esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The wind profiler, a weather observation tool that uses radar or sound waves to detect wind speed and direction in the vertical levels above the ground, can aid in data assimilation due to its high temporal and spatial resolution [17]. Wind profilers played a key role in several mesoscale experiments and were used for various boundary layer studies [22; 23]. Wind profile data have been used in the data assimilation system to improve numerical forecasts of overall short-range precipitation and wind [24; 25; 26; 27]. Three-dimensional variational (3 DVAR) and four-dimensional variational data assimilation analysis systems, with wind profile data from Profiler Network of National Oceanic and Atmospheric Administration during the winter of 2003-2004 revealed that wind profile data could provide details of wind fields and short-range forecasts of variables such as temperature and humidity temporally and spatially [28]. Data assimilation using the WRF model also aided in simulations of Typhoon Megi [29], low-level winds, and the intensity and location of rainfall [30]. Ground-based wind profile observations, such as those from lidar devices, have high potential for data assimilation in the atmospheric boundary layer [31; 32; 33]. However, high-resolution data from lidar wind profilers are rarely assimilated into numerical weather prediction models, and are also rarely used for ATs prediction. This study aimed to improve the forecasting of ATs near Pingtung Airport. To achieve this goal, Doppler lidar wind profiler observations were assimilated in the 3 DVAR system of the WRF model [34] for ATs and rainfall prediction during the summer season. An AT event on 5 August 2018 under weak synoptic weather conditions at Pingtung Airport, which is located in southwestern Taiwan, was analyzed. The observational data, configuration of the WRF model, data assimilation system, verification method and experimental design are described in Section 2. The results and conclusions are presented in Sections 3 and 4, respectively.
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
## 3 Results and Discussion
### Characterization of Original Fly Ash  
Before all experiments, a basic characterization analysis was conducted on the raw fly ash samples. From the SEM images of the raw fly ash (Figure 1a,b), it can be observed that the particles are spherical microbeads of varying sizes with smooth surfaces, which are the products of high-temperature combustion. The XRF data (Table 1) and XRD pattern (Figure 1c) indicate that the main components of the fly ash are mullite-quartz phases, SiO\({}_{2}\) glass phases, and iron microbead phases. Additionally, the EDX mapping images of the raw fly ash (Figure 2a-g) reveal the distribution patterns of the main elements in the fly ash. It is evident that the primary composition is a mixture of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), with other elements present in smaller amounts. The results also show that the mullite phase and the glass phase are intermixed.  
Figure 1: (**a,b**) SEM results of original fly ash and (**c**) XRD results of original fly ash.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Elements** & \multicolumn{4}{c}{**Compounds**} \\ \hline Element & Conc & Unit & Element & Conc & Unit \\ Si & 38.758 & wt\% & SiO\({}_{2}\) & 45.239 & wt\% \\ Al & 25.744 & wt\% & Al\({}_{2}\)O\({}_{3}\) & 30.777 & wt\% \\ Fe & 18.364 & wt\% & Fe\({}_{2}\)O\({}_{3}\) & 11.23 & wt\% \\ Ca & 5.204 & wt\% & CaO & 3.56 & wt\% \\ K & 4.58 & wt\% & TiO\({}_{2}\) & 3.524 & wt\% \\ S & 3.802 & wt\% & K\({}_{2}\)O & 2.253 & wt\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: XRF composition of the original fly ash.
