esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The analysis is based on more than 390 GPS stations (see Figure 1), Swarm-A satellite (SwA) of the European Space Agency's Swarm mission, and DMSP 5D-3 F18 spacecraft (F18) of the Defense Meteorological Satellite Program (DMSP). Our study is relevant because sources of ionospheric disturbances are a significant issue for space-based communication and radionavigation systems. Due to the fact that GPS is a radio-link-based technology, it can be affected by ionospheric fluctuations. Furthermore, our results were compared with previous studies about the ionospheric response to TSE2019, TSE2020, and TSE2021. We must emphasize that this is one of the first works where the ionospheric behavior due to two eclipses that occurred in South America and Antarctica under geomagnetic activity and in different seasons of the year are studied and compared, as well as the ionospheric effects that both eclipses had on the behavior of the EIA.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 2 The NOMA Link-Level Simulator
### Receiver Design
#### 2.4.5 Features and Considered Scenarios  
The NOMA link-level simulator was based on MATLAB simulations. The NOMA system works on the principle of user pairing with different channel gains; therefore, a minimum of two users are required in any simulation scenario. Owing to the restriction of a minimum of two users, a single-input-single-output system is not possible in NOMA systems. Thus, multiple users in a single cell are required in all the simulated scenarios. Although most research related to NOMA is limited to two-user pairing [32; 33], more than two users in NOMA systems and their power allocation is still under investigation by various research groups [1; 34]. The proposed NOMA link-level simulator also works on the pairing of two users only until now, but it can be extended to multiple users in the next release of this simulator.
# A Novel Intermittent Jumping Coupled Map Lattice Based on Multiple Chaotic Maps
## 3 Analysis of Dynamic Behaviors
### Power Spectrum Analysis  
Power spectrum is a significant tool to visualize whether a sequence has chaotic characteristics or not. The Fourier theorem sets forth that any periodic signal can be expressed as Fourier series, corresponding to a discrete frequency spectrum. By contrast,  
Figure 3: Diffusion energy analysis for (**a**) the CML system, (**b**) the NCML system, (**c**) the DCML system, (**d**) the NDCML system, and (**e**) the proposed IJCML system, respectively.  
an aperiodic signal is represented by Fourier integral with a continuous frequency spectrum. As such, if the power spectrum has one or more distinct peaks, the signal must be periodic or quasi-periodic. Conversely, if the power spectrum has a noise-like modality without obvious peaks, the signal is chaotic.  
Figure 4 shows results for the power spectrum analysis, in which we investigate twelve combinations of settings for the parameters \(\mu\) and \(e\). In this simulation study, the sequence with 1000 elements generated by 50 th lattice is selected for analysis. In addition, for the sake of observation, we swap the left and right halves of a power spectrum, in the sense of shifting the zero-frequency component to the center.  
Figure 4: Power spectrum analysis for the CML system, the NCML system, the DCML system, the NDCML system, and the proposed IJCML system.  
By comparison, we find that the parameter \(e\) has a relatively small influence on the power spectrum. Thus, the following discussion mainly focuses on the effect of different values of \(\mu\). As we see, for the baseline systems, almost all the power is concentrated at zero-frequency component when \(\mu=2.49\) or \(2.99\). This means that there is almost no fluctuation in the corresponding sequence, so that it belongs to a direct-current signal. When \(\mu=3.49\), the baseline systems distribute the power onto several frequency components. This implies that the corresponding sequence contains periodic transitions between several state variables, so that it belongs to a deterministic signal. When \(\mu=3.99\), five power spectrums share the similar noise-like modality, meaning that all the systems have turned into the complete turbulence pattern [4]. Remarkably, even when \(\mu=2.49\), \(2.99\), or \(3.49\), the IJCML system's power is evenly dispersed over all frequency components, in the sense that there exists only one small peak submerged in the noise-like fluctuations. This phenomenon demonstrates that the proposed system behaves better than the baseline ones, and indeed enlarges the parameter space of spatiotemporal chaos.
