esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Named Data Networking Architecture
### Names  
The NDN name space is structured hierarchically, for instance, a video file may be produced by uminho ndn/uminho/stream-app/video-1, similar to the uniform resource locator (URL), with name components separated by '/' in a readable address format.  
To request data by name, the consumer must build a name for the intended data. A name creation may be based on a deterministic algorithm, which lets the consumer and producer gain information for the same name. A consumer may also retrieve contents by a partially known name. This technique is also known as \"_longest prefix matching_\" by interest selectors which are used to specifically determine the content object. Retrieving data with a partially known name can be supported by a set of interest selectors.  
In addition, any NDN application and consumer can create their namespaces, to increase mapping data and its usage of the network. The naming of the data allows  
Figure 1: IP and NDN hourglass architectures (Adapted from [14]).  
Figure 2: NDN interest (consumer) and data (producer) packets form (Adapted from [15]).  
increasing mainly the functionality of data distribution, mobility, delay-tolerant networking, and multicast operations.
# The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature
###### Abstract  
The coefficient of thermal expansion (CTE) is an important property of ultra-low expansion (ULE) glass, and the ultrasonic velocity method has shown excellent performance for the nondestructive measurement of CTE in large ULE glass. In this method, the accurate acquisition of the ultrasonic velocity in ULE glass is necessary. Herein, we present a correlation method to determine the ultrasonic TOF in ULE glass and to further obtain the ultrasonic longitudinal wave velocity (\(c_{\rm L}\)) indirectly. The performance of this method was verified by simulations. Considering the dependence of \(c_{\rm L}\) on temperature (\(T\)), we carried out the derivation of the analytical model between \(c_{\rm L}\) and \(T\). Based on reasonable constant assumptions in the physical sense, a \(c_{\rm L}\)-\(T\) exponential model was produced, and some experimental results support this model. Additional experiments were carried out to validate the accuracy of the \(c_{\rm L}\)-\(T\) exponential model. The studies we conducted indicate that the \(c_{\rm L}\)-\(T\) exponential model can reliably predict the ultrasonic velocity in ULE glass at different temperatures, providing a means for the nondestructive CTE measurement of large ULE glass at a specified temperature.  
ultra-low expansion glass; ultrasonic velocity; correlation method; temperature coefficient; exponential model +
Footnote †: journal: Applied Sciences
# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 4 Experiments
### Model Complexity Analysis  
Foreign object detection in coal mines requires a balance between detection accuracy and computational efficiency. To evaluate this, we compare CAFOD with baseline models in terms of parameter size, GPU, and RAM usage, and computation time during training and inference, as shown in Table 6. All models were trained on the same dataset using an Intel Xeon E5-2666 v3 CPU, 20 GB RAM, and an NVIDIA Tesla V100 GPU (32 GB).  
We can draw the following observations from Table 6. (1) In terms of training efficiency, CAFOD requires 22.4 GB GPU memory and 61,200 s for training, making it one of the more resource-intensive models. However, its training time remains competitive with Sparse-RCNN (64,082 s) and CentripetalNet (79,235 s), which consume similar resources but fail to achieve CAFOD's superior detection accuracy. The increased computational demand stems from MVDA and CFP, which enhance feature extraction and generalization. (2) In term of inference speed and practical deployment, CAFOD processes an image in 0.2078 s, comparable to Deformable DETR (0.2186 s) and OZF (0.2015 s), while faster than CentripetalNet (0.7804 s) and VarifocalNet (0.6561 s). Although AFPN (0.0510 s) is the fastest, it sacrifices accuracy (77.42 mAP50 vs. 80.39 mAP50 for CAFOD). Moreover, CAFOD's 1.24 GB GPU memory usage is lower than Sparse-RCNN (9.56 GB) and CentripetalNet (8.74 GB), making it more suitable for deployment on edge devices.  
While CAFOD is not the fastest model, it maintains an optimal trade-off between accuracy and efficiency, justifying its computational cost with superior detection performance. This makes it practical for real-world deployment in coal mine conveyor belt monitoring, where high precision is essential alongside reasonable inference speed.
