esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 2 Air Quality Standards
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Pollutant** & **EPA**[37] & **WHO**[38, 39, 40] & **EC**[41] & **MEP**[42] & **EPD**[43] \\ \hline \multirow{3}{*}{Carbon Monoxide (CO)} & \multirow{3}{*}{9 ppm (8 h)} & \multicolumn{3}{c}{100 mg/m\({}^{3}\) (15 min)} \\  & & 15 mg/m\({}^{3}\) (1 h) & \multirow{3}{*}{10 mg/m\({}^{3}\) (8 h)} & \multirow{3}{*}{10 mg/m\({}^{3}\) (8 h)} & \multirow{3}{*}{30 mg/m\({}^{3}\) (1 h)} \\  & 35 ppm (1 h) & 10 mg/m\({}^{3}\) (8 h) & & & 4 mg/m\({}^{3}\) (24 h) \\  & & 7 mg/m\({}^{3}\) (24 h) & & & \\ \hline \multirow{3}{*}{Nitrogen Dioxide (NO\({}_{2}\))} & \multirow{3}{*}{100 ppb (1 h)} & \multirow{3}{*}{200 ug/m\({}^{3}\) (1 h)} & \multirow{3}{*}{200 ug/m\({}^{3}\) (1 h)} & \multirow{3}{*}{200 ug/m\({}^{3}\) (24 h)} & \multirow{3}{*}{200 ug/m\({}^{3}\) (1 h)} \\  & 53 ppb (1 year) & 40 ug/m\({}^{3}\) (1 year) & & & 40 ug/m\({}^{3}\) (1 year) \\ \hline Ozone (O\({}_{3}\)) & 75 ppb (8 h) & 100 ug/m\({}^{3}\) (8 h) & \multirow{3}{*}{120 ug/m\({}^{3}\) (8 h)} & \multirow{3}{*}{200 ug/m\({}^{3}\) (1 h)} & \multirow{3}{*}{160 ug/m\({}^{3}\) (8 h)} \\  & & & & & \\ \hline \multirow{3}{*}{Sulfur Dioxide (SO\({}_{2}\))} & \multirow{3}{*}{75 ppb (1 h)} & \multirow{3}{*}{500 ug/m\({}^{3}\) (10 min)} & \multirow{3}{*}{350 ug/m\({}^{3}\) (1 h)} & \multirow{3}{*}{500 ug/m\({}^{3}\) (24 h)} & \multirow{3}{*}{500 ug/m\({}^{3}\) (10 min)} \\  & 0.5 ppm (3 h) & 20 ug/m\({}^{3}\) (24 h) & & & 150 ug/m\({}^{3}\) (24 h)} \\  & & & & & 60 ug/m\({}^{3}\) (1 year) \\ \hline \multirow{3}{*}{Particulate Matter} & PM\({}_{2.5}\) & 35 ug/m\({}^{3}\) (24 h) & \multirow{3}{*}{25 ug/m\({}^{3}\) (1 year)} & \multirow{3}{*}{25 ug/m\({}^{3}\) (1 year)} & \multirow{3}{*}{75 ug/m\({}^{3}\) (24 h)} \\  & 12 ug/m\({}^{3}\) (1 year) & 10 ug/m\({}^{3}\) (1 year) & & & 35 ug/m\({}^{3}\) (1 year) \\ \cline{1-1} \cline{2-7}  & PM\({}_{10}\) & 150 ug/m\({}^{3}\) (24 h) & 50 ug/m\({}^{3}\) (24 h) & 50 ug/m\({}^{3}\) (24 h) & 150 ug/m\({}^{3}\) (24 h) \\  & & 20 ug/m\({}^{3}\) (1 year) & 40 ug/m\({}^{3}\) (1 year) & 70 ug/m\({}^{3}\) (1 year) & 50 ug/m\({}^{3}\) (1 year) \\ \hline \multirow{3}{*}{Lead (Pb)} & \multirow{3}{*}{0.15 ug/m\({}^{3}\) (3 month)} & \multirow{3}{*}{0.5 ug/m\({}^{3}\) (1 year)} & \multirow{3}{*}{0.5 ug/m\({}^{3}\) (1 year)} & \multirow{3}{*}{1 ug/m\({}^{3}\) (3 month)} \\  & & & & & 0.5 ug/m\({}^{3}\) (1 year) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different standards of the six common pollutants.
