esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Learning Power Control in Wireless Sensor Networks
## 5 Simulations
### Results  
QL-TPC is studied first in a scenario without interference, where there is only one transmitter and one receiver, in the network of Figure 4(a), and then in a cross-interfered network among eight nodes (four transmitters and four receivers), as per Figure 4(b). The former scenario is the equivalent of a system with one single agent in a Nakagami fading environment. Instead, the latter is a multi-agent system in a more complex environment, influenced by the interference of different agents. The results in QL-TPC are compared with HG, where the transmission power is constant for each node.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 5 Results and Analysis
### Metadata
#### 5.1.2 Access  
The access experiments are conducted by retrieving the metadata in a certain range, with longitude values ranging from seventy-five degrees to one-hundred-and-thirty degrees and latitude values ranging from twenty degrees to fifty degrees, which is a rectangular area and covers many provinces of China. The access performance of the remote sensing metadata under various volumes is tested by executing commands in the program. The query command is:  
\"db.meta.find([$and:[ [\"TopLeftLatitude\": [$lt: 50]], [\"TopLeftLongitude\":[$gt: 75]], {\"TopRightLatitude\": [$lt: 50]], {\"TopRightLongitude\": [$lt: 130]], {\"BottomRightLatitude\":[$gt: 20]], {\"BottomRightLongitude\": [$gt: 75]], {\"BottomLeftLatitude\": [$gt: 20]], {\"BottomLeftLongitude\": [$lt: 130]] } }}\".  
This paper chooses the execution time difference for comparison in this experiment. The experimental result under three shard key strategies is shown in Figure 6.  
As Figure 6 shows, the changing trend of the two curves is different. When choosing \"_id_hashed\" as the shard key, the access time tends to remain steady as the volume of metadata increases, which means the access performance is slightly influenced by the metadata volume in this situation.  
However, when the \"TopLeftLatitude\" field is chosen as the shard key, the access time increases along with the volume of metadata, because the metadata is distributed in different shard nodes according to the value of the \"TopLeftLatitude\" field, and the data is retrieved among these nodes. In particular, when the metadata volume is 1,000,000, the access time that the \"TopLeftLatitude\" shard key consumes is forty-eight times longer than that of \"_id_hashed\", which indicates that as for the \"TopLeftLatitude\" shard key, access performance is more affected by the metadata volume than with the latter.  
Figure 6: The comparison of the metadata access time with different shard keys.
# Diatomaceous Soils and Advances in Geotechnical Engineering--Part I
## 2 Evolution of Concepts Surrounding Diatomaceous Soils
### Diatoms, Diatomaceous Soils, or Diatomaceous Earth
The higher and larger ones are the epitheca (e); the lower and smaller ones are the hypotheca (h). Girdle bands (gbs) are indicated by arrows. Ligules (L) are bell-shaped structures within the girdle bands. **(B)** Diagram of a pennate diatom exposing the central node (CN) and raphe on the upper valve. the designation will be diatomaceous clay, diatomaceous silt, or diatomaceous mud. If the content of frustules is less than 10%, no diatomaceous reference will be made [63]. Regarding the chemical composition, Ref. [64] identified that the major elements contained in the diatoms deposited in the study area (Siachoque, Tunja, and Chivata, Boyaca--Colombia) were Si, Al, Fe, and K. It was observed that the diatom frustules were composed of silica, in concentrations higher than 98%, and with traces of aluminum. The presence of this element indicates a clear association with clay minerals. Diatomaceous earth has a high absorption and cation exchange capacity and is mainly used as an absorbent, filling, insulating, coating, and filter material. It is mostly composed of silica phases (quartz, opal), with some traces of clay minerals (illite and kaolinite). The large reserves of these minerals could be used in the future, either for the manufacture of composite materials or for the construction of advanced sorbents [38]. Although so far no techniques have been implemented to accurately define the content of diatom microfossils within a soil mass, microscopy tools have allowed approaching the determination of said quantity. Some reports of such content are, for example, Osaka Bay (Japan), with more than 50% in the observable surface [33]; Oregon (USA) at 50-80% [4]; Bogota (Colombia) at 50% quartz [11]; and Ciudad de Mexico (Mexico) at 65% [32].
