esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Dynamic Mechanical Properties of Sandstone Under Different Damage Degrees
###### Abstract  
To investigate the effects of impact loads during drilling under various geostress conditions on rock damage characteristics, strength properties, and energy absorption inotechnical engineering, dynamic uniaxial and dynamic triaxial compression experiments were conducted on sandstone specimens subjected to different impact loads using a self-designed drilling experimental bench. The results indicate that the compressive strength of rock specimens is inversely related to the impact power during drilling; higher impact power results in lower compressive strength. Additionally, the fragmentation mode of rocks after dynamic uniaxial and triaxial compression experiments is influenced by energy absorption, with greater energy absorption leading to smaller fragment sizes post-failure. Key factors affecting rock energy absorption include the intensity of dynamic loading, with higher loading intensity resulting in increased energy absorption; the level of damage sustained by the rock itself, with greater damage causing more energy absorption during dynamic loading; and the confining pressure, with higher confining pressure leading to reduced energy absorption. This study effectively evaluates the deformation characteristics, strength properties, and energy absorption characteristics of damaged rock under impact loads. It provides valuable guidance on the stability and damage conditions of surrounding rock during the rock drilling process in the field of geotechnical engineering.  
rock mechanics; impact load; uniaxial compression; triaxial compression Article
## 1 Introduction
assessed the preparation and reprocessing of grafted PLA/cellulose nanocrystals (1-3 wt%) during reactive extrusion using dicumyl peroxide as the initiator and demonstrated the utility of grafting for counteracting the consequences of one cycle of mechanical recycling [51]. Finally, [PERSON] et al. showed the potential reprocessability and recyclability of PLA/nanoclay (3 wt%) nanocomposites with a melt strength enhancer (4 wt%) after up to twenty reprocessing extrusions, which retained mechanical properties regardless of the decrease in the viscosity [52]. Nevertheless, recycling plasticized bionanocomposites has not been reported to date, and given the high number of newly developed PLA-based nanocomposites with plasticizers [23; 53; 54], the study of the consequences of subsequent processing stages becomes necessary and is indeed the novelty and core of this research. Altogether, even though promising results on the mechanical reprocessing of biopolymers and bionanocomposites have been achieved, it does not seem easy to close the recycling loop for particular applications such as food packaging, as it is currently applied for other polymers in this sector, as poly(ethylene terephthalate) (PET) [55]. A complementary perspective involves considering the downgrading of materials as an opportunity for applications with lower performance requirements. Indeed, using multiple-processed materials for downgraded applications has been reported as an added-value alternative for biobased polymers, including PLA [36; 56; 57]. This study aims, therefore, to evaluate the structural and morphological consequences of reprocessing PLA-based bionanocomposites containing PEG as a plasticizer and acetylated NFC as a bio-based nanofiller. A strategy based on the successive sequence of compounding, injection, and compression molding was applied to obtain pellets, dog-bone, and film specimens. The structural and morphological properties of the different materials were thoroughly assessed after each stage in terms of molar mass, crystalline structure, and thermo-oxidative stability. Finally, the performance of bionanocomposites was validated as a proof of concept for the foreseen applications from a downgrading perspective. In detail, the mechanical performance of dog-bone specimens after injection molding was validated, considering a first service life requiring high stability, strength, and resistance. Then, the water immersion performance of thermo-compressed films obtained after the second processing stage was evaluated to assess the contribution of PEG and NFC in the structures with an advanced degree of degradation for a foreseen second service life.
# Integrated Deep Ultraviolet Doublet Metalens for Projection Imaging
###### Abstract  
Metalens, with their unique modulation of light, are in great demand for many potential applications. As a proof-of-principle demonstration, we focus on designing SiO\({}_{2}\) metalenses that operate in the deep ultraviolet region, specifically around 193 nm. Based on the deep ultraviolet metalens proposed in this paper, an integrated deep ultraviolet doublet metalens is further offered. When the incident light is a plane wave with a wavelength of 193 nm, the integrated doublet metalens can reduce the beam size by a factor of 4:1, and the emitted light is flat. The integrated doublet metalens can project the reticle image proportionally, making the projection image clear. The integrated doublet metalens has the best imaging effect at the propagation distance of 2 \(\upmu\)m and can tolerate \(\pm\)3 degrees of incident angle deviation. Our findings establish general and systematic strategies to guide the design of traditional optical lens arrays with excellent integrated doublet metalenses and pave the way for enhanced optical performance in the application of large-relative-aperture deep ultraviolet detection, deep ultraviolet microscope systems, laser beam combining systems, deep ultraviolet lithography systems, etc.  