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 2 Materials and Methods
### Other Regression Methods
#### 2.4.2 Partial Least Square  
Partial Least Square (PLS) is a multivariate statistical method that analyses the relationship between variables to find a subspace of latent variables that synthesies the predicted or independent variables (\(X\)), with the aim of understanding the dispersion of the dependent or observed variables (\(Y\)) in a linear way [18]. The most important parameter of the PLS model is the number of components. The rank of \(X\) is the maximum number of components, but usually a lower number is used to avoid the description of the noise present in the data and to prevent the problem of collinearity (having a zero determinant for the matrix \(X\)). To determine the number of components in our experiment, we will use the method based on calculating the knee of the MSE curve when using cross-validation. The knee of the curve is defined as the point where the curve is best approximated by a pair of lines [19]. This method provides a consistent and mathematically justifiable answer when there is no obvious location of the inflexion point along the curve. In our experiment, the number of components usually turns out to be 2.
## 5 Conclusions  
Vegetation phenology has entered a stage of merging related disciplines and applying new approaches due to the growth of phenological observation techniques. We extracted vegetation phenology parameters from SIF and MODIS NDVI data in Northeastern China and compared spatial patterns and climatic response mechanisms to test the viability of SIF data in phenology estimation. The results showed that the greening stage of SIF started later (about 10 days), the yellowing stage ended earlier (about 36 days), and the growth peak of SIF was slightly earlier than NDVI. Although there are some differences in phenological dates between SIF and NDVI, their spatial distribution and future trend have some similarities. In addition, the vegetation phenology estimated by SIF was mainly controlled by temperature on the whole, while NDVI was mainly controlled by precipitation and relative humidity. Different phenological periods based on SIF and NDVI showed inconsistent responses to pre-season climate. SIF, as a novel remote sensing method, compensates for the shortcomings of classic vegetation indices in phenology extraction. Furthermore, this work may give scholarly references and proposals of major importance for the advancement of phenology and the understanding of ecological responses to global climate change.  
Conceptualization, R.Z., Y.Z. and T.H.; formal analysis, R.Z., Y.Z. and T.H.; funding acquisition, Y.Z. and T.H.; methodology, R.Z., Y.Z. and T.H.; data curation, W.S., S.Z., J.W. and H.W.; writing--original draft preparation, Y.Z. and T.H.; writing--review and editing, R.Z., Y.Z. and T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program (Grant No. 2021 xjkk0303).  
Not applicable.  
The authors declare no conflict of interest.
## 8 Impact and Shock Measurements on Critical Structures
The quantification of vibration, therefore, is required to be discussed in the assessment report. The research developed in [143] is a follow-up of the pioneering works in the U.S. Navy ship DDG-53, which is deployed in ship shock trials; in this work, measured data were later processed to find other vital coefficients, i.e., total damped ship vibration. The logarithmic decay rate was used to find the coefficient by fitting the peak strain values. After that, empirical mode decomposition (EMD) was used to decompose the measured data \(X(t)\) as a function of time \(t\) into intrinsic mode functions (IMFs). In this solution, the high frequency of the IMFs represents noise modes that are to be eliminated to acquire data \(\widetilde{\mathrm{y}}(t)\) without noise modes. Finally, the least-squares complex exponential (LSCE) method was employed to extract modal parameters effectively. Towards the end of the 2010s, intense underwater explosion was taken as an experimental reference. In [144], explosion was combined with the concept of material structure improvement to enhance structural performance against torpedos and mines. A polyurea coating was applied on ship hull steel to improve its underwater shock resistance, and the specimens were installed in an experimental rig. A Photron-Fastcam-SA1 high-speed camera was placed to measure the speed of the flyer plate before hitting the piston. In terms of pressure measurement, a piezoelectric pressure sensor produced by PCB Company was applied in the test rig. Later, the measured signal was acquired by a high-speed data acquisition (DAQ) instrument built by the German HBS. In the final stage, the digital imaging correlation (DIC) method was used to record the real-time displacement strain during the high-speed dynamic deformation and present the deformation process and strain field. This work offers a more complete experimental rig and setting (using piezoelectric pressure sensors), including sensors and measurement instruments, compared to the previously reviewed work in earlier years. In 2020, trials of underwater shock loading on cylindrical shells were conducted by [145]. The experimental subject in this work was very specific and quite different to that in other work, which mostly uses general plate geometry to represent thin-walled structures (except work that considers structural development). The test specimen in this work was set as a representative of the submarine pressure hull, submarine cable, torpedo, and tower structures for wind power generation. Furthermore, a different sensor type, namely, a fiber Bragg grating (FBG) sensor calibrated by a four-point bending test, was employed in this work to observe the strain of underwater structures. Other vital findings of the respective reviewed research studies are summarized in Table 11.
