esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Maturity  
In this study, a variety of methods were used to evaluate the thermal maturity of the source rock, and a detailed maturity evaluation was carried out on the Hengtongshan Formation. According to the reflectance (% Ro) test data of 21 samples obtained from eachsource rock horizon, the maturity of the samples is between 0.97 and 1.53 (average 1.10, Table 2), which indicates that the samples have entered the oil generation window and kerogen has begun to produce hydrocarbon compounds [18; 39]. Based on the T\({}_{\text{max}}\) values from a large number of samples, it was found that the T\({}_{\text{max}}\) values, which are indicative of thermal maturity, are highly correlated with Ro (Figure 12). The sections between 233 and 238 m and between 287 and 294 m are of high maturity, which indicates that, compared with other sections, these sections may have suffered more intense thermal action because the kerogen has a high maturity. Combined with the geological background of the Tonghua Basin, volcanic activity may have caused these sections to have especially high thermal maturities; however, in the depositional stage of the Hengtongshan Formation, there was no large-scale and long-term strong volcanic activity [7; 8]. Thus, local volcanic activity only caused a higher thermal effect on some strata.  
The biomarkers that can reflect the maturity of the organic matter in source rock extracts and shale oil, such as CPI, Ts/(Ts + Tm), C\({}_{30}\)M/C\({}_{30}\)H, C\({}_{32}\) 17\(\alpha\)(H)21(H)-hopanes, etc., were studied. These biomarker parameters all show that the source rocks in the Hengtongshan Formation have a high thermal maturity. The correlation parameters of biomarkers in the shale oil and shale extracts are similar, and the vertical variation throughout the entire formation is not significant, indicating that hydrocarbons have been discharged from the source rock. Figures 13 and 14 also confirm that the organic matter has reached the mature and highly mature levels and the source rock has crossed the hydrocarbon generation threshold.  
Kerogen can generate hydrocarbons as the thermal maturity increases, and the generated hydrocarbon gas and liquid hydrocarbons can have different molecular weights. Furthermore, hydrocarbon macromolecules can be heated further in the process of pyrolysis to produce solid asphalt and a large amount of pyrolysis gas [18]. The natural gas in the Hengtongshan Formation is mainly composed of methane and a small amount of ethane and propane. The methane content accounts for 90.06-98.53%, with an average of 94.36%. The gas is not dry gas. According to the interaction diagram of InC\({}_{1}\)/C\({}_{2}\) and InC\({}_{2}\)/C\({}_{3}\), the gas may be a mixture of kerogen degradation gas and oil cracking gas, and most of the gas was produced when the Ro exceeded 1.5% (Figure 15).
## 3 Results  
### 4 HR Increased Mitochondrial Stress  
To clarify the effect of 4 HR administration on mitochondrial stress, ATP level and MMP were measured. Mitochondria are the main cellular power factories for the production of ATP, a cellular energy source. Oxygen consumption decreased after 4 HR administration (Supplementary Figure S1), dropping to 94.7% of that of the untreated control when HUVECs were treated with 100 uM 4 HR (\(p<0.001\)). Thereafter, the production of ATP was decreased after 4 HR administration in HUVECs (Figure 1a). MMP increased by 20% after 100 uM 4 HR administration (Figure 1b). Increased MMP following cellular stress can protect cells from apoptosis [26].  
