esa-sceva/satcom-chunk-collection huggingface dataset:
# LAN Intrusion Detection Using Convolutional Neural Networks
## 2 Literature Review
It has been confirmed through thorough experimental testing that DNNs outperform traditional machine learning classifiers. Finally, the authors provide an AlexNet IDS with a hybrid scale, a highly hybrid and scalable DNN framework that can be used in real time to successfully monitor network host- and traffic-level events in order to forewarn of potential intrusions. The DNN with five layers obtained 78.9% accuracy on the dataset. [PERSON] et al. [25] introduced LIO-IDS, which is based on an LSTM classifier and an Improved One-vs-One approach. The introduced model is a two-layer anomaly-based network IDS that identifies various network intrusions with high accuracy in a short computing time. Using the LSTM classifier, layer 1 of LIO-IDS distinguishes intrusions from normal network data. Layer 2 classifies detected incursions into attack classes using ensemble methods. Because of its high attack detection rates and rapid computation durations, the recommended model is suited for deployment in the real world for network-based intrusion detection. The main aim of this paper is to check if DL algorithms are suitable for IDS based on anomalies systems. We created anomaly detection models using several DNN network topologies, such as auto encoders, recurrent neural networks, and CNN, for this study. These deep models were trained using the training dataset and evaluated using NSL-KDD training dataset, which is split by a sklearn split function. We conducted all of the tests in this work using a GPU-based test bed. Traditional ML-based models were created using well-known tools such as Nearest Neighbor, Decision Tree, Random Forest, Extreme Learning Machine, Support Vector Machine, Naive Bayes, and Quadratic Discriminate Analysis. The deep IDS models' test results demonstrate promise for practical applications in anomaly detection systems. DNN-based systems outperformed typical ML-based IDSs, according to the findings. DCNN, for example, attained 85% accuracy on the test data, a better performance than normal ML approaches, according to the trials. Finally, we concluded that DL is a promising technique for applicability in network security (InfoSec).
# ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
## 3 System Functionalities
### SeaCube: A Third Party Payload  
The SeaCube additional payload is a miniaturised and standardized structure designed to fit into the AUV hull, whereby one side is exposed to the sea water. Aim of the SeaCube is collecting and storing data independently by the other hosted sensors. This payload is inspired by the CubeSat technology, a miniaturized satellite with standard geometry and interfaces that often use off-the-shelf components. CubeSats are deployed from the International Space Station or launched to orbit as secondary payloads of a launch vehicle providing access to space for small experiments [83; 84]. Similarly to the CubeSat, the SeaCube aims to set-up a standard system for an additional \"third party\" payload allowing for an economic access to the ocean interior and affordable opportunities for Student, University or SME projects and finally to increase the sensor capacities of the AUV. Figure 5 summarize the structure of the SeaCube.
## 2 Material and Methods
### Experimental Design  
A total of 360 _M. rosenbergii_ juveniles (initial weight 0.028 \(\pm\) 0.004 g, mixed sex, male-female ratio 50:50%) were randomly allocated into 24 tanks (experimental units), with a 50 L capacity. Each tank had an individual aeration system and was coupled to a recirculation system (flow rate 800 L h\({}^{-1}\)) with mechanical (Perlon(r) wool filter; AquaUra, Uberaba, Minas Gerais, Brazil) and biological filters. Prawns were divided into four experimental groups with six replications (\(n\) = 6). The stocking density was 75 prawns m\({}^{-2}\), equivalent to 15 prawns tank\({}^{-1}\). The feeding trial lasted 42 days. The chosen duration of the feeding trial considered that indoor nurseries of _M. rosenbergii_ in tropical regions commonly last from two to eight weeks (43), with the usual time being 30 days. Prawns were fed five times daily (9:00, 11:00, 14:00, 17:00, 21:00) and 30 min after tanks were siphoned to remove feces and uneaten food. The initial feeding rate was equivalent to 5% of the biomass; the feeding rate was adjusted daily according to the feed consumption. Only animals in the intermolt period were used. Mortality was observed twice a day. The natural photoperiod was adjusted for a 12 h light/12 h dark cycle.  
