esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As seen, none of the works of ELM oriented to OFDM have proved its competency in tackling face the laser phase noise in a CO signal, which is one of the major limitations. By considering the overall OFDM frame, as well as the ELM potential against multipath fading channels, we introduce for the first time the SLFN prone to an improved ELM algorithm as equalizer for CO-OFDM systems with wide radio-frequency (RF) linewidths. The architecture of the artificial neural network is given by three layers: the first has two real-inputs (one for each component of the constellation symbol), the second possesses an activation function defined in the real domain for all hidden nodes, and the third supplies a single complex-output. The main contributions of the paper can be summarized as:  
1. We propose a modified ELM under supervised learning for maximizing the system performance (the BER minimization) of a phase-uncorrelated OFDM signal in the optical domain based on the adoption of the pilot subcarriers as training samples, as well as the consideration of the regularization parameter in the learning stage. 2. Taking into account the RF phase error as well as the subcarrier modulation format, we find the sub-optimal ELM parameters (the number of hidden nodes, penalty coefficient, and activation function) that yield the best BER via extensive simulations. This result is explained by the evaluation of the error vector magnitude (EVM) metric in the training as well as testing steps, which can properly quantify the root mean square error for complex numbers in the telecommunication industry. 3. We verify that when the Moore-Penrose generalized inverse of the hidden layer output matrix takes into account the regularization parameter, the ELM significantly improves in terms of stability and precision. As a result, the distortion induced by the laser oscillators is less within the constellation symbols. 4. For several signal to noise ratio (SNR) levels and RF-linewidth values, we respectively observe the superiority, and competitiveness of the novel ELM algorithm in terms of the BER metric among the benchmark PAE and a fully-real ELM, and the sophisticated ELM defined in the complex plane and non-effective bandwidth CPE compensator for binary phase-shift keying (BPSK) and QPSK modes. The content of the rest of the paper is organized as follows: Section 2 exposes in detail the foundations of this work (ELMs and OFDM-based CO systems). Section 3 presents the novel ELM algorithm for reducing the RF phase-noise effect in the BER performance. Furthermore, Section 4 shows the numerical-optimization process of the ELM parameters and compares its performance and computational cost with several state-of-the art phase-error mitigation mechanisms. Finally, Section 5 provides some concluding remarks.
## 3 Proposed Signal Processing Algorithm
### Hypothesis Test  
In hypothesis test, signal processing algorithm chooses the nine biggest peaks of \(i\left(n\right)\) as zero order fringe peak candidates. Ideally, \(p_{j}\)'s of \(j\)=0, \(\pm\)1, \(\pm\)2, \(\pm\)3, \(\pm\)4 are selected. Hypothesis test presumes that each candidate peak is the zero order fringe peak and calculates the parameter \(g(p_{j})\):  
\[g(p_{j})=\sum_{i=0}^{\infty}\left(J_{j+i}-J_{j-i-1}\right)=\sum_{i=0}^{\infty}d \left(j,i\right)\text{ for }\text{ }j=0,\pm 1,\pm 2,\pm 3,\pm 4 \tag{18}\]  
where \(j\) of notation \(d(j,i)\) corresponds to the candidate peak \(p_{j}\) on the hypothesis test and \(i\) is the distance from the zero order fringe peak candidate in terms of fringe. Then \(g(p_{0})\) is expressed as:  
\[g(p_{0})=d(0,0)+d(0,1)+d(0,2)+...\] \[=\left(J_{0}-J_{-1}\right)+\left(J_{1}-J_{-2}\right)+\left(J_{2}- J_{-3}\right)+... \tag{19}\]  
Ideally all the values of \(d(j\)=0,\(i)\) for the zero order fringe peak \(p_{0}\) is zero (Figure 7) due to the symmetric property of \(J_{i}\)= \(J_{i-l}\) and the zero order fringe peak candidate producing \(\left|\,g(p_{j})\,\right|=\)0 is announced as the estimated zero order fringe peak \(\hat{p}_{0}\). But, practically the zero order fringe peak candidate producing minimum \(\left|\,g(p_{j})\,\right|\) is announced as the estimated zero order fringe peak \(\hat{p}_{0}\). Note that ideally zero order fringe peak \(p_{0}\) happens at \(n\)= \(n_{d}\), first positive order fringe peak \(p_{1}\) at \(n\)= \(n_{d}+f_{S}\) and so on.
