esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this context, the sedimentary system development model established in this paper, which is based on sea-level fluctuations under a specific astronomical cycle, provides an in-depth discussion of the control mechanisms exerted by different climatic phases on the development of sedimentary features and sand bodies. Furthermore, it offers new perspectives and methods for understanding the coupling relationships among astronomical orbital forces, climate, sea level, and sedimentary systems. The upper section of the Zhujiang Formation in the Enping Sag offers unique research advantages, characterized by continuous sedimentary sequences that preserve abundant intercalations of terrigenous clastic and marine sediments. This provides an ideal material basis for identifying high-frequency astronomical cycles, such as Milankovitch orbital periods [13]. In this study, the theory of cyclost stratigraphy was applied to investigate this area in depth. A high-precision 'absolute' astronomical timescale was established by selecting natural gamma-ray data sequences (GR) as proxies for paleoclimate indicators and performing time series analysis and astronomical tuning. Based on this, the DYNOT (Dynamic Noise after Orbit Tuning) sedimentary noise model was applied to reconstruct the sea-level change curve during this period and elucidate the relationship between sea-level change and astronomical orbit. Additionally, the paleoclimate characteristics of the depositional period were analyzed in detail by integrating data on lithological changes, sand-silt ratios, and major geochemical elements, evaluating the impact of astronomical orbital cycles on paleoclimate. Ultimately, the coevolution of paleoclimate and sea-level change driven by astronomical orbital cycles and their depositional responses is revealed. This study contributes to understanding how astronomical factors drive climate and sea-level changes, thereby influencing sedimentary processes and the development and distribution patterns of sand bodies, providing new ideas and examples for oil and gas exploration in the Pearl River Formation of the Epping Depression.
# A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems
## 4 Simulation Results
Figure 6 also shows the acquisition probability of the composite code composed of these ranging codes under the same conditions. Compared with the acquisition performance of each sub-ranging code, the overall acquisition performance is much worse under low SNR. With the increase in SNR, the overall acquisition performance is approximately identical to the acquisition of each ranging code. Therefore, when the SNR is above \(-5\) dB, the OFDM system designed in this paper can complete the acquisition of the overall composite range code. After serial acquisition of ranging codes, the receiver will track the three ranging codes in parallel. The tracking loop is shown in Figure 3. Under different SNR, the average tracking accuracy obtained by tracking the three ranging pilots for 1000 ms is shown in Figure 7.  
From Figure 7, when the SNR is above 2 dB, the tracking loop enters a stable state. In addition, the tracking accuracy is proportional to the pseudocode period. Therefore, the tracking loop utilized in this paper can perform remarkably under the OFDM system framework. Based on the tracking loop output results, we can range the composite code slice offset and complete ranging by Equations (17)-(19). In Section 3, we established that the composite code chip offset is sensitive to the chip offset tracked by each ranging code. Moreover, according to the results of acquisition and tracking for each ranging code, the receiver can self-correct these tracking results through Equation (20). Figure 8 shows the average chip offset error calculated according to the tracking and corrected results of each ranging code under different SNRs. Figure 7: Tracking accuracy of different ranging codes. As can be seen from Figure 8, before self-correction, the receiver can only correctly calculate the composite code slice offset when the three ranging codes have been totally tracked. Moreover, the error of the calculated composite code slice offset is larger than the phase error of a single channel ranging code. After the self-correction of the tracking results, the system can correctly range the measured distance when the SNR is above 0 dB. Compared with the SNR requirement for stable tracking of a single channel ranging code, the SNR requirement for correct measurement of transmission distance is reduced by 2 dB. Moreover, the error at low SNR is also greatly reduced.
## 2 Materials and Methods  
Within the _IndustRiES_ study, a database was created in an interlinked top-down and bottom-up approach (see Figure 1). Examples for top-down determined data sets used for this study are, e.g., the FEC per energy carrier for every industrial sector and each useful energy category from available, national statistics databases as well as the Austrian potentials for renewable energy carriers. The statistical data (available from 1993 to 2019) on energy balances were used, as they reveal detailed information about the Austrian industrial FEC and the distribution of it according to the six useful energy categories:  
* Space heating and air conditioning,
* Steam generation,
* Industrial furnaces,
* Stationary engines,
* Lighting and IT and
* Electrochemical purposes.  
Thus, further analyses and results are performed for the FEC and the structure used in the statistics. Data for the primary energy consumption does not provide the same level of detail as the FEC statistics. Hence, the FEC is the basic data source in this work. The geographic system boundary for all scenarios is Austria. The calculated demands per energy carrier are always related to domestic potentials per energy carrier. If the demand cannot be covered by domestic supply potential, demand must be met by energy carriers from foreign energy markets. This circumstance would lead to energy imports in the future.  
