esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modeling Skills and ML of Energy Balance
This implies that the energy surplus and energy deficit can be predicted using the current model (ANN). It can be helpful for ports' energy managers to make rapid predictions without the need for expertise and time-consuming operations using the optimization models EMT v1, HY4 RES, or HOMMER. A second case was analyzed, considering possible additional energy beneficiaries for the installed power systems in the hybrid energy module previously defined. This allowed us to demonstrate the accuracy of the training model in making predictions, including for an increase in energy consumption of 80%. Table 7 presents the results of the MSE and R\({}^{2}\), which can be compared with the results in Table 6. The highest value of the MSE was found in Scenario 3, with a value of 0.0166 kWh (validation), while the lowest R\({}^{2}\) value was 0.9289, for validation purposes, in Scenario 2. Figure 20: Artificial neural network used to consider demand and energy balance for various scenarios: (**a**) No.1; (**b**) No. 2; (**c**) No. 3; (**d**) No. 4; and (**e**) No. 5. MATLAB R2024a was used to analyze the NNTS. The analysis employed a single hidden layer with ten (10) neurons. To account for the number of epochs used, Scenario 3 was considered, in which only 33 epochs were performed. The best result was achieved at epoch 27, with a mean squared error (MSE) value of 0.000956559, as shown in Figure 21.  
Figure 22 presents the results of the predictive model for Scenarios 1 to 5, considering an 80% increase in energy demand. The results allow us to conclude that, regardless of the energy demand, the predictive model can be used under different conditions as a valuable tool for making energy surplus/deficit predictions. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-5}  & **MSE (kWh)** & **R2** & **MSE (kWh)** & **R2** \\ \hline
1 & 0.0037 & 0.9655 & 0.0043 & 0.9651 \\ \hline
2 & 0.0017 & 0.9289 & 0.0015 & 0.9353 \\ \hline
3 & 0.0166 & 0.9637 & 0.0142 & 0.9671 \\ \hline
4 & 0.0150 & 0.9674 & 0.0167 & 0.9622 \\ \hline
5 & 0.0141 & 0.9693 & 0.0163 & 0.9617 \\ \hline \end{tabular}
\end{table} 
Table 7: MSE and R2 for validation and testing in scenarios with a higher energy demand. Figure 21: Analysis of the number of epochs for Scenario 3. Figure 22: _Cont._  
Due to the selected energy demands in the Port of Aviles and the characteristics of the defined hybrid energy module, the results for the different scenarios of this case study did not differ much, allowing us to consider the developed ML model as an efficient tool for predicting excess and shortage of energy in scenarios with diverse combinations of energy sources and types of storage.
# Study of the Critical Speed of Ultra-High-Speed Diamond Drilling Technology in Marble
## 5 Conclusions  
In this study, we investigated the critical speed challenge in ultra-high-speed diamond drilling through nonlinear dynamic simulations and experiments. Using marble as an example, we revealed the differences in rock fragmentation mechanisms between ultra-high-speed diamond drilling and traditional methods. Beyond a linear velocity of 7 m/s, WOB decreased, while MSE decreased, maintaining a stable cutting depth per diamond revolution. The equivalent plastic strain in rock fragmentation pits dropped by 7.14% compared to conventional speeds. Beyond 9 m/s, both MSE and PEEQ increased. Another viable speed range (7 m/s-9 m/s) was identified, effectively reducing MSE and enhancing rock fragmentation efficiency. However, there is a speed limit within this range, and further speed increases do not enhance drilling efficiency. Comparative analysis of rock fragmentation energy distribution under conventional and ultra-high-speed conditions revealed that the efficiency gain with ultra-high-speed diamond drilling within the critical speed range is achieved by altering the rock fragmentation mode. Notably, rock fragmentation transitions from mainly plastic deformation to predominantly brittle fracture. The ongoing advancement of this technology extends beyond the confines of marble, with future endeavors geared towards testing a broader spectrum of rocks with higher levels of hardness. This exploration aims to introduce novel paradigms to the realm of deep drilling, with a focus on enhancing drilling depth, maintaining or accelerating drilling speeds, and reducing energy consumption, thus holding substantial academic significance.  