## Materials and Methods
### Data Analysis  
The constituents were identified by matching their spectra with those recorded in the NIST 14 (National Institute of Standards and Technology, Gaithersburg, MD, USA) mass spectral library and with published data (NIST, [[http://webbook.nist.gov/chemistry/](http://webbook.nist.gov/chemistry/)]([http://webbook.nist.gov/chemistry/](http://webbook.nist.gov/chemistry/)); Pubchem, [[http://pubchem.ncbi.nlm.nih.gov/](http://pubchem.ncbi.nlm.nih.gov/)]([http://pubchem.ncbi.nlm.nih.gov/](http://pubchem.ncbi.nlm.nih.gov/)); Flavornet, [[http://www.flavornet.org/](http://www.flavornet.org/)]([http://www.flavornet.org/](http://www.flavornet.org/)); Chemspider, [[http://chemspider.com/](http://chemspider.com/)]([http://chemspider.com/](http://chemspider.com/))). The major components were then identified by analysis of the fragmentation data in the MS spectra. In addition, the constituents were confirmed by comparing the retention indices (RI) or GC retention time (r.t.) data with those of authentic standards or published literature. The RI's are calculated as shown in Equation (1) [27]  
\[\mathrm{RI}=100\times\mathrm{n}+[100\times(\mathrm{tx}-\mathrm{tn})]/(\mathrm{tn }+1-\mathrm{tn}) \tag{1}\]  
where RI is the retention index of the unknown compound x, n is the number of carbon atoms of the n-alkane eluted before x, n + 1 is the number of carbon atoms of the n-alkane eluted after x, tx is the retention time of x, tn is the retention time of the n-alkane eluted before x, and, tn + 1 is the retention time of the n-alkane eluted after x. All the indices were calculated via three replicate measurements by injecting pure compounds. The compounds were measured as relative contents (%) and calculated automatically from the peak areas obtained by the total ion chromatographic (TIC) analysis, using Equation (2) [27]:  
\[\mathrm{Relative\ contents}\ (\%)=(\mathrm{area\ under\ peak/total\ peak\ area})\times 100\%. \tag{2}\]  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments and reviewed the manuscript; [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
**Funding:** This research was funded by [Cooperative Research Program for Agriculture Science & Technology Development Rural Development Administration, Republic of Korea] grant number [PJ01141501].  
**Acknowledgments:** This work was carried out with the support of the \"Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01141501)\" Rural Development Administration, Republic of Korea and the 2017 Postdoctoral Fellowship Program of the National Institute of Horticultural and Herbal Science, Rural Development Administration, Republic of Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure
## 1 Introduction
The possibility of reducing the costs of the survey with low-cost equipment, such as digital and amateur cameras, smartphones, tablets and action cams, is one of the main advantages of the photogrammetric technique, which also allows for a high repeatability of the survey. In this contribution, we propose a standardized procedure of 3D reconstruction of underground caves, through a photogrammetric survey with a smartphone, which allows us to obtain a high-quality result in terms of geometric accuracy. To define the methodology, we have analyzed the limits of this approach as the distance between the objects and as how image overlapping can influence the processing and the products. To this purpose, preliminary tests have been conducted in a controlled environment, using a rock sample as the object of study. Finally, the proposed approach was tested in a real underground cave.
