esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intrusion Detection System for RPL-Based IoT Networks
###### Abstract  
The Internet of Things (IoT) has become very popular during the last decade by providing new solutions to modern industry and to entire societies. At the same time, the rise of the industrial Internet of Things (IIoT) has provided various benefits by linking infrastructure around the world via sensors, machine learning, and data analytics. However, the security of IoT devices has been proven to be a major concern. Almost a decade ago, the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was designed to handle routing in IoT and IIoT. Since then, numerous types of attacks on RPL have been published. In this paper, a novel intrusion detection system (IDS) is designed and implemented for RPL-based IoT. The objective is to perform an accurate and efficient detection of various types of routing and denial-of-service (DoS) attacks such as _version number attack_, _blackhole attack_, and _gravhole attack_, and different variations of flooding attacks such as _Hello float attack_, _DIS attack_, and _DAO insider attack_. To achieve this, different detection strategies are combined, taking advantage of the strengths of each individual strategy. In addition, the proposed IDS is experimentally evaluated by performing a deep analysis of the aforementioned attacks in order to study the impact caused. This evaluation also estimates the accuracy and effectiveness of the IDS performance when confronted with the considered attacks. The obtained results show high detection accuracy. Furthermore, the overhead introduced in terms of CPU usage and power consumption is negligible. In particular, the CPU usage overhead is less than 2% in all cases, whereas the average power consumption increase is no more than 0.5%, which can be considered an insignificant impact on the overall resource utilisation.  
RPL; industrial IoT; intrusion detection; routing attacks; DoS attacks Article  
An Intrusion Detection System for RPL-Based IoT Networks [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]
## 2 Materials and Methods
### Datasets Used  
This study used Level 1 Single-Look-Complex (SLC) SAR dataset acquired by Sentinel-1 radar system equipped with C-band. This C-band has a radar wavelength of 5.6 cm. The images were acquired using the Interferometric Wide (IW) swath mode with co-vertical polarization (VV). Sentinel-1, a constellation of two satellites, is in a near-polar, sun-synchronous orbit. Therefore, it passes over the AOI in both ascending and descending flights with a 12-day repeat cycle alone. The SAR data are freely shared in public. The Sentinel-1A was launched in 2014 and the Sentinel-1B in 2016 through the COPENICUS Programme of the European Space Agency (ESA). ESA's Sentinel-1 missions operate in Terrain Observation by Progressive Scans SAR (TOPSAR) mode which provides a SAR image with wide area coverage of around \(250\times 200\) km. The IW swath mode captures three swaths comprising a series of bursts. The SAR image has the moderate ground resolution of approximately \(14\times 4\) m in azimuth (parallel to the flight direction) and range (perpendicular to the flight direction) directions, respectively. Orbit state vectors in the metadata of the acquired SAR products are updated using the Sentinel-1 Precise Orbit Ephemerides (POE) in order to provide refined satellite position and velocity information. Aside from SLC dataset, Ground Range Detected (GRD) and Ocean (OCN) data can also be searched and ordered from the COPENICUS Sentinel Open Access Hub or Alaska SAR Facility. Table 10 lists the characteristics of Sentinel-1 missions.  
A total of 114 Sentinel-1 SAR scenes were processed: two images for Seogwipo (Site A, descending track 134), three images for Pohang (Site B, descending track 61), 78 images for South Chungcheong province (Site C, descending track 134), and 31 images for Gyeongju (Site D, descending track 61). All images were acquired at night to ensure high signal-to-noise ratio (SNR) and high coherence on  
Figure 3: Approximate area of slope failure and slope stabilization works. Photos **(a,b)** were taken in November 2019. Photos (**c,d**), and panoramic view were taken in August 2020.  
the AOIs and mitigate atmospheric delay in the interferometric phase [43; 44; 45]. Figure 4 shows the Sentinel-1 images connection graph for the A-DInSAR analysis performed in Site C and Site D.  
Table 1 lists the summary of applications of interferometry in various geotechnical engineering related problems investigated in South Korea.
