esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix E Simulation Assay on \(\lambda_{0}(u)\)
Figure 11 shows the spread of replicates \(y_{k(i)}(\xi)\) about the reference curve. Fitting [PERSON]'s model to \(\left(x_{i},\ \ y_{k(i)}(\xi)\right)\) pairs by DNLR produced \(\beta=1.403\times 10^{-12}\) and \(\alpha=2.739\ (r^{2}=0.977)\). Figure 11 compares reference and fitted curves. Again we can visualize remarkable agreement. Then, the linear model of Equation (150) was fitted on log-transformed replicates \(v_{k(i)}(\xi)=\log(y_{k(i)}(\xi))\) and covariates \(u_{i}=\log(x_{i})\). This resulted on estimates, \(lnp_{0}=-27.1\) and \(q_{0}=2.716\) (\(r^{2}=0.9987\)). Figure 11 shows spread about the fitted line \(\lambda_{0}(u)=p_{0}+q_{0}u\). Retransforming by Equation (6) produced \(E_{\lambda 0}(y|x)=\exp(p_{0}+q_{0}u)\delta(\xi)\), with \(\delta(\xi)\) produced by the criterion around Equation (7) since \(\xi_{k}\) is not normally distributed. Figure 10(b) compares \(E_{\lambda 0}(y|x)\) and reference curves. We can assert that in spite of non-lognormally distributed multiplicative error, a log scales identification procedure bears reliable results in this case. Table 12 presents model performance metrics. We can be aware that these statistics corroborates remarkable agreement shown in Figure 10(b). We can then assert that on spite of regression error \(\xi_{k}\) failing to be normally distributed, the TAMA device produces a dependable fit. Then, whenever [PERSON]'s model of simple allometry is consistent in direct scales assuming linearity in geometrical space, this will entail consistent analysis provided a proper CF form is chosen. As a result, it can be inferred that regardless of an error structure a log transformation step in the TAMA approach dos not induce curvature in geometrical space. We analysed performance of a \(\mathrm{H}_{TSK}(y|x)\) proxy fitted directly in arithmetical space by means of the dtsk_model_fit.m routine provided in the supplementary files section. For this assay the fuzzy clustering parameter was set at a value radius \(=2\). This in both \(\epsilon_{k}\) and \(\xi_{k}\) simulations returned an heterogeneity index of \(q=2\). Figure 11(a),b display resulting. Figure 11(c),d display agreeing dynamics of the \(\kappa(x)\) index. This corroborates that no curvature effects could be expected in geometrical space for this data. Table 12 presents performance metrics for proxies fitted on data simulated through a normal error structure. Table 12 pertains to results on data simulated based on a non-normal error structure. We can learn that \(H_{TSK}(y|x)\) seemingly adapts complexity as required by data.
## 5 Discussion
### Applicability and Significance  
The findings of this research hold significant implications, particularly in educational settings and broader scientific applications, owing to the demonstrated efficacy and accessibility of the smartphone-based methodology in determining \(g\) through free fall experiments. In educational contexts, where the understanding of fundamental physics principles is paramount, the utilization of smartphones equipped with built-in microphones presents a practical and accessible means to engage students in experimental physics. This methodology not only simplifies the experimental setup but also fosters hands-on learning experiences, enhancing students' comprehension of gravitational concepts while nurturing critical thinking and quantitative analysis skills. Moreover, the adaptability and simplicity of this approach make it accessible across diverse learning environments, regardless of resource constraints. By obviating the need for specialized equipment and leveraging commonly available smartphones, this methodology democratizes experimental physics education, ensuring broader inclusivity and participation. Beyond educational settings, the significance of this research extends to scientific and engineering domains where accurate measurements of gravitational acceleration are crucial. The demonstrated precision and reliability of the smartphone-based methodology highlight its potential for applications in fields requiring portable and accessible measurement tools for gravitational studies, such as geophysics, civil engineering, and materials science. Furthermore, the integration of SPARKvue for the real-time display and analysis of sound parameters enhances the methodology's practicality and usability, providing a user-friendly interface for data collection and analysis. This feature augments the methodology's value in both educational and research settings, streamlining experimental processes and facilitating accurate data acquisition.
## 2 Materials and Methods  
### The Study Case  
The presented research work focuses on the investigation and development of an innovative methodology for mapping vulnerability to seismic hazard at a regional scale by using collaborative geospatial methods and _ad hoc_ open source tools. The study area is a region of southern Italy (Campania, Figure 1), historically characterized by high intensity seismic events (between 6.5 and 7 Richter magnitude). For this area, seismic structural damage and loss estimation maps at regional scale have been generated by using advanced spatial analysis and geo-processing procedures, structural inventories, motion-damage relationships and scenarios modeling.  
