esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Machine Learning for Buildings' Designs  
#### 3.5.1 XGBoost and NN Results for the New Designs Dataset  
Among the models obtained using the XGBoost method (boosted trees), the best model was the one for which the MAE in the test set was 12.61, and the highest error was just over 34 m\({}^{2}\). This model outperformed all linear models in terms of MAE, but it predicted building floor areas on average worse than the \(D_{N}\) model.  
Among the neural network models, multiple architectures were tested--with all the selected explanatory variables in the input layer, and with one and two hidden layers of varying numbers of neurons. The _NN5-(10-5)_ network with five input variables (all except the area of the boiler room \(B\) and the height of the knee-wall \(h\)), ten neurons on the first and five on the second hidden layer, and the _NN6-(10-6)_ network with six variables (variables as in the previous model and additionally the square of the variable garage area \(G_{A}\)\({}^{2}\)) performed best. However, attention should be paid to the above-mentioned doubt concerning the variable garage area \(G_{A}\). The model _NN5-(10-5)_ was identified as the most effective model within this class, with regard to the mean absolute error (MAE) and root mean square error (RMSE) on the test set. However, it was observed that model _NN6-(10-6)_ exhibited a reduced error in instances where the prediction differed most significantly from the actual value of the usable floor area. Both neural network models outperform models \(D\) and \(D_{N}\) in terms of estimating the usable area of buildings on average. Moreover, it was noted that the estimates obtained for the worst case deviated more from the actual values than the estimates obtained for the base models \(D\) and \(D_{N}\) (the maximum error was higher).  
In the context of the boosted trees model, the study examined the contribution of individual variables to the estimation of individual results, expressed in the form of SHAP values. The result is presented in Figure 3. As can be seen, the values of the \(G_{A}\) variable have the most diverse effect on the estimated usable area, with both high and low values of this variable capable of increasing or decreasing the value of the modelled variable. Inthe case of the \(A_{C},H,\) and \(R_{S}\) variables, high values lead to an increase, while low values result in a decrease in the estimated usable area. Meanwhile, for the \(B\) variable, there is a noticeable, albeit not very strong, opposite effect on \(A_{U}\).  
It is worth noting that most of the models obtained were slightly overfitted, with MAE values for the learning data in the \(\langle 9,10\rangle\) range and RMSE values in the \(\langle 11,13\rangle\) range, while for the validation set, the respective values of MAE ranged from 11 to 13 and RMAE ranged from 15 to 16 (see Table 7). This means that some potential still exists here to improve the predictive properties of the model.
## 2 Literature Review
The work in [62] presents the principal strategies of object-oriented analysis, discusses how the combination with fuzzy methods allows implementing expert knowledge, and describes a representative example for the proposed workflow from remote sensing imagery to GIS. The strategies are demonstrated using the first object-oriented image analysis software on the market, e-Cognition, which provides an appropriate link between remote sensing imagery and GIS. The study in [63], demonstrates that the integration of satellite remote sensing and GIS was an effective approach for analyzing the direction, rate, and spatial pattern of land-use change. The further integration of these two technologies with Markov modeling was found to be beneficial in describing and analyzing land-use change processes. These wireless devices have sensors to collect data from the surrounding environment and pass the data onto some main location. Each wireless device also has a transmitter and receiver, which are used to communicate with other wireless devices or gateway within a network. The gateway, also known as middleware, transfers the sensed data to the main location, which logs in the data, and the data is then available to view, process, and analyze via a user interface. The work in [64], reports an investigation into the application of the integration of remote sensing and geographic information systems (GIS) for detecting urban growth and assessing its impact on surface temperature in the region. Remote sensing techniques were used to carry out land use/cover change detection by using multi-temporal Landsat Thematic Mapper data. Urban growth patterns were analyzed by using a GIS-based modeling approach. The integration of remote sensing and GIS was further applied to examine the impact of urban growth on surface temperatures.
