esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 CS Sparse Model
### Sparse Modeling of Pressure Differences  
The purpose is to estimate the location and size of the crack based on the collected data \(\mathbf{d}\). Directly estimating \(\mathbf{x}\) and \(\mathbf{v}\) is challenging due to the nonlinear relationship between \(\mathbf{d}\) and \(\mathbf{x}\) and \(\mathbf{v}\). Proposing to use spatial sampling to transform a portion of the linear model into a sparse representation simplifies the associated signal processing. Consider \(I\) spatial samples of crack positions \(\boldsymbol{\alpha}=\text{vec}[\alpha_{i}\mid\forall i=1:I]\), where \(\alpha_{i}\in[s_{U},s_{D}]\). These sample positions \(\boldsymbol{\alpha}\) represent potential crack positions.  
The corresponding column vectors of the basis matrix of the spatial sample points \(\alpha_{i}\) are:  
\[\phi(\alpha_{i})=\text{vec}[\psi_{m}(\alpha_{i};\omega)\mid\forall\omega\in \Omega,\forall m=1:M], \tag{11}\]  
where \(\psi_{m}(\alpha_{i};\omega)\) depends on \(\alpha_{i}\) and is given by Equation (8) with \(\alpha_{i}\) substituted. Let \(\mathbf{G}(\alpha)\in\mathbb{R}^{N_{\Omega}M\times I}\) be the base matrix, defined as:  
\[\mathbf{G}(\alpha)=[\phi(\alpha_{1}),\ldots,\phi(\alpha_{I})], \tag{12}\]  
where \(N_{\Omega}\) represents the number of frequencies utilized. Consequently, the measured value \(\mathbf{d}\):  
\[\mathbf{d}=\mathbf{G}(\alpha)\mathbf{t}+\boldsymbol{\epsilon}+\boldsymbol{ \epsilon_{s}}, \tag{13}\]  
The error \(\boldsymbol{\epsilon_{s}}\) in this is due to the fact that the spatial sample points set during the spatial sample modeling cannot be aligned with the sensor sampling points. The vector \(\mathbf{t}\) represents the sparse crack size signal, where its non-zero elements correspond to the actual crack sizes, and its support corresponds to the spatial samples closest to the actual crack positions. When setting up the spatial sample points, a larger number of spatial sample points are generally set up to obtain better results, so a small number of cracks in the tunnel rock mass can be regarded as sparse for the spatial sample point.  
As shown in Figure 4, this illustrates the graphical representation of sparse modeling for crack detection in tunnel rock mass, where \(\mathbf{d}=\mathbf{G}(\alpha)\mathbf{t}+\boldsymbol{\epsilon}+\boldsymbol{ \epsilon_{s}}\). From the figure, it can be observed that the measurement value \(\mathbf{d}\) can be expressed as the product of \(\mathbf{G}(\alpha)\) and the estimated value \(\mathbf{t}\), where each column of \(\mathbf{G}(\alpha)\) corresponds to \(\phi(\alpha_{i})\). The spatial sample points corresponding to the non-zero values in \(\mathbf{t}\) represent possible crack locations in the rock mass, and the magnitude of the non-zero values represents the crack sizes.
# Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
## 3 Results and Discussion
### Key Feature of Stress Classification  
From the first CART decision tree for classifying stresses with the GXBoost algorithm in the generalizing model, we learned that the most important root node was O1_HighGamma (as Figure 8); therefore, we examined the box plot for O1_HighGamma during the exploratory data analysis, as shown in Figure 9.  
The results in Figure 9 suggest that the X-axis is the stress levels, namely, high, mid, and low, and the Y-axis is the density of the power frequency spectrum in \(\mu\)V/\(\surd\)Hz, where the middle lines in the box are the medians, and the little triangles are the positions of the means. It is clear that the ranking of the means is high stress \(>\) mid stress \(>\) low stress. The means of the two types of O1_HighGamma attributes were compared using the independent sample \(t\)-test [45], and the results of the statistical analysis were as shown in Table 8.  
Figure 8: CART visualization.  
Figure 9: Box plot of O1_HighGamma.  
The results in Table 8 suggest that, in the data, the means of the high, mid, and low stresses had significant differences between one another. That verifies the possibility of causing different stress states by conducting the SCWT and MA.  