## 2 System Architecture and Requirements  
The proposed receiver with active SIC is shown in Figure 1. The analog RF canceler senses the TX signal from the PA and cancels out the modulated TX leakage signal at the input of the LNA. An auxiliary receive path is used to sense the broadband TX noise and cancel it out in the digital domain, as reported in [24]. Digital cancellation also allows to improve the cancellation level provided by the RF canceller and to extend the cancellation bandwidth. This issue however, is not covered in this work due to space limitations. The choice of cancelling the leakage signal at the LNA input relaxes the stringent linearity requirement of the entire receive chain, including the LNA, both in terms of IIP2 and IIP3, and reciprocal mixing is strongly reduced. However, the noise of the canceler is directly seen  
Figure 1: System block diagram.  
at the receiver input port and, therefore, the canceler must be designed with lower noise contribution. Canceling the SI after the LNA, e.g., as proposed in [25], could result in LNA clipping and receiver desensitization at large SI levels. Similar considerations apply to the SI architecture proposed in [22], where SIC is carried out at baseband. Furthermore, the introduction of a down-conversion mixer in the SIC path may cause reciprocal mixing with the LO phase noise, further increasing the RX noise.
## 3 Results  
### Correlation between Vegetation Indices at Different Phenological Stages  
Different weather conditions exist on different observation dates, and due to seasonal changes, variations in the solar zenith angle may cause changes in the correlation between vegetation indices and \(f\)PAR on different measurement dates. In this study, the near-ground reflectance of all 11 measurement days is simulated as Sentinel-2 vegetation indices, and three estimation models (linear, exponential, and logarithmic) were established with \(f\)PAR measured by SunScan. The coefficient of determination R\({}^{2}\) was also calculated. The results of linear correlation are shown in Table 4.  
In terms of linear correlation, MNDVI is the most stable. In the 11 measurement results, the correlation coefficient was in the top five for all vegetation indices ten times. NDVI and OSAVI followed with a total of eight occurrences, and NDPI had six occurrences within the top five. This indicates that MNDVI can achieve a high level of estimation capability for \(f\)PAR under different light conditions throughout the critical growth period. The results of the exponential correlation are shown in Table 5.  
In the exponential correlation between \(f\)PAR and vegetation indices, OSAVI is the most stable. On all eleven observation dates, the coefficient of determination is in the top five for all vegetation indices. NDVI and MNDVI follow with ten occurrences each, while NDPI appears in the top five eight times. The results of the logarithmic correlation are shown in Table 6.  
In logarithmic correlation, OSAVI still performs the best, followed by NDVI and NDPI. However, the coefficient of determination R\({}^{2}\) is lower compared to linear and exponential correlations.  
Through the analysis of these three correlations, the correlation results are consistent with [PERSON] [33], indicating the reliability of our observational accuracy. The differences in the correlations indicate that variations in weather conditions, water stress levels, and crop varieties during the observation period led to
# A Multi-Scale Video Longformer Network for Action Recognition
## 4 Experiments
### Hyperparameter Tuning
#### 4.4.3 Passing Global Tokens to the Next Stage  
In each stage, global tokens generate global features by attending to all local tokens. However, in the standard ViL structure, only input features were passed to the next stage, and old global tokens were discarded, with new global tokens initialized in the patch embedding. We conducted experiments to evaluate the impact of sequentially passing global tokens to the next stage. To ensure the smooth transfer of global tokens from the previous stage to the next stage, we trimmed a portion of the global tokens' data during the transfer process to match the dimensions of the next stage. The comparative results are presented in Table 5. It was observed that passing global tokens to the next stage had no impact on FLOPs and Params but improved top-1 accuracy by 1.56% and top-5 accuracy by 0.5%. Furthermore, we noticed that the practice of passing global tokens leads to faster network convergence during the early stages of training (within the first 20,000 steps), as shown in Figure 8.  
We believe that one significant reason for the improvement in accuracy is that these global tokens contain global features, which represent essential spatiotemporal features of the video and allow local tokens to better understand the video context. In Figure 10, we visualize the global tokens passed at different stages. It can be observed that the global tokens passed from lower stages contain more specific spatial features, while those passed from higher stages contain more abstract features. Additionally, we also noticed the presence of shadows of human actions from the previous frame in the global tokens passed from stage 1 to stage 2. This further validates our hypothesis that global tokens capture both temporal and spatial features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(n\) & **Top-1/\%** & **Top-5/\%** & **FLOPs/G** & **Params/M** \\ \hline (1, 1, 9, 1) & 69.41 & 88.37 & 156 & 28.5 \\ (1, 2, 8, 1) & 71.38 & 88.92 & 158 & 27.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablations about how many 3D Longformer AttenBlocks and MlpBlocks to insert.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _Passing_ & **Top-1/\%** & **Top-5/\%** & **FLOPs/G** & **Params/M** \\ \hline Yes & 72.94 & 89.42 & 158 & 27.3 \\ No & 71.38 & 88.92 & 158 & 27.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablations about whether to pass global tokens to the next stage.