An extensive seismic sensors network is located within the study area (ISNet, Irpinia Seismic Network, [[http://isnet.amracenter.com/](http://isnet.amracenter.com/)]([http://isnet.amracenter.com/](http://isnet.amracenter.com/))) supported by an integrated platform of broadband telecommunications. The sensor network is deployed around the fault zone that goes across the Appennini Mountains next to Irpinia. This area was struck hard by the disastrous earthquake of 23 November 1980: measuring 6.89 on the Richter Scale, the quake killed 2914 people, injured more than 80,000 and left 280,000 homeless.  
The seismic alert and emergency management includes by three sequential phases: early warning, near early warning, post-event. The early warning phase occurs 10-20 s after the main shock. In this short time interval, an EWS should predict the ground motion intensity, evaluate the epicenter and alert of a dangerous seismic event. The near early warning phase occurs 100-200 s after the main shock. During this time interval, firstly a ground motion map (shake map) is produced and, subsequently, a more detailed map (scenario) is produced based on simplified models of source/propagation and representing a simulation of expected damages. These maps assist in locating the area affected by the seismic event and supporting first search and rescue operations. The post-event phase consists of the response planning and emergency management tasks.
## 3 Results
### Molecular Docking Study  
Molecular docking of 11 phytocompounds from _Andrographis paniculata_ and 3 from _Thesesia populnea_ were performed with WT-EGFR, and the corresponding binding energies (BE) and intermolecular interactions are summarized in Table 4. First, all 11 compounds from _A. paniculata_ were predicted to form interactions with the active site residues. Among these, Andrographin scored the lowest BE of \(-\)8.3 kcal/mol via forming two hydrogen bonds, i.e., Met98... =O, Met98... OH, and forming ten hydrophobic interactions, i.e., Thr159, Lys50, Val31 (2), Ala48 (2), Leu149 (2), Leu23 (2) residues. Among these, seven interactions were found to span the active site residues. The intermolecular interactions of Andrographin with WT-EGFR are shown in Figure 5A. Second, all three compounds from _T. populnea_ were predicted to form stable interactions with active site residues. Among these, Gossypetin scored the lowest BE of \(-\)8.5 kcal/mol via forming three hydrogen bond interactions, i.e., Met98...OH, Thr95... OH (2) and eight hydrophobic bonds, i.e., Thr159, Ala48, Val31 (2), Leu149 (2), Leu23 (2). Among these, six interactions were found to span with active site residues. The interactions of Gossypetin with WT-EGFR are shown in Figure 5B.
# Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing
## 3 Power and Energy Models  
This section will delve into the power and energy models and is divided into two main parts. The first part will discuss the methodology of developing the power consumption model in detail, while the second part will cover the steps of developing the agnostic energy model. This work distinguishes between power and energy consumption as follows:  
Power consumption is the electrical usage when conducting a workload at a certain level of time; power consumption is measured in Watts (W).  
On the other hand, energy consumption is the amount of the average power consumption (\(P\)) within a period of time (\(T\)) until we finish executing the workload measured by Seconds (S); energy consumption (\(E\)) can be measured in Joules (J), and it is represented by Equation (1):  
\[E=P\times T \tag{1}\]  
According to Equation (1), there is a strong relationship between power and energy. When power is increased, energy consumption is also increased, and vice versa. The objective of this work is to predict energy consumption when GPU applications are executed on VMs in cloud-computing environments through ML techniques, more specifically, shallow neural networks and Deep Neural Networks (DNNs). We separately develop GPU power and energy consumption for several reasons. GPU power and energy consumption can be beneficially used for different purposes. GPU power consumption models are used to develop and optimize GPU architectures for power consumption [15]. GPU energy models are utilized for energy-aware GPU resource management [25]. Unlike power consumption, energy consumption is affected by other factors, such as execution time and temperature in the long execution time. Moreover, we develop GPU power consumption for applications with short execution times like [26], and we develop GPU energy consumption for applications having long execution times for measurable energy consumption. Cloud environments consist of heterogeneous infrastructures. The heterogeneity type in this research deals with heterogeneous GPUs, each of which contains different characteristics and capabilities. In this work, we handle two different GPU generations. The GPUs used for the power and energy models are Nvidia Fermi C2075 and Nvidia Kepler K40c, Taiwan Semiconductor Manufacturing Company (TSMC), Hsinchu, Taiwan.