Water parameters were within the recommended range for _M. rosenbergii_ farming (mean \(\pm\) SD): temperature (28.30 \(\pm\) 0.57 \({}^{\circ}\)C), dissolved oxygen (6.45 \(\pm\) 0.27 mg L\({}^{-1}\)), pH (8.46 \(\pm\) 0.10), hardness (124.67 \(\pm\) 20.77 mg CaCO\({}_{3}\) L\({}^{-1}\)), alkalinity (120.33 \(\pm\) 10.40 mg CaCO\({}_{3}\) L\({}^{-1}\)), total ammonia nitrogen (0.02 \(\pm\) 0.04 mg L\({}^{-1}\)), and nitrite (0.01 \(\pm\) 0.01 mg L\({}^{-1}\)). Temperature and dissolved oxygen were determined daily; total ammonia nitrogen and nitrite were monitored weekly; and hardness and alkalinity were measured biweekly.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 4 Results
### Number of Images  
Besides the sensor characteristics and environmental conditions, the number of images used for the developed time series approach is another important element that can influence the accuracy of the classification result. 25 S-1 images were used to derive flood-related classes for the study area in Namibia. The time series was used to obtain the statistical multi-temporal distribution of the backscatter values for the flood-related classes (see Section 3.2). For this purpose, it was analysed whether a lower number of images in the time series has an effect on the classification results. In each time series, fewer images were used starting with the original classification of 24 images decreasing in increments of 1, whereby the analysed flood date image was included in each classification run. Figure 15 shows the OAs and Kappa coefficients for 24 classification results, each based on time series with a different number of applied images. In addition, a confidence interval was calculated for each AO. The use of a single image, together with the image taken at the time of the flood results in the lowest accuracy. The same classification resulted in the largest confidence interval 0.19%. Overall, it can be observed that the fewer images are used in the time series, the lower is the OA and Kappa coefficient.  
Figure 13: Classification result of the S-1 FS (**a**), classification results of the S-1 FS supplemented by TOW areas of the time series approach (**b**), classification results of the S-1 FS supplemented by TOW and TFV areas of the time series approach (**c**), and validation image with merged flood areas (TOW + TFV) (**d**).
# Fuzzy Synthetic Evaluation of the Long-Term Health of Tunnel Structures
## 2 Determination of the Evaluation Methods and Ideas for the Tunnel Structural Health
### Choice and Implementation of Long-Term Health Monitoring Project  
The concept of structural safety based on stress is clear, and stress monitoring can easily and directly reflect the mechanical characteristics of the surrounding rock and supporting structure compared with the traditional deformation monitoring. Therefore, the tunnel structural health monitoring system based on long-term stress monitoring has been proposed and established gradually in recent years [3; 4]. At the same time, with the increase of tunnel service time, lining structures, especially primary supports, will deteriorate gradually during the operation. The load will be transferred to the secondary lining, which will gradually become an important bearing unit. Final tunnel safety will be reflected in the existing status of the secondary lining. Based on the above idea, the variation characteristics of the contact pressure (contact pressure means the pressure between the primary support and the secondary lining, similarly hereinafter) and the internal force of the secondary lining are major concerns for the long-term monitoring project of Cang Ling Tunnel (Figure 5). The embedded sensors, including pressure cells and strain gauges, are respectively located on vaults, both sides of the spandrel, and the side walls of the secondary lining. The pressure cells are located between the primary support and the secondary lining. They are fixed on the primary support with cross buckles before the secondary lining is applied. Two strain gauges are symmetrically buried in the inside and outside of each monitoring section. The embedding process is as follows: Firstly, the embedded strain gauges should be symmetrically strapped in the middle of the secondary lining main rebar with tying wires. Then, the wires should be guided along the waterproof to the reserved cavity located on the side walls of weak cable channel, which is convenient for networking and to subsequently transmit the monitoring data to the LAN. The variation of the contact pressure and the secondary lining stress are the focus when monitoring the entire life cycle of the Cang Ling Tunnel, which can help with judging the safety of the supporting structure. Figures of the reserved cavity, pressure cell, and strain gauge in the field are shown in Figure 6.  
Figure 4: The principal stress field distribution along the axial direction of Cang Ling Tunnel.  
Figure 5: Layout diagram of the long-term monitoring sensors in Cang Ling Tunnel.  
