esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
In this paper, we propose a CS-based framework for sparse signal transmission to solve the bottleneck of expensive lossy link utilization in the transitional region. Specifically, the lossy link transmission is modeled as a random compressive sampling process. Then, the original signal is reconstructed based on the correctly-received packets using CS reconstruction algorithms. Moreover, the packet length effect on signal/information transmission is investigated, which enlightens us to choose different optimal packet lengths to optimize different communication demands.  
Extensive simulations have been conducted, and the results show that compared to traditional link-layer automatic repeat request (ARQ) interpolation technique, the proposed method delivers a higher quality reconstructed signal while significantly reducing energy consumption at the transmitter side. Moreover, the packet length effect on signal transmission efficiency can be quite different compared to data transmission efficiency, which is influenced by the specific application and channel condition.  
Further experiments using STM32W108 chips conforming to the IEEE 802.15.4 standard verify the sparse signal transmission performance and packet length effect on sparse signal transmission. Interleaving's improvement is also investigated to validate its performance on enlarging optimal packet length.  
Our future work will concentrate on utilizing the link's characteristics to enhance our CS-based method's performance on medium access control and routing. Furthermore, the combination of source coding and channel coding will be further studied.
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 3 System Model
### Queueing Model  
As shown in Figure 1, the time is divided into time-slots, each of which spans \(\tau\) s and is indexed by \(t=0,1,2,\ldots\). We define \(a[t]\) as the number of packets that arrive at the base station in the beginning of time-slot \(t\). Meanwhile, we suppose that the data packet arrival follows a periodical process with the arrival period of \(T\) time-slots and the number of arriving packets set as \(B\).  
As such, \(a[t]\) evolves as  
\[a[t]=\left\{\begin{array}{rl}B&\text{if $t\bmod T=0$,}\\ 0&\text{if $t\bmod T\
eq 0$.}\end{array}\right. \tag{5}\]  
Figure 1: System model  
According to the classic BT problem proposed in [25], these \(B\) data packets that arrive periodically must be transmitted within \(T\) time-slots.  
Let \(s[t]\) represent the transmission rate, which is the number of packets transmitted in time-slot \(t\). Owing to the constrained throughput at the base station, we set an upper bound of \(s[t]\) by \(S\). This way, the feasible region of \(s[t]\) is defined as \(\mathcal{S}=\{0,1,2,\ldots,S\}\).  
Meanwhile, a buffer of size \(Q\) is utilized to store remaining packets at the base station. Let \(q[t]\) denote the queue length, i.e., the number of packets backlogged in the buffer, in the \(t\)-th slot. As such, we have \(q[t]\in\mathcal{Q}=\{0,1,2,\ldots,Q\}\). The queue state \(q[t]\) evolves as  
\[q[t+1]=\min\{(q[t]-s[t])^{+}+a[t+1],Q\}, \tag{6}\]  
where \((x)^{+}=\max\{x,0\}\).
# Investigation on Cutting Blasting Efficiency of Hard Rock Tunnels under Different Charge Diameters
###### Abstract  
Considering the low efficiency of cutting blasting in hard rock mine tunnels, a novel solution of increasing the charge diameter of the cutting holes was put forward. To investigate the influence of the charge diameter on the cutting blasting results, three different working conditions of \(\Phi\) 32 mm, \(\Phi\) 42 mm, and \(\Phi\) 50 mm blasting holes combined with \(\Phi\) 27 mm, \(\Phi\) 35 mm, and \(\Phi\) 45 mm cartridges, respectively, were taken as the investigation objects. At first, the theoretical destruction ranges of single cutting holes under the three different charge diameters were computed. The computed results showed that the destruction range of the cutting holes could be expanded by increasing the charge diameter, which would be beneficial to the destruction of the rock far away from the cutting holes in the cutting cavity. Subsequently, numerical simulations of cutting blasting under the three different charge diameters were performed to display the dynamic propagation process of the blasting stress wave. Importantly, the stress field intensity in the cutting cavity was enhanced significantly with the charge diameter. The stronger stress field intensities generated by the larger diameter charges were more conducive to breaking the rock in the cutting cavity into small fragments that were easy to be discarded. Ultimately, a hard rock vertical slope was used instead of the driving face to carry out the cutting blasting experiments, and the hole utilizations of the cutting blasting were 70.4%, 82.0%, and 94.0%, respectively, under the three different charge diameters, from small to large. The experimental results forcefully substantiated that a higher cutting blasting efficiency could be achieved by increasing the charge diameter of cutting holes in hard rock mine tunnels.  
