esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Literature Review Summary  
Previous studies have shown that the success of ML algorithms depends on the quality of data. Current real-life network activities are characterized by a massive volume of high-dimensional data with irrelevant and redundant information. This negatively impacts the detection accuracy and model-building time of IDSs. Thus, it is crucial to identify or propose an effective ML algorithm pipeline in intrusion detection accuracy and model-building time processes in order to reduce data dimensionality and improve the data balance. Moreover, it is essential to have a well-structured benchmark dataset suitable for IoT networks to train ML algorithm models in order to identify malicious attacks on networks [33].  
Additionally, the dataset should represent a large number of attacks when we wish to discover them. Currently (and in the simulation lab), most ML algorithm models are trained on general datasets, such as KDDCUP99, NSL-KDD, and UNSW-NB15. However, they are rarely suitable for IoT networks due to the limited support for dedicated protocols used in IoT networks. Even the datasets used in the simulation lab and those related to IoT (i.e., N-BaIoT, IoT-23, TON-IoT, and CICIDS2017) are not sufficiently comprehensive to cover the need for IoT networks in real-life attack situations. Moreover, HCBDA is used for disease prediction and cannot be used for IoT network intrusion attacks. Furthermore, in the field of IDS and GANs, researchers have not considered some of the critical aspects, such as the training and testing time performance of the generated dataset; generating IoT-focused datasets; unifying features across multiple datasets; applying feature selection methods; the adoption of train on synthetic, test on real data (TSTR) as a performance measure; and including statistical performance metrics, leaving significant contribution gaps for further research and investigation.  
Thus, as shown in Figure 2, this study was designed to select diverse datasets to cover a broader number of attacks from the TON-IoT, MQTT-IoT-IDS2020, and Bot-IoT datasets with 12 attacks and a normal traffic dataset. Then, these datasets are applied as input for generating a synthetic dataset using GAN. This study also aims to identify the appropriate pipeline of ML algorithms for detecting attacks with the highest detection accuracy and shortest time for the development of an efficient IDS in protecting the IoT network. Table 1 summarizes the reviewed ML algorithm studies that focused only on training and testing times. It contains the proposed ML algorithm approaches and the selected datasets. It compares their efficiencies in terms of accuracy, training time, and testing time.  
Figure 2: A flow diagram of the data generation and performance assessment.
## 3 Results
### The Regression Analysis for RPE  
When multiple regression was calculated for RPE, the predictors explained 31% of the variance (f\({}_{3,767}\) = 114.607, \(p\) = 0.001). The significant partial regressors were TD (\(\beta\) = 0.001, \(p\) = 0.001), PWvh (\(\beta\) = 0.008, \(p\) = 0.001), and ACh (\(\beta\) = 0.009, \(p\) = 0.001) (Table 1). The RPE regression model obtained was as follows: RPE = 2.37 + 0.001 \(\times\) \"TD\" + 0.008 \(\times\) \"PWvh\" - 0.038 \(\times\) \"ACh\".  
For RPE\({}_{\text{dur}}\), the predictors explained 58% of the variance (f\({}_{3,766}\) = 363.097, \(p\) = 0.001). The significant partial regressors were DUR (\(\beta\) = 4.777, \(p\) = 0.001), TD (\(\beta\) = 0.036, \(p\) = 0.001), and PWvh (\(\beta\) = 0.305, \(p\) = 0.001) (Table 2). The RPE\({}_{\text{dur}}\) regression model obtained was as follows: RPE\({}_{\text{dur}}\) = 2.37 + 4.777 \(\times\) \"DUR\" + 0.036 \(\times\) \"DIS\" + 0.305 \(\times\) \"PWvh\".  
