esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Vacant Parking Spaces in Multiple Parking Lots: A DWT-ConvGRU-BRC Model
## 3 Methodology
### Prediction Model
#### 3.2.2 Convolutional Gated Recurrent Unit (ConvGRU)  
A key difference between a ConvGRU and GRU is that the former uses a convolution operator rather than a fully concatenated operator. Therefore, a ConvGRU can better capture spatial-temporal correlations. Figure 8 shows the internal structure of the ConvGRU  
Figure 6: db3 wavelet decomposition diagram.  
Figure 7: Original and denoised time series.  
cell, where CAT and UCAT denote the concatenation and splitting operations, respectively. The detailed information flow of a ConvGRU is shown in the following equations:  
\[z_{t}=\sigma(W_{xz}*x_{t}+W_{hz}*h_{t-1}) \tag{4}\]  
\[r_{t}=\sigma(W_{xr}*x_{t}+W_{hr}*h_{t-1}) \tag{5}\]  
\[\overset{\wedge}{h_{t}}=\tanh(W_{xh}*x_{t}+r_{t}\odot(W_{hh}*h_{t-1})) \tag{6}\]  
\[h_{t}=(1-z_{t})\odot\overset{\wedge}{h_{t}}+z_{t}\odot h_{t-1} \tag{7}\]  
where \(z_{t}\) denotes the update gate, \(r_{t}\) denotes the reset gate, and \(\overset{\wedge}{h_{t}}\) denotes the candidate hidden state. \(x_{t}\) denotes the input of the current step and \(h_{t}\) and \(h_{t-1}\) denote the hidden state of the current and previous steps, respectively. \(x_{t}\) and \(h_{t-1}\) are the input and output vectors of the current time point, respectively, and \(W_{x}\), \(W_{r}\), and \(W_{h}\) are the convolutional kernels for each gate. In addition, \(\sigma\) and \(\tanh\) are the sigmoid and hyperbolic tangent activation functions, respectively, \(*\) is the convolution operation, and \(\odot\) denotes the Hadamard product.  
Specifically, given \(X_{t}\) (the denoised data) and \(h_{t-1}\) as inputs at time step t, the unit first obtains the output of the update gate and reset gates, respectively, using Equations (4) and (5). Then, using Equation (6), the temporal hidden state \(\overset{\wedge}{h_{t}}\) can be calculated, which considers both the input \(X_{t}\) and the hidden state \(h_{t-1}\) produced by the previous ConvGRU operator. The final hidden state \(h_{t}\) of the unit is produced by a linear combination of temporal hidden state \(\overset{\wedge}{h_{t}}\) and previous hidden state \(h_{t-1}\) using Equation (7). We denote the output of this part as follows:  
\[O_{c}=h_{t}\oplus\cdots\oplus h_{t-5(m-2)}\oplus h_{t-5(m-1)} \tag{8}\]  
where \(m\) denotes the number of data divided into 5 min intervals and \(\oplus\) is the concatenation operation.
# Integrated Fiber-FSO WDM Access System with Fiber Fault Protection
## 2 Proposed PON Access Architecture
As mentioned above, the corresponding AP can be also detached from ONU for FSO connection in the proposed WDM-PON system, as illustrated in Figure 2.1 The ONU and AP is linked by a short length of fiber path. If a fiber breakpoint is happened at the \"F1\" or \"F2\" points, the fiber access data can be routed through the FSO link path (lower connecting fiber) for fault protection. The ONU, AP and OWU modules of Figures 2 and 4 are similar and can be exploited in the proposed self-protected PON network simultaneously according to the actual field environment and requirement for deployment.
## 4 Application Discussion
### Discussion Application
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cable Number** & \(\mathbf{D1b}\) & \multicolumn{3}{c}{**D19b**} & \multicolumn{3}{c}{**C33**} \\ \hline Measured & & & & & & & & \\ fundamental & & 7.23 & & & & & 60.06 & \\ frequency (Hz) & & & & & & & & \\ \hline Boundary & Simple & Elastic & Fixed & Simple & Elastic & Fixed & Simple & Elastic & Fixed \\ Conditions & support & support & support & support & support & support & support & support \\ \hline Calculate & & & & & & & & \\ fundamental & 6.60 & 7.18 & 7.32 & 1.86 & 1.91 & 1.91 & 43.41 & 48.63 & 59.95 \\ frequency (Hz) & & & & & & & & \\ \hline Relative error (\(\delta\)) & 8.71\% & 0.69\% & 1.24\% & 1.06\% & 1.60\% & 1.60\% & 27.72\% & 19.03\% & 0.18\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the frequency values of each cable. measured frequency values, it is verified that the discussion of determining the boundary conditions of the corresponding cables from the corresponding cable length range proposed in this paper has a certain accuracy. The determination of cable boundary conditions by cable length has guiding significance in the analysis of cable dynamic characteristics. From the basic calculation of cable frequency to the measurement of cable force in the process of cable use, the determination of boundary conditions can clarify the application scope of previous formulas, solve the basic problem of difficult calculation of cable frequency in engineering practice, and provide a new way for its accurate test and calculation. The analysis can be applied in cable-stayed, short suspenders of arch bridges and other structures; however, determining the length of the cable in the elastic boundary needs further discussion.
