esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Standard Message-Passing Process  
Based on the available node and edge feature information, the standard _MP_ is defined in Equation (16), considering the updated graph \(G\) at each convolutional layer \(l\):  
\[MP=\begin{cases}h_{i}^{l+1}=\mathcal{F}_{h}(h_{i^{\prime}}^{l},\ \{h_{j}^{l}\}_{j})_{j\in\mathcal{N}_{i}^{\prime}},c_{ij}^{l} ),\\ \ \ \ \ \ e_{ij}^{l+1}=\mathcal{F}_{e}(h_{i}^{l},\ h_{j}^{l},\ c_{ij}^{l})\end{cases} \tag{16}\]  
where \(\mathcal{F}_{h}\) and \(\mathcal{F}_{e}\) are selected differently depending on the structure of the GCNNs. In addition, in the case of undirected graphs, the usage of edge features is optional because the edge features are not included in updating the node feature in the _MP_. The features \(h_{i}^{IN}\in\mathbb{R}^{\mathrm{d}_{v}}\) and \(e_{ij}^{IN}\in\mathbb{R}^{\mathrm{d}_{v}}\) of the input data are supplied to the input layer \(l=0\) as \(h_{i}^{l=0}\in\mathbb{R}^{\mathrm{d}}\) and \(e_{ij}^{l=0}\in\mathbb{R}^{\mathrm{d}}\) through the linear embedding function (\(LL\)) in Equation (17):  
\[\begin{cases}h_{i}^{l=0}=LL_{h}(h_{i}^{IN})=A^{0}h_{i}^{IN}+a^{0}\\ c_{ij}^{l=0}=LL_{e}(e_{ij}^{IN})=B^{0}e_{ij}^{IN}+b^{0}\end{cases}, \tag{17}\]  
where \(A^{0}\in\mathbb{R}^{\mathrm{d}\times\mathrm{d}_{v}},B^{0}\in\mathbb{R}^{ \mathrm{d}\times\mathrm{d}_{v}}\), and \(a^{0}\), \(b^{0}\in\mathbb{R}^{\mathrm{d}}\) are the learnable parameters of the linear layers. In the standard _MP_ of integrating positional information in Equation (18), embedded positional information based on the transformer method is commonly used by concatenating it with the initial node feature:  
\[h_{i}^{l=0}=LL_{h}\bigg{(}\begin{bmatrix}h_{i}^{IN}\\ p_{i}^{IN}\end{bmatrix}\bigg{)}=C^{0}\bigg{(}\begin{bmatrix}h_{i}^{IN}\\ p_{i}^{IN}\end{bmatrix}\bigg{)}+c^{0}. \tag{18}\]  
where \(p_{i}^{IN}\in\mathbb{R}^{k}\) is the embedded input positional information, and \(C^{0}\in\mathbb{R}^{\mathrm{d}\times(\mathrm{d}_{v}+k)}\) and \(c^{0}\in\mathbb{R}^{\mathrm{d}}\) are the learnable parameters of the linear layers, and \([\ ]\) is the concatenate operation.  
However, this _MP_ only initially merges the graph structural and positional information; thus, the GCNNs learn based on the fixed positional information. When the position is newly changed, it cannot be recognized.
## 3 Results
### HPLC Resolution
When a large amount of sample dissolved in the strong solvent, dichloromethane, is injected into the column running a weak mobile phase, the sample could precipitate immediately and redissolve during the run and cause peak splitting and distortion. In addition, as a result of the mismatch of elution strength, the sample may be weakly retained in the stationary phase, resulting in unretained elution of part of it. The explanation for the anomalous retentive behavior has physical reasons: the analyte has to be solvated in the mobile phase, but its solubility is lower. As a result, a large amount of sampling solvent perturbs the chiral stationary phase and a portion of the analyte is forced to stay in dichloromethane and, consequently, to be eluted with dichloromethane as an unretained peak. To overcome the effects due to sample-solvent mismatch, it is advisable to dissolve and inject the sample in the same solvents used for the mobile phase. Unfortunately, this strategy can be rarely adopted because of the low solubility of the sample in the mobile phase. Pure dichloromethane was initially used to dissolve **BT2-T6** because of dichloromethane's excellent solubility. In opposition to dichloromethane, n-hexane is a bad solvent for the compound. The mobile phase in use for chromatography was a mixture of n-hexane/MeOH/dichloromethane 90:5:5 (\(v/v/v\)) in which the \"bad solvent\" is in the large majority. The way to solve that problem consists of diluting the chlorinated solution with \(n\)-hexane. However, one should be aware that as the \(n\)-hexane volume increases, the solubility of the chiral analyte reduces. Thus, different samples containing the same concentration of **BT2-T6,** but increasing percentages of n-hexane were tested. As indicated in Figure 5, good results were obtained by dissolving 5.2 mg of **BT2-T6** in 0.75 mL of dichloromethane and adding 4.0 mL of \(n\)-hexane to the solution. Under these conditions, the peak splitting was hampered and two well-separated and symmetric enantiomeric peaks were obtained. The enantiomeric forms of **BT2-T6** were collected with high enantiomeric excess (ee > 99%) and yields (95%). Considering that the chromatographic resolution was completed in about 15 min, 10 mg of each enantiomer per hour could be isolated.