## 2 Literature Review
### Application of IFC to the AEC Industry with Extension  
IFC, as an ISO standard, is a popular, neutral, and open data exchange format for describing construction information [12]. In theory, based on IFC and information delivery standards, different software packages with good IFC interfaces can seamlessly conduct information sharing and exchange. In some construction fields, the latest version of the IFC standard (named IFC4 or IFC2x4) cannot meet information requirements. Thus, the IFC standard should be extended to meet specific project targets to ensure that the required engineering information is captured adequately and completely from certain system applications [16; 17]. Based on the engineering characteristics of certain projects, efforts of IFC extension have been implemented in various fields (e.g., bridge [13; 18], tunnel [19; 20]), mainly focusing on construction management [21; 22], cost estimation [23; 24], seismic components [25], and design change management [26].  
Extant review suggests that the IFC data model with extension can be potentially used as a formalized data format to support decision-making in various fields. On the one hand, it can be used to facilitate effective semantic interoperability with other software applications through IFC-based neutral interfaces [11]. On the other hand, the information integration and exchange of practical engineering conditions and IFC-based software applications can be ensured [21]. Thus, IFC is adopted in this research to support automatic safety risk identification and promote decision-making in the deep excavation field, thereby achieving the requirements for information integration and exchange. Based on an IFC data model, this research aims to integrate all risk-related construction information elements and involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases) that arise during the dynamic construction process. In this procedure, relevant extension work is developed because the current IFC4 standard is limited to meet the above expression requirements.
## 3 Methodology
### Energy Profile of LoRa Sensor Nodes
The spreading factor SF10 is favored since the areas in the selected scenarios are well covered by the LoRa signal, which can be concluded according to the previously presented field measurements [52]. Moreover, a lower spreading factor results in faster data reception, which causes a lower node energy consumption. However, since the LoRa gateway handles a downlink channel by allowing for a higher duty cycle and higher transmit power operation, a loss of packets due to lowering the spreading factor can be resolved by transmitting data at higher power, as also stated in [74]. Therefore, within the simulation process, it is assumed that the downlink message scheduled for the first receive window (Rx1) uses the same frequency and data rate as the uplink messages. In the second receive window (Rx2), a fixed predefined frequency and data rate (for SF10) are used.
## 3 Proposed Joint Resource Optimization for NOMA-Enabled Vehicular Networks
### Proposed Optimal Resource Optimization  
The proposed approach jointly optimizes RSU-VU association, channel assignment and OMA-NOMA switching for downlink transmission. Furthermore, the proposed approach optimizes the transmit power consumption at RSU, which is essential for reducing the OpEx of MNOs.  
Figure 1: Assumed power domain NOMA system where a vehicular user receives downlink service from its associated roadside unit with dynamic NOMA-OMA switching and resource optimization.  
To begin with, let us present the problem to be solved in this study as follows. Given \(N_{VU}\) number of VUs and \(N_{RSU}\) number of RSUs with \(N_{ch}\) number of orthogonal channels available, the goal is to minimize the downlink transmit power consumption (\(\mathbf{P}=\{\mathbf{P}^{h},\mathbf{P}^{l}\}\)) while fulfilling the QoS requirements of all VUs (\(\mathbf{r}=\{\mathbf{r}^{h},\mathbf{r}^{l}\}\)) which is defined by the requested bits per time unit. In the proposed approach, each VU is classified as either a high-power user (\(h\)) or low-power user (\(l\)) depending on the channel condition at the moment. The high- or low-power decision will control the precise transmit power for PD NOMA. To achieve the goal in an optimal manner, the proposed approach schedules RSU-VU association, VU-channel mapping and OMA-NOMA switching by using the decision variable \(\mathbf{X}=\{\mathbf{X}^{h},\mathbf{X}^{l}\}\)). In short, the considered problem can be summarized as follows: minimize \(\mathbf{P}\) by optimally scheduling \(\mathbf{X}\), while fulfilling \(\mathbf{r}\).  
Figure 2 outlines the proposed approach that consists of two phases of operations. Each of the sub-procedures under each phase will be elaborated on shortly. It is assumed that the resource optimization decision is made at a high-performance central scheduler, and the resulting decisions are distributed to RSUs with negligible delay.  
The chain of operations shown in Figure 2 proceeds as follows:  
In Phase A: Association, Channel Assignment, and NOMA-OMA Switching  
* VU-RSU Association: Each VU is paired with an RSU based on signal strength, accessibility, and load balancing to minimize interference and ensure optimal connectivity.