# Cleaning of Phototrophic Biofilms in a Show Cave: The Case of Tesoro Cave, Spain
###### Abstract  
Show caves have different grades of colonization by phototrophic biofilms. They may receive a varied number of visits, from a few thousand to hundreds of thousands of visitors annually. Among them, Tesoro Cave, Rincon de la Victoria, Spain, showed severe anthropic alterations, including artificial lighting. The most noticeable effect of the lighting was the growth of a dense phototrophic community of cyanobacteria, algae and bryophytes on the speleothems, walls and ground. The biofilms were dominated by the cyanobacterium _Phormidium_ sp., the chlorophyte _Myrmecia israelensis_, and the rhodophyte _Cyanidium_ sp. In many cases, the biofilms also showed an abundance of the bryophyte _Euclatum_. Other cyanobacteria observed in different biofilms along the cave were: _Chroococcidiopsis_ sp., _Synchocystis_ sp. and _Nostoc_ cf. _adphicum_, the green microalgae _Pseudococcomyaa simplex_, _Chlorella_ sp. and the diatom _Diadesemis contenta_. Preliminary cleaning tests on selected areas showed the effectiveness of hydrogen peroxide and sodium hypochlorite. A physicochemical treatment involving the mechanical removal of the thickest layers of biofilms was followed by chemical treatments. In total, 94% of the surface was cleaned with hydrogen peroxide, with a subsequent treatment with sodium hypochlorite in only 1% of cases. The remaining 5% was cleaned with sodium hypochlorite in areas where the biofilms were entrapped into a calcite layer and in sandy surfaces with little physical compaction. The green biofilms from the entire cave were successfully cleaned.  
cyanobacteria; chlorophytes; bryophytes; surface cleaning; hydrogen peroxide; sodium hypochlorite +
Footnote †: journal: Applied Sciences
## 3 Optimization Problem
### Power Constraint  
The allocated power for each user in the cell is a percentage of the overall power \(P_{t}\) transferred from base station; thus, the allocated power fraction for each user equipment must complies with [21]:  
\[\sum_{x=1}^{N}\alpha_{x}\leq 1 \tag{9}\]  
where \(\alpha_{x}\) is the power portion for the \(x\)th ser in the N-user NOMA cell.
# Feasibility of LoRa for Smart Home Indoor Localization
## 3 Experiment and Evaluation
### Calibration  
As the first step, we collect calibration data to identify the correlation between RSSI and distances. We use this correlation to map the RSSI measurements to distances in our ranging and localization experiments.  
We also collect the calibration data using one anchor and one tag in the same apartment as the rest of the experiments. We increase the distance from 0 m to 10 m and measured RSSI at every 30 cm with TX power levels of 5, 10, 15, 20, and 23 dB. Finally, we map RSSI to distance using linear and the 3 rd degree polynomial. Linear mapping results in the best fitting model.  
Figure 1: A furnished two-bedroom apartment used as our testing environment. We used three anchor nodes (red circles) and 5 tags (dark blue circles) at a fixed height of 1.06 m (3.48 ft). Furniture and appliances cause NLOS and reflections for LoRa signals and impact the localization accuracy.  
The result of linear mapping for TX power of 5 dB is shown in Figure 3. The equation is:  
\[Distance=-0.33\times RSSI-17.81 \tag{1}\]  
Although R2 is 0.72, in practice we observe that the performance of the linear model is better than the 3 rd degree polynomial. The calibration model is different for each TX power level, but we do not observe any difference in the ranging performance when using different TX power levels.  
Figure 3: RSSI to distance mapping for 5 dB of TX power, using a linear model.  
Figure 2: Floor plan of the apartment with anchor/tag locations.
## 3 Dynamic Forecasting Model Based on VMD-SLSTM Network
### Forecast Results Using the VMD-SLSTM Network  
In the experiment, the measured data from the first 260 days were used as a training set, and the remaining data were used as a testing set. To ensure the fidelity after displacement decomposition, the penalty parameter \(a=0.5\) and the rising step \(\tau=0.1\) were finally determined through multiple trial calculations. After several tests, it was considered that \(K=3\) has better decomposition effect. Figure 7 shows the decomposition results of original displacement for the training set.  
Next, the SLSTM network with \"3 + 1\" layers (3 LSTM layers and 1 fully connected layer) was built to learn the training set. To improve the convergence speed of the network, the data was normalized to have the same mean 0 and standard deviation 1 by Z-score. Figure 8 shows the forecast results of displacement subsequences using SLSTM networks. It can be seen that the forecast curve of each component conforms intuitively to the changing trend of the corresponding observed displacement subsequence.  
