esa-sceva/satcom-chunk-collection huggingface dataset:
# MpcNet: A Vision Transformer Network for Estimating Room Layouts from a Single RGB Panorama
## 4 Experimental Results and Discussion  
### Experimental Settings  
**Environment setting:** In our experiment, we used a server equipped with an Intel Xeon E5-2650 V2 CPU and GeForce GTX 3090 GPU for training. The network was implemented in PyTorch.  
**Datasets:** In terms of datasets, we used the PanoContext [24] and Stanford 2D-3D [25] datasets to train and evaluate the proposed network model. The PanoContext dataset contains 500 annotated cuboid layouts, including indoor environments such as bedrooms and living rooms. Since the resolution of panoramic images in the original PanoContext dataset was 9104 \(\times\) 4552, which did not meet the requirements of the model in this paper for input images, the resolution of all images in the PanoContext dataset was changed to 1024 \(\times\) 512 in this paper.  
**Hyperparameter setting:** We employed the Adam optimizer [26] to train the network for 300 epochs and a learning rate of 0.0003. The training process is analyzed in the following sections. We set the number of sparse attention layers as three and the number of enhancement layers as three. We also tested different numbers of sparse attention layers and enhancement layers, e.g., six and nine. With the increase of the number of layers, there was no significant improvement, but the training time was greatly increased. We set the batch size of the training as four and the batch size of the validation as two. Through experiments, we found that different batch sizes did not cause visible differences, but a big batch size easily caused an out-of-memory error from the GPU.  
**Training time:** The training time overhead of MreNet was 0.0184 h per epoch and that of HorizonNet was 0.0179 h per epoch. Due to the introduction of the multihead attention of Vision Transformer into MreNet which brought extra parameters, the time consumption of MreNet increased slightly. However, compared to HorizonNet, considering that MreNet could achieve more than 0.68% of improvement in 3D IoU, the 0.027% time overhead was tiny.
## 3 Network Model
### Primary User Model  
The PU is assumed either to be ON a channel or OFF the channel. The time spent ON and OFF the channel is modeled as independent and identically distributed random variables with mean values \(\mu_{on}\) and \(\mu_{off}\), respectively [1]. The probability of a PU being ON the channel, \(p_{on}\) is thus given by the following equation:  
\[p_{on}=\frac{\mu_{on}}{\mu_{on}+\mu_{off}} \tag{2}\]  
The probability of a PU being OFF the channel, \(p_{off}\) is given by the following equation:  
\[p_{off}=\frac{\mu_{off}}{\mu_{on}+\mu_{off}} \tag{3}\]  
The fact that we adopt the overly spectrum approach means that the bigger the size of \(p_{off}\), the more chances of having an SU on that particular channel.
# A Method to Reduce Non-Nominal Troposphere Error
## 6 Conclusions  
GBAS is unable to eliminate troposphere delay using the differential correction method when abnormal troposphere occurs, which leads to larger differential residual troposphere error.  
Based on the recorded abnormal troposphere conditions, this paper analyzed the relationship between non-nominal troposphere error and satellite elevation in detail. The results show that the non-nominal troposphere error decreases as the satellite elevation increases. Therefore, to reduce the non-nominal troposphere error, the number of low-elevation satellites within the constellation should be decreased while ensuring a good satellite geometry.  
To analyze the influence of non-nominal troposphere error, the VPLs of different satellite subsets are computed. Results showed that, once the constellation geometry is optimized, more satellites would increase the non-nominal troposphere error in the positioning solution and, as a results, increase the airborne VPL.  
This paper also analyzed the characteristics of optimal satellite subsets. The results showed that the number of satellites in the optimal subset is 16 and that the stable distribution ratios in the elevation ranges of (\(5^{\circ},30^{\circ}\)), (\(30^{\circ},60^{\circ}\)) and (\(60^{\circ},90^{\circ}\)) can be set to 1:2:1.  
In view of the above analysis, based on the proposed method, the average VPL increase caused by non-nominal troposphere error was found to be decreased by 1.17 m. Results showed that the proposed method can balance the satellite geometry and non-nominal troposphere error, which minimizes the airborne VPL and improves the GBAS integrity. In addition, the program running rate was clearly improved by 390.91% and 111.65% compared to the brute and greedy methods, respectively. These results revealed that the running rate is greatly improved, which indicates the feasibility in the practical operation.  