# On the Performance of Underlay Device-to-Device Communications
###### Abstract  
This paper comprehensively investigates the performance of the D2D underlaying cellular networks where D2D communications are operated concurrently with cellular networks provided that the aggregate interference measured on licensed users is strictly guaranteed. In particular, we derive the outage probability (OP), the average rate, and the amount of fading (AoF) of the D2D networks in closed-form expressions under three distinct power allocation schemes, i.e., the path-loss-based, equal, and random allocation schemes. It is noted that the considered networks take into consideration the impact of the intra-D2D networks, the inter-interference from the cellular networks and background noise, thus involving many random variables and leading to a complicated mathematical framework. Moreover, we also reveal the behavior of the OP with respect to the transmit power based on the rigorous mathematical frameworks rather than the computer-based simulation results. The derived framework shows that increasing the transmit power is beneficial for the OP of the D2D users. Regarding the cellular networks, the coverage probability (Pcov) of the cellular users is computed in closed-form expression too. Monte Carlo simulations are given to verify the accuracy of the proposed mathematical frameworks. Our findings illustrate that the power allocation method based on prior path-loss information outperforms the other methods in the average sum rate.  
attention from both academia and industry. In the next section, the state-of-the-art of the D2D networks itself, as well as in combination with other techniques, is provided.
# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
###### Abstract  
Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R\({}^{2}\) of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R\({}^{2}\) of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.  
K 6 June 2022  
5 July 2022  
Published: 7 July 2022  
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# Computational Vibro-Acoustic Time Reversal for Source and Novelty Localization
## 5 Examples
### Novelty Localization Examples  
The objective of this set of examples is to identify and localize novelties on certain components of a vibro-acoustic system. We formulate the problem by seeking which beam in the coupled structure acoustic system has undergone a modification with respect to its initial configuration. Generalizing, we may look for a vibro-acoustic system region that might have been subjected to some novelty. In order to succeed that, we utilize the time reversal property of the system using the difference response field given in Equation (7).  
In all following numerical experiments, quantities that remain unchanged are the fluid density and sound speed (\(\rho=1000\,\mathrm{kg/m^{3}}\) and \(c=1500\,\mathrm{m/s}\), respectively) and the numerical time integration step \(dt=1/F_{s}\), where \(F_{s}=3\times 10^{6}\) Hz. The simulation durations of the experiments are declared in time steps \(dt\) or equivalently in multiples of propagation domain lengths, \(L\). The minimum element size in the finite element mesh is also kept constant, so that the maximum distance traveled during a time step \(dt\) is 0.23 of its representative length. The Ricker pulse center remains at time \(t_{0}=3\times 10^{-5}\) s, and its half time span (see Figure 4a) is approximately \(\Delta t=2\times 10^{-5}\) s.
## 3 Performance Evaluation  
In this section, we derive new exact closed-form expressions for performance metrics, which include the capacity outage probability and weighted average BER. In addition, system energy efficiency, which considers the QoS constraints of users inside and outside the hotspot areas, is also analyzed.