Past studies suggested that Gamma waves are mainly associated with the extent to which minds are concentrated and lead to anxiety and stress [46]. Emotions are connected to Gamma waves [47; 48; 49], where the Gamma waves of negative emotions, in particular, are stronger than those of positive emotions [50]. Reference [51] stated that prefrontal lobe-corresponded Gamma power can serve as a biomarker for identifying stresses. The findings hereof indicated that a higher value of High Gamma at the O1 electrode position means higher stress, which is consistent with the literature and can serve as an important key feature for stress classification.
## 2 Background and Related Work
When surrounded by hazards, sensors will try to guide people as far away from emergency locations as possible. Based on this, [PERSON] _et al._[11] proposed a novel 3D emergency service that aims to guide people to safe places when emergencies happen. In their work, when emergency events are detected, the network can adaptively modify its topology to ensure transportation reliability; quickly identify hazardous regions that should be avoided and find safe navigation paths that lead people to exits. [PERSON] _et al._[12] presented a novel approach for safely evacuating persons from buildings under hazardous conditions. A distributed algorithm is designed to direct evacuees to an exit through arbitrarily complex building layouts in emergency situations. They find the safest paths for evacuees taking into account predictions of the relative movements of hazards, _i.e._, fires and evacuees. [PERSON] _et al._[13] solve a similar problem, but under conditions where hazards can change dynamically over time. When fire expands in an inner building, there may cause a lot of segmentation in the network. In this case, a lot of routing holes occur that lead to data routing failure. The \"Routing Hole Problem\" is a very important and well-studied problem, where messages get trapped in a \"local minimum\". Some existing \"face routing\" algorithms have been developed to bypass routing holes using geo-routing algorithms. GPSR [14] recovers holes by using the \"right-hand rule\" to route data packets along the boundary of the hole, combining greedy forwarding and perimeter routing on a planar graph. The authors of [15] proposed the first practical planarization algorithm with a reasonable message overhead, lazy cross-link removal (LCR). [PERSON] _et al._[16] presented an interesting approach, the BOUNDHOLE algorithm, which discovers the local minimum nodes and then \"bounds\" the contour of the routing holes. In the building fire situation, holes feature prominently and can be expected to grow in size rapidly as a fire spreads, thus demanding solutions that are robust and low complexity for quick reactions.
## 7 Conclusions  
In this paper, we proposed an APC technique to scale down the ICI level in the macro user while maintaining the necessary QoS of the femto user. This type of interference is caused by the downlink transmission of inadequately deployed femtocell. The proposed APC technique activates when the macro user sends an IM based on IDF to HeNB through the backhaul link. The HeNB then seeks for the RSRP feedback report from the FUE. Based on the IM and the feedback report, HeNB actively tunes its downlink power using different power levels and time levels, therefore, it reduces unnecessary consumption of power by saving 13.51% with respect to the baseline. The analysis of the green impact and CO\({}_{2}\) emission show that the proposed APC technique saved 11.17 kg/year of CO\({}_{2}\) emission for one full load HeNB, which is higher than the existing power control techniques. The simulation results have demonstrated that the proposed APC technique effectively reduces the inter-cell interference (ICI) and optimizes the throughput performance of MUE. Compared with the power control techniques based on the objective Signal-to-Interference-plus-Noise-Ratio (SINR) of FUE, MUE-assisted power control technique and range based approach, the APC technique provides a balanced trade-off in attaining necessary Quality-of-Service (QoS) for FUE and mitigation of ICI to the nearby macro user.  
[PERSON] proposed the original idea, designed the experiments and wrote the paper under the supervision of [PERSON]. All authors read and approved the final manuscript.  
This work was supported by the National Natural Science Foundation of China under grant 61620106001.  
The authors would like to thank all anonymous reviewers and editors for their helpful suggestions for the improvement of this paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Power Control Techniques** & **Power Saving (\%)** & **CO\({}_{2}\) Saving (kg/year)** \\ \hline FUE Assisted Power Control Technique & 7.5 & 6.20 \\ MUE Assisted Power Control Technique & 6.39 & 5.29 \\ Range Based Power Control Technique & 4.9 & 4.05 \\ Proposed Active Power Control Technique & 13.51 & 11.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Green Impact Calculation and CO\({}_{2}\) Emission.