The authors thank many of their colleagues at the National Key Laboratory of CNS/ATM for their advice and interest. The authors also acknowledge the data support provided by the International GNSS Monitoring Assessment System stations. This work was conducted with financial support from the National Natural Science Foundation of China (Grant Nos. 61501010, U1433114, and 61501014), the Aeronautics Science Foundation (Grant No. 2015 ZC51035) and the major project on the second satellite navigation system of China (Grant No. GFZX0305030106).  
[PERSON] and [PERSON] conceived of and designed the research. [PERSON] and [PERSON] performed the experiments and analyzed the result. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] were responsible for data acquisitions, data processing and project management.  
The authors declare no conflict of interest.
# Federated Learning for Secure In-Vehicle Communication
## 2 Backgrounds and Preliminaries
### Attacks Types
#### 2.2.1 Denial-of-Service (DoS) Attack  
A DoS attack or flood attack as described in [20] on the CAN bus attempts to overwhelm the network with a high rate of messages, consuming bandwidth and preventing legitimate traffic. This kind of interference can result in system instability, malfunction, or temporary disablement of some vehicle components.
## 1 Introduction
For the abovementioned reasons, in this paper, we decided to put our efforts into developing an unsupervised DL method for high-level semantic segmentation of buildings' point-clouds. We explored the possibility of learning a point-cloud segmentation network by only supplying limited task-specific labeled data. To conduct such a _label-efficient unsupervised DL network_ (3 DLEB-Net) we introduced the Autoencoder (AE) architecture to simultaneously learn reconstruction and discriminative features of the input 3D building point clouds without any label. To achieve this, we leveraged the Dynamic Graph Convolutional Neural Network (DGCNN) [21] as our _encoder_ and FoldingNet [22] as _decoder_ to acquire powerful embeddings from complex buildings' point clouds without any labeled data. To this end, with limited labeled data, we designed three fully connected layers in an end-to-end segmentation network to achieve the downstream segmentation task. In particular, our contributions may be summarized as follows:  
1. We proposed a novel AE network to learn powerful feature representations from a non-labeled complex-building point-cloud dataset, and the pre-trained AE may be used in the high-level downstream semantic segmentation task;
2. We trained an end-to-end segmentation network for the buildings' segmentation task. The output of our model is a semantically enriched LoD3 3D building representation; and
3. We experimentally demonstrated how to exploit limited labeled point clouds to segment input point clouds of buildings. The result shows that our result either surpasses or achieves performance comparable to the one of recent state-of-the-art methods, with only 10% of training data.
# Flexural Behavior of Corroded High-Speed Railway Simply Supported Prestressed Concrete Box Grider
## 3 Analytical Model  
### Impact of Strand Corrosion  
It was discovered that the effects of strand corrosion on flexural behavior mainly include the following four aspects: strand cross-section reduction, strand property degradation, concrete cracking in the tensile zone, and the deterioration of the bond between surrounding grouting and strands. As post-tensioned prestressed beams transfer force through anchorages, the deterioration of the bond has little effect on the flexural behavior of PC beams [31]. Thus, the analytical model in this paper takes into account the former three aspects.
# Variable Curvature Displays: Optical Designs and Applications for VR/AR/MR Headsets
## 4 Optical Design Example
### Performance Analysis  
To demonstrate the achieved image quality, we analysed the energy concentration in the point image. The reference element is \(11\times 11\)\(\upmu\)m, which corresponds to a typical display pixel size [11; 17]. Additionally, the energy concentration was compared for \(22\times 22\)\(\upmu\)m (i.e., \(2\times 2\) pixels) area. The ensquared energy plots for all the configurations are shown in Figure 4.  
The grid of subplots clearly demonstrates the advantages of introducing the image surface curvature and being able to variably adjust it. For example, the static curved (\"CS\") image surface allows increasing the energy concentration for the FoV corner by factor of 1.22. The variable curvature (\"CV\") solution makes the values higher by a factor of 1.54 and allows maintaining them with a large refocusing.  
Figure 3: Overview of the optical design illustrating the variable field curvature concept.  