# Multiresponsive Hybrid Microparticles for Stimuli-Responsive Delivery of Bioactive Compounds
## 2 Materials and Methods
### Preparation and Surface Modification of Zero-Valent Iron Microparticles  
The zero-valent surface-modified iron oxide (ZV) microparticles were prepared in two steps: (i) preparation of the iron oxide (IO) microparticles and (ii) surface modification by diazonium chemistry [12; 24]. In brief, FeCl\({}_{3}\)\(\times\) 6H\({}_{2}\)O (0.406 g, 1.5 mmol) and NaBH\({}_{4}\) (0.171 g, 4.5 mmol) were dissolved in deionized water (10 mL). Then, 5 mL of the FeCl\({}_{3}\)\(\times\) 6H\({}_{2}\)O solution and 5 mL of the NaBH\({}_{4}\) solution were combined in a triple-neck flask and stirred for 10 min under an argon atmosphere, forming a black-colored precipitate. Next, an additional 5 mL of the FeCl\({}_{3}\)\(\times\) 6H\({}_{2}\)O solution and 5 mL of the NaBH\({}_{4}\) solution were added simultaneously to the reaction mixture via syringe for the growth of microparticles. The resulting suspension was stirred for 10 min in an argon atmosphere. Then, 20 mL of the aqueous solution of 4-carboxybenzenediamine toxinylate (0.3 g) was added in the prepared suspension of microparticles in one portion and stirred for 40 min. The particles were isolated using a permanent magnet and washed with distilled water, ethanol and acetone in order to obtain a clear solution above the particles. To remove the traces of solvents, microparticles were freeze-dried.  
FTIR-ATR (Attenuated total reflection-infrared spectroscopy) determined the presence of an organic layer FTIR Spectrometer (Nicolet iS5, Thermo Fisher Scientific) equipped with an iD5 ATR accessory and ZnSe crystal; resolution 4 cm\({}^{-1}\); 64 scans).
# Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier
## 5 Conclusions  
This article is based on a hand gestures recognition system for controlling smart home appliances. Two benchmark datasets were selected for experiments: the IPN hand dataset and Jester dataset. Initially, images are acquired, in which hands are detected and landmarks are localized on the palm and the fingers. After that, the textures-based and point-based features are extracted. The hand skeletons are used for extracting the point-based features, whereas the full hand is used for extracting the texture-based features. For feature reduction and optimization, the fuzzy logic is adopted, and finally, the K-ary classification algorithm is used for classifying the hand gestures for operating smart home appliances. For the IPN hand dataset, we achieved the mean accuracy of 88.46% and for the Jester dataset, a mean accuracy of 87.69% was achieved. The proposed system's performance shows a significant improvement compared to existing state-of-the-art frameworks. The limitation of the proposed framework is due to the complexity in the videos, such as cluttered backgrounds and various illumination conditions, which make it difficult to achieve more accurate results.  
Conceptualization, H.A. and A.K.; methodology, H.A. and A.J.; software, H.A. and A.K.; validation, H.A., M.S., S.A.A. and A.A.; formal analysis, A.K., S.A.A. and J.P.; resources, M.S., A.K., A.A. and J.P.; writing--review and editing, H.A., A.K., S.A.A. and J.P.; funding acquisition, A.A., S.A.A. and J.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2018-0-01426) supervised by the IITP (Institute for Information & Communications Technology Planning and Evaluation). In addition, the authors would like to thank the support of the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University. This work was partially supported by the Taif University Researchers Supporting Project number (TURSP-2020/115), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.
## 1 Introduction
The data obtained from the simulation were successfully experimentally verified with a high degree of accuracy. [PERSON] et al. (2018) noted that for an accurate simulation, it is necessary to consider the powder surrounding the printed part, given that the heat is transferred through supports to the base plate and the surrounding powder insulates the heat. To verify the accuracy of the simulation, the results need to be experimentally verified, i.e., one needs to print the part and then measure its deformed state. There are several methods for this. One of the most widely used and non-destructive methods is 3D scanning, which generally allows one to compare a model from a real scan at an accuracy up to 0.025 mm. Alternatively, 3D full-field measurements can be taken using the digital image correlation (DIC) method, which is a method for measurement that also captures deformations/strains on the visible part surface. The advantage of this method is that no contact with the investigated part is required. This method utilizes optical testers that measure not only displacements and strains but also velocities and accelerations on the vibrated part. The DIC method is currently also applied in hole-drilling residual stress measurements ([PERSON] et al., 2018). X-ray diffraction can also be used for non-destructive testing, but it can only light up a very relatively thin layer ([PERSON] et al., 2018). As listed above, although numerical studies of the printing process and its products have been investigated intensively, there are few studies that consider the thorough process from the designing stage to fabrication of the optimal components so as to bridge the gap between academic works and practice. In view of this, this study presents a typical workflow from designing to manufacturing and finally quality control (QC) of parts fabricated with the state-of-the-art SLM technology. In particular, a part was designed and topologically optimized. Subsequently, two commercially available software programs, namely ANSYS Addictive Suite and MSC Simufact, were utilized to evaluate the deformation of the optimized part caused by the printing process. For both cases, the 3D non-linear finite element model based on thermo-mechanical fields was presented and compared. Finally, the part went through the QC process conducted with DIC and 3D scanning methods. The surface deviation results from the two methods were collected and compared. The manuscript answers the following questions:What are the roles of simulation in preparation for the fabricating process with SLM technology? How accurate are they in predicting the dimensional deviation of the printed parts? How dimensionally accurate is the SLM-fabricated part in comparison with the Computer Aided Design (CAD) design? What are the controllable factors that need to be considered in the whole process to obtain dimensionally, functionally, and economically optimal SLM-fabricated parts?