According to the rock condition, the grade of the surrounding rock, the section type, and the support parameters, the Cang Ling Tunnel is divided into 20 segments (Table 1). The characteristics of hydrogeology, stress field status, support types, and parameters are basically the same in each segment. In each segment, sensors are embedded in typical sections to carry out long-term monitoring.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## Appendix A Derivation of SINR Coverage Probability
### Laplace Transform for \(\zeta=\frac{2}{p+1}\)  
The LT for \(\zeta=\frac{2}{p+1}\) is derived in the following steps. Substituting \(s=\frac{\texttt{T}exp\left(\texttt{x}^{\zeta}\right)}{\texttt{C}_{\texttt{0}_{ BS}}\texttt{C}_{\texttt{0}_{UE}}}\) and \(\omega=uexp\left(-\texttt{x}r^{\zeta}\right)\) and letting \(a_{\texttt{x}_{k}}=\frac{a_{k}}{\texttt{C}_{\texttt{0}_{BS}}\texttt{C}_{ \texttt{0}_{UE}}}\), we obtain (A26)-(A31),  
\[\mathcal{L}_{l}(\mu_{N}) =\exp\left(\frac{2\pi\lambda T}{\zeta\left(\texttt{x}^{\zeta} \right)}\sum_{k=1}^{4}b_{k}\left[\int\limits_{0}^{1}\frac{a_{\texttt{x}_{1}} \left(ln(\omega)-\texttt{x}r^{\frac{2-\zeta}{1}}\right)^{p}}{1+Ta_{\texttt{x} _{0}}\omega}d\omega\right]\right)\] (A26) \[=\exp\left(\frac{\pi\lambda T(p+1)}{\left(-\texttt{x}\right)^{p+1 }}\sum_{k=1}^{4}b_{k}\left[\int\limits_{0}^{1}\frac{a_{\texttt{x}_{1}}\left(ln (\omega)-\texttt{x}r^{\frac{2-\zeta}{1}}\right)^{p}}{1+Ta_{\texttt{x}_{0}} \omega}d\omega\right]\right)\] (A27) \[=\exp\left(\sum_{q=0}^{p}\binom{p}{q}(-1)^{q}\texttt{x}q^{\frac{2 \pi}{p+1}}\frac{2q}{p+1}(-\texttt{x})^{p+1}\sum\limits_{k=1}^{4}b_{k}a_{ \texttt{x}_{k}}\prod\limits_{0}^{1}\frac{ln(\omega)^{p-q}}{1+Ta_{\texttt{x}_{ k}}\omega}d\omega\right)\] (A28) \[=\exp\left(\sum_{q=0}^{p}\binom{p}{q}\texttt{x}^{\zeta}r^{\frac{2 \pi}{p+1}}\frac{(p+1)\pi\lambda T}{\kappa^{p+1}}\sum\limits_{k=1}^{4}b_{k}a_{ \texttt{x}_{k}}\int\limits_{0}^{\infty}\left(\frac{-1}{\varepsilon+Ta_{\texttt{ x}_{0}}}\right)(c)^{p-q}dc\right)\] (A29) \[=\exp\left(\sum_{q=0}^{p}\binom{p}{q}\texttt{x}^{\zeta}r^{\frac{2 \pi}{p+1}}\frac{(p+1)\pi\lambda T}{\kappa^{p+1}}\sum\limits_{k=1}^{4}b_{k}a_{ \texttt{x}_{k}}\Gamma(p-q+1)Li_{(p-q+1)}(-Ta_{\texttt{x}_{0}})\right)\] (A30) \[=\exp\left(\sum_{q=0}^{p}\lambda a_{q}(Ta_{\texttt{x}_{k}})r^{ \frac{2\pi}{p+1}}\right)\] (A31)  
(A29) is obtained from substitution of \(\texttt{c}=ln(\omega)\) and from binomial expansion. (A30) is by using integral representation of polylogarithmic function where \(L_{i}(k)\) denotes \(k\)th order polylogarithmic function \(L_{i(p-q+1)}\left(-Ta_{\texttt{x}_{k}}\right)=-Ta_{\texttt{x}_{k}}\int_{0}^{ \infty}\left(\frac{-1}{\varepsilon+Ta_{\texttt{x}_{k}}}\right)^{p-q}cdc\). Letting \(\alpha_{q}\left(Ta_{\texttt{x}_{k}}\right)=\sum_{k=1}^{4}\frac{\pi(p+1)!}{q!a^ {p-q+1}}b_{k}L_{i(p-q+1)}\left(-Ta_{\texttt{x}_{k}}\right)\) and \(\alpha_{p+1}=-\pi\) in (A31) and averaging over the serving distance final expression for mmwave coverage probability in (12) is obtained.
