esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Use Case Description and Problem Statement  
The focus of this paper is on the opportunistic transmission of data using satellite links operating at the Ka-band. The terrestrial users coexisting with the satellite systems are considered as the incumbent users, whereas GSO and NGSO satellites are secondary users, with NGSO satellites having a lower priority than GSO satellites. In this study, we do not consider changing the spectrum management policies for the incumbent, but rather developing policies for GSO and NGSO entities so that the interference towards the incumbent is minimized. Therefore, only the satellite systems have DSS capabilities, whereas the terrestrial 5G Fixed Wireless Access (FWA) and MW links employ fixed spectrum allocation. The two most relevant and challenging use cases were selected: (1) opportunistic sharing of GSO/NGSO satellite and terrestrial systems in the downlink Ka-band; and (2) opportunistic sharing of GSO/NGSO satellite and terrestrial systems in the uplink Ka-band. These two use cases were selected based on the following criteria: (1) possible gains based on an initial qualitative evaluation; (2) importance and expected impact of the use case for the SATCOM industry; (3) complexity--coexistence of GSO satellites, NGSO satellites, ESIMs and terrestrial systems operating in same bands (4) implementation complexity; (5) need for new hardware support on satellites; and (6) need for new infrastructure on ground stations (GSs) and ESIMs.
## 3 Results and Discussion
### Mechanical Properties
The dimples on the tensile fractures of alloy 2 are deeper and more numerous, which indicates that the internal structure has been significantly refined. LNG storage represents a significant application area for invar alloy, where the low-temperature working environment imposes stringent requirements on the ability of invar alloy to absorb impact energy and resist fracture. The addition of Cr-Mo-V-N to invar alloy can effectively refine its microstructure and improve its impact toughness. As can be seen from Figure 15, both the alloy 1 specimen and the alloy 2 specimen have dimples and tearing edges distributed on the impact fracture. Tearing edges are usually found around the dimples and are the edges that form during crack extension when material fractures under impact loading. In Figure 15d, the second-phase particles in the alloy 2 specimen can change the crack propagation pathway, dissipate more energy, and thus improve the  
Figure 14: Tensile curve of **(a)** alloy 1 specimen, **(d)** alloy 2 specimen, and fracture morphology of **(b,c)** alloy 1 specimen, **(e,f)** alloy 2 specimen. Figure 13: Tensile and impact property of alloy 1 specimen (**a,c**) and alloy 2 specimen (**b,d**). impact toughness of the material. As can be seen from Figure 15a-d, there is an increase in the number of tearing edges on the impact fracture of the alloy 2 specimen, which is consistent with the results of impact toughness. In Figure 16, the average hardness of the alloy 1 specimen is 150 HV. The average hardness of the alloy 2 specimen is about 34% higher than that of the alloy 1 specimen, reaching 201 HV. The hardness distribution within the deposition unit has a corresponding rule due to the influence of the thermal cycle during the additive manufacturing process. The fine grain zone at the bottom of the deposition unit has a higher level of hardness due to the higher density of grain boundaries and greater resistance to plastic deformation. The columnar crystal zone and the heat-affected zone are more affected by the cyclic heat input, so the hardness level is lower in these areas and lowest in the heat-affected zone. The addition of V, N in the alloy 2 specimen can promote the generation of the V (C, N) composite phase, which has a high precipitation temperature, can effectively refine the grain, and is conducive to improving the hardness of the material. In addition to being in the form of carbides, Mo and Cr can also be dissolved in the matrix to play the role of solution strengthening, which is also conducive to the improvement of material hardness. Figure 16: Microhardness of alloy 1 specimen and alloy 2 specimen. (**a**) Distribution of microhardness test points. (**b,c**) The hardness values at different locations in alloy 1 specimen and alloy 2 specimen. Figure 15: Morphology of impact fracture: (**a,b**) alloy 1 specimen, (**c,d**) alloy 2 specimen, and (**e–g**) element composition of inclusions in the dimple of alloy 2 specimen.
## 4 Conclusions  
The following conclusions were drawn regarding the pattern and mechanism of the effect of NaSt on the microstructure and hydration of AASRSS gelling materials:  
1. The incorporation of NaSt effectively reduced the water absorption rate of AASRSS, with a dose of 1.5 wt.% reducing the water absorption rate of 28-day mortar specimens by about 80%.  
Figure 9: SEM images of AASRSS with and without NaSt.  