hard rock tunnel; cutting blasting efficiency; charge diameter; destruction range; numerical simulation; cutting blasting experiment Article
## 3 Experimental Methods
### Dynamic Light Scattering (DLS)  
The method of dynamic light scattering (DLS) consists in determining the broadening of the central (unshifted) line of the spectrum of light scattered by a suspension of nanoparticles and determining the particle sizes from this broadening. This is performed using correlation spectroscopy, i.e., measuring the autocorrelation function (ACF) of scattered light intensity \(G^{I}(\tau)\),  
\[G^{I}(\tau)=I\langle(t)I(t+\tau)\rangle, \tag{3}\]  
In the case of light scattering by noninteracting particles, \(G^{I}(\tau)\) has an exponential form with a characteristic decay time [25]  
\[\tau_{I}=\tau_{D}/2=1/2 Dq^{2}, \tag{4}\]  
where \(D=k_{B}T/6\pi\eta r\) is the diffusion coefficient of particles (in our case, drops), and \(q=|q|=|k_{S}-k_{L}|=4\pi n/\lambda\cdot sin(\theta/2)\) is the modulus of the scattering wave vector, i.e., the difference between the wave vectors of the scattered \(k_{S}\) and exciting laser \(k_{L}\) light, \(n\) is the medium refractive index, \(\lambda\) is the wavelength of the exciting light, and \(\theta\) is the scattering angle. The time \(\tau_{D}\) is the time of diffusion relaxation of the fluctuations of the particle concentration in the studied suspension at a characteristic fluctuation size \(\Lambda=2\pi n/q\)[26]. If the suspension contains particles of different sizes \(r_{i}\), then \(G^{I}(\tau)\) contains several exponentials with different \(\tau_{ii}\), and \(D_{i}\) corresponding to particles of these sizes.  
From the obtained diffusion coefficients, using the Stokes-Einstein relation, the radii of the scatterers are determined:  
\[r=\frac{k_{B}T}{6\pi\eta D}. \tag{5}\]  
Here, \(k_{B}\) and \(T\) are the Boltzmann constant and temperature, \(\eta\) is the dynamic viscosity.  
The droplet sizes of alcohol emulsions of oils and spreads were measured with a standard DLS installation [27]. We have used a He-Ne laser of 1-10 mW power, \(\lambda\) = 633 nm, and a Photocor-FC correlator [28]. The intensity autocorrelation functions (ACFs) of the scattered light were measured upon scattering angles of 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\); 3-4 ACFs were accumulated at each angle. The accumulation of each ACF lasted from 20 to 100 s. From the ACF measured, we determined the size of the drops of the emulsion, or more precisely, the distribution of the scattering intensity on droplet radii, using the commercial program DynaLS. The \(r\) values averaged over the main peak of the distribution (see Section 4 below) were considered as the results of individual measurements, the largest and smallest were discarded, and the average value and standard error were determined from the rest.  
It should be noted that correct DLS measurements require the absence of dust in the sample; that is, the cuvettes used for samples must first be de-dusted.
# A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
## 4 Challenges Still Exist in the Field of Clustering Algorithms
### Data Streams  
The clustering procedure is more difficult than clustering on static data because of clustering's unique nature. Several difficulties with clustering approaches on data streams were identified by [93]. Methods of clustering should be robust enough to handle the presence of outliers and other forms of noise. To aid in the study of trends in data streams, clustering algorithms should have the ability to precisely recognize the change in context and grouping of flowing data items. Clustering algorithms' computing capabilities and memory space optimization should also increase as the number of data streams generated from various sources, such as social networks, grows. To further adapt current context-based adaptive clustering algorithms and develop models for clustering dynamic data streams, more study is needed.