## 4 Our Solution: DeepChaos+
### Overview Process of DeepChaos+  
Let us define \(\mathcal{E}(\hat{\mathcal{X}})\) as the set of incorrect predictions (error bits) made by the model \(\mathcal{F}_{\theta}\) given the true label \(\mathcal{X}\) (transmitted bits). In our system, we employ the bit error rate (BER) metric, similar to the approach proposed by [PERSON] et al. (2019), defined as follows:  
\[\mathcal{B}(\hat{\mathcal{X}},\mathcal{X})=\frac{|\mathcal{E}(\hat{\mathcal{X }})|}{|\mathcal{X}|}. \tag{6}\]  
Generating a large dataset \(\mathcal{X}\) to train the model \(\mathcal{F}_{\theta}\) is time-consuming, and balancing the training \(\mathcal{X}^{train}\in\mathcal{X}\) and testing sets \(\mathcal{X}^{test}\in\mathcal{X}\backslash\mathcal{X}^{train}\) is challenging. Too much training data may cause overfitting, while too little can lead to underfitting, both increasing the BER \(\mathcal{B}(\hat{\mathcal{X}},\mathcal{X})\).  
DeepChaos+ (as depicted in Figure 2) combines a Variational Autoencoder (VAE) \(\mathcal{F}\theta\) and a lightweight Informer Network \(f^{student}\omega\) to optimize communication system performance while using limited original signal data \(\mathcal{X}^{train}\). The VAE is first trained on \(\mathcal{X}^{train}\) to generate synthetic data \(\mathcal{X}^{g}\), which is combined with \(\mathcal{X}^{train}\) to form an augmented dataset \(\mathcal{D}\). This process continues iteratively, refining the VAE until it can minimize the BER on \(\mathcal{X}^{test}\). Optionally, chaos can be added to \(\mathcal{X}^{g}\) to better represent original signal characteristics.  
In parallel, the Informer Network \(f^{student}\omega\) is trained using the augmented dataset \(\mathcal{D}\), with fewer parameters than the VAE's decoder but with similar functionality. Knowledge Distillation is used to transfer knowledge from the VAE to \(f^{student}\omega\) by training it to mimic the output of the VAE's decoder, enabling it to achieve comparable performance while enabling faster inference.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components
#### 4.1.20. KISTOR Was Identified as a DEPDC5 Binding Protein Using Endogenous Flag-DEPDC5-IPs  
In an effort to search for GATOR1 binding proteins, [PERSON] et al. used CRISPR to knock in a Flag tag in front of the _DEPDC5_ gene, an essential GATOR1 complex component and performed Flag-immunoprecipitations coupled with mass spectrometry analyses in HEK293T cells [62]. Four proteins including KPTN, ITFG2, C12 orf66, and SZT2 were determined as new DEPDC5 binding partners, and this protein complex was named as KISTOR. Further biochemical and mouse genetic experiments demonstrated a role for KISTOR in binding and recruiting GATOR1 to lysosome for mTOR1 suppression under nutrient-limited conditions [62].
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 4 Results and Discussion
### Results and Discussion
The F1-score values were lower when only a single prediction or reconstruction task was reserved, which indicates that the MTAD method is effective for anomaly detection because of the joint training of all proxy tasks. The detailed ablation study results are summarized in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Contamination = 0.005**} & \multicolumn{3}{c}{**Contamination = 0.002**} \\ \cline{2-7}  & **P** & **R** & **F1-Score** & **P** & **R** & **F1-Score** \\ \hline MTAD & 0.9966 & 1.0 & 0.9980 & 0.9957 & 0.9997 & 0.9977 \\ T1 (LSTM) & 0.9331 & 1.0 & 0.9653 & 0.8082 & 1.0 & 0.8939 \\ T2 (TCN-AE) & 0.9629 & 1.0 & 0.9811 & 0.7854 & 0.9564 & 0.8798 \\ T3 (LSTM-VAE) & 0.9357 & 1.0 & 0.9668 & 0.8344 & 1.0 & 0.9097 \\ T1 + T2 & 0.9948 & 0.9990 & 0.9969 & 0.9960 & 0.9927 & 0.9944 \\ T1 + T2 + T3 & 0.9959 & 0.9995 & 0.9977 & 0.9973 & 0.9970 & 0.9972 \\ T2 + T3 + T4 & 0.9953 & 0.9990 & 0.9971 & 0.9967 & 0.9970 & 0.9969 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Contamination = 0.005**} & \multicolumn{3}{c}{**Contamination = 0.002**} \\ \cline{2-7}  & **P** & **R** & **F1-Score** & **P** & **R** & **F1-Score** \\ \hline MTAD & 0.9966 & 1.0 & 0.9980 & 0.9957 & 0.9997 & 0.9977 \\ LSTM-AE & 0.7350 & 0.9998 & 0.8472 & 0.9223 & 0.9998 & 0.8043 \\ iForest & 0.9949 & 0.5315 & 0.6928 & 0.4139 & 0.9994 & 0.5854 \\ BP-VAE & 0.1596 & 1.0 & 0.2753 & 0.1596 & 1.0 & 0.2753 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection performance of alternative methods and the proposed MTAD method. Figure 5: Detection results of the proposed MTAD method on test data set. “High” and “low” values indicate normal and anomalous results, respectively. Figure 6: (**a,d**) Task 1 and 4 prediction errors, respectively, and (**b,c**) Task 2 and 3 reconstruction errors, respectively, on training (blue) and test (red) data. Figure 7: F1-scores for anomaly detection obtained by adding one proxy task at a time: (**a–c**) Ablation test results based on Tasks 1–3, respectively.
