esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Simulation Validation
#### 4.2.3 Case Study
As shown in Figure 7, in the absence of external disturbances, vehicle speeds remain relatively stable, showing minimal variation across segments. Figure 8 shows the changes in traffic speed upstream of the bottleneck under uncertainty and without control. The bottleneck significantly affects upstream traffic, causing speeds to decrease as vehicles approach it. Figure 9 shows the changes in traffic speed when the MVSPC strategy is implemented after the traffic bottleneck. This strategy promotes a gradual change in traffic speed upstream of the bottleneck, thereby alleviating congestion at the bottleneck point. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Flow** & **WT (s)** & **PC** & **CO2 (g) 1 \(\times\) 10\({}^{9}\)** & **CO (g) 1 \(\times\) 10\({}^{8}\)** & **NO (g) 1 \(\times\) 10\({}^{6}\)** & **NOx (g) 1 \(\times\) 10\({}^{5}\)** \\ \hline None & 1969 & 419.75 & 1196 & 7.88 & 1.80 & 3.14 & 1.44 \\ \hline DQN-VSL & 1667 & 344.24 & 5296 & 4.82 & 0.67 & 1.78 & 0.72 \\  & 17.99\% & \(-\)34.28\% & 38.91\% & 63.15\% & 43.50\% & 50.26\% \\ \hline OL-VSL & 1969 & \(\begin{array}{c}536.80\\ -27.89\%\end{array}\) & 914 & 6.65 & 1.18 & 2.56 & 1.05 \\  & \(\begin{array}{c}-27.89\%\end{array}\) & 23.58\% & 15.64\% & 34.52\% & 18.57\% & 27.05\% \\ \hline MVSPC & 1969 & \(\begin{array}{c}157.65\\ 62.44\%\end{array}\) & 56 & 5.80 & 1.55 & 2.31 & 0.96 \\  & 62.44\% & 95.32\% & 26.4\% & 14.11\% & 26.53\% & 32.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: System’s performance under high flow conditions. Figure 6: The system’s performance in terms of emissions.
# UAV Communication Recovery under Meteorological Conditions
## 4 UAV Communication Recovery Strategy with Results
### UAV Communication Recovery Algorithm under Meteorological Conditions  
We propose an algorithm as shown in Algorithm 1 to restore communications between the UAV and ground users under various meteorological conditions.  
**Input**: region type, carrier frequency \(f_{c}\), height of the UAV \(h\), parameters of LoS probability expression (\(i\), \(j\), \(k\), \(l\), \(m\), \(n\)), excessive path loss (\(\eta_{LoS}\), \(\eta_{NILoS1}\)), maximum path loss \(PL_{max}\), rainfall rate \(\gamma_{\text{rain}}\), liquid water density \(\gamma_{\text{fog}}\), snowfall rate \(\gamma_{\text{fog}}\)  
**Output**: maximum coverage area \(S_{max}\), current height of the UAV  
```
1:if weather is ideal then
2: keep supervising the weather and obtaining the maximum coverage radius (\(C_{r}^{\text{goss}}\)) and the corresponding optimal height (\(H_{opt}^{\text{goss}}\)) of the UAV;
3:if\(h\
eq H_{opt}^{\text{goss}}\)then
4: update the height of the UAV (\(h\)) to the value of \(H_{opt}^{\text{goss}}\);
5:endif
6:endif
7: calculate the specific attenuation of rain (\(R_{\text{rain}}\)), fog (\(R_{\text{fog}}\)) or snow (\(R_{\text{snow}}\));
8: calculate the maximum coverage radius (\(C_{r}^{\text{rain}}\), \(C_{r}^{\text{fog}}\) or \(C_{r}^{\text{snow}}\)) and the corresponding optimal height (\(H_{opt}^{\text{rain}}\), \(H_{opt}^{\text{fog}}\) or \(H_{opt}^{\text{snow}}\)) of the UAV;
9: calculate the compensated path loss (\(PL_{cps}^{\text{rain}}\), \(PL_{cps}^{\text{fog}}\) or \(PL_{cps}^{\text{snow}}\));
10: update the value of \(PL_{max}\) to \(PL_{max}+PL_{cps}\);
11: obtain the maximum coverage radius (\(C_{r}^{\text{rain}}\), \(C_{r}^{\text{fog}}\) or \(C_{r}^{\text{snow}}\)) and the optimal height (\(H_{opt}^{\text{rain}}\), \(H_{opt}^{\text{fog}}\) or \(H_{opt}^{\text{snow}}\)) after the UAV communications are recovered;
12: calculate the corresponding maximum coverage area \(S_{max}\);
13:if\((H_{opt}^{\text{rain}}\
eq H_{opt}^{\text{rain}})\vee(H_{opt}^{\text{fog}} \
eq H_{opt}^{\text{fog}})\vee(H_{opt}^{\text{snow}}\
eq H_{opt}^{\text{snow}})\)then
14: update the height of the UAV to \(H_{opt}^{\text{rain}}\), \(H_{opt}^{\text{fog}}\) or \(H_{opt}^{\text{snow}}\).