Active microwave remote sensing images have the ability for land surfaces mapping and have been widely used to map the forest because of the advantage of all-weather use and have been more applicable especially as wall-to-wall global observations become available. The L-band synthetic aperture radar (SAR) data has a relatively longer band wavelength (23.6 cm), which has greater penetration capacity into forest biomass under the canopy and more sensitive to forest structure interacts with large trunk and branch components [18; 19; 20; 21]. That is particularly suitable for forest monitoring with stronger volume scattering signals than that of X-band sensor and C-band sensor. Therefore, it is preferred to map the forests at regional scale. However, the accuracy of SAR data will be influenced by the underlying soil conditions, moisture content, and high biomass croplands with backscatter coefficients like forests [22; 23; 24]. To map forests with a high accuracy and low uncertainty is a very challenging task using either optical or SAR remote sensing images. Recently, several studies [2; 25; 26; 27] have focused the integration of both optical and SAR images to map forests. [PERSON] et al. [28] utilized the PALSAR data with 25 m spatial resolution and optical remote sensing data (Landsat) with 30 m spatial resolution to extract the forest information of Sub-Humid and Semi-Arid Regions from Oklahoma, in 2010, which has the highest overall accuracy compared with other forest products, with an overall accuracy of 88.2%. [PERSON] et al. [29] utilized the same method to produce a forest product with 30 m spatial resolution to analyze the agroforests in North China Plain. Considering the above successful cases and complementary advantages between optical and SAR remote sensing data, the integration of SAR and optical remote sensing images may enable improvement of forest mapping [30]. In this study, we aim to analyze the forest cover changes in the Loess Plateau after GGP based on the integration of PALSAR and Landsat imagery. We tried to answer the following questions: (1) Is the approach integrating Landsat and PALSAR data for mapping forests effective and robust in fragile regions like the Loess Plateau? (2) What is the spatiotemporal pattern of forest cover changes after GGP in the Loess Plateau? To achieve this object, firstly, we produced a new forest map using the 25 m PALSAR and 30 m Landsat data in the Loess Plateau located in the north of central China. Secondly,we compared our forest map with the existing forest maps with 30-50 m resolutions. Thirdly, we analyzed the spatiotemporal pattern of forest dynamics in the Loess Plateau by using the new forest map. This study expects to facilitate an assessment on the effectiveness of GGP on environmental restoration, and to offer a valuable reference for future detection of forest changes.
## 4 Comparative Analysis of Test Results
### Comparative Analysis of Numerical Simulation and Triaxial Photogrammetry
#### Comparative Analysis of Local Deformation  
Due to the inability to obtain local deformation values from traditional triaxial tests, the local deformation values derived from numerical simulations were compared to those obtained through photogrammetry at a peripheral pressure of 300 kPa. The results of this comparison are illustrated in Figures 19 and 20.  
Figure 18: Shear strength envelope.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Local Site**} & \multicolumn{4}{c}{**ABAQUS**} & \multicolumn{4}{c}{**Photogrammetry**} \\ \cline{2-10}  & & **Pre-Test** & **Post-Test** & **D deflection** & **Post-Test** & **D deflection** & **Absolute Error** & **Relative Error** & **Ralitic Error** & **Magnification** \\  & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/\%** & **Magnification** \\ \hline \multirow{3}{*}{Axial direction} & Zone I & 20.00 & 17.64 & 2.36 & 17.69 & 2.31 & 0.05 & 0.25 & 1.003 \\ \cline{2-10}  & Zone II & 40.00 & 32.93 & 7.07 & 33.12 & 6.88 & 0.19 & 0.47 & 1.006 \\ \cline{2-10}  & Zone III & 20.00 & 17.63 & 2.37 & 17.67 & 2.33 & 0.04 & 0.20 & 1.002 \\ \hline \multirow{3}{*}{Radial direction} & Zone I & 39.10 & 40.19 & 1.09 & 40.68 & 1.58 & 0.49 & 1.25 & 1.012 \\ \cline{2-10}  & Zone II & 39.10 & 41.48 & 2.38 & 43.79 & 4.69 & 2.31 & 5.91 & 1.056 \\ \cline{2-10}  & Zone III & 39.10 & 40.05 & 0.95 & 40.51 & 1.41 & 0.46 & 1.18 & 1.011 \\ \hline \end{tabular}
\end{table}
Table 9: Comparison table of axial and radial deformation (300 hPa).
# Review of Web Mapping: Eras, Trends and Directions
## 3 Web Mapping Eras of Development
### Web Mapping Eras
#### 3.2.2 Dynamic Web Mapping Era  
Once multiple users started to share web maps, the next logical progression was to be able to modify the map for a user or customize for different users. Dynamic maps created using Dynamic HTML (DHTML), Common Gateway Interface (CGI), Java applets and servlets, plugins and ActiveX technologies enabled users to retrieve maps that were dynamically created and constructed on the server according to user preferences and choices [9]. Users clicked on the map or on hyperlinked buttons around the map to identify their map selections, which were sent as user requests to the server where the map was dynamically constructed and sent back to the client. In fact, the two-tier client-server architecture evolved into a multiple or n-tier architecture where the presentation, application and database server functions are separated and able to be provided on different computers. Hence, the data could be requested from multiple database servers, assembled by server-side application (map server) and provided as a response to a user request, all from different computers on the Internet.  
Dynamic maps provided a measure of interaction with maps, so that users could control and manipulate what they were viewing. This opened the way for interactive online atlases and the beginnings of GIS functionality on the web. For example, one of the first online atlases in the world was the Atlas of Canada launched in 1994 [31], which gave users the opportunity to select layers and submit the request to view the resulting map. The United States Geological Survey implemented the National Geospatial Data Clearinghouse in 1995 as a web portal that allowed users to search for maps, geospatial data and satellite images using keywords and map windows [32]. This dynamic web mapping technology opened the way to increasing user interaction through subsequent developments (refer to Section 3.2.4).
