Flow-Related Electricity Generation Mechanisms
### Pseudo-Streaming  
Another Korean research group also successfully fabricated carbon-coated fabric for evaporation-induced electricity [19]. The main novelty lies in the absence of a water reservoir by using CaCl\({}_{2}\) to absorb water moisture from the ambient atmosphere. Subsequent works also demonstrated a scaled-up device for improved performance [55] and even hybrid functions with water purification [56].  
Considering the material used is conductive carbon, which cannot be fully explained by the ionovoltaic effect alone, they proposed a mechanism termed pseudo-streaming. In essence, this mechanism can be deemed a combination of the streaming potential and the ionovoltaic effect. On one hand, the flow of water over the conductive carbon surface induces an electrokinetic charge transfer, known as a pseudo-streaming current. Unlike conventional streaming currents that occur in liquids, the pseudo-streaming current occurs in the conductive channels of the carbon fabric. On the other hand, a potential difference arises due to the electrical double layer formed on the wetted carbon surface against the dry carbon, which is also crucial for generating power (Figure 5). However, for a conductive material, i.e., conductive carbon, how the electrical double layer is formed is not elucidated in detail.  
The authors provided a mathematical description of pseudo-streaming, but it is primarily a qualitative relationship based on experimental observations. Similar to the challenges faced with the ionovoltaic effect, this model cannot fully explain the steady, continuous electricity generation, as its theory relies on the movement of the water contact line. While this represents a significant advancement in understanding the complexity behind capillary flow-related electricity, further efforts are clearly needed to refine and complete the model.
## 2 A More Flexible MHz, Serial Crystallography Experiment  
Through optimizing parameters such as resolution, detector geometry, and focal size, the SPB/SFX instrument is leading the charge in next-level MHz SFX experiments and advancing the capabilities to produce movie-like protein dynamics.  
It is evident that the unprecedented MHz repetition rate SFX experiments now possible at the EuXFEL provide vast quantities of quality data for better statistical analysis than is currently possible at other XFELs. The experiments mentioned herein demonstrate the novel capabilities of the EuXFEL and provide examples for future research. At the time of writing, preliminary experiments have already utilized a 4.5 MHz pulse rate with 250 pulses per train, and future experiments have access to more than double the rate of data collection than that of [PERSON] et al., while also decreasing the minimum time point separation by a factor of 4.  
Currently, improvements are under way to increase the number of pulses per train so as to fill all AGIPD memory cells with 352 frames per train, that is, 3520 frames measured per second. Initial plans to implement a veto system are also in discussion, with the goal to increase the number of data-containing frames measured per train. At present, data are collected regardless of whether diffraction is present, resulting in a typical 1% hit rate for GDVN sample injection. Detection of crystal-X-ray interaction prior to detector read-out could effectively use more than 352 pulses per train, where the frames that do not contain data are overwritten in the memory cells. This potentially allows for up to 352 data-containing images recorded per train.  
Further improvements in detectors include the development of an AGIPD 4 Mpx detector (AGIPD-4M). Design specifications for AGIPD-4M use the same-sized 200 um\({}^{2}\) pixels as its smaller 1 Mpx cousin [48]. AGIPD-4M is therefore four times bigger and, as discussed in Section 1.8, a larger detector will provide better flexibility in experimental design in cases similar to those discussed by [PERSON] et al. [56].
# Adsorption of Ag (I) Ions at the Zirconium Phosphate/KNO3 Aqueous Solution Interface
## 4 Conclusions  
X-ray amorphous precipitated zirconium phosphate Zr(HPO\({}_{4}\))\({}_{2}\) was subjected to the hydrothermal and microwave modifications in the form of wet gel and dried xerogel. The specific surface area of the modified samples was within 195-450 m\({}^{2}\)/g. Their crystal structure was almost the same. On the other hand, the FTIR tests showed stronger bands characteristic of the Zr-OH groups and the appearance of vibrations for various phosphate groups. The presence of these groups caused a shift of pH\({}_{\text{iep}}\) = 3.9 for the Initial ZrP sample to pH\({}_{\text{iep}}\) = 2.2 for the MWT treated Xerogel ZrP sample. The studies of the adsorption of Ag (I) ions from the solutions with the initial concentrations of 1, 10, and 100 umol/dm\({}^{3}\) on zirconium phosphates modified with MCHT, MWT, and HTT showed that the xerogel and MWT modified zirconium phosphate gel samples were characterized by high adsorption affinity for Ag (I) ions. Compared with the ZrP initial sample, there was a shift of the Ag (I) ion adsorption edge towards lower pH values, e.g., with adsorption of Ag (I) ions from the solution with the initial concentration of 1 umol/dm\({}^{3}\) for the ZrP Initial sample pH\({}_{\text{50\%}}\) = 3.2, while for the sample MWT ZrP\({}_{\text{nero}}\) pH\({}_{\text{50\%}}\) = 2.6. The effect could be of practical importance in removing the \({}^{110\text{m}}\)Ag radioisotope from the nuclear wastewater.  