## 3 Proposed Method
### Stability Analysis of Satellite Ground Repeat Period and Original CNR
Figure 5: RMS of the CNR difference between two consecutive adjacent days for other satellites and stations at L1 frequency. found that the CNRs of other adjacent days remain unchanged for the same satellite. This phenomenon can be found in the color bar--the more yellow the color, the more similar it is. In addition, R denotes the correlation coefficient between the CNRs, which is presented in each subplot. It is obvious that all of the coefficients perform very well and are larger than 0.997. Although the coefficient decreases as the time interval increases, it can still reach approximately 0.997. Thus, the above results indicate that the CNR maintains consistency on consecutive adjacent days. However, it should be noted that there are some epochs that have larger bias (blue color), but all of them concentrate on CNRs lower than 40 dB-Hz. As is well known, these epochs are at low elevation angles and are mainly affected by random noise. Thus, to mitigate the influence of this bias, random noise and the CNR should be considered at low elevation angles.
## 2 Background
### Chloride Content Analysis  
Corrosion is initiated once the chloride content present in the concrete exceeds a certain threshold, called the chloride threshold level (CTL). This threshold value is also referred to as the critical chloride content in the literature. The CTL is often expressed in terms of \"chloride content relative to the weight of the cement\" [15]. The CTL can also be expressed as the total chloride content relative to the concrete weight or as a ratio of free chloride to hydroxyl threshold or (Cl\({}^{-}\)):(OH\({}^{-}\)) [15; 16; 17]. Different standards provide guidance on the CTL for concrete structures. For instance, ACI 318 [18] limits the CTL in concrete in the range of 0.08 to 0.1% by weight of concrete. Table 1 indicates the CTLs provided by other standards, expressed in terms of chloride content relative to percentage weight of the cement.
# An Easy Technique for Focus Characterization and Optimization of XUV and Soft X-ray Pulses
## 2 Experimental Implementation
The ion microscope that is used in the focus for linear and nonlinear ionization measurements can be shifted out without breaking the vacuum to make space for the CMOS camera. There are two windows in front of the CMOS chip to protect this type of camera from dust. For the application in the XUV spectral region, those windows have to be carefully removed in order to enable the XUV photons to directly hit the surface of the sensor. Furthermore, common CCD or CMOS cameras are not designed for the use in a vacuum environment. This can presumably negatively impact the performance of the camera and increase the backing pressure inside the vacuum chamber. However, after months of operation, the only negative impact of the vacuum environment on the camera that we have found is overheating. If the pressure in the vacuum chamber is <1 mbar, the cooling of the camera is not sufficient anymore and it slowly heats up. However, an increase in the noise level due to the higher temperature was not detected. After \(\approx\)30 min of continuous operation, the camera shuts down due to the temperature being too high. However, when unplugged, the camera cools down within <5 min and is useable again afterwards. No negative long-term effect for the camera has been observed from this operation. Minimal additional cooling, such as heat loss due to \(\approx\)1 mbar of gas or an extra small Peltier cooling, is already enough to prevent overheating completely. As in many other XUV experiments, the focal point of our XUV beam is located in an area, where it can  
Figure 2: XUV spectrum of the attosecond beamline of the Relativistic Attosecond Physics Laboratory at Umeå University with 3\(\times\) 150 nm Zr filters (blue), 2\(\times\) 150 nm Zr + 1\(\times\) 100 nm Pd filters (red), and 2\(\times\) 500 nm Al filters (green). The spectrum with the Al filters was simulated using the transmission of the Al filters, and a plateau odd harmonic spectrum as the spectrometer was operating in a shorter wavelength range. The area below the Al spectrum was scaled to be 38% of the area below the Zr spectrum, corresponding to the energy in this region. interact with a localized gas source. Hence, the vacuum in this part is only on the order of 10\({}^{-5}\) mbar. At this vacuum level, the backing pressure is not negatively influenced by the implementation of the camera. For an ultrahigh vacuum environment, this could be different; however, the focused XUV beam at the camera is extremely small, which provides an ideal opportunity for differential pumping, reducing the impact of the camera onto the vacuum even further.
