esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurements and Analysis of Sound Reflections from Selected Building Facades
## 3 Results and Discussion
Differences in average values of equivalent sound level \(L_{eqM2}-L_{eqM3n}\) in the range of 0.6 to 2.2 dB were obtained. This means that at a distance of 3 m from the facade of the building there is a reflection-disturbed sound field. In the conducted measurements, the location of the microphones and the sound source were determined to be the same in relation to each other and the analyzed building facade. However, the ground cover at the surveyed locations varied and was not affected by the investigators. At location A, the ground cover was 80% soft (grass), while, at locations B and C, the ground cover was 80% hard (concrete paving stones). The results of the measurements at the examined locations may include the influence of sound reflected from the ground, which is difficult to estimate. It should be noted that the ISO standard does not condition the \(-3\) dB correction value on the type of ground. The greatest impact of this phenomenon should be observed on the first measurement level (A1, B1, C1). However, no such confirmation was obtained in tests at elevation C. The average values of differences at a height of 2 m (C1) are less than or equal to those obtained at a height of 6 m (C2). It is interesting to note that at location A with soft cover, the largest tested differences \(L_{eqM1}-L_{eqM2}\) and \(L_{eqM1}-L_{eqM3}\) were obtained, as well as differences between the results at 2 and 6 m height (A1 and A2).
# Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives +
## 3 Motivation and Related Work
### Summary and Related Work
Other approaches exist for general specification and analysis of dynamic architectures, including _DR-BIP (Dynamically Reconfigurable - Behaviour Interaction Priority model)_[34] and _DReAM (Dynamically Reconfigurable Architectural Modelling)_[35], using meta-entities like _components_ (for modelling behaviour), _connectors_ (for modelling interaction), _maps_ (for modelling logical connections), and _deployments_ (for mapping components to deployment loci), and _motifs_ (modelling architectural configurations that may change at runtime); however, they are not tailored to self-organising systems. * **Self-organisation algorithms and their control. ** Our reference example leverages self-organising algorithms such as gradients [30] and _information flows_ which are typically used in multi-scale and situated systems [36; 37]. Our emphasis is not on the algorithms themselves; rather, we show how self-organisation, for example, based on information flows, can be improved through virtual devices. The general idea of using a kind of internal or external intervention for driving the emergent behaviour of a \"given\" system (for which e.g., the behavioural and interactional rules of the member agents are not considered to or cannot be re-designed) is not new, and has been referred to with different terms like _soft control_[13; 38], _control kernel_[39], and _pseudo-emergence_[40]. For instance, in [13], a \"shill agent\" is used for soft control of a flock of birds--idea leveraged in subsequent works proposing multiple \"influencing agents\" [41]. In [42], the authors introduce a notion of \"virtual leader\" to control groups of vehicles; however, the virtual leader is not really an entity but rather a construct used to reify additional artificial potentials. Similarly, in [43], decoy objects are introduced to improve performance in swarm searching problems. Finally, in [14], notions of virtual individuals, virtual targets, and virtual pheromones, are proposed as a way to combine WSNs and robot swarms to steer collective behaviours of the latter. In this paper, we bring and extend these ideas to cyber-physical aggregate computing settings, where the influencing agents are virtual and the control target is the collective physical twin of a virtually-augmented collective digital twin. Moreover, differently from the aforementioned related work, the introduced virtual entities will play _the same control behaviour_ of the other, regular entities--hence showing an interesting, original \"structural\" interplay with self-organisation algorithms. Indeed, we also suggest further uses for virtual entities (see Section 3.1) that, to the best of our knowledge, are not present in literature. Additionally, we suggest that dynamic addition and removal of virtual devices can be key from a self-improving system integration perspective [15].
# Research on Performance Prediction Model of Impeller-Type Breather
## 4 Data Analysis
### Goodness of Fit Test  
According to the 50 groups of calculation conditions determined by the orthogonal experiment design, the corresponding numerical calculation was completed. The calculation process was simplified, and the least squares method was used for fitting. Finally, each index in the dimensionless group equation was determined, and each prediction model is shown in Equations (15)-(17).  