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 7 Experiment Validation  
In this section, the performance of MLE on underwater radiated noise measurement is validated using experimental data. The experiment was implemented in a reservoir in December 2019 [32]. Figure 8 illustrates the reservoir environment, as well as the geometric configuration of the source and receiving array. The sound speed profile was measured using a CTD sensor. The properties of the sediment layer and semi-infinite layer, including sound speed, density, and attenuation coefficients, were determined based on seabed material. A UW-350 transducer submerged at a depth of 50 m emitted two types of signals: (1) a 750 Hz Continuous Wave (CW) tone, and (2) a 10 s duration Linear Frequency Modulated (LFM) pulse with a frequency range of 100-900 Hz. A nested vertical hydrophone array, comprising a 24-element uniform linear subarray with a 1 m spacing and a 17-element subarray with a 0.5 m spacing, was deployed vertically at a horizontal range of 34.5 m from the source, spanning depths from 73 to 97 m. Additionally, a reference hydrophone, located at the same 50 m depth as the source but horizontally offset by 5.2 m, was used to monitor the source level. Due to the short propagation distance, the recorded signals exhibited high SNRs, allowing the single hydrophone-derived power estimates to serve as real value for radiated signal validation. All data were sampled at 48 kHz.  
During post-processing, data from the central 15 hydrophones of the nested array with 1 m inter-element spacing were analyzed. To investigate the impact of snapshot number on measurement performance, the CW signal duration was varied from 2 s to 10 s. Frequency-domain conversion was performed via DFT with a 0.2 s rectangular window and 50% overlap. Figure 9 presents the estimated powers of CW signal across different snapshot numbers, while Figure 9 shows the corresponding estimation biases. These experimental results are basically consistent with the simulated trends in Figure 4. It can be seen that the estimation bias of RPCA exhibits a decreasing trend as snapshot numbers increase. Moreover, the MLE method demonstrates the best estimation performance, highlighting its superior robustness to snapshot limitations compared to other methods.  
Figure 8: The sketch of the experimental reservoir environment and the configuration of the source (marked by a black pentagram) and the array (marked by a set of black solid circles).  
To comparatively evaluate frequency-dependent performance, the power and associated estimation biases of the LFM signal across the 500-900 Hz are analyzed. The results are presented in Figure 10. The comparative analysis reveals that MLE achieves the closest agreement with real values across the entire frequency band. However, it should be noted that under specific spectral regions, MLE exhibits a slight negative bias, likely caused by noise covariance matrix mismatch. In contrast, other methods demonstrate bias fluctuations exceeding 3 dB.