## 3 Construction of the Microseismic Monitoring System  
### Composition of the Microseismic Monitoring System  
When rock mass in underground engineering suffers from external disturbance, the surrounding rock stress changes, and the elastic energy accumulates locally. Once the energy reaches the critical value, microfractures are formed in the rock mass and elastic waves are released simultaneously, which are called microseismic activities. The source  
Figure 1: The location of the hydropower station.  
Figure 2: Tunnel profile. (**a**) Profile information; (**b**) 3D model; (**c**) typical section of the diversion tunnel.  
information, such as the occurrence time, location, and energy of microfractures, is obtained based on analysis of the acoustic signals of rock fractures.  
Relying on the diversion tunnel of this hydropower station, a microseismic monitoring system was established. Figure 3 shows the network topology of the monitoring system. The elastic waves emitted by rock fractures are received by the sensors and transmitted via the cable to the signal conversion device to form the original waveforms recording the source information, which is transmitted to the camp office by wireless network and finally sent to the analysis center for data interpretation via the internet.
###### Abstract  
To study the effect of different freezing methods on the quality changes of cuttlefish during the frozen storage of cuttlefish, fresh cuttlefish was treated with six freezing methods (refrigerator direct-freezing, saline solution impregnation freezing, flat freezing, tunnel type continuous freezing, air-blast freezing and liquid nitrogen freezing) and then stored at \(-18\)\({}^{\circ}\)C for 90 days. The time to pass the maximum ice crystal generation zone for the above six freezing methods in this experiment was 165.5, 67.5, 34.5, 21.8, 20.4 and 1.5 min, respectively. In this study, water retention (thawing loss rate, centrifugal loss rate, and cooking loss), pH, malondialdehydedehyde content, TVB-N value, and sulfhydryl content were measured to evaluate the quality after thawing. Protein secondary structure was measured by attenuated total reflection infrared spectroscopy (ATR-FTIR), water migration was determined by low-field NMR, and muscle microstructure was observed by scanning electron microscopy. The results showed that among the six freezing methods, liquid nitrogen freezing took the shortest time to pass through the maximum ice crystal generation zone. And it had the highest water retention, the lowest TVB-N content, the highest sulfhydryl content and the least irregular curling of protein secondary structure after 90 days of frozen storage. However, liquid nitrogen freezing can cause cracks and breakage in cuttlefish due to cryogenic fracture caused by ultra-low temperature, which affects its sensory evaluation. Although the freezing speed of flat freezing is faster than refrigerator direct-freezing and saline solution impregnation freezing, the muscle is extruded and deformed during the freezing process, and the damage is more serious, and the frozen storage quality is the worst. The comprehensive analysis results showed that the freezing speed of air- blast freezing was faster and the quality of cuttlefish in the freezing process was better, which was the more recommended freezing method, and this study provided some theoretical basis for the selection of freezing method in the actual production of cuttlefish.  
cuttlefish; freezing method; liquid nitrogen freezing; frozen storage; air-blast freezing Article
# Autonomous Orbit Determination of LLO Satellite Using DRO-LLO Links and Lunar Laser Ranging
## 4 Autonomous Orbit Determination Simulation
### Simulation Result Analysis  
Tables 10-12 are the orbit determination statistic results of three simulation experiments. The position accuracy and velocity accuracy of the simulation experiments refer to the 3D Root Mean Square (RMS) values calculated from the relative position residual sequence and the relative velocity residual sequence within the previous 24 days (the latter 80%).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **DRO Position** & **DRO Velocity** & **LLO Position** & **LLO Velocity** \\  & **Accuracy/m** & **Accuracy/(mm\(\cdot\)s\({}^{-1}\))** & **Accuracy/m** & **Accuracy/(mm\(\cdot\)s\({}^{-1}\))** \\ \hline LiAISON & 22.00 & 0.07 & 0.59 & 0.44 \\ LiAISON + two lunar & 13.12 & 0.05 & 0.17 & 0.13 \\ corner reflectors & 6.71 & 0.03 & 0.08 & 0.06 \\ LiAISON + ten lunar & 6.70 & 0.03 & 0.07 & 0.06 \\ \hline \end{tabular}
\end{table}
Table 10: Orbit determination accuracy with lunar surface laser ranging data added.  