## 4 3D QoS-Aware Routing (3 DQR)
### E2E Routing and Path Allocation Approach  
The paths are requested by the External Requester (ExtReq) entity (#1) such as UP entities, Management and Orchestration (MANO), M-SDNC applications, etc. M-SDNC, which constitute a single entry point to the framework by exposing path allocation Application Programming Interfaces (APIs). The E2 EP allocation process is shown in Figure 3. The path allocation process also includes the reservation of resources for the traffic flow with respect to the flow's QoS class.  
First, M-SDNC sends the E2 EP request containing flow-specific data (source node, target node, and QoS class) to M-QR (#2). M-QR calculates the E2 EP (#3), which consists of BNs and overlay links visible at the M-SDNC level (#4). After verification of E2E path allocation feasibility, M-SDNC splits the path into segments based on the BNs domain membership (#5). For each segment, M-SDNC requests IDP calculation from the SDNC-DQR pairs (#6, #7). Then, IDPs' feasibility is verified by SDNCs, which ends with the pre-allocation of resources of the flow (#8). Finally, SDNCs notify M-SDNC about the successful pre-allocation of resources for the IDPs (#9), including the QoS parameters of the selected paths (e.g., delay, expected error rate, etc.). After receiving a positive notification from each domain, M-SDNC verifies if connected IDPs satisfy the E2E QoS requirements (#10). If so, M-SDNC notifies respective SDNCs about successful E2 EP verification (#11) to finalise enforcement (#12), and notifies ExtReq about successful allocation (#13). If IDP allocation is impossible, e.g., due to dynamic changes in the network (nodes mobility, faults, etc.), SDNC notifies M-SDNC about it; then, M-SDNC requests respective SDNCs to revert IDPs allocations. The same approach is taken by M-SDNC if E2 EP verification fails.  
While the centralised approach increases procedure duration, it ensures E2 EPs even in disjoint NTN topology graphs, i.e., missing ISLs, or in a multi-stakeholder environment (e.g., NTN roaming). The inclusion of an M-SDNC overlay view enables routing the traffic via TN or other NTN segments to connect the disjoint parts of topology and attempt the allocation.  
Figure 3: Path allocation in 3 DQR concept.
## 2 System Model of Multiple-Access Relay Networks with Cooperative Relay  
We consider a communication system based on multiple-access relay networks, as shown in Figure 1. Multiple users \(\{S_{1},S_{2},\ldots,S_{n}\}\) transmit information to destination \(D\) via a common relay \(R_{1}\). We assume that the channel conditions of the source-relay links between \(R_{1}\) and \(t\) sources \(\{S_{n-t+1},S_{n-t+2},\ldots,S_{n}\}\) deteriorate dramatically due to obstacles blocking or unexpected damage to the links. In this situation, traditional distributed networks can no longer provide reliable and efficient transmissions. We propose a novel multiple-access multiple-relay network by introducing an additional relay node in a distributed network. The additional relay can be regarded as a cooperative relay to provide enhanced access to certain sources with poor channel conditions. As can be seen from Figure 1, the additional relay is a mobile relay in contrast to a traditional static fixed relay,which can be implemented using drones. It can move to the appropriate locations where the sources with poor link conditions to ensure all sources can be accessed. By making use of a drone as a cooperative relay, this network can provide an additional degree of freedom in terms of movement. As a result, the coverage of the relays can be extended, and the transmission efficiency and reliability of the network can be improved.  