Examples for information determined in a bottom-up approach are the knowledge of alternative production routes and technology options, the potentials of alternative energy sources, the production process temperatures, the efficiency gains possible in different sectors, the partly purchased energy and self-supply (e.g., combined heat and power plants, CHPs) on site level. For this purpose, production companies were interviewed, and data were gathered from company reports. In addition, production process descriptions, parameters of reference plants and alternative production routes were retrieved from research reports. Information from personal interviews, publications, research and company reports was used for scenario development and parameter selection. Besides, the initial results  
Figure 1: Visualization of the methodical approach used for this paper.  
were presented to relevant industrial companies and sector representatives as part of a participatory stakeholder process. This feedback loop allowed strengthening the assumptions and thus led to more robust results and derived decarbonization strategies. In the following subsections, more insight is provided into these topics (data and scenarios).
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 6 Results
### Low SNR Case  
Figures 15 and 16 show the SINR for a SNR of 10 dB. As the noise level was high, the observed decrease in SINR in the other cases caused by compensating the channel with an obsolete CSI was not appreciated in the X3 sensor. In the IMX249, the temperature effect was still appreciable, but much less than in the previous cases. Therefore, considering the temperature effect in compensation is significant only at medium-high SNR levels.  
Figures 17 and 18 depict the BER performance. Because of a low input SNR, the BER increased as the temperature increased. This effect was slightly appreciable in both devices. However, there were no BER values below the FEC limit in any case. This poor outcome occurred as a result of the ZF method, which highly penalized the BER performance in those cases when the noise level was considerably high.  
Figure 14: BER at different temperatures, compensating the channel using the CSI corresponding to 60 \({}^{\circ}\)C and with a medium SNR value (20 dB). (**a**) Sony IMX249 and (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively.  
Figure 13: BER at different temperatures, compensating the channel using the CSI corresponding to 27 \({}^{\circ}\)C and with a medium SNR value (20 dB). (**a**) Sony IMX249 and (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively.  
Figure 16: SINR at different temperatures, compensating the channel using the CSI corresponding to 60 \({}^{\circ}\)C and with a low SNR value (10 dB). (**a**) Sony IMX249 and (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively.  
Figure 15: SINR at different temperatures, compensating the channel using the CSI corresponding to 27 \({}^{\circ}\)C and with a low SNR value (10 dB). (**a**) Sony IMX249 and (**b**) Foveon X3. Dashed line, dotted line, and dash-dot line correspond to red, green, and blue, respectively.
# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 4 Proposed ML Techniques, MILP Framework, and Methodology  
### Network Model  
Figure 3 highlights that this study operates within the defined network model, with K numbers of transport network technology (connectivity alternatives between the access layer node and the core node). Even though each transport technology possessed various links to form mesh connectivity in optical and DOCSIS cables, the technology was treated as a single connectivity line. This observation was similar to the inter-satellite links from satellite technology. The transport network to the provided list mentioned in the previous section was scoped using a K value of 3. Meanwhile, the different annotations of arrow colors and directions indicating the downlink (DL) and uplink (UL) traffic represented the traffic classifications. Specifically, the code color represented the traffic types exchanged inside the infrastructure. Figure 3a,b illustrate the difference in traffic classification treatment between the conventional and the proposed intelligent traffic engineering for 6G architectures.  
The traffic classification in the conventional architecture was treated with different priorities and scheduling. Nonetheless, the proposed architecture in this study complemented the approach by adding preliminary traffic segregation works before accessing the transport networks. The post-clustering and assignment process indicates that a group of packets that shares the same attributes is steered to optimal-matched transport network attributes and subsequently undergoes a conventional traffic prioritization process. This proposed architecture mission aimed to maximize end-to-end efficiency by enabling relevant packets to be steered into optimal paths following their respective transport network technologies. Equation (1) describes the calculation of the total volume of packets, \(U\), from the \(i\)-th index of packets that sums up the total traffic volume in an instance, \(T(t)\).  
\[T(t)=\sum_{i=1}^{N}U_{i}(t)=\sum_{m=1}^{5}\sum_{n=1}^{N}u_{mn}(t) \tag{1}\]  
Figure 3: The coexistence of heterogeneous transport networks for the 6G infrastructure using (**a**) conventional and (**b**) the proposed architectures.  
where \(U_{i}(t)\) is the packet in the instance, and \(u_{mn}(t)\) represents a packet based on their traffic classification types denoted by m, representing traffic classification types ranging from 1 to 5 and the \(n\)-th index of packets. \(T(t)\) equals the total number of packets in an instance in the respective m category, where \(n\) ranges from 1 to \(N\). \(T(t)\) underwent unsupervised learning, a clustering technique to group the total UE and traffic per instance.