## 1 Introduction  
Computer networks and telecommunications play a significant role in information exchange. An increase in valuable information, along with enabling technology expansions, have led to increases in threats. The sources of these threats are not only from outside but, also, from within the organization. Such threats possess a large security risk and are seemingly difficult to detect [1; 2; 3]. Insider threats can inflict critical damage on the reputation, financial assets, and intellectual property of enterprises. A 2018 report on the insider threat has stated that slightly more than half of threats (53%) in the past 12 months came from inside of organizations. Moreover, 27% of surveyed organizations have stated that attacks originated from within the organizations [4]. In the previous decade, many incidents of insider threats have gradually reached the media; these include well-known cases of data leakage conducted by [PERSON] and [PERSON] [1]. Thus, most organizations implement cybersecurity techniques, such as firewalls, intrusion detection, and electronic access system, to protect data not only from outsider threats but, also, from potential malicious insiders.  
To fend off malicious insiders, organizations should have an insider threat detection system that can detect and mitigate malicious insiders before they perpetuate threats. Unfortunately, the insider threat field is not sufficiently understood. Moreover, the detection mechanisms or approaches that can be used and the limitations of existing solutions remain unexplored. Thus, an urgent review on available insider threat detection systems is needed [5]. The current scenario is primarily due to the lack of knowledge on insider threat and its potential damage to organizations.  
This study aims to discuss the insider threat problem in a descriptive and analytic manner by studying the insider or attacker to understand its nature, such as insider access, types of insider, insider motivation, insider profiling, effect security property, level of insider, and method and actions used by the insider, and highlighting the researchers' achievements. Furthermore, our objectives in this study are, first, to summarize previous studies conducted on insider threat detection systems and, second, to categorize the field of insider threats based on insider and detection systems; we classified them mainly by the type of study, such as development scenarios based on behaviors analyzed. The detection mechanisms or approaches can differ through the type of analyzed behavior [5], techniques, datasets used to evaluate the proposed solution, detection methodology, and evaluation metrics. In this work, the major contributions are summarized as follows:  
* Classifying the field of insider threats based on the insider as an actor and insider threat detection systems.
* Analyzing real insider threat cases based on the subsection of the proposed classification.
* Discussing challenges and recommendations in the field of insider threats.  
Figure 1 presents the remaining sections of the article in detail.  
Figure 1: Article outline.
###### Abstract  
To evaluate the flavor characteristics of Chinese bayberry alcoholic beverages, fermented bayberry wine (FBW) and integrated bayberry wine (IBW) were investigated for their volatile and soluble profiles using HS-SPME GC-MS and UHPLC Q-TOF and were analyzed with multidimensional statistical analysis, including PCA and OPLS-DA. The volatile compounds 1-pentanol, \(\beta\)-caryophyllene and isopentanol were only detected in IBW. \(\beta\)-caryophyllene, the key flavor component of bayberry, was found to be the most abundant volatile compound in IBW (25.89%) and was 3.73 times more abundant in IBW than in FBW. The levels of ethyl octanoate, ethyl nonanoate, and ethyl decanoate were also several times higher in IBW than in FBW. These compounds contributed to the strong bayberry aroma and better fruits flavor of IBW. On the other hand, high levels of ethyl acetate and octanoic acid in FBW, representing pineapple/overripe or sweat odor, were key contributors to the fermented flavor of FBW. Soluble sugars, such as sucrose, D-glucose, and D-tagatose, as well as amino acids, such as L-glutamate and L-aspartate, had much higher levels in IBW. The anthocyanin pigment cyanidin 3-glucosidase, which generates red color, was also higher in IBW. On the other hand, most of the differentially expressed alcohols, acids, amino acids, purines/pyrimidines and esters were present in higher concentrations in FBW compared to IBW. This demonstrated that IBW has a much sweeter and more savory taste as well as a better color generated by more anthocyanins, while FBW presents a more acidic and drier taste as well as a complex formation of alcohols and esters. The study also prompts the need for further research on the flavor profiles of IBW and its potential application and market value.  