# A Hierarchical Federated Learning Algorithm Based on Time Aggregation in Edge Computing Environment
## 1 Introduction  
Federated learning (FL), a distributed machine learning paradigm proposed by Google [1], enables data owners to train local models and collaboratively build a global model without exchanging sample data. FL only requires sending a representative to each location to record data, reducing privacy concerns and the issue of \"qland\" [2, 3]. FL leverages cloud computing to access scalable storage and computing resources but faces challenges in meeting low latency, high reliability, and data security requirements in the Internet of Everything. As the number of participants and model complexity increases, FL communication overhead becomes a non-negligible issue, and the unreliable network connection between participants leads to high network delays during model transmission and aggregation tasks [4, 5, 6].  
In the context of the heterogeneous edge environment, the development of a hierarchical centralized FL architecture and aggregation mechanism is essential [7]. A hierarchical architecture reduces the number of direct communication connections and the burden on computing centers. An aggregation mechanism balances the model's accuracy and training efficiency to speed up model convergence and overall training [8]. These optimizations address issues such as high model interaction overhead and poor convergence of heterogeneous data in the training process, promoting the deployment of FL algorithms to the edge and maximizing the benefits of collaborative training and privacy protection [9]. However, research on FL algorithms in edge scenarios is still in its early stages, and further optimization of the system architecture and training strategies is needed.  
The main contributions of this paper are as follows:1. A hierarchical federated learning algorithm with dynamic weighting is proposed to optimize the conventional FedAvg algorithm and address the heterogeneity of client data by adjusting the contribution of each local model to the global model using the Earth mover's distance method during edge aggregation.
2. A time-effective hierarchical federated learning aggregation algorithm is proposed to address the issue of varying completion times for local training tasks and model parameter uploads due to heterogeneous devices and data among participating clients, using a combination of synchronous and semi-asynchronous aggregation methods based on a dynamic packet-weighted aggregation strategy.  
The remainder of this article is organized as follows: Section 2 presents a review of related work in the field of federated learning. Section 3 describes the hierarchical federated learning algorithm based on dynamic weighting (FedDyn). Section 4 presents the hierarchical federated learning algorithm based on time-effective aggregation (FedEdge). Section 5 analyzes the performance of the proposed method on the MNIST and the CIFAR-10 datasets. Finally, Section 6 concludes this paper.
## 2 Materials and Methods
### Point Cloud Data Collection and Generation
#### 2.2.2 UAV-LS-Based Point Cloud  
In comparison to TLS technology, UAV-LS technology is more suited for large-field scenarios, allowing for multi-scale, real-time, and close-range data collection of field crops [13]. This technology is extensively used for tasks such as crop biomass assessment [80], height  
Figure 1: Data collection of terrestrial laser scanning. (**a,b**) FARO FocusS70 scanner. (**c**) Data collection scenario (rapeseed). (**d**) Point cloud data processing pipeline (maize and cotton).  
estimation [81], and canopy structure analysis [82; 83]. We carried out data collection using the DJI Matrice 350 RTK (made by DJI, Shenzhen, China) (Figure 2d) equipped with the DJI Zenmuse L2 (made by DJI, Shenzhen, China) (Figure 2e) laser scanner. The DJI Zenmuse L2's ranging accuracy at a distance of 150 m is 2 cm, with a laser wavelength of 902 nm and a laser frequency of 204 kz. To capture complete structural information of plants, data collection was executed in weather conditions with wind speeds below 5 m/s. The UAV-LS operated in a non-repeat scanning mode, moving from north to south and east to west. The flight altitude was set at 12 m, with a runway overlap of 65%, a flight speed of 1.5 m/s, and a gimbal pitch angle of \(-60^{\circ}\). In this scenario, we collected UAV-LS-based point clouds of maize plants as depicted in Figure 2.  
