esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Error by Covariates  
A relevant question to ask about the model is how the uncertainty of predictions changes with respect to specific covariates. An important issue of interest, for instance, is how the uncertainty changes by depth. To answer this, a slightly simpler definition for the accuracy of the model is needed, since the upper and lower 95% confidence intervals are asymmetric about the estimated mean, particularly for small response values. A useful definition adopted here is that the average accuracy is defined as: 'the average of the differences between each of the upper and lower 95% confidence intervals and the fitted value.' That is, the average accuracy is defined as \(\left(\left(\hat{f}_{0.975}-\hat{f}_{0.050}\right)+\left(\hat{f}_{0.500}-\hat{ f}_{0.025}\right)\right)/2\), which is \(\left(\hat{f}_{0.975}-\hat{f}_{0.025}\right)/2\), where \(\hat{f}_{q}\) is the \(q\)-quantile prediction estimate of the distribution of the soil water content at some specific tension or values such as TAW or MP, for some quantile \(q\) (\(q=0\dots 1\)).  
Figure 7 shows a plot of the predicted TAW as a function of the sample depth, by soil order, along with the average accuracy (as defined above). Note that the maximum value of the sample depth for the combined training and validation datasets was 155 cm, although this varies by soil order. The general trend is for the predicted value to be high near the surface, reducing to a more-or-less plateau level as depth increases. Exceptions to this rule included Podzol soils, where the predicted value increased below a depth of about 75 cm, and to a lesser extent Pumice, Semiarid, and Mature Gley soils. The average accuracy, by contrast, was generally low for samples near the surface, increasing slightly with sample depth. Again, there were exceptions, such as Granular and Ultic soils where the accuracy diminishes with depth. It is important to remember when interpreting Figure 7 that the average accuracy depends to a significant extent on the texture (sand, silt, and clay content), in addition to sample depth and all the other lesser-influential explanatory variables.  
Figure 7: Plot of the estimated median of total available water content (TAW) (left axis, solid line) and average accuracy (mean of the upper and lower 95% confidence values of TAW; right axis, dashed line) as a function of the sample depth. The shaded region in each case is plus-and-minus one standard error of the estimate.
# Early Ransomware Detection with Deep Learning Models
## 5 Experimental Evaluation
### Results
#### 5.5.3 Competing Model  
In reviewing the literature on ransomware detection, we learned that most studies do not share their code, which hinders reproducibility and comparative analysis. After examining numerous papers in this field, such as [26; 31; 39; 63; 64; 65], we found that they provide method descriptions but not the implementation code. The only work that shares its code is [29]. Therefore, we ran the two models presented in this work on our datasets and compared them with our models.  
To evaluate the effectiveness of our proposed models, we compared our results with those obtained using a previous methodology described in the paper [29]. The method of [29] utilizes windows with a length of 100. We have used the publicly available implementation of this method. We ran the two deep graph neural network (DGNN) models (denoted by DGNN1 and DGNN2) contained in this implementation.  
Table 5 shows the comparison of this method and our top three models on the PARSEC-500 dataset. All our models yielded higher F1 scores, demonstrating the robustness and effectiveness of our approach. These results highlight the improvements in detection accuracy achieved by incorporating operation and result features with \(1\)-hot encoding.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multicolumn{8}{c}{Top 3 models for the PARSEC-500 dataset} \\ \hline model & text repr & FLIST & sensitivity & specificity & F1 & W & ep & test time (s) \\ \hline CNN & 1-hot & OpsRes & 0.9978 & 0.9827 & 0.9903 & 7 & 10 & 0.20 \\ CNN & 1-hot & OpsRes & 0.9957 & 0.9805 & 0.9882\(-\) & 7 & 30 & 0.33 \\ LSTM & 1-hot & OpsRes & 1.000 & 0.9705 & 0.9877\(\downarrow\) & 7 & 10 & 0.53 \\ \hline \multicolumn{8}{c}{Top 3 models for the PARSEC-5000 dataset} \\ \hline model & text repr & FLIST & sensitivity & specificity & F1 & W & ep & test time (s) \\ \hline LSTM & 1-hot & Ops & 0.9998 & 0.9885 & 0.9942 & 7 & 10 & 1.42 \\ DNN & 1-hot & OpsRes & 0.9997 & 0.9870 & 0.9934\(-\) & 7 & 10 & 0.59 \\ CNN & 1-hot & OpsRes & 0.9972 & 0.9890 & 0.9931\(\downarrow\) & 7 & 10 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Top-performing models for PARSEC-500 and PARSEC-5000 datasets (the best scores are marked in gray, \(\downarrow\) and \(-\) indicate the statistical significance of differences in the results, or the lack thereof).