15:endif
```  
**Algorithm 1** UAV Recovery Communications Strategy under Meteorological Conditions
## Appendix: Proof of the faultiness of the Hypothesis 1
According to the condition 2 of the definition of \(\mathbf{MI}\), if all the vectors in \(\left\{\boldsymbol{h}_{j}\mid\boldsymbol{h}_{j}\in\mathbf{MI}\right\}\) make \(\boldsymbol{h}_{j}\times C=0\), then also for all the vectors in \(\left\{\boldsymbol{h}_{j}\mid\boldsymbol{h}_{j}\in\mathbf{H}\right\}\), we have \(\boldsymbol{h}_{j}\times C=0\), so the calculation of Equation (36) should be:  
\[P_{r}\left[\mathit{Hb}_{\min}(\alpha^{i})\times C=0\right]=\prod_{t=1}^{rank \left(\mathit{Hb}_{\min}(\alpha^{i})\right)}P_{r}\left(\boldsymbol{h}_{j} \times C=0\right)=\left(0.5\right)^{rank\left(\mathit{Hb}_{\min}(\alpha^{i}) \right)} \tag{41}\]  
where \(\left\{\boldsymbol{h}_{j_{t}}\mid 1\leq t\leq rank\left(\mathit{Hb}_{\min}(\alpha^{i}) \right)\right\}\) are the vectors in \(\mathbf{MI}\), _i.e.,_ a maximum linearly independent vector group of the rows of \(\mathit{Hb}_{\min}(\alpha^{i})\). According to Equation (41), **Hypothesis 1** is true only if all the \(\mathit{Hb}_{\min}(\alpha^{i})\), where \(1\leq i\leq 2^{m}-1\), have the same rank. But unfortunately, this condition cannot always be met. For example, when considering the BCH(63, 51) codes, we have the following results:  
\[ \begin{cases}rank\left(\mathit{Hb}_{\min}(\alpha^{1})\right)=6\\ rank\left(\mathit{Hb}_{\min}(\alpha^{9})\right)=3\\ rank\left(\mathit{Hb}_{\min}(\alpha^{63})\right)=1\\ \ldots\end{cases}  \tag{42}\]  
Therefore, we have:  
\[ \begin{cases}P_{r}\left[\mathit{Hb}_{\min}(\alpha^{1})\times C=0\right]=\left( \frac{1}{2}\right)^{6}\\ P_{r}\left[\mathit{Hb}_{\min}(\alpha^{1})\times C=0\right]=\left(\frac{1}{2} \right)^{3}\\ P_{r}\left[\mathit{Hb}_{\min}(\alpha^{1})\times C=0\right]=\left(\frac{1}{2} \right)^{1}\\ \ldots\end{cases}  \tag{43}\]  
so we find that the **Hypothesis 1** is not correct.
# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
## 5 Results
### Validation of Classification of VGG19-Based CNN for Step-1  
First, the VA classifier for Step-1 where Classes A and B are identified was tested by using VGG19-based CNN, and the confusion matrix is as shown in Figure 14.  
