esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analytical Method  
The model materials used in this study were austenitic stainless-steel SUS304 and DP980 sheets with thicknesses of 0.3 mm and 1.2 mm, respectively, and widths of 1.5 mm and which were studied in the literature [6; 7]. An in-plane uniaxial tension-compression test apparatus was used to prevent buckling of the specimens during the compression tests. In addition, a specially designed pure bending test apparatus used to measure the bending moment-curvature diagrams. Details of these devices can be found in the literature [6; 7].  
Some basic assumptions were considered in developing the analytical solution of pure bending as follows:  
* The plane section remains planar during pure bending;
* The sheet thickness is assumed to be constant during the process;
* The bending strain is proportional to its distance from the neutral surface.  
The 1-, 2- and 3-directions were assumed to be the circumferential (\(\theta\)), width (\(z\)), and radial (\(r\)) directions, respectively. The strain in the circumferential direction due to the pure bending was defined as:  
\[\varepsilon_{1}=\varepsilon_{b}=ln\bigg{(}1+\frac{y}{R}\bigg{)}\approx\frac{y }{R} \tag{1}\]  
where \(\varepsilon_{b}\) is the bending strain in the circumferential direction, \(y\) is the distance measured from the neutral plane and \(R\) is the bending radius.  
The neutral surface of pure bending is not always the same as the mid-surface. It can shift from the mid-surface for several reasons, some of which are applied tensile or compressive forces, TCA in yield points or the subsequent hardening. The TCA is only observed during the plastic bending and disappears for elastic bending. In this case, the neutral surface shifts to the outer or inner radius of the bending whenever the compressive yield point is lower or greater than the tensile yield point (RD and TD specimens in this study, respectively). Figure 1 shows the stress distribution through the thickness for an asymmetric material under pure bending. As it can be seen, the neutral surface with the length of \(L_{n}\) and the radius of \(R_{n}\) is not coinciding with the mid-surface with the length of \(L_{m}\) and radius of \(R_{m}\). The parameter \(d\) shows the coordinate from the mid-surface to the neutral surface.  
In this paper, to consider the effects of neutral surface shift, a pseudo-neutral surface was defined as suggested by [PERSON] et al. [16]. It is a surface where the strain equals the membrane strain and coincides with the real neutral surface in case of no applied tensile force. Hence, the general form of strain distribution of an asymmetric material for pure bending is given as:  
\[\varepsilon_{1}=\varepsilon_{b}=\frac{y-d}{R} \tag{2}\]
## Appendix A Theoretical Overview  
### The K-Means Clustering Algorithm  
K-means clustering is an unsupervised machine learning algorithm used to allocate data points into groups or clusters by identifying the mean distance between data points [69]. This process is then iterated to provide more precise classifications over the course of time. In k-means clustering, the basic concept is to choose a number (k) of centroids for each cluster. Since different positions of centroids produce distinct results, it is possible to place the centroids strategically. The better option, however, is to position them at large distances from each other. After defining the centroids, a point is taken from the dataset and linked to the closest centroid. This process is first performed for all the points to generate an initial grouping, then the \(k\) new cluster centroids are calculated accordingly. Based on the \(k\) new centroids, new associations are then made between the same dataset points and the closest new centroids. Thus, the centroids change their positions iteratively until no further adjustments can be made (i.e., the problem has converged to one solution). In brief, the algorithm minimizes an objective function (a squared error function in the present case) to form an association between centroids and dataset points. The objective function is given by Equation (2) [69]:  
\[J=\sum_{j=1}^{k}\sum_{i=1}^{n}\left\|x_{i}^{(j)}-c_{j}\right\|^{2}, \tag{1}\]  
where \(\|x_{i}^{(j)}-c_{j}\|^{2}\) is a chosen distance measure between data-point \(x_{i}^{(j)}\) and the cluster center \(c_{j}\) as an indicator of the distance of the \(n\) data points from their respective cluster centers.