metalens; deep ultraviolet; projection imaging Article
## 4 Conclusions  
This paper proposed an implementation of the frequency-domain-based bit flipping control strategy [1] for stabilizing the single-bit high-order interpolative sigma delta modulator. In particular, a frequency detector was used to detect the resonance frequencies of the input sequence of the sigma delta modulator. Moreover, a neural-network-based controller was used for finding the solution of the integer programming problem. Moreover, the buffers and the combinational logic gates as well as an inverter were used for implementing the control strategy. The implementation cost was low. The computer numerical simulation results show that the stability region in terms of the input dynamical range increased from 0.24 to 0.58. Moreover, the control strategy could be applied to a wider class of the input signals compared to the clipping method.  
Figure 9: (**a**) Output of the loop filter under our proposed control strategy when the input is an electromyogram. (**b**) Output of the loop filter under the clipping control strategy [7] when the input is an electromyogram. (**c**) Magnitude response of the output of the loop filter under our proposed control strategy when the input is an electromyogram. (**d**) Magnitude response of the output of the loop filter under the clipping control strategy [7] when the input is an electromyogram.  
**Author Contributions:** Conceptualization, H.Z. and B.W.-K.L.; methodology, software, H.Z. and B.W.-K.L.; validation, B.W.-K.L.; formal analysis, H.Z. and B.W.-K.L.; investigation, H.Z. and B.W.-K.L.; resources, B.W.-K.L.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z.; visualization, H.Z.; supervision, B.W.-K.L.; project administration, B.W.-K.L.; funding acquisition, B.W.-K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded partly by the National Nature Science Foundation of China [no. U1701266, no. 61372173 and no. 61671163], the Team Project of the Education Ministry of the Guangdong Province [no. 2017 KCXTD011], the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent [no. 501130144], the Guangdong Province Intellectual Property Key Laboratory Project [no. 2018B030322016] and Hong Kong Innovation and Technology Commission, Enterprise Support Scheme [no. S/E/070/17].  
**Conflicts of Interest:** The authors declare no conflict of interest
## 3 Experiments
### Compared Methods  
We use six state-of-the-art algorithms for comparison, including three methods based on Tucker decomposition: low tensor-train rank (LUTR)-based method [34], coupled sparse tensor factorization (CSTF) [28], nonlocal sparse tensor factorization (NLSTF) [29], and a method based on CP decomposition Super-resolution TEnsor-ReConstruction (STEREO) [30] (we use the non-blind version). In addition, we use the matrix decomposition-based method Hysure [13] and the spectral unmixing-based method non-negative structured sparse representation (NSSR) [20]. To demonstrate the reliability of the experimental results, all source codes were obtained through open access, and the codes for the various experiments were run in the same environment. This paper's experiments were implemented in MATLAB R2014a on localhost with 2.40 GHz Intel i7-7700 CPU and 8.0 GB DDR3.
## 3 Results and Discussions
### Results of Predicting Age Information for Unlabeled Datasets and Exploration
** & **Z** & **_p_-Value** \\ \hline \multirow{5}{*}{Politics} & 10s & 0.0121 & 0.0132 & 0.0089 & 0.0123 & 0.0093 & 0.0124 & 15.5958 & 0.0000 \\ \cline{2-7}  & 20s & 0.1132 & 0.0659 & 0.1402 & 0.1193 & 0.1365 & 0.1139 & \(-\)14.9812 & 0.0000 \\ \cline{2-7}  & 30s & 0.2350 & 0.0673 & 0.2306 & 0.1337 & 0.2312 & 0.1267 & 2.1891 & 0.0286 \\ \cline{2-7}  & 40s & 0.3201 & 0.0601 & 0.2528 & 0.1085 & 0.2619 & 0.1058 & 40.2355 & 0.0000 \\ \cline{2-7}  & \(\geq\)50s & 0.3199 & 0.1053 & 0.3675 & 0.2104 & 0.3610 & 0.2000 & \(-\)15.1047 & 0.0000 \\ \hline \multirow{5}{*}{Eonomy} & 10s & 0.0085 & 0.014  
Table 9 shows similarities within a section measured for each age group pair, with their maximum and minimum values for similarities highlighted in red and blue fonts, respectively. For example, (10s, 20s) was the most similar in the \"World\" section, but least similar in the \"Economy\" section. Overall, (20s, 30s) had the greatest similarity in the \"World\" section, while (10s, \(\geq\)50s) had the lowest similarity in the \"Economy\" section. The largest and smallest values of all age group pairs are marked in bold in Table 9.  
