esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 4 Reservoir Modeling of the Sangonghe Formation in the Central Junggar Basin
### Property Modeling  
Once the structural framework has been established, the next step is to grid the model. To enhance the accuracy of the geological model and achieve a finer characterization of reservoir heterogeneity, the model is divided into a grid with a horizontal resolution of 20 m by 20 m and a vertical resolution of 0.5 m. This grid resolution takes into account the size of the target area and the distribution of wells, ensuring that the model captures the geological features and variations in a more detailed manner.  
The physical properties of the reservoirs vary among different sediment microfacies and even within the same microfacies. The hydrodynamic force is the strongest during the deposition of the middle and lower parts of submarine distributary channels, as well as the middle and upper parts of the mouth bars. This results in relatively coarse particle sizes with good sorting and roundness, leading to favorable reservoir physical  
Figure 11: Heterogeneous reservoir geological model of the Sangonghe formation in the MXZ area.  
Figure 10: The width–thickness ratio statistics of the submarine distributary channel (**A**) and mouth bar (**B**) of the second member of the Sangonghe Formation in the MXZ area. Dots are measurements and lines are regression curves.  
properties. Conversely, the sand bodies located between channels exhibit the poorest physical properties. To establish the property model of the reservoir, the porosity and permeability of unit sand bodies in different sedimentary microfacies are randomly assigned based on the statistical analysis results of their physical properties. This property model provides a representation of the reservoir's characteristics, allowing for the simulation and analysis of fluid flow behavior within the reservoir (see Figure 12).  
The heterogeneity of the braided reservoir can be observed in Figure 13, which showcases local amplification sections of the unit elements filling model. The sections of the porosity model demonstrate that the internal architecture of the reservoir is well-preserved after property modeling. In the porosity model, it can be observed that single bars exhibit the highest porosity values, ranging from 0.2 to 0.26, represented by a yellowish to orangish color. Channel sands follow, with porosities ranging from 0.1 to 0.18, indicated by a greenish color. The other two fine sediments display a bluish color and exhibit lower porosities ranging from 0.01 to 0.08. The background shale, on the other hand, is assigned the lowest porosity values, ranging from 0.005 to 0.01.  
Figure 12: Porosity model of the Sangonghe Formation in the MXZ area.  
Figure 13: I-direction section of the porosity model (Z scale: 15, **(A)**) and its local amplification (**B,C**).
## 2 State of the Art Energy Efficiency and IoT in SBE
### Challenges and Opportunities  
Despite the transformative potential of IoT in SBEs, several challenges and opportunities persist in this researching scope. Data interoperability remains a significant hurdle, as heterogeneous devices and platforms often lack standardised communication protocols [24]. Furthermore, privacy and security concerns complicate the deployment of IoT solutions in sensitive environments. Nevertheless, emerging technologies, such as hybrid twin models that combine real-time IoT data with predictive simulations, offer innovative solutions. These models enhance decision-making by providing insights into complex behaviours of the SBE and enabling proactive management strategies.  
This work builds on the existing literature by proposing a multidimensional framework for SBE that integrates IoT ecosystems with an in-depth understanding of the modes of inhabiting. Unlike previous studies (focused on technical or operational aspects), this work emphasises the interrelations between environmental, social, and cultural factors in shaping energy efficiency outcomes. By deploying a comprehensive IoT-driven methodology, this research contributes to the following: (1) enhancing data through sensor networks to collect and analyse contextual data, providing actionable insights for energy management; (2) user-centric design that incorporates user behaviour into decision-making processes to ensure thermal comfort while minimising energy use; and (3) policy integration, demonstrating compliance with international energy efficiency directives and offering evidence-based recommendations for further guidelines.