2. The incorporation of NaSt did not change the type of hydration products of AASRSS, and the trend in the exothermic rate and cumulative exothermic curve of the hydration exothermic process did not change either, but it had some adverse effects on the intensity of the exothermic activity peak and cumulative hydration exothermic heat.
3. The addition of NaSt inhibited the hydration reaction of AASRSS, leading to an increase in porosity. The combination of an increased porosity and reduced hydration products mainly accounts for the decrease in the compressive strength of AASRSS due to NaSt.
4. Based on the experimental results, the improvement mechanism of NaSt can be summarized through two main reasons. The first is the optimization of the pore structure of AASRSS, which involves a reduction in the defects of the microstructure and an alteration of the pore size distribution. On the other hand, when NaSt is added, a hydrophobic membrane is formed on the surface of the pore wall of the AASRSS, which is effective at preventing water from entering the AASRSS.  
Conceptualization, K.Y.; data curation, K.Y., J.L., P.W. and W.T.; writing--original draft preparation, K.Y.; writing--review and editing, K.Y., Q.L. and P.C.; supervision, P.C. and Q.L.; project administration, Q.L.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the following institutions for providing facilities and financial support: the National Natural Science Foundation of China (No. 52268039); the Guangxi Natural Science Foundation, China (Nos. 2021 GXNSFBA220047 and AD22035104); Open funds (No. 22-J-21-21) from the Guangxi Key Laboratory of New Energy and Building Energy Saving and the College Student Innovation Project from the Guilin University of Technology (No. 202210596314).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Factors Influencing the Productivity of Wells  
#### 4.2.1 Rock-Type Impact  
To investigate the impact of rock type on well productivity, specific capacity values were categorized into two main groups: alluvial and hard rock types. Analysis of the relationship between well productivity and the geological nature of the rocks revealed that alluvium (66%) was the most productive formation, followed by limestones (17%), shales (9%), and schistose limestones (8%) (Figure 6b).  
Alluvial sediments are known for their high permeability properties, as evidenced by various studies [17; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54]. Many paleochannels have been identified as subsurface counterparts of surface rivers or creeks, and water circulation in these subsurface aquifers follow preferential flow paths within alluvial deposits [55]. These flow paths can occur in both saturated and unsaturated soils and play a critical role in groundwater flow [56; 57].  
On the hydrodynamic level, pumping tests conducted in the alluvial aquifer show values of transmissivity ranging from 4 \(\times\) 10\({}^{-3}\) m\({}^{2}\)/s to 7 \(\times\) 10\({}^{-4}\) m\({}^{2}\)/s, with an average value of 3 \(\times\) 10\({}^{-3}\) m\({}^{2}\)/s in the alluvial unit upstream Tagmout and 4 \(\times\) 10\({}^{-4}\) m\({}^{2}\)/s in the alluvial unit of Tata.  
The second most common lithotype in the area, in terms of the number of wells, is limestone, which presents itself in karst form when it contains water resources. Despite its large surface area in the Tata basin (>65%), only 18% of all boreholes in the basin exploit this type of aquifer. Studies of the Moroccan karst suggest that drought is progressively impacting water resources of this aquifer type [58; 59; 60; 61; 62; 63; 64; 65]. This problem is particularly severe in the southern regions. [PERSON] et al. [66] investigated the origin of groundwater from the lower Cambrian carbonate aquifer of the Lakhssas Plateau in the Anti-Atlas mountains of southwestern Morocco, revealing that the limited water resources in this semi-arid zone of Morocco may be less renewable in some parts.  
The third formation is schist, it contains intercalations of carbonates and quartz, as well as micaceous and clay minerals. When fractures are filled with clay or silty material, the fracture permeability is considerably reduced [67]. This may explain the low number of wells capturing this aquifer.  
Statistical analysis of the parameters influencing well productivity (i.e., lithology, fracturing, and depth) has identified the optimal well depth (43 m) for good productivity. This, in addition to the influencing parameters, will depend on the renewal and recharge rate of the alluvial aquifer.
## 3 Results
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Modified Thomas Test, deg (\({}^{\circ}\))**} \\ \hline Right hip flexion & 4.5 \(\pm\) 3.9 (0–13) \\ Left hip flexion & 4.9 \(\pm\) 4.4 (0–15) \\ Right knee flexion & 67.0 \(\pm\) 10.1 (42–86) \\ Left knee flexion & 67.5 \(\pm\) 9.8 (48–85) \\ \hline
**Sit-and-Reach Test, cm** & 27.2 \(\pm\) 10.5 (6–44.5) \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\) Data are reported as mean \(\pm\) standard deviation (minimum–maximum).  