Conceptualization, Y.L. and K.Z.; methodology, Y.L. and K.Z.; software, Y.L. and K.Z. and Y.W.; validation, Y.L. and K.Z.; formal analysis, Y.L. and K.Z.; investigation, Y.L. and K.Z.; resources, K.Z.; data curation, Y.L. and K.Z.; writing--original draft preparation, Y.L. and K.Z.; writing--review and editing, Y.L. and K.Z. and Y.W.; visualization, Y.L. and K.Z.; supervision, K.Z.; project administration, K.Z.; funding acquisition, K.Z.; All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (Nos. 42202346) and Special Fund for Scientific Research of Shandong Bureau of Coal Geology (No. 2023-5 and No. 2022-33).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 6 User Satisfaction Ratio
### Modified Intra-Tier and Cross-Tier Cooperation (MICTC)  
The Algorithm 2 is time consuming and requires extensive signalling and more energy because unserved request has not only to be passed through all cluster of each tier but also from one tier to another tier on the other hand Algorithm is better one regarding energy overhead and E-E delay in executing the existing frame work. Therefore, Algorithm 3 is used which initially caches popular contents on each BS utilizing CF. Then clusters are formed based on the similar files cached in all tiers. Hence, a cluster may have BSs of Pico cells and micro cells if they have similar contents. Now, if a request is received by a local BS and if it is not fulfilled, then it is moved to the other BSs within the cluster. There may be a fair chance that the requested contents may be found on the micro BS or Pico Base station of the cluster as the cluster now has micro BSs and pico BSs. These clusters are shown in Figure 2. The proposed Algorithm 3 is:  
```
Input: Xt
0 Output: Yr
1. Cache the data using CF in each tier based on past requests.
2. Cluster all BSs from all the tires(including pico, micro and macro) based on similar cached data files
3. For pico BS do
4. If request arrives at Pico BS then
5. Serve the request at associated pico BS
6. Else if
7. Send the request to neighboring BS (that may be of Pico, micro or macro ) but within the same cluster
8. Else if
9. Send the request to another cluster
10. Else
11. Send it to content server
12. End
13. For micro BS do
14. If request arrives at micro BS then
15. Serve the request at associated micro BS
16. Repeat steps 6-12
17. For macro BS do
18. If request arrives at macro BS then
19. Serve the request at associated macro BS
20. Repeat steps 6-12
21. Repeat for all requests
22. Return Y
```  
**Algorithm 3** Modified Intra-Tier and Cross-Tier Cooperation  
Figure 2: Cluster formation considering all tiers.
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 3 Proposed Solution
### 5G Core Network (5 GCN)
#### 3.5.2 CUPS--Architecture for Control and User Plane Separation
The protocol stack has the following data:  
* Device UP (Device/UE) connects to the UPF network function via IP-to-IP;
* CP is connected via NAS-to-NAS by the AMF network function;
* The NAS and RRC sublayers are unique to the PC;
* The message-based IP transport protocol known as SCTP is used between the eNB (Evolved NodeB) and the MME (Mobility Management Entity) to transport the NAS messages;
* Reliable message transfer between eNB and MME;
* RRC deals with the general configuration of a cell including the PDCP, RLC, MAC and PHY sublayers [40];
* The SDAP sublayer deals with the mapping between the QoS flow and the radio bearers;
* IP (Internet Protocol) packets are mapped to radio bearers according to their QoS requirements;
* The PDCP sublayer is primarily responsible for IP header compression/decompression, reordering and duplicate detection, encryption/decryption and integrity protection;
* The RLC sublayer performs error correction through an automatic repeat request (ARQ) mechanism, segmentation/resegmentation of IP packets (header compression) and sequential delivery of data units to upper sublayers;
* The MAC sublayer is responsible for error correction through the ARQ3 hybrid mechanism (HARQ), uplink and downlink scheduling [40];
* The PHY sublayer handles encoding/decoding, modulation/demodulation, multi-antenna processing and mapping signals to physical time-frequency resources;
* The CP is responsible for control signaling for connection configuration, mobility and security;
* Control signaling originates from a core network or from an RRC sublayer in the gNB;
* The PHY sublayer is the key component of the NR physical layer technology that undergoes modulation, waveform, multi-antenna, transmission and channel coding [38].