_Article_  
**Fatigue Evaluation of Steel Bridge Details Integrating Multi-Scale Dynamic Analysis of Coupled Train-Track-Bridge System and Fracture Mechanics**  
**[PERSON] *** and [PERSON]**  
Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, National and Local Joint Engineering Research Center for Intelligent Construction and Maintenance, Southeast University, Nanjing 211189, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 29 March 2020; Accepted: 4 May 2020; Published: 7 May 2020  
**Featured Application: The presented framework has a potential application of enhanced fatigue assessment of steel railway bridges with respect to fatigue-prone structural details.**  
**Abstract: Increased running speed and axle weight in the transportation network lead to significant dynamic interactions between the vehicles and bridges. It is essential to capture these interactions in fatigue analysis of steel bridges. This paper presents a framework for fatigue evaluation of critical steel bridge details through multi-scale dynamic analysis of the train-track-bridge system and linear elastic fracture mechanics. The multi-scale coupled dynamic analysis allows accurate and efficient computation of fatigue stresses produced by the moving trains in structural details based on a vehicle-bridge analysis model composed of a 3D vehicle model, multi-scale bridge finite element model including the track system, and a wheel-rail interaction model. Field data from an existing steel-truss railway bridge are used to validate the multi-scale analysis method. Enhanced fatigue evaluation of the bridge detail is performed using the computed fatigue load effects and linear elastic fracture mechanics. The effects of the track irregularity and operating train speed on fatigue crack propagation life are investigated. The presented framework is general and can be applied to other types of steel bridges such as the steel-box girder bridge with orthotropic decks.**  
**Keywords: fatigue evaluation; steel bridge; train-track-bridge system; multi-scale dynamic analysis; linear elastic fracture mechanics; fatigue crack propagation**
# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 5 Discussion  
In the introduction of our paper, we first analyze the pipeline-based approach, which leads to the joint learning approach since the pipeline approach ignores the interaction between entities and relations and is prone to error propagation. Our model is also based on the joint learning approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sentence** & **SGNet** \\ \hline Barcelona will discharge [PERSON] to Brazil, & \\ [PERSON] to Portugal and the young star [PERSON] & ([PERSON], person, Brazil) \\ [PERSON] to Argentina. & ([PERSON], person, Argentina) \\ \hline Ms. [PERSON] met with China’s leaders in Beijing in & (Beijing, administrative\_division, China) \\ March specifically to ask them to pressure & (China, location, Beijing) \\ North Korea. & (China, country, Beijing) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Case study for SGNet.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **Training Time** & **Inference Time** & **F1** \\ \hline \multirow{3}{*}{NYT} & TPLinker * & 1592 & 46.2 & 90.6 \\ \cline{2-5}  & SGNet\({}_{\text{WG}}\) & 1390 & 43.2 & 90.2 \\ \cline{2-5}  & SGNet & 2165 & 69.6 & **91.3** \\ \hline \multirow{3}{*}{WebNLG} & TPLinker * & 599 & 40.1 & 90.9 \\ \cline{2-5}  & SGNet\({}_{\text{WG}}\) & 142 & 37.4 & 90.2 \\ \cline{1-1} \cline{2-5}  & SGNet & 631 & 63.4 & **91.9** \\ \hline \hline \end{tabular} Bold data in the table indicates the best results.  
\end{table}
Table 5: Comparison of the model efficiency. Training time(s) means the time required to train one epoch, and inference time (ms) is the time to predict triples of one sentence. Our re-implementation is marked by *.  
First of all, we consider a joint decoding approach to decompose triples into quintuples to strengthen the information exchange between tasks and prevent the problem of information imbalance in the feature extraction stage. In experiments, the F1 score of \(\mathrm{SGNet_{WG}}\) is higher than most of the models by comparing with other models, and in the exploration of triple overlapping experiments, the F1 score of \(\mathrm{SGNet_{WG}}\) is higher than the best baseline model. Next, in order to better learn the semantic relationship information of sentences, we introduce a graph neural network. Modeling text as a graph, nodes can better pick up local and non-local information. In the experimental results, it can be seen that the F1 score of \(\mathrm{SGNet_{WG}}\) is improved compared with \(\mathrm{SGNet}\).  
Finally, due to the deepening of the model depth, the training time and inference time of our model are longer in the analysis of the model software complexity, but it is acceptable. In exchange for this, the advantage is that the extraction accuracy is improved.
## 4 Discussion
### Cost-Benefit Analysis  
For large areas, in comparison to ground-based or drone surveys, the monoscopic satellite-based method is more cost-effective, as it allows for the analysis of extensive areas with less time and resource investment. In fact, almost all operations can be performed remotely. High- to medium-resolution DEMs are often available for free from public agencies, either through cartographic data or, in some cases, from LiDAR flights. Only some GCPs need to be acquired on-site; for an RPC (Rational Polynomial Coefficient) model, fewer than 10 GCPs are required, which, depending on the extension of the study area, can be collected with a few hours of work. Ground-based methods require surveying each individual tree, which, in areas spanning several hectares, may take days of work. However, for very small areas, the proposed method may not be as cost-efficient, as the setup and processing time could become excessive relative to the area covered. Furthermore, ground-based laser surveys are even more time-consuming than drone surveys, as they require more extensive on-site data collection.  