Figure 6: Displacement-time curve of the Dashuitian landslide.  
Figure 7: Decomposition results of original displacement for training set.  
Finally, the forecast results of the landslide displacement were obtained by reconstructing the forecast results of all displacement subsequences. Figure 9 shows the forecast results and errors of landslide displacement. It can be seen that the forecast curve can follow the observed curve well, indicating that the proposed model is valid. It is noted that after 320 days, the forecast results are not ideal. This is because although the forecast values are retrained, the accumulated errors will continue to impair the network performance.
# Multimodal Biometric Template Protection Based on a
## 1 Introduction
### Related Work
#### 1.1.2. Cancelable Multimodal Biometrics with Deep Learning  
Multimodal biometrics with template protection is not a new topic, with many papers having been published on this subject [18; 19; 20; 21; 22]. However, deep-learning-based multimodal biometric systems that include template protection remain very scarce.  
[PERSON] et al. [23] designed a multi-instance CB for the face using multiple CNNs to extract features from multiple regions of a face image, such as face, eyes, nose, mouth, etc. After fusion of several deep features, a cancelable template was generated via bioconvolving encryption. Their method achieved a better performance than when a unimodal biometric was applied. However, a detailed analysis of CB design criteria was not provided.  
[PERSON] et al. [24] introduced a method for generating CB templates from face and iris biometrics. Each biometric image was subjected to feature extraction via a CNN, with a random component selected from the generated features and used as a transformation key. The transformed templates generated from the CB module were then converted to a secure sketch via a forward error correction (FEC) decoder and cryptographic hashing. However, a compromised transformation key may pose the risk of CB template inversion.  
[PERSON] et al. [25] put forward a finger vein and iris-based CB scheme in which a CNN was applied to perform feature extraction and a support vector machine (SVM) was used for user verification. The template was protected with a random projection-based approach.  
More recently, [PERSON] et al. [26] proposed a multibiometric CB system using fingerprint, finger vein, and iris images. First, feature extraction was performed for each biometric modality through a CNN and fusion of the three modalities was achieved using feature maps. The DeepDream algorithm was then applied to give a cancelable template. However, a security analysis was not performed.  
A detailed comparison with the above papers is provided in Section 4.6.
## 2 Materials and Methods
### Headspace Solid-Phase Microextraction (HS-SPME)  
The HS-SPME analysis was carried out using a CombiPal G6500-CTC (Agilent Technologies, Inc., Santa Clara, CA, USA). The extraction conditions were previously optimized by our research group [15]. Homogenous cork samples (1 g) and 20 \(\upmu\)L of 4-nonanol (0.05 g/L in absolute ethanol) used as an internal standard were placed in a 20 mL vial sealed with a screw-capped top containing a Teflon-lined septum. A triple SPME fiber, previously conditioned according to the manufacturer and coated with 50/30 \(\upmu\)m of divinylbenzene/carboxoxen/polydimethylsiloxane (DVB/CAR/PDMS) was exposed to the headspace of the sample at 70 \({}^{\circ}\)C for 40 min under continuous stirring (250 rpm). The fiber was then retracted and inserted into the gas chromatograph injector. Volatile compounds absorbed in the SPME fiber were thermally desorbed at 250 \({}^{\circ}\)C for 5 min (splitless mode for 0.5 min). This desorption time allowed the fiber to be left clean for the next extraction. Each extraction was performed in duplicate.
## 3 System Model  
### Network Model  
In this paper, the information is generated by the underwater source node and transmitted upward to the destination node using relay forwarding. Subsequently, the destination node transmits the data packets to the ground base station for data analysis via electromagnetic waves. The network model is depicted in Figure 1. We make the following assumptions:  