## 3 Data Routing Approaches in WSNs
### Fault-Tolerant Approaches
#### 3.3.2 Fault Recovery
After this detection, the FCS nodes' information including their state, transmission rate and delay is updated. In general, data forwarding in the proposed scheme is done in two modes: with failure or without failure. When there is no failure, the next hop node is selected from FCS based on the local information and the transmission rate of the packet. But in case of failure, it considers the remaining transmission time of packet to do jumping transmission. When the remaining time's value is below than jumping threshold, it switches to jumping mode. For selecting the next hop, the jumping probability is calculated. This calculation is done based on the results of previous jumping transmission or feedbacks from other nodes. By performing a jumping transmission, packets can pass over the failed nodes to be received by a proper node. Figure 12 depicts the process of jumping transmission.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
###### Abstract  
For the development of a global navigation satellite system (GNSS), the third generation of BeiDou Navigation Satellite System (BDS-3) achieved full constellation for worldwide service on 23 June 2020. The new signals, B1C and B2a of BDS-3, further enhance the compatibility and interoperability between different GNSSs. In this study, we first assessed the quality of all the signals in BDS-3/GPS/Galileo. Then, to achieve the interoperability among BDS-3/GPS/Galileo, the inter-system bias (BSB), which appears if an inter-system difference exists between two GNSSs, was estimated at overlapping frequencies. Finally, we used the estimated ISBs in real-time kinematic (RTK) positioning. The results show the higher quality of the overlapping frequency B2a/L5/E5a than B1C/L1/E1 in terms of pseudo range multipath. The ISBs are stable both in the short term for one day and in the long term for over a year, which fit a zero-mean normal distribution well when the identical type of receiver is applied. Thus, it is reasonable to ignore the ISBs in the inter-system differences. With the estimated ISBs, the inter-system double-difference RTK can be achieved, which is called a tightly combined model (TCM) RTK. Compared with the traditional intra-system double-difference RTK, which is called a loosely combined model (LCM) RTK, the TCM RTK can achieve a higher success rate (SR) in terms of ambiguity resolution and higher positioning accuracy. In addition, the higher the cutoff elevation angle set, the greater the promotion can be obtained in SR. Even with a cutoff elevation angle of 50\({}^{\circ}\), the SR of TCM is over 80%. Thus, it is important to apply TCM RTK when the observation conditions are limited, such as in dense jungles or the urban canyons.  
BDS-3; inter-system bias; real-time kinematic; tightly combined RTK +
Footnote †: journal: Remote Sens.  
13 July 2021  
Accepted: 1 September 2021  
Submitted: 3 September 2021
# Simulation Analysis of Nanosecond Laser Processing of Titanium Alloy Based on Helical Trepanning
## 3 Results and Discussion  
### Simulation Parameter Setting  
The schematic diagram of the laser scanning path for a nanosecond laser to process titanium alloy is shown in Figure 1. A nanosecond laser beam moves along the spiral scanning. The default laser parameters in the simulation were as follows. The laser power was 18 W, the pulse energy 1.8 mJ, the laser spot-size 27 \(\mu\)m, the repetition rate 10 kHz, the scan speed 2.4 m/s, and the number of iterations 20. The laser spot center moved spirally along a spiral path with an increasing radius ranging from 14 \(\mu\)m to 60 \(\mu\)m.  
The numerical model of micropore ablation was established by COMSOL simulation software. The thermodynamic parameters of TC4 used in the simulation are shown in Table 1.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 2 Thermal Modeling
### Radiometry
#### 2.4.1 DN-Temperature Sensor Function  
The first approach links DN to temperature with the following relation [15]:  
\[DN=a\cdot T^{\pi}+b\,. \tag{11}\]  
Figure 4: Method workflow.  
To compute \(a\), \(b\), and \(\alpha\), a calibration is performed with a black body at different temperatures. The coefficient values are reported in [15]. To use such a model, the heat flux is recovered for each pixel using the image scale values, and then it is converted into the corresponding radiative temperature assuming the object as a black body and accounting for the object distance.  