Comprehensive separation efficiency prediction model:  
\[\eta=e^{4.4221}(\frac{\vec{d}}{r_{1}})^{0.0092}(\frac{n}{{\it{vr}_{1}}^{-1}})^ {0.0458}e^{0.0089}(\frac{\mu}{{\it{pr}_{1}}})^{-0.0260}\alpha^{0.0252}\] \[(\frac{r_{2}}{r_{1}})^{-0.0267}\beta^{0.0059}(\frac{h}{r_{1}})^{0.0582}(\frac{d}{r_{1}})^{-0.0036} \tag{15}\]  
Breather separation efficiency prediction model:  
\[\eta_{0}=e^{4.4338}(\frac{\vec{d}}{r_{1}})^{-0.0577}(\frac{n}{{\it{pr}_{1}}^{- 1}})^{0.1562}\varepsilon^{-0.0079}(\frac{\mu}{{\it{pr}_{1}}})^{0.0405}\alpha^{ 0.1604}\] \[(\frac{r_{2}}{r_{1}})^{-0.0505}\beta^{0.0132}(\frac{h}{r_{1}})^{ 0.2209}(\frac{d}{r_{1}})^{-0.2869} \tag{16}\]  
Ventilation resistance prediction model:  
\[\begin{array}{l}\frac{\Delta P}{\rho v^{2}}=e^{-2.2685}(\frac{\vec{d}}{r_{1} })^{-0.0228}(\frac{n}{{\it{pr}_{1}}^{-1}})^{0.0871}\varepsilon^{0.0169}(\frac{ \mu}{{\it{pr}_{1}}})^{0.0524}\\ \alpha^{0.0257}(\frac{r_{2}}{r_{1}})^{-0.4739}\beta^{0.0124}(\frac{h}{r_{1}})^ {-0.8326}(\frac{d}{r_{1}})^{2.1699}\end{array}. \tag{17}\]  
The prediction model validation mainly includes two aspects--one is to analyze the errors in the process of prediction model fitting, and secondly, the reliability of the equation and model parameter estimation is tested by mathematical statistics, including goodness of fit test, significance test of equation, etc. The mean absolute percentage error (MAPS) of the prediction model fitting for the comprehensive separation efficiency, breather separation efficiency, and ventilation resistance were 3.46%, 5.49%, and 9.99%, which indicate that the accuracy of the model was relatively high. The F values of the models were 149.83, 141.43, and 1566.96, which were larger than the corresponding value of F\({}_{0.001}\) (9, 40), 4.02. The coefficients of determination (R\({}^{2}\)) were 0.6196, 0.6059, and 0.9445, indicating that the equation has a high significance and a good fitting degree.
## 3 Results and Discussion
### Binder Characterisation
This indicates that the final heat of reaction of the curing reaction of the geopolymer binders were strongly related to the types of additives. In other words, the heat release \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Additives in** & **Amount (wt \%)** & **Viscosity** & **Gel Time (s)** & \multicolumn{3}{c}{**DSC Parameters**} \\ \cline{4-7}
**Geopolymer** & **\%** & **(cP)** & **Onset Temperature** & **Peak Temperature** & **Delta H (J/g)** \\ \hline No additives & 0 & 1040 (48) \({}^{\star}\) & 83.7 (1.7) \({}^{\star}\) & 79 & 98.8 & 160.4 \\ \hline Phenol flakes & 5 & 6282 (387) & 89.7 (1.8) & 78 & 96.0 & 118.1 \\  & 10 & 10,740 (417) & 91.3 (2.4) & 79 & 95.2 & 85.4 \\ \hline Conventional silica & 5 & 1564 (27) & 80.3 (1.9) & 80 & 99.4 & 168.2 \\  & 10 & 2237 (13) & 79.7 (1.1) & 81 & 99.6 & 174.3 \\ \hline Grafted silica fume & 5 & 1194 (9) & 79.0 (0.7) & 80 & 99.2 & 166.8 \\  & with APTES & 10 & 1773 (6) & 74.3 (1.8) & 79 & 99.5 & 169.1 \\ \hline \hline \end{tabular} \({}^{\star}\) Numbers presented in brackets are standard deviation.  
