esa-sceva/satcom-chunk-collection huggingface dataset:
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 2 Related Works
These models, although powerful, are highly complex, which requires careful management of the parameters of the component networks. Recently, there has been an emphasis on spatio-temporal models using deep neural networks. For example, in [43; 44; 45], LSTM or attention-based networks were proposed to perform this prediction; however, they use the historical values of the geographical point to be predicted. They also use additional values such as PM\({}_{2.5}\) measurements given by cheap sensors. A similar work was provided by [46]; they proposed a novel hybrid deep learning model that combines convolutional neural networks (CNN) and long short term memory (LSTM) together to forecast air quality at high resolution. In general, for the study of pollution in Santiago de Chile, there is not much literature on the use of modern architectures of neural networks. The majority of studies focus on air pollution in other countries, which is often characterized by different spatial and temporal features. For these reasons, we explored the use of LSTM networks for the space-time prediction of PM\({}_{2.5}\) in Santiago de Chile. We emphasize the data pre-processing because it is required for correct validation of models; the prediction is performed in a spatial point without historical data.
## 2 Forward Model for O\({}_{2}\)_A_-Band Night Glow Limb Radiation in MLT
### Radiative Transfer  
The airglow of the target is detected in limb mode, and the spectral brightness observed by the instrument is the path integral along the LOS (Line of sight):  
\[I(\
u)=\int\limits_{-\infty}^{\infty}\eta(s)_{rot}D(\
u,s)\exp\left[-\int\limits _{s}^{\infty}n\big{(}s^{\prime}\big{)}\sigma\big{(}s^{\prime}\big{)}D\big{(}\
u,s ^{\prime}\big{)}ds^{\prime}\right]ds \tag{1}\]  
Among them, \(s\) is the distance along LOS, \(n(s)\) is O\({}_{2}\) numerical density, and \(\sigma\) is the absorption cross section. \(D(\
u)\) is Doppler line shape at wavenumber \(\
u\), while the pressure broadening and other light sources, including stray light, can be ignored in the MLT. On this basis, the simulated radiance spectra of the O\({}_{2}\)\(A\)-band night glow are convolved with the instrument line shape function, and then observation spectra of \(A\)-band night glow are simulated. The limb observation model combined with the simultaneous separate field of view imaging technique of the atmospheric vertical profile is used to detect the fine spectral structure of O\({}_{2}\)\(A\)-band night glow. Based on the forward model mentioned above, the observed radiance spectra in all fields of view covering 80-120 km with vertical resolution better than 2 km are obtained by simulation, as shown in Figure 3, and the simulation configurations which are directly related to the observed radiance spectra, including oxygen number density, atomic oxygen number density, nitrogen number density, and temperature are shown in Figure 4. The observed spectra in the field of view of 92 km are selected. In the \(A\)-band spectral rotational structure, the spectral lines to be resolved appear in pairs, and the interval between the adjacent lines is about 1.92 cm\({}^{-1}\). Therefore, in order to make a tradeoff between bandwidth and instrument size, this simulation is through the instrument model of the spatial heterodyne spectrometer with spectral resolution 0.7 cm\({}^{-1}\).  
Figure 3: Simulated limb observation spectrum in a field of view of 92 km.  
Figure 2: Fine spectral structure of O\({}_{2}\)\(A\)-band airglow.
## 3 Theoretical Model of Slope Seepage and Stability under Rainfall Conditions
### Slope Stability Theory  
The double-variables strength theory proposed by [PERSON] and [PERSON] [20] is used to describe the shear strength of fractured rocks and soils:  
\[\tau_{\mathrm{f}}=c^{\prime}+(\sigma_{\mathrm{n}}-\mu_{\mathrm{a}})\tan\varphi^{ \prime}+(\mu_{\mathrm{a}}-\mu_{\mathrm{w}})\tan\varphi^{\mathrm{b}} \tag{6}\]  
where \(\tau_{\mathrm{f}}\) is the shear strength of unsaturated material (kPa); \(c^{\prime}\) is the effective cohesion (kPa); \(\sigma_{\mathrm{n}}\) is the effective normal stress (kPa); \(u_{\mathrm{a}}\) is the pore air pressure (kPa); \(u_{\mathrm{w}}\) is the pore water pressure (kPa); \(\varphi^{\prime}\) is the effective friction angle (\({}^{\circ}\)); \(\varphi^{\mathrm{b}}\) is the suction internal friction angle (\({}^{\circ}\)); and \(\mu_{\mathrm{a}}-\mu_{\mathrm{w}}\) is the matrix suction (kPa). Without considering the matrix suction or when the material is close to saturation, \((\mu_{\mathrm{a}}-\mu_{\mathrm{w}})\tan\varphi^{\mathrm{b}}=0\), and Equation (6) is the formula for saturated materials.  