## 1 Introduction
This paper presents the integration of fabricated vertically stacked SiNW photodetectors with a switched capacitor stimulator circuit to demonstrate their feasibility for retinal prosthesis applications. The SiNW photodetector demonstrated excellent electrical and optical characteristics, including linear I-V characteristics that confirm ohmic contact formation and high photoresponsivity exceeding \(10^{5}\) A/W across the 400-800 nm wavelength range. The device performed effectively under various lighting conditions, especially at low light intensities. Figure 1 illustrates an overview of the proposed top-level system design of the retinal stimulator incorporating SiNWs. The system stores electrical charge proportional to the light intensity and the digital signal. The voltage-controlled current source generates the stimulation current, while the biphasic current generator facilitates biphasic stimulation. Upon integration with the switched capacitor stimulator circuit, the SiNW photodetector showed a proportional increase in stimulation current with increasing light intensity and SiNW density. In vitro experiments revealed that the device successfully evoked neural responses from retinal cells, with more neural spikes generated at higher light intensities and SiNW densities. These results indicate the potential of high-density vertically stacked SiNW photodetectors in high-resolution retinal prostheses, highlighting their high photosensitivity in low-light conditions. The remainder of this paper is organized as follows: Section 2 presents the fabrication method and working principle of the SiNW photodetector. It also explains the schematic design of the switched-capacitor biphasic current stimulator and the fabrication method of the nanostructured MEA using Pt-black. Section 3 presents the fabrication results and provides an analysis of the electrical and optical characteristics of the vertically stacked SiNW photodetector. Additionally, the SiNW photodetector is integrated with a stimulator circuit, and the resulting stimulus current is measured. In vitro experiments are conducted to analyze the effect of the stimulator-integrated circuit combined with the vertically stackedSiNW photodetector on retinal responses. Finally, Section 4 presents the discussion and conclusions regarding the proposed retinal prosthesis.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
###### Abstract  
In this study, the satellite data of ASTER and Landsat 8 OLI were used for the discrimination of lithological units covering the Khyber range. Of the 24 tested band combinations, the most suitable include 632 and 468 of ASTER and 754 and 147 of OLI in the RGB sequence. The data were also tested with two conventional machine learning algorithms (MLAs), namely maximum likelihood classification (MLC) and support vector machine (SVM), for lithological mapping. Principal component analysis (PCA), minimum noise fraction (MNF), band ratios, and color composites in combination with available lithological maps and field data were utilized for training sample collection for the MLC and SVM models to classify the lithological units. The accuracy assessment of SVM and MLC was performed using a confusion matrix, which revealed a higher accuracy of 74.8419% and 72.1217% for ASTER and an accuracy of 58.4833% and 60.0257% for OLI, respectively. The results indicate that ASTER imagery is more suitable for lithological discrimination in the study area due to its high spectral resolution in the VNIR to SWIR range. The experiment revealed that the SVM classification offered the highest overall accuracy of nearly 75% and the kappa coefficient value of 0.7 on ASTER data. This demonstrates the effectiveness of SVM classification in exploring lithological mapping in dry to semi-arid regions.  
remote sensing; machine learning; SVM; MLC; lithology; ASTER; OLI; Khyber
## 4 Methodology and Methods  
To address the problem described in Section 2, we propose a methodology that (i) identifies frequent unbalanced sets of nearby stations in critical situations, representing recurrent critical patterns, analysing past data \(\mathcal{D}\) offline and then (ii) plans rebalancing actions to apply online (in at most \(t_{reb}\) minutes) leveraging on the mined historical patterns.  
The general methodology proposed in this work is fully configurable according to system administrators' needs and can be adapted through time by performing offline periodic updates to find potential new patterns that could emerge due to changes in the city assets or people's habits.  
The proposed methodology is composed of two main phases: (i) pattern-based model training on historical data and (ii) online planning of rebalancing operations and their application. The first phase extracts frequent patterns (association rules) that represent sets of recurrent critical situations among nearby stations. Each of the considered pattern contains both positively and negatively critical stations and can be used to plan a rebalancing operation. Those patterns model recurrent critical situations in the analysed data. Based on the patterns extracted from historical data, rebalancing actions can be planned and applied to dynamically manage critical stations status.  
For instance, suppose that during the fist phase the frequent pattern \(+s_{4},+s_{7}\to-s_{11},sup=60\%,conf=80\%\) is mined. This rule highlights a frequent critical unbalanced behaviour and can be used to plan at time \(t_{new}\) a rebalancing operation among the nearby stations \(s_{4}\), \(s_{7}\), and \(s_{11}\) moving bicycles from stations \(s_{4}\) and \(s_{7}\) to \(s_{11}\), supposing \(s_{11}\) is negatively critical while \(s_{4}\) and \(s_{7}\) are positively critical at time \(t_{new}\).  
Since different time periods are characterized by different behaviours, the proposed methodology trains and applies a set of contextualised models, each one tailored for a specific time period.
## 2 Materials and Methods  
### Research Object  
In this work, wood pellets produced from waste generated during the cultivation of plants used for industrial purposes were used for the research. In the case of plant biomass, waste from the cultivation of sunflower (_Helianthus_ L.), Jerusalem artichoke (_Helianthus tuberosus_ L.), and tobacco (_Nicotiana_ L.) were used. The pellets were supplemented with coniferous sawdust obtained from a local sawmill. The material for the analysis was various types of pellets (diameter 6 mm) produced in the laboratory of the Department of Bioenergetics, Food Analysis and Microbiology.  
