esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background Work
### Zigbee IP Protocol Stack Architecture
The 6 LoWPAN protocol is the next layer in the protocol stack, acting as an adaptation layer to deliver services to the internet layers. This protocol transports data from Zigbee end devices across the MAC and PHY layers to the top layer. The 6 LoWPAN protocol provides several services, including neighbor discovery in the top layer, fragmentation, reassembly, stateless autoconfiguration, and header compression. IoT devices that use Zigbee can talk to IPv6 nodes through the 6 LoWPAN protocol, which handles the larger payload. The maximum frame length for IEEE 802.15.4 is 127 bytes, and the maximum transmission unit for IPv6 is 1280 bytes [14; 15]. The network layer provides a wide range of services to the Zigbee-enabled IoT devices, including IPv6 addressing, packet framing, messaging through ICMPv6, neighbor discovery, and duplicate address detection. Further, this layer propagates 6 LoWPAN configuration information and forwards IPv6 packets and multicast. The Internet Control Message Protocol for IPv6 is a fundamental IPv6 protocol that reports errors and data generated during packet processing using error or informational messages. Link-local scoped multicast is used by \"Neighbour Discovery\" and other IPv6 parts to find routers, find duplicate addresses, and resolve addresses. \"Stateless address autoconfiguration\" simplifies IPv6 device configuration and maintenance by allowing nodes to provide meaningful addresses. The routing layer uses the AODV-RPL protocol. It is a reactive peer-to-peer route discovery protocol that can find routes for both symmetric and asymmetric network traffic [16]. This routing system allows for point-to-point communication between 6 BRs and Zigbee devices, as well as multipoint-to-multipoint transmission from Zigbee devices to 6 BRs. DODAG, a destination-orientated directed acyclic graph with a root-based design,  
Figure 1: Zigbee IP protocol stack architecture. manages diverse traffic flows. The management function, which is the application support sublayer, is in charge of the different protocols so that the node can do what it needs to do. The application support sublayer offers services to the application and network layers [17; 18]. The application support sublayer is in charge of node bootstrapping, node power management, non-volatile storage, and restoration of critical network parameters. The definition for various application profiles employing application layers will be included in the application layer, letting users take on the burden of developing applications. The application profile specifies the over-the-air communication mechanism. Communication across devices is possible if they have the same application profiles. This part presents the preliminary information and fundamental building elements needed for the proposed system. We then cover the architecture, design, and assumptions of the system that support our work.
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 2 Setup Design and Realization
### Setup for TC-011: Accuracy of SOG/COG (Constant Speed)  
This test verifies the accuracy of speed and course over ground (SOG and COG) under different constant speed conditions, including 0.5 knots (A), 10 knots (B), and 20 knots (C). The SOG error threshold is 0.2 knots for cases A and B and 0.4 knots for case C. The COG error has to be lower than 3 degrees for case B and 1 degree for case C, and there is no check for case A due to the low speed of the vessel. For the test, simulated signals are used.