The similarities in Table 9 were investigated within each section and they demonstrated that four sections, \"Politics,\" \"Lifestyle/Culture,\" \"World,\" and \"IT/Science\" had the greatest similarity in (20s, 30s). In comparison, the other two sections \"Economy\" and \"Society\" had the greatest similarity in (30s, 40s). However, all sections had the lowest similarity in (10s, \(\geq\)50s). These results indicate that age differences in terms of topics of interest were small in (20s, 30s) and (30s, 40s), whereas they were large in (10s, \(\geq\)50s). A more thorough analysis of the similarity of topics of interest between age groups was performed using the obtained \(similarity_{subsection}(s,g_{i},g_{j})\) values. Its results are shown in Supplementary A. To summarize the results, both the top and bottom five subsections for \(similarity_{subsection}(s,g_{i},g_{j})\) were identified for each age group pair, as shown in Table 10. The data in Table 10 can also help to examine and discuss the more detailed reasons for the similarities and differences in topics of interest revealed between the age groups. For example, according to Table 9, overall, the greatest similarity in topics of interest existed in (20s, 30s) in the \"World\" section, but a closer look at Table 10 revealed that the two age groups had the greatest similarity in the \"Weather\" subsection of the \"Lifestyle/Culture\" section. Moreover, Table 9 shows that overall, (10s, \(\geq\)50s) showed the lowest similarity relating to topics of interest in the \"Economy\" section. However, based on Table 10, the \"Society General\" subsection of the \"Society\" section had the lowest similarity relating to topics of interest for (10s, \(\geq\)50s).
## 5 Conclusions  
Aiming at the addressing the issues of multipath spreads and Doppler effects in underwater environments, this paper proposes the TRM-SBDC UWA communication method to improve the performance of UWA communications. TRM based on a single sound pressure sensor is used to reform the multipath structure and achieve energy focusing of the signal, so as to mitigate the ISI. Subsequently, the Doppler effect of each symbol is compensated in real-time by the SBDC method, thereby reducing the time delay error and obtaining a low BER. The results of simulations with real sounding channels indicate that the proposed method can greatly improve the UWA communication performance. In UWA communication experiments conducted in the Songhua River, Harbin, the proposed method achieved nearly error-free performance with a communication rate of 100 bit/s in the 5.5-7.5 kHz band, confirming the feasibility and robustness of the TRM-SBDC communication method, and indicating that the method proposed in this paper could be of great significance for mobile UWA communications.  
[PERSON] and [PERSON] proposed the method described in the paper; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] processed and analyzed the data; [PERSON] wrote the original draft of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant Nos. 61371171, 11374072, 61501061) and the Open Foundation of National Key Laboratory of Science and Technology on Underwater Acoustic Antagonizing (Grant No. SSDKKFIJ)-2017-02; Grant Name: Research on Surface and Underwater Target Resolution Using Two-Dimensional Vector Sensor and Array).  
The authors declare no conflict of interest.
## 4 Discussion
### Accident Rates Due to Natural Hazards Compared to Overall Accident Rates  
In the years 2020-2024, a total of 48 hazardous events associated with the manifestation of methane hazards, rock bursts, and endogenous fires occurred. These events included methane ignitions and explosions, rock bursts, and stress relief events, as well as endogenous fires (Table 7):  
In cases in which methane and rock burst hazards manifested, these events resulted in accidents (Table 8).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Number of Events**} \\ \cline{2-7}
**Hazard** & **2020** & **2021** & **2022** & **2023** & **2024** & **2020–2024** \\ \hline Methane & 1 & 3 & 4 & 1 & 1 & 10 \\ \hline Rock bursts & 3 & 3 & 6 & 2 & 11 & 25 \\ \hline Endogenous fires & 2 & 2 & 5 & 0 & 4 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Hazardous events resulting from natural hazards in the years 2020–2024.  