# Hardware Model Checking Algorithms and Techniques
## 7 Igr
### Cone Unwinding and Rewinding
#### 7.3.1 Cone Unwinding  
At a given iteration \(i\) of the forward traversal, given \(k\) the bound of the cone, if the formula  
\[F_{i}(V^{0})\wedge T(V^{0},V^{1})\wedge Cone(1,k) \tag{65}\]  
is Sat, then a possibly spurious counterexample is found. In such a scenario, standard interpolation would unwind the bad cone by one step and then start a new traversal from the initial states. When step \(i\) is then reached once again in the traversal, the overapproximation \(F_{i}\) could have been strengthened enough to exclude the previously found spurious counterexample. If that is not the case, the algorithm restarts the traversal again, incrementing \(k\) until either the spurious counterexample is excluded from the overapproximation or the counterexample is confirmed to be a concrete one. With cone unwinding, the cone is unwound of the minimum depth necessary to strengthen a frame \(F_{j}\), with \(0<j\leq i\), in order to eliminate the spurious counterexample directly.  
Upon encountering a spurious counterexample, the BMC-like problem described in Equation (65) is Sat. Therefore, the algorithm starts an iterative process checking BMC problems of fixed bound in which the cone is unwound by one step and the algorithm moves to the previous frame. Starting from frame \(F_{i}\) and cone \(Cone(1,k)\), at each \(j\)-th iteration it considers the BMC-like problem:  
\[F_{i-j}(V^{0})\wedge T(V^{0},V^{1})\wedge Cone(1,k+j) \tag{66}\]with \(0<j\leq i\). At each iteration it swaps an overapproximated image in \(F_{i-j}\) with an exact image in \(Cone(1,k+j)\). If the BMC-like problem in Equation (66) is Sat at a given \(j\), then \(F_{i-j}\) is not strong enough to refute the counterexample and the process iterates. Eventually, either it finds Unsat thus refuting the spurious counterexample, or it reaches the initial states thus confirming the counterexample as a concrete one. In fact, for \(j=i\) we have exactly \(\textsc{bmc}(i+k)\). Assuming that it can find a \(j\), with \(0<j<i\), such that Equation (66) is Unsat, then it can restart the forward traversal from \(F_{i-j}\) with \(Cone(1,k+j)\). This is guaranteed to generate an interpolant and therefore refine the current trace.
# Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis
## 2 Methods
### Training  
The proposed network was trained with a database of 689 annotated videos from a clinical trial involving 30 patients [24], which were processed with the semi-automated labeling algorithms presented in the previous section. The selection criteria for each training step are as follows:  
Among the 30 patients, 27 were chosen for training the model. The videos of these patients contained images of all the artifacts to be detected, to ensure maximal learning for the model. The remaining videos were reserved for a final validation phase to perform an extra verification of the model's behavior. Since consecutive frames in a video often have a close resemblance, the _K-means_ clustering algorithm [29; 30] was applied to select the most distinctive images from each video; similar frames are grouped into clusters, and the frames closest to the centroids of these clusters are selected as key frames, avoiding overfitting during training. In this study, three key frames were selected per video.  
Additionally, data augmentation methods, including image saturation with random gain values and the introduction of horizontally flipped images, were utilized (Figure 10). Image saturation is particularly important in ultrasound imaging because the gain control is arbitrarily adjusted by the physician during the examination, based on their eye perception. Therefore, in some high-gain images, the pleura line can be artificially thickened by saturation, and the model should learn to identify it, even in that scenario. Furthermore, detection of B-Lines should be based on their shape and variation along the scan line, but not on their absolute amplitude, which can be induced by introducing images with different gains but correctly labeled. These techniques are aimed at enhancing the model's adaptability to differences in ultrasound image gain, and to introduce greater image variability.  
The application of these criteria resulted in a dataset comprising 9159 annotated images, which were randomly divided into 70% for training and 30% for tests.  