## 3 Results
### Evaluating Data Latency, Coverage, and Display  
Meeting latency, spatial coverage, and display requirements are challenges when introducing a new satellite product to the tropical cyclone forecasting workflow. To estimate the minimum possible NUCAPS latency, we delivered the data to campaign organizers in real time using direct broadcast receivers. We found that the direct broadcast pathway reduced the NUCAPS latency to \(\approx\)30 min in the Atlantic region using the Miami, Florida and Puerto Rico antenna sites. Of the five flights, all had at least one satellite overpass within the range of the receiver antennas. The NOAA-20 overpass on 29 September 2019 was not in direct broadcast range over Hurricane Lorenzo (22.9\({}^{\circ}\)N, 45.0\({}^{\circ}\)W), which was much further east than the other flights. In fact, Lorenzo became the strongest category 5 storm to form over the eastern Atlantic basin [34]. However, while NOAA-20 was too far to the east, the Suomi NPP overpass was further west and in range, so NUCAPS was still delivered in real time. While tropical cyclones in the eastern Atlantic can be out of range, we found that the rapid delivery of NUCAPS was consistently possible over the southwestern Atlantic and Caribbean.  
We delivered NUCAPS to NOAA Hurricane Field Program organizers in a graphical Skew-T format and through web-based displays, which we customized for the campaign. For example, Skew-T plots included collocations with aircraft dropsondes, NUCAPS, and the NOAA Global Forecast System (GFS) to provide real-time validation to all team members. We delivered retrieval variables, such as cloud fraction and cloud top height, and profiles from multiple satellites, neither of which are available in operations due to SBN bandwidth limitations. The web display showed spatial maps of temperature and water vapor DOFs to visually assess the vertical resolution. These tools were a component of daily tropical weather map discussions presented by the NOAA/AOML/Hurricane Research Division.  
Through the JPSS proving ground and risk reduction program (PGGR), ideas demonstrated in these display tools were synthesized into the NASA Short-term Prediction Research and Transition Center (SPoRT) web display tool [35]. This work also led to the integration of real-time NUCAPS into the Sounding and Hodograph Analysis and Research Program in Python (SHARPpy) [36], which is an open-source software that emulates how AWIPS displays sounding data. Hurricane forecasters thus have an assortment of tools to  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Temperature (km)** & **Water Vapor (km)** \\ \hline
0–300 mb & 7.5 (3.5) & 5.5 (1.5) \\ \hline
300–600 mb & 5.9 (1.8) & 4.4 (1.2) \\ \hline
600–1000 mb & 1.7 (0.2) & 2.2 (0.4) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean vertical resolution (km) from successful IR + MW retrievals of temperature and water vapor for flight matches. Standard deviations are shown in parentheses. Statistics incorporate 112 NUCAPS profiles.  
access NUCAPS to augment NUCAPS in AWIPS-II once the migration from N-AWIPS is complete.
## 4 FRA Short Circuit Test  
This test was conducted similar to the above case study but with the low voltage windings shorted together to eliminate the effect of the magnetizing inductance of the magnetic core. This configuration can be modeled using the equivalent circuit shown in Figure 12 with the parameters listed in Table 5[7]. The proposed lumped RLC circuit model represented the transformer at normal, coking, and pitting at the end-to-end short circuit measurement.  
The measured and simulated FRA signatures for this configuration under coking and pitting conditions of different levels are shown in Figures 13 and 14, respectively. For the normal signature, the resonance frequencies were shifted toward a higher range due to the absence of a transformer core effect. The low and mid frequency regions' response was dominated by the leakage inductance and was characterized by a negative slope. At higher frequencies, the transformer was considered purely capacitive, and the response was approximately similar for the investigated cases shown in both figures.  
Figures 13 and 14 revealed a significant effect of the tap changer contact coking on the FRA signature in the low and mid-frequency ranges. This effect occurred due to the increment of transformer leakage inductance and the reduction in the winding resistance. Additionally, the resonance frequency was shifted towards higher frequencies due to the decrease in the magnetizing impedance. On the other hand, pitting shows no variation in the FRA signature at low frequencies. However, a hardly observable deviation in the FRA curve occurred at higher frequencies because of the slight reduction in the series and ground capacitances with no deviation in the resonance frequency.  
Figure 12: The RLC circuit model for the tap changer under the FRA short circuit test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **Lp (mH)** & **Cp (pF)** & **Rs \(\Omega\)** & **Ls (mH)** & **Cs (nF)** \\ \hline Normal & 1 & 0.10 & 1 & 100 & 1.12 \\ Fitting & 1 & 0.10 & 1000 & 100 & 1.12 \\ Coking & 1 & 0.83 & 1000 & 50 & 1.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Values of simulated short circuit model parameters.