Figure 12: DRO position residual.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **DRO Position** & **DRO Velocity** & **LLO Position** & **LLO Velocity** \\  & **Accuracy/m** & **Accuracy/(mm\(\cdot\)s\({}^{-1}\))** & **Accuracy/m** & **Accuracy/(mm\(\cdot\)s\({}^{-1}\))** \\ \hline LiAISON & 293.67 & 0.81 & 7.69 & 5.89 \\ LiAISON + two lunar & 96.22 & 0.73 & 1.55 & 1.17 \\ corner reflectors & 13.70 & 0.11 & 0.15 & 0.12 \\ LiAISON + ten lunar & 13.17 & 0.12 & 0.11 & 0.09 \\ \hline \end{tabular}
\end{table}
Table 11: Orbit determination accuracy under large initial errors.  
The results of the simulation experiment indicate the following:  
* In the autonomous orbit determination of the LLO spacecraft in cislunar space, relying solely on LiAISON for autonomous orbit determination will encounter issues like a long orbit convergence time and vulnerability to initial orbit errors and dynamic errors.
* Introducing lunar laser ranging technology can substantially enhance the convergence time and accuracy of the LLO satellite. Moreover, it improves the robustness of the navigation systems to initial orbit errors and dynamic model errors.
* The number of lunar corner reflectors also has a significant impact on navigation performance. When the number of lunar corner reflectors is relatively small, increasing the number of lunar corner reflectors can notably enhance the convergence time and accuracy of the orbit. However, when the number of lunar corner reflectors increases to six, further increasing it does not yield the same level of improvement.
## 4 Discussion
### Electrical Conductivity in the General Case: Spatial Dispersion  
In the general case, we must take into account the dyadic nature of the conductivity. For the sake of simplicity, let us refer to the components of any vector to the parallel and orthogonal directions with respect to the vector, \(\mathbf{\beta}\), denoting them with the symbols \"\(\parallel\)\" and \"\(\perp\)\", respectively. In this system, the four components, \(\sigma_{\parallel\parallel}(\omega,\mathbf{\beta}),\sigma_{\perp\perp}(\omega,\bm {\beta}),\sigma_{\parallel\perp}(\omega,\mathbf{\beta})\) and \(\sigma_{\perp\parallel}(\omega,\mathbf{\beta})\), of the dyad, \(\sigma(\omega,\mathbf{\beta})\), reduce to the following expressions:  
\[\sigma_{\parallel\parallel}(\omega,\mathbf{\beta})=\sigma(\omega) \frac{2}{1+\gamma^{2}+\sqrt{1-\gamma^{2}}}\,,\quad\sigma_{\perp\perp}(\omega, \mathbf{\beta})=\sigma(\omega)\frac{2}{1+\sqrt{1-\gamma^{2}}} \tag{24}\] \[\sigma_{\parallel\perp}(\omega,\mathbf{\beta})=\sigma_{\perp\parallel }(\omega,\mathbf{\beta})=0, \tag{25}\]  
where:  
\[\gamma=\frac{\
u_{F}\beta}{\omega}\frac{1}{1-\
u i/\omega} \tag{26}\]  
Figure 7: The ratio, \(\sigma_{0}/\sigma_{c}\), for metallic and semiconducting armchair GNRs at 300 K.  
The relation between the dyadic nature of the conductivity and the spatial dispersion is consistent with the result obtained in [25], where the graphene conductivity dyad is derived from the Kubo formula and numerically evaluated with an integral formulation.
## 3 Results and Discussion
### Evaporated Volume and Crater Depth  
In terms of material removed per single shot, the high-power PALS laser appears to be the most efficient source. It is capable of ablating up to \(\sim\)1.7 mm\({}^{3}\) of chondritic material in one pulse, with a 1 mm focus and an energy of 650 J, which corresponds to a fluence of about 83,000 J/cm\({}^{2}\) and an irradiance of 240 TW/cm\({}^{2}\). For comparison, the Bivoj laser, reaching a fluence of 50 J/cm\({}^{2}\) and an irradiance of 5 GW/cm\({}^{2}\), was able to remove approximately 0.08 mm\({}^{3}\) of chondritic material in 10 pulses. If we consider a linear increase [80,81] in the depth of the crater ablated by the Bivoj laser, approximately 200 pulses would be sufficient  
Figure 11: Clearly defined edges of the ablation spot from the Ti-Sapphire femtosecond laser on the chondritic meteorite JaH 815 (**panel A**), in comparison with a detailed picture of ejected molten Fe–Ni droplets in the vicinity of the PALS ablation crater on the chondritic meteorite NWA 8212 (**panel B**).  