For the selection of the hyper-parameters for training, several approaches were tested with the aim of improving the performance of the model while balancing computational cost during training and results. The behavior of the model was studied using different loss functions during training (DICE loss and binary cross-entropy) and varying the learning rate between values of \(1\times 10^{-3}\) and \(1\times 10^{-5}\).  
As a result, the final training was carried out using two NVIDIA 2080 TI in parallel, applying a dropout of 0.3 to avoid overfitting during the process, as well as a binary cross-entropy loss function being 100 the number of epochs, 64 the size of the batches, and a learning rate of \(5\times 10^{-4}\).
# Low Discrepancy Sparse Phased Array Antennas
## 4 Aperture Shape Effects on the Performance of Sparse Phased Array Antennas
As expected, the uniform case has the poorest performance in terms of SLL as well as directivity and efficiency. Both LDS techniques are effective in removing the grating lobes, but it can be seen that the Poisson technique provides the best results. It should be noted that in comparison between array aperture types, the circular aperture provides the best performance in terms of peak SLL, directivity, and efficiency. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Method/Aperture Type**} & **Average Minimum** & **Peak SLL** & **Directivity (dB)** & **Aperture Efficiency (\%)** \\  & **Element Spacing (\(\lambda\))** & **(dB)** & **Directivity (dB)** & **Aperture Efficiency (\%)** \\ \hline Uniform/Rectangular & 1.3333 & 0 & 31.5778 & 11.175 \\ Uniform/Circular & 1.3333 & 0 & 31.5848 & 11.261 \\ Uniform/Elliptical & 1.3333 & 0 & 31.5181 & 11.228 \\ Halton/Rectangular & 0.8481 & \(-\)6.74 & 32.4857 & 13.774 \\ Halton/Circular & 0.8967 & \(-\)9.27 & 32.6474 & 14.383 \\ Halton/Elliptical & 0.7724 & \(-\)8.94 & 32.5883 & 14.365 \\ Poisson Disc/Rectangular & 0.9094 & \(-\)12.18 & 32.8062 & 14.829 \\ Poisson Disc/Circular & 0.9016 & \(-\)15.30 & 32.8643 & 15.119 \\ Poisson Disc/Elliptical & 0.9245 & \(-\)14.34 & 32.6634 & 14.616 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance metrics for sparse antenna arrays with rectangular, circular, and elliptical apertures and different element distributions. Figure 7: Bar plot of minimum element spacings on rectangular, circular, and elliptical apertures with different distributions: (**a**) uniform (rectangular), (**b**) Halton (rectangular), (**c**) Poisson disk (rectangular), (**d**) uniform (circular), (**e**) Halton (circular), (**f**) Poisson disk (circular), (**g**) uniform (elliptical), (**h**) Halton (elliptical), (**i**) Poisson disk (elliptical).
# Multi-Lidar System Localization and Mapping with Online Calibration
## 5 Conclusions  
This paper designs a laser SLAM system that can realize online external parameter calibration and synchronous positioning and mapping of multiple laser radars at the same time, and the feasibility of the scheme is verified by real vehicle experiments. Firstly, the point cloud preprocessing is carried out, and the motion distortion of the lidar point cloud is corrected based on the uniform motion model, and the ground extraction, point cloud segmentation, and feature extraction are completed. Then the realization of online calibration is carried out by judging the convergence of multi-lidar external parameters through the degradation factor. This was followed by matching the feature point cloud of the auxiliary lidar with the local map of the main lidar so as to solve the pose transformation relationship between multiple lidars. In the implementation process of the front-end odometer, based on the smoothness of the multi-radar feature point cloud and its distribution, high-quality features are screened for fusion, and then the Ceres library is used for inter-frame matching to solve the estimated pose. Then the GTSAM library is used to build a graph optimization model at the back end. The loop closure detection factor is added based on the Scan Context descriptor for global pose optimization and the ground constraint factor based on plane feature matching to reduce the z-axis error of the overall trajectory. Finally, the real vehicle experiment verification was carried out, the software and hardware platform of the real vehicle experiment was built, and the data was collected in Tianwenlu, Chang'an University, which verified the feasibility of the scheme.  