Conceptualization, W.J. and V.S.; Investigation, E.S. and S.K.; Methodology, W.J. and E.S.; Visualization, S.K.; Writing--original draft, W.J., E.S. and V.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
## 1 Introduction
Further, the detailed vertical structure [40] and shadowing effects among tree crowns implicitly contained within ALS data allow us to estimate the proportions of four forest components without using a statistical-based method. Meanwhile, the ALS-based digital elevation model with high spatial resolution [41, 42, 43] can be beneficial to accurately estimate the proportions of the four forest components. [PERSON] et al. [44] derived the shaded component proportion of forest canopy using a hillshade algorithm [45] from airborne Lidar data. However, few studies have paid attention to estimating the four forest component proportions of a forest stand with a real 3D forest scene using the ALS data. An ALS-based approach is needed to quantify the proportions of the four forest components. Additionally, the effects of ALS data characteristics and forest structure on the four forest component proportions estimation are still not clear. Therefore, our specific goals are:  
(1) To develop an ALS-based method to estimate and validate the four forest component proportions of a forest scene and  
(2) To investigate the spatial variations of the proportions of the four forest components, and further explore the factors affecting the estimation accuracy of the four forest components.
## 1 Introduction
Therefore, the combination of antimicrobial NPs with electrospun membranes is a very promising strategy to improve the overall performance of the facemasks, namely the filtration efficiency as well as elimination of microorganisms [5; 18; 22]. The incorporation of the materials into electrospun fibers can be carried out using different techniques, namely the surface functionalization. In this method, the electrospun membranes are firstly produced, and then functionalized with the desirable materials in order to obtain improved properties. Using the surface functionalization method, the NPs stay available at the surface of the fibers instead of within them [24]. This also reduces the costs compared to the conventional inclusion approaches, due to the use of fewer nanomaterials for an equivalent efficacy and fine-tuning of surface properties [25]. Thus, the aim of this work involves the development of an active, fully biodegradable facemask which is able not only to exhibit high filtration efficiency but also to degrade the microorganisms, taking advantage of the biodegradability and excellent properties of natural fibers and biopolymers. The first part of the work was focused on the development of PCL electrospun membranes functionalized with different types of NPs (Ag, TiO\({}_{2}\) and MgO) to act as the filter layer. Ag NPs were synthetized using a green methodology, whereas commercial TiO\({}_{2}\) and MgO NPs were used. All the developed membranes were characterized by Field Emission Scanning Electron Microscopy (FESEM), Energy DispersiveSpectroscopy (EDS), Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR), Ground State Diffuse Reflectance (GSDR) and Thermogravimetric Analysis (TGA). The functional properties were also evaluated, including the antibacterial activity and the filtration efficiency. Finally, the PCL/NPs electrospun membranes with the most promising results were selected for the production of a facemask prototype, using cotton natural fiber for the inner and outer mask layers. Three different configurations of facemasks were evaluated regarding their filtration efficiency, air permeability and thermal comfort.
# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 3 Materials and Methods
### System Architecture
#### 3.1.2 Path Model  
Obviously, travel time prediction is a crucial aspect of the multiple bus route transfer problem [10]. The travel path from the source to the destination can be represented as a series of time periods, as shown in Figure 3. Inspired by study [3] dividing the bus travel time, in this paper, the path model comprises 4 blocks: Current Driving Segment Travel Time, Bus Stop Dwelling Time, Stop-to-Stop Travel Time, and Transfer Point Waiting Time. It should be noted that CDS refers to the path segment connecting the bus's present position and the next stop.  
Figure 3: The path model of the entail journey.  