The rock-soil body is assumed to be ideal elastic-plastic bodies, and the constitutive model of the double-variables strength theory is used [21]. Based on SRM [22], the shear strength of the material is reduced when the three shear strength parameters are simultaneously divided by an increasing reduction factor until the slope is in a state of instability and failure [23]:  
\[\frac{\tau_{f}}{SRF}=\frac{c^{\prime}}{SRF_{c^{\prime}}}+(\sigma_{\mathrm{n}}- u_{\mathrm{a}})\frac{\tan\varphi^{\prime}}{SRF_{\varphi^{\prime}}}+\chi(u_{ \mathrm{a}}-u_{\mathrm{w}})\frac{\tan\varphi^{\mathrm{b}}}{SRF_{\varphi^{ \prime}}} \tag{7}\]  
where \(SRF\) is the strength reduction factor--the slope is critically unstable with \(SRF=FOS\); \(SRF_{c^{\prime}}\), \(SRF_{\varphi^{\prime}}\), \(SRF_{\varphi^{\mathrm{b}}}\) are the reduction factors of effective cohesion, effective friction angle and suction internal friction angle, respectively; and \(\chi\) is the empirical coefficient.  
It is usually assumed that \(SRF_{c^{\prime}}=SRF_{\varphi^{\prime}}=SRF_{\varphi^{\mathrm{b}}}=SRF\). Combined with Equation (7), the shear strength parameters of the rock-soil body when the slope is in ultimate equilibrium can be obtained as follows:  
\[\left\{\begin{array}{l}{c^{\prime}}_{\mathrm{F}}=\frac{c^{\prime}}{FOS}\\ \\ {\varphi^{\prime}}_{\mathrm{F}}=\arctan\left(\frac{\tan\varphi^{\prime}}{FOS} \right)\\ \\ {\varphi^{\mathrm{b}}_{\mathrm{F}}=\arctan\left(\frac{\tan\varphi^{\mathrm{b}}}{ FOS}\right)}\end{array}\right. \tag{8}\]
# A Comprehensive Review on Music Transcription
## 2 Overview of Music Transcription
### Stream-Level Transcription  
Stream-level transcription is also called multi-pitch streaming (MPS), which can group estimated pitches or notes into separate streams. The grouped or separated stream typically corresponds to an individual instrument. Figure 1c shows an example of stream-level transcription which was obtained from a multi-task multitrack music transcription (MT3) model [31]. The estimated pitches and notes for each instrument in this model have been grouped into separate streams using various music transcription datasets. The pitch streams of different instruments in this transcription can be visually distinguished using varying colors. This level of transcription offers a broader perspective, involving the transcription of musical events or attributes across entire segments or streams of music. These segments may span longer durations and encompass multiple notes or musical phrases. Stream-level transcription has the capacity to capture higher-level musical structures, such as chords, sections, or phrases, thereby aiming to provide a comprehensive and holistic representation of the analyzed music. This level of transcription is useful when the goal is the simplified arrangement of a musical piece. It works well for music with a smaller number of notes. However, it reduced some level of detail compared to note-level transcription, which may limit the accuracy of the transcription. Stream-level transcription is less suitable for complex music because it captures larger musical sections, which means it provides a more generalized overview of the music but does not capture the complex compositions such as rapid note sequences or variations in dynamics. In stream-level transcription, recent research efforts [32] have addressed the challenge by considering three key inputs: the original audio mixture, an existing multi-pitch estimation system, and the number of sources within the audio mixture. The approach undertaken in this work formulates the transcription problem as a clustering task, with a focus on maintaining timbral consistency. This is achieved by assuming that sound objects originating from the same source exhibit similar timbral characteristics. Similarly, other studies [33; 34] serve as additional examples of stream-level transcription. Both of these works also operate under the assumption that notes belonging to the same source stream demonstrate comparable timbral traits in contrast to notes from different source streams.
## 5 Results and Discussion
### Improving the Method  
The image processing method developed in this study was designed for use in other study areas, and also at a national scale. During the study described here, the image processing method was implemented in Languedoc, which allowed us to assess its potential for application at the regional scale in a Mediterranean setting, and to adapt it accordingly.  