Conceptualization, F.W. and L.W.; methodology, X.Z. and Y.H.; software, H.G.; resources, X.Z.; data curation, H.G. and Y.H.; writing--original draft preparation, S.W.; writing--review and editing, L.W. and F.W.; supervision, F.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Construction of the \"Scientist + Engineer\" team of QinChuangYuan, Shaanxi Province, grant number 2022 KQJ-021.  
The study did not involve humans or animals.  
The study did not involve humans.  
The data is unavailable due to privacy or ethical restrictions.  
The authors declare no conflict of interest.
## 2 Preliminaries
### Co-Evolution  
In this study, we introduce the co-evolutionary algorithm into the multi-preference search mode based on evolutionary demand. Through the co-evolution of the main population and the auxiliary population, the overall optimization performance is enhanced. This collaborative mechanism enables the algorithm to dynamically balance feasibility and convergence at different stages of evolution. The cooperative co-evolutionary algorithm is an optimization method based on evolutionary algorithms. Its main aim is to decompose a complex problem into several relatively simple sub-problems, solve these sub-problems separately, and then combine their solutions to obtain the overall solution. During this process, the solutions of different sub-problems interact with each other through cooperation and competition, thereby optimizing the overall problem.  
The basic steps of the co-evolutionary algorithm are as follows:  
Problem decomposition: Decompose the original problem into multiple subproblems. These sub-problems should be relatively independent and can be solved relatively easily.  
Individual representation: Designing a suitable individual representation for each subproblem, usually by encoding the solution of the subproblem as an individual.  
Subproblem solving: Use evolutionary algorithms (e.g., Genetic Algorithm, Particle Swarm Algorithm, etc.) to solve each subproblem separately, and get the local optimal solution of each subproblem.  
Merging and competition: Combining locally optimal solutions to individual sub-problems and influencing each other competitively or collaboratively to further optimize those solutions.  
Iterative optimization: The above steps are repeated until the stopping condition is reached (e.g., the maximum number of iterations is reached, the convergence condition is satisfied, etc.).  
The advantage of the co-evolutionary algorithm is that it can deal with complex, high-dimensional problems and make full use of the structural information of the problem to improve the solution efficiency. It is often used in multi-objective optimization, large-scale optimization, and optimization problems with complex structures.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.9 Multi-User (MU) Aggregation  
This method defined by the IEEE 802.11 ac, supports the aggregation of MPDUs from multiple receivers into a single PDU only used for transmission from AP to multiple stations.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 2 Background and Motivation  
### Fanets  
An unmanned aerial system (UAS) comprises small unmanned aerial vehicles (UAVs) that are small in size, flexible, and quick to deploy, as seen in Figure 2.  
When a single UAV is employed, a star topology network is present where the UAV is at the center. A ground node can indirectly connect with other ground nodes via the UAV [19; 20]. On the other hand, single UAV systems have specific complex challenges in peer-to-peer communication [26], such as decreasing interference, improving transmission range, and transmitting more data to multiple UAV systems [27].  
The use of high gain directional antennas, rather than omnidirectional antennas, can be found as one of the possible solutions to these issues [21]. If the UAV or a sensor/hardware fails in a single UAV system, the UAV should return to its home base to be repaired or replaced [5]. The fault tolerance of a multi-UAV system is increased since other UAVs may share duties among themselves, which raises the overall fault tolerance of the system. It is feasible to take advantage of the capabilities of other UAVs while working with a heterogeneous UAV cluster [22]. FANET is also considered a component of the VANET network. Due to the excellent mobility of nodes in FANETs networks, conventional routing methods are also not practical in FANETs and do not offer sufficient throughput [19; 20; 21]. FANET topologies change significantly more often than MANETs or vehicle ad hoc networks, which is typical [15]. FANETs, which are not only applicable for multi-UAV systems but can also be formed by single-UAV systems, according to the specification seen in Figure 3.  
