esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data  
### GRACE/FO-TWSA Data  
Currently, data from the widely used GRACE and GRACE-FO missions are categorized into three main levels: Level-1B, Level-2, and Level-3. GRACE/GRACE-FO Level-1B products primarily consists of essential raw observational measurements, including variations in inter-satellite distance, satellite velocity, changes in acceleration due to distance variations, and acceleration caused by non-conservative forces. GRACE/GRACE-FO Level-2 products, on the other hand, encompass various components such as Gravity Signal Model, Atmosphere Model, Ocean Model, Combined Atmosphere and Ocean Model, and De-aliasing Product for Atmosphere and Ocean. GRACE/GRACE-FO Level-3 products are derived from Level-1B and Level-2 data through further post-processing and analysis. These Level-3 products provide gridded data representing global changes in terrestrial water storage [39].  
In this study, three Level-3 mason solutions from GRACE and its successor missions were utilized, sourced from the University of Texas Center for Space Research (CSR) [40], NASA's Jet Propulsion Laboratory (JPL) [41], and NASA's Goddard Space Flight Center (GSFC) [42]. The data cover a unified time range from April 2002 to December 2023. However, within this dataset, there are 33 months of missing data, including an 11-month gap between July 2017 and May 2018 [43]. Derived from GRACE, the TWSA exhibits a spatial resolution of approximately 300 \(\times\) 300 km\({}^{2}\) and a temporal resolution of monthly intervals, providing information on various forms of surface water [44], soil moisture (SM), and groundwater.
## 3 CRKG in Massive MIMO and mmWave Systems  
Several key enabling technologies have been identified, such as massive MIMO and mmWave communications, to achieve high throughput and serve a large number of terminals in the 5G and beyond wireless networks. In massive MIMO systems, one base station (BS) employs a large number of (e.g., hundreds of) antennas simultaneously serving several (e.g., tens of) user terminals in the same time frequency resource. By employing a large number of antennas, energy can be focused into small spatial regions, providing high spatial resolution and reducing intra-cell interference. Hence, massive MIMO can significantly improve the spectral efficiency and power efficiency. The performance bottleneck is the pilot contamination caused by the pilot reuse [40].  
MmWave communications is another key technology to meet the explosive user data demand for the future wireless communications. It corresponds to 30-300 GHz frequency bands to support data rates of multiple gigabits per second. Compared with sub 6 GHz bands, mmWave bands provide large  
Figure 4: Comparison of secret key rates for different duplex modes. In TDD systems, the time interval between the uplink and downlink is 0.5 ms with velocities of 10 m/s and 50 m/s. In FDD system, the center carrier frequency is 2 GHz, and the frequency interval is 5 MHz and 40 MHz. Under IBFD operation, the power of RSI is set \(-\)10 dB and \(-\)20 dB less than that of the received signals.  
available bandwidths. Moreover, thanks to small wavelength at mmWave frequencies, a large number of antennas can be packed into a physically limited space. For example, the number of antennas at 70 GHz can go up to 1024 at the base station and 64 at the user equipment [41]. In addition, without spatial coding gain, the attenuation at mmWave is too large. Thereby, mmWave communications are usually combined with massive MIMO technology.  
In massive MIMO and mmWave communication systems, the channel exhibits some novel characteristics, bringing opportunities and challenges to CRKG and opening a new and promising area.
# WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds
## 2 Methods
### Defectology Analysis
#### 2.3.2 Cross-Sections Generation and Defects Analysis  
Once the bead and the plaques have been extracted, an automatic cross-sectional analysis can be performed using the extracted axis as reference. With this analysis, it is possible to locate flaws and imperfections according to the four defects previously defined (Figure 2). Cross-sections are plotted successively and automatically along the direction of the weld axis. The distance between sections could be freely chosen by the inspector according to the level of resolution required, the type of weld, the material, and the defects or imperfections to be detected and measured [9].  