# A Low-Profile Dielectric Resonator Antenna Array for OAM Waves Generation at 5G NR Bands
## 3 Results and Discussion
### Phase and Intensity Distributions  
For experimental validation, the phase and intensity distributions of the proposed antenna were performed in an anechoic chamber. The measurement setup is shown in Figure 6. As shown in Figure 6, a 2-axis linear guide rail and a receiving antenna were used to scan the phase over a sampling plane that is perpendicular to the propagation of the radiation produced by the transmitting antenna. To ensure consistency of results, the transmitting antenna was placed at a constant distance, \(D\) of 1.2 m from the receiving antenna. The positioning of the receiving antenna on the linear guide rail was controlled by a computer with a suitable step size. The signal measured by the receiving antenna is sent to a vector network analyzer to determine the phase, and the data are then stored in a file on a computer. The data obtained were used to plot the phase distribution and were analyzed with Python to determine the purity of the OAM waves.  
Figure 5: Reflection coefficient of the proposed antenna. (**a**) Measurement setup; (**b**) simulated and measured results of the reflection coefficient.  
The simulated phase distribution of the proposed antenna from 0\({}^{\circ}\) to 360\({}^{\circ}\) is shown in Figure 7a. It was proven that the proposed antenna exhibited a clockwise spiral phase distribution with a continuous phase change of 2\(\pi\) around the center. This is a characteristic feature of the OAM wave. The multicolor in Figure 7a indicates that the phase change has taken place by 2\(\pi\) around the propagation axis. In addition, the measured phase distribution also exhibited a spiral phase distribution, as shown in Figure 7b. As shown in Figure 7c, a hollow structure that resembles a donut shape which is called a \"vortex core\" can be observed at the center of the beam. This black dot inside the blue circle shape of the beam determines the intensity distribution of the produced OAM mode. The measured intensity distribution is shown in Figure 7d. As stated in [32], the OAM vortex beams with mode \(\
eq\) 0 have a donut-shaped structure, and this donut-shaped structure expands in dimensions as the value of OAM modes increases. Therefore, both spiral phase distribution and donut-shaped intensity were evident from the simulated results. These confirm the generation of OAM mode +1 using the proposed design.  
Figure 6: Measurement setup of phase and intensity distributions.  
Figure 7: Radiation and intensity distributions at 3.56 GHz. (**a**) Simulated phase distribution; (**b**) measured phase distribution; (**c**) simulated intensity distribution; (**d**) measured intensity distribution.
## 2 Case Presentation
### Case 1: SCC on Right Mandibular Buccal Mucosa
#### 2.1.1 Computer-Assisted Surgical Planning  
For computer-assisted surgery, CT data of the patient's pre-surgical ilium and fractured ilium were collected. The collected CT data were saved as a DICOM file and superimposed onto an ilium using Mimics software 18.0 (Materialize, Leuven, Belgium). After setting the threshold value based on the bone HU value, we performed 2D masking. Segment separation in the ROI area and artifact removal were performed, and 2D masking was laminated to produce a 3D shape. We created a natural bone shape through smoothing work. We reconstructed the defect area using the CT data of the patient before the fracture, and applied the porous structure of the interior and designed the fixation part. Using the electron beam melting (EBM) technique, a high-energy electron beam was exposed inside the vacuum chamber to melt metal powder (T\({}_{6}\)Al\({}_{4}\)V alloy powder) and print the final implant (Q10 Plus, GE Additive). Sanding was performed to remove the powder of the final printed implant, and removal of the support of the printed implant was carried out. The T\({}_{6}\)Al\({}_{4}\)V alloy implant reproduced the shape of the natural ilium, while the interior was porous so as to improve cell proliferation and mesenchymal stem cell differentiation. On the outer surface, a plate shape with a hole for fixation with iliac bone was added (Figure 2).  