\end{table} 
Table 4: Data on flexibility tests \({}^{\mathrm{a}}\). \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Vertical Jump, cm**} & 32.4 \(\pm\) 5.9 (20.2–48.7) \\ \hline \hline \multicolumn{2}{c}{**Balance**} \\ \hline \hline \multicolumn{2}{c}{**Absolute Reach Distance, cm**} \\ Anterior with right LL & 68.5 \(\pm\) 5.7 (58.3–79.5) \\ Anterior with left LL & 69.2 \(\pm\) 6.9 (57.8–82.7) \\ Postero-medial with right LL & 77.4 \(\pm\) 8.3 (60.5–88.5) \\ Postero-medial with left LL & 78.7 \(\pm\) 7.6 (60.4–92.7) \\ Postero-lateral with right LL & 80.0 \(\pm\) 6.8 (58.6–91.9) \\ Postero-lateral with left LL & 79.7 \(\pm\) 8.2 (60.6–92.3) \\ \hline \hline \multicolumn{2}{c}{**Normalized Reach Distance, \%**} \\ Anterior with Right LL & 72.6 \(\pm\) 6.7 (56.1–82.4) \\ Anterior with Left LL & 73.3 \(\pm\) 8.2 (55.6–84.9) \\ Postero-Medial with Right LL & 82.0 \(\pm\) 8.8 (58.4–91.9) \\ Postero-Medial with Left LL & 83.2 \(\pm\) 7.5 (62.5–92.8) \\ Postero-Lateral with Right LL & 84.8 \(\pm\) 7.7 (71.4–101.7) \\ Postero-Lateral with Left LL & 84.3 \(\pm\) 8.1 (58.2–94.3) \\ \hline \hline \multicolumn{2}{c}{**Composite Reach Distance, \%**} \\ Right side & 84.9 \(\pm\) 10.4 (59.8–103.4) \\ Left side & 85.3 \(\pm\) 10.3 (56.5–100.9) \\ \hline \hline \multicolumn{2}{c}{**20 m sprint time, s**} & 3.26 \(\pm\) 0.19 (2.94–3.67) \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\) Data are reported as mean \(\pm\) standard deviation (minimum–maximum); LL: lower limb.  
\end{table} 
Table 3: Data on vertical jump height, balance and speed tests \({}^{\mathrm{a}}\). Similarly, correlations between the vertical jump and the flexibility of the knee flexors were also weak and not statistically significant (\(\rho=0.330\); \(p=0.133\)). In balance, the composite reach distance of the right side presented a moderate and statistically significant correlation (\(\rho=0.411\); \(p=0.040\)) with the flexibility of the knee flexors, with the composite reach distance of the left side, although not diverting much from the contralateral, had no statistically significant correlation (\(\rho=0.364\); \(p=0.096\)). It should also be noted that weak correlations were recorded, but very close to moderate, between the speed with the flexibility of knee extensors (Table 5), which were also not statistically significant (right: \(\rho=0.360\); \(p=0.100\); left: \(\rho=0.386\); \(p=0.076\)). Similarly, speed did not correlate with the flexibility of the knee flexors (\(\rho=-0.173\); \(p=0.440\)).
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Key-Space Analysis  
Sufficiently large key size is necessary for any good encryption algorithm. In the proposed method, we have used four keys: wavelet coefficient permutation and diffusion keys, SPIHT bit-stream cyclic shift with permutation and matrix vocabulary keys, logistic chaotic map keys and quadratic chaotic map keys. At each stage, we have used 4-real-number keys of double data type. In these four stages, keys that are composed of a minimum of 16 real numbers are used. These 16 double real numbers provide a key size of 1024 bits. Thus, the size of the key space is \(2^{1024}\), which is large enough to resist any brute-force attack. Table 2 shows the comparison among key sizes of the proposed and pseudorandom number based encryption schemes.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 7 Conclusions  
We reviewed activity-based congestion management (ABC) for fair resource sharing, gave an introduction to P4, and demonstrated the technical feasibility of ABC on programmable software switches by a prototype implementation in P4. We presented performance results illustrating the ability of ABC to enforce fairness.  
The implementation leveraged several extern functions that can be utilized on a software switch but may not be available on hardware switches. Thus, P4-capable hardware switches should provide a wider range of externs to support richer use cases. Moreover, access to queue lengths of transmission interfaces are desirable to support implementation of simple AQMs.  