In any case, we can assess that, regarding both time and economic costs, they remain relatively contained when using satellite imagery (from archives, approximately EUR 1000 per image), with the acquisition of fewer than 10 GCPs using differential GPS, followed by remote processing time (georectification, then measurements on individual trees), and when using a drone (for drone, insurance, and pilot, approximately EUR 5000 in initial costs), it is still necessary to survey dozens of GCPs. However, it is important to emphasize that to acquire the approximately 3600 km\({}^{2}\) of a single satellite image, a drone would theoretically require 360 flight hours, which, of course, cannot be performed consecutively. Additionally, battery recharge time limits the number of flights per day, meaning that acquiring the same area covered by a WorldView-3 image using a drone could take several weeks of field survey work.  
Thus, the choice of method will depend on the specific size and nature of the area to be surveyed.
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 5 Discussion
### Comparison between Techniques in Geophysical Exploration
#### 5.3.3 Comparative Studies  
Some studies have compared the effectiveness of differential geometric methods and Euclidean techniques in geophysical imaging and subsurface modelling.  
For instance, Ref. [59] finds that Riemannian wavefield extrapolation provides more accurate seismic images in complex geological settings, such as salt domes, by better accounting for the curvature of the subsurface. In contrast, Eikonal-based ray tracing, while faster, showed limitations in accurately representing wave paths in these environments.  
Also, Ref. [60] uses finite difference methods on curved grids, inspired by differential geometry, and demonstrated improved accuracy in modelling wave propagation through complex structures compared to traditional Euclidean grid methods. This approach reduced numerical artefacts and better captured the effects of geological heterogeneities.  
On the other side, Ref. [61] compares the geometric optics approaches in Riemannian and Euclidean spaces, concluding that the Riemannian approach offers superior resolution and accuracy in complex subsurface models. The Euclidean methods, while computationally efficient, often missed finer details in areas with significant curvature.  
The choice between differential geometric methods and Euclidean techniques in geophysical exploration depends on the specific requirements of the application. In Section 5.3.1, it is explained that differential geometry provides enhanced accuracy and detailed modelling capabilities, making it suitable for complex geological structures like salt domes. However, it comes with higher computational costs and implementation complexity. On the other hand, Euclidean methods (see Section 5.3.2) offer simplicity and speed, beneficial for real-time applications but may fall short in accurately representing complex subsurface features.  
Integrating insights from both approaches, leveraging the strengths of differential geometry for detailed modelling and Euclidean methods for computational efficiency, can provide a balanced and effective strategy in geophysical exploration. Continued comparative studies and advancements in computational techniques will further enhance the capabilities and applications of these methods in the field.
## 2 Materials and Methods
### Data Labeling  
The EEG signal recorded in epileptic patients can be categorized into four main stages: (1) the interictal state, which is a period of regular brain activity between two consecutive seizures; (2) the preictal state, which refers to the period included between approximately 60 and 90 min before seizure onset; (3) the ictal state, which is when the seizure occurs; and (4) the postictal state, which is the period immediately following a seizure for a few minutes [28; 29; 30]. The beginning and end of the ictal state were manually marked by the clinicians A.D. and P.B. based on the electroclinical and video-recorded information derived from video EEG monitoring. Our prediction task was designed to discriminate between the portion of the preictal state immediately preceding the seizure and a normal signal recorded during the interictal state (see Figure 2). To this end, we defined two binary categories based on the distance from the upcoming seizure: class 1 contained signals sampled from the time window between 0 and 30 min before the seizure, while class 2 had signals sampled from 30 min randomly selected from the interictal state activity.  
Figure 1: Scalp positioning of the 20 common EEG channels used in our data set.