We may note here that some secondary performance targets were not included directly into the merit function during the optimisation, yet some gain was obtained. For instance, the **\"CS\"** demonstrates significant advantage in the relative illumination and the **\"CV\"** configuration shows a good illumination stability with refocusing. At the same time, the image distortion for these configurations appears to be slightly higher than that for the **\"FS\"**. However, the distortion values remain low for all configurations. The exact numbers are summarised in Table 2.
# Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
## 2 Literature Review
### Breath Hold  
During the breath-hold technique, the patient holds their breath for specific intervals, during which images are acquired. In comparison to scans taken with slow CT, images under breath-hold are less blurry, with reduced artifacts. [PERSON] et al. [10] performed a study to evaluate the effectiveness of the breath-hold technique in replicating the tumor location during radiotherapy. It was possible to reproduce the tumor location at an average of within 1 millimeter for the thirteen patients included in the study. It was also highlighted that the motion margin for dose delivery can be lowered to less than 2 millimeters with the breath-holding technique. In order to assist patients with breath-hold, active breath coordinator (ABC) devices can be used. The most prominent limitation of this method is that it causes discomfort to the patient, as it requires them to hold their breath for at least 10 s.
# A Nodal Analysis Based Monitoring of an Electric Submersible Pump Operation in Multiphase Flow
## 5 Results
### Changes in the PIP and \(P_{\text{disch}}\).--A Field Example  
A field example was used to evaluate the observed changes in PIP and Pd. Example 1 (Figure 13a) shows that the ESP was operating stably when a sudden gas increase occurred. This was followed by an abrupt increase in the PIP and a decrease in the Pd. The ESP was gas locked briefly but quickly resumed normal operation. A PIP increase and a Pd decrease normally signal a gas lock [34]. Example 2 (Figure 13b) illustrates automatic ESP shutdown when the differential pressure becomes zero. In this example, an increase of gas at the ESP intake caused the PIP to rise and the Pd to fall. The ESP operated at increasingly low production rates until the PIP and Pd attained the same values; the ESP automatically shut down. Example 3 (Figure 13c) shows the PIP and Pd variations caused by severe gas degradation. When the pump ingests substantial gas, ESP efficiency is reduced as the Pd falls and the PIP rises. These trends continue until the lift is insufficient and the ESP shuts  
Figure 10: PIP and Pd deviations from the design conditions for two cases. (**a**) Case 1: ESP deployed in a dead single-phase well at a fixed GOR = 100 SCF/ST and a water cut = 0; (**b**) case 1: ESP deployed in a dead multiphase well at a fixed GOR = 2218 SCF/STB and a water cut = 92.9%; (**c**) case 2: ESP deployed in a naturally flowing single-phase well at a fixed GOR = 100 SCF/STB and a water cut = 0; (**d**) case 2: ESP deployed in a naturally flowing multiphase well at a fixed GOR = 2218 SCF/STB and a water cut = 92.9%.  
down. The wellhead production rate becomes zero, as shown by the constant wellhead pressure [39]. These field examples show that our model will find practical applications.
## 7 Discussion  
This article aims to comprehensively understand the magnitude of external advertising's influence on customer decision-making in the digital domain. To enhance the precision of the research and data, this study measures the effects of external advertising on drivers and their decision-making processes. To simulate the online environment, the researchers employed A/B testing, wherein drivers were promptly presented with two options after driving. This testing method was designed to elicit a choice between the brand and the product itself, resembling the process of online shopping. To ensure that drivers would adapt their behaviour and preferences accordingly, they were given prior notification of this experimental setup.  
To elucidate conscious consumer preferences, an extensive interview was conducted. The primary objective of this interview was to ascertain the frequency with which the participating drivers engage in online purchasing. It is worth noting that over two-thirds of the sample reported engaging in monthly online transactions. This substantial figure lends credence to the research outcomes and their relevance in the context of consumer behaviour within the digital landscape. Given drivers' familiarity with this form of shopping, their decision-making is inherently connected to the online realm.  