The classification between Classes A and B is performed by VGG19-based CNN, of which the classification accuracy is around 94% as shown in Table 5. Our 4-Class VA classifier at Step-1 adopts the VGG19-based CNN, thus it cannot outperform the classification accuracy of VGG19-based CNN for VA classification.  
In Figure 14a, the images in Class A are more misclassified than those in Class B. It is in accordance with the fact that the recall of Class A is 60% in Figure 7d. We investigated the probability value of Softmax of images misclassified in the confusion matrix and the characteristics of these images. In Table 2, the images in Class A look generally blurred and partially poorly observed. On the other hand, the images in Class B look relatively cleaner than Class A, and the macula and optic disc are clearly observable. However, the misclassified images of Class A in this experiment have the more observable optic disc and macula images than the images of Class A Table 2.  
Figure 13: Training results of EfficientNet-B7-based CNN for Step-2-2. (**a**) Accuracy; (**b**) Loss; (**c**) Confusion matrix; (**d**) Classification report.  
Figure 15 shows some misclassification examples where the fundus images in Class 1 or Class 2 of Class A are misclassified as Class B. Note that the decision probability of Softmax of the fundus images in the first line is both close to 50%, and that means that they can likely be misclassified. Furthermore, the macular in fundus images in the second line look cleaner, in contrast to those in Table 2.  
For the misclassification cases of Class B, the misclassified images in Class B look very blurred or macularly unclear, as shown in Figure 16.
# User Identity Linkage across Social Networks by Heterogeneous Graph Attention Network Modeling
## 4 Methods
### Embedding
#### 4.1.1 Embedding of Every User-to-User Edge  
In the heterogeneous social network graph, the edges between users have several types. For example, in Figure 1, the edge between user A and user B is the \"friend\" edge, and the edge between user A and user C is the \"blacklist\" edge. We use the first-order proximity [26] to embed  
Figure 3: User identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN).  
Figure 2: User identity linkage.  
every user-to-user edge. Given two users \(v_{i},v_{j}\in\mathcal{V}^{Ul}\) in the heterogeneous social network graph, the probability that the edge whose type is \(t\), existing between \(v_{i}\) and \(v_{j}\) can be calculated as follows:  
\[p(v_{i},v_{j})=\sigma(z_{i}^{T}\cdot z_{j})=\frac{1}{1+e^{-z_{i}^{T}\cdot z_{j} }}, \tag{2}\]  
where \(z_{i},z_{j}\in\mathbb{R}^{d^{\prime}}\) are \(d^{\prime}\)-dimensional vectors of \(v_{i},v_{j}\) in the latent space, and \(\sigma\) is the sigmoid function.  
To obtain the vectors \(z_{i},z_{j}\) in the latent space, combining with (2) and the log-likelihood function, we need to minimize the following:  
\[-\sum_{(i,j)\in\mathcal{E}}\log p(v_{i},v_{j}). \tag{3}\]  
To avoid the existence of trivial solutions, the loss function of the embedding can be written as follow:  
\[L_{t}=-\sum_{(i,j)\in\mathcal{E}}\log\sigma(z_{i}^{T}\cdot z_{j})-\sum_{(i,k) \
otin\mathcal{E}}^{K}\log(1-\sigma(z_{i}^{T}\cdot z_{k})), \tag{4}\]  
where \(t\) is the type of edge, and \(K\) is the total number of negative sampling edges. For all edge types \(t\in T\), the total loss function is written as:  
\[L_{\text{SE}}=\sum_{t\in T}L_{t}. \tag{5}\]  
Then, to facilitate the subsequent feature aggregation, we transform the embedding vector into \(d\)-dimension in the latent space, and the transformation process is as follow:  
\[h=\tanh(W\cdot z+b), \tag{6}\]  
where \(W\in\mathbb{R}^{d^{\prime}\times d}\) is the transformation matrix, \(b\in\mathbb{R}^{d}\) is the bias vector, \(h\in\mathbb{R}^{d}\) is the transformed vector as input to subsequent attention layers, and \(\tanh\) is the hyperbolic tangent function.  
Finally, according to the type of edges, the structural embedding nodes are created in the heterogeneous social network graph, whose embedding vector is \(h^{\prime}\) in the latent space.
# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 3 Materials and Methods
### Object Detection Model
#### 3.3.3 YOLOv9  
YOLOv9 is built on YOLOv7 [8]. Several innovations have been integrated into the architecture to improve model performance, accuracy, and efficiency. In YOLOv9, Dynamic Neural Networks (DyNN) have been added to the architecture, which can dynamically adjust the model's structure according to the complexity of the input data. This tuning enables YOLOv9 to utilize computational resources more effectively, increasing processing speed while improving detection accuracy [8]. DyNN can be effective in scenarios with different levels of object complexity, such as environments with mixed-scale objects or rapidly changing dynamic scenes.  
In YOLOv9, the Advanced Spatial Pyramid Pooling (ASPP) module was added to the architecture, which improves the model's ability to capture multi-scale content information in the input image [8]. This module enables YOLOv9 to better understand and interpret spatial relationships between objects, resulting in more accurate localization and classification, even in cluttered or highly dynamic scenes. Furthermore, YOLOv9 incorporates the Self-Regularizing Convolution (Self-RegConv) technique, which further improves the convolutional layers of the model by automatically adjusting them during training to prevent overfitting and improve generalization [8]. The general structure of the YOLOv8 algorithm can be seen in Figure 7.  
Figure 6: The network structure of the YOLOv8 algorithm.
# Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms
## 1 Introduction
For example, the encoder-decoder model could be trained in a regularized manner such that the output score was close to the ground-truth drum score and at the same time was preferred by a language model pretrained from an extensive collection of drum scores [19]. More specifically, a repetition-aware bi-gram model and a gated recurrent unit (GRU) model were used as language models for evaluating the probability (musical naturalness) of a drum score. Assuming that drum patterns were repeated with an interval of four beats as was often the case with the 4/4 time signature, the bi-gram model predicted the onset activations at each tatum by referring to those at the tatum four beats ago. The GRU model worked better than the bi-gram model because it had no assumption about the time signature and could learn the sequential dependency of tatum-level onset activations. Although the grammatical knowledge learned by the GRU model was expected to be transferred into the RNN-based decoder, such RNN-based models still could not learn the repetitive structure of drum patterns on the global time scale. To overcome this limitation, in this paper, we propose a global structure-aware frame-to-tatum ADT method based on an encoder-decoder model with a self-attention mechanism and transfer learning (Figure 1), inspired by the success in sequence-to-sequence tasks such as machine translation and ASR. More specifically, our model involves a tatum-level decoder with a self-attention mechanism, where the architecture of the decoder is similar to that of the encoder of the transformer [26] because the input and output dimensions of the decoder are the same. To consider the temporal regularity of tatum for the self-attention computation, we propose a new type of positional encoding synchronized with the tatum times. Our model is trained in a regularized manner such that the model output (drum score) is preferred by a masked language model (MLM) with a self-attention mechanism that evaluates the pseudo-probability of the drum notes at each tatum based on both the forward and backward contexts. We experimentally validate the effectiveness of the self-attention mechanism used in the decoder and/or the language model and that of the tatum-synchronous positional encoding. We also investigate the computational efficiency of the proposed ADT method and compare it with that of the conventional RNN-based ADT method. In Section 2 of this paper, we introduce related work on ADT and language modeling. Section 3 describes the proposed method, and Section 4 reports the experimental results. We conclude in Section 5 with a brief summary and mention of future work.
## 3 Experiments and Results
### Experimental Setup
#### 3.1.2 Experimental Parameters  
All experiments in this paper were conducted in the following configuration: the operating system was Windows 11, the processor was a 13 th generation Intel CoreTM i5-13400F (Intel Corporation, Santa Clara, CA, USA) with 16 GB of RAM (Kingston Technology Corporation, Fountain Valley, CA, USA), and the GPU was an NVIDIA GeForce RTX 3060 (NVIDIA Corporation, Santa Clara, CA, USA) with 12 GB of video memory. The software environment utilized Python 3.8 and the experimental framework was based on PyTorch 2.0.1 + cu11.8, using a GPU for training.