The effect of the distance is taken into account, as reported in Equation (12), in the computation of \(T_{rad}\). If the distance is not compensated, then the equivalent \(T_{rad}\) values would be extremely low. Then, the value is fed into the sensor function to compute the corresponding DN. Such values are modified according to the noise models, and the noisy temperature is recovered, inverting the sensor function (Equation (11)).  
The scale of the image can then be adjusted accordingly to the selected temperature range. The steps are summarized in Equation (12):  
\[\begin{split} q_{fc}\to T_{rad}=\left(\frac{q_{fc}\,\pi\,| \mathbf{r}_{fc}|^{2}}{\sigma\,\Delta A_{c}}\right)^{1/4}\to DN=a\cdot T_{rad}^{ \alpha}+b\rightarrow\\ \to DN_{noisy}=DN+Noise\to T_{rad+noise}=\left(\frac{ DN_{noisy}-b}{a}\right)^{1/\alpha}.\end{split} \tag{12}\]  
It must be noted that in using such an approach, the computation of the received heat flux must be coherent with the thermal camera response function, meaning that we need to write \(q_{fc}\) using the same input that is taken by the function that provides the DN. For example, in this case, the function \(DN=a\cdot T^{\alpha}+b\), uses \(T\) as the input; thus, we cannot use Equation (10) because (as explained in the next paragraph) if we invert the equation to recover the emitted heat flux, we obtain \(F(T)\), when instead we need to recover \(T\). Thus, a slightly different formulation of \(q_{fc}\) that allows obtaining \(T\) is needed. A workaround, in this case, is given by the following equation, which has been used in Equation (10):  
\[q_{fc}=\frac{\varepsilon\sigma T_{rad}^{4}}{\pi}\,\,\,\frac{(\mathbf{\hat{n}}_{ f}\cdot\mathbf{r}_{fc})\,(\mathbf{\hat{n}}_{c}\cdot\mathbf{r}_{cf})}{| \mathbf{r}_{fc}|^{4}}\,\Delta A_{c}\,. \tag{13}\]  
In using this approach, the sensors attenuation caused by the optical lenses and the sensor responsivity (i.e., the parameter \(R(\lambda)\) in Equation (10)) is contained directly in the formula \(DN=a\cdot T^{\alpha}+b\) in the exponent \(\alpha\) as stated in [15].  
Given the fact that the camera response is directly proportional to the heat flux received, it is preferred to use the thermal camera model presented in the next paragraph, where the third calibration parameter \(\alpha\) can be eliminated by simply calibrating the camera with the total emissive power of the emitter.
# Self-Supervised Learning across the Spectrum
## 8 Conclusions  
In this paper, we introduced S4, a multimodal self-supervised training framework for satellite image time series segmentation. S4's design can be characterized by (1) multimodal learning, (2) temporal alignment, and (3) a pixel-wise feature space. To enable improved self-supervision for SITS, S4 proposes the following:  
Figure 9: Updated graph cloud cover robustness prediction on optical image inference over PASTIS-R test set.  
* Novel joint pixel-wise space-time contrastive learning;
* Reconstruction loss for multimodal satellite imagery;
* A SITS preprocessing strategy to temporally align SITS across modalities.  
We also curate M2S2-SITS, a new multimodal SITS dataset that enables our new geographic ablations and highlights the greater opportunities to benefit from multimodal SITS pretraining. Using our datasets, we demonstrate how S4 can outperform a variety of other self-supervised baselines on the downstream task of semantic segmentation, and we conduct detailed ablations to better illustrate the robustness of our model in specific situations, such as cloud cover and geographic diversity. We envision that S4 will unlock the potential of using satellite imagery for emerging Earth-scale applications like climate monitoring and precision agriculture by reducing the requirement for large, labeled datasets.  
Conceptualization, J.S. and D.V.; methodology, H.Z.; software, R.Y.; validation, S.M., X.D.Z. and B.T.; formal analysis, X.D.Z.; investigation, B.T.; resources, D.V. and H.Z.; data curation, X.D.Z.; writing--original draft preparation, J.S.; writing--review and editing, H.Z.; visualization, J.S.; supervision, D.V.; project administration, J.S.; funding acquisition, D.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSF grant number 2237474, Cisco Systems Inc., IBM-IL Discovery Accelerator Institute, and CloudBank [70] under NSF grant 1925001. [PERSON] is funded by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE - 1746047.  