* Channel Assignment: Channels are allocated to VUs associated with each RSU, focusing on reducing interference and improving spectral efficiency.
* NOMA-OMA Switching: For each assigned channel, the system dynamically decides between OMA and NOMA, considering channel conditions and the number of associated VUs. NOMA is enabled for two users with distinct channel gains, while OMA is used for single-user assignments.  
In Phase B: Transmission Power Control
* Power Allocation: The optimal transmit power for each RSU-VU pair is determined to satisfy Quality of Service (QoS) requirements while minimizing overall power consumption.
## 4 Results of the Flow Field of the Model
### Whole Description of the Flow Field  
The pressure contour of different reference planes is partly shown in Figure 5. There exists the adverse pressure gradient at the junction between the leading edge of the sail hull and the submarine body. The surface pressure at the leading edge of the sail hull is the largest, which is also called the stagnation point.  
The contours of the pressure, velocity and turbulent kinetic energy at plane A are shown in Figure 6.  
At first, the pressure decreases gradually from the leading edge of the sail hull to both sides. The area with the minimum surface pressure is in the chord length of 0.006 m, which is also known as the transition area. Then, the pressure on the surface increases after the transition area.  
The velocity at the stagnation point is zero. Then, the velocity increases gradually when the points approach both sides of the sail hull. The velocity reaches the maximum value at the transition area. After the transition area, the velocity decreases.  
Due to the effect of viscous drag on the surface of the sail hull, the boundary layer is formed. The thickness of the boundary layer is smaller at the front of the transition point. Then, with the decrease of the velocity of the flow, the thickness of the boundary layer increases.  
Figure 4: Diagram of the reference planes. Plane A is in the longitudinal direction, plane B is also in the longitudinal direction, which is perpendicular to plane A, and plane C is in the transverse direction.  
Figure 5: Pressure contours at different reference planes: (**a**) plane A; and (**b**) plane B. The pressure gradient is adverse when the points are approaching the leading edge.  
In Figure 6, the wake is evident in the downstream, and the structure of the vortices continues to spread. The high levels of turbulent fluctuation pressure at the leading edge and the trailing edge of the sail hull are found.  
According to the velocity contour in Figure 6, we have found that the stagnation point at the leading edge, the boundary layer separation, and the tail vortex shedding can produce fluctuation pressure to excite the sail hull. The flow is converted to be unstable by the listed three parts, and the radiation noise will be generated. When the fluid flows around the sail hull, the structure of the horseshoe vortex will be formed by the influence of the stagnation point at the leading edge, which is the most important flow phenomenon around the sail hull compared to other flow phenomena at the stagnation point. Therefore, the fluctuation pressure generated by the unsteady flows can be ascribed to the horseshoe vortex, the boundary layer separation, and the tail vortex shedding.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 2 Proposed End-to-End Video Steganography System
### Training
#### 2.2.2 Dataset  
The biggest difference between the end-to-end video steganography algorithm in this paper and the image steganography algorithm is that the video steganography works on the VVC decoding video frames. The unique color space conversion between RGB and YUV, CU block division, intra prediction, inter prediction, motion estimation and motion compensation, discrete cosine transform (DCT) from spatial domain to frequency domain, quantization, and other processes of VVC bring about a series of irreversible losses to video frames. In practical applications, it is inevitable to compress the original video due to occupying a lot of memory, and the decoding video frames may provide more favorable hidden features for secret messages. Therefore, the video frames decoding by VVC compression are selected as the covers in this paper.  
DIV2K [36] and MSCOCO [37] are selected as the datasets. DIV2K contains 900 natural scene images with a high resolution, including 800 pictures of the training set and 100 pictures of the verification set. The MSCOCO dataset has 118,060 pictures of the training set,including many common pictures in life with a complex background and small size. In this experiment, 5000 images of the MSCOCO dataset are randomly selected, which are divided into five equal parts. Plus, for the training set of DIV2K, a total of six training datasets are used as the final network training dataset. The verification set of DIV2K is selected for verification.  
