esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-Wideband Analog Radio-over-Fiber Communication System Employing Pulse-Position Modulation
## 5 Conclusions  
This research demonstrates the use of TR-PPM for data transmission in an ARoF system, with signal detection carried out by a commercial time tagger. The experimental results confirm the feasibility of this approach and highlight the potential of PPM over RoF for wireless communication applications, as well as the possibility of extending this concept to PPM-based ISAC implementations.  
The primary factor influencing the performance of the ARoF TR-PPM communication system is the time bin duration, which directly determines the system's tolerance to jitter. The results validate the effectiveness of the implemented encoding algorithms, as enabling ECC reduces the BER in both the 20 km SMF and back-to-back setups. Tests with a 20 km SMF show only a slight BER increase over the back-to-back setup, which remains negligible for data transmission. In both configurations, multiple combinations of pulse widths and time bin durations resulted in a BER of \(<\)8.17 \(\times\) 10\({}^{-7}\), which is below the threshold corresponding to a single bit error after PPM signal demodulation. Within the context of this research, all BER values below this limit can be interpreted as error-free data transmission.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15084222/s1](https://www.mdpi.com/article/10.3390/app15084222/s1)]([https://www.mdpi.com/article/10.3390/app15084222/s1](https://www.mdpi.com/article/10.3390/app15084222/s1)).  
Conceptualization, S.M., S.S. and T.S.; methodology, T.S. and O.O.; software, S.M. and N.T.; validation, S.M., N.T., S.S. and K.R.; formal analysis, S.M. and K.R.; investigation, K.R., S.M. and N.T.; resources, A.A., S.S., V.B. and O.O.; data curation, S.M., N.T. and K.R.; writing--original draft preparation, S.M., K.R. and N.T.; writing--review and editing, A.A., S.M., N.T. and S.S.; visualization, N.T. and S.M.; supervision, A.A. and V.B.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Latvian Council of Science grant No. Izp-2021/1-0475, \"Picosecond-resolution pulse position modulation for beyond-the-limits energy efficiency of communications\".  
Not applicable.  
The original contributions presented in this study are included in the article and Supplementary Materials. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & analog-to-digital converter \\ ARoF & analog radio-over-fiber \\ AU & antenna unit \\ AWG & arbitrary waveform generator \\ BER & bit error ratio \\ BPF & bandpass filter \\ CCSDS & Consultative Committee for Space Data Systems \\ CW & continuous wave \\ DAC & digital-to-analog converter \\ D-MIMO & distributed multiple-input multiple-output \\ \end{tabular}
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 2 Background and Preliminaries
### Significance of Prediction Metrics in Energy Efficiency  
The accurate prediction of energy demand and consumption is paramount for optimizing energy use and minimizing waste. This accuracy is largely enabled by various prediction metrics, which contribute significantly to the following key aspects of energy efficiency:  
* Optimization of energy use: Prediction metrics facilitate the development of methods to maximize energy use, ensuring that it is utilized effectively without compromising occupant needs. Buildings can operate more economically and efficiently by anticipating peak demand periods and adjusting energy consumption accordingly.
* Cost reduction: Precise energy forecasting enables improved financial planning and budgeting, resulting in lower operating expenses. Organizations can use these measurements to negotiate lower energy prices, participate in demand response initiatives, and make investments in energy-saving technologies that, over time, result in significant cost savings.
* Enhanced energy planning: In markets where energy prices fluctuate, energy managers can make well-informed decisions about whether to purchase, sell, or store energy by using dependable forecast metrics. This enhances the ability to respond to market fluctuations and to capitalize on reduced energy expenses during off-peak periods.
* Improved grid reliability: Prediction metrics facilitate the equilibrium of supply and demand within the broader energy system, mitigating overloads and reducing the likelihood of outages. They are crucial in regulating the variability and inconsistency associated with renewable energy sources, making them essential for integration.
* Sustainability and environmental impact: Prediction metrics can help reduce carbon emissions and achieve sustainability goals by facilitating more efficient energy  
Figure 3: The stages of ML implementation in energy management systems.  
use. They provide the information necessary to assess the success of energy-saving programs and the progress toward environmental goals.  
Table 1 describes some of the commonly used prediction metrics and explains their importance.
