esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation with Field Test
### Dataset Configuration  
To validate the effectiveness of the proposed method and confirm the stability of the simulated cross-sectional relationships under varying traffic and temperature conditions, we carefully selected monitoring data from three distinct periods, as shown in Figure 15. The data were collected on March 7 th, August 28 th, and October 6 th, 2018, with a 30 min monitoring period each day to ensure a representative sample of the bridge's behavior. The daily air temperatures were recorded as 4 \({}^{\circ}\)C/0 \({}^{\circ}\)C, 31 \({}^{\circ}\)C/22 \({}^{\circ}\)C, and 22 \({}^{\circ}\)C/13 \({}^{\circ}\)C, respectively. As depicted in Figure 15, strain measurements--using the monitoring data from 02-S01 as a representative example--demonstrated varying ranges on these days. It should be noted that the strain values depicted in Figure 15 represent the unprocessed raw data. The initial strain recorded throughout the entire monitoring period ranged from \(-2500\) to \(-3000\)\(\mu\)e. Furthermore, the monitoring data exhibited several anomalies, such as the \"sudden jump\" shown in Figure 16, which may have artificially inflated the measured strain values. As a result, the raw strain measurements for the bridge, particularly the maximum strain values presented in Figure 15, are unrepresentatively high. The strain distribution also reflects the impact of temperature on the bridge's structural response. We specifically analyzed the high-frequency bridge vibrations. Figure 17 shows that the primary natural frequency of the bridge exhibited minor variations across the three different days, indicating that the dataset captured the variability in bridge stiffness due to air temperature fluctuations. It is important to observe that, as depicted in Figure 17, the average value of the high-pass-filtered high-
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 3 Results and Discussion  
### Correction of the Drought Index through Analysis of Drought Forecast and Warning Data  
The KMA produces meteorological drought forecasts and warnings based on cumulative precipitation information and SPI data over the previous six months. Nationwide warnings were issued 5.54 times per year on average from 2017 to 2020 (Figure 2). In the agricultural sector, drought forecast warnings at the attention, caution, alert, and severe levels are issued based on information about the average year water reserve rate and the effective water content in the soil. On average, such warnings were issued 0.74 times per year on average from 2017 to 2020. In the case of the hydrological field, forecasts and warnings are provided based on information about the water level and water storage volume of rivers and dam reservoirs that provide public water. On average, warnings were issued 3.61 times per year from 2017 to 2020.  
The procedure for correcting the monitoring results of the meteorological, agricultural, and hydrological drought indices calculated in this study was performed based on the current status of the forecasts and warnings of the analyzed meteorological, agricultural, and hydrological droughts. Although the correction of meteorological, agricultural, and hydrological drought indices calculated based on satellite and ground data may differ academically, as a way to minimize the deviation from the results monitored in the field, the optimization technique was applied to the extent that the calculation results of the raw data (drought index) did not change.  
Drought specificity and accuracy before and after weight correction for meteorological, agricultural, and hydrological drought indices calculated using satellite data were analyzed (Figures 3 and 4). In the case of the meteorological drought index (SPI) calculated based on satellite data, drought specificity and overall accuracy significantly improved after correcting the weight through drought forecast data analysis. The drought specificity in the meteorological field improved significantly from 0.25 to 0.88 after index correction (Figure 3), and the overall accuracy was slightly enhanced from 0.47 to 0.55 after index correction (Figure 4). However, in the case of VHI, there was no change in drought specificity and overall accuracy before and after weight correction. There was little change in the correction of the drought index through the application of weights to vegetation or soil moisture data. Regarding the hydrological drought index (WBDI), although the drought specificity improved from 0.13 to 0.50 after weight correction, the overall accuracy did not significantly change. Therefore, although there was relatively little forecast and warning information, it showed the possibility of improving drought specificity and accuracy by correcting the drought index via weight application.  
were provided for 167 municipalities, and the LSCORE method, showing a relatively high correlation, was applied to basins when only two drought indices were provided.