The method would be worth modifying for use in a different context and then assessing its feasibility. Certain lines of research could improve the processing time. Since 1 January 2011, IGN has made available its land cover databases to public services free of charge. Knowing how the databases were updated, these cannot replace the image processing step in our method. However, it would be  
\begin{table}
\begin{tabular}{|l|c|c|c|} \cline{2-4} \multicolumn{1}{c|}{} & \multicolumn{2}{c|}{**Area in hectares**} & \multirow{2}{*}{**Artificial**} \\ \cline{2-4} \multicolumn{1}{c|}{} & **Artificialized patch** & & **Artificial** \\  & **in 1996-1997** & **2009** & \\ \hline Artificialized patch with roads & 242,396 & 261,707 & 8\% \\ \hline Artificialized patch without roads & 113,469 & 133,280 & 18\% \\ \hline \end{tabular}
\end{table}
Table 5: Impact of roads on artificialized patches on the entire study area.  
Figure 9: Increase in artificial sprawl between 1996–1997 and 2009.  
useful to combine them with satellite images in order to stratify processing and hence reduce confusion between areas of bare soil, agricultural areas, and artificial areas. Using these data would allow us to significantly reduce or even eliminate the post classification step and consequently reduce total processing time.  
A second improvement to the artificial patch production method would involve adapting the choice of the building continuity distance to the type of area under study (urban peri-urban or rural). This would involve varying the width of the buffer zone applied to the constituent objects of the artificial area produced by the dilation/erosion operation. The nomenclature would have to be extended to be able to distinguish the component artificial areas of rural, peri-urban and urban areas, and then to define criteria for distances between the corresponding building objects.  
The second possible improvement of the method mentioned above would be particularly useful to generate data that could be used for studies of the shape of towns and their spatial distribution.
## 2 Materials and Methods
### Classification Accuracy Assessment  
The areas correctly detected (True Positive--TP: correctly extracted pixels), omitted (True Negative--TN: correctly unextracted pixels), or committed (False Positive--FP: incorrectly extracted pixels) were computed by comparison to the manually-extracted guano stains. Based on these areas the Correctness, Completeness, and Quality indices were computed as follows [73; 74]:  
\[\text{Correctness}=\ \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{Completeness}=\ \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{Quality}=\ \frac{\text{TP}}{\text{TP}+\text{FP}+\text{TN}} \tag{6}\]  
Correctness is a measure ranging between 0 and 1 that indicates the detection accuracy rate relative to ground truth. It can be interpreted as the converse of commission error. Completeness is also a measure ranging from 0 and 1 that can be interpreted as the converse of omission error. The two metrics are complementary measures and should be interpreted simultaneously. For example, if all scene pixels are classified as TPs, then the Completeness value approaches 1 while the Correctness approaches 0. Quality is a more meaningful metric that normalizes the Completeness and Correctness. The Quality measure can never be higher than either the Completeness or Correctness. The Quality  
Figure 3: Possible changes of membership function during adaptation. (**a**) initial fuzzy function; (**b**) adapted: upper and lower border shifted but range fixed; (**c**) adapted: upper and lower border shifted and range stretched; (**d**) upper and lower border shifted and the range reduced.  
parameter (Equation (6)) is then used as q in Equation (3) to compute the robustness of the master ruleset in each adaptation.
## 2 Methods
### Proposed Routing Scheme  
#### 2.2.1 Routing Metrics  
Cross layer design has been widely used to improve the network performance in wireless networks [24]. Such networks are expected to support various types of applications with different and multiple QoS and grade-of-service (GoS) requirements. Our proposed algorithm exploits cross-layer information exchange to obtain the most optimal routing path, which depends on three different parameters, i.e., forwarding probability, link duration, and wireless bandwidth allocation.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 2 Research Methodology and Proposed System
### Proposed System  
This paper proposed an integrated system of IoT and WSN for illegal logging detection, monitoring, and event tracking in the forest. The design and architecture of the proposed system follow the open standard of LoRaWAN architecture that consists of a network server, gateways, and sensor nodes as end nodes [14]. The proposed system architecture is shown in Figure 2. The outline of the system is divided into local access network and remote access networks. Local access networks are equal with the connection between the sensor nodes and the gateways in the open standard LoRaWAN architecture. End nodes are defined as a measurement layer to supervise the real environment; the networks can consist of a hundred of end-devices connected in STAR of STAR topology. A dozen gateways are placed in the link-layer. The connections between a measurement layer and link layer are through LoRa Radio Frequency.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{1}{c}{**LoRaWAN**} & \multicolumn{1}{c}{**ZigBee**} \\ \hline Standards & LoRa Alliance & IEEE 802.15.4 \\ Modulation & CSS & DSSS, QPSK \\ Frequencies & ISM:433 MHz, 868 MHz, 915 MHz & ISM: 868 MHz, 2.4 GHz \\ Coverage & 1–10 Km & 10–100 m \\ Bandwidth & 125 kHz, 250 kHz & 2 MHz \\ Tx Limit & Duty Cycle Lim & Unlimited \\ Network Size & 10,000 per BS & 65,000 per BS \\ Max Date Rate & 50 kbps & 250 kbps at 2.4 GHz \\ Network Topologies & STAR of STAR & P2P, Tree, STAR, Mesh \\ Private & Yes & Yes \\ Energy Consumption & Low & Low \\ Security & High & High \\ Cost & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the LoRaWAN and Zigbee.  