Following a meticulous interview process and subsequent A/B testing, it was revealed that most drivers did not consciously perceive external advertising while driving. Nevertheless, the measurements obtained from the study unveiled that drivers are most strongly attracted to advertisements featuring visually appealing graphics, appropriate sizes, and captivating colours. This finding is paramount for advertisers, as it enables them to effectively engage consumers through three key mechanisms: establishing contact, capturing attention, and placing advertisements in optimal locations. Armed with this knowledge, advertisers can leverage visually appealing graphics, appropriately sized ads, and captivating colours to ensure that their advertisements are seen and noticed by drivers.  
The subsequent inquiry pertains to the impact of external advertising on drivers' purchasing behaviour. Most participants reported limited awareness of the influence exerted by such advertising on their decision-making. However, it is important to note that further investigation and comprehensive assessment utilizing additional tools would be required to corroborate or refute this observation definitively. It is pertinent to acknowledge that the research presented in this article was conducted from the driver's perspective, with data collection facilitated by an eye tracker, thus limiting the scope of aspects that could be evaluated. When questioned regarding the influence of external advertising on their purchasing decisions, nearly all drivers asserted that it does not affect their decision-making process. The collected measurements partially substantiate this finding. Nonetheless, the impact of external advertising on drivers' purchasing decisions, albeit minor, warrants further exploration and investigation.
## 3 Proposed Differential Coherent Bit Synchronization Algorithm  
### Differential Coherent Algorithm  
The differential coherent algorithm for bit synchronization was first introduced in [19]. The differential coherent algorithm is implemented on the coherent integration output, by multiplying the present coherent integration result with the delay conjugate of the last one. The mathematical representation of the conventional DC method is:  
\[\begin{array}{l}Q(k)=Y(k)\cdot Y(k-1)^{*}=[0.5 AR(\delta\tau)]^{2}\exp(j\cdot \pi\delta f_{d}T_{c})\\ \times D_{k}D_{k-1}H_{k}H_{k-1}\text{sinc}[\delta f_{d}(k)T_{c}]\text{sinc}[ \delta f_{d}(k-1)T_{c}]\end{array} \tag{10}\]  
where \(()^{*}\) denotes the conjugate operation, the coherent integration time \(T_{c}\) is 1 ms, \(\pi\delta f_{d}T_{c}\) is the carrier phase deviation between \(Y(k)\) and \(Y(k-1)\). The sign of \(Q(k)\) changes due to bit transitions.  
Assuming the integration time is 20 ms, the accumulation output of differential coherent values is defined as:  
\[\begin{array}{l}\sum\limits_{k=1}^{u}Q(k)=\sum\limits_{k=1}^{u}Y(k)\cdot Y(k- 1)^{*}=[0.5 AR(\delta\tau)]^{2}\exp(j\cdot 2\pi\delta f_{d}T_{c})\\ \times\sum\limits_{k=1}^{u}D_{k}D_{k-1}H_{k}H_{k-1}\text{sinc}[\pi\delta f_{d} (k)T_{c}]\text{sinc}[\pi\delta f_{d}(k-1)T_{c}]\end{array} \tag{11}\]  
where \(u\) is the accumulation time.  
For GPS signals, because the sign of product can only change at each bit boundary, there is at most one change in 20 ms and it corresponds with the real bit boundary, but for BeiDou signals, the data bit rate increases to 1 kbps and 10 bit transitions occur in every navigation data bit, so the algorithm for BeiDou signals must be different.
# Change and Driving Factor Analysis of Eco-Environment of Typical Lakes in Arid Areas
## 4 Discussion
### Analysis of the Suitable Area of Taitema Lake
From the current water surface formation of Lake Taitema, it is observed that the water volume and peak flow rate of the Tarim River into the lake are both high [47; 48], indicating the limited capacity of the Tarim River source, and upper and middle reaches regulation projects. Due to various factors, such as source water inflow and losses along the way, the lake area of Lake Taitema can only be rapidly expanded in the short term using ecological water conveyance during the flood season, while ensuring the supply of water for domestic and industrial use. Despite improvements in water supply efficiency in the lower reaches of the Tarim River, there is still room to optimize water resource use and achieve the unified regulation of water resources throughout the entire basin. Therefore, it is necessary to further strengthen the capacity of source and mainstream regulation projects in the Tarim River basin [49]. Water resources are the primary limiting factor in ecological environment safety, and sustainable economic and social development in arid areas [50; 51]. For Taitema Lake, it is important to use the currently available ecological reservoirs for artificial regulation and operation. To prevent ecological water from entering Taitema Lake all at once and to avoid large-scale attenuation of the lake area during hot and dry periods, leading to environmental deterioration, a more scientific and reasonable water resource regulation plan should be developed based on the relationship between water discharge from the Tarim River and the lake area [52]. According to analysis and research on the average water level change process and frequency of Taitema Lake over the past 20 years, taking into account ecological water conveyance and climate factors, maintaining a water area of 30-110 km\({}^{2}\) for Taitema Lake is relatively suitable.