The steps to make the dataset are as follows:  
1. Because the video compressed by VVC must be YUV color space, six datasets are spliced into six videos and transformed from the RGB color space to the YUV color space.
2. VVC compresses them using the intra prediction mode, with the quantization step (_QP_) selected as 37. In this process, Make the CU mask of the corresponding cover video frame according to the division of VVC. Figure 7 shows a video frame decoding by VVC compression and the corresponding CU mask. Set the pixel value of the corresponding CU division edge area to 255 and the interior of the CU block to 0, and obtain the CU mask picture, as shown in Figure 6(b), which is a single channel gray image. The CU mask in Figure 6(b) roughly represents the outline of the whole video frame content in Figure 6(a). The content of the background area is flat and its CU block is relatively large. The human body has many textures and its CU block is relatively small and diverse.
3. Convert the decoded videos from the YUV domain to the RGB domain.
4. These, together with their CU masks, are as the whole network training dataset. This is true for the whole network verification dataset.  
Models are trained on six training datasets. Then, they are evaluated separately on the test set and the average is obtained.
# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 1 Introduction
* The introduction of Partitioned Shadow Removal, which enables more accurate shadow removal in complex driving scenes. * Demonstrating significant improvements in segmentation performance under nighttime conditions, particularly in challenging road environments with complex shadow patterns.
## 3 Performance Analysis Methods
### Expressions for Path Loss and Average Path Loss
#### 3.2 Expressions for Received Optical Power  
The received optical power is an important factor in VLC systems. Consider (1), where \(x\) denotes the on-off keying (OOK) symbol corresponding to the intensity modulation-direct detection (IM/DD) scheme and \(y\) is the received signal at Rx. Accordingly, the received optical power of the rays arriving at the Rx through the LOS/NLOS link can be expressed as follows:  
\[P_{r,j}=P_{t,j}h_{j}, \tag{15}\]  
where \(P_{t,j}\) corresponds to the emitted power of the \(j^{th}\) headlight LED and \(h_{j}\) can be calculated from (2). In the proposed system, \(h^{PL\_av}\) denotes the average path loss of the LOS/NLOS link as shown in (12), (13), and (14). Therefore, the power and the total power of the LOS/NLOS link received by Rx from the two Txs can be respectively expressed as follows:  
\[P_{r}^{LOS}=\sum_{j=1}^{2}P_{t,j}h_{a}h_{LOS}^{PL\_av}=h_{a}h_{LOS}^{PL\_av} \sum_{j=1}^{2}P_{t,j}, \tag{16}\]  
\[P_{r}^{NLOS\_M}=\sum_{j=1}^{2}P_{t,j}h_{a}h_{NLOS\_M}^{PL\_av}=h_{a}h_{NLOS\_M }^{PL\_av}\sum_{j=1}^{2}P_{t,j}, \tag{17}\]  
\[P_{r}^{NLOS\_RS}=\sum_{j=1}^{2}P_{t,j}h_{a}h_{NLOS\_RS}^{PL\_av}=h_{a}h_{NLOS \_RS}^{PL\_av}\sum_{j=1}^{2}P_{t,j}, \tag{18}\]  
and  
\[P_{r}^{Total}=P_{r}^{LOS}+P_{r}^{NLOS\_M}+P_{r}^{NLOS\_RS}. \tag{19}\]
## 3 Multi-Layer Stacked Bidirectional LSTM Neural Network for Short-Term Load Forecasting
### Multi-Layer Stacked Bidirectional LSTM Based Load Forecasting  
The essential concept of the proposed improved LSTM neural network consists of obtaining the statistical analysis of the power load by reconstructing the training sample data. The multi-layer stacked bidirectional LSTM network is trained to perform the forecast of the power load for the next 24 h. The prediction process of the proposed model can be divided into the follow steps and shown in Figure 5.  