# Accelerated Inference of Face Detection under Edge-Cloud Collaboration
## 4 Evaluation
### Comparison of Different Input Resolutions  
Input resolution is an important factor affecting inference latency. This section tests the inference latency of models with different pruning strategies at different resolutions. The test environment was performed on a GPU equipped with an NVIDIA GeForce 3090 [35]. Second, the input batch size was set to 1, and the final result was an average of 50 runs for testing accuracy. As shown in Table 5, compared with backbone pruning, deconvolution pruning can achieve a greater speedup in any input resolution at the same compression rate. However, it should be noted that the acceleration effect will be more obvious at smaller resolutions. For example, for a resolution of \(3\times 1280\times 720\), deconvolution pruning is 23.6% of the model without any pruning at a compression rate of 0.6, but it reaches 41.1 at a resolution of \(3\times 320\times 240\).  
In addition, this paper makes comparisons with other advanced methods. The experimental environment was carried out under NVIDIA GeForce 3090, 640 was used as the input of the long side, and the image was scaled proportionally. Both Blazeface [36] and Retinaface [37] are the results of recent [PERSON] experiments. As can be seen from Figure 5, this is a clear advantage over the deconvolution pruning model accelerated by TRT.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pruning Strategy** & **Compression Ratio** & **NO TRT Inference Delay** & **TRT Inference Delay** & **TRT Load Delay** & **Per Image Delay** \\ \hline \multirow{3}{*}{Backbone pruning} & 1 & 265.919 ms & 145.392 ms & 3287.6 ms & 2.496 ms \\  & 0.80 & 251.741 ms & 140.882 ms & 3066.7 ms & 2.276 ms \\  & 0.60 & 242.892 ms & 135.738 ms & 2674.7 ms & 1.939 ms \\ \hline \multirow{3}{*}{Deconvolution pruning} & 1 & 265.919 ms & 145.392 ms & 3287.6 ms & 2.496 ms \\  & 0.80 & 221.656 ms & 130.265 ms & 2817.5 ms & 1.955 ms \\ \cline{1-1}  & 0.60 & 198.333 ms & 119.374 ms & 2563.9 ms & 1.451 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inference speed of different pruning strategies on the server.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pruning Strategy** & **Compression Ratio** & **3\(\times\) 320\(\times\) 240** & **3\(\times\) 320\(\times\) 240** & **3\(\times\) 1280\(\times\) 720** \\ \hline \multirow{3}{*}{Backbone pruning} & 1 & 1.514 ms & 2.657 ms & 4.728 ms \\  & 0.80 & 1.252 ms & 2.387 ms & 4.417 ms \\  & 0.60 & 1.146 ms & 2.239 ms & 4.206 ms \\ \hline \multirow{3}{*}{Deconvolution pruning} & 1 & 1.514 ms & 2.657 ms & 4.728 ms \\  & 0.80 & 1.071 ms & 2.279 ms & 3.810 ms \\ \cline{1-1}  & 0.60 & 0.891 ms & 1.687 ms & 3.613 ms \\ \hline \hline \end{tabular}
\end{table}
Table 5: Inference latency at different input resolutions.
