esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proof of Concept
### The Data Plane Low-Level Metric  
The adopted low-level metric, P4 NEntropy [7], estimates the normalized network traffic entropy in a given time interval, as described by Equation (3) in Section 2. It is implemented in P4, thus overcoming the lack of this language which does not natively support basic yet relevant arithmetic operations such as division, logarithm, and exponential function calculation as well as any operation on floating numbers or for loops.  
P4 DDoS is an application developed by the authors of [7] to trigger an alarm when the entropy metric P4 NEntropy reaches a value crossing a given threshold. We modified the P4 DDoS application to be compatible with GRAPH4 so as to be able to install the detection rules only for the vulnerable hosts that result from the AG.  
The DDoS attack is detected when the P4 NEntropy metric value drops below the adaptive threshold: as network traffic varies over time, the adaptability of the threshold is essential to avoid false positives. Figure 4 shows the detection of a DDoS attack in our simulation: when the metric value decreases under the threshold, initially set to 0.5, the controller installs MATs to block the malicious flow. As soon as the new rules are activated, the malicious traffic is automatically dropped by the switches, as proven by the return of the metric to its normal value. In our simulations, these actions take an average of 2.5 s from the detection of the attack to the installation of the new rules.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 11 Sample Outdoor Localization Results
The mean error for determining the position of the UE using the BGU method here was 4.16 m (RMS error 4.64 m). For comparison, the mean error in the GNSS track was 5.85 m (RMS error 6.31 m). The error in the GNSS positions is about what is expected of smartphone's GNSS (4.9 m with open sky [42]). The corresponding CDF plots are shown in Figure 1 above. Note that any remaining uncertainty in the position of the APs, although small, contributes a bit to the errors in UE positions.
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 5 Digital Twin Technology for Energy Sector Development
### Algorithms and Deep Learning for Energy Efficiency  
Table 2 shows the summary of 75 application literature related to building energy management that uses machine learning technology, indicating the year of publication, the machine learning technology used and the application content. According to the application direction of the literature, the research topics can be roughly divided into energy prediction, energy efficiency improvement, energy model calibration and evaluation, energy system control, etc. It is worth noting that many of these studies focus on the establishment of forecasting models, such as \"short-term energy consumption forecasting\", \"heating and cooling load forecasting\", \"building energy use forecasting\", and so on. Among the paper reviewed, the most widely used algorithms are Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Network (DNN). These algorithms can predict overall or specific energy demand and even further predict environmental impact or related parameters such as electricity prices, carbon emissions, etc. The establishment and application of these predictive models cover various types of buildings, including residences, offices, commercial buildings, educational buildings, and hotels. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **References** & **Year** & **Energy Simulation ML Technologies** & **Application** \\ \hline
1 & [113] & 2012 & RF \\
2 & [114] & 2014 & FFNN, HME-REG, SVR, HME-FFNN, \\  & & & HME-LSSVM, FCM-FFNN \\
3 & [115] & 2016 & SSL, K mean & Building energy systems modeling \\
4 & [115] & 2016 & SSL, Clustering & Building energy systems modeling \\
5 & [116] & 2017 & DUE-S & Building performance simulation and analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Energy simulation ML technologies in various applications.