Figure 1: Initial examinations: (**a**) intraoral photograph showed the granulomatous lesion on right lower gingiva; (**b**) no bone invasive lesion was seen in the panoramic view.  
Figure 2: (**a**) Fractured iliac bone; (**b**,**c**) computer-based planning and designed titanium implant.
## 4 Conclusions  
In summary, sputtering-processed CuIn\({}_{1-x}\)Ga\({}_{x}\)Se\({}_{2}\) nano-crystals have been successfully applied as a novel Cu-based inorganic HTM for solar cells. The PCE of the CuIn\({}_{1-x}\)Ga\({}_{x}\)Se\({}_{2}\) HTM-based photovoltaic device has been reached at 14.2%, which is quite encouraging for conventional solar cells with Cu-based chalcopyrite semiconductor HTMs. In addition, the CuIn\({}_{1-x}\)Ga\({}_{x}\)Se\({}_{2}\) HTM/MAPbI\({}_{3}\) perovskite layers that involved MoSe\({}_{2}\) exhibited a good ohmic-contact. This work provided a promising candidate of Cu-based inorganic HTM for stable perovskite solar cells, and dedicated investigation about characteristics of silver/C\({}_{60}\)/CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\)/CIGSe/bi-layer Mo/FTO multilayer of nano-structured devices. The SEM morphology observation on CuIn\({}_{1-x}\)Ga\({}_{x}\)Se\({}_{2}\) HTM film following thermal annealing at 400, 500, and 600 \({}^{\circ}\)C revealed crystal grains ranging from tens nm to one \(\upmu\)m in size. The pinhole and defect density wasalso decreased with the increasing annealing temperature. The experimental results of MAPbI\({}_{3}\) perovskite solar cells with the optimal 200 nm CuIn\({}_{1-x}\)Ga\({}_{x}\)Se\({}_{2}\) HTM nano-structures that were annealed at 600 \({}^{\circ}\)C showed a better PCE of 14.2%. This device exhibited that the V\({}_{\text{OC}}\), JSC, FF, PCE, Rs, and Pmax were 1.02 V, 20.9 mA/cm\({}^{2}\), 66.6%, 14.2%, 15.8 \(\Omega\), and 1.42 mW, respectively.  
Investigation, L.-B.C., C.-C.T., G.W., W.-S.F. and M.-J.J.; methodology, L.-C.C. and K.-L.L.; resources, E.P., L.J. and K.G.; writing--original draft preparation, C.-C.T.; writing--review and editing, G.W.  
This work was supported in part by the Ministry of Science and Technology under research grants MOST105-2221-E182-059-MY3, MOST105-2221-B182-059-MY3, and MOST106-2221-E182-060. The APC was funded by CGMH/CGU under CMRPD3G0062 and BMRP246.  
The authors declare no conflict of interest.  
Figure 9: The measured results of external quantum efficiency (EQE) spectra based onsilver/C\({}_{60}\)/MAPbI\({}_{3}\)/CIGSe/Mo/FTO solar cells.
