esa-sceva/satcom-chunk-collection huggingface dataset:
# An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction
###### Abstract  
With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.  
Keywords:passenger flow prediction; real-time data; LSTM +
Footnote †: journal: Applied Sciences
# A Review of Machine Learning and IoT in Smart Transportation
## 1 Introduction
### Smart Transportation  
As the IoT technology expands, new applications are created in order to make people's lives better. Cities are getting \"smarter\" and smart city applications are developed to take advance of the latest technological improvements. With the introduction of IoT in the field of transportation, transportation systems begin to \"feel\" and \"think\", leading to the development of Intelligent Transportation Systems (ITS). Smart transportation has attracted the attention of many researchers since there are plenty of opportunities for further enhancements. One of the most significant areas of interest in smart transportation is navigation or route optimization. Using data from the users' mobile devices [12], or with side units placed in specified locations on the road [13], applications try to estimate traffic congestion and propose optimal route options to minimize traveling times, and therefore reduce car emissions and energy consumption. Furthermore, to support the energy consumption reduction, street lights are proposed that can detect traffic conditions and operate accordingly, instead of being constantly on with a time schedule. IoT devices have been widely used to create smart parking systems, too. Using cameras [14], or other wireless sensors like magnetic field or IR sensors [15], researchers have proposed new parking reservation systems that allow to maximize a parking lot's availability and capacity and minimize the searching time. Moreover, systems that help detect road surface anomalies based on input data from sensors attached to cars or the driver's phone have been proposed. By detecting bad road conditions, accidents could possibly be avoided. There have also been efforts to detect or prevent road accidents using IoT devices. Finally, the IoT M2M communication option has given the opportunity to develop vehicle to vehicle communication and vehicle social networks, where vehicles can exchange useful information with each other and give many more possibilities for new applications [16].  
The rest of the article is organized as follows. In Section 2.1, there is a presentation and a detailed analysis of the ML algorithms used in the reviewed researches to support smart transportation applications. Additionally, in Section 2.2, there is a review of the IoT and ML applications about smart transportation, organized in categories, based on the application type. In Section 3, a discussion on the schemes presented is performed, and concluding remarks are drawn.
# A Dual-Gas Sensor Using Photoacoustic Spectroscopy Based on a Single Acoustic Resonator
## 2 Materials and Methods
### System Setup
The PA cell was sealed by two CaF\({}_{2}\) windows with a high optical transmission of \(>\)90% from 180 nm to 8 \(\upmu\)m. Eight electret microphones (FG-2332-P07, Knowles, Itasca, IL, USA) were placed in the middle of the acoustic resonator to detect the PA signal. PA signals from the eight microphones were first summed up and then filtered by a homemade noise-reduction band-pass filter. After pre-amplification, the PA signal was connected to the lock-in amplifier and sampled with an NI data acquisition card. A laptop was used to perform the data processing and display the results via LabVIEW software. The properties of the PA cell were reflected by measuring its first longitudinal resonance. Before the acquisition of PAS, it was imperative to confirm the value of the fundamental vibration frequency of the PA cell. A certified 500 ppm NH\({}_{3}\)/N\({}_{2}\) gas mixture was used to select the best frequency. Figure 3 shows the normalized frequency response curves of the PA cell, indicating the first longitudinal resonance of the PA cell at the resonance frequency of \(f_{0}=6800\) Hz. Hence, the modulation frequency of the sine signal was 6800 Hz. Figure 1: Schematic of the PA instrument. FC: fiber collimator. BS: beam splitter. CW1, CW2: CaF\({}_{2}\) window. DFB: distributed feedback laser diode. Mic: electret microphone. DAQ card: data acquisition card.
# Low Temperature Plasma Strategies for
###### Abstract  
The quarantine bacterium _Xylella fastidiosa_ was first detected in Salento (Aptula, Italy) in 2013 and caused severe symptoms in olives, leading to plant death. The disease, named Olive Quick Decline Syndrome (QQDS), is caused by the strain \"De Donno\" ST53 of the subspecies _pauca_ of this bacterium (XfDD), which is spread by the insect _Philaneus spumarius_. The epidemic poses a serious threat to the agricultural economy and the landscape, as _X. fastidiosa_ infects several plant species and there is yet no recognized solution. Research on QQDS is focused on finding strategies to control its spread or mitigate its symptoms. As a perspective solution, we investigated the efficacy of the low-temperature plasma and plasma-activated water to kill bacterial cells. Experiments were conducted in vitro to test the biocidal effect of the direct application of a Surface Dielectric Barrier Discharge (SDBD) plasma on bacteria cells and Plasma Activated Water (PAW). PAW activity was tested as a possible biocidal agent that can move freely in the xylem network paving the way to test the strategy on infected plants. The results showed a high decontamination rate even for cells of XfDD embedded in biofilms grown on solid media and complete inactivation in liquid culture medium.  