After the tobacco, Jerusalem artichoke, and sunflower plants are harvested, waste (i.e., stems) is generated, which is difficult to manage because it is very slowly mineralized in the soil, due to the problem of using the residues from the cultivation of these plants grown for industrial purposes in order to be used to produce pellets. Then, the collected material was dried in the open air and ground in an Essa-CM 1000 grinder (Atest Sp. z o. o., Kielce, Poland). In addition, coniferous sawdust was used for the production of pellets. After preparing the raw materials in the Prime-200 pelletizing machine (TechnoMaszBud, Darke, Poland), six types of pellets with different composition were made:  
-P1-pellet made of 100% sunflower stalks,  
-P2-pellet made of 50% coniferous sawdust and 50% sunflower stalks,  
-P3-pellet made of 100% tobacco stalks,  
-P4-pellet made of 50% coniferous sawdust and 50% tobacco stalks,  
-P5-pellet made of 100% Jerusalem artichoke stalks,  
-P6-pellet made of 50% coniferous sawdust and 50% Jerusalem artichoke stalks.  
The prepared pellets were stored in a closed room free from excessive moisture in perforated foil bags weighing 5 kg. Bags with pellets were placed on a pallet, to keep distance from the ground. Mechanical tests were performed on the 1 st, 30 th, 60 th, and 90 th day of storage.
## 2 Related Works
### Video Control Stage  
This stage involves the process of providing an NFoV that shows the user the main objects in the 360-degree video, as shown in Figure 6. In the 'video reception' process, a 360-degree video shot at the micro UAS is input. The 360-degree image input at time \(t\) is Image \(v_{i,t}\). Algorithm 3 is a pseudocode that generates a video control signal to provide NFoVs by using the received 360-degree video.  
The deep learning model-based video control inference process calculates video control signals based on the 360-degree video; 360-degree images are input to the deep learning model to infer control labels, which are in turn used to generate the video control signals in the video control signal postprocessing process. The Control Label \(l_{i,t}\) is converted to the Video Control Signal \(s_{i,t}\) as shown in Equation (5). The Video Control Signal \(s_{i,t}\) is set to the defined video control signals of the index with the highest value of the Control Label \(l_{i,t}\).  
\[s_{i,t}=s^{\prime}\underset{k}{\text{arg max}}l_{i,t} \tag{5}\]  
Figure 6: Process of generating NFoV based on a deep learning model that is based on end-to-end control of 360-degree video.  
During video control signal transmission, the calculated video control signal is transmitted to the user; the video is controlled based on the video control signal to output the NFoV containing the objects. An NFoV \(n_{i,t}\) is calculated as shown in Equation (6) using an Image \(v_{i,t}\) and a Video Control Signal \(s_{i,t}\).  
\[n_{i,t}=SetNFoV\left(v_{i,t},s_{i,t}\right) \tag{6}\]  
where \(SetNFoV\) denotes a function accepting Image \(v_{i,t}\) and Video Control Signal \(s_{i,t}\) and predicting the corresponding NFoV \(n_{i,t}\).  
