esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Electron Trajectories in XUV-Initiated High-Harmonic Generation
## 3 Theoretical Methods
### Numerical Integration of the Time-Dependent Schrodinger Equation  
We calculate XUV-initiated HHG in helium using a numerical integration of the TDSE employing the QPROP solver [PERSON] _et al._ (2016). In the single active electron approximation, the atomic potential of helium is well described by a parametrized model potential [PERSON] _et al._ (2016). We use 787 nm laser pulses with a peak intensity of \(8\times 10^{13}\,\mathrm{W}\,\mathrm{cm}^{-2}\) and a FWHM intensity duration of 25 fs. The APT is modeled as a pulse train consisting of the H11-H17 harmonic orders, with a FWHM duration of 8 fs (for detailed definitions of the fields see Appendix B). We calculate the time-dependent dipole as a function of the XUV-IR delay \(\Delta t\) and perform a Fourier transformation in order to retrieve the resulting XUV dipole spectrum. Here we do not include pulse propagation effects [PERSON] _et al._ (2016), but focus solely on the single-atom response in XUV-initiated HHG. Figure 2b shows the dipole spectrum calculated with the TDSE without and with the IR field at zero delay. XUV-initiated HHG is clearly visible, in good agreement with the experimental results although macroscopic effects are not included.
## 3 Results
### Response Time of Slave and Master Modules
#### 3.4.1 Mathematical Model without Multiprocessing or Virtual Wires  
Let T_slave be the function representing the response time of the slave module in seconds.  
Let T_master be the function representing the response time of the master module in seconds.  
Let C be the workload (number of tasks) assigned to both modules.  
N is the degree of the polynomial function used to model the response time of the slave and master modules.  
The choice of N in the polynomial equations for the response times of the master and slave modules is based on the complexity of the relationship between response time and workload. Specifically, N determines the degree of the polynomial that best fits the actual data. To find the optimal value of N, model fitting and validation techniques are used.  
This polynomial function allowed us to express the response time (T) of each module as a function of the workload (C) assigned.  
Equations (2) and (3) are fundamental because they provide a mathematical framework for modeling the response times of the master and slave modules as polynomial functions of the workload. These models allow for predicting how response time will vary with different workloads, which is necessary for designing efficient and predictable systems.  
General mathematical model:  
* For the slave module: The response time of the slave module (_T_slave_) can be expressed as a polynomial function of degree N: \[T\_slave(C)=a^{0}+a^{1}C+a_{2}C^{2}+a_{3}C^{3}+\ldots+a_{n}C^{n}\] (2)
* For the master module: The response time of the master module (_T_master_) can also be expressed as a polynomial function of degree N: \[T\_master(C)=b_{0}+b_{1}C+b_{2}C^{2}+b_{3}C^{3}+\ldots+b_{n}C^{n}\] (3)  
In these equations, the coefficients (a_0, a_1, a_2, \(\ldots\), a_N) and (b_0, b_1, b_2, \(\ldots\), b_N) represent the parameters of the model that need to be adjusted based on real data for each module. The choice of N will depend on the complexity of the relationship between the response time and the workload, and will be determined through model fitting techniques and validation.
# TMCrack-Net: A U-Shaped Network with a Feature Pyramid and Transformer for Mural Crack Segmentation
## 4 Experiments and Results
### Other Methods  
1. U-Net: U-Net uses an encoding-decoding structure to build a U-shaped network. It uses convolutional and pooling layers to extract features and contextual information and can maintain a certain performance level on a small amount of data by linking contextual information through hopping.
2. PSPnet: PSPnet is designed with a pyramid pooling module (PPM) to obtain global information at different scales by using global pooling at different scales and fusing the obtained global features with the original input features. The feature map carries both local and global contextual information.
3. DeepLab v3+ obtains feature information of different perceptual fields by atrous convolutions, uses spatial pyramid pooling (SPP) to fuse feature information under different perceptual fields, and uses an encoder-decoder structure to recover spatial information to obtain target boundaries.