## 4 Model Dynamics
Proposition 3 demonstrates that if the CS cell population survives, then the CR must also survive. Biologically, this makes sense, as the CR will always receive new mutated CR cells as ADT continues. Next, we study the extinction of cancer cell populations and stability conditions for each of these steady states when feasible. Observe that we can not linearize at the steady state \(E_{0}\) since the last term of \(dQ/dt\) is not differentiable at \(E_{0}\). This prevents us from carrying out a routine local stability analysis of \(E_{0}\). Proposition 4 below simply confirms the intuition that if both cancer cell populations growth rates are too low, they will die out eventually. For ease of computations in the following propositions, we shall define \(S_{1}(Q)=G_{1}(Q)-D_{1}(Q)-\lambda(Q)\) and \(S_{2}(Q)=G_{2}(Q)-D_{2}(Q)\). **Proposition 4**. : _Assume that \(S_{1}(Q_{m})<0\), then CS population will die out. If, in addition, \(S_{2}(Q_{m})<0\), then both cancer populations will die out._  
**Proof. ** Observe that both \(S_{1}(Q)\) and \(S_{2}(Q)\) are strictly increasing with respect to positive values of \(Q.\) Since,  
\[\frac{x_{1}^{*}(t)}{x_{1}(t)}=G_{1}(Q)-D_{1}(Q)-\lambda(Q)-\delta_{2}x_{1},\]  
and \(S_{1}(Q_{m})<0\), we know that \(G_{1}(Q)-D_{1}(Q)-\lambda(Q)\leq S_{1}(Q_{m})<0\) for any \(Q\). Let \(m=-S_{1}(Q_{m})\), and, since \(x_{1}(t)>0\), we have that  
\[\frac{x_{1}^{*}(t)}{x_{1}(t)} \leq -m\] \[x_{1}(t) \leq ee^{-mt}.\]  
Therefore \(\lim_{t\to\infty}x_{1}(t)=0.\) Applying a similar but slightly more delicate comparison argument to \(x_{2}(t)\) with \(\lim_{t\to\infty}x_{1}(t)=0\) yields \(\lim_{t\to\infty}x_{2}(t)=0.\) This completes the proof of this proposition. The following proposition provides a simple set of conditions that yields the biologically realistic final outcome when sensitive cells are overtaken by resistant cells. **Proposition 5**. : _The CR only steady state \(E_{1}\) is locally asymptotically stable when \(S_{1}(Q^{1})<0\) and \(S_{2}(Q^{1})>0.\)_  
**Proof. ** The Jacobian matrix evaluated at \(E_{1}\) is given by:  
\[J(E_{1})= \begin{pmatrix}S_{1}(Q^{1})&0&0\\ \lambda(Q^{1})&-S_{2}(Q^{1})&(\frac{\mu q_{2}}{Q^{2}}+\frac{d_{2}}{(R_{2}+Q^{ 2})})\frac{G_{2}(Q^{1})-D_{2}(Q^{1})}{\delta_{2}}\\ \frac{\mu S_{2}(Q^{1}-q_{2})}{G_{2}(Q^{1})-D_{2}(Q^{1})}&0&-\gamma-\mu\end{pmatrix} .\]The eigenvalues are the diagonal elements. We see that when \(G_{1}(Q^{1})-D_{1}(Q^{1})-\lambda_{1}(Q^{1})<0\) and \(G_{2}(Q^{1})-D_{2}(Q^{1})>0\), all diagonal elements are negative. Hence, \(E_{1}\) is locally asymptotically stable. If both CS and CI cells can proliferate under treatment, then the coexistence equilibrium may be stable. Figure 2 displays the regions where this could happen. If CS cells have a high growth rate \(\mu\), they may survive under relatively low levels of androgen. Alternatively, if these cells have a very low death rate \(d_{1}\), they may persist as well.
# Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples
## 4 Experimental Verification  
### Case 1: Bearing Dataset with One Missing Failure Sample  
The rolling bearing dataset used in this section is from Case Western Reserve University (CWRU). As shown in Figure 6, the test bench includes a 2.72 kW motor, a torque sensor, a power test meter, and an electronic controller (as shown in the Figure 7) [18]. The bearings to be tested support the motor shaft, the drive side bearing is SKF 6205 and the fan side bearing is SKF6203. The 6203 deep groove ball bearing is chosen for the roller bearing study in this work, and the dataset includes normal condition (NC), outer ring fault (OF), inner ring fault (IF), and roller element fault (RF). The fault is handled by electrical discharge machining (EDM) single point damage.
# Evaluating Deep Learning Techniques for Natural Language Inference
## 5 Experimental Results
### Discussion  
In Table 2, the best accuracies of each model on each of the eight datasets are presented. The most obvious observation is that the pre-trained transformers very clearly outperform the older models. The differences in accuracies between them are 0.64-20%. Out of the three BERT models, RoBERTa comes out to be the best performing one, given that its large version has the highest accuracy on almost all tasks. This is a trend across all BERT models; larger models perform better than their base versions. ALBERT seems to do exceptionally well, given its size. It generally performs comparative results with respect to the other two, and in one dataset (QNLI), it outperforms all others. Better performances can be achieved with ALBERT's large and xxlarge versions, but we are more interested in looking at the inference capabilities of the smaller versions.  