Figure 2 outlines the process of our UAV survey. To generate an authentic maize point cloud, preprocessing was necessary for the data collected by the UAV laser scanner. This involved three primary tasks: registration, stitching, and filtering of the point cloud. DJI Terra software was employed to handle the raw data collected, opting for the CGCS2000 coordinate system. Subsequently, the point cloud in LAS format was exported. To achieve individual plant segmentation in the field environment, LAS-formatted point clouds were divided into 2.5 m \(\times\) 1.3 m sections using the CloudCompare (version 2.13.1) software, with each plot containing approximately 15 maize plants. Following Successive Over-Relaxation (SOR) filtering for each plot, these were manually annotated. The annotation work for the point cloud instance segmentation dataset involved two primary attributes: semantic attributes and instance attributes. The annotation process began by manually segmenting the plants and ground in each small area, followed by assigning these two types of scalar field properties to every isolated instance object. In semantic category division, the ground was labeled as 0, and plants were labeled as 1. Finally, a unique identifier starting from 0 was assigned to each instance to complete the instance segmentation annotation.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 4 Results
### Doppler Search  
Figure 3 shows contours of the Doppler least-squares estimator \(L_{\
u}\) plotted over the globe centered on the receiver location. This shows a global and local minimum at the receiver location. This estimate is not accurate, however it is sufficient to locate the receiver within a small enough region for the next step which is the minimisation of the relative codephase estimator \(L_{k\phi}\). Figure 4 shows a histogram of the Doppler-only location error, showing a median error of 175 km and a 95 th percentile error of 537 km.  
Figure 2: The FastFix positioning algorithm, showing the Doppler-search, space and space-time least-squares optimization processes.  
Figure 4: Histogram of Doppler position errors (in km) (\(N=118\)). The median Doppler-only positioning error is 175 km. The 95 th percentile is 537 km. The vertical axis represents the number of doppler-fixes that fall within the corresponding range of positioning error.  
Figure 3: Global Doppler least-squares contours of \(L_{\
u}\). This shows a global minimum close to the receiver position in New Zealand. This map is an orthographic projection of the globe, centered over New Zealand at longitude 170\({}^{\circ}\), and latitude 45\({}^{\circ}\) south. The grey lines are 30\({}^{\circ}\) intervals in longitude and latitude.
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
## 4 Results
### Adaptive Adjustment Results of Dynamic Feedback Network and Long-Term Transfer Function Drift  
Based on the open-loop frequency response data of the hydrophone, we applied the designed dynamic feedback network to obtain the closed-loop frequency response of the hydrophone, as shown in Figure 9.  
It can be seen that in the frequency range of 1.0 Hz to 100 Hz, the closed-loop system has a flatter frequency response, and the sensitivity is adjusted to \(-180\) dB ref 1 V/\(\upmu\)Pa.  
To simulate long-term transfer function drift, we fit the open-loop data and add a bias to obtain the drifted open-loop frequency response and closed-loop frequency response. That is, the fitting parameters \(A\), \(B\), and \(C\) are shifted to \(A\cdot\Delta A\), \(B\cdot\Delta B\), and \(C\cdot\Delta C\).  
As shown in Figure 11. It can be seen that after an adaptive adjustment, the system's frequency response has returned to the initial design state after the system characteristics of the open-loop system have shifted. The feedback not only restores the low-frequency cutoff to 1.0 Hz but also ensures good consistency in passband sensitivity.  
Although the proposed method achieves a flatter sensitivity response across the extended bandwidth, it reduces peak sensitivity values in specific frequency ranges compared to the open-loop system. However, this trade-off brings critical advantages for practical applications. The flattened response ensures a consistent detection capability over a wider frequency band (1-100 Hz), avoiding significant sensitivity fluctuations at different frequencies, thereby enhancing system stability and reliability. Experimental results demonstrate that after feedback implementation, the system exhibits a more balanced detection performance across the entire operational bandwidth, leading to more stable identification of low-frequency signals. Ultimately, this improvement enhances rather than compromises the hydrophone's weak signal detection capability in complex underwater environments.  
The above experimental results verify the effectiveness of the proposed dynamic feedback network and adaptive adjustment strategy, which can effectively compensate for the impact of a long-term transfer function drift on system performance.  
Figure 11: Hydrophone closed-loop frequency response adaptive adjustment after drift.
# Analysis of the Impact of Multipath on Galileo System Measurements
## 4 Multipath Analysis
However, in the case of observations at the WUT2 station, the spatial distribution is very uniform, and it is impossible to determine directions that are particularly susceptible to recording signals reflected from terrain obstacles.