4. UNet++ [24]: U-Net++ is an improved jump-join method for U-Net networks. It uses nested and dense jump-joins to flexibly fuse features at different scales, compensate for semantic loss between the encoder and decoder, and enhance segmentation capabilities.
5. Swin-Unet: Swin-Unet is the first pure transformer-based u-architecture; both encoder and decoder are built based on the Swin-transformer block. The extracted contextual features are upsampled by the decoder with the patch extension layer, and the spatial resolution of the feature map is recovered for segmentation prediction by fusing with the multi-scale features of the encoder through jump connections.
6. UCTransNet: UCTransNet proves that not all jump-joins are powerful for the segmentation task and enhances the effective feature layer by computing the importance of features at different scales via the transformer. An attention mechanism is used to guide the feature fusion between encoder and decoder, reducing the semantic gap between them.
## 3 Auction-Based Cooperative Rescue Plan
### Bidders
#### 3.2.3 The Bidding Strategy of Bidders  
When determining the bid price, in addition to utility \(u_{i}\), cost \(c_{ij}\) and opportunity cost \(c^{\prime}_{ij}\), bidder \(B_{j}\) has to consider the number of buried sites within the scope of cooperation \(y_{j}\) and the number of available rescue teams within the scope of cooperation \(l_{j}\). Let \(\rho_{j}=y_{j}/l_{j}\), the larger the value of \(\rho_{j}\), the more likely for \(B_{j}\) to be involved in a task. Therefore, in order to make as many tasks as possible to be assigned to enough rescue teams, a coefficient \(\lambda_{j}\) is introduced in this model to adjust bid price, as shown in Equation (8). The coefficient \(\lambda_{j}\) needs to be tuned when applied in different cases. The bid price \(p_{ij}\) is defined in Equation (9).  
\[\lambda_{j}=1-\frac{\rho_{j}-\rho_{\min}}{\rho_{\max}-\rho_{\min}},\ \ \lambda_{j}\in(0,\ 1) \tag{8}\]  
\[p_{ij}=\lambda_{j}(u_{i}-c_{ij}-c^{\prime}_{ij}) \tag{9}\]
## 2 Communication Challenges and Service Types
### Demands and Requirements of Communication Services
Furthermore, they can be divided into two types from an aspect of the action objection: those related to the safety of the train itself (TCS and OCS data) and public safety ones (including OVCS, TOSM, VS, and PIS). Similar to the HSR, all safety-related communication services of Hyperloop should be categorized as the SIL4, the highest safety level that means a catastrophic impact once it happens [27]. With regards to the passenger multimedia services (PMS), the data rate, latency, and BER requirements are not as stringent as that of the mission-critical services. In addition, the communication demands vary among different passengers even for the PMS. For example, most Internet applications of the business users usually involve web browsing and emailing, which requires a less real-time process and a small throughput. The entertainment users generally involve Internet applications requiring a large bandwidth or real-time process, e.g., online games, video, chatting, etc.