Figure 12: PALS ablation crater morphology, indicating different resistances of metal and chondritic material of the Seymchan pallasite (**panel A**, dashed white circle). **Panel B** introduces an example of the olivine mineral chipped in the Seymchan pallasite by the PALS laser.  
to reach the same ablated volume as for the PALS laser, but the total cumulative energy delivered per unit area would be eight-times lower (\(\sim\)10,000 J/cm\({}^{2}\)). This limited ablation efficiency, even at high fluences, for lasers with longer pulse durations (ps-ns) is primarily attributed to the plasma shielding, resulting in a reduction in the laser energy able to reach the surface [80; 82; 83].  
An investigation of the evaporated volume dependence on the PALS laser's radiation fluence, summarized in Figure 13, showed its apparent dependence on the type of the ablated material. Fe-Ni alloy areas on the Seymchan pallasite surface evinced greater resistance against ablation. Moreover, melting dominanates over ablation itself. Chondritic material poor in iron (SaU 571-L chondrites) is ablated significantly easier and, therefore, shows a much steeper trend. This can be attributed to material properties such as reflectivity, strength, surface structure, morphology, and thermal conductivity.  
Furthermore, the bottom surface profilometry of the Ti:Sapphire ablation incisions (see Figure 14) indicates significant ablation non-uniformity. This rugged relief is due to the grainy inhomogeneous structure of meteorites and the different resistances of the individual mineralogical components comprising the meteorite sample.  
Figure 14: The bottom surface profilometry of the Ti:Sapphire ablation incision for the mesosiderite JaH 267.  
Figure 13: Dependence of the evaporated volume of several meteorite samples on the laser fluence for the PALS laser.
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 4 Experimental Results
### Experiment with Proximity-Based Adaptive Algorithm  
To measure the performance of the proposed method in real-world environments, experiments are conducted in an indoor environment with the layout shown in Figure 12. The thick lines in Figure 12 represent concrete walls with a thickness of approximately 0.2 m, while the gray dots indicate the positions of the beacons placed inside the building. Three adjacent beacons are installed to form an equilateral triangle with a side length of 15 m. There is a total of eight Beacons used for the experiment, covering an approximate region of 585 m\({}^{2}\). Figure 12 represents the positioned locations for accuracy measurement. In Figure 12, triangles indicate points proximate to the AP within 1 m, squares represent the center positions of three adjacent beacons, and stars denote arbitrarily designated points.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Measurement Noise** & **Original** & \(R\) = **20** & \(R\) = **1.25**[24] & \(R\) = **2.5**[36] \\ \hline LOS MAE & 4.47 & 2.43 & 2.48 & 2.45 \\ NLOS MAE & 3.96 & 2.77 & 2.82 & 2.78 \\ \hline \end{tabular}
\end{table}
Table 4: Distance MAE according to \(R\).  
Figure 11: RSSI histogram at 1 m.
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 3 The Adaptive Fusion of KCF and Kalman Filter  
The GM-APD LiDAR developed by our team can obtain the intensity image of the target through photon accumulation detection while acquiring the target point cloud. The intensity image can represent the surface reflectivity and shape of the target, and the point cloud can represent the distance and orientation of the target. Inspired by article [20], we propose an adaptive fusion method of point cloud tracking and intensity image tracking, combining the advantages of two different data sources to improve the tracking performance of GM-APD LiDAR. In addition, since the two kinds of images do not need to be calibrated jointly, the calculation speed of the algorithm is greatly improved. In this paper, the KCF tracking method based on multi-feature fusion is used to process the intensity image, while the Kalman filter tracking method is used to process the point cloud data, and finally the results of two methods are adaptive fused. Combining the advantages of the two methods, the proposed method has the advantages of high tracking speed and high tracking accuracy, and can avoid the situation of tracking accuracy decline or even tracking failure caused by target occlusion to a certain extent, improving the accuracy of tracking targets in different occlusion states effectively.