Chinese bayberry; fermented bayberry wine; integrated bayberry wine; HS-SPME GC-MS; UHPLC Q-TOF 2023  
24 February 2023  
1-28 March 2023  
Accepted: 3 April 2023  
Published: 6 April 2023
## 3 Methods
### Interpretable Methods
#### 3.4.2 Shapley Additive Explanation (SHAP)  
The objective of interpretable ML is to understand how models make predictions and to answer questions such as what relationships between input and output are most important in driving the prediction [52]. SHAP is a method based on the cooperative game theory that gives close results to the original predictive model to explain complex ML models [53; 54]. SHAP is capable of providing the analysis of local interpretability and the effects of feature interaction.  
SHAP calculates the contribution of each feature to a particular prediction using Shapley values, allowing local interpretation. The Shapley value is defined as follows:  
\[\Phi_{i}=\sum_{S\subseteq N\setminus\{i\}}\frac{|S|!(|N|-|S|-1)!}{|N|!}[f_{x} (S\cup\{i\})-f_{x}(S)] \tag{10}\]  
where \(N\) denotes the set of all features. \(S\) is any subset of features that does not contain feature \(i\). \(|S|\) is the number of features in the set \(S\). The meaning of the bracket part of Equation (10) is that the contribution of feature \(i\) can be defined as a marginal contribution, i.e., the difference between the profit obtained by subset \(S\) only: \(f_{x}(S)\) and that of both feature \(i\) and the subset: \(f_{x}(S\cup\{i\})\).  
In addition, SHAP interaction values measure the effects of feature interaction, revealing more complex relationships between input variables. The SHAP interaction value of each of the \(i\)th features with the \(j\)th features is calculated as Equation (11).  
\[\Phi_{i,j}=\sum_{S\subseteq N\setminus\{i,j\}}\frac{|S|!(N-|S|-2)!}{2(N-1)!} \bigtriangledown_{i,j}[f_{x},S] \tag{11}\]  
When \(i\) is not equal to \(j\), we have:  
\[\bigtriangledown_{i,j}[f_{x},S]=f_{x}(S\cup\{i,j\})-f_{x}(S\cup\{j\}-[f_{x}(S \cup\{i\})-f_{x}(S)]) \tag{12}\]  
where \(\Phi_{i,j}\) denotes the SHAP interaction value of the \(i\)-th feature with the \(j\)-th feature. \(\bigtriangledown_{i,j}[f_{x},S]\) is the joint incremental gradient of the \(i\)-th feature and the \(j\)-th feature in the set S. The incremental gradient of the \(j\)-th feature in the set S is the incremental gradient of the \(i\)-th feature.  
Moreover, based on the SHAP values, it is possible to plot a SHAP feature summary plot, providing a global interpretation of SHAP. Each point in the SHAP feature summary plot represents a real sample, and the color of the point reflects the size of the factor value, the larger value being redder and the smaller value being bluer. The horizontal coordinate is the SHAP value to measure the degree of contribution and impact of the influencing factors on the predicted values of the model, with positive and negative values representing positive or negative contributions, respectively. The vertical coordinate represents the importance of each feature.
# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 2 Materials and Methods  
### iCTU-Net  
The design of our iCTU-Net displayed in Figure 1a is based on the iCT-Net by [PERSON] et al. [26], which in turn is inspired by the classical FBP. The reconstruction is trained end-to-end, i.e., the inputs of the iCTU-Net are sinograms and the outputs are reconstructed images. The network includes pre-processing layers and aims to emulate the filtration of the sinograms and the back projection into the image domain. Post-processing layers were used to further refine the reconstruction. The network performs the complete CT image reconstruction and does not require a predefined back projection operator or the exact X-ray beam geometry.  
In a first step, disturbances in the raw measurement data, such as excessive noise, are supposed to be suppressed using \(3\times 3\) convolutions (refining layers). The corrected sinogram is then filtered via \(10\times 1\) convolutions (filtering layers). By using \(1\times 1\) convolutions after the refining and filtering layers and by applying padding in all convolutions, the refined and filtered sinogram maintains the same size of the input sinogram. The convolutions in the refining layers employ a shrinkage activation function with a threshold of 0.0001 [26]. For the filtering layers, a tanh activation function is used. Afterwards, the refined and filtered sinogram is projected into the image space in a back projection step. This is realized by a \(d\times 1\) convolution with \(N^{2}\) output channels without padding, where \(d\) is the number of detector elements and \(N\) is the output image size. This convolution connects every detector element with every pixel in the image space. Since the back projection is learned, sinograms acquired with different beam geometries can be used to train the network, such as parallel beam and fan beam. Then, the results for each view angle \(v\) are reshaped to images of size \(N\times N\) and rotated according to the acquisition angle. The acquisition angle of the projections is the only geometrical information provided to the network. The rotated images are linearly interpolated and cropped to maintain an image size of \(N\times N\). The back projected image is then obtained by combining all views with a \(1\times 1\) convolution using a leaky Rectified Linear Unit (ReLU) activation function [27]. Finally, the image output is further refined by a U-Net. The U-Net is a popular choice for post-processing to reduce artifact in CT imaging [28].