\end{table} 
Table 4: Viscosity, gel time, and differential scanning calorimetry (DSC) parameters of the geopolymer binder. and reaction temperature depended notably on the raw materials, activators, and reaction conditions. The effect of the silica fume on the final heat of reaction of the curing reaction could be explained by the increase in the reactivity of reactive groups (fast formation of three-dimensional networks) in the curing system. The phenol flakes were negatively influenced by the binder characteristics and created the lowest heat of reaction during curing. As shown in Figure 5, the endothermic reaction before binder curing was observed for all geopolymer binders with and without additives. This endothermic reaction should not have been related to vapor loss because the analysis was conducted under pressure. This endothermic reaction could be due to the release of water prior to polycondensation of geopolymer components. Furthermore, an endothermic reaction after geopolymerization was also observed only for the geopolymer binder with phenol flakes. The hydrolysis of the phenol or reacted components (e.g., phenoxide anion) might have contributed to this endothermic phenomenon.
# JITScanner: Just-in-Time Executable Page Check in the Linux Operating System +
## 4 Assessment
### Memory Usage  
The final test we performed was aimed at measuring the memory utilization of our system under normal conditions. We simulated a typical user interaction with software applications while monitoring the quantity of page snapshots awaiting analysis; we report our findings in Figure 6:  
* From second 0 to second 60, our user was interacting with the web browser, checking their Facebook notifications, then moving to YouTube to start watching a video--for the rest of the test, the video kept playing in the background, while the user continued with their normal/foreground activity;
* From second 60 to 80, the user played a game of Mine Sweeper;
* From second 80 to 100, the user modified and saved a LibreOffice document;
* From second 100 to 130, the user opened Gimp and tested various options.  
It can be observed that the vast majority of page snapshot creations (and their transfer to the user agent) occur at the beginning of the programs and when programs such as web browsers start new processes, resulting in extremely small memory usage spikes; after the  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Language** & **Benchmark Name** & **No Sync Slowdown** & **Sync Slowdown** \\ \hline C & spectralnorm & 0.00\% & 1.47\% \\ C & fannkuchreducx & 0.00\% & 1.61\% \\ C & matmul & 2.13\% & 2.13\% \\ C & binarytrees & 0.00\% & 0.92\% \\ C & fasta & 0.00\% & 2.86\% \\ Luajit & spectralnorm & 0.58\% & 2.33\% \\ Luajit & fannkuchreducx & 1.06\% & 7.45\% \\ Luajit & matmul & 4.62\% & 7.69\% \\ Luajit & binarytrees & 2.74\% & 9.95\% \\ Luajit & fasta & 0.00\% & 1.44\% \\ PHP & spectralnorm & 1.04\% & 8.33\% \\ PHP & fannkuchreducx & 0.00\% & 28.12\% \\ PHP & binarytrees & 0.00\% & 15.38\% \\ PHP & fasta & 0.91\% & 7.27\% \\ Ruby2 & spectralnorm & 0.86\% & 3.45\% \\ Ruby2 & fannkuchreducx & 1.79\% & 4.46\% \\ Ruby2 & matmul & 2.56\% & 10.26\% \\ Ruby2 & binarytrees & 0.93\% & 4.63\% \\ Ruby2 & fasta & 0.47\% & 0.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Slowdown of JIT benchmark applications.  
Figure 6: Memory usage of JITScanner.  
application has started, the number of page transfers decreases sharply, resulting in a near zero extra memory usage.  
Furthermore, it is important to note that the test was conducted with a limited number of threads on the user side, due to the limitations of the used virtual machine. As a result, the software did not massively utilize our hash table supporting concurrent accesses, whose deep exploitation would further reduce memory usage and improve the overall performance.
## 3 Experiments and Data Analysis
### Establishment of the Coordinate System and Arrangement of Control Points  
For the convenience of arranging control points, the inner frame of the turntable was locked and formed a body rigid body with the middle frame, whereas the outer frame was a world rigid body. The origins of the B and W systems were both set at the center of the turntable. According to the construction of the turntable, this point was also the center of mass of the body rigid body, as shown in Figure 4. Figure 5 shows the scene of the experiment, which truly reflects the layout of each marking point. Eight control points were arranged on the body rigid body (i.e., the middle frame) and eight control points were arranged on the world rigid body (i.e., the outer frame).  
Figure 3: Schematic of the overall structure of the turntable.  
As the geometric structure and dimensions of the three-dimensional turntable were fixed, the three-dimensional coordinates of the control and test points on the middle and outer frames could be measured easily.