###### Abstract  
Using the rock mechanics test (RMT) and acoustic emission acquisition system (DS9), based on the energy principle, uniaxial compression, uniaxial cyclic loading, and unloading tests are used to study the energy transformation characteristics of the process of sandstone absorbing axial strain energy, accumulating and releasing elastic strain energy, plastic deformation, and crack extension dissipation energy. The study results show the increase of loading rate, rock fracture surface increases, number of fragments increases, and size of fragments decreases; the sandstone damage process causes: shear damage to tensile shear damage and then splitting damage for change; the input energy and elastic energy increase nonlinearly with an increase of stress, and the dissipative energy is larger at the beginning of loading. After a small decrease, it enters the nonlinear growth stage, and the input energy density grows the fastest. The elastic energy density is the second fastest, and the dissipative energy density grows the slowest; with an increase of loading rate, in any deformation stage, the elastic energy density and dissipation energy density are increased, proportion of elastic energy density is decreased, and proportion of dissipation energy density is increased. Near the peak stress stage, the proportion of elastic energy decreases, and the proportion of dissipative energy increases, the damage variable stress curve of sandstone is \"weakly concave\", which is consistent with the logistic function, and the damage evolution process has chaotic dynamics properties; the acoustic emission energy of sandstone in cyclic loading and unloading test has a similar variation with the theoretical calculation of dissipated energy. The cumulative energy curve shows a step-up law, and the stress corresponding to the step point is near the historical maximum stress.  
rock mechanics; energy evolution; damage; deformation; acoustic emission +
Footnote †: journal: Applied Sciences
## 3 Experiments
### Photovoltaic Power Measurements  
The goal of this experiment was to verify that a miniature photovoltaic (PV) panel can replenish the energy the sensing node consumes each day. The procedure followed four analytical steps drawn directly from the design notes in the document:  
1. Translate the daily current budget into energy. The battery must recover the 30 mAh that the node is expected to draw every 24 h. Converting to watt-hours: \[\mathrm{Energy(Wh)} =\mathrm{Capacity(Ah)}\times\mathrm{Voltage(V)}\] (7) \[30\,\mathrm{mAh} =0.030\,\mathrm{Ah}\] (8) \[0.030\,\mathrm{Ah}\times 5\,\mathrm{V} =0.15\,\mathrm{Wh}\] (9)
2. Convert the energy target into a required panel power rating. Assuming an average of 4 h of effective sunshine per day (Athens climate), the mean electrical power that must reach the battery is \[\mathrm{Power(W)} =\frac{\mathrm{Energy\,\,Wh}}{\mathrm{Sunlight\,\,hours(h)}}\] (10) \[=\frac{0.15\,\mathrm{Wh}}{4\,\mathrm{h}}=0.0375\,\mathrm{W}\] (11)
3. Derate for charging and weather losses. To cover charger inefficiency, temperature effects, and cloudy days, we apply a 0.75 system-efficiency factor, giving a name-plate power \[\mathrm{Require\,\,Power}=\frac{0.0375\,\mathrm{W}}{0.75}=0.05\,\mathrm{W}\] (12)
4. Translate the power rating into physical size.  
With peak irradiance \(G=1000\,\mathrm{W}/\mathrm{m}^{2}\) and 18% efficient monocrystalline cell, the active area required is  
\[\mathrm{Area}(\mathrm{m}^{2}) = \frac{\mathrm{Power}(\mathrm{W})}{\mathrm{Efficiency}\times\mathrm{ Solar}\mathrm{Irradiance}(\mathrm{W}/\mathrm{m}^{2})} \tag{13}\] \[= \frac{0.05\,\mathrm{W}}{0.18\times 1000\,\mathrm{W}/\mathrm{m}^{2}} = \frac{0.05}{180}\,\mathrm{m}^{2}=0.000278\,\mathrm{m}^{2} \tag{14}\]  
So a solar panel of 30 cm\({}^{2}\) will be enough to sustain the system's needs [36].
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 2 System Model
### RIS-T Scheme  
In this case, we consider an RF signal generator, a RIS with N reflecting meta-surfaces, and a destination (D), where RIS is used as a transmitter along with the RF signal generator, as shown in Figure 2. In addition, we assume that the RIS and RF signal generators act together as a transmitter as in [59]. Thus, the signal at D is given as follows.  