## 1 Introduction  
The generation and use of electrical energy is crucial to modern society. The term economic cost dispatch (ECD) that is associated with the power system, which may minimize the cost of power generating while still satisfying the operating requirements, is a highly essential problem given that thermal power generation is the predominant power generation at present. But, as environmental issues worsen, more people are realizing that we need to consider emissions of dangerous gases such as NOx, SO\({}_{2}\), and CO\({}_{2}\) in addition to just the generation cost. The cost of producing electricity and the need to reduce emissions are both taken into account by CEED. In common ECD problems, generator cost functions are approximated by quadratic functions. The most frequent form of expressing the CEED issue is as a quadratic function, reflecting the fact that it is a multi-objective optimization problem. Higher order polynomials have been shown to enhance solution methodologies, and studies have indicated that advanced functions can provide an accurate representation of the power generating system's response; however, this will only serve to further exacerbate the issue at hand. To address CEED issues, some scientists use optimization strategies grounded on conventional mathematical modeling. The traditional approach may be used in several power-generating test environments. Benefits include optimality shown mathematically and the absence of problem-specific factors. The conventional method that relies on a coordination equation to tackle the issue of economic emission load dispatch takes into account the limitation of line flow. Using the Min-Max price penalty factor brings about a decrease in the total fuel expense of CEED. Nevertheless, standard mathematical approaches face significant difficulties in resolving these nonlinear issues because of intrinsic nonconvexity and nonlinearity of the existing power generating system and other constraints in the actual production process. Many evolutionary programming and AI-based strategies have been proposed to address CEED issues, including but not limited to the bat algorithm [1], artificial bee colony [2], teaching-and-learning-based optimization [3], cuckoo search [4], flower pollination algorithm [5], firefly algorithm [6], bacterial foraging optimization [7], genetic algorithm [8], differential evolution [9], and particle swarm optimization [10].
# A Multi-Analytical Approach to Investigate Fresco Paintings in a Hypogeum Environment
## 3 Results
### Spectroscopic Investigations
Yellow, brown, and red areas all display markedly higher iron than the lime substrate, plus variable calcium and sulfur that originate from the plaster and salt efflorescences rather than the pigments themselves. The absence of cadmium and silicon excludes cadmium yellow (CdS) and enamel yellows (SiO\({}_{2}\) + CdS). The iron abundance instead points to iron-oxide earths: yellow = yellow ochre (limonite, FeO(OH)), brown = burnt sienna (limonite with hematite), and red = red ochre (hematite, Fe\({}_{2}\)O\({}_{3}\cdot\)\(n\)H\({}_{2}\)O). Figure 7: XRF spectra of the fresco “Teoria di Santi” measured at the following points: (**a**) red pigment; (**b**) white pigment; (**c**) yellow pigment; (**d**) brown pigment; (**e**) green pigment; (**f**) black pigment. The bottom right of each spectrum shows the image of the measurement point: red circles indicate the exact point of measurement. The overlayed spectra (blue, orange, and magenta) are referred to the different voltages (8, 15, and 45 kV) used for the detection of light and heavy elements. The green strokes exhibit the same iron signature but with detectable Si, K, Mg, and Al. The presence of low amounts of silicon and potassium and traces of magnesium and aluminum (see the magnification in Figure 6e) can be attributed to the use of Green Earth (silico-aluminates of Fe\({}^{2+}\)/Fe\({}^{3+}\)). The black pigment, on the other hand, is the only organic pigment detected. Calcium, sulfur, and traces of strontium are the only elements detected; as carbon itself is invisible to XRF, this indicates the possible presence of charcoal black.
## 6 Conclusions  
1. A dynamic characteristics test was conducted on a 1/5 scale and 10-storey concrete-encased steel frame-reinforced concrete core tube building model. The natural frequency was measured by the high precision USB (Universal Serial Bus) acquisition instrument, and higher order frequencies through modal analysis were obtained. As a result, first five order frequencies were obtained according to experimental data.
2. Modal analysis was carried out using a nonlinear finite element program based on the fiber model. The simulation results agreed well with those of experiments. The damage process under El-Centro wave was analyzed. Compared with the intact state, the first two frequencies of the ultimate state had an obvious degradation of about 56.16%, and the higher frequencies had an obvious degradation of about 21.87%. The max story drift angle increased from 0.819% to 4.062%, and the proportion of the total shear in the core tube increased from 53.37% to 77.36%.