## 4 Discussion
While coordinated antagonist activation is likely crucial for accurate peak-force control in such contractions [14], it remains to be determined if antagonist activity contributed to the differences in the RFD between ballistic and sustained contractions. Further studies comparing pulse and sustained contraction, preferably using high-density electromyography, are needed to reveal the exact underlying mechanisms of the differences observed in this study. During submaximal ballistic contractions, there is a linear relationship between the peak force and RFD [14; 30]. A previous study [15] introduced a theoretical peak RFD, which was extrapolated from the submaximal ballistic pulses of varying intensities. This theoretical peak RFD was higher than the actual RFD in explosive sustained contractions (\"fast-and-hard\" instruction) in females, but the opposite was true in males. This is partially in line with our findings, as the differences between ballistic and sustained contractions in our study were more pronounced in females compared to males. The reason for this difference is difficult to decipher. Differences in the RFD between sexes are minimal when normalized to maximum strength and/or body size [31; 32; 33]. This was also the case in our study, as the sexes had an equal RFD normalized to peak force (all \(p>0.129\)). Males and females also show very similar scaling of the RFD with peak force in ballistic contractions [30; 34]. It could be speculated that females have more difficulties in attaining the highest possible RFD in sustained contractions. Females have been reported to exhibit longer electromechanical delay [35], which could be a modulating factor in the RFD, but would likely primarily affect the early phase of force rise. On the other hand, sex differences in neural mechanisms, such as cortical and intracortical inhibition, could also play a role. The RFD in females was also more sensitive to whole-body vibration intervention, compared to the RFD in males [36], which points to possible sensory and central mechanism. However, the exact mechanism underlying the sex differences in RFD changes are still to be determined. This study does not come without limitations. Our findings should be further verified using different joints and movements. Moreover, our sample consisted of individuals who were moderately trained with resistance exercise and moderately physically active. Further studies are needed to reveal if the results are applicable to other populations (e.g., elite resistance-trained athletes, older adults and patients with neuromuscular diseases). Moreover, intersession reliability of RFD in pulse contractions should also be verified. Finally, future experimental protocols should include in-depth investigation by recording motor unit behavior and possibly even spinal and corticospinal excitability, to provide an understating of the underlying mechanisms for the differences observed in this study.
# Design of a Digital Array Signal Processing System with Full Array Element
## 3 Implementation and Verification of the DBF Algorithm  
### Principle and Simulation of the MIMO Phased Array DBF Algorithm  
The MIMO phased array radar is a combination of conventional MIMO radar and phased-array radar with the advantages of MIMO radar without sacrificing the coherent processing gain of phased-array radar. The essence of the technology is to divide the transmit array into several subarrays that are allowed to overlap, where each subarray transmitting waveforms orthogonal to the other subarray transmits waveforms based on the weight vector. This technique allows for the optimal design of the emission direction map or emission waveform of a MIMO radar to concentrate the emission energy to the region of interest, thus effectively improving the detection and tracking performance of the radar system [24, 25, 26].  
The array used in this algorithm validation has a total of 11 array elements and the array is divided into four 8-element subarrays through full overlap subarray division, as shown in Figure 16.  
Based on the subarray division method described above, each subarray emits mutually orthogonal waveforms and emission beamforming, with each beam pointing at different angles. The directional maps of the subarrays are simulated, in which four subarrays point in four directions \(-45^{\circ}\), \(-15^{\circ}\), \(15^{\circ}\), and \(45^{\circ}\). Results of the directional map simulation are shown in Figure 17.  
Figure 16: Array element arrangement.  
Figure 15: Comparison of amplitude-frequency responses before and after broadband calibration: (a) Amplitude-frequency responses between channels before broadband calibration; (b) Amplitude-frequency responses between channels after broadband calibration.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 3 Dry Methods for Black Silicon Fabrication
### Reactive Ion Etching (RIE)
#### 3.1.1 Deep Reactive Ion Etching (DRIE)  
The Deep Reactive Ion Etching (DRIE) method is a commonly used technique for fabricating BSi or nanostructured Si with a high aspect ratio, enabling efficient light absorption. DRIE involves cyclic time-multiplexed etching, alternating between two cycles as in Equations (1) and (2): a cycle of octafluorocyclobutane (C4F8) plasma deposition utilizes fluorocarbon species to deposit a polymer film of diffuorocarbene (\(CF_{2}\)) on the Si surfaces, including the sidewalls followed by a plasma etching cycle that employs ions to etch the Si and the deposited film. The desired etching profile is achieved by repeating the cycle process multiple times. In the etching cycle, the \(SF_{6}\) splits into chemically metastable sulfur tetrafluoride (\(SF_{4}\)) and two individual fluorine (\(F\)) atoms.  