###### Abstract  
Since its introduction in 2010, self-referenced spectral interferometry (SRSI) has turned out to be an analytical, sensitive, accurate, and fast method for characterizing the temporal profile of femtosecond pulses. We review the underlying principle and the recent progress in the field of SRSI. We present our experimental work on this method, including the development of self-diffraction (SD) effect-based SRSI (SD-SRSI) and transient-grating (TG) effect-based SRSI (TG-SRSI). Three experiments based on TG-SRSI were performed: (1) We built a simple TG-SRSI device and used it to characterize a sub-10 fs pulse with a center wavelength of 1.8 \(\upmu\)m. (2) On the basis of the TG effect, we successfully combined SRSI and frequency-resolved optical gating (FROG) into a single device. The device has a broad range of application, because it has the advantages of both SRSI and FROG methods. (3) Weak sub-nanojoule pulses from an oscillator were successfully characterized using the TG-SRSI device, the optical setup of which is smaller than the palm of a hand, making it convenient for use in many applications, including sensor monitoring the pulse profile of laser systems. In addition, the SRSI method was extended for single-shot characterization of the temporal contrast of ultraintense and ultrashort laser pulses.  
femtosecond pulses; spectral interferometry; four-wave mixing; transient-grating effect; pulses characterization; SRSI +
Footnote †: journal: Applied Physics Letters
## 4 Results and Discussion
### Results and Discussion of Geo-Environment Indicator Distribution
#### 4.1.2 Land Subsidence  
According to the statistics data with PS-InSAR measurements from January to December in 2016, the land subsidence rate in most areas was between 30 mm/a and 10 mm/a, except the urban district and the northern area of Rongcheng County, Jiaguang, Bayu, and the western area of Dahe, with a rate of 30-40 mm/a, and the southern area of Pingwang, with a rate of less than 10 mm/a (Figure 4). The land subsidence rate for suitability for urban construction, making 0 mm/a, 10 mm/a, and 30 mm/a as grading standards [42], could be divided into four grades, corresponding to very high (<0 mm/a), high (0-10 mm/a), moderate (10-30 mm/a), and low (>30 mm/a) (Table 3).  
Figure 4: Zonal distributions of land subsidence rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & **Subcriteria** & \multirow{2}{*}{**Indicator Layer**} & \multirow{2}{*}{**Indicator Layer**} & \multicolumn{4}{c}{**Grading Criteria of Suitability**} \\ \cline{4-7}
**Layer** & & & **Very High** & **High** & **Moderate** & **Low** \\ \hline \multirow{8}{*}{Engineering geological status} & & 0–5 m (C1) & 125–130 kpa & 115–125 kpa & 105–115 kpa & / \\ \cline{3-7}  & \multirow{4}{*}{Engineering geological status} & & 5–10 m (C2) & 140–180 kpa & 130–140 kpa & 120–130 kpa & 110–120 kpa \\ \cline{3-7}  & & capacity of foundation & 10–15 m (C3) & 190–250 kpa & 170–190 kpa & 150–170 kpa & 110–150 kpa \\ \cline{3-7}  & & 15–30 m (C4) & 185–240 kpa & 175–185 kpa & 165–175 kpa & 155–165 kpa \\ \cline{3-7}  & & 30–50 m (C5) & 220–280 kpa & 210–220 kpa & 200–210 kpa & 190–200 kpa \\ \cline{3-7}  & & Land subsidence rate (C6) & \textless{}0 mm/a & 0–10 mm/a & 10–30 mm/a & \textgreater{}30 mm/a \\ \cline{3-7}  & & Geological faults (C7) & None & Away & Near & Existing \\ \cline{3-7}  & & Ground fissures (CS) & None & Away & Near & Existing \\ \cline{3-7}  & \multirow{4}{*}{Environmental geological status} & & Potential liquefied sands (C9) & None & Slight & Moderate \\ \cline{3-7}  & & & & & Can be used as a source of drinking water & Not suitable to \\ \cline{3-7}  & & Quality of groundwater & Can be used as a source of drinking water & drinking water & be a source of drinking water \\ \cline{3-7}  & & chemistry (C10) & Very clean & Clean & Mildly polluted & Serious polluted \\ \cline{3-7}  & & Quality of soil chemistry (C11) & Very clean & Clean & Mildly polluted & Serious polluted \\ \hline \multirow{8}{*}{Resource conditions} & & Chemical corrosion of concrete & & & Slight & \\ \cline{3-7}  & & by groundwater (C12) & & & & \\ \cline{3-7}  & & Chemical corrosion of steel by & & Slight & & A little \\ \cline{3-7}  & & groundwater (C13) & & & & \\ \cline{3-7}  & & Enrichment of deep & & & & \\ \cline{3-7}  & & groundwater (C14) & & & & \\ \cline{3-7}  & & Geothermal & & & & \\ \cline{3-7}  & & resource/Geothermal & & & & \\ \cline{3-7}  & & gradient (C15) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The evaluation criteria for geo-environment suitability for urban construction.