1. Numerous underwater sensor nodes are randomly scattered throughout a three-dimensional underwater network;
2. The destination nodes are energy unconstrained and can obtain their position information through GPS;
3. All underwater nodes can obtain their location information through positioning algorithms;
4. All nodes have access to their buffer status.
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 5 Experimental Results and Analysis
### Intra- and Inter-SF Interference  
The analysis of intra- and inter-SF interferences during 600 s of simulation time is shown in Figure 4. The simulation scenario includes 100 EDs in a radius of 5 km, where SFs are assigned based on our proposed ED sensitivity-based approach. Every ED transmits a single packet of 51 bytes for the UL in unconfirmed mode using the 125-kHz bandwidth. The analysis in Figure 4 depicts collision overlap time occurred due to the collision of packets with the same and different \(SF_{(i,j)}\) over the same channel. Considering the interference among the same SF, at the simulation time of 50 s, two packets using \(SF_{(i,j)}=\text{ SF 12}\) (SF 12 with the ToA equals to 2.46 s) arrive at the GW with an overlap of 1.3 s. After computing \(\gamma_{\hat{C}}\) (which is -5.39 dB), the respective SINR after equalization is not greater than 6 dB, resulting in a packet loss [11; 50]. Let us consider the case where a collision occurs between two packets with different SF (i.e., \(SF_{(i)}=9\) and \(SF_{(j)}=12\)). This collision occurs at the simulation time of 240 s and lasts up to 1.1 s. The \(\gamma_{\hat{C}}\) value for this collision is -86.63 dB, which is smaller than \(\beta_{(i,j)}\) (i.e., -36 dB), resulting in a packet loss. From Figure 4, we can observe that both the intra- and inter-SF collisions are certainly an issue in LoRaWAN networks. Besides, the recent works also confirmed the fact that SFs are not completely orthogonal among themselves [10; 11; 12; 13; 14]. We also observed that if two packets using the same SF overlap on the same channel for a small amount of time and their respective SINR is greater than \(\beta_{(i,j)}\), then both packets can be correctly decoded.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulation time [h] & 24 \\ Uplink interval & 24 packets/day \\ GW radius [m] & 3410 \\ Number of GWs & 1 \\ Receive paths at GW & 8 \\ Packet size [bytes] & 51 \\ Mode of communication & confirmed and unconfirmed \\ Maximum packet transmission limit & 8 \\ GW antenna height [m] & 15 \\ ED antenna height [m] & 1.2 \\ Path loss exponent & 3.76 \\ Path loss model & log-distance \& Okumura-Hata \\ Shadowing & \(d_{0}=110\) m, variance = 6 dB [2; 38] \\ Buildings & height = 6 m, number of floors = 2 [51] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters utilized in simulation.
## 5 Application of Metro Station Excavation Model Test  
### Overview of the Model Test  
Chegongmiao Station, a four-line transfer station of Shenzhen metro, is located adjacent to two other underground structures, as show in Figure 7. The existing structures around the metro expansion project are complex. The minimum clear distance between the metro station and the existing building on the east side is only 3.45 m, and the station structure is built in conjunction with the viaduct on the west side; as a results, its foundation pit construction can only be carried out in a narrow environment.  
Based on this practical project, a geomechanical model test was carried out to study the earth pressure changing law of the soil mass during the excavation of the long, narrow metro station foundation pit. In this study, the geometry similarity ratio is \(C_{h}=50\), while the elastic modulus similarity ratio is \(C_{E}=10\). For ease of conversion, the stress similarity  
Figure 6: Curves of the compressive strength and elastic modulus at different cementing agent contents.  
Figure 7: Location site of the metro station foundation pit.  
ratio was taken as \(C_{\sigma}=50\). The dimensions of the prototype foundation pit were converted according to the similar scale. The stacking height of the model soil was 103 cm, the bedrock was 58 cm, the excavation depth of the foundation pit was \(H=40\) cm, and there was a distance \(B\) between the station retaining structure and the existing structure. The aspect ratio was \(n=B/H\).  