###### Abstract  
The Lhasa to Nyingchi Expressway in Xizang made efforts to protect the ecological environment during its construction, but it still caused varying degrees of damage to the fragile ecosystems along the route. Accurately assessing the process of change in the ecological environment quality in this region holds significant research value. This study selected the Linzhi-to-Gongbo'gyamda section of the Lhasa-to-Nyingchi Expressway as the research area. Firstly, based on the remote sensing ecological index (RSEI), this study constructed an ecological environmental quality evaluation system for the Xizang region. Subsequently, using the Google Earth Engine (GEE) platform, sub-indicators were extracted, and the combination weighting method of game theory was employed to determine indicator weights. This process resulted in the calculation of the MBSEI for the study area from 2012 to 2020. Finally, by utilizing the spatial distribution of the MBSEI, monitoring the level of MBSEI changes, and employing the transition matrix, this study analyzed the changing trend of the ecological environmental quality from 2012 to 2020. The results indicate that the MBSEI are 0.5885, 0.5951, 0.5296, 0.6202, 0.59, 0.5777, 0.5898, 0.5703, and 0.5987, showing a gradual increasing trend with an initial decrease followed by an ascent. This trend is mainly attributed to concentrated road construction and subsequent ecological restoration, leading to an improvement in the restoration effect. Simultaneously, the ecological environmental quality remains relatively stable, with 69.5% of the region showing no change, and the remaining 30.5% experiencing improvement exceeding degradation. Specifically, there were significant improvements in the land with ecological quality levels categorized as poor, fair, moderate, and good. The types of degradation primarily involved lands originally classified as excellent and good degrading to good and moderate levels, respectively. The above results serve as a theoretical reference for the ecological restoration project of the Lhasa-to-Nyingchi Expressway.  
GEE; RSEI; ecological environmental quality; Lhasa-Nyingchi Motorway 2024 16 265 10.3390/rs16020265
## 3 Results
### Evaluation of Model Prediction Performance
#### 3.5.3 Performance Evaluation Metrics  
There are several ways to evaluate the performance of a classifier. A confusion matrix can retain correct and incorrect classification results, thus measuring the quality of the classifier, where _TP_, _TN_, _FP_, and _FN_ denote true positive, true negative, false positive, and false negative, respectively. We evaluate the model performance using the following four evaluation metrics, specifically calculated as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1-score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{4}\]  
In order to measure the performance of the model, the optimization results of the above model are compared with XGBoost, KNN, and SVM. The experiments use a confusion matrix to classify the prediction results, which are categorized into four classes, _TP_, _TN_, _FP_, and _FN_, as shown in Table 2.  
The ROC curves for the four models are shown in Figure 6, where AUC is the area under the ROC curve enclosed with the axes; the closer the AUC is to 1.0, the higher the accuracy of the model. Based on the AUC values of the four models, it can be seen that the random forest model has the highest accuracy (AUC = 0.90).  
In this study, the model was evaluated by AUC (Figure 6) and the values accuracy, precision, recall, and F1-score (Table 3).  
The assessment metrics for the four models are shown in Table 3 and Figure 9, The blue dashed line in Figure 9 represents the reference line.  
In this paper, the four indicators of accuracy, precision, recall, and F1-score are selected for the comprehensive evaluation of model performance, and the specific values are shown in Table 4 and Figure 10.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Predicted** & \multicolumn{2}{c}{**Confusion Matrix**} \\ \cline{2-3}  & **0** & **1** \\ \hline
0 & _TN_ (True Negative) & _FP_ (False Positive) \\
1 & _FN_ (False Negative) & _TP_ (True Positive) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
In summary, the accuracy, precision, recall, F1-score, and AUC values of the four models were compared, and the results showed that the prediction model based on the Random Forest was the most effective.
# Use and Abuse of Personal Information, Part I: Design of a Scalable OSINT Collection Engine
## 3 _U&A_ Framework
### A Custom VoIP Phone Exchange
#### 3.2.1 Requirements/Assumptions  
Our first decision is selecting the type of connectivity to host our phone server. A locally hosted VoIP (Voice over Internet Protocol) server is deemed preferred over cellular communication for many reasons. First, it is more cost effective, meaning the possibility of scaling up the quantity of phone numbers we need for the experiment does not prevent execution on financial grounds. Next, VoIP has a flexible network infrastructure that allows users to access the services from different platforms; these include dedicated VoIP phones and several other devices. Its flexibility helps us customize elements of 6000 phone numbers uniformly and efficiently. Without this malleability, we would encounter an error-prone and time-consuming process to adjust the settings of the phone numbers and phone-answering profiles.  