Mitochondrial stress would decrease the cytochrome c level in mitochondria. The administration of 4 HR decreased cytochrome c levels at 100 uM but not at other concentrations in the mitochondrial fraction (Figure 1c). The level of PDH-E1\(\alpha\) was also decreased following 100 uM 4 HR administration, which might have caused the decreased ATP level and oxygen consumption (Figure 1c and Supplementary Figure S2). The administration of 100 uM 4 HR significantly decreased the cytochrome c level of mitochondria when compared to the 1 and 10 uM 4 HR administration groups (\(p=0.029\) and 0.005, respectively, Figure 1d). Decreased cytochrome c following 100 uM 4 HR administration was not detected in the cytoplasmic fraction. This may be due to a negligible amount of cytochrome c leakage. Interestingly, there was no significant difference in cytochrome c level  
Figure 1: ATP and mitochondrial membrane potential (MMP) assay. (**a**) Administration of 4 HR slightly decreased the ATP levels in HUVECs (* \(p<0.05\) compared to the control group; RLU, relative light units). (**b**) MMP was slightly increased by 4 HR administration in HUVECs. In the case of 10 and 100 uM 4 HR administration, MMP was increased by approximately 20 % compared to the untreated control (* \(p<0.05\)). (**c**) Administration of 4 HR (100 uM) decreased the cytochrome c level in the mitochondrial fraction in HUVECs. However, cytoplasmic leakage of mitochondrial cytochrome c was not clearly detected in the cytoplasmic fraction. (**d**) Cytochrome c level relative to mitochondrial \(\beta\)-actin was calculated and compared. There was no significant difference in cytochrome c level of the mitochondria among the untreated control and the 1 and 10 uM 4 HR administration groups (\(p>0.05\)). In the case of 100 uM 4 HR administration, the cytochrome c level of the mitochondria was decreased significantly compared to the untreated control and the 10 uM 4 HR administration group (* \(p<0.05\)).  
c level between the 100 uM 4 HR administration group and the untreated control group (\(p>0.05\)). This finding was confirmed through subsequent confocal microscopy observations (Supplementary Figure S3).
## 3 Results
### Conceptual Synthesis: How AI Interventions Reinforce the Three Pillars of Regional Sustainability  
This scoping review reveals a coherent pattern: regardless of the domain, AI operates as a data-driven feedback loop that (i) senses the state of the system, (ii) learns causal relations, and (iii) delivers prescriptions that translate into environmental, economic, and social co-benefits. Table 2 maps every empirical study from Section 3.1 to Section 3.9 to the sustainability pillars.  
\begin{table}
**AI Domain/Section Techniques and Data** & **Representative AI Techniques and Data** & **Environmental Impact** & **Economic Impact** & **Social Impact** \\ \hline Ecological monitoring and land use (3.1) & Remote sensing (Landsat, Sentinel, GEE); K-ELM, Random Forest, XGBoost, SHAP & Early detection of wetland and vegetation degradation; soil-nutrient and land-cover maps for conservation & Optimized zoning and infrastructure siting; lower restoration costs & \
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline Sustainable agriculture and agro-ecosystems (3.2) & ML Stacking, Cubist, IoT sensors, U-Net DL, agent-based crop models & Reduced fertilizer, water and pesticide loads; healthier soils & Higher and more stable yields; input–cost savings; precision-agriculture markets & Enhanced food security; stronger rural livelihoods; less agro-chemical exposure \\ \hline Water and coastal ecosystems (3.3) & LSTM, object-based ML on SAR + optical, XAI, Random Forest & Improved water quality; mangrove and wetland protection; eutrophication control & Efficient irrigation and aquaculture sitting; lower treatment costs; sustained fisheries & Flood-risk reduction; safer drinking water; coastal livelihood security \\ \hline Forest ecosystems (3.4) & Fire-risk ensembles (XGBoost, GBM), Vision-Transformer 3D & Fire prevention; carbon-sink conservation; deforestation monitoring & Verified carbon credits; sustainable timber planning & Health protection from smoke; recreation and cultural services for planning & \\ \hline Carbon monitoring and climate resilience (3.5) & TanSat-2 GHG data, Random Forest, CatBoost GTP, CMIP6 + MaxEnt & High-resolution emission inventories; climate-risk hotspot mapping & Lower compliance/offset costs; climate-smart investment guidance & Reduced losses from extremes; policy transparency; cleaner air \\ \hline Ecosystem services and natural capital (3.6) & XGBoost-SHAP, GWANN, InVEST, Rotation Forest & Preservation of regulating services (water, climate, habitat) &
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \end{tabular}
\end{table}
Table 2: Qualitative mapping of AI interventions in regional ecosystems to the three pillars of sustainability.  