## 6 Conclusions  
This paper investigates the maximum power transfer capability of grid-connected inverters, which is jointly determined by the SCR, the R/X ratio of grid impedance, and the PCC voltage amplitude. The maximum power curves in the inductive grid and resistive grid cases, with different SCRs and PCC voltages, are illustrated and benchmarked. It is revealed that increasing the SCR or reducing the R/X ratio of grid impedance can increase the maximum power transfer capability of the system. In order to have higher power transfer capability, SCR > 2 and R/X < 1 are recommended to be used. Moreover, under resistive grid conditions, a higher PCC voltage than the rated value is beneficial for power transmission. Based on this research, a suitable SCR can be designed for high  
Figure 10: Simulation results of the resistive grid case with \(R_{g}/X_{g}\) = 7 and SCR = 10. (a) \(V_{pcc}\) = 1 pu; (b) \(V_{pcc}\) = 1.05 pu.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Steady-State Error on \(P_{inv}\)**} & \(V_{pcc}\) = 1 pu & \(V_{pcc}\) = 1.05 pu & \(V_{pcc}\) = 1.1 pu \\ \hline \multirow{3}{*}{\(R_{g}/X_{g}=0.3\)} & SCR = 1 & 2.68\% & 0 & 0 \\ \cline{2-5}  & SCR = 2 & 0.08\% & 0 & 0 \\ \cline{2-5}  & SCR = 5 & 1.82\% & 0 & 0 \\ \cline{2-5}  & SCR = 10 & 2.90\% & 0 & 68.39\% \\ \hline \multirow{3}{*}{\(R_{g}/X_{g}=1\)} & SCR = 5 & 22.57\% & 4.97\% & 0 \\ \cline{2-5}  & SCR = 10 & 25.84\% & 0 & 22.22\% \\ \hline \multirow{3}{*}{\(R_{g}/X_{g}=7\)} & SCR = 5 & 76.03\% & 50.76\% & 25.33\% \\ \cline{2-5}  & SCR = 10 & 80.93\% & 31.78\% & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Power steady-state error comparison in different cases.  
penetration renewable generation systems in practice. Finally, simulation results verified the effectiveness of the theoretical analysis.  
Conceptualization, C.W.; methodology, L.H.; validation, L.H.; writing--original draft preparation, L.H.; writing--review and editing, C.W., D.Z. and F.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by THE VELUX FOUNDATIONS under the VILLUM Investigator Grant--REPEPS, grant number 00016591.  
Not applicable.  
The authors declare no conflict of interest.
# Seismic Risk Analysis of Existing Link Slab Bridges Using Novel Fragility Functions
## 2 Seismic Reliability Framework
### Annual Failure Rates  
Once both the hazard and the fragility are characterized, it is possible to proceed with the estimation of the mean annual failure rate, \(\
u_{E}(e)\), i.e., the mean annual rate of exceedance of a given failure event \(E\). For instance, the failure event \(E\) might be represented by the attainment of the collapse limit state for a combined damage scenario involving piers and bearings simultaneously (using fragility function of the type of Equation (3); or the attainment of a serviceability condition on the link bars according to one of the possible shades of analysis: at least one link damaged (using fragility of Equation (1)), a given percentage of them damaged (fragility of Equations (6) and (7)) and so on.  
The estimation of \(\
u_{E}(e)\) follows from the application of Total Probability Theorem [41] and stems from the following convolution integral  
\[\
u_{E}(e)=\int_{IM}P(e|im)\cdot|dv_{\text{IM}}(im)| \tag{8}\]  
between fragility function \(P(e|im)\) (any of the proposed ones) and seismic hazard \(\
u_{\text{IM}}(im)\). The above integral can be easily solved numerically, as graphically shown in the charts of Figure 5, which provide visual representations of the convolution between hazard (yellow curve) and fragility (blue curve) performed via the rectangular numerical integration technique (proposed for its simplicity). It is worth noting how fragility functions do not need to be monotonic within their domain of definition (as we are used to when classic fragility is adopted) but convolution can be performed regardless of this, as specifically shown in the explicative chart of Figure 5b where a non-monotonic fragility is assumed. This is important because the proposed fragility functions (Equations (3), (6) and (7)) can be notably non-monotonic because of the particular type of information they carry.