## 6 Conclusions  
This paper surveys 40 wireless sensor network deployments described in the research literature. Based on thorough analysis, design rules for WSN operating system design are proposed. The rules include suggestions related to the task scheduler, networking protocol and other aspects of OS design. Some of the most important concluding design rules:  
* In many cases, customized commercial sensor nodes or fully custom-built mites are used. Therefore, OS portability and code reuse are very important.
* Simplicity and extensibility should be preferred over scalability, as existing sensor networks rarely contain more than 100 nodes.
* Both preemptive and cooperative task schedulers should be included in the OS.
* Default networking protocols should be sink-oriented and use CSMA-based MAC and proactive routing protocols. WSN researchers should be able to easily replace default networking protocols with their own to evaluate their performance.
* Simple time synchronization with millisecond (instead of microsecond) accuracy is sufficient for most deployments.  
The authors believe that these design rules will foster more efficient, portable and easy-to-use WSN operating system and middleware design.  
Another overall conclusion based on analyzed data-existing deployments is rather simple and limited. There is still the need to test larger, more complex and heterogeneous networks in real-world settings. Creation of hybrid networks and \"networks of networks\" are still open research topics.
## 3 Results  
### Actual NPP Simulated by BEPS  
The total actual NPP (ANPP) simulated by the BEPS model in eastern Inner Mongolia ranged from 120.6 Tg C yr\({}^{-1}\) to 198.5 Tg C yr\({}^{-1}\)(1 Tg = 10\({}^{12}\) g), with an average of 152.3 Tg C yr\({}^{-1}\) over the period of 2000-2017 (Figure 3), and increased at a rate of 3.1 Tg C yr\({}^{-1}\) since 2000. The grasslands' total ANPP ranged from 36.3 Tg C yr\({}^{-1}\) to 75.6 Tg C yr\({}^{-1}\), with an average of 51.9 Tg C yr\({}^{-1}\). This ANPP of the grasslands took about 34.1% of the total ANPP in eastern Inner Mongolia, where the grasslands, however, covers 64.9% of the area.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Livestock** & **Cow** & **Horse** & **Donkey** & **Rule** & **Camel** & **Sheep** \\ \hline Young livestock & 2.5 & 2.5 & 2 & 2.5 & 3.5 & 0.7 \\ Adult livestock & 5 & 5 & 5 & 4 & 7 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: The coefficient of one sheep unit for livestock.  
The total ANPP of the grasslands varied significantly in Xilingol League, Xing'an League, and Hulunbubuir City, and the average value of the above three regions were 19.9 Tg C yr\({}^{-1}\), 12.2 Tg C yr\({}^{-1}\), and 19.7 Tg C yr\({}^{-1}\), accounting for 96.9%, 68.9%, and 17.3% of the total vegetation ANPP in the corresponding regions, respectively. With over 50% forest coverage, the proportion of the grasslands ANPP to the total vegetation ANPP in Hulunbuir City is the smallest among the three regions.  
The average annual ANPP of the grasslands during 2000-2017 in eastern Inner Mongolia is shown in Figure 4a. The annual ANPP showed a gradient increasing from southwest to northeast, and the values were mostly below 200 g C m\({}^{-2}\) yr\({}^{-1}\) in extensive areas of eastern Inner Mongolia. The annual ANPP of he grasslands in Xilingol League ranged from 74.5 to 146.1 g C m\({}^{2}\) yr\({}^{-1}\) during 2000-2017, with an average of 102.5 g C m\({}^{2}\) yr\({}^{-1}\), which was obviously smaller than that of Xing'an League (292.4 g C m\({}^{2}\) yr\({}^{-1}\)) and Hulunbuir City (242.1 g C m\({}^{2}\) yr\({}^{-1}\))during the same period. As shown in Figure 4b, the annual ANPP showed an increasing trend in most areas of eastern Inner Mongolia, and the average increase rates were 2.3 g C m\({}^{2}\) yr\({}^{-1}\), 11.6 g C m\({}^{2}\) yr\({}^{-1}\), and 5.7 g C m\({}^{2}\) yr\({}^{-1}\) in Xilingol League, Xing'an League, and Hulunbuir City, respectively.