## 3 Beamformer Design
### LS-Based AP Pre-Coder Optimization  
For the LS-based problem formation, we need the following definition of a \(K\times M\) matrix as \(\mathbf{G}\doteq[\mathbf{\hat{g}}_{1},\dots,\mathbf{\hat{g}}_{K}]^{T}\). For a certain signal level \(\mu\) (\(\geq 0\)), we consider the following magnitude-constrained LS estimation problem as  
\[\begin{split}(\mathbf{w})=\min\|\mu\mathbf{1}_{K}-\sqrt{P} \mathbf{G}\mathbf{w}\|^{2},\\ \|\mathbf{w}\|\leq 1.\end{split} \tag{5}\]  
By introducing vector \(\mathbf{1}\) as the estimation target signal, we turn the LS estimation into an equalization problem, where the signal levels at the MC UTs are almost uniformly equalized with the LS criterion. The solution of (5) can be found through the approach of [30], as follows. First, we define  
\[\mathbf{S}_{G}\doteq(\mathbf{G}^{H}\mathbf{G})^{-1}\mathbf{G}^{H}/\sqrt{P}.\]  
For a positive \(\mu\) (\(\mu\leq\lambda_{max}(\mathbf{G})\sqrt{P}\)), the LS estimate of \(\mu\mathbf{1}_{K}\) with the magnitude constraint is achieved with  
\[\mathbf{w}=\begin{cases}\frac{\mathbf{S}_{G}\mathbf{1}_{K}}{\|\mathbf{S}_{G} \mathbf{1}_{K}\|},&\text{if }\mu\|\mathbf{S}_{G}\mathbf{1}_{K}\|\geq 1,\\ \mu\mathbf{S}_{G}\mathbf{1}_{K},&\text{o.w.}\end{cases} \tag{6}\]  
The LS formulation above results in the determination of \(\mathbf{w}\) depending on the signal level \(\mu\) as in (6), which makes the \(\mathbf{w}\) decision a sub-part of optimizing \(\mu\). For the optimization of \(\mu\), we define  
\[\mathbf{A}(\mu)\doteq\mu\mathbf{1}_{K}-\sqrt{P}\min\biggl{\{}\mu,\frac{1}{\| \mathbf{S}_{G}\mathbf{1}_{K}\|}\biggr{\}}\mathbf{G}\mathbf{S}_{G},\]  
and the square of the LS estimation error is given as  
\[\varepsilon=\|\mathbf{A}(\mu)\mathbf{1}_{K}\|^{2}.\]  
The above LS-based equalization allows us to consider the following equalized signal level as \(1+\mu^{2}-\varepsilon=1+\mu^{2}-\|\mathbf{A}(\mu)\mathbf{1}_{K}\|^{2}\), which is an increasing function of \(\mu\) until the point \(\mu=\frac{1}{\|\mathbf{S}_{G}\mathbf{1}_{K}\|}\) is reached since \(\varepsilon\) is a proportional fraction of \(\mu^{2}\) for the interval. However, it is easy to show that \(\varepsilon\) increases faster and approaches \(\mu^{2}\) as \(\mu\) grows beyond \(\frac{1}{\|\mathbf{S}_{G}\mathbf{1}_{K}\|}\). Therefore, the overall shape of \(1+\mu^{2}-\|\mathbf{A}(\mu)\mathbf{1}_{K}\|^{2}\) over \(\mu\) is assured to be concave, and the maximum of the equalized signal level can be obtained at \(\mu^{*}\) by conducting the golden section search (GSS) over \(\mu\) within the feasible range of \(0\leq\mu\leq\beta=\lambda_{max}(\mathbf{G})\sqrt{P}\). This makes the overall optimization a concave one of \(\mu\), as summarized in Algorithm 1, where the AP beamformer \(\mathbf{w}\) is found as a by-product.  
```
1. Find \(\mathbf{S}_{G}\).
2. Calculate \(\mathbf{A}(\mu)\) and conduct the GSS over \(\mu\) to locate \(\mu^{*}\) where \(1+\mu^{2}-\varepsilon\) peaks.
3. Calculate \(\mathbf{w}\) with the found \(\mu^{*}\).
```  
**Algorithm 1** : AP pre-coder optimization algorithm
# An Ensemble Model for Multi-Level Speech Emotion Recognition
## 3 Proposed Method
### Expert 3: Multi-Level Model Based on HSFs and CRNN
#### 3.4.3 Design of Multilevel Model Based on HSFs and CRNN  
Features learned through the LLD-HSF and CRNN models describe the state of emotions from different aspects, and there are complementary characteristics between them. Combining the above learned features, expert 3 was designed and a multilevel emotion recognition model was proposed. Two types of features are connected through the hidden layer and are projected into the same space. Compared with the traditional way, the discrimination of emotion-related features is enhanced.  
As shown in Figure 4, the entire multi-level model is trained in an end-to-end manner, with given segmented speech being processed simultaneously on 2 parallel levels. On one level, these segments are input to the CRNN based on the spectrogram segmentation. The output of the CRNN model is a 32 \(\times\) 513-dimensional vector, which is then added to an attention layer and a hidden layer of 128 dimensions, and the high-dimensional features are successively mapped to the low-dimensional 1024-dimensional feature space. On another level, the original waveform is segmented into frames. The LLD feature is extracted from each frame, and the corresponding HSFs are further counted to obtain a global feature vector with a size of 20 dimensions.  