Figure 2: FANETS structure.
###### Abstract  
Water use efficiency of vegetation (WUE), the ratio of carbon gain to water loss, is a valid indicator to describe the photosynthetic carbon-water coupling relationship. Understanding how and why WUE changes are essential for regional ecological conservation. However, the impacts of various factors and their interactions on the spatial variation of WUE remain uncertain in the arid land of Northwest China. Here, we selected the Qilian Mountains (QM) and Hexi Corridor (HC) as the study areas. Supported by the Google Earth Engine, we explored the spatio-temporal variations of WUE in QM and HC for 2002-2021 using STL decomposition (a seasonal-trend decomposition procedure), trend analysis, and the Hurst index. Then, the Geodetector method was applied to quantify impacts of geographical and eco-meteorological factors on the spatial variation of WUE. The WUE in HC was higher than that in QM. Interestingly, the opposite longitude zonality characteristics are shown in the QM and HC. In QM, the WUE showed an upward trend with longitude increasing, while a downward trend with longitude increasing in the oases of HC. The WUE of cropland was the highest (1.15 \(\pm\) 0.35 gC kg\({}^{-1}\) H\({}_{2}\)O), and that of alpine vegetation was the lowest (0.2 \(\pm\) 0.15 gC kg\({}^{-1}\) H\({}_{2}\)O). WUE showed a decreasing trend across the study area, almost certainly due to a drop from May to July during 2002-2021. The air temperature is the dominant factor influencing the spatial variation of WUE. In addition, the interaction of any two factors is greater than the independent influence of either factor alone. The Geodetector method proved to be effective for quantifying the impact of complex multi-factors on the spatial variation of WUE. This study provides a new technical scheme to analyze the spatio-temporal pattern and quantify the impact of multi-factors on the spatial variation of WUE. These findings aid in understanding underlying mechanisms of WUE variation and thereby will be beneficial for clarifying the response of vegetation to climate change.  
WUE; MODIS; STL decomposition; Geodetector; Google Earth Engine; vegetation dynamics +
Footnote †: journal: Remote Sensing  
2022145926
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
## 2 Materials and Methods
### GNSS Real-Time PPP Model  
#### 2.2.1 Multi-GNSS SF RT-PPP Model  
The standard uncombined single-frequency PPP model can be expressed as follows:  
\[\begin{cases}L_{r,j}^{sys,s}=\rho_{r}^{sys,s}+c\left(dt_{r}^{sys}-dt^{sys,s} \right)+J_{j}^{sys}\left(b_{r,j}^{sys}-b_{j}^{sys,s}\right)+A_{j}^{sys}N_{r,j}^ {sys,s}-I_{r,j}^{sys,s}+T_{r}^{sys,s}+\varepsilon_{r,j}^{sys,s}\\ P_{r,j}^{sys,s}=\rho_{r}^{sys,s}+c\left(dt_{r}^{sys}-dt^{sys,s}\right)+d_{r,j}^ {sys}-d_{j}^{sys,s}+I_{r,j}^{sys,s}+T_{r}^{sys,s}+\varepsilon_{r,j}^{sys,s}\end{cases} \tag{6}\]  
where \(L\) and \(P\) are the carrier phase and pseudo-range observation, respectively; _sys_ represents the GNSS system; \(\rho_{r}^{s}\) is the satellite-receiver geometric distance; \(dt\), and \(dt^{s}\) represent the clock bias; \(\lambda_{j}\) is the wavelength of frequency \(j\); \(b\) and \(d\) are the phase delay and the code biases; \(N\) is the integer ambiguity; \(I_{r,j}^{s}\) is the ionospheric delay at frequency \(j\); \(T_{r}^{s}\) is the tropospheric delay; and \(\varepsilon_{r,j}^{s}\) and \(\varepsilon_{r,j}^{s}\) denote the pseudo-range and carrier phase observation noise.