## 3 Mi-Sdp Reformulation
### Sdp Model for Power Flow Equations  
To obtain a convex equivalent of the constraints (2) and (3), let us introduce the matrix of variables \(X\) as follows:  
\[X=VV^{T}, \tag{10}\]  
Figure 1: Characterization of the mathematical model associated with the optimal location and sizing of DGsin DC grids.  
where  
\[V=\begin{bmatrix}v_{1}\\ v_{2}\\ \vdots\\ v_{n}\end{bmatrix},\qquad X=\begin{bmatrix}v_{1}^{2}&v_{1}v_{2}&\cdots&v_{1}v_{n}\\ v_{2}v_{1}&v_{2}^{2}&\cdots&v_{2}v_{n}\\ \vdots&\vdots&\ddots&\vdots\\ v_{n}v_{1}&v_{n}v_{2}&\cdots&v_{n}^{2}\end{bmatrix}.\]  
Note that each column of the matrix \(X\) contains all the products between the voltage variables of each node. In addition, this matrix exhibits asymmetric behavior; i.e., \(X=X^{T}\).  
Now if we remember that the conductance matrix \(G\) is a positive semidefinite matrix (also symmetric) with the structure presented below:  
\[G=\begin{bmatrix}G_{11}&G_{12}&\cdots&G_{1n}\\ G_{21}&G_{22}&\cdots&G_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ G_{n1}&G_{n2}&\cdots&G_{nn}\end{bmatrix},\]  
and that with the vectors of the power in slack nodes, power generation in distributed sources and power demands can be rewritten as the following:  
\[P^{s}=\begin{bmatrix}p_{1}^{s}\\ p_{2}^{s}\\ \vdots\\ p_{n}^{s}\end{bmatrix},\qquad P^{sd}=\begin{bmatrix}p_{1}^{gd}\\ p_{2}^{gd}\\ \vdots\\ p_{n}^{gd}\end{bmatrix},\qquad P^{d}=\begin{bmatrix}p_{1}^{gd}\\ p_{2}^{gd}\\ \vdots\\ p_{n}^{d}\end{bmatrix};\]  
then, the power balance constraint (2) can be expressed using a vector structure as follows:  
\[P^{s}+P^{gd}-P^{d}=\mathbf{diag}\left(GX\right), \tag{11}\]  
where \(\mathbf{diag}\left(\cdot\right)\) is an operation that takes the diagonal of a matrix with dimensions \(n\times n\) and translates it into a vector with dimensions \(n\times 1\). Note that the power flow constraint defined in Equation (11) is now an affine constraint with the form \(Ax=b\), which has allowed the rewriting of the optimal power flow problem for optimal dimensioning of DGs in DC grids as a convex constraint.  
In the case of power flow in lines, this expression is easily written by observing in the definition of the matrix \(X\) that \(X_{km}=v_{k}v_{m}\) and \(X_{kk}=v_{k}^{2}\), which implies that (3) takes the following form:  
\[p_{km}=g_{km}\left(X_{kk}-X_{km}\right),\ \forall km\in\mathcal{L} \tag{12}\]  
**Remark 4**: _It is important to highlight that in the case of semidefinite programming, for the \(X\) matrix, a nonlinear constraint associated with the rank of this matrix appears. This implies that for recovering exactly all the voltage variables \(V\), it is mandatory that \(\mathbf{rank}\left(X\right)=1\). However, these conditions are a non-convex constraint that we can relax as recommended in [26] to obtain a complete convex reformulation of the optimal power flow model associated with the problem of the optimal dimensioning of DGs in DC networks._