###### Abstract
In the same spectrum, the proposed scheme can be blended with other known security mechanisms such as firewalls or Security Information and Event Management (SIEM) systems to further enhance access control policies and enable more efficient incident response and threat intelligence sharing across the entire security infrastructure. Regarding the nature of the attacks, there are only two assumptions. First, the attacks are based on long-lived network connections. While this method is generic within this context, attacks such as _LDoS_, _False Data injection_, _TCP session hijacking_, _reverse shells_, _data exfiltration_ constitute potential candidates for our proposed scheme. Second, the impact of the attack is _observable_ and possibly quantifiable. For example, in the case of LDoS attacks, an application or the entire system becomes unresponsive for a short amount of time; note, however, that the malicious connection(s) are hidden in plain sight, among the plethora of benign ones. Upon discovery, thanks to the proposed scheme, the malicious connections can be isolated, examined in further detail, or redirected to _honeypot_ systems. Behind the scenes, the proposed partitioning strategy is based mainly on two mechanisms. Namely, the _containerization_ technology, which is used to spawn replicas of existing server systems in a fast and inexpensive manner. Additionally, the mechanism of _live-migration_ of connections is employed to transfer living connections from an existing running system to another with minimal downtime. We anticipate that the described technique can provide another active defense tool in the quiver of defenders, allowing them to adapt to attacks in real time. Our key contributions can be summarized as follows. * We formulate the problem, presenting its parameters and key performance indicators. * We provide the blueprints of a generic solution strategy. * We detail an implementation of our connection partitioning strategy prototype. * We perform simulations based on the provided metrics to demonstrate the feasibility of our approach at scale. The next section provides a brief overview of the relevant works in the area. In Section 4, we delve into the problem, the proposed methodology, key assumptions, and suggest performance metrics. The evaluation of the prototype implementation of the proposed scheme is presented in Section 5. Finally, the conclusions and future directions of our research are outlined in Section 6.
## 4 Results--A Case Study
### Case Study Description  
Commonly, the development of a metro network is a process lasting many years. The considered case differs here, because the center of Upper Silesia region is an agglomeration of a dozen of cities, which practically form a uniform urban structure, but due to the lack of funding there was no attempt to build a common metro communication system in the past. There are lots of bus and trans lines, which serve over 1 million passengers daily. There is also a network of railways, but the train frequency is much too low to serve as typical urban trains. Very dense road network including two motorways and an urban motorway crossing the centers of most of cities cause many citizens to use their own  
because metro networks often cross the city boundaries and the size of serviced population cannot be determined exactly.  
The dependence between the population and the number of metro lines is not strict, but some regularity can be observed (Figure 2).  
As is can be noted except the case of New York (off-standing red square), all other points are more or less aligned along the regression line. The conclusion is as follows: one metro line should be for about 800,000 citizens. In the same way, the overall number of stations can be determined (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**City** & **No of Lines** & **No of Stations** & **Total Length [km]** & **Population [10\({}^{6}\)]** & **Area [km\({}^{2}\)]** \\ \hline London & 11 & 270 & 402 & 8.20 & 1579 \\ Berlin & 10 & 173 & 146 & 3.40 & 892 \\ Paris (agglomeration) & 16 & 302 & 218 & 11.90 & 12,000 \\ Moscow & 12 & 200 & 333 & 12.20 & 2511 \\ Beijing & 19 & 344 & 574 & 18.59 & 16,800 \\ Prague & 3 & 61 & 65.5 & 1.28 & 496 \\ Shanghai & 14 & 364 & 588 & 24.15 & 6341 \\ New York & 27 & 468 & 368 & 9.00 & 1213 \\ Los Angeles & 6 & 80 & 141 & 3.93 & 1302 \\ Rome & 3 & 60 & 74 & 2.87 & 1287 \\ Istanbul & 5 & 73 & 95.3 & 14.80 & 1539 \\ \hline \end{tabular}
\end{table}
Table 2: Selected metro networks in the world.  
Figure 3: Dependency between the number of metro lines and the number of the stations.  
Figure 2: Dependency between the population size and the number of metro lines.  
In contrast to the previous issue, there is no exception here--the regression line fits the data points very well, and the average value of 19 stations per line can be stated as a conclusion.  