plasma agriculture; low-temperature plasma; surface dielectric barrier discharges; _Xylella fastidiosa_; plasma activated water
# A LoRaWAN Network Architecture with MQTT2 MULTICAST
## 7 Results
The tree topology consists of a root node and two levels, similar to those in Figures 13 and 14. For simplicity, we assume that the fanouts of each level are equal. We also assume the worst case scenario, in which all leaf nodes subscribe to all other nodes. Multicast trees have been calculated using a virtual machine with 1 core and 8 GB of RAM in VirtualBox. Ten repetitions have been performed for each number of edge nodes in order to improve the reliability of the results. Figure 17 shows the time required to compute all the possible paths between edge nodes. Figure 16: Comparison of traffic due to multicast routing between PIM & IGMP and MQTT2 MULTICAST. As shown, the total number of possible paths increases exponentially with the number of edge nodes. The behaviour is also similar for the time to compute those paths. This time is lower than four minutes for up to 2500 nodes, being lower than 30 s for 1000 nodes. These values are tolerable for large networks, especially considering that (1) this is the worst case scenario (all possible paths are created), (2) the executing machine may have better performance than the one used in our experiments (e.g., more CPU cores, possibly using parallel execution of the shortest path algorithm), (3) the shortest path algorithm may be optimized for our tree topology and (4) in a real network, all the paths are not required to be computed simultaneously but progressively, based on publishers and subscribers' behaviour. These calculations are performed by an application at the SDN controller. Thus, the SDN controller will be able to accommodate large networks (up to a few thousands of edge nodes) without scalability problems. Finally, since each edge node can manage tens of LoRaWAN gateways, our architecture will be able to support tens of thousands of them.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 4 Theoretical Basis of Performance Metrics
### Range Estimation  
The basic working principle of an FMCW radar is the transmission of a chirp in the radar field of view (FOV) which is reflected off targets in its path and received with a delay depending upon the distance of the target from the radar. This RTOF of the EM wave traveling at the speed of light constitutes the basis for range estimation. The terms _target_ and _object_ have been used interchangeably in this article. Section 4.1 provided general expressions for the I and Q components of IF. A simplified alternative using the equation of the line may be leveraged for modeling the process of IF generation. Since this section aims to present a simplified discourse on the theoretical aspect of performance metrics, the ensuing discussion considers a real baseband architecture.  
The received chirp is multiplied with its time-shifted version from the output of the FMCW generator yielding the sum and difference terms. The summed component is suppressed by the low-pass filter, leaving only the difference component, \(f_{IF}\). Figure 3 exhibits this phenomenon with a constant IF signal generated for the duration of the overlap between the transmitted and received chirp. IF signal generation can be explained with the line equations of the transmitted (Tx) and received (Rx) chirps.  
For the sake of simplicity, the carrier frequency term \(f_{c}\) is neglected, and the starting point of the Tx chirp is assumed at the origin of the cartesian plane. The line equation for the Tx chirp with a y-intercept at the origin and slope \(S\) can then be given as  
\[f_{Tx}(t)=St. \tag{12}\]  
The Rx chirp has an x-intercept, \(\tau\), the RTOF. The y-intercept for the Rx chirp using the line equation comes out to be \(-S\tau\). The line equation for the Rx chirp can then be expressed as  
\[f_{Rx}(t)=St-S\tau. \tag{13}\]  
Then, subtracting (13) from (12) yields  
\[f_{IF}=f_{Tx}(t)-f_{Rx}(t)=S\tau. \tag{14}\]  
Figure 3: IF signal generation [28].  