Figure 2: The influence of environment factors. (**a**) Temperature. (**b**) Holiday. (**c**) Air quality. (**d**) Peak/off-peak time. (**e**) Week.  
Therefore, based on the aforementioned path model, the whole travel time T can be denoted as the sum of the time consumed by the 4 blocks mentioned above, which can be formulated as follows:  
\[T=t_{CDSTT}+t_{SSTT}+t_{BSDT}+t_{TPWT} \tag{3}\]  
Note: \(t_{BSDT}\) and \(t_{SSTT}\) include p sub-time blocks, which are denoted as Equation (4), where \(t_{BSDT}(k)\) is the dwell time at \(Stop_{k}\), and \(t_{SSTT}(k,k+1)\) is the travel time between \(Stop_{k}\) and \(Stop_{k+1}\).  
\[t_{BSDT}=\sum_{k=0}^{p}[t_{BSDT}(k)],\quad t_{SSTT}=\sum_{k=0}^{p}[t_{SSTT}(k,k+1)] \tag{4}\]
###### Abstract
(2018) utilized the cluster analysis method to demonstrate the rainfall threshold triggering the landslides in Amboori, Kerala, India, which is a tropical area. In the study, 2-, 3-, and 5-day antecedent rainfall clusters were modelled, and regression analysis was performed. When the 5-day antecedent rainfall was zero, a single instance of rainfall of 80.7 mm was found to trigger a landslide. In addition, almost 20% of triggering in the whole study was found to have happened following the 5-day antecedent rainfall. The study suggested the use of the rainfall threshold criteria and did not calculate but recommended the factor of safety values as well. Moreover, [PERSON] et al. (2018) argued that the soil became overconsolidated when it experienced a higher suction value than its residual suction value which led to the soil having a higher average skeleton stress, resulting in a state of overconsolidation. The authors concluded that, similar to the stress history, the suction history is found to be incredibly effective for the behavior of the overconsolidation process. Therefore, the antecedent rainfall conditions in terms of creating a suction history become more important to the evaluation of the triggering mechanism of a landslide. As per Intergovernmental Panel on Climate Change (IPCC), the global average surface temperature is suggested to increase by 1.4 \({}^{\circ}\)C to 5.8 \({}^{\circ}\)C degrees within the period of 1990 to 2100. Due to this increase, the rainfall patterns are expected to be more intense and less frequent. Today, rainfall exceeding 3 days is a small possibility; however, with the global warming effect, intense rainfalls with longer durations will not be surprising. Future conditions will likely include challenging precipitation patterns, such as prolonged heavy rainfall and/or extreme rainfall events occurring after periods of heavy rainfalls. However, these are rarely incorporated into models, particularly in the case of rainstorms that occur after a preceding period of a rainfall ([PERSON] et al., 2018). This is a significant issue because the antecedent rainfall saturates the soil, thereby preparing it for the subsequent heavy rainfall which is the actual trigger for slope failure. This study investigates the effect of antecedent rainfalls on the slope stability when the soil was subjected to different rainfall intensities such as 2.5 mm/h, 5 mm/h, and 10 mm/h. Moreover, the slope stability was also investigated regarding the historical extreme rainfall data given on the web site of the Turkish State Meteorological Service (TSMS) when subjected to a prior antecedent rainfall or not. The time required for failure for a particular slope subjected to an extreme rainfall record was investigated and the susceptible time zone for the given situation is suggested. An application for a real case situation was also carried out and the results were discussed in terms of the antecedent conditions in the slope failure model. These analyses are expected to form a basis for evaluating the potential slope failure mechanisms which are prone to be affected by climate change due to global warming.
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 3 Model and Method
### Delay Model and Energy Consumption Model Design  
According to [39], if the spread spectrum factor (SF), coding rate (CR), and signal bandwidth (BW) are known, the calculation formula issued by the Semtech company can calculate the air transmission time of a single LoRa node packet. By understanding the key parameters that users can control and according to the definition of the symbol rate, the LoRa symbol rate \(R_{s}\) and symbol period \(T_{s}\) can be calculated by the following formula:  
\[R_{s}=\frac{BW}{2^{SF}} \tag{12}\]  
\[T_{s}=1/R_{s} \tag{13}\]  
The LoRa data transmission time is equal to the sum of preamble time and packet transmission time. The length of the preamble can be calculated by the following formula:  
\[T_{p}=(n_{p}+4.25)T_{s} \tag{14}\]  
where \(n_{p}\) represents the set preamble length, and its value is determined by two registers in the transmission chip.  