Figure 7: Number of rock bursts, stress relief events, and related accidents in the years 2020–2024 [22].  
Against the background of overall accident rates in the years 2020-2024, the share of accidents resulting from hazardous events caused by the manifestation of natural hazards (methane-related hazards and rock bursts) is illustrated in Figure 8.  
With regard to methane hazards, the share of accidents resulting from methane ignitions and explosions compared to overall accident rates is presented in Figure 9.  
With regard to rock burst hazards, the share of accidents resulting from rock bursts and stress relief events compared to overall accident rates is presented in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Hazard**} & \multicolumn{4}{c}{**Type of Accident**} \\ \cline{2-5}  & **Total** & **Fatal** & **Serious** & **Accidents Causing** \\  & **Accidents** & **Accidents** & **Accidents** & **Temporary Work Incapacity** \\ \hline Methane & 56 & 16 & 7 & 33 \\ \hline Rock bursts & 140 & 32 & 4 & 104 \\ \hline Endogenous fires & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accident rates resulting from natural hazards in the years 2020-2024.  
Figure 8: Accident rates resulting from natural hazards compared to overall accident rates (in %).  
Figure 9: Accident rates resulting from methane hazards compared to overall accident rates (in %).  
In summary, the manifestation of natural hazards (methane-related hazards and rock bursts) in Polish hard coal mines during the years 2020-2024 was responsible for the majority (62%) of all fatal and serious accidents. The occurrence of hazardous events resulting from these natural hazards can not only cause accidents but may also lead to the limitation, temporary suspension, or even cessation of mining operations, thereby reducing coal production. The exclusion of parts of deposits affected by natural hazard events from mining activities may further decrease production capacities and, in some cases, lead to the premature closure of mining operations.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Discussion of Different ML Methods in Network Measurement  
We introduce the application of supervised learning, unsupervised learning, reinforcement learning, and transfer learning to network measurements in the second section. The aforementioned findings demonstrate that most datasets in the field of network measurement contains labeled data. Hence most researchers still opt for supervised methods to teach and train their models. In terms of network metrics, supervised learning has developed and is at a good level of development. In comparison to other learning algorithms, including unsupervised learning, it is popular and performs well. Advanced training data are not necessary for unsupervised learning. A dataset can be directly modeled. Unsupervised learning's main goals are to train the model that will be used to learn the dataset's structure and to give users meaningful knowledge about fresh samples. As a result, all we need to do to apply the clustering algorithm to get the desired outcomes is to understand how to calculate data similarity. Semi-supervised learning has not been used for network measurement compared to supervised and unsupervised learning due to its rapid development and scant theoretical backing. Both reinforcement learning and transfer learning are now in use; however, due to the differences between the two learning techniques, few academics are interested in them. In conclusion, supervised learning is currently the most widely used technique for measuring networks, which is consistent with tendencies in future development.
# Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery
## 3 Network Model
### Intelligent Receiver Model
#### 3.3.2 The Training Algorithm of the Intelligent Receiver  
The parametric optimizer we used was Stochastic Gradient Descent with momentum (SGDM). A momentum mechanism was added compared to Stochastic Gradient Descent (SGD). The current momentum \(V\) is determined by the last iteration momentum, together with the current gradient. By adding the momentum factor, the model parameter update can maintain the previous update trend and avoid the local optimum. The iterative process of parameter update can be expressed as:  
\[V_{dW}=mV_{dW}+(1-m)dW \tag{18}\]  
\[W=W-\alpha V_{dW} \tag{19}\]  
where \(m\) is the momentum factor, \(W\) is the parameter to be updated in the network, and \(\alpha\) is the learning rate.  