# The Exploitation of Lysosomes in Cancer Therapy with Graphene-Based Nanomaterials
## 5 The Role of Lysosomes in GNM-Based Cancer Therapy
The resulting GPP complex was then loaded with the ICG. The ICG/GPP entered MCF-7 breast cancer cells by endocytosis. ROS induced the breakage of diselenide bonds between GO and PAMAM-Poloxamer 188, resulting in the release of ICG. The proton sponge effect combined with oxidative stress led to the destabilization of the lysosomal membrane, allowing for the lysosomal escape of the drug. Finally, upon NIR irradiation, GO produced heat while ICG produced both heat and ROS, leading to tumor cell death [13]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Graphene-Based DDSs** & **Cells** & **Drug Detachment Mechanism** & **Escape Mechanism** & **Ref** \\ \hline GPCP/miR-2 li/ICG & MDA-MB-231 & \begin{tabular}{c} Low pH \(\rightarrow\) charge conversion of PLL-Cit from \\ positive to negative \(\rightarrow\) conformational changes of \\ PAMAM \(\rightarrow\) cleavage ICG/graphene interaction \\ \end{tabular} & Proton sponge effect & [12] \\ \hline ICG/GPP & MCF-7 & \begin{tabular}{c} ROS \(\rightarrow\) cleavage of diselenide bond between GO \\ and PAMAM-Poloxamer 188 \(\rightarrow\) ICG release \\ \end{tabular} & Proton sponge effect & [13] \\ \hline GO/AuNS-PEG/Ce6 & EMT6 &? & Heat and ROS disrupted \\ \hline FPS-Cur & HeLa & \begin{tabular}{c} Hydrolysis of amide links + charge reversion to \\ positive FPS \(\rightarrow\) electrostatic repulsion \(\rightarrow\) structure \\ opening \\ \end{tabular} &? & [14] \\ \hline ICG/DOX/GO-PPF68 & MCF-7/ADR & NIR \(\rightarrow\) ICG \(\rightarrow\) ROS \(\rightarrow\) cleavage of diselenide bond & Proton sponge effect & [15] \\ \hline DOX@-GO@AuNR & HeLa & Low pH \(\rightarrow\) DOX release &? & [63] \\ \hline Ag-GO/DOX & Ca Ski & Low pH \(\rightarrow\) breakdown of \(\pi\)-\(\pi\) DOX/GO interaction &? & [59] \\ \hline NGO/DOX@SPC-FA & HeLa & Low pH \(\rightarrow\) protonation effect \(\rightarrow\) DOX release &? & [60] \\ \hline NGO-PEG-DA/DOX & MCF-7 & \begin{tabular}{c} Low pH \(\rightarrow\) DOX protonation \(\rightarrow\) \(\uparrow\) DOX \\ hydrophilicity/solubility + electrostatic repulsion \\ with charge reversion to positive NGO-PEG-DA \\ \end{tabular} &? & [40] \\ \hline PKSE7(PEI-GO) & HeLa & Low pH \(\rightarrow\) DOX protonation \(\rightarrow\) electrostatic &? & [39] \\ \hline GPC-NP and GDC-NP & HeLa & \
\begin{tabular}{c} Low pH \(\rightarrow\) DOX and proflavine release + cleavage \\ of carboxylate bonds and cisplatin release \(\rightarrow\) GO \\ converts from 3D into 2D \\ \end{tabular} &? & [61] \\ \hline GT/IR820/DP-CpG & EMT6 &? &
\begin{tabular}{c} Heat and ROS disrupted \\ lysosomal membrane \\ \end{tabular} & [64] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Lysosomes enable drug release from GNMs, which is followed by endosomal/lysosomal escape of the drug. \(\rightarrow\) denotes causal connection; \(\uparrow\) denotes increase; \(\uparrow\) denotes unknown mechanism.