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 4 Proposed Ensemble of Deep Learning Models for IoT Intrusion Detection
### Step 5: Training, Testing, and Evaluation  
Rigorous testing and evaluation procedures are undertaken following the configuration and training of each deep learning model within the ensemble. This involves partitioning the dataset into training, validation, and test sets, training the models on the training data, and assessing their performance on the test set. Performance metrics such as accuracy, precision, recall, F1-score, and false alarm are employed to quantify the model's effectiveness in detecting security threats.  
This research project adopts a structured approach encompassing data preprocessing, balancing, feature selection, and classification using diverse deep-learning models. The aim is to create an IoT security model that enhances threat detection accuracy and provides a robust and adaptable solution for safeguarding IoT environments.
# Unsupervised Transformer-Based Anomaly Detection in ECG Signals
## 3 Materials and Methods
### ECG Time Series Data
#### 3.1.1 The ECG5000 Dataset  
We used a five-thousand ECG (ECG5000) time series dataset ([PERSON] et al., 2017) in our experiment. The original data were obtained from PhysioNet's BIDMC Congestive Heart Failure Database (CHFDB). In two processes, the data were pre-treated: each heartbeat was first extracted, and then each heartbeat was interpolated to be the same length, equaling 140 time steps [32]. According to Figure 1, there are five different heartbeat types: normal (N), R-on-T VPC (R-on-T), PVC, SP, or EB, and unclassified beat (UB).  
The ECG5000 dataset was created by selecting 5000 sequences from the extrapolated ECG, of which 2989 sequences were normal, and the remaining 2011 sequences were anomalous. For our proposed model, the data were divided into three sets--training, validation, and testing--in an 80:10:10 ratio. Since we applied unsupervised learning, we eliminated anomalous data from the training and validation data. In the training stage, the model learned the latent space of normal heartbeats. Table 1 lists the number of sequences in each dataset.
# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 2 Background
### Cybersecurity Data  
The availability of cybersecurity data drives machine learning in cybersecurity [46]. Machine learning techniques in cybersecurity are built on datasets, which are collections of records that contain information in the form of numerous qualities or features and related facts. As a result, it is vital to understand the nature of cybersecurity data, which encompasses a wide range of cyber events and critical components. The reasoning is that raw security data acquired from similar cyber sources can be used to investigate distinct patterns of security incidents. They can also be used to detect malicious behavior in order to develop a data-driven security model. This model will assist researchers to accomplish their objectives. In the field of cybersecurity, many datasets are available for various purposes, such as network intrusion analysis, malware analysis, phishing detection, fraud, anomalies or spam analysis. Numerous types of datasets have been outlined, including their varied aspects and incidents that are accessible on the internet, in Table 2, and we emphasize their use in diverse cyber applications based on machine learning techniques, analyzing and processing these networks effectively.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Dataset** & **Description** \\ \hline IMPACT [47] & Mostly known as the Protected Repository for the Defense of Infrastructures Against Cyber Threats (PREDICT), a community that produces security-relevant network operation data and research. Repository provides regularly updated network operations data of cyber defense technology \\ \hline SNAP [48] & Not specific to security, but there are several relevant graph datasets. \\ \hline KYOTO [49] & Traffic data from Kyoto University’s Honeypots. \\ \hline KDD’99 Cup [50] & Contains 41 features that could be used to evaluate ML models. Threats are categorized into four major target labels, such as remote-to-local (R2L), denial of service (DoS), probing and user-to-remote (U2R). \\ \hline NSL-KDD [51] & Updated variant of KDD’99 Cup dataset. Records that are redundant have been removed. It also addresses issues associated with class imbalance. \\ \hline DARPA [52] & LLDOS 1.0 and LLDOS 2.0.2 attack scenario data from the Authenticated Intrusion Detection System (IDS). MIT Lincoln Laboratory collects data traffic and threats from the DARPA dataset in order to evaluate network intrusion detection systems (NIDS). \\ \hline UNSW-NB15 [53] & It has 49 independent features spread over nine different threat types, including DoS, which were gathered from the University of New South Wales (UNSW) cybersecurity Lab in 2015. \\ UNSW-NB15 can be used for evaluation of ML-based anomaly detection systems in cyber applications. \\ \hline ADFA IDS [54] & This is an intrusion dataset with different versions, named ADFA-LD and ADFA-WD, that is issued by the Australian Defense Academy (ADFA). This dataset is designed to evaluate host-based IDS. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of cybersecurity databases.