## 3 Results and Discussion
### Effect of Inner Diameter of Heating Tube  
By changing the inlet flow rates and the inner diameters of the heating tube, the performance of the electric heater under different conditions is studied. The range of the inner diameter of the tube is 5-8 mm, and the range of the inlet mass flow rate is 7.5-10 Nm\({}^{3}\)/min. Figure 4a-c show the curves of the heat-transfer coefficient, pressure drop, and comprehensive index, \(h/\Delta p^{1/3}\), versus the inner diameter of the heating tube, respectively. It is found that a large inner diameter of the heating tube leads to the reduction of the heat-transfer coefficient and the pressure drop. As shown in Figure 4c, the comprehensive index, \(h/\Delta p^{1/3}\), changes non-monotonically within the range of 5-8 mm, showing a uniformly decline trend.  
Figure 4: \(Cont.\)  
Figure 3: Grids of electric heater: (**a**) grid independence validation and (**b**) meridian-section views of the inlet region.  
Figure 5 compares the temperature nephogram and pressure nephogram of the electric heater. From Equations (8) and (9), when the heating current is maintained the same, a large inner diameter gives rise to a small electrical resistance, resulting in a small heating power of a single tube. Therefore, the maximum temperature of the heating pipe with a smaller tube diameter is expected to be larger. Meanwhile, the small inner diameter of the heating-tube results in a large pressure loss along the tube, as shown in Figure 5. As a consequence, the relatively small tube is beneficial to the operations of the downhole electrical heater, whose convective heat transfer coefficient and comprehensive index are larger; meanwhile, the pressure loss (~18,500 Pa) and surface temperature of the heater (~850 \({}^{\circ}\)C) are still affordable.  
Figure 6 shows the typical velocity vectors in meridian slices. In the inlet chamber, the secondary vortex flows are formed, which ensures that the flow rates within the heating pipes are uniformly distributed. When nitrogen gas flows through a series of heating pipes, the velocity increases from about 2.8 m/s to about 6 m/s, due to the dramatic decrement in density as the temperature increases. When the high-speed nitrogen gas is injected into the outlet chamber, several small secondary vortex flows are formed, and the outlet velocity of the electric heater is estimated to be less than 1 m/s.  
Figure 4: Heat transfer and flow performance versus inner diameter of heating tube at different flow rates: (**a**) \(h\), (**b**) \(\Delta p\), and (**c**) \(h/\Delta p^{1/3}\).  
Figure 5: Temperature nephogram and pressure nephogram of electric heater at different inner diameters of heating pipes with flow rate \(G\) =10 Nm\({}^{3}\)/min, tube length \(L\) = 5 m, and tube thickness \(\delta\) = 1 mm.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
###### Abstract  
A major concern when dealing with financial time series involving a wide variety of market risk factors is the presence of anomalies. These induce a miscalibration of the models used to quantify and manage risk, resulting in potential erroneous risk measures. We propose an approach that aims to improve anomaly detection in financial time series, overcoming most of the inherent difficulties. Valuable features are extracted from the time series by compressing and reconstructing the data through principal component analysis. We then define an anomaly score using a feedforward neural network. A time series is considered to be contaminated when its anomaly score exceeds a given cutoff value. This cutoff value is not a hand-set parameter but rather is calibrated as a neural network parameter throughout the minimization of a customized loss function. The efficiency of the proposed approach compared to several well-known anomaly detection algorithms is numerically demonstrated on both synthetic and real data sets, with high and stable performance being achieved with the PCA NN approach. We show that value-at-risk estimation errors are reduced when  
methods less sensitive to anomalies can be used. Yet, existing robust estimators are computationally expensive, with a polynomial or even exponential time complexity in terms of the number of market risk factors. A faster approach was suggested by [PERSON] et al. [3], but this algorithm only applies to the estimation of the covariance matrix in the case of a Gaussian distribution. Financial risk models used by banks are widespread and various. Therefore, instead of searching for a robust version for each of them, we propose to detect anomalies directly in the time series.
# Investigation of Guidewire Deformation in Blood Vessels Based on an SQP Algorithm
## Materials and Methods
### Solving of Deformation of the Guidewire
#### 2.3.2 Constraints  
During the advancement of the guidewire in the blood vessel, there are two constraints at the contact points, namely the positional constraint and deflection angle constraint. Assuming that the H-th light rod is in contact with the vessel wall, the coordinate of the contact point is (a,b), and the deflection angle of the light rod at the contact point is \(\theta_{H}\).  