\[C_{4}F_{8}\rightarrow-(CF_{2})_{4}-\to Polymer \tag{1}\]  
\[SF_{6}\to SF_{4}+2F\to Etching \tag{2}\]  
An improved DRIE method introduced by [PERSON] et al. systematically tuned the polymer deposition time to \(SF_{4}\) fabricate nanostructured BSi (n-BSi) in micrometer-scale  
Figure 2: (**a**) The top-view SEM images of BSi with nanopores, (**b**) the side-view SEM images of BSi with nanopores, (**c**) an inclined view of the BSi surface with hollow cylinders, and (**d**) an inclined view of chimney-like BSi with a hierarchical structure [52], © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.  
absorptive regions of high-contrast optical alignment marks [56]. Adjusting the deposition time, while maintaining other parameters as fixed, resulted in three distinguished etching regimes. The unruffled etching regime, characterized by equal or longer etching times compared to deposition, produced a vertical profile with scalloped sidewalls. The n-BSi regime, occurring when etching time closely matched the deposition time, utilized the nano-masking effect to create n-BSi structures. The etch-stop regime occurred when deposition time significantly exceeded etching time, halting the etching process. Increasing the time within the n-BSi regime led to a morphological transformation from nanopillars to nanopores. In our prior work, we noted a substantial enhancement in the growth of grass structures when the substrate temperature dropped below \(-20\,\mathrm{\SIUnitSymbolCelsius}\). We identified the cyclic Bosch process, involving etching and passivation stages, as a crucial factor in the formation of these structures. Furthermore, we found that the interaction between \(SF_{6}\) and O\({}_{2}\) gases during the Bosch process played a pivotal role in the formation of microstructures [57]. Figure 3 shows an SEM image of BSi formation (as grassy-looking residuals) by controlling the etch profile due to micro-masking.
## 2 Fault Diagnosis Algorithm Selection  
There are a variety of algorithms that belong to neural networks, such as feedback neural networks, self-organizing competitive neural networks, and probabilistic neural networks. Compared with other neural networks, probabilistic neural networks are neural networks based on statistical principles. They are equivalent to the optimal Bayes classifier in classification function. The essence of them is a parallel algorithm based on Bayesian minimum risk criterion. At the same time, unlike the traditional multi-layer forward network which needs to use the feedback algorithm to calculate the reverse error propagation, this method is the complete forward calculation process [13]. It has the advantages of short training time, less local optimum, and high classification accuracy.  
Support vector machine is a new machine learning method proposed by [PERSON] on the basis of statistical learning theory in 1995 [14]. It is based on VC dimension theory and structural risk minimization principle. The algorithm of it is a convex quadratic optimization problem, which can guarantee that the found extremum solution is the global optimal solution. It has unique advantages in solving nonlinear and high dimensional pattern recognition and small sample classification problems [15]. In addition, the support vector machine also has the advantages of strong universal type and simple calculation.  
Due to the superior performance of probabilistic neural networks and support vector machines in pattern recognition, they have the potential for diagnosing faults. In this paper, they are chosen to be used to diagnose faults in the 3 W engine control systems.
## 3 Experimental Section
### Use of Diseases to Measure Patients' Similarity  
Some examples of how problematic diseases (see Section 2.4) are encoded and used in similarity calculation are:  
* Diseases not encoded to a single ICD-10 category. _Myopathy_ is encoded as _primary disorders of muscles_ (G71), _other myopathies_ (G72) and _disorders of muscles_ (M60-M63) and, if no additional information is available, the first option is chosen as it is the most representative according to an experts' consensus.  
\begin{table}
\begin{tabular}{l l l} \hline \hline _vMR Class:_ **attribute** & **Standard classification** & **EMR reference** \\ \hline _Person:_ name & Patient name & \\ \hline _EvaluatedPerson_ (subclass of Person): birthTime, gender & Date of birth, gender & \\ \hline _Problem_: problemCode & ICD-10 & Patient’s static \\ \hline _ProblemBase_ (subclass of Problem): & Date of diagnosis, severity & information \\ diagnosticEventTime, severity & score & \\ \hline _AdverseEventBase_: adverseEventcode & Event & \\ \hline _GoalBase_: goalFocus, goalAchievementTarget Time & Therapy, timing milestone & SDQ \\ \hline _ObservationBase_: observationFocus & ICF &
\begin{tabular}{l} FIM, SCIM, HAD, \\ PWBI, WHOQOL-BREF \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between Virtual Medical Record (vMR), terminologies and Electronic Medical Record (EMR) classes.  