In terms of hardware, an on-site bare metal FreePBX Server is selected. Being an open-source Linux-based platform, we know that most devices (including Raspberry Pis) could test FreePBX configurations. The benefits are that we maintain the hardware on-premises and have full control over configurations and usage. Due to the unique nature of the experiments, we find it important to have control over the data to ensure no additional  
Figure 3: U&A mail server design.  
sharing by a third-party service, such as the previously used Zadarma tools. On the other hand, we need to invest in the security, physical space, and supporting infrastructure that it takes to manage the server on site. The server (CIP 790 PBX) is selected for its large user capacity--2000 extension numbers. The CIP 790 PBX comes preloaded with the FreePBX Distro and has a call capacity of 350 concurrent calls. The hardware specifications include a Quad Core i7 Processor, 32 GB RAM, dual 500 GB SSD storage, multiple Ethernet ports (allowing flexibility for separated networks), all within a 1 U chassis design.  
As a service provider, we compare and analyze six different SIP (Session Initiation Protocol) providers that would enable us to use SIP trunking. SIP trunking is the technology used in telecommunications to enable the delivery of VoIP traffic. Some positives of using SIP trunking are the powerful caller ID options and the quick development. ClearlyIP is chosen as the best aligned service provider option based on scalability and the pay-per-channel policy. During the design and planning phase of the experiment, we plan to dynamically scale the number of phone numbers based on new research questions and monitoring of activity to the phone lines; for lines where activity goes dormant for an extended period of time, we can release our lease on the number.
# Practical Approach for Developing Lateral Motion Control of Autonomous Lane Change System
## 2 Controller Design in Cylinder Domain
### Interpolation Based Gain-Scheduling
\begin{bmatrix}\mathbf{0}_{1\times 2}& \frac{2\pi}{L_{w}}\theta_{1}&\mathbf{0}_{1\times 2}\\ \mathbf{0}_{1\times 2}&0&\mathbf{0}_{1\times 2}\\ \mathbf{0}_{3\times 5}\end{bmatrix} ,\,\hat{A}_{\xi}^{(2)}= \begin{bmatrix} \mathbf{0}_{1\times 2}&0&\mathbf{0}_{1\times 2}\\ \mathbf{0}_{1\times 2}&\frac{2\pi}{L_{w}}\theta_{2}&\mathbf{0}_{1\times 2}\\ \mathbf{0}_{3\times 5}\end{bmatrix} .\]  
Here, \(\hat{A}_{\xi}^{(j)}\) are the real fixed nodal matrices associated with \(\theta(x_{1},L_{w})= \begin{bmatrix}\theta_{1}&\theta_{2}\end{bmatrix} ^{T}\). Now, a gain-scheduled optimal control gain \(K(\theta)\) will be considered using convex interpolation. As the control problem for vehicle lateral motion is reformulated for the LPV systems, we can design a linear controller. In this study, the process of LPV controller design is proposed in the cylinder domain, so the validity is verified through the basic linear quadratic (LQ) controller for reference tracking. We can pre-calculate the optimal gain \(K^{(i)}\) for each vertex \(A^{(i)}\) from the well-known Riccati equation. As we mentioned before, once the varying parameter is updated, the convex interpolation parameter is uniquely determined as \(\eta(\theta)=\hat{\mathbf{V}}^{-1}\hat{\Theta}\) using the Equation (23). From the measured varying parameters, we can calculate and update the sub-optimal gain for the lateral motion controller as follows [19,20]:  
\[K(\theta)=\sum_{i=1}^{4}\eta_{i}(\theta)K^{(i)}. \tag{26}\]  
Note that the gain is not optimal but sub-optimal because the proposed method uses simple interpolation from each vertex. Nevertheless, the proposed method exhibits reasonable performance with a small amount of computation, which can be confirmed by the simulation results in the next section.