## 2 Mechanism Description
### Packet Processing Procedure  
Upon the arrival of a packet at a PE node, it undergoes processing along a pipeline, presenting two potential scenarios:  
1. If a packet matches an existing flow in the DFT, it is processed based on the actions specified for that entry. The packet is then tagged with an indicated MPLS label and forwarded to the specified output interface.
2. If a packet does not match the DFT, it is redirected to the CFT. This table contains entries with a destination network and associated actions. A predefined MPLS label is pushed, and the packet is directed to a specified output interface. Upon finding a match, the designated actions are executed, and a comprehensive flow entry is generated in the DFT. The action list of the new entry is duplicated from the CFT, and its idle timeout is configured.  
It is important to note that, in the second scenario, our mechanism deviates from the OpenFlow specification. In this case, the switch autonomously installs the flow in the DFT, accelerating the flow insertion process and eliminating the need for additional signaling messages to be sent to the controller, such as Packet_IN and Flow_MOD messages.  
For the P nodes, a single flow table is sufficient. Upon a packet's arrival, it is matched on the basis of the label, and then sent to the appropriate output interface with the same label. If a legacy MPLS router serves as a P node, it executes standard label swapping operations. Legacy MPLS routers support static label switched path configuration, enabling the SDN controller to add or remove static MPLS entries via SNMP or NetConf. Therefore, the proposed mechanism can be implemented incrementally within current networks.
## 4 Discussion
### Co-Citation and Cluster Analysis
#### 4.3.2 Research Base  
On the way to intelligent transportation systems (ITS), vehicular ad hoc networks (VANET), a subclass of the mobile ad hoc network (MANET), caught researchers' attention [9; 11; 13; 58]. Researchers conducted a lot of surveys on VANETs and the relevant issues from 2010 to 2013. The paper of [PERSON] et al., the most cited reference, introduces VANETs from architecture to characteristics, from theory to simulation and application. It is a comprehensive study that helps researchers understand VANETs from the very beginning, including features surrounding VANET, tools to simulate VANET protocols, and applications up to 2013 [9]. As vehicles are expected to carry communication systems, computing systems, infotainment systems, and so on, vehicular networks are facing a lot of challenges [10; 11; 12; 55]. To improve ITS on aspects of road safety, traffic efficiency, privacy, and security issues, researchers try to classify the problems cryptographically [11] and present solutions such as Vehicular Cloud Computing (VCC) [10], long-term evolution-wireless (LTE) for vehicular networking [63], as well as dedicated short-range communication (DSRC) technology [56]. The VCC is a hybrid technology that allows the vehicle to make decisions instantly to promote traffic and road safety [10]. [PERSON] et al. alsosummarize the applicable properties that support VCC [10]. [PERSON] et al. discuss LTE for vehicular networking critically on merit and demerit [63]. DSRC is a technology for communication between vehicles and the roadside. [PERSON] (2011) interprets the concept of DSRC and discusses the standards in the United States and whether the technology should be adopted [56]. [PERSON] et al. also conducted a review study of wireless access standards and presented the worldwide concerns of VANET on protocols, technologies, and services [12]. [PERSON] et al. discuss requirements, challenges, standards, and solutions for vehicular networking protocols in the United States, Japan, and Europe in their survey [55].  
In other words, researchers realized the potential of VANET in the early 2010s and conducted surveys on technique issues and ethical issues.