# Robustness of Deep Learning Models for Vision Tasks
## 3 Adversarial Attacks and Defenses
### Adversarial Defenses
#### 3.2.4 Defense-GAN  
Defense-GAN [146] is a technique for defending against both white-box and black-box adversarial attacks on classification networks.  
GANs are trained on a classification dataset through unsupervised learning. Classification in a typical GAN can be trained using the original training images, the reconstructed images generated by G, or a combination of both. Ideally, if the GAN is properly trained and has a strong representation of the data, the original clean image and its reconstruction should be similar. Therefore, these two classifier training methods are expected to perform similarly. From this point of view, as shown in Figure 24, the defensive strategies of Defensive-GAN that differ from other defense mechanisms can be represented as follows:  
1. Defense-GAN can be utilized with any classifier and does not alter the classifier's design. It functions as an auxiliary step or a preprocessing step before the classification process.  
Figure 23: Pipeline of randomization-based defense mechanism. The RRP method involves transforming the input through random resizing and padding to make it difficult for an attacker to calculate the gradient of the loss on the input.  
2. If the GAN has a strong representation, there is no need to retrain the classifier and the integration of Defense-GAN should not cause a significant drop in performance.
3. Defense-GAN can be applied as a defense against any type of attack as it does not rely on a specific attack model. It simply takes advantage of the generative capabilities of GANs to reconstruct adversarial examples.
4. Defense-GAN is highly nonlinear, making it challenging for white-box gradient-based attacks due to the gradient descent loop.  
[PERSON] [146] showed that Defense-GAN is effective in identifying and rejecting adversarial examples. In particular, they revealed that this method is highly effective in detecting FGSM adversarial examples in MNIST and fashion MNIST datasets, resulting in high AUC scores. While its robustness may not match that of adversarial training, Defense-GAN demonstrates greater stability against FGSM compared to MagNet and adversarial training when tested on MNIST and F-MNIST datasets in black-box settings. In addition, Defense-GAN performed better than MagNet and adversarial training against FGSM and CW attacks for both MNIST and F-MNIST. However, [PERSON] et al. [147] found that Defense-GAN is not effective when applied to CIFAR-10 datasets and is vulnerable to back-pass differentiable attacks (BPDAs).
# Defacement Detection with Passive Adversaries
## 4 System Architecture and Implementation
### Adaptive Tests (Based on the Learned Classifier)
#### 4.4.1 Feature Choice  
Following our previous discussion on keyed learning, we face the goal of identifying a large number of features, where only a subset of such features will actually be used for training. The choice of this subset will depend on the learning key, that is selected at deployment time, can be changed at run time, and is unknown to our adversary.  
The features to be used should be adequate for addressing the defacement problem, and hence should be  
* easy to compute for general Web pages, and preferably available from previously computed values in the custom tests \(T_{1},...,T_{n}\)
* applicable to dynamic pages and pages containing different types of client-side code
* adequate for describing \"normal\" page content and \"defaced\" page content
* capable of capturing the page look and feel
* easy to extend and change, so as to allow for larger keys  
Based on the above guidelines, and using some of the features also suggested by [6,7], we have identified a large number of features, including:  
* Global page features, e.g.,
* Empty space to Filled-in ratio
* Prevalent colors
* Text to image ratio
* Static to moving content ratio
* Amount and type of client side code
* Simple numeric features, e.g.,
* Number of lines
* Number of characters
* Number of images
* Number of imperlinks *
* Words and semantics, e.g.,
* Number of words in a given semantic classes or dictionaries
* Prevalent languages (e.g., 1 st English, 2 nd Italian, 3 rd French)
* Presence of mandatory words / lines (e.g., company motto)
* DOM tree
* Depth, arborescence
* Number of leaves and nodes
* Character size and color  
* Color map (e.g., RGB mean in given areas)
* Average image size
* Prevalent image position within page
* Presence of predefined images in a given page area (e.g., company logo)
* Text from image OCR
* Word to size ratio
* Prevalent semantic class in image words
* Total number of words in images
* Network data
* Download time
* Number of different IP addresses contacted
* Number of TCP connections  
Some of the above features can easily be changed or extended, so that an even larger number can be produced. Some can even be parameterized, for example number_of_links(k,n) is defined as the number of external links in the k-th portion of the page, after dividing the page in n segments of equal size.