Cross-cutting enablers: Across all clusters, success hinges on open data infrastructures, interpretable AI, and local capacity building. These factors articulate why AI, when responsibly governed, can act as a unifying lever for the three sustainability pillars in regional ecosystems.
## 5 Discussion  
In this section, we discuss the results of the numerical outcomes of our model. We discuss the effect of some of the network parameters considered throughout this study on the exposure, and we  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Variable** & **Total Sobol Indices** \\ \hline \(\alpha\) & 0.447 \\ \(\eta\) & 0.932 \\ \(\lambda\) & 0.365 \\ \(P\) & 0.254 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Total Sobol indices of the inputs contributing to the 90 th percentile of the exposure in the network.  
Figure 8: Comparison between the current model developed in this paper, its verification using Monte-Carlo simulations assuming the gain distributions from Section 2.2, and the old model from [25], versus a Monte-Carlo simulation of the exposure using the gain simulated by NYUSIM.  
discuss the result of the sensitivity analysis considering the importance of each parameter on the global exposure.  
From the results presented in Figure 5, we can show the accuracy of the developed model in estimating the total exposure in the cell. The result shows a good overlap between the analytical framework and the numerical validation. Here, the blue solid line is obtained by considering the fitted distribution of the channel and antenna gain, e.g., Equations (10) and (11). The purpose of Figure 5 is to verify the correctness of the analytical framework. It should be noted that the execution time in obtaining results from the analytical framework is much quicker than the numerical simulations. The sensitivity analysis shows the big effect the path loss exponent has in comparison to the other variables. In terms of exposure, this effect is desirable especially knowing that the path loss exponent is relatively high in mmWave channels leading to lower power in the cell. Although, since cellular networks are usually designed to maintain a sufficient SNR, it would give clearer insight analyzing the exposure in relation with the SNR. On the other hand, our study sheds the light on a usually ignored, but increasingly discussed, aspect of cellular network design and analysis which is the total electromagnetic wave power present in the cell area.  
As previously mentioned in Section 1.1, the 5G NR architecture make it difficult to accurately measure the exposure because of its beam behavior. In terms of in-situ measurements of the exposure, they are being conducted without consideration of the variability in the system utilization in order to have constant beam behavior. This assumption will ignore the effect the system usage will have on the actual exposure in the cell. As we can see from our analysis, the transmission probability, which is directly related to the system utilization, it is the second-most important variable affecting the exposure since it accounts for 0.44 of the total exposure's variance, and simply measuring at constant full transmission may lead to major overestimation of the exposure.
# Near-Nyquist-Limit Optical Communication and Ranging Method Based on Waveform Matched PPM
## 4 Simulation Results
### Bit Error Rate  
The proposed method was compared with the traditional method shown in (12) via simulation. We employed RS(255,223) code as the forward error correction approach. This code is suggested by the Consultative Committee for Space Data Systems (CCSDS). A sampling rate of \(F_{s}=2.5\,\mathrm{Gsps}\), as shown in Figure 7, was obtained. Compared with the other two scenarios, about 1.3 dB loss is inevitable for \(R_{s}=1.24416\,\mathrm{Gslot}/\mathrm{s}\) when the bit error rate is \(10^{-6}\) by (12). The reason for this is that timing errors have a greater impact on the lower oversampling ratio scenario. It can be seen that the BER performances of the traditional method and the proposed method are basically the same far from the Nyquist sampling limit, but a gain of around 1.1 dB is achieved near the limit for the proposed method. The proposed algorithm is more advantageous than the traditional algorithm when it is near the Nyquist sampling limit. The results with a sampling rate of \(F_{s}=625\,\mathrm{Msps}\) are similar to that of \(F_{s}=2.5\,\mathrm{Gsps}\).  