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 2 Materials and Methods  
### Study Area  
The study area encompasses the Algonquin Provincial Park (7630 km\({}^{2}\), 763,000 ha) and the surrounding area (hereafter, Algonquin Park and Greater Park Ecosystem (AGPE)), covering a total of about 15,000 km\({}^{2}\) or 1.5 M ha (Figure 1). The area is located in south-central Ontario (Canada), on Precambrian Canadian Shield bedrock, and contains about 15% water and 2500 lakes [36]. Elevation varies from about 150 m above sea level (masl) in the east to about 600 masl in the west (Figure 1a) [37]. This gentle altitudinal gradient hasimportant climatic consequences--the west is relatively cooler and wetter than the east [37]. Most of the older forests are located in the eastern part of the AGPE (Figure 1b) and about 16% of the area is protected (Figure 1c) with little or no timber harvesting activities.  
The AGPE has seen its share of disturbances. Most of Algonquin Park has burned at least once over the past 150 years [36] and timber harvest and insect pest outbreaks have occurred periodically in the area (Figure 1d). Active forest and fire management (fire suppression and prescribed burning) take place in the study area [36]. The AGPE can be considered a working landscape [38] that has been impacted by natural disturbances and human activities. This makes the AGPE a great case study to track forest vegetation change across time and space and shed light on the implications of this change for forest ecosystem services.
## 2 Study Area and Methods
### Data Sources
#### 2.2.2 Meteorological Data  
The monthly average temperature and precipitation data from 43 meteorological stations in the research region from 2001 to 2020 were taken from the China Meteorological Data Network ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)), accessed on 1 June 2022) (Figure 1a). Long-term meteorological raster data of the research region were tailored using ANUSPLIN interpolation software, using elevation as a covariate. The spatial distribution maps of precipitation and temperature in different seasons were obtained (Figure 2).
## 5 Conclusions  
In this paper, we investigate algorithms for both independent and coupled phase shift scenarios, focusing on weight optimization and secrecy rate enhancement in STAR-RIS-aided MISO systems. We mainly discuss the coupled phase-shift scheme, employing a PDD-based optimization algorithm for BCD. This algorithm finds high-quality suboptimal solutions by performing an exhaustive search on the set of magnitude and phase offsets. By integrating the transmission and reflection characteristics with the algorithm for solving the coupled phase-shift problem, this approach offers a comprehensive method for the design and analysis of STAR-RIS systems. Our simulations demonstrate the superior WSSR of STAR-RIS and the algorithm outperforms the existing methods. STAR-RIS can effectively enhance PLS by intelligently adjusting the paths of reflected and transmitted signals. It dynamically adjusts the direction and strength of signals so that legitimate users receive stronger signals and eavesdroppers receive weaker signals, thus improving the confidentiality of communications.  
In the future, the deployment of the STAR-RIS and hardware impairments will be discussed in more detail. These two aspects are important because the physical location of STAR-RIS, as well as hardware impairments, will directly affect the quality of the transmission/reflection links. Additionally, the PDD-based BCD algorithm would be enhanced by incorporating a deep learning (DL) model, which can be integrated with a PDD framework to optimize the coupled phase-shift subproblem. By alternately optimizing the coupled phase-shift coefficients, the algorithm can achieve rapid convergence. The CP scheme has the potential for practical applications, especially in scenarios where security and system performance need to be balanced. Many research directions for the CP scheme are worth pursuing, including the Internet of Medical Things (IoMT), unmanned aerial vehicle (UAV) communications, and massive MIMO systems. In addition, the IP scheme applied to STAR-RIS-enhanced robotic communications and STAR-RIS-assisted visible light communications (VLC) has potential for future research.  
Conceptualization, B.W. and Y.W.; methodology, Y.W.; software, B.W.; validation, B.W. and Y.W.; formal analysis, B.W.; investigation, Y.W. and B.W.; resources, Y.W.; data curation, Y.W.; writing--original draft preparation, B.W.; writing--review and editing, B.W. and Y.W.; visualization, Y.W.; supervision, B.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of the Higher Education Institutions of Anhui Province grant number 2022 AH050591 and by Science Research Foundation of Anhui University of Finance and Economics grant number ACKYB22022.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.