# Experimental Study of Influence of Plant Roots on Dynamic Characteristics of Clay
## 3 Experimental Results Analysis
### Influence of Plant Root Systems on Soil Damping Ratio
#### 3.2.1 Different Root Distribution Patterns  
The maximum damping ratio and the damping ratio-shear strain curves for different root distribution patterns are depicted in Figure 11 and Figure 12, respectively. The figures reveal that, under all three confining pressures, the damping ratio of soil with roots distributed in the upper part exceeds that of soil with roots uniformly distributed or distributed in the lower part. This observation can be attributed to the introduction of roots into the soil, enhancing the stiffness of the specimen. Soil with roots in the upper part exhibits greater stiffness in the upper portion and reduced stiffness in the lower section, while soil with roots in the lower part displays diminished stiffness in the upper portion and elevated stiffness in the lower segment. The heightened upper stiffness results in a more rigid and swift response of the vibration system to vibrations, leading to a heightened rate of energy dissipation and, consequently, an increased damping ratio. Conversely, increased lower stiffness yields a softer and slower response of the vibration system, resulting in a comparatively reduced rate of energy dissipation and a diminished damping ratio. Furthermore, soil with roots in the lower part is typically constrained by the upper soil, restricting  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\sigma_{c}\)(kPa) & \multicolumn{2}{c}{**100**} & \multicolumn{2}{c}{**200**} & \multicolumn{2}{c}{**300**} \\ \hline \(p_{g}\)/(g\(\cdot\)cm\({}^{-3}\)) & \(G_{max}\)/(MPa) & \(\gamma_{r}\)(\(\times\)10\({}^{-4}\)) & \(G_{max}\)/(MPa) & \(\gamma_{r}\)(\(\times\)10\({}^{-4}\)) & \(G_{max}\)/(MPa) & \(\gamma_{r}\)(\(\times\)10\({}^{-4}\)) \\ \hline
0 & 84.75 & 3.97 & 108.08 & 4.66 & 134.03 & 5.1 \\
1\% Uniform & 92.55 & 3.95 & 116.67 & 4.41 & 140.28 & 4.64 \\
1\% Upper Distribution & 97.57 & 3.67 & 120.83 & 4.19 & 138.91 & 4.34 \\
1\% Lower Distribution & 91.86 & 4.4 & 114.71 & 4.81 & 138.4 & 5.22 \\
1.5\% Uniform & 108.16 & 4.02 & 136.64 & 4.75 & 155.19 & 4.72 \\
2\% Uniform & 101.81 & 4.27 & 130.49 & 5.23 & 147.99 & 5.79 \\
2.5\% Uniform & 100.9 & 4.26 & 131.8 & 4.73 & 148.23 & 5.59 \\ Distribution & 99.8 & 4.23 & 131.97 & 4.44 & 148.58 & 5.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardin–Drnevich hyperbolic model’s dynamic shear modulus fitting parameters.  
the transfer of vibrational energy, which contributes to a relatively lower damping ratio. Therefore, placing roots in the upper part of the soil proves advantageous for elevating the soil's damping ratio and facilitating energy dissipation. This aligns with practical scenarios where roots are predominantly distributed on the surface of embankments, which corresponds to the upper part of the soil and fosters energy dissipation.