\(\theta_{H}\) can be Hexpressed by Equation (16), which is the sum of the relative deflection angles of all the light rods between A and H.  
\[\theta_{H}=\sum_{\text{i=1}}^{\text{H}}\Delta\theta_{i},\ -\pi\ \leq\ \theta_{H}\ \leq\ \pi,\ 1\ \leq\ \text{H}\ \leq\ \text{n}. \tag{16}\]  
The linear equality constraint function constructed from the deflection angle of the contact point is:  
\[\mathbf{w}_{1}\mathbf{x}^{\text{T}}=\mathbf{b}_{1}, \tag{17}\]  
where \(\mathbf{w}_{1}\) is a strictly n-order lower triangular matrix, and \(\mathbf{x}\) is the design vector of the algorithm. They can be expressed as:  
\[\mathbf{w}_{1}=\left[\begin{array}{cccc}1&0&\cdots&0\\ 1&1&\cdots&0\\ \vdots&\vdots&\ddots&0\\ 1&1&\cdots&1\end{array}\right],\ \mathbf{x}^{\text{T}}=\mathbf{\Delta}\theta^{ \text{T}}=\left[\begin{array}{c}\Delta\theta_{1}\\ \Delta\theta_{2}\\ \vdots\\ \theta_{n}\end{array}\right],\ \mathbf{b}_{1}=\left[\begin{array}{c}\theta_{1}\\ \theta_{2}\\ \vdots\\ \theta_{n}\end{array}\right]. \tag{18}\]  
When the guidewire advances in the blood vessel, if the H-th light rod is in contact with the blood vessel wall, the H-th row of \(\mathbf{w}_{1}\) is retained, other elements are discarded, and \(\theta_{H}\) of \(\mathbf{b}_{1}\) is retained accordingly.  
\(\bullet\) Relative deflection angle constraints  
In order to ensure that the curve of the guidewire deformation simulation is smooth and continuous, the magnitude of relative deflection angle is constrained by the linear inequality constraint function (19).  
\[\pm\mathbf{w}_{2}\mathbf{x}^{\text{T}}\leq\mathbf{b}_{2}, \tag{19}\]  
where \(\mathbf{w}_{2}\) is an n-order unit diagonal matrix. The value of \(\mathbf{b}_{2}\) is related to n. The larger n is, the smaller \(\mathbf{b}_{2}\) is.  
\(\bullet\) Position constraints  
Assuming that the horizontal displacement of the H-th light rod is \(\mathbf{x}_{i}\), and the vertical displacement is \(\mathbf{y}_{i}\), we can obtain:  
\[\begin{array}{c}\mathbf{x}_{i}=\frac{\text{L}}{\text{n}}\cos\theta_{i},\\ \mathbf{y}_{i}=\frac{\text{L}}{\text{n}}\text{sin}\theta_{i},\end{array} \tag{20}\]  
So the position constraint function of contact point is:  
\[\begin{array}{c}\mathbf{a}=\frac{\text{L}}{\text{n}}\sum_{\text{i=1}}^{ \text{h}}\cos(\sum_{\text{j=1}}^{\text{i}}\Delta\theta_{\text{j}}),\\ \mathbf{b}=\frac{\text{L}}{\text{n}}\sum_{\text{i=1}}^{\text{h}}\text{sin}( \sum_{\text{j=1}}^{\text{i}}\Delta\theta_{\text{j}}),\end{array} \tag{21}\]  
where L indicates the length of the guidewire.
## 3 Results and Discussion
### Sampling Protocol  
The taxa accumulation curves are shown in Figure 5 for the studied months. The percentage of recorded richness varied from 67.25 to 75.51% in the case of the arthropods and from 76.29 to 90.17% in the case of the weeds. The quality of the inventories was always higher than 0.1 for arthropods, indicating that a more intensive sampling would be required for a representative inventory. The number of samples for \(C_{i}=0.1\) varied from 54 to 80. In the case of the weeds, the \(C_{i}\) was in all months lower than 0.1, indicating a good quality of the inventory, and the number of samples for \(Ci=0.1\) varied from 22 to 24. These parameters were not calculated for weeds in June, when only one species was found.  
Thus, in this study, the minimum number of samples needed to elaborate a comprehensive inventory of arthropods and plants in a sisal crop was determined. However, the biology and distribution of the species in the field can bring several constraints to optimizing a sampling protocol [16], and different sampling techniques, such as entomological net or chromotropic traps, could improve the quality of the inventory. For example, [PERSON] et al. [43] (2007) compared pit-fall traps and litter bags and showed that litter bags most frequently succeeded in collecting certain groups of arthropods associated with moisture and sheltered areas and pit-fall traps most often captured taxa considered active at the ground level. Additionally, the minimum number of samples may be sufficiently satisfactory depending on the study's goals [16].