\[\begin{split}& y_{D}=\big{(}\beta_{D}\beta_{S}+\bar{\beta}_{D} \bar{\beta}_{S}^{*}\big{)}\sqrt{\frac{P_{S}}{d_{2}^{X}}}\sum_{n=1}^{N}h_{D,n}e^{ \mathrm{i}\phi_{n}}x\\ &+\big{(}\beta_{D}\bar{\beta}_{S}+\bar{\beta}_{D}\bar{\beta}_{S} ^{*}\big{)}\sqrt{\frac{P_{S}}{d_{2}^{X}}}\sum_{n=1}^{N}h_{D,n}e^{\mathrm{i} \phi_{n}}x^{*}+\beta_{D}n_{0}+\bar{\beta}_{D}n_{0}^{*},\end{split} \tag{6}\]  
Based on [59], the maximum SNR at user D is given as follows:  
\[\begin{split}&\gamma_{D}=\frac{P_{S}\left[\sum\limits_{n=1}^{N}A_{n} \right]^{2}\varpi_{1}}{P_{S}\left[\sum\limits_{n=1}^{N}A_{n}\right]^{2}\varpi_ {2}+d_{2}^{X}N_{0}\varpi_{3}}\\ &=\frac{\bar{\eta}\varpi_{1}}{\bar{\eta}\varpi_{2}+\varpi_{3}}, \end{split} \tag{7}\]  
where \(A_{n}\) is a Rayleigh random variable with mean \(\sqrt{\pi}/2\) and variance \(\frac{(4-\pi)}{4}\) and \(\bar{\eta}=\frac{\rho_{S}}{d_{2}^{2}}X^{2}\).
## 3 Numerical Modeling of HDS
### Heat Exchangers  
When the heat exchanger operation is stable, the air and refrigerant achieve stationary energy balance, and heat transfer capacity can be calculated by the refrigerant heat transfer capacity, air import and export state parameters, or according to convective heat transfer capacity. It should be noted that there is water vapor condensing on the evaporator, so the air sensible heat is not equal to all the heat exchange of the evaporator. Therefore, the heat transfer capacity of the evaporator and condenser can be calculated by the following equations:  
\[Q_{e}=m_{r}(h_{\sigma,i}-h_{\sigma,\rho})=m_{a}(h_{ea,i}-h_{ea,o})=K_{e}A_{e} \Delta t_{e} \tag{4}\]  
\[Q_{c}=C_{pa}m_{a}(t_{ca,i}-t_{ca,o})=m_{r}(h_{cr,i}-t_{cr,o})=K_{c}A_{c}\Delta t _{c} \tag{5}\]  
where subscript \(e\) indicates the property for the evaporator and subscript \(c\) indicates the property for the condenser. The subscripts \(i\) and \(o\) indicate inlet and outlet, respectively. The subscripts \(r\) and \(a\) indicate refrigerant and air, respectively. \(t\) is the temperature. \(Q\) is the heat transfer capacity. \(m_{a}\) is the mass flow of air. \(h\) is enthalpy. \(K\) and A are the heat transfer coefficient and area of the heat exchanger. \(\Delta t\) is the logarithmic mean temperature difference between the air and the refrigerant. \(C_{p}\) is the specific heat at a constant pressure.  
The formula for the \(K_{e}\) and \(K_{c}\) is shown in the literature [26].  
The finned tube heat exchanger is used in this system, and its structural parameters are shown in Table 1.
# 3D-STARNET: Spatial-Temporal Attention Residual Network for Robust Action Recognition
## 4 Experiments
### Datasets and Evaluation Metrics  
The NTU-RGBD120 [31] dataset is a large-scale 3D human action recognition dataset developed to address some limitations in existing action recognition benchmarks, including the lack of large-scale training samples, the limited number of different categories of actions, the insufficient diversity of camera viewpoints, the limited changes in environmental conditions, and the insufficient diversity of human subjects. It also provides multiple data modalities, including RGB, skeleton points, depth map, and infrared.  
In this study, we only used the skeleton modality. In the action category, we selected 12 actions, including throwing, kicking something, squatting down, moving heavy objects, cross arms, falling, punching other person, kicking other person, pushing other person, yielding knife towards other person, hitting with body, grabbing other person's stuff. The action description is shown in Table 1. In addition, in order to verify the generalization ability of the model, we also used another dataset--UCF101 [32]. UCF101 is a real action dataset that provides 13,320 videos from 101 action categories. We used acc/top1 as the accuracy indicator of the model, and used the confusion matrix to intuitively understand the recognition effect of the model.  