It should be noted that \(f_{IF}\) is constant for the duration of the overlap between the Tx and Rx chirp. Every object in the radar FOV generates a unique IF termed as the beat frequency. Its value depends on the chirp slope and RTOF. Using (5) and (14), the expression for IF as a function of the target range can be rewritten as  
\[f_{IF}=\frac{2 SR}{c}. \tag{15}\]
## 5 RF Switch Model
Moreover, the number of array observations depends on the number of antennas, that is, \(N=\left\lfloor\frac{75}{(2M-1)}\right\rfloor\). Notably, \(75\) is the number of IQ samples and \(2M-1\) is the number of antennas in the L-shaped array, such that \(2M-1\in[3,75]\). Observe that, if \(75\) is not divisible by \(2M-1\), the last \(75\mod(2M-1)\) IQ samples are not used. MUSIC was devised considering that all antennas perform IQ sampling at the same time, which is not the case for Bluetooth specification. Thus, without a phase compensation, the accuracy of Unitary R-D Root MUSIC is totally compromised, as shown experimentally in Section 7. The DOA method receives \(N\) array observations as the input shown in Equation (35),  
\[\mathbf{H}_{\mathrm{ss}}=\big{[}\mathbf{h}_{\mathrm{ss}}(t_{1})\quad\mathbf{h }_{\mathrm{ss}}(t_{2})\quad\ldots\quad\mathbf{h}_{\mathrm{ss}}(t_{N})\big{]}. \tag{35}\]  
From Equation (32), we know that \(\mathbf{O}^{-1}\mathbf{h}_{\mathrm{ss}}(t)=\mathbf{h}(t)\). Thus, the DOA method needs to apply the RF switch compensation matrix (\(\mathbf{O}^{-1}\)) into the N array observation matrix (\(\mathbf{H}_{\mathrm{ss}}\)) as expressed in Equation (36),  
\[\mathbf{H}=\mathbf{O}^{-1}\mathbf{H}_{\mathrm{ss}}, \tag{36}\]  
where \(\mathbf{H}=\big{[}\mathbf{h}(t_{1})\quad\ldots\quad\mathbf{h}(t_{N})\big{]}\). Note that in the real world, the equality in Equation (36) is an approximation, since the RF switch compensation matrix (\(\mathbf{O}^{-1}\)) takes an estimation of \(\Delta f\) calculated in Equation (30). Moreover, the Unitary R-D Root MUSIC needs to obtain matrices \(\mathbf{X}\) and \(\mathbf{Y}\), which are the \(N\) array observations from x-axis and y-axis ULA, respectively, as defined in step 1 Section 4. To do that, observe that \(\mathbf{X}\) is equal to the first \(M\) rows of \(\mathbf{H}\), and \(\mathbf{Y}\) is equal to the last \(M\) rows of \(\mathbf{H}\). As a result, step 1 needs an additional operation to obtain matrices \(\mathbf{X}\) and \(\mathbf{Y}\) prior to covariance calculations.
## 4 Vulnerability Assessment and Simulation Platform
### Vulnerability Assessment Aspects  
In China, the data of the electrical communication systems in power grid systems are not open currently. These communication systems are equipped with special transmission lines which guarantee high bandwidth for data exchange. Hence, in the following discussion, we assume that bandwidth is not a concern. In this section, we assess the impacts of power grids in ECPSs.  
In the power grids, the major electrical properties include node (generator or load) constraints, branch capacity limits, and flow direction. The major topological properties include numbers of nodes, connectivity, and the degree of nodes. All of these properties have an effect on the vulnerability of the power grids. Therefore, in this section, we propose thorough assessment procedures from five aspects which take the above properties into account.  
1. The stability and robustness of the system. In this procedure, we randomly cut off a branch, balance the power supplies and demands, redistribute the power flow, and count the number of remaining buses. At last, we calculate the proportion of the numbers of remaining buses. A system is said to have good stability against a single tripped branch if none of the nodes will be split during the procedure, while the system has bad stability if the system will suffer a cascading failure owing to the tripped branch. As for robustness assessment, the statistics show that a system has good robustness if the system is stable in most of the faulty situations, but not vice versa.
2. The vulnerable branches which will cause higher economic costs. When a branch is randomly cut off from the grid, the consequential balance of power supplies and demands leads to economic cost. The economic cost can be represented by the sum of adjusted outputs of the generators (Algorithm 4) along the shortest path which is defined in Section 3. Generally speaking, the tripped branches which will lead to higher economic costs should be protected by some specific methods.
3. The vulnerable branches which will lead to a serious damage. If we randomly cut off a branch, the degree of damage will be represented by the number of remaining buses or branches after cascading failures. The tripped branch which leads to less remaining buses or branches is more vulnerable.
4. Vulnerable nodes against extra power injection. Randomly choose a power node (generator or load), inject the same amount of power, and recalculate the power flows on the branches. A node is vulnerable if the power injection will cause other lines to overload. The result is influenced by both the topological and electrical properties.
5. The trip point of the cascading failure propagation. It reflects the controllability of a system. The trip point is the point when the number of remaining buses decreases the fastest. In a discrete system, the duration of cascading failures is replaced by the number of loops in the procedure. The system has more time to deal with the emergence if the trip point appears slowly.