Figure 4: Correlation between RPE, RPE\({}_{\text{dur}}\), SLP, SORENES, FTG, and EXLD parameters at MIC7. RPE—rate of perceived exertion; RPE\({}_{\text{dur}}\)—RPE \(\times\) training duration; SLP—sleep perceived responses; SRN—soreness perceived responses; FTG—fatigue perceived responses; DUR—training duration; ACh—accelerations 2–3 m/s\({}^{2}\); ACh—accelerations \(>\)3 m/s\({}^{2}\); DCM—decelerations \(-\)2—\(-\)3 m/s\({}^{2}\); DCM—accelerations \(<\)\(-\)3 m/s\({}^{2}\); TD—total distance, SPm—distance 14.4–19.7 km/h; SPh—distance 19.7–25.2 km/h; SPvh—distance 25.2 km/h; SPA—number of actions \(>\) 19.7 km/h; MSP—maximal speed; PWm—metabolic distance 15–25.5 w/kg; PWh—metabolic distance 25.5–50 w/kg; PWh—metabolic distance 25.5 w/kg. * denotes significant correlation at level \(<\) 0.05; ** denotes significant correlation at level \(<\) 0.01; *** denotes significant correlation at level \(<\) 0.001.  
Figure 5: Correlation between RPE, RPE\({}_{\text{dur}}\), SLP, SORENES, FTG, and EXLD parameters at MIC9. RPE—rate of perceived exertion; RPE\({}_{\text{dur}}\)—RPE \(\times\) training duration; SLP—sleep perceived responses; SRN—soreness perceived responses; FTG—fatigue perceived responses; DUR—training duration; ACh—accelerations 2–3 m/s\({}^{2}\); ACh—accelerations \(>\)3 m/s\({}^{2}\); DCM—decelerations \(-\)2—3 m/s\({}^{2}\); DCM—accelerations \(<\)\(-\)3 m/s\({}^{2}\); TD—total distance, SPm—distance 14.4–19.7 km/h; SPh—distance 19.7–25.2 km/h; SPvh—distance 25.2 km/h; SPA—number of actions \(>\) 19.7 km/h; MSP—maximal speed; PWm—metabolic distance 15–25.5 w/kg; PWh—metabolic distance 25.5–50 w/kg; PWh—metabolic distance 25.5 w/kg; PWh—metabolic distance 25.5 w/kg. * denotes significant correlation at level \(<\) 0.05; ** denotes significant correlation at level \(<\) 0.01; *** denotes significant correlation at level \(<\) 0.001.
# Licence to Simulate: When Agent-Based Models Are More Fiction than Function +
## 5 Green and Red Flag Models
### Contextualisation of the Classification Scheme
#### 5.2.4 Red Flag Model  
Ref. [43] presents a novel approach for optimising supply chain ordering management, by combining an ABM with reinforcement learning (RL). It models a four-echelon SC, comprising a retailer, distributor, manufacturer, and supplier, where each agent makes weekly decisions based on its local state and demand, which is non-stationary. The merits of the paper are undeniable, as it introduces a novel and intelligent simulation-based optimisation framework and enables the definition of near-optimal ordering policies that contribute to stabilising inventory levels under fluctuating demand. Nonetheless, ref. [43] is classified as a Red Flag model because the nature of the agents included in the simulation fails to meet our inclusion criteria. Specifically, the agents lack meaningful autonomy, interactivity, and adaptive capacity in relation to the system they inhabit. Firstly, their behaviour is constrained to static rule-based policies, with no evidence of social or systemic adaptation. The integration of Q-learning into individual agent decision making is, in practice, a reinforcement of local optimisation rather than agent-level learning in a broader sense. In addition, the agents are optimised in isolation, with their policies shaped only by internal cost functions and inventory states, rather than by the evolving behaviour or the performance of other agents in the network. Secondly, there is a complete absence of interactions and influence between the agents. They only respond to system events without any other strategies or actions. For these reasons, the agents in this model function more as independent optimisation units than as autonomous, socially situated entities. The absence of system-aware adaptation, interactive decision making, and behavioural complexity suggests that the model could just as effectively have been implemented using a standard discrete event simulation or a similar approach.  
Finally, it is worth noting that this article clearly demonstrates that a Red Flag classification does not imply a \"poor model\" but rather reflects the limited suitability of an ABM for the specific case under study.
## 3 Numerical Simulations  
### Verification of Numerical Results  
In the author's previous study, a fixed offshore structure was analyzed using the developed program to verify natural periods, dynamic responses, and bending stresses with commercial programs [39]. The dynamic response of the floating body was also verified through regular wave analysis and free-decay tests in the previous study [19].  