Figure 7: (**a**) Validation accuracy graph and (**b**) training loss graph of best model (RoBERTa-Large) for RTE.  
One dataset that proved to be more of a challenge to the BERT models is RTE. It took an MNLI fine-tuning approach to achieve results above 80%. Even then, it is the hardest dataset we tried, and only RoBERTa-large managed to break the line of 90% accuracy. This was also true with the MNLI task. The easiest task to solve, on the other hand, seems to be SciTail. All models performed well above 90%, with RoBERTa-large scoring 96.52% on the test set. One explanation is that Wikipedia, where all BERT models were trained, contains the answers to the science questions in SciTail.  
In Table 3, a comparison of the best of our models (RoBERTa-Large) with state-of-the-art approaches is attempted. In the table, we have included models that deal with the same datasets. It is evidence that our model does better than the others on three datasets (RTE, SNLI, SciTail), especially on the RTE, which is one of the toughest, and is second on one (MNLI).
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 2 UWAC Physical Layer Techniques
### Multicarrier Modulation for the UWAC  
Communication systems struggle to attain high data rate communication. Increasing the data rate requires a decrease in the duration of the symbol \(T_{s}\), which effectually escalates the system bandwidth. The signal undergoes frequency selectivity if the bandwidth of the system is very high in comparison to the coherence bandwidth [59; 60]. Usually, an equalizer is compulsory at the receiver to manage the ISI initiated by the frequency-selective channel [61]. However, small symbol duration and high bandwidth need a longer equalizer, effectively improving the computational work and the probable instability [60]. Multicarrier modulations have proposed an alternative method to achieve high data rates and are the dominant modulation techniques for wireless systems.  
For propagations over multipath channels of communications, multicarrier modulation systems are extensively used. The traditional methods assume the symbol for the signal transmitted to be of longer duration in comparison to the channel delay spread. This provides a subcarrier bandwidth that is comparably lower than the coherence bandwidth of the communication channel, and a low-rated \"sampling\" of its impulse response (in each sub-channel it is once per symbol) [62]. There are two reasons for the popularity of multicarrier modulations in the UWA channels. Firstly, processing a signal with multicarrier modulations in the receiver significantly reduces interference and noise that may result from linear equalization of a single carrier signal. Secondly, the long symbol duration used in multicarrier modulation guarantees fast fades and better immunity to impulse noise [63].  
The transmitted bit-stream is distributed into different sub-channels and numerous sub-streams in multicarrier modulation [59]. The key is to assign bandwidth to every sub-channel, which is lower than the total coherence bandwidth that in effect ensures a flat fading in each sub-channel. Therefore, each sub-channel experiences a low data rate as compared to the total data rate and large symbol period. The UWA multicarrier modulation technology's merits and demerits are depicted in Table 3.