There is a population of about 2,700,000 inhabitants living in considered region; therefore, three metro lines were assumed with, respectively, 54 stations. Additionally, some calculations were done for four lines, keeping the same number of stations.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 3 Related Work
Because of the lack of diversity in the experimental design, their results may not cover many particularities or situations of real-world scenarios. The literature review is summarized in Table 1 considering drift detectors and types of drift explored. Regarding the approaches mentioned in this manuscript to deal with concept drift, they are generally applied in specific scenarios with limited comparisons among drift detectors. The detectors may exhibit different behaviors in each type of scenario. Thus, considering \begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference \#** & **Drift Detectors** & **Types of Drift Explored** \\ \hline [PERSON] et al. [2] & DDM & Abrupt; gradual \\ \hline [PERSON] et al. [13] & EDDM, and DDM & Abrupt; gradual \\ \hline [PERSON] et al. [14] & HDDMW, DDM, ADWIN, and ECDD & Abrupt; gradual \\ \hline  & DDM, EDDM, ADWIN, & \\  & ECDD, STEPD, SEQDRIFT, & \\ [PERSON] and [PERSON] [24] & SEED, HDDMA, HDDMW, & \\  & FHDDM, FTDD, RDDM, & \\  & and WSTD & \\ \hline [PERSON] et al. [25] & DDM, EDDM, ADWIN, & \\  & STEPD, ECDD, SEQDRIFT, & \\  & HDDMA, HDDMW, FHDDM, & \\  & RDDM, and WSTD & \\ \hline  & ADWIN, DDM, EDDM, & \\  & ECDD, FHDDM, FTDD, & \\  & GMA, HDDMA, HDMMW, & \\  & PH, RDDM, SEED, STEPD, & \\  & SEQDRIFT, and WSTD & \\ \hline [PERSON] et al. [27] & DDM, EDDM, ADWIN, & \\  & HDDMA, and HDDMW & \\ \hline  & NB, PL, ADWIN, DOF, DDM, & \\  & ECDD, EDDM, PHT, & \\  & and STEPD & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of most commonly applied, drift detectors and types of concept drift by contributions in related works. them in a general context, most authors do not provide useful information on specific data distributions. Therefore, this paper aims to provide further information about drift detectors using the proposed synthetic dataset technique, exploring different metrics in different contexts.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 4 Experiment
### Evaluation Criteria for Target Recognition Results  
This study employed four metrics, namely, accuracy, precision, recall, and F1 Score [29], to assess the experimental outcomes. Accuracy (_Acc_) is a measure that calculates the ratio of correctly predicted samples by the classification model to the total number of samples. It provides a measure of the overall prediction accuracy of the model across all samples. Precision (_Pre_) is the quotient obtained by dividing the number of accurately anticipated positive samples by the total number of samples predicted as positive. It measures the precision of the model's predictions for the positive category. Recall (_Rec_) is the quotient obtained by dividing the number of accurately predicted positive category samples by the total number of actual positive category samples. It quantifies the rate at which the model correctly identifies positive category samples. The F1 score (_F1_) is a metric that quantifies the balance between prediction accuracy and recall by calculating their harmonic mean. It considers both aspects to provide a comprehensive evaluation of the model's performance. For multi-classification problems, the construction of the confusion matrix can be shown in Table 3.  
Among the four sets of formulas, _PP_ is the count of positive recognition outcomes from the recognition model, where the actual label is also positive. _PN_ denotes the count of recognition outcomes from the model that is accurate, but the corresponding true label is  
Figure 17: Time domain data framing results.  
Figure 18: Delay-Doppler (**a**) and Time–Frequency (**b**) features of a passenger ship.  
incorrect. \(NP\) denotes the count of recognition results from the recognition model that is incorrect, while the actual label is correct. \(NN\) represents the number of recognition results from the recognition model that are both false and have a false actual label. The formulas for accuracy, precision, recall, and F1 score are derived from the confusion matrix.  
\[Acc = \frac{PP+NN}{PP+PN+NP+NN} \tag{5}\] \[Pre = \frac{PP}{PP+PN}\] (6) \[Rec = \frac{PP}{PP+NP}\] (7) \[F1 = \frac{2\times Pre\times Rec}{Pre+Rec} \tag{8}\]
## 5 Discussion
### Limitation of the Simulation Study  
A limitation of our work is that the simulation study did not cover all possible types of scenarios that may occur in real-world applications and did not incorporate coefficient settings from real-world data. In addition, we considered only classical linear models, assuming linearity, no interactions, no outliers or influential points, and homoscedasticity. These issues could have been included to improve the reliability of the methods recommendations.