In general, the motion characteristics of a floating body can be evaluated by the Response Amplitude Operator (RAO), which expresses the relationship between the wave amplitude and the body motion according to the period of the incident wave.  
In this section, the RAO of the floating body for linear and body-nonlinear analysis was obtained through the time-domain simulations under irregular wave conditions, and the validity of the developed program was verified by comparing the RAO with previous studies.  
The RAO was calculated using cross- and auto-spectral densities [40]. The frequency interval between regular waves was set randomly to prevent repeatability of the generated wave.  
\[RAO=\frac{S_{xy}(\omega)}{S_{xx}(\omega)} \tag{22}\]  
where \(S_{xy}(\omega)\) and \(S_{xx}(\omega)\) are the cross- and auto-spectral densities, respectively. In this process, the input signal \(x(t)\) represents a time history of a random wave, and the output signal \(y(t)\) represents system responses, such as the heave or pitch motion of the float. The wave was generated using the white noise spectrum shown in Figure 2 to calculate the RAO in the assigned frequency range.
# Vertical Seismic-Profile Data Local Full-Waveform Inversion Based on Marchenko Redatuming
###### Abstract  
Local full-waveform inversion (FWI) methods use redatumed seismic responses of virtual receivers within the subsurface to build the local objective function based on the convolution-type representation theorem. The Marchenko method is widely used to obtain the redatumed data. The method only requires a smoothed velocity model with correct kinematic characteristics of seismic responses for redatuming of the single-sided reflection data. However, the standard Marchenko method is insensitive to lateral propagation of the wavefield. By injecting the standard Marchenko redatumed wavefield along the boundary of the target, the local wavefield propagation modeling produces errors, which affects the accuracy of the local FWI. In this paper, a method to obtain more accurate Green's functions is proposed by incorporating vertical seismic profile data (VSP) into the calculation process of the Marchenko source-receiver redatuming. This method allows one to obtain the accurate laterally propagating waveform, resulting in a significant improvement of lateral resolution. The proposed method is applied to a benchmark model dataset and compared with the local FWI based on standard Marchenko redatuming.  
full-waveform inversion; Marchenko redatuming; seismic interferometry; vertical seismic-profile; target-oriented inversion +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Vertical Seismic-Profile Data Local Full-Waveform Inversion Based on Marchenko Redatuming. _Appl. Sci._**2023**, 13, 4165. [[https://doi.org/10.3909/app13074165](https://doi.org/10.3909/app13074165)]([https://doi.org/10.3909/app13074165](https://doi.org/10.3909/app13074165))
## 1 Introduction
### CubeSat Software State-of-the-Art  
The whole idea of making a CubeSat cheap and easy to launch lies in the domain of using commercial-off-the-shelf (COTS) or modifiable-off-the-shelf (MOTS) components in both software and hardware parts of a typical development project. The use of COTS components opens up a wide variety of open-source and proprietary software to be used [14; 15]. However, the use of the COTS software creates a huge number of troubles by way of the developing and proper testing of the CubeSat on-board software. Why? Well, many of the software parts are rather provided as the sample or _\"take it on your own risk\"_ and thus simply do not fulfill any reasonable metrics for production-ready software. This means that an increasing number of development teams are leaning towards the very comprehensive testing of the final assembled flight software, simply to ensure its quality  
Figure 1: CubeSats launch statistics (annual by mission status).  
and readiness to go to space. The majority of the research is focused on a very thorough verification & validation (V&V) approach [16], using complex data validation models and techniques [17]. Other research papers propose software in the loop (SIL) and/or hardware in the loop (HIL) simulation [18]. Various efforts are also made in the field of failure emulation mechanisms (FEM) [19], as well as the introduction of different fault injection platforms (FIP) [20]. All these methods are very typical for the \"waterfall' software development lifecycle and overall project management, as well as for rigidly and thoroughly planned projects. The rigidity and thoroughness are truly nontypical for academia and research projects where the mindset of \"greenfield exploration\" is commonly used.