Figure 6: Ranging accuracy (\(\sigma_{R}\)) simulation results.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 3 Proposed Target Speech Enhancement Method  
The diagram of the proposed speech enhancement method (termed as AVS-SMASK) is shown in Figure 4, which is processed in the time-frequency domain. The details of each block in Figure 4 will be addressed in the following context.  
Figure 4: Block diagram of our proposed AVS-SMASK algorithm (STFT: Short-Time Fourier Transform; FBF: a fixed beamformer; ISTFT: inverse STFT; y(n): enhanced target speech).
## 5 Conclusions  
This paper describes the BLE protocol stack, provides a performance evaluation of this technology and explores its potential applications. In BLE, there exists a trade-off between energy consumption, latency, piconet size, and throughput that mainly depends on the _connInterval_ and _connSlaveLatency_ parameters. Evaluation results show how these parameters can be tuned wisely in order to meet application requirements. On the other hand, the paper points out several implementation constraints that may reduce BLE performance in a real scenario, in comparison with the theoretically expected one. BLE emerges as a strong low-power wireless technology for single-hop communication use cases which may contribute to connecting a dramatically large amount of new devices to the Internet of Things.
# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
### Performance of RTKLIB and GSDC Participants  
Coupled with commercial receivers such as u-blox, RTKLIB has been able to provide centimeter level of accuracy [13]. However, as RTKLIB was originally designed for commercial GNSS receivers with survey type measurements, multiple adaptations are required for its use in GSDC, where only the GNSS measurements from smartphones are available. The smartphones used in GSDC (Google Pixel4 variants, Samsung S series variants, and Xiaomi Mi8) have several differences from commercial GNSS receivers, particularly with respect to antenna design/performance [14]. Consequently, the quality of the measurements is worse, leading to greater signal noise, increased number of carrier phase cycle slips, and increased multipath. This issue is illustrated by [PERSON] [15], when raw GNSS measurements from a Xiaomi Mi8 smartphone and a geodetic receiver collected at the same time were processed using RTKLIB. When GPS L1 measurements from both devices were processed using the same kinematic PPP mode, the geodetic receiver had horizontal position errors of approximately 1 m, while the smartphone's errors were approximately 3-5 m, due to fewer observed satellites and lower carrier to noise ratio density (C/N\({}_{0}\)) for the tracked satellites.  
In the 2021 GSDC, the 1 st-place winner used factor graph optimization, coupled with [PERSON]'s version of the RTKLIB, to obtain a score of 1.62 m [11]. Other teams used Google's baseline Android navigation engine [16] or their own proprietary navigation engines [17]. In the Results section of the paper, the performance of the updated RTKLIB against other navigation engines will be provided using the official Kaggle scoring system. The comparison candidates are Google's baseline solution and the scores of other participants. The goal will be to assess where the performance of the suggested RTKLIB adaptation stands among other available navigation engines used in the competition.
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 2 Background
### Problem Formulation  
The basic GM-PHD filter described in Section 2.1 does not adapt to the radar dynamic scanning model and trajectory estimation, especially for detecting weak targets. The method we proposed earlier in Sections 2.2 and 2.3 solves the problem of explicit tracking maintenance and is able to solve the problem of misdetection, false state estimation and adverse interference caused by close targets.  
In radar systems, the antenna beam width is limited, and it takes a long time to scan the entire airspace, which causes the targets at different locations to be detected at different times during a scan, this leads to a variety of sampling times. However, in a basic PHD filter, targets are considered to be sampled at the same time, and the measurements generated by different targets are used to update the prediction prior to the same moment, which leads to a mismatch between the measurement time and the prediction time.  
As shown in Figure 2, the \(k\)th scan of a radar is clockwise, as indicated by \(\theta_{1}\), and the \(k+1\)th scan is in the counterclockwise direction, indicated by \(\theta_{2}\).  