## 4 Discussion
Unfortunately, not all publications present results in a similar manner, which can make interpretation difficult [41; 42; 43; 44; 45; 46; 47]. While the issue is complex, it can be illustrated through the use of accuracy metrics, specifically the interchangeable use of \"average accuracy\" (ACC) and \"overall accuracy\" (OA), often unintentionally. When a publication reports only the ACC metric as \"accuracy\" without also providing OA, it can give the impression that the metric reflects the commonly used OA. This can lead to confusion, as average ACC values in multi-class classifications often exceed 90%. Interpreting such results is challenging, especially when dealing with new ML models, where higher accuracy levels are typically expected. Unfortunately, in the intriguing publication [41] on various ML models, the authors use the metrics ACC and OA interchangeably, comparing them directly. Naturally, the reported accuracies exceed 90%. On the other hand, reference [46] focuses on precision and recall metrics, which correspond to UA and PA rather than OA. This makes it challenging to draw reliable conclusions about the analyzed models. A typical approach to accuracy estimation in ML image classification on satellite images with deep learning, including code, can be found in [42]. This work explicitly presents the ACC metric along with high values for this metric when comparing different models. Eight models were compared (three variants of RESNET 50, two variants of CNN, and three variants of MobileNet). The differences between seven of the models ranged from 0.946 to 0.993, while one model (MobileNet) recorded an accuracy of 0.852. In [42], the formula for ACC is presented as OA, and the accuracy of the seven analyzed models exceeds 95%, with differences among them being only a few percentage points. Similarly, other publications [43,44] have employed the ACC metric, reporting accuracies above 90-95%. However, in those cases, the metrics were not referred to as OA. Our paper demonstrates the discrepancies between ACC and OA values. In both cases examined here (LUCAS 2018 accuracy/divergence analysis and classification accuracy analysis), reporting accuracy with ACC creates an artificial impression of higher accuracy, overestimating it by approximately 10%. Our other studies demonstrate that the smaller the OA, the greater this difference, as ACC often exceeds 90% in multi-class classifications [48]. A notable aspect of our study is the use of the widely accessible Google Earth Engine (GEE) cloud platform, which provides both harmonized Sentinel-2 image collections and seasonal composite analysis algorithms, as well as machine learning algorithms for image classification. Additionally, the LUCAS database is freely available across Europe, enabling its application in any region within Europe. The data and GEE code used in this research have also been shared. Accuracy analyses, including the computation of accuracy metrics, were conducted using custom Python scripts, which have been made available on GitHub.
## 3 The Inversion Strategy of FPAR  
The Heihe area was selected in this experiment, and, for this area, the FY-3C MERSI data and MODIS surface reflectance data (MOD09 GA) were used to extract the required data. In order to obtain two inverted FPAR products, namely FY-FPAR and MOD09-FPAR, from two different satellites sources, the same PROSAIL model and the LUT algorithm were applied.
## 5 Industrial Application  
In this section, an online cutting pattern recognition system through the cutting sound based on the proposed FCM-FGOA was established and applied in a coal mine as shown in Figure 10. The industrial experiment was organized at the 2115 coal mining working face in the No. 13 Mine of the Pingdingshan Coal Industrial Group Corporation (Pingdingshan, China). According to the production schedule of the 2115 working face, the expected current of the cutting motor was 27 A. In addition, the changing range of the current should be [\(-20\%\), \(20\%\)]. Blast protection was conducted on the microphone and the cutting sound was transmitted to the airborne industrial personal computer (IPC). To guarantee the that shearer was working steadily, the IPC calculated the cutting pattern of the shearer online and sent control commands to the hydraulic height adjusting system and variable frequency speed control system of the shearer. The remote working state representation interface was applied to monitor the field condition in the ground control center. In order to exhibit the effectiveness of the system, the cutting current of the shearer was collected and the curve was shown in Figure 11. The current data was from 9:17:00 to 9:47:00 on 22 January 2016, and the changing range of the signal was from 24.3827 to 31.0185 A. The average value of the series was 27.6271 A, and the standard deviation was 0.7816. The maximal value was about 14.883% larger than expected and the minimum was about 9.694% smaller. The shearer kept working steadily and the occurrence of cutting roof was avoided, which proved the stability and reliability of the online system.  
Figure 10: Online cutting pattern recognition system.