# A Method to Determine BeiDou GEO/IGSO Orbital Maneuver Time Periods
## 3 Experimental Validation
### Data Description  
Data from the Multi-GNSS Experiment (MGEX) stations XMIS (located on Christmas Island), DARW (in Darwin), and SIN1 (in Singapore) with a 30 s sampling period were selected to analyze the experimental results of orbit maneuvering detection of BeiDou GEO and IGSO satellites, which combine predicted orbits by using the precise orbit products from CHD. The station coordinates were obtained from the IGS Solution Independent Exchange (SINEX) product. The distributions of trajectories on station XMIS and DARW are shown in Figure 3.  
Figure 3: Satellite trajectories on the ground and station locations. Red circles denote selected station locations. Satellites with distributions of trajectories in dotted yellow circle can be monitored by the XMIS (Christmas Island) station.  
Thresholds of orbital maneuver detection are shown for XMIS station in Table 1, calculated by normal observations and the predicted orbit in 2017.
# The YouGovern Secure Blockchain-Based Self-Sovereign Identity (SSI) Management and Access Control
## 5 Experimental Evaluation
### Comparative Evaluation of SSI Frameworks  
To provide context for YouGovern's capabilities, we compare its core architectural and operational features with four prominent SSI frameworks: Microsoft ION [49; 50], Sovrin [42], uPort [41], and DNS-IdM [44]. Table 4 presents a summary of the comparison across key dimensions, including decentralization, revocation support, identity storage, interoperability, and transaction costs.  
Although YouGovern leverages a semi-decentralized blockchain (BSC) for scalability and cost-efficiency, it maintains strict compliance with W3C DID standards and supports advanced capabilities such as smart contract-based access control, revocation, and hybrid decentralized storage via Web3 Storage. Microsoft ION benefits from Bitcoin's high decentralization and tamper resistance, but lacks integrated access control mechanisms and incurs slower confirmation times due to Bitcoin's base-layer constraints. Sovrin introduces a well-defined governance framework and identity guardianship features, but is constrained by its permissioned validator pool, which may limit openness and inclusivity. In contrast,  
Figure 12: Access request view.  
Figure 13: Dashboard To Verify Data.  
uPort simplifies identity creation using Ethereum and IPFS but lacks robust revocation and recovery processes. DNS-IdM provides a domain-name-centric identity layer but relies on centralized DNS updates and lacks decentralized credential verification. Collectively, these comparisons highlight YouGovern's balance between operational autonomy, scalability, and compliance with SSI principles.
## 5 Proposed Framework
### FL Framework
#### 5.2.3 Convergence Analysis  
We guarantee convergence of the global model under standard assumptions:  
**Theorem 3**.: _Given \(N\) participants with at most \(k=15\%\) Byzantine nodes, assuming bounded gradient variance \(\mathbb{E}\|\
abla F_{i}-\
abla F\|^{2}\leq\sigma^{2}\) and a learning rate schedule \(\eta_{t}=\frac{1}{\sqrt{t}}\), the global model \(W_{G}\) converges to a critical point with rate \(O(1/\sqrt{T})\) after \(T\) communication rounds, even in the presence of adversaries._  
Proof.: Following the analysis in [56], we model the aggregation error introduced by malicious clients as an additive bounded perturbation. The trimmed mean aggregation reduces the impact of outliers by discarding extreme values.  
Let \(W_{G}^{t}\) denote the global model at round \(t\). The update satisfies  
\[W_{G}^{t+1}=W_{G}^{t}-\eta_{t}\big{(}\
abla F(W_{G}^{t})+\xi_{t}\big{)} \tag{11}\]  
where \(\xi_{t}\) is the aggregation noise due to Byzantine participants, bounded as \(\|\xi_{t}\|\leq\beta\).  
Under standard smoothness assumptions on \(F(\cdot)\),  
\[F(W_{G}^{t+1})\leq F(W_{G}^{t})-\eta_{t}\big{\langle}\
abla F(W_{G}^{t}), \
abla F(W_{G}^{t})\big{\rangle}+\frac{L}{2}\eta_{t}^{2}\|\
abla F(W_{G}^{t}) +\xi_{t}\|^{2} \tag{12}\]  
Taking expectations and rearranging, we derive  
\[\mathbb{E}\Big{[}\|\
abla F(W_{G}^{t})\|^{2}\Big{]}=O\bigg{(}\frac{1}{\sqrt{T }}\bigg{)} \tag{13}\]  
thus establishing convergence at a sublinear rate \(O(1/\sqrt{T})\) even under Byzantine faults.  
This result indicates that, even with a moderate fraction of malicious participants, our FL protocol ensures stable convergence that is suitable for RAN optimization tasks.