3. The max story drift angle was chosen to reflect the damage state and to quantify the damage degree on five levels. A criterion of seismic damage assessment was proposed with the consideration of higher-order vibration modes and its validity was confirmed by finite element analysis. The influence of earthquake intensity and structural stiffness ratio on dynamic characteristics were discussed. This work is hoped to provide references for structural health monitoring and quantifying the degree of post-earthquake restoration.
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of Charge Rates on Battery Performance
#### 3.4.2 Effects of Charge Rates on Battery Power  
In this study, the effects of different charge rates on the change rate of battery charge-discharge power were tested with different SOC values--80%, 50%, and 20%. The test results are shown in Figures 16-18.  
The test results produced by the battery modules from manufacturer A show that the 0.5-C charge rate presented itself as the best approach to maintain the power of Ni-MH battery modules with 80% SOC, followed closely by the 1-C rate. The other test results show that the 1-C charge rate produced the lowest (absolute value) power change rate. In general, we may consider that the 1-C charge rate is more conducive to maintaining the power of Ni-MH battery modules.
## 4 Results and Discussion
### Muting of All Vehicles Except Target  
A muted model in which only the target vehicle is considered in the prediction was used as a relative comparison to assess whether information from additional vehicles in the network will improve the prediction accuracy of the model. To test this, vehicles were muted during attention application. After the calculation of the weighted values, all values that were not from the target vehicle were multiplied by 0. This removes information about the network state and, as a hypothesis, should perform with lower accuracy compared to the model, which can leverage network information. The results for the eastbound direction showed that on a small subset of the dataset, the addition of network-based information improved the performance of the model, and a model without network information is inferior to the one with the information from the entire bus network (Figure 8) thus confirming the hypothesis. The results are not as clear cut for the westbound direction, where the network-based model outperforms the muted version in ETA prediction, but there is no clear competitive advantage in the trajectory prediction. This can be explained by the fact that the generalisation error remains negative in the training process, suggesting that the model is currently underfitting. This is intuitive if the data contribution of the bus lines shown in Figure 9 is considered, where the proportion of data contributed by target vehicles is greater than in the eastbound direction, which could indicate that longer training times are needed.
## 3 Establishment of the Scale Factor Model
### Time Alignment Simulation of \(\beta\)  
Based on the Gauss curve obtained from the simulation, a velocity of 1.5 m/s was set. Using this velocity value, we calculated the corresponding \(\beta\) and plotted the orthogonal basis energy curves. Figure 15 displays the new orthogonal basis energy curve and the traditional orthogonal basis curve.  
In Figure 15, \(\Delta t_{1}\) and \(\Delta t_{2}\) represent the time difference of the peak of orthogonal basis energy achieved by the adaptive scale factor \(\beta\) and the conventional OBF method, respectively. Research indicates that the meticulous selection of the adaptive scale factor \(\beta\) markedly enhances the time alignment between the peak of orthogonal basis energy and the theoretical result, leading to a more precise time synchronization. The data of orthogonal basis energies are shown in Table 2.  
Based on the bias analysis of the above data, the following conclusions were drawn:  
1. The peak value that can be achieved with OBF detection under the traditional approach is 885 nT, while the peak value of the adaptive scale factor \(\beta\)-OBF detection is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Emax/nT** & **Distance/m** & **V m/s** & \(\Delta T\) \\ \hline OBF & 885 & 0.24 & 1.5 & 0.164 \\ \hline \(\beta\)-OBF & 1233 & 0.11 & 1.5 & 0.073 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Orthogonal basis energy data.  
Figure 14: Fitting Gauss curves of scale factor and velocity matching under different methods.  
Figure 15: Orthogonal basis energy values.  
1233 nT. In comparison, the adaptive scale factor approach improves the peak value by 39.3%.
2. In the OBF detection method under the traditional approach, the deviation \(\Delta t_{2}\) at the peak of the near CPA is 0.164, while the deviation \(\Delta t_{1}\) of the scale factor \(\beta\)-OBF at the peak of the near CPA is 0.073, which is improved by 55.4% compared with that of the traditional approach.  
The comprehensive simulation results show that the proposed method using Gaussian curve fitting can accurately determine the optimal scale factor \(\beta\) at a specific speed, thus maximizing the energy peak in the detection of magnetic anomaly signals. Additionally, this method improves the detection probability of magnetic anomaly signals of the acquisition device under an unknown motion state. The method significantly optimizes the matching of the moment of passing CPA at a simulated speed with that of the theoretical moment.