Experimental results with the prototype implementation illustrated that ABC provides an ecosystem where users can maximize their throughput by sending at their fair share in case of congestion, which incentivizes the use of congestion controlled transport protocols. Moreover, the experimental results are in line with a more comprehensive simulation study [6].  
As ABC does not require per-aggregate states in core nodes, it is a scalable technology for core networks. As it requires trusted network devices, it should be applied only in closed networks. Extensions for QoS differentiation exist. Thus, ABC provides scalable, QoS-aware congestion management for closed networking domains. Therefore, it may be an attractive technology for 5G transport networks, data center networks, or residential access networks of ISPs.  
Figure 6: Resource sharing with CBR and TCP traffic; Client 0 sends CBR traffic as indicated on the \(x\)-axis while Client 1 has one saturated TCP connection.  
**Author Contributions:** Conceptualization, M.M.; Investigation, H.M.; Project administration, M.M.; Software, H.M. and M.H.; Supervision, M.M.; Validation, D.M.; Visualization, D.M. and M.H.; Writing--original draft, H.M. and D.M.; Writing--review and editing, M.M and H.M and D.M.  
**Funding:** This work was supported by the Deutsche Forschungsgemeinschaft (DFG) under Grant ME2727/2-1.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions  
The integrated logging-geochemical analysis reveals that Gamma Ray (GR) logs, with a uranium enrichment coefficient of 0.89, and bulk density (DEN) measurements, corrected for multicollinearity (variance inflation factor, VIF = 4.7) through PCA, exhibit superior TOC sensitivity compared to acoustic transit time (vertical resolution: \(\pm\)0.3 m) and resistivity logs (radial difference coefficient > 0.8). For expedited TOC evaluation in field applications, we strongly advocate the prioritized adoption of GR-DEN crossplots, which demonstrate robust predictive capability (R\({}^{2}\) = 0.83) across diverse lithofacies.  
The DW-RFR model, incorporating Bayesian-optimized hyperparameters (200 trees, max depth = 12) and SHAP-weighted feature engineering, reduces TOC prediction errors by 35.2% compared to conventional methods, achieving an RMSE of 0.171 wt.%--the first sub-0.2 wt.% benchmark for Cambrian systems. Monte Carlo cross-validation confirms model stability (R\({}^{2}\)\(\sigma\) = 0.032 \(\pm\) 0.004), while Gamma-Ray-guided dynamic time warping resolves core-log depth mismatches with \(\pm\)0.06 m precision, critical for ultra-deep shale evaluation (2800-4200 m).  
Spatial validation across the Deyang-Anyue Rift Trough reveals maximum prediction accuracy in the intra-trough center (TOC = 2.0-3.2%, RMSE = 0.171), where continuous organic-rich deposition minimizes diagenetic overprinting. The methodology demonstrates transferability to the Longmaxi Formation, establishing a replicable framework for layered shale gas exploration.  
Conceptualization, M.Z. ([PERSON]); Methodology, M.Z. ([PERSON]); Validation, Y.W.; Data curation, D.L.; Writing--original draft, J.Z.; Writing--review & editing, X.T.; Project administration, K.C. 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 (grant no. 42372144).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: Authors [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company PetroChina Southwest Oil & Gasfield Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic
## 2 Experiments
### Description of Remote-Sensed Observations
The sun-photometers-measured AODs are regarded as truth because the ground observations are not affected by surface reflectivity. Thus, in the study, we employed the available AERONET AOD data at five monitoring sites (Hornsund, Thule, Andenes, Kuopio, and Hy Hy Hytiala stations) to evaluate the CMAQ-calculated, MODIS-retrieved, and assimilated-AODs over the Arctic. As shown in Figure 1a, the Thule, Horsund, and Andenes stations are adjacent to the coast, so the AODs in the areas is easily affected by sea salt particles. On the other hand, the Kuopio and Hyytiala stations are located in the inner parts of Finland, which the air quality is affected by anthropogenic sources. The level 2.0 product of AOD obtained from the AERONET archive ([[https://aeronet.gsfc.nasa.gov](https://aeronet.gsfc.nasa.gov)]([https://aeronet.gsfc.nasa.gov](https://aeronet.gsfc.nasa.gov))) is cloud-screened, quality-controlled, processed via version 3.0 algorithm [79]. Here, AOD at 550 nm was selected as the same as \(\tau_{MODIS}\) and calculated by Equations (1) and (2):  
\[\tau_{\lambda}=\tau_{\lambda_{0}}\left(\frac{\lambda}{\lambda_{0}}\right)^{- \alpha} \tag{1}\]  