Finally, the four defects considered in Figure 2 can be checked for each cross-section deriving a complete analysis of the whole weld, as follows:  
* The excess weld metal, \(H\), was computed automatically, extracting the width of the bead based on the semantic classification of the bead along the axis and taking the median as a robust reference [51].
* The linear misalignment, \(d\), was computed automatically, extracting the orthogonal distance between plaques based on the classification of the plaques along the axis and considering the median as a robust reference. The thickness of the weld, \(t\), was provided by the manufacturer or measured directly in the 3D model.
* The angular misalignment, \(\delta\), was computed by applying principal component analysis (PCA) to those points in \(P_{l},\ P_{r}\) that belong to the left and right plaques previously classified. Particularly, the third eigenvector was estimated as a normal vector for each plaque, \(n_{l},\ n_{r}\) and thus used for computing the angular misalignment, \(\delta\), between plaques. Here, a threshold value of 0.9 was empirically defined, so normal vectors with the lower vertical component were discarded.
* The presence of notches or pores, \(dz\), was computed adjusting a plane to the plaques previously classified using RANSAC algorithm [50] and analysing the orthogonal distance from the cross-sections to the fitted plane. All those points that generate an orthogonal distance \(\geq 2\) mm were considered as notches or pores.
## 4 Evaluation of the Developed Unit
### Effect of the Granular Objects  
This section presents the evaluation of the effect of granular objects inside the pouch. As described in Section 3.1.2, granular objects amplify the function of friction reduction through producing an air pocket around the contact area and maintaining the airflow under external loads. To verify the former role, the maximum static and kinetic friction forces were measured under two conditions: with granular objects inside the pouch (nominal configuration, as shown in Figure 5) and without granular objects. The experimental setup is shown in Figure 9. Experiments were performed through running the device with and without blowing dry air for each configuration (with and without granular objects). The results are expressed as the ratio of the friction coefficients after the friction change (decrease) to those of the nominal case and are presented in Figure 12. The results demonstrate that friction was effectively reduced using the dry-air blowing unit, regardless of the presence of granular objects. The presence of granular objects enhanced the friction reduction.  
The experimental setup shown in Figure 13 was used to verify the capability of granular objects to maintain airflow under an external load. When granular objects were used, the tests were performed using the configuration described in Section 2.2, as shown in Figure 13a, where the air tube passed through the pouch of the heating unit, including the granular objects. One side of the tube was connected to the air source, whereas the other side was connected to a flowmeter (8550 MC, Kofloc, Kyoto, Japan). For comparison, experiments were also performed without granular objects. In this case, only the air tube was set up, as shown in Figure 13b. The airflow rate from the air source was set to 10 L/min. An external load was applied to the air tube and pouch using a force gauge attached to an automatic positioning stage. The output airflow rate from the air tube was monitored using  
Figure 11: Friction coefficient ratios after friction changes through injecting water (white) and blowing dry air (black) over nominal conditions (red line) under different environmental conditions. (**a**) Maximum static friction. (**b**) Kinetic friction.  
Figure 10: Representative results of the behavior of the friction force under the environmental condition of t20h30.  
a flowmeter until the magnitude of the external force reached 25 N. The output airflow rates under both conditions (with and without granular objects) are shown in Figure 14. In the presence of granular objects, the output airflow rate was maintained under an external load. By contrast, the airflow rate decreased under an external load when the air tube did not pass through the pouch containing granular objects. Thus, the results demonstrated the importance of the granular objects in maintaining airflow under external loads.
## 2 OBS Networks
### 2.3. QoS Support
#### Traffic Engineering  
Traffic engineering (TE) has long been considered an essential mechanism for any next generation networks (NGN) used to improve its overall performance. A network without TE is, in most cases, operated under the simplest way to route traffic flows based on the fixed shortest path. Despite the benefits of such simplicity, it can cause some traffic load unbalance in the network, _i.e._, a large amount of traffic may traverse a certain set of links, while there can be little traffic load on other links. The disadvantage of such an unbalance is poor network capacity utilization, making it difficult to guarantee a certain level of performance to data flows requiring, for example, less than a certain level of delay, jitter or dropping probability. To improve such an unbalanced situation and increase the network capacity utilization, TE tries to best balance link utilization in the network to avoid congestion on some links, creating ground for QoS guarantees. This can be accomplished by load balancing and offline routing (see Figure 5).  