The improvements we introduced above, including the improvement of residual structure and shortcut, are all carried out without increasing the model parameters and complexity, and the performance of the final network is also significantly improved.
## 4 Results and Discussion
### Influence of Battery Size
A rough estimation from the data from Figure 8 shows that, by using a battery pack of 2 kWh, an average savings of EUR 2.10/day, hence EUR 760/year, can be obtained when compared to a scenario without batteries. Consequently, assuming installation costs of EUR 1800 for sodium metal halide batteries, the investment could be paid off in less than 3 years. The status of battery charge during operation is depicted in Figure 9 for every time step. In this example, the most economical operative profile for the MGT operating in partial-load for a two-living-unit building is shown. The 2 kWh battery starts at 40% and the water tank at 75% of their respective maximum capacity. It can be seen that, when the system is not running, the electrical charge and the available hot water reduce due to the energy demand (not depicted here). When the system is activated (each column indicates the SP), the batteries are charged and hot water is produced. In can also be noted that, when the batteries are empty and the system is not running, electricity is taken from the grid (continuous line).
# Influence of Surface State in Micro-Welding of Copper by Nd:YAG Laser
## 4 Conclusions  
High efficiency and high quality laser welding of copper is expected by using an appropriate approach. Stable micro-welding with 1064 nm Nd:YAG laser can be enhanced by utilizing the effect of increasing light absorption rate with controlled variations of surface topology. The following are the main conclusions deduced:  
1. Absorption rate and molten volume under 1064 nm wavelength laser is increased by controlled surface undulations such as creating appropriate concave holes, and by controlled surface roughness variations. There is correlation between the surface roughness and concave holes. Concave depth of around 30 um and a similar value of surface roughness \(R_{z}\) leads to stabilization of the micro-welding process.
2. For copper welding using 1064 nm laser, by optimizing the surface texture in transitional processing condition between heat conduction and keyhole welding, light absorption was improved and the process was stabilized leading to deep penetration and good surface quality.  
Conceptualization, M.R.M., Y.O., and R.I.; Methodology, M.R.M., Y.O., and R.I.; Formal analysis, M.R.M., Y.O., and R.I; Investigation, M.R.M. and R.I.; Resources, Y.O., A.O., S.-i.N., and T.S., Writing--original draft preparation, M.R.M.; Writing--review and editing, M.R.M., Y.O., and A.O.; Supervision, Y.O. and A.O.; Funding acquisition, Y.O. and A.O.; Visualization, M.R.M. and Y.O.  
This research was funded by JSPS KAKEN Grant Number 15K05725.  
The authors acknowledge the support given by Japanese Ministry of Education, Culture, Sports, Science and Technology, and Jomo Kenyatta University of Agriculture and Technology, Kenya.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## Materials and Methods
### MIMO Channel Matrix, Beamforming and Precoding
The normalization \(\sqrt{1/K}\) is needed for the transmit vector \(\mathbf{t}\) in (9) with \(\mathbf{W}\) given by (3) or (5) to satisfy the overall transmit power constrain.
## 2 CET Systems and EV
### Appropriateness for EV  
The CET using acoustic waves presents advantage of being operational in situations where electromagnetic emissions are proscribed. In addition, transmission can happen across metal walls, which is appropriate for sensing in e.g., nuclear structures. Conversely, the powers implicated do not match to EV applications. Moreover their efficiency diminutions severely with the gap between the transmitter and the receiver, which handicap the use in EV.  
The optical power laser ray might be an answer, but that uses concentrated huge energy on a slight exceptionally directional beam. This presents evident safety complications in the perspective of EV case whilst driving. Furthermore, the present powers are very low.  
CET by capacitive coupling [44; 45; 46; 47; 48] can be envisaged in case of EV. This solution has the advantage of being less sensitive to the shift in the coupler than transmission by induction, but the functioning seems complicated and particularly involves high voltages at the terminals of the coupling capacitors. In addition, the distance between reinforcements leads to low capacities and implies operating at very high frequencies, greater than one mega Hertz [49].  