where, \(\lambda\) and \(\lambda_{0}\) represent wavelengths at 550 nm and 500 nm, respectively. The Angstrom exponent (\(\alpha\)) was calculated from the 870 nm (\(\lambda_{1}\)) and 440 nm (\(\lambda_{2}\)) channels [80]. \[\alpha=\frac{Log\frac{\tau_{\lambda_{1}}}{\tau_{\lambda_{2}}}}{Log\frac{ \lambda_{1}}{\lambda_{2}}} \tag{2}\]  
Figure 2 showed the seasonal scatter plots and statistical analysis between \(\tau_{AERONET}\) and \(\tau_{MODIS}\) over the 5 AERONET stations. For synchronization with the AERONET observation, the MODIS data were collected within at most 10 km and 30 min in the spatial and temporal differences. As expected from Figure S1, the synchronized hourly dataset were small (N = 139 during the periods of April-September 2008) because the MODIS data-set was filtered due to the high surface albedo. The overall correlation coefficients (Index of Agreement) were 0.73 (0.81) and were good, particularly for April and August 2008. From the analysis, the slopes between \(\tau_{AERONET}\) and \(\tau_{MODIS}\) were smaller, in particular in warm seasons, which indicates that the MODIS AOD was possibly underestimated. Therefore, the underestimated \(\tau_{MODIS}\) would affect the surface PM\({}_{10}\) and PM\({}_{2.5}\) estimations from the assimilated AOD. In the study, AERONET AODs (\(\tau_{AERONET}\)) were also used to determine the free parameter for the data assimilation. The details were discussed later in Section 2.3.
## 2 Dense Feature Extraction and Matching Theory and Mismatch Cause Analysis
### Image Feature Matching Algorithm  
After extracting a large number of SIFT features from two frames of images, feature matching is performed to identify the same points in both frames. Frequently used feature matching methods are the Brute Force (BF) matching algorithm and the FLANN algorithm [23]. FLANN can quickly locate the nearest neighbors to search similar feature descriptors and perform matching and is more often used in feature matching applications that demand a large number of feature points. FLANN constructs an index for all feature descriptors in the reference image and automatically selects the nearest neighbor based on the characteristics of the feature descriptor set. For each feature descriptor in the detected image, FLANN searches for the nearest neighbor from the established index, finding the most similar feature descriptor in the reference image. FLANN performs an approximate search instead of accurate matching, so the absolute accuracy of the matching cannot be guaranteed for the nearest neighbor it finds, which can meet the requirements of traditional image processing. The approximations between two feature vectors in feature nearest neighbor search are measured by the Euclidean distance, which is expressed as:  
\[d(x_{1i},x_{2i})=\sqrt{\sum_{i=1}^{128}(x_{1i}-x_{2i})} \tag{3}\]  
where \(x_{1i}\) and \(x_{2i}\) (\(i=1\), \(2\), \(\ldots\), \(128\)) are the coordinates of the 128-dimensional feature vectors in the two images, respectively. In the FLANN algorithm, the shorter the Euclidean distance between two feature vectors, the more similar these two feature vectors. When this distance is zero, the two features are identical. The FLANN algorithm performs well in terms of accuracy and efficiency in high-dimensional feature matching, meeting the requirements in real-time health monitoring and also maintaining a sufficient number of feature point matches.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 2 Mission Concept
### Ground Segment
From the parsed information, data will be encoded in the database through an online webpage by the ground stations, which receive the data. A dedicated user account is provided for each country for data encoding, access, and download. While all data could be accessed by an admin user account, the dedicated user account is restricted to access only for the GSTs of the relative countries. The data collected from the GSTs of other countries are not accessible to anyone else except the country that transmitted the data. To facilitate data sharing, the country could access their own data in  
Figure 8: PCB design of GST. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Dimensions & 100 mm \(\times\) 100 mm \\ Operating Voltage & 3.3 V \\ Current consumption & 35 mA standby, 90 mA transmit \\ Frequency range & 400 MHz–440 MHz \\ Transmit power & Up to 100 mW (20 dBm) \\ Data rate & 46 bps–781 bps \\ Reconfigurable SF & 6–12 \\ Bandwidth & 7.8 kHz to 125 kHz \\ Communication ports & IEC, SPI, UART, analog \\ \hline \end{tabular}
\end{table} 
Table 3: Specifications of the fixed GST board. the database, and reports could be generated based on their selected data in TSV, CSV or TXT format.