The key form of TE is load balancing, in which traffic from congested areas is diverted to lightly loaded areas. In doing so, load balancing frees up network resources at bottleneck links and helps the network to provide better QoS to end users [27]. Several load balancing algorithms have been proposed for OBS networks, some of them [51, 52] for example, adopting an offline approach formulated as an optimization problem and solved by ILP. Dynamic and adaptive approaches for best effort traffic can be found in [11, 53] and [54] respectively, considering wavelength conversion restrictions in [12, 55], and considering the streamline effect in [56].  
Offline routing is another load-balanced related form of TE focused on the selection of paths with the objective of balancing the traffic across the network links in order to reduce congestion and improve the overall performance. This are complex problems usually solved by ILP optimization or heuristics. Examples can be found in [10, 15, 57] and also in the routing strategies next discussed in Section 4 of this article.
## 4 Performance Analysis  
In this section, analytical expressions for important performance metrics of the scheme under consideration will be provided. More specifically, its performance will be evaluated using the criteria of OP, average NLE, and SP.
## 4 Experiment Results and Comparison
### Performance Metrics
#### 4.3.2 Recall  
The recall metric is used to measure the model's predictive accuracy for the positive class, and it is defined as the number of correctly classified flows divided by the total number of flows in each class. For example, the recall for the flow type 1 (or normal flow type) is the number of the correctly predicted flow type 1 out of the number of the flow type 1 in the testing data set. In particular, the recall for each class( or flow type) is calculated as follows:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]
# EchoIA: A Cloud-Based Implicit Authentication Leveraging User Feedback
## 3 The System Overview  
Implicit authentication (IA) identifies users by constantly comparing current users' behavioral data with legitimate users' historical behavioral data. If current users' behavioral data is different, IA will lock the device and ask the users to input passwords. Due to the noise and behavioral change, it is common that legitimate users are falsely blocked by the device [14]. In implicit authentication, if users input correct passwords, they can continue to use the device, where the setting of the original IA system will not change. In EchoIA, however, if users input correct passwords and prove their identities, the system will unlock the device and adjust the features to align with legitimate users' behavior.  
As shown in Figure 1, EchoIA contains two phases, _Initialization_ and _Authentication_. The _Initialization_ phase only takes place for the first time of the usage. Meanwhile, we assume the users at the _Initialization_ phase are legitimate. In the _Initialization_ phase, candidate features are sent to legitimate users, who may rank the features based on their behavior. The combination of top-ranking features and system-default features is used as personal features to identify users. Note that the personal features are dynamically adjusted according to different users and devices. In the _Authentication_ phase, EchoIA utilizes the correct rate of inputted passwords to adjust personal features. Specifically, user feedback is implicitly obtained through the process of inputting passwords (Section 3.1), which keeps IA's transparency. However, to prevent illegitimate users from taking advantage of the system, it only updates personal features after users entered a correct PIN number, which must be different from passwords used for unlocking the device. A secured channel is established to transmit data between client and server. We adopted a Wind Vane module [14] to optimize the data transmission efficiency.  
From the system's point of view, inputting incorrect passwords will enhance its confidence in using existing personal features; inputting correct passwords will reduce its confidence in using existing personal features and encourage it to choose different features. Since most of the time, the system is running at the _Authentication_ phase, the users will not be able to notice the existence of EchoIA during the usage. The following sections will discuss the detail of the _Initialization_ phase and the _Authentication_ phase.