By linking the feature vectors of the 2 levels, on the basis of integrating the 2 types of features, they are sequentially added to a bi-directional 3-layer LSTM model, an attention channel, and a hidden  
Figure 4: Joint model of high-level statistics functions (HSFs) and CRNN.  
Figure 3: Convolutional recurrent neural network (CRNN) model.  
layer. The hidden layer projects them into the feature space, passes them to the Softmax layer for classification, and finally outputs the classification results.  
Expert 3 is characterized by the effective combination of local and global features. Local features can maximize the weight of features of certain important areas, while global features measure the standard of speech over the entire time range. Both types of features have their own characteristics and focus on the direction. An effective combination can increase the significant area while taking into account the information of all features. Multi-level fusion can improve the basic elements of local and global features. On the basis of combining attention mechanisms, the salient regions in the input features are reversely acquired, and the recognition accuracy is improved in the reverse direction.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 4 Performance Characterization
### Scenario Description
#### 4.1.1 Trajectory  
The user trajectory (Figure 8) is generated using the Balco software [29]. It is a six- and seven-degree-of-freedom (6-7 DOF) trajectory simulation program. The user initial speed is set to 820 m/s and the initial spin rate is set to 200 rad/s. The scenario lasts 80 s, the travelled range in the \(x\)-\(y\) plane is \(\sim\)22 km for a maximum height of \(\sim\)7.8 km. We set the trajectory starting point at the latitude 75\({}^{\circ}\) N and the longitude 10\({}^{\circ}\) E in order to obtain sufficient satellite visibility for the positioning, since the Iridium satellites have polar orbits.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions  
Although most researchers leveraged ML techniques in wireless communication and D2D networks, it is crucial to identify and address different problems and challenges in practical D2D networks. In this section we present the following exciting and challenging future research directions that worth further studies.
# User Experience of 5G Video Services in Indonesia: Predictions Based on a Structural Equation Model
## 2 Underlying Studies and Theories  
### 5G in Indonesia  
According to the ITU-RM.0283-02 recommendation from the International Telecommunication Union (ITU), the speed of 5G technology is around 20 Gbps and the latency is only 1 ms. Besides the technical side, 5G technology demands increased affordability and transformation of the user experience that provides innovation and solutions [12; 13]. The launch of 5G technology to be used commercially has been starting in several countries since 2019. For example, commercial launches in several countries have taken place, including in South Korea in 2019 and Turkey in 2020 [14; 15].  
In Indonesia, 5G was only launched officially by a cellular operator in May 2021 [16]; however, 5G technology has been tested in Indonesia since 2018. The height of this was during the 2018 Asian Games, when two major cellular operators in Indonesia provided the public with the opportunity to participate in testing the advantages of 5G technology [17; 18]. In May 2021, the commercial use of new 5G technology began through two major operators in Indonesia, although it was technically still superimposed on the infrastructure of 4G technology. Conditions that are still early mean that data on the existence of 5G technology customers in Indonesia cannot be obtained; in other words, the service is available but does not have commercial customers [19]. For the development of 5G infrastructure, Indonesia is considering placing 5G on the radio spectrum frequency, including using the mid-band of the 3.5 GHz and 2.3 GHz spectrum because it can cover a wide geographical area and provide a more efficient platform to serve urban and suburban areas [5]. The frequency of 3.5 GHz is currently used by satellite communications, but this is planned to be shifted to another frequency to support 5G communications.  
Regarding the number and distribution of mobile video streaming users in Indonesia, in January 2021, statistical data indicated that Indonesia has a population of 275 million, of which around 40.52% or 111.43 million are mobile video streaming users [20]. In terms of demographics, about 60% of Indonesia's population resides on the island of Java, followed by 20% on the island of Sumatera, 7% on the island of Sulawesi, 6% on the island of Kalimantan, 4% on the islands of Nusa Tenggara, and 3% on the islands of Maluku and Papua [5]. Urban areas are home to 57% of Indonesia's population, while 43% of the population lives in rural areas.