Link to name's pretrain datasets can be found at [[https://datavverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CCOGD0](https://datavverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CCOGD0)]([https://datavverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CCOGD0](https://datavverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CCOGD0)).  
We thank all members of the Illinois WoW Lab for their input and feedback on multiple versions of this work.  
The authors declare no conflicts of interest.
## 3 Results
### Study Characteristics  
The study characteristics are presented in Table 1. There were seven studies on pre-professional dancers [18; 19; 20; 21; 22; 23; 24] and five on professional dancers [25; 26; 27; 28; 29], with none reporting on both pre-professional and professional dancers. The age range of pre-professional dancers was 11 to 29 years old and professional dancers 17 to 42 years old. Eleven studies focused on ballet [18; 19; 20; 21; 22; 23; 25; 26; 27; 28; 29] and one on contemporary dancers [24], with no studies on musical theatre or opera. All studies were conducted in Western countries, with 50% based in the USA [20; 21; 25; 27; 28; 29]. Publication dates ranged from 1985 to 2021. Only three studies included male participants, two of which were on professional ballet dancers [25; 26] and one on pre-professional ballet dancers [19]. The studies on pre-professional dancers had larger sample sizes, ranging from 25 to 160 participants, whereas samples of professional dancers ranged from 10 to 22 participants.
## 4 Probability Distribution of Loss Degree Based on Three-Point Estimation Method
### Correlation Processing  
In the actual operating system, there is often a certain correlation of wind power between and load between, so it is not suitable to directly perform a probabilistic power flow calculation. To eliminate the correlation between random variables, the Nataf transform is used to map the input correlation non-normal random variables into independent standard normal random variables. The specific steps of correlation processing are as follows:  
1. [label=(0)]
2. Supposing the input \(n\) correlation non-normal random variables are \(X=[x_{1},x_{2},\ldots,x_{n}]\), the correlation coefficient matrix is \(C_{X}\) and the corresponding element is \(\rho_{x_{i}x_{j}}\), which can be made certain by Equation (17): \[\rho_{x_{i}x_{j}}=\frac{cov(x_{i},x_{j})}{\sigma_{x_{i}}\sigma_{x_{j}}}=\frac{ cov(x_{j},x_{i})}{\sigma_{x_{i}}\sigma_{x_{j}}}=\rho_{x_{j}x_{i}}\] (17) where \(cov\big{(}x_{i},x_{j}\big{)}\) is the covariance of variables \(x_{i}\) and \(x_{j}\), and \(\sigma_{x_{i}}\) and \(\sigma_{x_{j}}\) represent the standard deviations of variables \(x_{i}\) and \(x_{j}\), respectively.  
(2)  
Supposing the independent standard random variables are \(Y{=}\left[y_{1},y_{2},\ldots,y_{n}\right]\) and its correlation coefficient matrix and corresponding elements are represented by \(C_{Y}\) and \(\rho_{y_{i}y_{j}}\) respectively, variables \(X\) and \(Y\) satisfy the following conversion relation:  
\[y_{i}=\Phi^{-1}(F_{i}(x_{i})) \tag{18}\]  
where \(\Phi^{-1}(\cdot)\) and \(F(\cdot)\) represent the inverse distribution functions and the cumulative distribution functions of \(X\), respectively. \(\rho_{x_{i}x_{j}}\) and \(\rho_{y_{i}y_{j}}\) satisfy the following formula:  
\[\begin{array}{rl}\rho_{x_{i}x_{j}}&=\int_{-\infty}^{+\infty}\int_{-\infty}^{ +\infty}\frac{x_{i}-u_{x_{i}}}{\sigma_{x_{i}}}\cdot\frac{x_{j}-u_{x_{j}}}{ \sigma_{y_{j}}}\cdot f_{j}\big{(}x_{i},x_{j}\big{)}\,{\rm d}x_{i}{\rm d}x_{j}\\ &=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}\frac{F_{-1}^{-1}(\Phi(y_{i }))-u_{x_{i}}}{\sigma_{x_{i}}}\cdot\frac{F_{-1}^{-1}(\Phi(y_{j}))-u_{x_{j}}}{ \sigma_{x_{j}}}\cdot\phi_{2}\Big{(}y_{i},y_{j},\rho_{y_{i}y_{j}}\Big{)}\,{\rm d }y_{i}{\rm d}y_{j}\end{array} \tag{19}\]  
The calculation using the above formula is too complicated, and the correlation coefficient between wind power is usually calculated by empirical formula [27].  