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 4 Case Study
### Ablation Experiment
Additionally, the training process becomes more complex, requiring more computational resources and time for tuning and optimization. Secondly, although the proposed model demonstrates a consistent performance across multiple datasets, its generalization ability may still be limited in certain specific scenarios or data distributions, necessitating further validation and adjustment. Lastly, while attention mechanisms can effectively capture dependencies and feature importance in time-series data, their interpretability remains challenging, particularly in practical applications where explaining and visualizing these attention weights to support decision-making is still an ongoing issue. Therefore, future research could focus on model simplification, computational efficiency improvement, and interpretability enhancement to better balance model performance and practicality. Figure 9: The performance comparison of different models on three datasets.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 2 Materials and Methods
### Wind Speed Observations in HSR Systems  
The wind speed sensor used to monitor wind speeds affecting the HSR utilizes an ultrasonic anemometer with a measurement accuracy of 0.1 m/s and a frequency of 100 Hz, with data recorded every second. The sensor and associated equipment are mounted on 4 m high pillars located 1 m from the track, in accordance with railway department requirements. This analysis focuses on four representative HSR stations: Dashengguan Station (K1005), Jurong Zouwang Road Station (K1066), Yangcheng West Lake Station (K1245), and Yangcheng Lake West Bridge Station (K1256). K1005 is centrally located on the Dashengguan HSR bridge over the lower Yangtze River, where the wind speed on the bridge deck is often higher than on surrounding land, posing a risk to high-speed trains during high winds. The maximum recorded wind speed over the past three years reached 30 m/s. K1066, situated in the Baohua Mountain area, features a low track bordered by high sides, creating a valley effect that accelerates wind through the area. Both K1245 and K1256 are positioned along the Yangcheng Lake section of the Beijing-Shanghai HSR; however, K1245 is situated over the lake, while K1256 is located on the shore. Wind speed data for these stations span the entire year of 2020. The exact locations of the four HSR stations are shown in Figure 1.
## 2 Materials and Methods
### Experimental Procedures  
A UF unit, an O\({}_{3}\) unit and their combination (Figure 2) were operated to investigate the removal efficiency of EDCs. For the O\({}_{3}\) unit, the procedure is briefly described as follows: the secondary effluent containing 100 \(\upmu\)g\(\cdot\)L\({}^{-1}\) EDCs was firstly put into an O\({}_{3}\) reaction tank, in which the O\({}_{3}\) was generated by the O\({}_{3}\) generator at the concentration of 5.5 mg\(\cdot\)L\({}^{-1}\) react with secondary effluent. After the reaction, the high purity nitrogen was introduced to blow O\({}_{3}\) out, which was not completely reacted, to an off-gas bottle filled with potassium iodide solution. For the UF unit, the secondary effluent containing 100 \(\upmu\)g\(\cdot\)L\({}^{-1}\) EDCs was put into the feed tank, then filtered into the UF cup by a peristaltic pump. The filter fluid was pumped into the weighing bottle on the electronic balance by another peristaltic pump. The quality of the filtrate was weighed by an electronic balance, and the data were transmitted to a computer for processing and analysis. For the combination process of O\({}_{3}\) and UF, the secondary effluent treated by O\({}_{3}\) was put into the feed tank, and the subsequent filtration experiment was carried out as above.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & **Unit** & **Amount** \\ \hline TOC & mg\(\cdot\)L\({}^{-1}\) & 6.47 \\ conductivity & \(\upmu\)s/cm & 1001 \\ pH & - & 7.8 \\ SS & mg\(\cdot\)L\({}^{-1}\) & 10.0 \\ TP & mg\(\cdot\)L\({}^{-1}\) & 0.32 \\ TN & mg\(\cdot\)L\({}^{-1}\) & 23.05 \\ UVA254 & - & 0.084 \\ specific ultraviolet absorbance & L\(\cdot\)mg\({}^{-1}\cdot\)m\({}^{-1}\) & 1.3 \\ Ca\({}^{2+}\) & mg\(\cdot\)L\({}^{-1}\) & 66.94 \\ Mg\({}^{2+}\) & mg\(\cdot\)L\({}^{-1}\) & 9.35 \\ K\({}^{+}\) & mg\(\cdot\)L\({}^{-1}\) & 20.00 \\ Na\({}^{+}\) & mg\(\cdot\)L\({}^{-1}\) & 75.51 \\ E1 & ng\(\cdot\)L\({}^{-1}\) & 42.04 \\ E2 & ng\(\cdot\)L\({}^{-1}\) & 157.73 \\ E3 & ng\(\cdot\)L\({}^{-1}\) & 2.67 \\ EE2 & ng\(\cdot\)L\({}^{-1}\) & 138.34 \\ BPA & ng\(\cdot\)L\({}^{-1}\) & 34.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Water quality of secondary effluent.