## 3 Results and Discussion
### Evaluation of Insulin Microparticle Formulations  
#### 3.2.1 Assay  
Table 1 shows the percentage of insulin content as determined by the assay of the insulin microparticle formulations prepared with different manufacturing techniques.  
The assay results displayed in Table 1 show that the insulin content was the highest (89.6%) in the microparticle formulation consisting of Eudragit\({}^{\otimes}\) L100 combined with \(A\). _vera_ polysaccharides (E + AVP emulsion), followed by the microparticle formulation consisting of \(A\). _vera_ polysaccharides only (AVP emulsion, 56.2%) that was prepared using the emulsion-solvent evaporation method. The microparticles prepared by the ionic gelation method, consisting of the \(A\). _vera_ polysaccharides only (AVP ionic gelation), showed the lowest insulin content (42.9%). During the emulsion-solvent evaporation technique, the drug molecules were concentrated in the droplets of the internal phase of the emulsion together with the polymer molecules, which formed the microparticles after solvent evaporation. A relatively low portion of the insulin molecules could be lost during microparticle formation into the continuous phase (mineral oil) due to low solubility in this phase. A portion of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Microparticle** & **Theoretical Insulin** & **Experimental Insulin** & **\% Insulin** & **Content** \\
**Formulation** & **Content** & & \\ \hline E + AVP emulsion & 1 mg of dried powder mass & 1 mg of dried powder & \\  & contains 0.0515 mg insulin & contains 0.0462 mg insulin & 89.6\% \\ AVP emulsion & 1 mg of powder contains & 1 mg of powder contains & 56.2\% \\ AVP ionic & 1 mg of dried powder & 1 mg of dried powder & \\ gelation & contains 0.0780 mg insulin & contains 0.03345 mg insulin & 42.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage insulin content of the microparticle formulations.  
the insulin may have been lost during the washing step from the microparticles consisting of _A. vera_ polysaccharides only. The relatively low insulin content of the microparticles produced by the ionic gelation technique can possibly be explained by the homogenous distribution of the dissolved insulin molecules throughout the entire solution, and only a portion of the insulin molecules could be captured in the microparticles formed through ionic gelation, while some of the insulin molecules remained in the solution surrounding the microparticles.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Data Imputation
#### 2.3.3 Data Segmentation  
After filling the space of missing samples with NULL values, the segmentation of the samples was performed to apply the imputation technique to extrapolate the missing samples. The samples were segmented in each excerpt with respect to its sample frequency rate. In the case of the accelerometer and gyroscope, the samples were segmented into a window of 100 samples having 90 known samples and the first 10 unknown samples. While in the case of the magnetometer, the samples were segmented into a window of 10 samples having 9 known samples and one unknown sample. If the missing samples count was less than or equal to 10 in the case of the accelerometer and gyroscope, then all unknown samples were included with the known samples to make a window of 100 samples. While in case of the magnetometer, if the missing samples count was less than or equal to 2, all unknown samples were included with the known samples to make a window of 10 samples.
# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
###### Abstract  
Edge detection methods are significant in medical imaging-assisted diagnosis. However, existing methods based on grayscale gradient computation still need to be optimized in practicality, especially in terms of actual visual quality and sensitivity to image contrast. To optimize the visualization and enhance the robustness of contrast changes, we propose the Contrast Invariant Edge Detection (CIED) method. CIED combines Gaussian filtering and morphological processing methods to preprocess medical images. It utilizes the three Most Significant Bit (MSB) planes and binary images to detect and extract significant edge information. Each bit plane is used to detect edges in 3 x 3 blocks by the proposed algorithm, and then the edge information from each plane is fused to obtain an edge image. This method is generalized to common types of images. Since CIED is based on binary bit planes and eliminates complex pixel operations, it is faster and more efficient. In addition, CIED is insensitive to changes in image contrast, making it more flexible in its application. To comprehensively evaluate the performance of CIED, we develop a medical image dataset and conduct edge image and contrast evaluation experiments based on these images. The results show that the average precision of CIED is 0.408, the average recall is 0.917, and the average F1-score is 0.550. The results indicate that CIED is not only more practical in terms of visual effects but also robust in terms of contrast invariance. The comparison results with other methods also confirm the advantages of CIED. This study provides a novel approach for edge detection within medical images.  
medical image; edge detection; bit plane; contrast invariance