It can be seen that the sampling times corresponding to the measurements of \(z^{\prime}_{k}\) and \(z^{\prime}_{k+1}\) during the \(k\)th and \(k+1\)th scans are \(t^{\prime}_{k}\) and \(t^{\prime}_{k+1}\) instead of \(t_{k}\) and \(t_{k+1}\).  
If time \(t_{k}\) and \(t_{k+1}\) are used in the prediction and update step, the clutter near positions \(x_{k}\) and \(x_{k+1}\) will obtain larger weights and is more likely to be considered as a target measurement, while the actual target measurements \(x^{\prime}_{k}\) and \(x^{\prime}_{k+1}\) will be considered as false alarms, resulting in additional estimation errors and affecting the accuracy of target state estimation and trajectory generation.  
Figure 1: This is the probability density of a filter output for a representative signal-to-noise ratio. The area under the curve to the right of \(U_{T}\) is the probability of detection, \(P_{D}\). Note that while increasing \(U_{T}\) decreases \(P_{fa}\), it also decreases \(P_{D}\).  
In order to solve the above problem and implement a PHD filter-based radar multi-target tracker, a sector-matching PHD filter framework is designed in this paper, which involves the measurement time as part of the multi-target state information to participate in prediction, update and state extraction operations.
## 5 TSCH Link Scheduling Visualization and Data Processing
### The Design of TLS-VaD  
#### 5.1.1 System Design  
TLS-VaD is an open platform for anyone who wants to observe the performance of a scheduling algorithm. To observe or examine an algorithm, all users of TLS-VaD must have an executable file of the algorithm to be observed. The use case diagram for this tool can be seen in Figure 26, which shows that the application contains two main feature: simulation and experiment. The simulation feature will visualize the process of transmitting and receiving the data on a network that is controlled by a scheduling algorithm. Whereas with the experiment feature, besides being able to see animations, users can also obtain data that are related to the performance of the selected scheduling algorithm.  
The activities that are carried out in the simulation feature can be seen in Figure 27, which shows that to obtain the desired results, user must first enter the network topology. Network input can be done by either drawing node by node or by inputting the adjacency matrix of the network. After accomplishing the input process, the next activity is applying the scheduling algorithm and the running animation procedure to the network. As for the experiment feature, Figure 28 shows that the activities that must be carried out are almost the same as those for the simulation feature. The difference lies in that the experiment feature also includes a process for inputting the network topology iteratively as well as a process for displaying experimental data.  
Figure 26: Use case diagram.  
Figure 27: The activity diagram of the simulation feature.  
As stated in the previous section, to test a scheduling algorithm using TLS-VaD, researchers must attach the executable file of the scheduling algorithm to be observed. The processes of developing the algorithm and the programming language used are left to each researcher. For success with TLS-VaD, researchers must abide by the following rules: (1) the attached executable file must be able to accept input in the form of an adjacency matrix that represent the network topology (see Figure 29). The digit 1 in the matrix shows a connection between a pair of nodes where its node number corresponds to the row and column numbers of the digit. (2) The attached executable file must be able to produce scheduling matrices that represent the activities of transmitting and receiving data at nodes in the network. The number of matrices shows the number of cycles needed to send all data to the master node (see Figure 30 for more details).
## 4 Discussion  
The detection of surface defects on automotive engine parts is very important for automobile production. The traditional manual detection methods have problems of low accuracy and being inefficient. Deep learning methods have been partially applied in the field of defect detection. However, the existing methods have an inadequate consideration of minor defects. To solve this problem, some researchers proposed a more complex network to improve the adaptability to minor defects. Although the accuracy has been higher, the efficiency has decreased. This paper does not modify the network, but focuses on the sample distribution, especially the analysis of size and ratio. On this basis, this paper improves the anchor design in Mask R-CNN. Meanwhile, this improvement has no additional cost in training and testing. We made a statistical analysis on the dataset and design the corresponding anchors. The effectiveness of the improved anchor design is verified by experiments. Moreover, the proposed method can be easily combined with networks with an anchor design.