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 3 Experimental Results
### Matching Performance Analysis
#### 3.3.3 RANSAC Estimation Performance Analysis  
RANSAC estimation is used to evaluate the matching performance of LGHD and DDCE. The performance of RANSAC estimation depends on both the percentage and the number of correct matches [44]. A group of experimental results of LGHD and DDCE is illustrated in Figure 6. It can be found that LGHD achieves a small number of correct matches, as presented in Table 5. As shown in Figure 6a, the experimental result of LGHD contains wrong matches indicated by non-horizontal lines. In addition, the building that is the main scene of infrared and visible images is missing. As shown in Figure 6b, however, DDCE successfully identifies the building as indicated by horizontal lines. Although LGHD achieves a slightly higher percentage of correct matches than DDCE, DDCE obtains a significantly larger number of correct matches than LGHD, which leads to a better RANSAC estimation performance by DDCE.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Method** & **Precision** & **Recall** & **F1** \\ \hline single-modality & SIFT & 0.028 & 0.024 & 0.026 \\ image & ORB & 0.001 & 0.003 & 0.002 \\ matching & LIFT & 0.002 & 0.019 & 0.004 \\ methods & SuperPoint & 0.022 & 0.031 & 0.027 \\ \hline  & PIIFD & 0.054 & 0.024 & 0.033 \\  & MMSURF & 0.082 & 0.205 & 0.117 \\ multi-modality & PCEHD & 0.143 & 0.333 & 0.200 \\ image & LGHD & 0.164 & 0.327 & 0.218 \\ matching & RIFT & 0.107 & 0.192 & 0.137 \\ methods & MSCB & 0.142 & 0.217 & 0.172 \\  & DDCE & 0.156 & 0.301 & 0.205 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of feature match.
## 5 Conclusions  
This paper developed a quick and applicable approach for monitoring regional forest degradation in the Three-North Forest Shelterbelt Program area in China from multi-scale remote sensing data. Firstly, six indicators (NDVI, EVI, RVI, LAI, FPAR, NPP) from satellite observation data were selected to describe forest degradation. Then an approach to detect forest degradation using time series MODIS and Landsat ETM+ images was developed. At last, the degradation results were validated by ground survey data. Results of this paper demonstrated that multi-scale remote sensing data have great potential in monitoring regional forest degradation.  
Generally, indicators used in this paper could detect a long term forest degradation effectively. And accuracy of forest degradation from MODIS-Landsat data was higher than that from MODIS data. Results of this paper also indicated that great achievements had been made since the implication of the Three-North Forest Shelterbelt Program in China. Most forest in the Three-North Forest Shelterbelt area was in good condition. The forest degradation was mainly located in the middle areas in Inner Mongolia, the northwestern areas in Xinjiang, the middle areas in Inner Mongolia, northern areas in Shannxi and Ningxia. In Liaoning, Jilin and Heilongjiang, some slight and moderate degradation also occurred in the forest.  
The following are available online at [[https://www.mdpi.com/2072-429](https://www.mdpi.com/2072-429)]([https://www.mdpi.com/2072-429](https://www.mdpi.com/2072-429)) 2/13/6/1131/s1, Figure S1: Histograms of the indicators in a sliding window in the Koppen-Geiger BWL climate class, Figure S2: Histograms of the indicators in a sliding window in the Koppen-Geiger BSk climate class.  
Data curation, T.Y., P.L. and Y.R.; writing--original draft preparation, T.Y. and Q.Z.; writing--review and editing, P.L.; visualization, J.Y.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"The Three North Shelterbelt System Engineering Management and Service Platform\" from the Three North Shelterbelt Program Construction Bureau of National Forestry and Grassland Administration of China.  
All data used during the study are available at [[https://modis.gsfc.nasa.gov/data/,https://earthexplorer.usgs.gov/](https://modis.gsfc.nasa.gov/data/,https://earthexplorer.usgs.gov/)]([https://modis.gsfc.nasa.gov/data/,https://earthexplorer.usgs.gov/](https://modis.gsfc.nasa.gov/data/,https://earthexplorer.usgs.gov/)) (accessed on 8 February 2021).  
This research was funded by \"The Three North Shelterbelt System Engineering Management and Service Platform\" from the Three North Shelterbelt Program Construction Bureau of National Forestry and Grassland Administration of China. We also thank for the reviewer's insightful suggestions.  
The authors declare no conflict of interest.