# A New Security Proof for Twin-Field Quantum Key Distribution (QKD)
## 3 Protocol
After applying the BS to Equation (20), but before measuring the output of the BS, the state evolves to (after permuting subspaces) the following:  
\[q\left|v\right\rangle_{01}\left|01\right\rangle_{AB}+\sqrt{q(1-q)}(\left|0 \right\rangle_{01}\left|\phi_{0}\right\rangle_{AB}-\left|1\right\rangle_{01} \left|\phi_{1}\right\rangle_{AB})+(1-q)\left|\psi_{2}\right\rangle_{01}\left| 10\right\rangle_{AB}. \tag{21}\]  
At this point, a measurement of the BS output register is performed and the outcome is broadcast. Assuming that \(1-q\) is \"small\", whenever a \"\(D_{0}\)\" or \"\(D_{1}\)\" is measured, [PERSON] and [PERSON]'s state should collapse to an entangled Bell state; when the outcome is \(D_{1}\), [PERSON] will apply a Pauli \(Z\) gate to transform the state \(\left|\phi_{1}\right\rangle\) to \(\left|\phi_{0}\right\rangle\). Since \(1-q>0\), there will be some error in the multi-photon case, and this is something that users must optimize. Thus, interestingly, for this TF-QKD protocol, even when there is no channel noise and everything is ideal, there will always be some error in [PERSON] and [PERSON]'s raw key, which error correction must later repair. At this point, we comment that two varieties of the above protocol may be introduced, which we denote as \(\Pi\)-Zero and \(\Pi\)-One. For \(\Pi\)-Zero, [PERSON] and [PERSON] will only use rounds where the server reports an outcome of \(D_{0}\) (if any other outcome is reported, including \(D_{1}\), that round is discarded); similarly, \(\Pi\)-One is defined to be the same, but [PERSON] and [PERSON] will only use rounds where the server reports an outcome of \(D_{1}\). The original protocol, where [PERSON] and [PERSON] use rounds where the server reports either \(D_{0}\) or \(D_{1}\), will be denoted \(\Pi\)-Total. While \(\Pi\)-Zero and \(\Pi\)-One may discard more rounds, we show later that improvements in key-rates can be found in some instances based on channel statistics. This is due to the asymmetric nature of the protocol (which we discuss in more detail in Section 5.1). We are not aware of these slight modifications being analyzed in prior literature.
# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 3 Review Findings
### Analysis Domain
#### 3.2.2 Video  
Few studies utilized the CE video domain for automatic bleeding detection. The frames of the video are sequentially arranged and applied to an algorithm for identifying bleeding frames or regions. Time is an important parameter of the video domain. The video domain has the best likelihood of detecting bleeding by using the previous and next frames of the video as well as the time information. However, lengthy videos require high processing power. The authors of [28] utilized a video CE dataset with a sequence of 600 frames, which was collected from a PillCam SB3 video. Among them, 73% of the total frames were red lesions. In [29], if the last frame of the CE video was not found, the authors presented a pixel-based approach using the Support Vector Classifier method in which the model continued the process to the next frame of the video. In [30], a novel, full reference, video quality metrics method named Quality Index for Bleeding Regions in Capsule Endoscopy (QI-BRiCE) was proposed, which evaluated the perceptual and diagnostic qualities of damaged WCE videos with bleeding regions. Videos of 15 patients ranging in duration from 12,000 to 20,000 frames were used for automatic bleeding detection [31]. A semi-automatic method was proposed to extract the bleeding region from successive video frames containing bleeding. Three video files were used that included 589, 500, and 428 frames [32]. [PERSON] et al. improved the model by skipping one or a few frames from the sequence of bleeding frames [33]. A study utilized a CNN-based model to screen high-risk suspicious images from CE videos with a focus on high sensitivity but potentially lower specificity [34]. Using 84 full-length videos, another study [35] proposed an algorithm that was comparable with the Suspected Blood Indicator (SBI).  
Five publicly accessible CE bleeding videos were tested in [36; 37] to construct automatic bleeding detection models to identify bleeding. Ten real patient video files were used in [38], which consisted of 200 frames and about 40 s in duration. Instead of dealing with a complete video, consecutive small portions of videos were used. Each video consisted of several frames, which were tested sequentially [39]. Another study used the time domain information of CE videos for a bleeding localization technique. The approach used eight different videos collected from eight subjects at West China Hospital [40]. A total of 4166 third-generation small bowel CE videos were applied in [41], which were collected from the Computer-Assisted Diagnosis for Capsule Endoscopy database (CAD-CAP) endorsed by the Societe Francaise d'Endoscopipe Digestive.