## 3 Results  
No serious adverse effects were observed in participants with rosuvastatin use. A Sidak's multiple comparisons test (Table 3) revealed significant differences between HAM-D ratings collected at the start of the study and after three months for both groups. On \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Formula** \\ \hline Accuracy & \(TP+TN\) \\ \cline{2-3}  & _Total Number of Observations in Both Classes_ \\ Sensitivity & TP \\ \cline{2-3}  & _Total Number of Observations in Positive Class_ \\ Specificity & _Total Number of Observations in Negative Class_ \\ Precision & TP \\ \cline{2-3}  & _Total Number of Positive Predictions_ \\ FOR & FN \\ MCC & \(\frac{Total Number of Negative Predictions}{(TP*TN)-(FP*FN)}\) \\ \cline{2-3}  & \(\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Formulas for the classification performance indices. Figure 3: Format of the confusion matrix format used to report results. Number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) were represented and used to calculate other performance metrics. The placebo group was arbitrarily taken as the “positive” class. the other hand, the difference between BDI-II ratings at the beginning and the end of the study were only significant for the statin group. The BDI-II ratings for the statin group significantly decreased compared to their initial values (\(p<0.0001\)), suggesting improvement in perceived mood due to stain use. No significant difference in BDI-II ratings before and after placebo administration was observed. The variable with the highest percentage of missing data was the \(\Delta\) of urea levels in the blood with 28.6%, while 4.8% of the data was missing for \(\Delta\) variables with information of levels of lactate dehydrogenase, alkaline phosphatase, total bilirubin, total protein, albumin, blood urea nitrogen (BUN), uric acid, calcium, and phosphorous in the blood, and from \(\Delta\) variables of MDA, IL-6, TNF\(\alpha\), and Rho-kinase activity factors from hemostasis data. Meanwhile, 9.5% of data was missing from levels of glycemia and 4 CANTAB variables, all from the spatial span (longest sequence recalled by subject) or spatial working memory tasks. The rest of the CANTAB variables had 4.8% of data missing. All perfusion data, absolute and \"abnormal\", had no missing data. Multiple comparison tests revealed no significant differences in any non-behavioral variables before and after the study in either group. In contrast, feature selection techniques ranked several notable non-behavioral variables as the ones that changed most differently after three months due to statin use compared to placebo. The five features selected by the mRMR and ReliefF algorithms are shown in Table 4.  
According to the mRMR algorithm, the top five features that contributed to the distinction of the two groups included perfusion abnormality in left middle temporal gyrus, perfusion abnormality in the left temporal pole, perfusion abnormality in the right Brodmann Area (BA) 6 located in the frontal lobe, perfusion abnormality in right BA
## 4 Situational Information Propagation Level Prediction
This linguistic information shows the peoples emotions and sentiments in th specific context; they express themselves more freely on social media, which could reveal their perceptions about certain situations and about how they think [55]. The values of following and followers were log transformed to avoid zeros during analysis and these two features were directly calculated from the dataset; verified users values also come from the dataset. As we have already described, our data context is the US, so the location attribute was also available in the dataset, and the NearState attribute was obtained by the following definition: whether the location showed the hardest-hit state, which is California according to data by center of disease control and prevention (CDC) ([[https://covid.cdc.gov/covid-data-tracker](https://covid.cdc.gov/covid-data-tracker)]([https://covid.cdc.gov/covid-data-tracker](https://covid.cdc.gov/covid-data-tracker)) (accessed on 20 April 2020)); if location showed California, then we assigned it a value of one, otherwise we assigned it a value of zero. As far as content features are concerned, they also come from the dataset, specifically from the tweet content and its parameters; there is a feature word count category option that counts the words of each post, so we use that feature and obtained the WC (word count) in terms of length and count of likes already available in our dataset, so we obtained this from our dataset. The like attribute contains how many likes a specific post receives. Table 4 shows the summary of all the extracted features, their LIWC values and log-transformed values of features from the dataset that we computed, for all types of situational information. By using the above-defined features of each type of situational information, we first selected the features using random forest (RF) and linear regression and, by comparing performance of the selected features, we found out that linear regression performs better and we finally choose this method. In addition to this, multiple linear regression and negative binomial regression were implemented to predict the amount of re-tweets (log-transformed) of each type of information using the selected features. The reason for choosing the negative binomial regression model as one of the model is because our predicted dependent variable have a counted no. of values; simply saying it is a counter variable and negative binomial regression is better in predicting counter variables and we can see the effect of this in the root mean square error (RMSE) value table which show that the negative binomial is better compared to the multiple linear regression model. Table 5 shows the Root mean square error value of each model; as in the regression models, the root mean square error is the most important metric. We check the model efficiency by using this, as negative binomial regression gives lower RMSE values; therefore, we finally choose the negative binomial regression to predict the retweeted amount of COVID-19-related Twitter posts to analyze the propagation scale of each type of situational information.