The payload transfer time can be calculated by the following formula:  
\[T_{l}=\varepsilon*T_{s} \tag{15}\]where \(\varepsilon\) is the number of payload symbols, which can be calculated by the following formula:  
\[\varepsilon=8+\text{max}\Bigg{(}ceil\Bigg{(}\frac{8 PL-4 SF+28+16-20H}{4(SF-2 DE)} \Bigg{)}(CR+4),0\Bigg{)}. \tag{16}\]  
where \(PL\) is the number of bytes of payload; when the header is used, \(H\) = 0, otherwise \(H\) = 1; \(DE\) is determined by the chip register; and \(CR\) is the coding rate, and the value range is 1-4.  
Finally, the data transmission time of LoRa is equal to the preamble transmission time plus the payload transmission time. The formula is as follows:  
\[T_{ALL}=T_{p}+T_{l} \tag{17}\]  
The energy generation process of LoRa data transmission is shown in Figure 5. In this paper, the energy consumption model was established based on the calculation method of communication equipment.  
If \(E_{TX}\) is the transmission energy consumption, \(E_{RX}\) is the receiving energy consumption, \(n\) is the data size, and \(d\) is the data transmission distance, then:  
\[E_{TX}(n,d) =E_{TX-dec}(n)+E_{TX-amp}(n,d) \tag{18}\] \[=E_{Tele}\times n+\varepsilon_{amp}\times n\times d^{k}\]  
\[E_{RX(n)}=E_{RX-dec(n)}=E_{Relec}\times n \tag{19}\] \[E_{LoRa}=E_{TX}(n,d)+E_{RX}(n)\] (20) \[E_{all}=E_{LoRa}+E_{CC}(n) \tag{21}\]  
Among them, \(E_{Tele}\) and \(E_{Relec}\), respectively, represent the energy consumption of LoRa communication equipment for sending and receiving data; \(\varepsilon_{amp}\) is the energy consumption of power amplifier transmitting data per unit distance; \(k\) is the propagation attenuation index of transmission environment with the value range of \(2\leq k\leq 5\); \(k\) should be taken as 4 in a mountain environment; and \(E_{cc}(n)\) refers to the energy consumption of cellular wireless data transmission module when transmitting \(n\) byte data.
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 5 Conclusions
### Ffe  
The addition of the taps in the feedforward part did not affect the performances consistently. The advantage of the addition of taps manifested notably in the high fiber length of 2.5 km.  
Increasing the number of taps enlarges the observation range in the time domain, and the resolution in the frequency domain; however, it did not always yield a performance improvement. In the case that a certain number of taps is optimum, any addition on top of that number will not induce any improvement and may even degrade the results because of unnecessary manipulation on the signal.  
The FFE in this research is linear, so the taps are spaced at \(T_{s}\). This tap spacing is reciprocal, in frequency domain, to the symbol rate, and it is not compatible with the Nyquist criterion of sampling, which means we would like to demand sampling space \(T^{\prime}=\frac{T_{s}}{2}\). In general, this approach leads to an equalizer performance that is very sensitive to the choice of sampling time; this may be the reason for the noise sensitivity and the lack of taps increasing the contribution.  
In order to eliminate the equalizer sensitivity, it is possible to use a fractionally spaced equalizer (FSE). In the FSE, the taps are spaced at \(T^{\prime}=\frac{M_{*}T_{s}}{N}\), where \(N>M\). The most used space is one that is compatible with the Nyquist criterion \(T^{\prime}=\frac{T_{s}}{2}\)[13]. Theoretically, the decrease in time space enables high-frequency equalization and can contribute to neutralizing the noise. In practice, because we are discussing a very high-rate communication, it is impossible or very expensive to implement this kind of equalizer.  
Figure 6: Isolated noise model—penalty graph for desirable BER = 1 \(\times\) 10\({}^{-4}\), where 0 dBm OIP penalty corresponding to \(-\)14 dBm. (**a**) FFE with \(N_{1}=6\) (**b**) DFE with 6 \(N_{1}=6\) and \(N_{2}=3\).