Step 1: Data preparation. Historical data of the power load profiles are collected and pre-processed to remove any outlier or incorrect data before the training process. However, the original data are not standard enough to use directly. Normalization is a common method to normalize original data structures in system modeling, and the original data become dimensionless after normalization, which can increase the convergence speed of the neural network. After normalization, the value of the original data is between the range of [0,1]. There are many normalization methods such as min-max scaling, Z-score standardization method, and decimal scaling. In this paper, a linear normalization method based on min-max scaling is used, which can be written as follows:  
\[x^{*}=\frac{x-x_{min}}{x_{max}-x_{min}}. \tag{15}\]  
\(x_{max}\) and \(x_{min}\) are the maximum and minimum values of the sample data of power load, \(x\) is the original value of the sample data, and \(x^{*}\) is the normalized value of the original data.  
Step 2: Network training. The forward value of input at t = 1 and the reverse state value of input at t = T (T is the last sampling time of the training dataset) are unknown, which are generally set to a fixed value (0.5) in the training process. Additionally, the derivative of the forward value of input at T = t and the original value of the reverse state of t = 1 are generally set to zero. It is assumed that the later information is not very important for the current information updated. The process of network training contains the following:  
(1) Forward transfer. With the time sequence of 1 < t <= T, training data are input from the cell of the bidirectional LSTM, and the predicted outputs are determined. Forward  
Figure 5: The framework of the proposed method for load forecasting.  
passes are only for forward states (from t = 1 to t = T) and backward states (from t = T to t = 1). The output cells were transferred forward, and the n-th layer forward predicted output is calculated.  
(2) Backward transfer: The derivative of the partial objective function is calculated for the forward transfer time period with 1 < t <= T. The backward LSTM cells are calculated based on the forward value of 1 < t <= T and the reverse value of 1 < t <= T. The reversed prediction output is calculated.  
(3) Weight matrix updating. Based on the loss function of the neural network during the training process, the weight matrix is calculated and updated.  
(4) Result output. Based on the bidirectional calculation, the parameters of the prediction model of LSTM neural network are estimated.
## 3 Materials and Methods  
### Experimental System  
We set up the experimental system based on VPI Transmission Maker 9.0 to generate three widely used optical signals (QPSK, 8 PSK, 16 QAM) modulated at 10G Baud in Figure 1. At the transmitter terminal, a pseudo-random binary sequence (PRBS) is mapped into MQAM and MPSK signals. The system utilizes a continuous wave (CW) laser with a center frequency of 193.1 THz, a linewidth of 0.1 MHz, and a power of 10 dBm to generate the optical carrier signal. It is used to drive a dual Mach-Zehnder modulator (dual MZM). The modulated signal is transmitted through a standard single-mode fiber (SSMF) extending by 1 km, characterized by an attenuation of 0.2 dB/km. At the optical receiving terminal, the optical signal was subsequently converted into an electrical signal. The local oscillator (LO) operates at a wavelength of 1550 nm with a linewidth of 0.1 kHz. It is mixed with the transmitted optical signal, and a demodulated signal is generated using a 90-degree Optical Hybrid. Subsequently, a photodetector is employed to convert the optical signal into an electrical signal. Following synchronized sampling by two analog-to-digital converters (ADC), two digital signals encapsulating in-phase (I) and quadrature (Q) information are acquired. These signals are processed using an offline DSP module to extract the two required datasets: PSD and constellation phase histogram.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & \begin{tabular}{c} **Explanation** \\ **Scope** \\ \end{tabular} & \begin{tabular}{c} **Computational** \\ **Complexity** \\ \end{tabular} & \begin{tabular}{c} **Theoretical** \\ **Basis** \\ \end{tabular} & **Limitation** \\ \hline LIME & Local & Moderate & \begin{tabular}{c} Linear \\ approximation \\ \end{tabular} & \begin{tabular}{c} Depends on the \\ surrogate model \\ \end{tabular} \\ \hline SHAP & Global and local & High & \begin{tabular}{c} Shapley values \\ (cooperative \\ game theory) \\ \end{tabular} & \begin{tabular}{c} Requires \\ complex \\ calculations \\ \end{tabular} \\ \hline Grad-CAM & Local & Moderate & \
\begin{tabular}{c} Can be less \\ precise for small \\ computation \\ \end{tabular} &
\begin{tabular}{c} Can be less \\ precise for small \\ object \\ localization \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of common interpretability techniques.  
Figure 1: Simulation system setup for data acquisition. CW: continuous wave; LO: local oscillation; AD: analog-to-digital converter.