Figure 2 shows the multiple-access relay network model with a cooperative relay for the proposed communication system. The proposed network consists of \(n\) source nodes \(\{S_{1},S_{2},\ldots,S_{n}\}\), a destination node \(D\), and two relay nodes \(R_{1}\) and \(R_{2}\). We assume that all \(n\) sources transmit information to destination through relay \(R_{1}\). The links between \(\{S_{n-t+1},S_{n-t+2},\ldots,S_{n}\}\) and \(R_{1}\) may be in poor channel conditions, so we assume that \(t\) sources \(\{S_{n-t+1},S_{n-t+2},\ldots,S_{n}\}\) transmit information to the destination via relay \(R_{2}\) additionally.
## 3 Results and Discussion
### Stage Performance  
In order to explore the influence of flow rate of seal cavity leakage on the performance of all stages of the compressor, the pressure coefficient (\(\psi\)) and work coefficient (\(\psi\prime\)) of each stage under the corresponding state were calculated by using the flow field data of selected working points. The definition of \(\psi\) and \(\psi\prime\) are as follows  
\[\psi=\frac{\left(H_{out}^{*}-H_{in}^{*}\right)_{isen}}{U_{m}^{2}}\]  
\[\psi^{\prime}=\frac{\left(H_{out}^{*}-H_{in}^{*}\right)}{U_{m}^{2}}\]  
where \(U_{m}\) is the tangential speed of the rotor at 50% of the span at the leading edge of the stage. Referring to the analysis method in Figure 11, configurations 1-3 are a group and configuration 1 is considered as a benchmark to study the effect of changes in c1 on the characteristics of each stage. Configurations 3-6 are another group and configuration 3 is considered as a benchmark to study the effect of changes in c2 on the characteristics of each stage. Figures 12 and 13 show the variation of work coefficient attenuation and pressure coefficient attenuation with respect to tip clearance, respectively. From the first stage characteristic attenuation (blue line) of Figures 12b and 13b, it can be seen that the leakage flow in the seal cavity 2 under the second stage stator has little effect on the performance of the first stage compressor. As it can be seen from Figure 12a, the leakage flow caused by the seal cavity 1 has little influence on the work capacity of the first stage (current stage), but has a relatively large impact on the latter two stages, thus reducing its work capacity. From Figure 12b, it can be concluded that seal cavity 2 has little effect on the work capacity of the current stage, almost no effect on the front stage (the first stage), and reduces the work capacity of the later stage (the third stage). On the other hand, as it can be seen from Figure 13, the seal cavity leakage flow has little effect on the pressurization capacity of  
Figure 11: Efficiency penalty for varying seal-tooth clearance.  
the front stage (the change of cavity 2 in Figure 12(b) does not affect pressure coefficient of the first stage), but reduces the pressurization capacity of the current stage and the next one. Considering that the work capacity is mainly related to the rotor blades, and the pressurization capacity is affected by both the rotor and stator, the following conclusion can be drawn: Seal cavity leakage flow affects the performance of the stator row and the blade row located at downstream, but the impact on the upstream blade row (including the rotor blade row of the same stage and the more upstream blade row) is minimal. This conclusion will be further confirmed in the analysis of the spanwise distribution of the flow parameters of the blade row.
###### Abstract  
The Chang 8 oil group within the Luo 1 well area of Jiyuan Oilfield, situated in the Ordos Basin, exemplifies an ultra-low-permeability reservoir with an average permeability of 0.84 mD. Despite primary development efforts through acid fracturing, suboptimal recovery efficiency has been observed due to inadequate injection-production matching. To mitigate this issue and enhance reservoir utilization, a comprehensive understanding of sand body architecture is imperative. This study employs a detailed reservoir architecture element analysis approach, integrating core samples, thin-section petrography, and geophysical logging data. The objective is to elucidate the internal structure and heterogeneity of sand bodies, which significantly influence hydrocarbon recovery. Key findings reveal that the study area is characterized by a shallow-water deltaic depositional system, featuring three principal sedimentary microfacies: subaqueous distributary channels, sheet sands, and lacustrine muds. Notably, subaqueous distributary channel sand bodies dominate, forming composite units via lateral accretion or vertical stacking of 2-5 individual channels, with widths exceeding 2000 m. Individual distributary channels range from 83 to 535 m in width, exhibiting both isolated and stacked contact styles. Importantly, only 25.97% of channels demonstrate connectivity, underscoring the critical role of channel scale and continuity in ultra-low-permeability reservoir development. By addressing the previously identified gap in architectural configuration knowledge, this study contributes foundational data for future development improvements. In conclusion, the detailed characterization of reservoir architecture offers pivotal insights into tailoring development strategies that align with the specific characteristics of ultra-low-permeability reservoirs, thereby improving overall recovery rates.  