For the loss function, the binary cross-entropy loss function was used, as shown in Equation (17). The training batch size was 256 and the number of training epochs was 10. The specific training method is shown below (Algorithm 1):  
Figure 6: Structure comparison between traditional receiver and intelligent receiver.
## 4 Simulation Results under Irregular Waves in the Time Domain  
We carry out simulations of the OWC system associated with the control strategies in the time domain. For time domain calculations, a Runge-Kutta method [48] was applied for solving the above dynamics under irregular wave condition.  
We obtain the hydrodynamic parameters of the first stage from ANSYS AQWA[49] using the assumption that a trapped water column is regarded as a rigid body of a cylindrical shape. In the simulation, the body has a 6-m diameter with a 5-m draft (\(h_{0}\)) floating in the ocean cube that has a 500 by 500 m\({}^{2}\) surface and a 50-m water depth, and only the heave motion of the body is allowed. In order to properly calculate heave impulse response function, the frequency domain response should have a truncation frequency of 2 rad/s with a frequency spacing of 0.05 rad/s [50].  
The solid lines in Figure 3 display the impulse response of excitation force \(f_{e}\) and radiation force \(f_{r}\) for calculations of \(F_{e}\) and \(F_{r}\), respectively, and the dashed line is for the state space approximation for \(f_{r}\).  
A Pierson-Moskowitz (PM) wave model is applied to analyze the performance of the device. When a constant wind has blown for a sufficiently long time along a sufficiently long stretch of the ocean, the semi-empirical PM spectrum [51]:  
\[S_{PM}(\omega)=5\pi^{4}\frac{H_{s}^{2}}{T_{p}^{4}}\frac{1}{\omega^{5}}\exp \left(-\frac{20\pi^{4}}{T_{p}^{4}\omega^{4}}\right), \tag{26}\]  
matches relatively well with the experimentally-obtained wave spectra, where \(H_{s}\) and \(T_{p}\) denote significant wave height and the peak wave period, respectively.  
Figure 3: Impulse response functions: (**a**) excitation force; (**b**) radiation force and its state-space approximation.  
For time series calculations, the spectral distribution (26) is discretized as the sum of a large number \(N\) of regular waves as written as:  
\[\lambda(t)=\sum_{n=1}^{N}\lambda_{n}\cos{(\omega_{n}t+\theta_{n})}. \tag{27}\]  
Here, \(\omega_{n}=\omega_{l}+(n-1)\Delta\omega\), where \(\omega_{l}\) is the lowest frequency, \(\Delta\omega\) is a small frequency interval, \(n=1,\ 2,...,\ N\) and the spectrum is not to contain a significant amount of energy outside the frequency range \(\omega_{l}\leq\omega\leq\omega_{l}+(N-1)\Delta\omega\). \(\lambda_{n}=\sqrt{2S_{PM}(\omega_{n})\Delta\omega}\) and \(\theta_{n}\) are the amplitude of the wave component of order \(n\) and the initial phase randomly chosen in the interval \((0,2\pi)\), respectively. A summary of the parameters and values used in calculations is given in Table 1.  
We performed the simulation under various wave heights and periods, but only one condition was chosen for verification of the controller operation under irregular waves with \(H_{s}=2\) m and \(T_{p}=10\) s. The sampling time interval is 0.02 s.
# Product Demand Prediction with Spatial Graph Neural Networks
## 2 Related Works
### Graph Neural Network in Modeling Spatial Dynamics Events  
Graph Neural Networks offer several advantages over traditional neural networks. They can be trained on datasets that include both input data and pairwise relationships between items, making them particularly effective for modeling spatial dynamics events [16]. Furthermore, demand predicting in online marketplaces also involves spatial dynamics [17], as the location from which products are sold can influence the demand for geographically proximate products.  
In this paper, we creatively apply GNN to predict e-commerce product demand by leveraging the geographic information inherent in the data. We construct an adjacency matrix based on the spatial connectivity of administrative regions in Russia, defining a graph where each geographic region is treated as a node, with various features from the dataset serving as attributes for each node. By utilizing this graph structure, GNN effectively integrates location-specific information into the prediction model, capturing spatial dependencies that other neural network models do not consider. This approach not only enhances the accuracy of demand predictions but also provides a more comprehensive understanding of the factors influencing demand across different regions.