## 3 Synthesis of Crown Ethers and Their Derivatives
### Electrochemical Sensor  
Crown ether-based sensors were constructed to determine serotonin in human serum. Crown ether was mixed with carbon nanotubes-ionic liquid crystal and at glassy carbon electrode surface (GC/(CNTs-ILC) allowed a stable host-guest inclusion complex between crown ethers and neurotransmitters. The crown cavity of 18-Crown-6 allows H-bond formation with the indole ring of serotonin. The sensor enables to examine drugs at a low cost [67]. Furthermore, potassium ion (K\({}^{+}\)) microsensors were made using electrochemical impedance spectroscopy (EIS) to understand potassium ions transport across the cellular membrane. The building block of sensors was 1-aza-18-crown-6 functionalized graphene oxide (Crown-GO). The crown ether part captures potassium ions, whereas graphene oxide provides the binding ability [68]. A study reports K\({}^{+}\) chemical sensor development based on a self-assembled monolayer of 4-aminobenzzo-18-crown-6 ether as selective ionophore that shows reproducible activity in disease diagnosis [69]. The 4-aminobenzzo-18-crown-6 modified gold nanoparticles have been used by researchers to make the sensor to detect K\({}^{+}\) in human urine samples through colorimetric assay. The K\({}^{+}\) detection was compared with few other ions through UV-Vis and showed excellent anti-interference activity [70].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Studied Compounds** & **Concentrations** & **Anti-Amyloid Genesis Activity (\%)** \\ \hline
**Difunisal** & 0.5 \(\upmu\)M & 80 \(\pm\) 9.0 \\  & 1.5 \(\upmu\)M & 35 \(\pm\) 1.0 \\ \hline
**18** & 0.2 mM & 99 \(\pm\) 0.25 \\  & 0.6 mM & 92 \(\pm\) 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Amyloid genesis inhibitory potential of **18**.  
Figure 8: Structure of 4′-carboxybenzo-18-crown-6 ether **18**.
## 3 A Simple Self-Indexing Method
### Using the LZ-index  
The LZ-index of a \(\$\)-terminated text \(T[1,u]\) is based on the LZ78 parsing, which divides \(T=Z_{0},\ldots,Z_{n}\) into \(n+1\) phrases \(Z_{i}\) so that \(Z_{0}=\varepsilon\) and each other \(Z_{i}\) is is equal to the longest possible phrase \(Z_{j}\), \(j<i\), plus a single character in \(\Sigma\). Because the set of phrases is prefix-closed (_i.e._ every prefix of a phrase is also a phrase), the set of phrases \(\{Z_{i}\}\) can be conveniently organized into a trie, so that each trie node is the path-label of some \(Z_{i}\). This trie, called the _LZ-trie_, is one of the structures of the LZ-index, where it is represented in compressed form. Any phrase can be extracted in reverse order from the LZ-trie, by starting from its corresponding node and moving to the parent in the trie. This requires constant time per character and is efficient in practice [50]. The LZ-index requires \(O(uH_{k})\) bits. For example the LZ78 parsing of the string \(T=abababcdddbdbc\) is \(a,b,ab,abc,d,db,dbc\). We show the corresponding LZ-trie in Figure 3, moreover we also show a tree composed of the reverse phrases which is also an important component of LZ-indexes.  
The occurrences of a pattern are classified into three categories in the LZ-index. Those of type 1 fall within a single phrase \(Z_{i}\). Those of type 2 span two consecutive phrases, that is, fall within some \(Z_{i}.Z_{i+1}\). Those of type 3 span more than two phrases. Because in a LZ78 parsing all the phrases are different, there are at most \(O(m^{2})\) occurrences of type 3 overall. Occurrences of type 1 are found by first  
Figure 3: (left) LZ-trie for strings \(\{a,b,ab,abc,d,db,dbc\}\). (right) Reverse tree of the LZ78 trie.  
spotting the type-1 occurrences that lie at the end of their phrase, that is, where \(P\) is a suffix of \(Z_{i}\). Let us call those occurrences _primary_. Primary occurrences are found using other LZ-index structures (in particular, the trie of reverse phrases) in time \(O(m)\), plus \(O(1)\) per primary occurrence found. Now, it turns out that every type-1 occurrence descends from a primary occurrence in the LZ-trie (alternatively, each occurrence where \(P\) is inside \(Z_{i}\) but not a suffix of it, is inherited from an occurrence of \(P\) within the phrase copied by \(Z_{i}\), from which \(Z_{i}\) descends in the LZ-trie). Thus all occurrences of type 1 are obtained by simply traversing all LZ-trie subtrees of the primary occurrences. Occurrences of type 2 and 3 are costlier to obtain.  
A simple method for ASM is thus to split \(P\) into \(k+1\) pieces, as evenly as possible, search for those pieces in the LZ-index, extract a context of \(m+2k\) characters around the occurrence, and run DP over it. This approach will be called _LZI_ in the experiments.