ultra-low-permeability reservoir; shallow-water delta front; sand body architecture analysis; sedimentary microfacies; Ordos Basin
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 2 Literature Review and Related Work
### Commercial mmWave Radar Altimeter Offerings for UASs  
Plug-and-play radar altimeter solutions specifically designed for mini-drone platforms, based on mmWave automotive radars, are now being offered [39]. In alignment with the feasibility objectives of this article, the existence of such solutions in a compact and cost-effective package congeals the motivation behind this exploration. Moreover, the technical specifications of these radars are expected to serve as a valuable repository for conducting comparative analyses and validation of theoretical findings. Nevertheless, it is important to underscore that the focus of commercial solutions is not aimed at contributing to academic literature or scientific discourse. Conversely, this article delves into the rationale, theoretical foundations, and mathematical groundwork necessary for designing a mmWave radar altimeter for UAS.
# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
###### Abstract  
A reliable estimate of the gross primary productivity (GPP) is crucial for understanding the global carbon balance and accurately assessing the ability of terrestrial ecosystems to support the sustainable development of human society. However, there are inconsistencies in variations and trends in current GPP products. To improve the estimation accuracy of GPP, a deep learning method has been adopted to merge 23 CMIP6 data to generate a monthly GPP merged product with high precision and a spatial resolution of 0.25\({}^{\circ}\), covering a time range of 1850-2100 under four climate scenarios. Multi-model ensemble mean and the merged GPP (\(\text{CMIP6}_{\text{DL}}\) GPP) have been compared, taking GLASS GPP as the benchmark. Compared with the multi-model ensemble mean, the coefficient of determination between \(\text{CMIP6}_{\text{DL}}\) GPP and GLASS GPP was increased from 0.66 to 0.86, with the RMSD being reduced from 1.77 \(\text{gCm}^{-2}\text{d}^{-1}\) to 0.77 \(\text{gCm}^{-2}\text{d}^{-1}\), which significantly reduced the random error. Merged GPP can better capture long-term trends, especially in regions with dense vegetation along the southeast coast. Under the climate change scenarios, the regional average annual GPP shows an upward trend over China, and the variation trend intensifies with the increase in radiation forcing levels. The results contribute to a scientific understanding of the potential impact of climate change on GPP in China.  
G 2023, 14, 1201. [[https://doi.org/10.3390/f14061201](https://doi.org/10.3390/f14061201)]([https://doi.org/10.3390/f14061201](https://doi.org/10.3390/f14061201))
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 3 State-of-the-Art
### State-of-the-Art in the Encryption Aspect
#### 3.3.1 Symmetric versus Asymmetric Cryptography  
Symmetric key encryption involves the use of the same key in both encryption and decryption and is common in many algorithms such as the Advanced Encryption Standard (AES). [PERSON] et al. [114] proposed an image encryption algorithm for encrypting medical images using a chaotic variant of the Simplified Advanced Encryption Standard (S-AES). Asymmetric key encryption involves the use of different (but related) keys for encryption and decryption. [PERSON] et al. [115] proposed an algorithm that utilizes a complex and irreversible function that causes the algorithm to exhibit asymmetric properties.