According to the requirements of the various mechanical parameters of the model tests, the proportion of the similar material in the model test was determined by conducting several proportioning tests. The mechanical parameters of the raw soils and the similar material are shown in Table 5, and the proportions of the model test similar material is shown in Table 6:
# Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization
## Extended Particle-Aided Unscented Kalman Filter
### Particle Prediction  
The prediction step predicts the state one timestamp ahead of the current state. According to Bayesian rule, the prediction always refers to the prior belief. It provides prior information of the vehicle based on the previous posterior belief, which is often the final estimated result of the previous timestamp. In this research, the road was assumed to be a flat XY plane, and movement in the vertical direction z was considered random noise. The state vector is given as Equation (1) and the vertical direction movement is given as Equation (2). Here, the value of \(\sigma_{\
u_{\mathrm{z}}}\) is 0.3.  
\[\mathrm{X}=[\mathrm{x},\ \mathrm{y},\ \mathrm{z},\ \mathrm{\theta}], \tag{1}\]  
\[\mathrm{z}_{\
u}\ \sim\ \mathrm{N}\big{(}0,\ \sigma_{\
u_{\mathrm{z}}}^{2}\big{)}. \tag{2}\]  
Equation (3) shows the transition model of the particles. Each particle is generated randomly and calculates the next timestamp value based on the transition model. The predicted value provides the prior guess of each particle and all the particles will be weighted based on the predicted value and current measurement value [27].  
\[\overline{\mathrm{X}}_{\mathrm{k+1}}=\left[\begin{array}{c}\overline{ \mathrm{x}}\\ \overline{\mathrm{y}}\\ \overline{\mathrm{z}}\\ \overline{\mathrm{\theta}}\end{array}\right]_{\mathrm{k+1}}=\left[\begin{array} []{c}\frac{\
u_{\mathrm{k}}}{\theta_{\mathrm{k}}(\Delta t)}\bigg{[}\mathrm{ sin}\bigg{(}\mathrm{\theta}_{\mathrm{k}}+\mathrm{\theta}_{\mathrm{k}}( \Delta t)\bigg{)}-\mathrm{sin}(\mathrm{\theta}_{\mathrm{k}})\\ \frac{\
u_{\mathrm{k}}}{\theta_{\mathrm{k}}(\Delta t)}\bigg{[}\mathrm{cos}( \mathrm{\theta}_{\mathrm{k}})-\mathrm{cos}\bigg{(}\mathrm{\theta}_{\mathrm{k} }+\mathrm{\theta}_{\mathrm{k}}(\Delta t)\bigg{)}\bigg{]}\\ \mathrm{z}_{\
u,\mathrm{k+1}}+\\ \mathrm{\theta}_{\mathrm{k}}(\Delta t)\end{array}\right]+\left[\begin{array}[] {c}\mathrm{x}\\ \mathrm{y}\\ \mathrm{0}\\ \mathrm{\theta}\end{array}\right]_{\mathrm{k}}. \tag{3}\]  
When the yaw rate equals zero, Equation (3) can be shown to become infinite. A different prediction model should therefore be used when the yaw rate is zero as shown in Equation (4).  
\[\overline{\mathrm{X}}_{\mathrm{k+1}}=\left[\begin{array}{c}\overline{ \mathrm{x}}\\ \overline{\mathrm{y}}\\ \overline{\mathrm{z}}\\ \overline{\mathrm{\theta}}\end{array}\right]_{\mathrm{k+1}}=\left[\begin{array} []{c}\
u_{\mathrm{k}}\cos(\mathrm{\theta}_{\mathrm{k}})\Delta t\\ \
u_{\mathrm{k}}\sin(\mathrm{\theta}_{\mathrm{k}})\Delta t\\ \mathrm{z}_{\
u,\mathrm{k+1}}\\ \mathrm{0}\end{array}\right]+\left[\begin{array}{c}\mathrm{x}\\ \mathrm{y}\\ \mathrm{0}\\ \mathrm{\theta}\end{array}\right]_{\mathrm{k}} \tag{4}\]