Figure 2: Architecture of the LoRaWAN.  
The remote access network is the structure of the networks on the middleware layer and application layer as a development of the network server in the open standard architecture of LoRaWAN. A LoRa network server and database cloud computer are placed as a middleware layer consisting of the database information and server to compile the data from end-devices. The application and the end-user are placed in the application layer. The application layer could be in the form of an IoT cloud with the web application or mobile application.
###### Abstract  
Structural damage inspection is a key structural engineering technique that strives for ensuring structural safety. In this regard, one of the major intelligent approaches is the inverse analysis of structural damage using evolutionary computation. By considering the recent advances in this field, an efficient hybrid objective function that combines the global modal kinetic and modal strain energies is introduced. The newly developed objective function aims to extract maximum dynamic information from the structure and overcome noisy conditions. Moreover, the original methods are usually vulnerable to the associated high multimodality and uncertainty of the inverse problem. Therefore, the oppositional learning (OL) for population initialization and convergence acceleration is first adopted. Thereafter, the unified particle swarm algorithm (UPSO) mechanism is combined with another newly developed algorithm, the gradient-based optimizer (GBO). The new algorithm, called the oppositional unified particle swarm gradient-based optimizer (OL-UPSGBO), with the convergence acceleration feature of (OL), enhances balanced exploration-exploitation of UPSO, and the local escaping operator of GBO is designed to specifically deal with the complex inverse analysis of structural damage problems. To authenticate the performance of the OL-UPSGBO, the complex benchmark set of CEC 2017 is adopted to compare the OL-UPSGBO with several original metaheuristics. Furthermore, the developed approach for structural damage identification is tested using several damage scenarios in a multi-story frame structure. Results show that the developed approach shows superior performance and robust behavior when tackling the inverse analysis of structural damage.  
inverse analysis; structural damage identification; modal kinetic energy; modal strain energy; evolutionary algorithms; soft computing +
Footnote †: journal: Applied
###### Abstract  
In the electromagnetic design process of the outer rotor of a low-speed permanent magnet motor (LSPMM), due to the different heat dissipation conditions of the stator core and the stator winding, the selection of different magnetic loads and stator current densities will produce different temperature distributions even under the same efficiency. In the existing literature, the effects of magnetic load and current density on temperature distribution are rarely studied, which makes it difficult for designers to select optimal electromagnetic parameters to achieve better temperature performance. Therefore, in this paper, the comparison of temperature characteristics considering magnetic load and current density is conducted based on an outer rotor LSPMM. Firstly, the structure and parameters of an initial scheme of a 200 kW 56 rpm motor is determined, and the electromagnetic and temperature characteristics of the initial scheme are obtained through two-dimensional finite element analysis (2D-FEA) using Ansys Maxwell and Motor-CAD software. Secondly, by comparing the temperature and loss characteristics under different magnetic loads and different current densities, the effect of magnetic load on temperature and the effect of current density on temperature are obtained. Furthermore, four different schemes are proposed, and the loss and temperature characteristics of the four schemes under rated load are also compared to obtain the comprehensive effects of magnetic load and current density on temperature. Next, a final scheme is determined based on the above analysis, and the temperature characteristics of the final scheme and the initial scheme are compared to verify the validity of the conclusions. Finally, a prototype is built and tested to verify the feasibility of the conclusions. For LSPMM design, the results and several measurements provided in this paper can help researchers to choose a better optimization scheme to achieve good temperature performance.  