## 3 Results
### Evaluation of Implant Stability  
The assessment of implant stability, as delineated through IT and ISQ values, indicated a pronounced enhancement in the stability parameters in the groups supplemented with XB (Figures 4 and 5). In particular, the xenograft bone bolstered the IT and ISQ metrics significantly compared with the CW group alone, a trend that was statistically significant (\(p<0.05\)).  
Unusually, the evaluations indicated marginal differences between the CW + XB and CCW groups, establishing no significant divergence (\(p>0.05\)). Conversely, the CCW + XB cohort emerged as the group presenting the zenith in both IT and ISQ values, a statistically significant observation (\(p<0.05\)).  
The different osteotomy approaches produced different density aspects (Figure 3). The use of XB produced stronger densification at the body and apex of the implantation site regardless of drilling direction, with improvement in bone volume and trabecular thickness and a reduction in trabecular separation inside the VOI (Table 2). The CW + XB group produced the same level of porosity as CCW. CCW + XB reduced the lowest percentual porosity inside the VOI (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **CW** & **CW + XB** & **CCW** & **CCW + XB** \\ \hline
**BV (\%)** & 37.0 \(\pm\) 1.1 \({}^{\text{A}}\) & 55.2 \(\pm\) 1.9 \({}^{\text{B}}\) & 50.6 \(\pm\) 2.3 \({}^{\text{C}}\) & 56.8 \(\pm\) 2.1 \({}^{\text{B}}\) \\
**Tb.Th (mm)** & 0.41 \(\pm\) 0.02 \({}^{\text{A}}\) & 0.76 \(\pm\) 0.02 \({}^{\text{B}}\) & 0.61 \(\pm\) 0.03 \({}^{\text{C}}\) & 0.78 \(\pm\) 0.03 \({}^{\text{B}}\) \\
**Tb.Sp (mm)** & 2.40 \(\pm\) 0.16 \({}^{\text{A}}\) & 2.11 \(\pm\) 0.12 \({}^{\text{B}}\) & 2.14 \(\pm\) 0.12 \({}^{\text{B}}\) & 2.01 \(\pm\) 0.11 \({}^{\text{B}}\) \\
**Po(tot) (\%)** & 64.0 \(\pm\) 3.1 \({}^{\text{A}}\) & 48.3 \(\pm\) 1.9 \({}^{\text{B}}\) & 49.9 \(\pm\) 2.9 \({}^{\text{B}}\) & 43.5 \(\pm\) 2.4 \({}^{\text{C}}\) \\ \hline \hline \end{tabular} Characteristic quantitative analyses of microstructural properties (mean + SD) pertaining to bone volume (BV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), and overall porosity (Po(tot)), as influenced by osteotomy techniques (clockwise (CW) and counterclockwise (CCW)) coupled with the presence or absence of xenograft bone (XB) granules. Different superscripts denote significant differences between the groups as determined via ANOVA and corroborated with [PERSON]’s post hoc test (\(p<0.05\)).  
\end{table}
Table 2: Comparative analysis of microstructural attributes.  
Figure 3: Micro-CT reconstructions illustrating representative middle sections of each osteotomy group. This delineates the variation in bone density and microstructure characteristics resultant of osteectomies conducted in clockwise (CW) and counterclockwise (CCW) drilling directions, with or without xenograft bone (XB) granule integration.
# MpcNet: A Vision Transformer Network for Estimating Room Layouts from a Single RGB Panorama
## 2 Related Work
### Methods Based on Deep Learning  
[PERSON] et al. [11] proposed a method for estimating the edge character of an indoor layout from a panoramic image, which could extract the edge of the room scene characteristic and used it to analyze the room layout, assuming that the edge referred to each plane's intersecting lines, such as ceiling and metope, ground and metope of the line intersection curve, etc. They used a structured forest to extract the local information of the input image, and a full convolutional neural network to extract the global information of the input image to obtain the edge image, and then extracted its features and used a maximum margin classifier to estimate the room layout. [PERSON] et al. [12] proposed a new optimization framework based on a convolutional neural network, which could generate a room layout estimation with high accuracy in the face of complex room scenes. [PERSON] et al. [13] pro posed a network model with an encoder-decoder structure based on self-attention, which could predict the key points of an indoor layout. This method showed that an indoor layout estimation result and segmentation result could be obtained according to some specific sequence key points. AtlantaNet [14] proposed a neural network structure, which double-projected the original panoramic images to better encode the required modeling information. Recursive neural networks (RNNs) were used to capture remote geometric patterns, and custom training strategies based on domain-specific knowledge were utilized. Most of the deep-learning-based methods use a CNN as their backbone, but, being limited by the CNN's smaller receptive field, their performance is hard to improve further.