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 4 Results  
### Ablation Experiments  
In this study, ablation experiments were conducted on the light enhancement network, color restoration network, and denoising network to analyze the impact of these three modules on the network's enhancement performance. The experimental results are presented in Figure 6 for comparison and analysis.  
The function of the illumination enhancement module is to iteratively enhance the decomposed light map. The absence of this module would result in decreased image contrast and brightness. The denoising module aims to remove image noise while preserving as much detail as possible. The absence of this module would lead to noticeable noise  
Figure 6: Results of ablation experiments. (**a**) Comparison of ablative experiments on kitchen images; (**b**) Comparison of ablative experiments on toy images.  
artifacts and overall degraded image quality. The color restoration module is responsible for restoring the image colors. Without this module, there would be significant color deviations between the output image and the reference image. From the results of the ablation experiments, it is evident that each module plays a distinct role in enhancing the algorithm's performance. The absence of any module would significantly deteriorate the quality of the output image.
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 2 Backpack-Based Sensor System
### LiDAR Sensors  
Two Velodyne VLP-16 (Figure 2) were mounted on the backpack system. Since its release in 2014, VLP-16 has been extensively utilized in mobile applications for both research and industry. The specifications of VLP-16 are summarized in Table 1. The sensor consists of 16 pairs of simultaneously rotating laser emitters and receivers within a compact sensor pod, and each laser has a fixed vertical angle of 2\({}^{\circ}\) resolution. The rotation rate varies from 5 to 20 Hz, with a set default value of 10 Hz, which gives 0.2\({}^{\circ}\) of horizontal angular resolution. Based on the default settings, VLP-16 rotates every 0.1 s and acquires approximately 28,800 points in each scan.
# Investigation of the Pressure Coefficients Correlation Field for Low-Rise Building Roofs
## 3 Experimental Wind-Tunnel Results
### Mean Wind Direction 45\({}^{\circ}\)  
A wind direction 45\({}^{\circ}\) generates a very complex flow field for this kind of geometry, because the asymmetrical shape of the model with respect to the mean wind direction implies a completely unpredictable aerodynamics without wind-tunnel tests or CFD analyses.  
In this case, the flow detached from one of the roof edges and partly followed the curvature of the diagonal line of the roof, partly splits into vortices that decayed close to the roof edges. This caused a trend of correlation coefficients completely different from that observed at 0\({}^{\circ}\).  
Observing for example the results shown in Figure 9 for the model RLC, it was noted how the correlation coefficient was rapidly reduced for points at a short distance compared with findings for the 0\({}^{\circ}\) angle, even in the central region of the model, which was strongly affected by the detachment of vortices from the edges. In comparison with experimental results for the 0\({}^{\circ}\) angle, the spatial decay of the correlation function was more accentuated around the pressure taps along the two windward edges. Comparing models with smaller curvatures, Figures 10 and 12 (RLF and RHF), and with larger curvatures, Figures 9 and 11 (RLC and RHC), on high (RHC and RHF) and low (RLC and RLF) models, the same behavior was observed with some small differences in the extension of the correlation in the central region: a wider correlation was found for flatter roofs than for more curved roofs.  
For the pressure taps along the leading edge with upward curvatures (the edge with smaller dimensions), comparison of models with the same height and different curvatures showed a significant increase for the flatter models in the extension of the region strongly correlated with the pressure tap under consideration (for example, see pressure taps Ac, Ad, Ae in Figures 10 and 12 with the analogous pressure taps Ac, Ad, Ae in Figures 9 and 11), similar to observations for the leading edge in lower models and those with wind direction 0\({}^{\circ}\).  
In conclusion, the way in which the flow at 45\({}^{\circ}\) detached from two edges of different lengths with opposite curvatures, both equally affected by the incident flow (oriented at 45\({}^{\circ}\) with respect to both), influenced the trend of the correlation coefficient much more than the curvature or height of the building. When the latter varied, differences in correlation were found for most of the roof that were lower than differences found when the same parameters (height and curvature) were varied for the other two angles considered (0\({}^{\circ}\) and 90\({}^{\circ}\)), with the exception of pressure taps along the leading edge with upward curvatures (the smallest edge).