## 4 Simulation and Performance Evaluation
### Performance Analysis under Different Parameters
#### 4.2.3 The Impact of Period of Changing Event Area  
In this section, we study the impact of period of changing event area. As we defined in Section 3.3.2, the time sink spend to complete a column running is 2\(T\), so we appoint 2\(T\) as a unit to study this topic. We call 2\(T\) as column period. The parameters we set is shown as follow: (1) the number of all nodes is 500; (2) \(r_{e}\) is 30 m; (3) \(Th\) is 70%; (4) one source node produces one data packet in one second; (5) the velocity of sink is 5 m/s. The period of changing varies from 2 to 16 column period.  
In Figure 16, the black line represents the lifetime of the network. The network's lifetime increases firstly, and it gets the peak value at 10. Then the network's lifetime decreases. When the period of changing is 2 column period, the lifetime is the shortest. The blue line represents the number of data collected. The blue line is almost coincident with the black line. The red line represents variance of  
Figure 15: The impact of \(r_{e}\) on the network performance.  
residual energy. Before 14 column period, the variance is relatively stable and small. When changing period is greater than or equal to 14 column period, variance increases fast. As we introduced in Section 3.3.2, sink has a trend to move to the area with more source data. The sink also has the trend to stay for longer in this area. Hence sink moves to event area in the experiment. According to the relationship between grid length and communication radius, our virtual structure has \(6\times 6\) grids. So when changing period adopts 2 column period, sink always can not reach the event area before event area changes. Energy couldn't be saved and network dies soon. By the increasing of changing period, sink has sufficient time to reach event area and stays for more time in the event area. Then much energy can be saved and network works longer. We can find that network's lifetime is longest when 10 column period is adopted. When changing period is greater than 10 period column, lifetime of the network decreases quickly. In this situation, event area stays in someplace for long time, and sink will always travel in the columns with source nodes. Although much energy is saved in overall, the nodes in the same columns with source nodes consume much energy. The nodes in other columns consume almost no energy. The network's lifetime becomes shorter, and the variance of residual energy becomes higher. According to this experiment, the network can get a good performance when 10 column period is adopted.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps from Other Data Sources
#### 3.5.4 GEOCARBON Global Forest Biomass Map  
The GEOCARBON global forest biomass map at 0.01-degree resolution was derived by integrating the pan-tropical biomass map ([PERSON] et al., 2013) with the boreal forest biomass component ([PERSON] et al., 2013). It only included areas defined as forests according to the GLC2000 map.
###### Abstract  
Knowledge of ionospheric plasma altitudinal distribution is crucial for the effective operation of radio wave propagation, communication, and navigation systems. High-frequency sounding radars--ionosondes--provide unbiased benchmark measurements of ionospheric plasma density due to a direct relationship between the frequency of sound waves and ionospheric electron density. But ground-based ionosonde observations are limited by the F2 layer peak height and cannot probe the topside ionosphere. GNSS Radio Occultation (RO) onboard Low-Earth-Orbiting satellites can provide measurements of plasma distribution from the lower ionosphere up to satellite orbit altitudes (~500-600 km). The main goal of this study is to investigate opportunities to obtain full observation-based ionospheric electron density profiles (EDPs) by combining advantages of ground-based ionosondes and GNSS RO. We utilized the high-rate Ebre and El Arenosillo ionosonde observations and COSMIC-2 RO EDPs colocated over the ionosonde's area of operation. Using two types of ionospheric remote sensing techniques, we demonstrated how to create the combined ionospheric EDPs based solely on real high-quality observations from both the bottomside and topside parts of the ionosphere. Such combined EDPs can serve as an analogy for incoherent scatter radar-derived \"full profiles\", providing a reference for the altitudinal distribution of ionospheric plasma density. Using the combined reference EDPs, we analyzed the performance of the International Reference Ionosphere model to evaluate model-data discrepancies. Hence, these new profiles can play a significant role in validating empirical models of the ionosphere towards their further improvements.  
ionosphere; COSMIC-2; ionosonde; electron density; empirical ionospheric model; radio occultation