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Distribution**} & \multicolumn{1}{c}{**Uniform**} & \multicolumn{1}{c}{**Exponential**} & \multicolumn{1}{c}{**Pareto**} & \multicolumn{1}{c}{**Rayleigh**} & \multicolumn{1}{c}{**Weibull**} \\ \hline \multirow{4}{*}{Mean} & Random & 47.1070 & 50.5568 & 43.1053 & 42.9436 & 44.4931 \\  & Optimal & 40.4067 & 40.9428 & 40.3052 & 40.0660 & 40.0651 \\  & Proposed10 & 40.4411 & 41.0199 & 40.3109 & 40.0062 & 40.0694 \\  & Proposed50 & 40.4159 & 40.9638 & 40.3063 & 40.0060 & 40.0660 \\ \hline \multirow{4}{*}{Standard} & Random & 13.6106 & 16.2251 & 4.8572 & 8.5445 & 11.0222 \\  & Proposed10 & 0.0269 & 0.0483 & 0.0048 & \(1.2614\times 10^{-4}\) & 0.0035 \\ \cline{1-1}  & Proposed50 & 0.0119 & 0.0255 & 0.0011 & \(3.0954\times 10^{-5}\) & 0.0012 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison (Case A). Note that “Proposed10” and “Proposed50” stands for “Proposed algorithm with 10 iterations” and “Proposed algorithm with 50 iterations”, respectively. Tables 3 and 4 show the performance of the proposed algorithm compared with the other two target channel visiting methods: the random target channel visiting and the optimal target channel visiting. Two cases are considered respectively: (Case A) Nine target channels with the mean vacant time durations of 170 ms, 30 ms, 210 ms, 300 ms, 52 ms, 5 ms, 130 ms, 59 ms, 111 ms, respectively, and (Case B) Eight target channels with the mean vacant time durations of 10 ms, 60 ms, 25 ms, 170 ms, 83 ms, 5 ms, 54 ms, and 155 ms, respectively. The optimal target channel visiting order is obtained by exhaustive search. It can be seen that the proposed method performs far better than the random channel visiting scheme. The solutions obtained by the proposed algorithm are very close to the optimal solutions. The small standard deviation values also indicate that the proposed scheme is quite stable.
## 3 Results
### Hyperparameter Tuning Results  
The hyperparameter tuning experiments yielded insights into model performance and computational requirements under two cross-validation (CV) approaches--10-fold cross-validation (10\(\times\) CV) and 5 \(\times\) 2 cross-validation (5 \(\times\) 2 CV)--across all seven machine learning algorithms included in this study: Artificial Neural Networks (ANN), Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), LightGBM (LGBM), CatBoost (CB), and XGBoost (XGB). The fine-tuned hyperparameters for each model are reported in Table 3.  
Figure 2 illustrates the hyperparameter tuning time per trial for the 10-fold CV and 5 \(\times\) 2 CV approaches accordingly. The chart reveals that Random Forest and CatBoost models incurred noticeably higher tuning times with the 5 \(\times\) 2 CV compared to the 10\(\times\) CV approach, indicating that models exhibiting greater complexity or sensitivity to data partitioning may experience additional computational overhead due to different train/test split ratios. By contrast, Logistic Regression, SVM, and LightGBM produced minimal differences in tuning time between the two referent strategies, reflecting the stability and lower complexity of these algorithms. Unlike the other models--where tuning times for 10\(\times\) CV were generally lower than those for 5 \(\times\) 2 CV--ANN and XGBoost models exhibited slightly higher tuning times for 10\(\times\) CV compared to 5 \(\times\) 2 CV. Figure 3 presents the optimal loss values (negative performance metric) obtained during the hyperparameter tuning process for each model. The performance metric used in the tuning process is the F1 score for the optimization. However, it is represented as a negative value because the Hyperopt algorithm minimizes the objective function during the search for the optimal parameter configuration. Consequently, lower loss values indicate better performance. CatBoost, for instance, achieved the best loss under 10\(\times\) CV (approximately \(-\) 0.812) with a 'depth' parameter of six and a 'learning rate' of 0.116, while under 5 \(\times\) 2 CV, its performance decreased moderately to \(-0.791\). A similar trend was noted in XGBoost, where \(10\times\) CV yielded \(-0.807\) but declined to \(-0.784\) with fewer training samples per fold. ANN also benefited from the larger training set, achieving \(-0.807\) when two hidden layers were employed under \(10\times\) CV, although a simpler single-layer design under \(5\times 2\) CV maintained competitive performance at \(-0.792\).