## 4 Discussion
### Evaluation of High-Resolution Remote Sensing Images in Urban Carbon Research  
Compared to Landsat-8 and Sentinel-2, the lower resolution of MODIS lost many details inside the city (Figure 4b-d). In the built areas, vegetation usually accounts for a small proportion at 500 m resolution. As a result, the reflection characteristics of vegetation will be affected by the background. Meanwhile, due to the variety of land cover types within a built area, low-resolution remote sensing images fail to accurately depict the spatial variability of urban surfaces (Figure 10), thereby excluding changes in vegetation characteristics within the city. As a result, the uncertainty in estimating GPP from low remote sensing images increases. As Figure 11 shows, with the increment of spatial resolution, the contribution of the urban forest to total GPP was increased. Therefore, the photosynthetic ability of urban forests can be better figured out by fine-resolution remote sensing images. Landsat having similar performance as MODIS is partially due to its coarser resolution and lower NDVI relative to Sentinel-2, and also partially owing to missing data of high values in summer with large fractions of cloudy days.  
Figure 10: _Cont._  
Figure 11: The contribution of urban forests to total GPP. The percentages of 0.073\({}^{\circ}\) and 0.05\({}^{\circ}\) are from Ju [51] and Zhang [50], respectively. Additionally, results of 500, 30, and 10 m resolutions were calculated by MODIS, Landsat-8 and Sentinel-2 in this study.  
Figure 10: The GPP variation coefficient (CV) of Sentinel-2 (**a**) and MODIS (**b**).  
With its 10 m resolution, the Sentinel-2 GPP can provide more information about vegetation dynamics in built areas. Despite the fact that Landsat-8 GPP has a relatively high resolution to explore the dynamic of urban forests, the 16-day revisiting period made the images of Landsat-8 subject to cloud cover. For example, for July and August of 2021, the whole regional average NIRv of Landsat 8 cannot be generated. Vegetation is at a growing peak during these two months, which is vital for the yearly total GPP. Therefore, the 5-day revisiting period and higher spatial resolution of Sentinel-2 images make them ideal for studying GPP on an urban scale.
# An Intelligent Warning Method for Diagnosing Underwater Structural Damage
## 3 Experiment and Result Analysis
### Image Acquisition and Processing  
An underwater micro-damage visual diagnosis system was established according to the characteristics of underwater structures, as shown in Figure 4. The system mainly contains a HCCK-102 series crack detection system (including industrial camera, industrial endoscope, control interface), maintenance pool, concrete test block, computer etc. Among them, the industrial endoscope has a magnification of 40x and a measurement accuracy of 0 to 0.08 mm. The micro-damage image of the underwater concrete test block was obtained based on the diagnosis system. The image definition was extremely reduced due to the turbid water quality, insufficient light, and the configuration of the protective cover for the micro-probe. Underwater micro-damage images were processed by ROI (region of interesting) interception, histogram equalization, and a median filter.  
Figure 3: The diagram of the diagnostic process.
# Atomicity Violation in Multithreaded Applications and Its Detection in Static Code Analysis Process
## 5 Multithreaded Application Source Code Model
### Relation Breakage  
The order of execution of an operation within one thread may cause the result of one operation to be overwritten by the result of another operation. As a result, a further operation in relation to both operations will always receive the result only from the one that occurs later. Such a situation is called a relation breakage and takes place in the code from the Figure 5, the graphical representation of this code is shown in Figure 6. According to the model from Section 5 and Table 2 the set of relations backwards is as follows:  
\[B_{P}^{BWD}=\{(o_{1,1},o_{1,12}),(o_{1,A},o_{1,12}),(o_{1,6},o_{1,12}),(o_{1,11 },o_{1,12})\} \tag{2}\]  
Relationship between operations of pairs \((o_{1,1},o_{1,12})\) it is always broken because in a series of subsequent operations there will always be other operations forming a relationship with the operation \(o_{1,12}\). The relationship between operations \((o_{1,A},o_{1,12})\) will also be broken. The other two pairs of operations \((o_{1,6},o_{1,12})\) and \((o_{1,11},o_{1,12})\) are placed in two mutually exclusive code blocks, so there will be no breakage, but each of them is the cause of breaking the previous two pairs.  