## 2 Materials and Methods
### Analysis
#### 2.4.5 GC/MS Analysis of Volatile Aroma Compounds  
Prior to GC/MS analysis, volatile aroma compounds were extracted by HS-SPME following the protocol by [PERSON] et al. (2018), with slight adjustments. Wine samples were diluted fourfold with deionized water and placed in 10 mL glass vials containing 1 g of ammonium sulfate. An internal standard solution (2-octanol, 1-nonanol, and heptanoic acid) was added in a volume of 50 uL. The DVB/CAR/PDMS fibre was conditioned above the sample for 15 min at 40 \({}^{\circ}\)C and then exposed to the headspace vapours for 40 min at 40 \({}^{\circ}\)C while stirring at 800 rpm. Desorption of volatiles took place in a GC-MS injector at 248 \({}^{\circ}\)C for 10 min (3 min in splitless mode). Identification and quantification of volatile compounds were performed on a Varian 3900 gas chromatograph (GC) coupled with a Varian Saturn 2100T ion trap mass spectrometer (Varian Inc., Harbour City, CA, USA). The GC was equipped with a 60 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 um d.f. capillary column (Rtx-WAX, Restek). The temperature program started at 40 \({}^{\circ}\)C; then, the temperature was increased by 2 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C and was held constant for additional 10 min. Helium was used as the carrier gas at a flow rate of 1.2 mL/min. Mass spectra were acquired in EI mode (70 eV) within the 30-350 \(m/z\) range. Compound identification was carried out by comparing retention times and mass spectra with pure standards, as well as using the NIST05 library. A reverse match score above 750 was the criterion for identification. Spectra with lower scores were manually verified using characteristic ion ratios. Additional confirmation was achieved by comparing linear retention indices (calculated from C10 to C28 n-alkanes) with literature values obtained on equivalent columns. Calibration curves were constructed with internal standard normalization applied before quantification (\(n=5\), \(r^{2}>0.99\) for all the compounds). Major volatile compounds were quantified using total ion current (TIC) peak areas, while minor compounds were quantified using quantifier ion peak areas. Method validation details can be found in [PERSON] et al. (2018). Relative standard deviations in repeatability conditions (\(n=5\)) ranged from 4.02% to 13.04% for terpenoids, from 2.25% to 9.22% for alcohols, from 7.78% to 11.77% for fatty acids, from 1.68% to 7.33% for ethyl esters, and from 7.30% to 12.33% for acetate esters. For volatile compounds without commercially available standards, semi-quantitative analysis was performed by assuming equivalent detector response for similar compounds.
## 2 Theory
### Regulatory Comparisons with Other Countries  
Table 3 shows the regulation comparison among five countries. Municipal network initiatives are part of the digital strategies and broadband plans [50]. Some countries have adopted these. However, to accelerate the provision of broadband services, applications, and infrastructure, we suggest the area classification (four grids) to make development priority targets clearer in any country.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
* With SDG unique number/identity code.  
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regulation** & **Indonesia** & **India** & **Malaysia** & **Thailand** & \
\begin{tabular}{c} **Korea** \\ **(Rep. of)** \\ \end{tabular} \\ \hline
\begin{tabular}{c} Regulatory \\ Incentives for \\ Telecom/ICT \\ Operators \\ \end{tabular} & 2022: No, there are \\ & 2022: Yes, for other \\ & 2022: No, there are \\ & 2022: Yes, for other \\ & 2022: /  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Regulation** & **Indonesia** & **India** & **Malaysia** & **Thailand** & **Korea** \\ \hline  & & National Digital & & & \\  & & Communications & & & \\  & & Policy, 2018. & & & 2021: The Sixth \\  & 2016: Indonesia & [[https://dot.gov](https://dot.gov)]([https://dot.gov](https://dot.gov)). & 2021: 11 th & & \\  & Broadband Plan. & in/sites/default/ & Malaysia Plan. & 2022: National & Information to \\  & //kominfo.go.id/ & EnglishPolicy- & //www.skmm.gov. & 2022: National & Realize Intelligent \\  & content/detail/68 & NDCP.pdf & /www.skmm.gov. & 2022: National & \\  & Includes & 59/program- & (accessed on 5 & \\  & Prioritas- & January 2023) & & \\  & & National & & \\  &  
In addition, there are other lessons to be learnt from implementing a broadband plan, such as establishing a central hub that connects all parties/institutions involved in implementing a broadband plan as a focal point connecting to all related parties/institutions, task delegation, support from the highest-ranking officials and having it put in regulation, concurred indicators for implementation and monitoring-evaluation, multi-stakeholder active participation, dynamic plans, maintaining the momentum, and a context which relates to national agenda to create urgency.