outer rotor; low-speed permanent magnet motor; magnetic load; current density; temperature comparison Article
## 3 Analysis of MRSMC Strategy  
### Stability and Robustness  
In sliding mode control design, a Lyapunov approach is usually used for deriving conditions on the control law that will drive the state orbit to the equilibrium manifold. The quadratic Lyapunov function is selected as  
\[W_{j}=\frac{1}{2}\mathbf{S}_{j}{}^{T}\mathbf{S}_{j}\geq 0;j=r,g \tag{16}\]  
The time derivative of \(W_{\rm r}\) and \(W_{\rm g}\) on the state trajectories of (16) is given by  
\[\frac{dW_{j}}{dt}=\mathbf{S}_{j}{}^{T}\big{(}\mathbf{d}_{j}-\mathbf{k}_{j\rm u}sat(\mathbf{S}_ {j})\big{)};j=r,g \tag{17}\]  
For stability to the sliding surfaces, it is sufficient to have \(dW_{j}/dt\) (\(j\) = \(r\) or \(g\)). Outside the boundary layer of the sliding surface, the saturation functions of \(\mathbf{S}_{\rm r}/\mathbf{S}_{\rm g}\) are equal to their sign functions. In this condition, it is worth noting that if the positive control gains fulfill the following condition, viz.,  
\[\mathbf{k}_{j\rm u}>\big{|}\mathbf{d}_{j}\big{|};j=r,g \tag{18}\]  
The time derivative of Lyapunov function \(dW_{j}/dt\) (\(j\) = \(r\) or \(g\)) is definitely negative, which means that the system state moves asymptotically toward to the sliding surface outside the boundary layer. Hence, the system features strong robustness and is insensitive to variations of system parameters and external disturbances outside the boundary layer.  
Inside the boundary layer of the sliding surface, if time derivative of Lyapunov function \(dW_{j}/dt\) (\(j\) = \(r\) or \(g\)) is definitely negative, it is sufficient to have  
\[\mathbf{k}_{j\mathrm{u}}\big{|}\mathbf{S}_{j}\big{|}/\lambda_{j}>\big{|}\mathbf{d}_{j}\big{|} ;j=r,g \tag{19}\]  
It can be seen that the validity of (19) is affected by the forms of sliding surface \(\mathbf{S}_{\mathrm{j}}\) (\(j\) = \(r\) or \(g\)) and disturbance \(dj\) (\(j\) = \(r\) or \(g\)). The sliding surface of the MRSMC system is different from that in the ISMC system. And the unbalanced, 5\(-\) and 7\(+\) harmonics distorted grid voltages will introduce the disturbances \(dj\) (\(j\) = \(r\) or \(g\)). The stability and tracking behavior of MRSMC and ISMC strategy with disturbances \(dj\) (\(j\) = \(r\) or \(g\)) will be investigated in Section 3.2.
## Appendix A  
This section presents all the commercial LEO small platforms that have been considered in the survey with their corresponding references. Then, the commercial platforms are assessed in terms mass, power consumption, communications, pointing control, and knowledge. Tables A1-A3 summarize each platform and manufacturer with the available capability to support a wide range of available payload mass and power. These small platforms were categorized into three groups' nano-, micro-, and mini- platforms based on the criteria of the International Academy of Astronautics [110]. Nano-satellites have a mass smaller than 10 kg, micro-satellites have a mass between 10 kg and 100 kg, and mini-satellites have mass in the range from 100 kg to 1000 kg. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Available Instruments**} & \multirow{2}{*}{**Frequencies \& \& Resolution**} & \multirow{2}{*}{**Swath**} & \multirow{2}{*}{**Mass**} & \multirow{2}{*}{**Power**} & \multirow{2}{*}{**Rate**} & \multirow{2}{*}{**Altitude**} \\  & & & [km] & & & & [kkps] & [km] \\ \hline SGR-ReSI & L1 C/A Code (Options: Galileo E1, (TechDemoSat-1 (TDS-1), (CYGNSS) [57] & 20–50 & 740 & 1.4  & **Orbit Rate** & **Altitude** \\  & & [km] & & [kps] & [km] \\ \hline FMMPL-2 & & & & & \\ (FSSCAT) & L1 C/A Code (Options: Galileo E1) & 0.3 & \(\sim\)350 & 1.5 & \(>\)8.0 & 40 & 500–550 \\
[49] & & & & & & \\ \hline \hline \end{tabular} The background color in the table indicates the type of platform suitable for the instrument according to the power and mass requirements: Very light gray: Nano-platform; Light gray: Micro-platform; Gray: Mini-Platform;  
\end{table} 
Table 6: Survey of GNSS-R instruments.