## 3 Results
On the second running day (Table 3), the clinically relevant results appeared unequivocally in the LDH activity, since when comparing the difference in NAB vs. CON results, both just before supplementation (60\({}^{\prime}\)Post-0\({}^{\prime}\)Post) and before running (60\({}^{\prime}\)Post-Pre), the ES was large in both cases (d = 0.80 and d = 1.59, respectively), not including the 0 value within its IC. Qualitatively, this could be interpreted as most likely harmful because of the positive ES for LDH. Furthermore, this was accompanied by other medium ES values in thermography, closer to large than to small (as in Table 2), since in ThQ, NAB vs. CON d = 0.72 (suggesting likely harmful). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Interval Time** \\ **Comparison** \\ \end{tabular} } & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Threshold**} & \multicolumn{3}{c}{**Day 1**} \\  & & & **CON (Mean \(\pm\) SD)** & **NAB (Mean \(\pm\) SD)** & **ES (CI 95\%)** \\ \hline \multirow{4}{*}{60\({}^{\prime}\)Post-0\({}^{\prime}\)Post} & GPx (U) & \(-\)0.20 & 71.52 \(\pm\) 698.25 & 3.72 \(\pm\) 754.20 & \(-\)0.09 (\(-\)0.99; 0.81) \\  & GR (U) & \(-\)0.20 & \(-\)19.56 \(\pm\) 633.77 & \(-\)88.84 \(\pm\) 240.35 & \(-\)0.10 (\(-\)0.78; 0.58) \\  & Cat (U) & \(-\)0.20 & 0.47 \(\pm\) 12.18 & \(-\)1.38 \(\pm\) 5.72 & \(-\)0.14 (\(-\)0.91; 0.64) \\  & LDH (U) & 0.20 & 5.67 \(\pm\) 66.20 & 8.91 \(\pm\) 50.72 & 0.04 (\(-\)0.73; 0.82) \\ \hline \multirow{4}{*}{60\({}^{\prime}\)Post-Pre} & GPx (U) & \(-\)0.20 & \(-\)1.94 \(\pm\) 727.11 & \(-\)66.17 \(\pm\) 748.06 & \(-\)0.08 (\(-\)0.96; 0.79) \\  & GR (U) & \(-\)0.20 & 56.89 \(\pm\) 342.68 & \(-\)143.75 \(\pm\) 574.94 & \(-\)0.54 (\(-\)1.75; 0.68) \\ \cline{1-1}  & Cat (U) & \(-\)0.20 & 3.59 \(\pm\) 11.94 & 2.20 \(\pm\) 8.14 & \(-\)0.10 (\(-\)1.02; 0.82) \\ \cline{1-1}  & LDH (U) & 0.20 & 19.70 \(\pm\) 83.42 & 36.16 \(\pm\) 53.11 & 0.18 (\(-\)0.55; 0.91) \\ \hline \multirow{2}{*}{15\({}^{\prime}\)Post-15\({}^{\prime}\)Pre} & ThQ (\({}^{\circ}\)C) & 0.20 & 0.37 \(\pm\) 0.66 & 0.69 \(\pm\) 1.12 & 0.44 (\(-\)1.03; 1.91) \\ \cline{1-1}  & ThG (\({}^{\circ}\)C) & 0.20 & 0.41 \(\pm\) 0.79 & 1.00 \(\pm\) 0.77 & 0.68 (\(-\)0.29; 1.65) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values variation of plasma enzyme activity and lower limb thermography after non-alcoholic beer supplementation on the first running day. CON, n = 10; NAB, n = 10. Data in CON and NAB are expressed as mean \(\pm\) SD.
# Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
## 3 Improved BSGA* Algorithm  
The traditional A* algorithm is improved to design a new global path planner, whose inputs include the values of grids, the starting point, the target point, and the attitude of the DDMR. The global path calculated by the improved BSGA* algorithm is divided into a series of waypoint coordinates as the outputs of the algorithm, which are relied upon to generate the linear velocity and angular velocity to guide the DDMR to move to the target point. In this planner, the constraints include kinematic constraints, that is, the maximum velocity of 0.6 m/s and the maximum angular velocity of \(\pi\)/6 rad/s. The environmental constraints, that is, the side lengths of the grids and the locations of the obstacles. Cost constraints: the sum of the actual costs and heuristic costs of each grid.