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 4 Results
### Comparison with State-of-the-Art
#### 4.2.2 Comparison on MOT20  
As shown in Table 2, the method proposed in this paper is close to the state of the art in MOTA, IDF1, FP, IDs and other metrics on the MOT20 dataset. TransMOT leverages graph transformers to efficiently model the spatial and temporal interactions among numerous objects. In addition, TransMOT proposed a cascade correlation framework to deal with low score detection and long-term occlusion, which further improved the tracking speed and accuracy. However, it requires large computational resources to model in TransMOT. Our two-stage tracker can outperform other methods except TransMOT for tracking effect without a lot of calculations.
(2) AI can uncover patterns and relationships in data that might not be apparent or easily modeled through traditional methods. (3) Once trained, AI models can provide rapid predictions, which is beneficial in practical applications. AI offers enhanced predictive accuracy, especially in systems where interactions are nonlinear and multifaceted, such as in the settling of non-spherical particles in non-Newtonian fluids. The ability of AI to learn from data allows it to adapt to various conditions without the need to re-derive formulas or extensively recalibrate models. However, these studies have limitations in terms of particle shape and fluid type. For instance, some models are only suitable for spherical particles [30,37] or Newtonian fluids [31,32,34,35,38], while others are only applicable to non-spherical particles in power-law fluids [28,36]. Moreover, some studies lack experimental data for non-spherical particles in non-Newtonian fluids [40], making it difficult to predict their settling velocity. Therefore, developing an AI model to accurately predict spherical and non-spherical particles' settling velocity in Newtonian and non-Newtonian fluids is crucial in petroleum engineering. In this paper, we aim to achieve the following objectives: (1) developing an ANN model to accurately predict the settling velocity of both spherical and non-spherical particles in both Newtonian and non-Newtonian fluids, (2) incorporating the impact of particle settling orientation in the prediction of settling velocity, and (3) performing a sensitivity analysis on the various input parameters of the proposed ANN model to evaluate their individual effects on settling velocity prediction.
## 2 Two-Stage Time-Domain Equalization Method
### Data Preprocessing  
In this study, in order to take advantage of LSTM in time series processing, we combined the concept of shift register in a traditional equalizer and used sliding window to preprocess the collected signal. Similar to the shift register, our sliding step is 1 and the time window length is \(L\). At the same time, in order to ensure that the amount of data after the sliding window remains unchanged and that the subsequences at the boundary can be fully extracted, we used zero padding to pre-fill \(L-1\) zero-value samples at the front-end of the original transmission signal.  
This processing method is structurally consistent with the shift register: the generation process of each new sample is equivalent to moving a fixed-length region backward as a whole by one time unit and inserting the new value at the current moment. After this step, the transmission signal is organized into multiple overlapping subsequences, which can be used for subsequent training of the equalization model.  
The processed signal is represented as  
\[\mathbf{y}=\left[\begin{array}{cccccc}0&0&\cdots&0&y_{1}\\ 0&0&\cdots&y_{1}&y_{2}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ y_{1}&y_{2}&\cdots&y_{L-1}&y_{L}\\ \vdots&\vdots&&\vdots&\vdots\\ y_{N-L+1}&y_{N-L+2}&\cdots&y_{N-1}&y_{N}\end{array}\right] \tag{10}\]  
The received signal after data preprocessing corresponds one-to-one with the transmitted signal to form the original dataset. For the two-stage time-domain equalization module, both trainings will go through the data preprocessing part.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 2 Study Area and Materials
### Materials  
#### 2.2.1 Cambodia Electricity Consumption Data  
Data on electricity consumption in Cambodia from 2005 to 2019 were obtained from the EAC (Table 1). The EAC is authorised by the Royal Government to act as an autonomous body for regulating electricity services and managing the relationship between electricity transmission, receipt, and use. The EAC is also responsible for controlling electricity services and electricity use throughout the country. In addition, it has the duty to issue, modify, revoke, or suspend licences for electricity services, approve tariffs, issue regulations for control, impose fines, and settle disputes relating to electricity services and use.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Electricity Consumption (GWh)** \\ \hline
2012 & 3527 \\
2013 & 4051 \\
2014 & 4713 \\
2015 & 5990 \\
2016 & 7175 \\
2017 & 8073 \\
2018 & 9739 \\
2019 & 12,015 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electricity consumption in Cambodia, 2012-2019.