Based on the previous paragraph and on the definition of the subgraph \({}_{i}^{s}G_{P}\)[2], the definition of a relationship breaking up is as follows.  
Figure 4: Subgraph representing the code from Figure 3.  
Figure 5: Pseudocode showing the process of breaking relationships.  
Figure 6: A subgraph representing the code from Figure 5.  
Definition 2: If there are pairs of operations in the i-th thread, \((o_{i,j},o_{i,l})\) and \((o_{i,k},o_{i,l})\) belong to any subset of the \(B_{P}\) sequence, and in each subgraph path there are \({}_{i}^{s}G_{P}\) operations \(o_{i,j}\), \(o_{i,k}\) and \(o_{i,l}\), such that \(j<k<l\) occur, then the relationship \((o_{i,j},o_{i,l})\) is called completely broken.  
Definition 3: If, in the i-th thread, there are pairs of operation \((o_{i,j},o_{i,l})\) and \((o_{i,k},o_{i,l})\) belonging to any subset of \(B_{P}\) sequence, and there is a path of subgraph \({}_{i}^{s}G_{P}\), in which between operations \(o_{i,j}\) and \(o_{i,l}\) the operation \(o_{i,k}\) does not occur, then the relationship \((o_{i,j},o_{i,l})\) is called conditionally broken.  
Broken relationships are a consequence of two operations, of which the second invalidates the result of the first operation--e.g., by overwriting the value of the shared resource. If a relationship is broken, it should not be present in the sets of \(B_{P}\) sequences. Therefore, if there are relationships that are not broken or are conditionally broken, they may be disrupted by an operation from another thread, leading to atomicity violation.
# Pseudorange Measurement Method Based on AIS Signals
## 7 Conclusions  
This paper studied the most basic pseudorange measurement problem in AIS-R mode in depth. The theoretical analysis and simulation verification of the key problems in the AIS signal transmission delay measurement (such as timestamp option, the detection method of the timestamp object, and the optimization of the measurement accuracy) are carried out. On this basis, a set of completed measurement programs is put forward. The results of this study make the AIS system add a new pseudorange measurements function without changing the original equipment.  
By considering that the AIS uses the frequency modulation method, the carrier phase was not suitable as a timestamp to be measured. Instead, the bit 0 phase was selected as the timestamp object. Through the mathematical modeling analysis for an AIS message frame (in addition to each two bits of the header and the tail), the timestamp objects corresponding to the other bits have two characteristics: the amplitude is equal to 0 and the differential result is the peak. The comparison shows that in the AWGN channel, the accuracy of the zero-crossing detection method was higher than that of the differential peak detection method. As the ship is a low-speed and low-dynamic motion carrier, the measurement accuracy of the pseudorange is greatly improved by the optimal estimation algorithm based on the MMSE. The Monte Carlo simulation results show that the pseudorange measurement accuracy of the optimal estimation algorithm based on the zero-crossing detection method was consistent with the conclusion deduced from the modified Cramer-Rao Bound in [23]. Furthermore, the measurement results obtained by combining the zero-crossing detection and differential peak detection were further improved under low SNR conditions.  
The final field test results show that the pseudorange measurement accuracy of the functional prototype was better than 28 m (\(\sigma\)). For such large vessels as ships, the results have been able to meet the positioning requirements. Furthermore, as the measurement scheme proposed in this paper had no special requirements for the message format, the channel capacity of the AIS was not increased. In conclusion, the theoretical measurement accuracy has achieved or exceeded that of other visible studies.  
Our future work will look at optimizing the RF circuit by reducing system noise and optimizing the performance of the pre-filter to improve the pseudorange measurement accuracy of the principle prototype.  
This research was partially supported by the Chinese National Science Foundation (Nos. 61231006 and 61501078) and the Fundamental Research Funds for the Central Universities (No. 3132016208).  
[PERSON] designed the measurement scheme, carried out the simulations, and wrote the paper; [PERSON] supervised the work, arranged the architecture, and contributed to the writing of the paper; and [PERSON] performed the field test and analyzed the data.  