Figure 4: Summary of [PERSON]’s health status in 2007 using international classification of functioning, disability and health (ICF) taxonomy.  
* Diseases encoded to the same ICD-10 category. Both _complete parapleggia_ and _incomplete parapleggia_ are encoded as _spastic parapleggia_ (G82.1), therefore their similarity is considered equal to 1.
* Diseases encoded to catch-all ICD-10 categories. _Other ischemic stroke_ is encoded as _other cerebral infarction_ (I63.8).
* Diseases encoded to ICD-10 categories with scattered exclusions. In this case, if no additional information is available, the most representative ICD-10 category is chosen according to an experts' consensus. For example, for parapleggia various categories exist which include the concept, such as _hereditary spastic parapleggia_ (G11.4), but the _spastic parapleggia_ (G82.1) category is chosen for being more representative.
* Diseases that may occur as a complication in the course of some other disease. This problem has not been studied in the scenario.  
In this way it is possible to calculate the similarity between two diseases with ambiguous representation in the ICD, such as, for example, _Myopathy_ and _Other ischemic stroke_; similarity which has a value in this case of 0.5, considering related classes G71 and I63.8 respectively.  
Regarding patients' similarity calculation, a 10-NN classifier was developed using the jColibri framework.
# The Application of an Impedance-Passivity Controller in Haptic Stability Analysis
## 3 Planning and Design of Impedance-Passivity Controller (IPC)
### Passive Stable Region of Haptic Feedback  
To obtain the stable passive region, some parameter identification processes needs to be performed. The mass and physical damping of the haptic device can be obtained by means of a least square algorithm [25], and the identified values of the mass and damping of the haptic device are 0.0886 kg and 6.2036 Ns/m with a sampling period of 1 ms, respectively. Thus, from (13), a stable passivity region of VE parameters, i.e., \(K\) and \(B\), is shown in Figure 4. In practice, due to the limitation of the haptic device, the maximum reaction force must be restricted. In this paper, the maximum stiffness constant \(K\) is set to be 4000 N/m.
## 2 Materials and Methods
### Deployment Platforms
#### 2.7.1 Mobile Application Development  
To deploy trained machine learning on mobile devices, the models were converted to TensorFlow Lite termed interpreters in the format of \".tflite\" files using the TensorFlow Lite Task Library API. TensorFlow Lite is a collection of tools built for edge, embedded, and mobile devices, allowing for on-device machine learning. The benefits of edge machine learning include real-time latency (no data offloading), privacy, robustness, connectivity, a smaller model size, and efficiency (costs of computation and energy in watts per FPS). It supports Linux, Android, iOS, and the MCU. The TensorFlow Lite converter and TensorFlow Lite interpreter are two components for the deployment of TensorFlow models on mobile devices [35]. To begin, the Keras model that is created using the TensorFlow 2.0 library was exported to pb (protocol buffer) models. Second, the PB models were converted to TensorFlow Lite models using the TensorFlow Lite Converter. Finally, the TensorFlow Lite interpreter was set to execute the TensorFlow Lite models on smartphones and take advantage of smartphone hardware resources to boost detection performance even further [36]. To facilitate the use of TensorFlow Lite models on mobile phones, the mobile application was developed for Android OS using the Java programming language  
Figure 3: Confusion matrix for multi–class classification.  
and the TensorFlow Lite library. The application takes live camera image streams as input and processes them individually. Each image was processed by a Keras-based Tensorflow Lite model and the model produced a list of confidence scores, indicating the probability of the image belonging to a particular class. The application would take the highest confidence score index and display the image with a label with that index. A screenshot of the application is given in Figure 4.