At this level, only data associated with the load (active and reactive powers) of each substation and the power injection by each wind farm, uploaded in real-time from the SCADA systems, will represent the input data for the OPF module that manages the OLTC of the transformer by simultaneous commands based on the proposed methodology. Regarding the changes in the wind generation, these are managed within the SCADA system implemented at the wind farm level, as presented above, and only the data sent to the SCADA master system are used by the methodology, see Figure 3. The methodology has, as the starting point, the ability of the TSOs and DNOs to hold the control capability of the transformers due to the advanced technologies based on the automatic voltage control (AVC). It can be integrated considering both operation modes (real-time and off-line). The real-time implementation requests the technical features relating to a wide-area communication network with high-speed data transmission [16], an OPF module with a fast processing speed [17], and the OLTC of the transformer to perform quick changing of the taps [18; 19].
## 3 Methods and Materials
### Materials
The second dataset was custom-made by us, using exercise database recorded using two different depth sensors and coordinated by the experts from Vilnius Santara clinics. An example of the dataset can be seen in Figure 5. The first depth sensor _Intel Realsense L515_ was placed in front of the subject, the second depth sensor _Intel Realsense D435i_ positioned 90\({}^{\circ}\) right of the subject. Both of the sensors were placed 1.4 m above ground level and 1.8 m away from the subject. The database consisted of 23 subjects of varying physical conditions, age from 26 to 71, 61% male, 32% female, and the rest identified as other genders). In total, 168 recordings were made for each participant. We provided a demonstration by an expert rehabilitation specialist explaining the purpose and methodology behind the exercises. Each of the subjects performed these action movements from the diagnostic rehabilitation functional assessment methodology [87; 88]:  
1. Shoulder flexion (90\({}^{\circ}\));
2. Shoulder flexion and internal rotation;
3. Shoulder flexion and internal rotation, elbow flexion;
4. Shoulder extension and internal rotation;
5. Shoulder flexion;
6. Full shoulder flexion (180\({}^{\circ}\));Shoulder adduction. Each of the videos in both datasets was pre-processed to be used for neural network training. This was performed by extracting every 0.5 s to minimize the number of similar frames in the video feed; a breakdown can be seen in Table 1. The frame was then passed to our network to extract its joints and, subsequently, each of the joint orientations was calculated for IK neural network training. Each person also wore eight HTC VIVE trackers to gather the data to check and tune the accuracy of the kinematics model. The HTC trackers provide an IK solution of their own, allowing us to increase the accuracy of the joints containing the HTC trackers by obtaining a mean orientation of the HTC joints and the calculated joints.
## 2 Materials and Methods  
### Design of Smart Hopper System  
In this study, a smart hopper system was developed to solve jamming caused by rocks dispensed into a hopper at a mining site. The proposed system identifies and removes unfiltered rocks by integrating technologies such as an RGB-D camera, machine vision technology, and a robotic arm. Figure 2 depicts the overall structure of the proposed smart hopper system. First, the upper part of the hopper was photographed in real-time using the RGB-D camera, and the images were transmitted to a program on a notebook personal computer. Then, the rocks were classified using an image-processing technique in a virtual hopper system implemented in the virtual world, and the appropriate motion was simulated in the robotic arm. Based on the simulation, the robotic arm was operated in the real world to remove the rocks causing the blockage.  
Figure 3 depicts the operational flowchart of the smart hopper system including data acquisition, processing, virtual simulation, and movement of the robotic arm. First, an RGB image and a depth image were captured using the color sensor and infrared (IR) sensor in the RGB-D camera. Then, the RGB images were converted into grayscale and binary images using machine vision technology and were processed. Only the shapes of rocks were extracted from the entire image. By comparing this with the depth image processed in binary form, the heights of the rocks were estimated. A rock was ignored if it had fallen below the hopper; however, its coordinates were calculated if it was in the hopper. To move the robot arm to the calculated rock coordinates, they were converted to six-axis joint coordinates for the robot arm. Based on this information, the robot arm was moved along the planned path and the targeted rocks were removed from the hopper. Table 1 lists the detailed specifications of the sensors and controllers used in this study.  
Figure 2: Overall structure of the proposed smart hopper system (**a**) and simulation of the robot arm in virtual space (**b**).