The authors declare no conflicts of interest.
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 3 Analyses of Possible Solutions and Their Adaptation to mURLLC
### MCS Selection
#### 3.6.2 Two TXs Case  
In this case, the gNB selects two MCSs. The simplest approach considered in many papers is to use the same MCS for the first and the second transmissions: \(MCS_{2}=MCS_{1}\), where \(MCS_{1}\) is selected using the OLLA algorithm with the target BLER \(p_{1}=\sqrt{PLR^{QoS}}\). By taking into account HARQ combining gain, it is assumed that \(p_{2}\leq p_{1}\) and, thus, the overall reliability requirement is satisfied.  
Since 3 GPP specifications allow the use of different MCSs for various transmission attempts, we propose the following approach. We select two target BLERs such that \(p_{1}\cdot p_{2}\leq PLR^{QoS}\) (the specific configurations are analyzed in Section 4). For each TX, the target BLER is provided by a separate OLLA adjustment. Note that in the case of ACK/NACK feedback, SINRs for the second TX might be higher than for the initial transmission and, thus, selecting \(MCS_{2}>MCS_{1}\) reduces resource consumption for retransmissions.
## 3 Experiments
### Experiment Design  
One can verify the torque compensation strategy by comparing the steady-state speed error (SSSE) before and after the compensation. As for the HIL experiment, one can easily compare the cogging torque model and its estimation at each position to verify the performance of the proposed observer. Three different experimental conditions were implemented.  
* In order to verify the accuracy of the online cogging torque compensation, the procedures are conducted in the following steps: 1. Control the motor speed to rated speed under no load condition. 2. Decrease the speed command to find the most appropriate operation speed. 3. Apply the online torque compensation with the cogging torque learning strategy and compare the SSSE before and after compensation. 4. Compare the observed cogging torque \(\hat{T}_{Cog}(\theta_{m})\) with the HIL cogging torque model.
* In order to verify the accuracy of offline cogging torque compensation, the procedures are conducted in the following steps. 1. Control the motor speed to the most appropriate operation speed and apply the offline torque compensation strategy. 2. Verify the offline compensation, which can lower the noise compared with online compensation. 3. Decrease the speed command to lower controllable speed and verify the speed performance.
* As for the rated load condition, the procedures are conducted in the following steps. 1. Control the motor speed to obtain the most appropriate operational speed under the rated load condition. 2. Apply the offline torque compensation and compare the SSSE before and after compensation. 3. Decrease the speed command to lower controllable speed and then verify the speed performance.
## 2 Distributed Inertial Network System and Aircraft Elastic Deformation
### Aircraft Elastic Deformation Model  
The inertial sensors are installed in different parts of the aircraft with a distributed structure, and the deflection motion of the carrier structure limits the further improvement of the navigation accuracy of the inertial network system. In order to effectively overcome this adverse effect, it is necessary to further establish a more accurate and practical model of structural deflection motion and study a certain algorithm to correct and compensate for the influence of structural deflection.  
The elastic deformation causes an additional rotation angle \(\theta\) from the master node to the child node, and when the elastic deformation changes with time, there will be an additional angular velocity \(\dot{\theta}\) measured by the child node gyroscope, which cannot be measured by the master node. Assuming that there is an installation error angle \(\psi\) between the primary and secondary nodes, the total misalignment angle between the primary and secondary nodes is \(\delta\), then the relationship between the three can be given by the following:  
\[\delta=\theta+\psi \tag{6}\]  
The body between the main and sub-nodes constitutes a large elastic system, and in the case of deformation, process noise is selected to describe the motion on each axis. Therefore, this kind of deflection motion can be regarded as a second-order Markov process, the parameters of the model can be determined by structural mechanics experiments [32].  
The flexural motion model represented by the second-order Markov process can be written as  
\[\ddot{\theta}+2\beta\theta+\beta^{2}\theta=\rho \tag{7}\]  
In Formula (7), \(\theta=\left[\begin{matrix}\theta_{x}&\theta_{y}&\theta_{z}\end{matrix} \right]^{T}\) is the elastic deformation angle vector, where the variance \(\sigma^{2}=\left[\begin{matrix}\sigma^{2}_{x}&\sigma^{2}_{y}&\sigma^{2}_{z} \end{matrix}\right]^{T}\); moreover, the parameter \(\beta=2.146/\tau\), \(\tau\) is the correlation time of the random process of the elastic deformation of three axes; \(\rho\) is a white noise with a certain variance, and its spectral density \(Q=4\beta^{3}\delta^{2}\), that is, \(\rho\sim N(0,Q)\).  