## 4 Numerical Verifications  
### Numerical Finite Element Model of this Precast Segmental Beam  
A 3D precast segmental beam model of the tested structure was built by using commercial finite element package ANSYS to numerically investigate the accuracy of the proposed theoretical method using the neutral axis location of the segmental beam for obtaining the prestress force in externally prestressed tendons. The developed finite-element model is illustrated in Figure 9. Geometric and material properties of this numerical model are the same as those in the experimental studies, as described in Section 3. In this numerical simulation, the concrete segments are simulated using SOLID92 elements, which consider stress stiffening and large deflection geometrical nonlinearity. This element is defined by ten nodes with three degrees of freedom at each node, that is, translational displacements in the nodal x, y and z directions. SOLID45 elements are used to simulate two 20-mm-thick steel plates with external loading added to the top surface of the beam. This is because the SOLID45 element can be used to obtain a more uniform stress distribution over the pressurized area. The locations of the steel plates placed on the segmental beam, which remain at the same location as that in the experimental tests, are denoted as the green area in Figure 9. They are placed equally at one-third and two-thirds of the beam. LINK8 elements are employed to simulate steel reinforcements, including steel bars and stirrups inside the concrete, and the prestressed tendon. For the prestressed tendon, different initial strain values are applied to simulate the different prestressing levels based on the measured prestress value in the experiment. The dry joints between segments of the beam model are simulated using contact elements. TARGE170 and CONTA174 elements are adopted for target and contact elements, respectively. Node coupling is applied to the contact point between the concrete and external prestressed tendons at the anchorage location.
# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 3 Materials and Methods
### Evaluation Criteria  
To rigorously evaluate the performance of WT-YOLO in weld defect detection, we adopt standard object detection metrics that provide a balanced assessment of detection accuracy and reliability.  
Mean Average Precision (mAP) [44] is used to measure the model's detection capability across different localization thresholds. Specifically, we report:  
* mAP50-95: The mean of average precision values computed at IoU thresholds ranging from 0.50 to 0.95 with a step size of 0.05. This metric reflects the model's ability to consistently detect defects at varying localization accuracies.
* mAP50: The average precision at an IoU threshold of 0.50, commonly used in object detection tasks as a baseline measure of performance.
* mAP75: The average precision at an IoU threshold of 0.75, representing a more stringent requirement for precise localization.  
Recall quantifies the model's ability to detect actual defects, calculated as  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
where TP denotes true positives and FN denotes false negatives. A higher recall indicates that fewer defects are missed.  
Precision measures the proportion of correctly identified defects among all detections, given by  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
where FP represents false positives. A model with high precision makes fewer incorrect predictions.  
F1-score is the harmonic mean of precision and recall, providing a balanced evaluation when considering both false positives and false negatives:  
\[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{5}\]  
This metric is particularly useful when precision and recall need to be considered jointly, ensuring that the model is neither too conservative nor overly permissive in its detections.  
By combining these metrics, we gain a comprehensive understanding of the model's performance, assessing not only its ability to detect defects but also its reliability in distinguishing true defects from false alarms.