## 3 Results and Discussion  
### Ultimate Load Analysis for CPC  
In order to evaluate the ultimate loads of the offshore wind turbine with aerodynamic imbalance, ultimate load analysis was performed by the CPC state using the GH-BladedTM V4.1. The reference wind speeds were 11 m/s, 14 m/s, 17 m/s, 20 m/s, 22 m/s, and 24 m/s in the CPC state. The ultimate loads (i.e., maximum force (\(F_{xc}\), \(F_{yc}\), \(F_{xc}\)), and maximum moment (\(M_{xc}\), \(M_{yc}\), \(M_{zc}\))) were obtained through the ultimate load analysis, with reference to each wind speed. Using these results, resultant force and moment was calculated by Equations (1) and (2), respectively. Figure 4 represents the coordinate system of the hub. Table 4 shows the ultimate resultant loads and pitch angles of the CPC for each reference wind speed. The CPC is the principle of reducing the load by increasing the pitch angle of the three blades equally as the wind speed increases. Here, the CPC control used the basic algorithm built into the GH-BladedTM V4.1.  
\[F_{Rc}=\sqrt{F_{xc}^{2}+F_{yc}^{2}+F_{zc}^{2}} \tag{1}\]  
\[M_{Rc}=\sqrt{M_{zc}^{2}+M_{yc}^{2}+M_{zc}^{2}} \tag{2}\]
# Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal
## 2 Materials and Methods  
### Materials and Equipment  
This investigation used pure bentonite (BNT) (Fisher, Waltham, MA, USA) and other chemicals of high purity, including ammonium hydroxide (NH\({}_{4}\)OH), 2-methyl imidazole (2-MIM), cobalt (II) nitrate hexahydrate (Co(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O), and methyl orange [(CH\({}_{3}\))\({}_{2}\)NC\({}_{6}\)H\({}_{4}\)N\({}_{6}\)H\({}_{4}\)SO\({}_{3}\)Na)]. All respective stock solutions were prepared using deionized water.  
Figure 1: The chemical structure of methyl orange (MO).  
A Rigaku Miniflex-II diffractometer was used for X-ray diffraction (XRD) analysis to evaluate the crystalline structure of the synthesized ZIF-67@BNT. The respective materials were scanned at a rate of 7\({}^{\circ}\) per minute throughout a 2-theta angle range of 5\({}^{\circ}\) to 40\({}^{\circ}\). Further, thermogravimetric analysis (TGA) was conducted using TA Instruments to evaluate the thermal stability of the synthesized nanocomposite within a temperature range of 25 to 800 \({}^{\circ}\)C under an inert environment (i.e., in nitrogen) at a heating rate of 10 \({}^{\circ}\)C per minute. Also, ZIF-67@BNT surface functional groups were determined by employing a Fourier transform infrared spectroscopy (FTIR) setup (Smart iTR NICOLET iS10). Furthermore, a Shimadzu UV-Vis spectrophotometer setup was used to determine the residual methyl orange (MO) concentration in aqueous samples (that had been duly pre-filtered using 0.2 \(\upmu\)m (Whatman, Dassel, Germany) filters to exclude the adsorbent particles) at 464 nm. Standards for MO ranged from 12.5 to 100 mg/L and the same were used to develop the calibration curve. Further details on the synthesis and application of ZIF-67@BNT for the adsorptive removal of MO from wastewater samples are provided in the sections below and also summarized in Figure 2.
# SHS Produced TiB\({}_{2}\)-Si Powders for Selective Laser Melting of Ceramic-Based Composite
###### Abstract  
One of the main limitations for widespread additive manufacturing is availability and processability of the precursor materials feedstock. For the first time, this study reports the development of a \"pomegranate-like\" structured TiB\({}_{2}\)-Si ceramic-metalloid powder feedstock suitable for selective laser melting (SLM) of ceramic-based composite. The powder was produced via self-propagating high temperature synthesis (SHS) at a moderate combustion temperature of 1530 \({}^{\circ}\)C. The effective activation energy in the Ti-B-Si system for the slow step of the combustion process was estimated as 184 kJ. Conditions of SHS process demonstrated a strong influence on the properties of produced powders and, therefore, on SLM parameters and properties of the printed materials. The powders have demonstrated a high performance for manufacturing bulks of 56 wt%TiB\({}_{2}\)-44 wt%Si ceramic-based composite.  
titanium diboride; ceramic composite; selective laser melting; self-propagating high-temperature synthesis +
Footnote †: journal: Applied Sciences