```
FUNCTION VideoControlSignalGenerationUsingDeepLearningModel WITH \(V_{i,t}\) SET\(f_{i,t}\)\(\leftarrow\)CALL Inference Deep Learning Model-based Feature (\(v_{i,t}\)) SET \(l_{i,t}\)\(\leftarrow\)CALL Softmax(\(f_{i,t}\)) SET \(s_{i,t}\)\(\leftarrow\)CALL Generate video control signal (\(l_{i,t}\), \(S^{\prime}\)) RETURN \(s_{i,t}\) END
```  
**Algorithm 3** Pseudocode for generating 360-degree video control using trained deep learning model
# Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
## 5 Simulations
### Case II: The Enhanced Integration Control  
We evaluate the effectiveness of the enhanced integration control (25), (27), and (56) with simulation. The results are presented in Figure 8a-1. These figures are arranged in the same order as Case I to facilitate a direct comparison with the results of Case I. We first observe that the vehicle speed \(V_{0}\) in Figure 8a becomes lower after the control process, and yaw moment \(M_{z}\) and wheel torques \(T_{j}\) appear larger during the control process in comparison to those of Case I. In Figure 8e,f, the performance of the side slip angle \(\beta\) appears similar to that of Case I, but the yaw rate \(\gamma\) shows an improved response to the reference state \(\gamma_{ref}\), meaning the enhanced integration control meets the urgent lane change demand with a faster yaw rate response. We see in Figure 8g the corresponding lateral acceleration \(a_{y}\) still operates within the safety range \(\left|a_{y}\right|\leq\widetilde{a}_{y,\text{lim}}=0.68\text{ g}\). Figure 8h-1 depicts the history of the combined wheel slip magnitudes \(\left\|\mathbf{S}_{j}\right\|\) and the combined wheel slips \(\mathbf{S}_{j}=\left[s_{Lj},s_{Sj}\right]^{T}\) of the four wheels. We observe that all the combined wheel slips still fall within the linear region, i.e., \(\left\|\mathbf{S}_{j}\right\|\leq\widetilde{s}_{Res}\approx 0.08\). When compared with the longitudinal slips \(s_{Lj}\) of Case I, the enhanced control performs with larger longitudinal slips \(s_{Lj}\) in three intervals around 0.5, 2, and 3.5 s, and therefore gives rise to a noticeable variation in longitudinal acceleration \(a_{x}\) as shown in Figure 8g. However, the enhanced control still exhibits levels of lateral acceleration \(a_{y}\) similar to that of Case I. As demonstrated in the simulation, the enhanced integration control (56) prioritizes safety while driving by improving the agility of yaw rate response to its reference. Noticeably, it still operates within the linear region of tire/road friction and allows the DYM to effectively handle other disturbances like \(\left(\mathbf{d}_{v},\dot{\mathbf{x}}_{ref}\right)\) described in (35).
# Effect of a Hypoxia-Controlled Atmosphere Box on Egg Respiration Intensity and Quality
## 3 Results  
### Effect of Different O\({}_{2}\) Concentrations on the Respiration Intensity of Eggs  
The respiratory intensity of eggs is closely related to the changes in quality and can be divided into three levels, namely, weak (0-1 mg/(kg x h)), moderate (1-3 mg/(kg x h)), and strong respiratory intensity (>3 mg/(kg x h)). Fresh eggs have a strong respiration effect. As the quality decreases, the respiration intensity decreases gradually. However, when the quality of the eggs deteriorates, the respiratory intensity will increase and peak [20].  
There were distinct differences in the respiration intensity of eggs in environments with different O\({}_{2}\) concentrations (Figure 2). The breathing intensity of Group A, Group B and Group C can be regarded as constant: Q(A) = 0.10 mg/(kg x h); Q(B) = 0.18 mg/(kg x h); Q(C) = 0.25 mg/(kg x h).The fitted regression line of Group D was described by the Equation (11) and \(r\) (\(D\)) = 0.9563, \(RMSE\) (D) = 0.1728. The fitted regression line of Group E was described by the Equation (12) and \(r\) (\(E\)) = 0.8451, \(RMSE\) (\(E\)) = 0.4582.  
\[Q(D)=1.561+1.055\times cos(0.09748x)-0.3874\times sin(0.09748x) \tag{11}\]  
\[Q(E)=26.08\times sin(0.002732x+2.983)+0.4007\times sin(0.2182x+1.817) \tag{12}\]  
\[+0.505\times sin(0.5418x+1.319)+0.4196\times sin(0.8301x+1.957)\]  
The respiration intensity of eggs stored in 20% O\({}_{2}\) was higher than that of eggs in a reduced O\({}_{2}\) environment. Strong breathing intensity was recorded on days 0-7. With the passage of storage time, the freshness of the eggs gradually decreased. Respiration intensity showed a downward trend as a whole, but an upward trend was observed during the process. Respiratory peaks appeared on the 8 th, 10 th, 14 th, 21 st, and 30 th days of storage. The eggs stored at 15% O\({}_{2}\) concentration exhibited a gradual decrease in respiratory intensity from moderate to weak and the amplitude of respiratory intensity was small. When the O\({}_{2}\) concentration was \(\leq\)10%, the respiration intensity of eggs was \(<\)1 mg/(kg x h), indicating weak respiration. These results showed that O\({}_{2}\) concentration in the storage environment of \(<\)10% led to a better inhibitory effect on the respiration of eggs.  
Figure 2: Trend of egg respiration intensity during storage.