Inductive CET seems the most appropriate for EV applications. It allows energy to be exchanged between two systems without any electrical connection between them. It, as mentioned before, consists mainly of a big air gap transformer and resonant converter. The main applications of inductive power transfer IPT include in addition to EV and battery chargers, robots, winches, mobile devices, sensors and actuators. In the case of transport sector, the technologies based on inductive coupling are used for power transfer to electric or hybrid vehicles, whether they are buses, trucks, or automobiles. This include static charging when stationary and dynamic charging while driving the vehicle.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 4 Demineralized Dentin Matrix (DDM)
### Clinical Studies
#### 4.2.3 Histologic and Histomorphometric Outcomes  
Histologic and histomorphometric analysis showed a good tissue integration with a direct union between the new bone and the graft material, evidencing osteoconductive and osteoinductive properties [39; 66]. It was reported a dense lamellar bone formation [48; 49; 50] associated with connective tissue reach in angiogenesis [37], fulfilling the goal of minimizing the alveolar bone loss in extraction sockets [44]. The follow-up period on which these results were found varied from 3.5 to 6 months [39; 44; 48; 66].  
The most important limitations when comparing these studies are the variety of protocols and adjuvant materials used, for instance absorbable [44; 50] or non-absorbable membranes [67], or the use of platelet-rich fibrin (PRF) [40].
###### Abstract  
For the very first time, a study on the crystallization growth of zinc silicate glass and glass-ceramics was done, in which white rice husk ash (WRHA) was used as the silicon source. In this study, zinc silicate glass was fabricated by using melt-quenching methods based on the composition (ZnO)\({}_{0.55}\)(WRHA)\({}_{0.45}\), where zinc oxide (ZnO) and white rice husk ash were used as the raw materials. The control crystallization technique was used in which the sample was sintered at 700-950 \({}^{\circ}\)C; then, the physical, structural, and optical properties of the glass and glass-ceramics were investigated by using a densitometer, linear shrinkage, X-ray diffraction (XRD), Fourier transform infrared radiation (FTIR), field-emission scanning electron microscopy (FESEM), and photoluminescence spectroscopy (PL). The density and linear shrinkage increased as the crystallinity increased and the XRD results showed the progression of the crystal formation, in which the sample was still in an amorphous state at 27 \({}^{\circ}\)C and 700 \({}^{\circ}\)C; the crystalline phase started at 750 \({}^{\circ}\)C. Based on the FTIR spectra, all samples showed sharpened absorption bands as the sintering temperature was increased, and the FESEM image showed the progression of crystal growth, indicating the formation of zinc silicate glass-ceramics. Lastly, the PL spectra emitted three emission peaks, at 529, 570, and 682 nm for the green, yellow, and red emission, respectively.  
glass; glass-ceramics; sintering; crystallinity; crystal growth; luminescence +
Footnote †: journal: Applied Physics Letters
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 2 Analysis of Key Technologies for the Transition from Open-Pit to Underground Mining
### Analysis of Mine Development Systems in the Transition from Open-Pit to Underground Mining
#### 2.1.1 Independent Development System  
The independent development system fully separates underground and open-pit operations, ensuring no interference between the two in terms of working areas, transportation routes, and auxiliary facilities. This system is particularly suited for mines with deep ore bodies, significant disturbances from open-pit mining, or marked differences in the operational conditions of the two methods. Examples of mines employing this system include China's Yishui Iron Mine and Yinchangou Copper Mine as well as international mines such as Chuquicamata [13], Geita [14], and Tropicana [15]. The primary advantage of this system is its high safety, as it effectively prevents open-pit blasting from affecting underground ventilation, drainage, and equipment operation. However, this system typically involves higher infrastructure costs, as it requires the construction of independent shafts, transportation, and ventilation facilities. As a result, it demands a longer economic return period.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
###### Abstract  
With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types.  
BiFPN; signal blind detection; automatic modulation identification; short-time Fourier transform; EIOU 2023231525. [[https://doi.org/10.3390/s23031525](https://doi.org/10.3390/s23031525)]([https://doi.org/10.3390/s23031525](https://doi.org/10.3390/s23031525))