## 5 Deployment and Evaluation
### Implementation  
As we mentioned, UWB radios and an in-circuit radio L4 radio module were used to implement our solution for UWB communication. This radio chip uses the DW1000 radio chip as the UWB interface along with an STM32 micro-controller for managing the radio chip and other interfaces. Raspberry pi running Rasbian was used to monitor UWB anchors. The anchors were connected to the server for data communication using a WiFi infrastructure. The data from the anchors are collected and processed on a DELL Precision 7720 acting as the server. The data flow from anchors is also monitored using the server to ensure their functionality. The TDMA approach used in the tags for transmission allows the location of tags to be updated at 0.2 s intervals.
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 3 Buried Oil & Gas Pipelines
### Typical Approaches for Pipeline Monitoring  
There are several best practices reported in the scientific literature regarding pipeline integrity management (PIM; see, e.g., [33]). These can be categorized between maintenance surveys, which can potentially include some non-destructive testing (NDT) techniques, and long-term SHM apparatuses, which apply embedded sensors. Techniques and approaches for both categories have been extensively reviewed, e.g., in [34; 35].  
Regarding NDTs, some approaches worthy of note include ultrasonic techniques (reviewed by [36]), acoustic emissions (AE, [37]), eddy currents, etc. These point-wise, localized approaches require, however, a nearby (robotic or human) operator. In this sense, for scheduled maintenance and surveys, the use of instrumented pipeline inspection gauge (PIG) [38] represents one of the main options for unmanned inspections to date. However, pipelines dedicated to the transportations of explosive fluids are generally deemed as \"unpiggable\" due to safety reasons. For these and other reasons, man- or robot-made periodical inspections are not cost-efficient. Thus, output-only, vibration-based approaches are preferable from a practical and economical point of view.  
The procedure proposed in this work falls in this latter group, utilizing optical fibers deployed along the pipe length for strain measurements. In this regard, a concise yet quite complete review of sensors for vibration-based and non-vibrational SHM in buried pipes can be found in [39] (the specific application is for underground water supply systems, but the differences are irrelevant for what concerns the sensing technologies).
## 5 Conclusions  
Three VN foams were characterized in compression and combined compression and shear at different strain rates. VN foams displayed strain rate dependent behavior, and the stress at plateau increased with the foam density. In compression, VN-A, the lighter foam (97.5 kg/m\({}^{3}\)) absorbed 1.5-2.5 times less energy at maximum efficiency than VN-B (125 kg/m\({}^{3}\)) and VN-C (183 kg/m\({}^{3}\)), respectively. VN-B presented the highest maximal efficiency (49%) in both compression and combined loading. Compared to EPP foams, VN foams were denser for the same amount of energy absorbed, but presented higher maximal efficiency, reducing stress for the same amount of impact energy. Finally, when VN foam samples were submitted to combined loading, more than 65% of the stress was in the compression direction, which is more than in EPP, where the stress is more evenly distributed between compression and shear. These characteristics make VN foam an interesting candidate for energy absorption purposes, especially in helmets where increased shear force can induce increased rotational accelerations of the head. For this characterization, a methodology to obtained stress vs. strain curves at constant strain rates from a drop test was refined. This methodology enables a more consistent comparison between tested foam materials and could support finite element modeling in future works.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/22/8286/s1](http://www.mdpi.com/2076-3417/10/22/8286/s1)]([http://www.mdpi.com/2076-3417/10/22/8286/s1](http://www.mdpi.com/2076-3417/10/22/8286/s1)), Figure S1: Example of full field strain measurement using digital image correlation, Figure S2: Sensibility analysis of the quality of the fit, Figure S3: Impact stress vs. strain data, Table S4: Coefficient of the equation of the fitted surface, Figure S5: Surface plot of the Strain rate dependent behavior of VN foams in combined compression and shear, Video S1: Quasistatic compression of VN foams.  
Conceptualization, N.B., Y.P. and E.W.; Funding acquisition, N.B., Y.P. and E.W.; Investigation, N.B., J.-M.D., O.L. and S.L.; Methodology, N.B., Y.P., J.-M.D. and E.W.; Supervision, Y.P., S.L. and E.W.; Writing--original draft, N.B.; Writing--review & editing, N.B., Y.P. and E.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Sciences and Engineering Research Concil of Canada and by the company CCM Hockey.  
The study was partly funded by the company CCM Hockey which design, manufacture and sell helmets for hockey players, one of the authors was employed by CCM Hockey at the time of the study.