(3)  
The obtained \(C_{Y}\) is a positive definite matrix. By Cholesky decomposition \(C_{Y}=GG^{T}\) can be obtained and the lower triangular matrix \(G\) can be obtained. The independent random variable is obtained by using \(Z=G^{-1}Y\).
# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 5 Discussion
### The Fwd Packet Length Mean as an Indicator of Botnet Activity  
Our correlation analysis highlights the 'Fwd Packet Length Mean' feature as significantly associated with botnet activity. This result underscores its importance for detecting the anomalies caused by botnets in IoT environments. The identified correlation supports its utility in differentiating normal traffic patterns from malicious activities, particularly in resource-constrained IoT devices.  
Botnet attacks can be identified in the IoT network by considering the 'Fwd Packet Length Mean' feature, which captures and, thus, provides insights into the average size of the packets that are traversing in a network session. Because botnet attacks are accompanied by packets of abnormal length, thereby causing deviations from typical network patterns, the length is one of the significant metrics that can be captured to detect the attack. Furthermore, these deviations are more prominent and easily detected in low-powered IoT environments with limited processing capabilities. Botnets generally flood the target with traffic using packets of unusual sizes to overwhelm the system during attacks, such as in DDoS attacks; hence, the existence of packets with abnormal lengths indicates the execution of malicious activities [51; 52]. To maintain the security and operational integrity of IoT networks, these anomalies must be detected [53].  
Features such as 'Fwd Packet Length Mean' have been effectively analyzed using machine learning and deep learning models, such as support vector machines, random forest, and long short-term memory, to identify and mitigate botnet attacks in IoT networks [51; 52]. Several datasets, such as IoT-DH and IoT-23, include various scenarios related to attacks, which are used for learning based on realistic IoT environments, thereby developing robust detection mechanisms [54]. As IoT ecosystems are dynamic, several novel, low-frequency attacks can arise. Such attacks are addressed by allowing the models to adapt to new attack patterns with minimal data by detecting them using few-shot learning and federated learning approaches [53]. The integration of advanced learning techniques enables networks to proactively respond to emerging threats, thus maintaining resilience against the vulnerabilities induced by botnets [55]. Other methods, such as the Stockwell transform, can complement the analysis of important features, such as packet length, in time-frequency analysis. This helps to improve the detection of intrusion anomalies in complex environments [56].  
Overall, 'Fwd Packet Length Mean' is an important indicator of potential vulnerabilities in IoT networks. Analysis of this feature via several machine learning frameworks is essential to enhance the level of security against various botnet attacks [57].
## 3 Sensitivity Optimization
### Finite Element Modelling
#### 3.1.3 Experimental Studies for Frequency Optimization
However, the same frequency shift was not observed in the experimental results; (2) 100 kHz was found to be the optimal frequency for uniform corrosion of 1 mm in 8 mm and 7 mm thick plates, but then the optimum frequency moved to 140 kHz in the simulation. In experimental studies, 100 kHz was the sensitive frequency for all plate thicknesses. This can be explained by considering the limitations of the FE modeling of magnetic materials especially close to magnetic saturation. The FE modeling requires the assignment of complete magnetic properties of the material on point-to-point basis [36]. It is not always possible to address the complete magnetic characteristics of a material. Such detailed information on the magnetic properties of the material is not always available. The FE models are indicative rather than definitive. It was observed that the error in the FE models is more obvious near saturation because of the limited capability of the simulation software to solve for the magnetic saturation phenomena. The results for the uniform corrosion of 1 mm in 8 mm and 7 mm plates are comparable for both the FE model and the experiments as depicted in Figure 10. Based on these results, 100 kHz was used to design the wireless MEC sensor as it has high sensitivity and is not too close to the zero-crossing point. Though a similar trend is observed, the values of reactance computed from the FE model are different than those obtained from the tests. This can be explained by the calibration system used by AD5933 evaluation board for reactance measurement. The reference resistor can be used for the amplification of the signals at lower frequencies up to a certain value, and therefore the variation in reactance in experiments is higher than the FE results. However, careful consideration is required in the calibration process as using a very large resistor can saturate the impedance measurement circuit. Figure 9: The schematic representation of the MEC principle.