## 3 Results and Discussion
### Preliminary Economic Evaluation  
A preliminary parametric techno-economic analysis is performed to evaluate the viability of the proposed technological solution for the brine treatment. The analysis is based on the estimation of the levelized cost of water (LCOW) [\(\xi\)/m\({}^{3}\)]. Levelized costs are commonly utilized in investment planning to compare the different proposed technological solutions. For the proposed system, LCOW represents the average cost that its owner  
Figure 14: Specific energy consumption as the recirculation ratio increases for \(\Delta\)p = 0.7, 0.75 and 0.8 bar.  
Figure 13: Coefficient of performance (COP) as recirculation ratio increases for \(\Delta\)p = 0.7, 0.75 for the set point at 80 \({}^{\circ}\)C and \(\Delta\)p = 0.75, 0.8 bar for the set-point at 80 \({}^{\circ}\)C.  
will have to pay for every m\({}^{3}\) of produced freshwater throughout its life cycle. LCOW is calculated by the following formula:  
\[\text{LCOW}=\ \ \sum_{i=1}^{N_{t}}\frac{I_{i}+M_{i}+E_{i}}{(1+r)^{i}}\Bigg{/} \sum_{i=1}^{N_{t}}\frac{Q_{i}}{(1+r)^{i}} \tag{7}\]  
where \(N_{t}\) [years] is the anticipated life cycle of the unit, \(r\) [%] is the discount rate, whereas \(I_{i}\) [\(\xi\)], \(M_{i}\) [\(\xi\)], \(E_{i}\) [\(\xi\)], and \(Q_{i}\) [m\({}^{3}\)] represent the investment cost, the O&M cost, the total cost of electricity, and the total freshwater production in year \(i\), respectively.  
The following assumptions were considered for the analysis:  
1. The anticipated life cycle \(N_{t}\) of the unit is 20 years.
2. The specific electricity consumption is taken equal to 150 kWh/m\({}^{3}\), as it is the minimum electricity consumption of the unit.
3. The discount rate \(r\) is 3%.
4. The annual production of freshwater is equal to 920 m\({}^{3}\) (corresponding to 8000 operation hours annually), and it remains constant throughout the unit's life cycle.
5. The investment cost \(I_{1}\) in year 1 is estimated to be about 80,000\(\xi\), and no investments are necessary thereafter.
6. The annual cost \(M_{i}\) is considered constant and equal to 1% of \(I_{1}\).
7. An annual increase of 2% in the price of electricity is considered.  
A parametric analysis was performed with the cost of electricity \(e\) [\(\xi\)/kWh] as the varying quantity, because of the great differentiation in its value based on the installation location and the acute fluctuations noticed in the global energy market during the past few years. The values of LCOW as a function of \(e\) are listed in Table 4.  
The values of LCOW are in accordance with the values documented in the literature [29], taking into consideration that brine has an adverse impact on the environment due to its high salinity. The economic viability and competitiveness of the unit will be increased by reducing the cost of the necessary electricity input because the brine treatment process is highly energy-consuming. Ideally, this will be accomplished by integrating renewable energy sources into the system, such as PV collectors.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Basic Watermarking Scheme
#### 2.1.1 Watermark Generation Scheme  
If the embedded watermark bits are the same in each data frame, the watermark can be easily obtained by other receivers. Therefore, to implement identity authentication, the basic watermark generation method is to determine a common key, then hash the key with the user bits, and take the hashed result as watermark bits [19]. The watermark generation scheme can be shown in Figure 2. An interception step is added after the hash operation to get the appropriate watermark length. The hash algorithm used in this paper is SHA-1 [20].