The coordinates of the sub-node \(i\) in the main node coordinate system are \(\left[\begin{array}{ccc}x_{i}^{1}&y_{i}^{1}&z_{i}^{1}\end{array}\right]^{T}\). Considering the constraints of the wing structure, the relative displacement caused by the deflection deformation is set as  
\[P_{f}^{1}=\begin{bmatrix}P_{fx}^{1}\\ P_{yy}^{1}\\ P_{zz}^{1}\end{bmatrix}=\begin{bmatrix}0\\ x_{i}^{1}\theta_{fz}-x_{i}^{1}\theta_{fx}\\ -x_{i}^{1}\theta_{fy}\end{bmatrix}\quad V_{f}^{1}=\begin{bmatrix}V_{fx}^{1}\\ V_{yy}^{1}\\ V_{zz}^{1}\end{bmatrix}=\begin{bmatrix}0\\ x_{i}^{1}\omega_{fz}-x_{i}^{1}\omega_{fx}\\ -x_{i}^{1}\omega_{fy}\end{bmatrix} \tag{8}\]
# Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
## 5 Experiments
### Road Segmentation
#### 5.1.2 Results on BDD100K  
The model is validated on the Drivable Area Detection benchmark of the BDD100K [21] dataset. The mIoU of Swin-APT on the BDD100K [21] dataset is reported, and a comparison with previous works is presented in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **mIoU (\%)** \\ \hline MultiNet [18] & 71.6 \\ DLT-Net [19] & 72.1 \\ YOLOv8n(seg) [[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)]([https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)) & 78.1 \\ (accessed on 12 February 2023) & 89.6 \\ PSPNet [26] & 90.5 \\ HybridNets [27] & 91.0 \\ A-YOLOM [28] & 91.0 \\ Swin-APT & **91.2** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison results of mIoU on the BDD100K dataset. Numbers in bold represents the best performance.  
Swin-APT showcases a clear performance advantage over the other methods in this comparison. Swin-APT stands out as the top-performing model with an impressive mIoU of 91.2%. Swin-APT significantly outperforms the baseline methods like MultiNet and DLT-Net, achieving mIoU scores of 71.6% and 72.1%, respectively. PSPNet and HybridNets are specialized models for semantic segmentation, yet Swin-APT outperforms both. A-YOLOM is a recent state-of-the-art model, but Swin-APT surpasses it with a higher mIoU of 91.2% versus A-YOLOM's 91.0%. This indicates that Swin-APT represents a significant advancement in this task. In conclusion, Swin-APT distinguishes itself as a top-performing model in road segmentation on the BDD100K dataset, consistently outperforming various previous models and even recent advancements.
# Multi-formalism Models for Performance Engineering
## 3 Queueing Networks Techniques
### Approximate Computing with Finite Capacity Region  
The evolution of hardware and software architectures is making approximate computing an emerging technology with great potential. This computing paradigm is based on a very simple concept: an approximate result obtained in a short amount of time is very often sufficient to achieve the objectives of the application with the required accuracy. Essentially, this method trades off accuracy of results with the requested time for their computation.  
Figure 5: Behavior of Response Time for timeout ranging from 1 to 30 s, \(\lambda=0.9\) r/s for a variable number \(K\) of VMs.  
Figure 6: Trade-off between deployment cost (number \(K\) of VMs) and user satisfaction for different arrival rates \(\lambda\).  
Examples of applications range from parallel search engines to data analytics, video streaming compression, statistical analysis of large datasets, MapReduce transactions, and special hardware implemented for this purpose, e.g., Neural Network Accelerators. The wide range of applications requires the implementation of many approximation techniques. Among them, the one based on the parallel execution of several tasks of an application and a stopping condition based on the accuracy level required is very popular. Deploying approximate computing in a cloud computing scenario is particularly relevant, as this approach allows containing the costs of autoscaling algorithms by imposing a threshold to the acquired VM numbers (without affecting the performance and maintaining the system responsiveness to the requests' bursts). The VMs parallelism degree is not increased, at the cost of lowering down the results quality.  
In the following, we show a simple model of approximate computing based on parallel computations, implemented with a _Fork_ primitive, and a control condition based on the accuracy level required, implemented with a _Join_ primitive and advanced synchronization [29].  
Thus, approximate computing has the potential to benefit a wide range of application frameworks, e.g., data analytics, scientific computing, multimedia and signal processing, machine learning and MapReduce.