esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Related Work  
**Malware detection**. [PERSON] et al. [3] proposed to detect Android malware by relying on critical API calls, their package level information, and their parameters. [PERSON] et al. [4] proposed a semantic-based malware classification to accurately detect both zero-day malware and unknown variants of known malware families, in which they model program semantics with weighted contextual API dependency graphs. For ransomware, existing detection approaches mainly monitor typical file system activities [5; 6; 7; 8] or analyze cryptographic primitives [6; 8; 30]. For example, Unveil [5] generates an artificial user environment and monitors desktop lockers, file access patterns and I/O data entropy; CryptoDrop [7] observes file type changes and measures file modifications using a similarity-preserving hash function and Shannon entropy to detect ransomware. The aforementioned malware detection mechanisms work under the assumption that the malware cannot obtain the OS privilege, which is unfortunately not true for the OS-level malware. On the contrary, mobiDOM specifically targets high-privileged malware that can compromise the operating system of the victim device.  
**Secure data recovery in mobile devices**. [PERSON] et al. [9] proposed Bolt, which can allow the system to be restored after bare-metal malware analysis. However, Bolt runs the malware in a controlled manner (for the analysis purpose), and the system has a clear knowledge of when the malware starts to function. Therefore, Bolt can simply disable garbage collection of the flash storage medium so that none of the original data stored in the flash will be deleted, which enables restoration of the original data later. Different from Bolt, mobiDOM aims to defend against malware, which is uncontrollable, and the system has no knowledge of when the malware comes. In addition, the use of TrustZone in Bolt is to enable restoration of memory state of the system; however, the use of TrustZone in mobiDOM is to ensure the trusted execution of the user-level manager of the malware detector (note that mobiDOM does not target restoration of the memory state as it is not designed for bare-metal malware analysis).  
FlashGuard [31], MimosaFTL [32], SSD-Insider [33; 34], Amoeba [35] aimed to combat ransomware by enabling data recovery after the ransomware attacks. The major differences between them and mobiDOM are: First, mobiDOM targets the general data corruption malware and is not specific for ransomware. Second, mobiDOM is more practical by allowing the FTL to securely communicate with the user-level app, which is necessary for further actions after malware is detected. mobiDOM makes it possible by leveraging both the ARM TrustZone (ensuring the security of the app) and the steganography (ensuring the security of the communication between the malware detector and the user app) technique. On the contrary, the other frameworks [31; 32; 33; 34; 35] do not consider interacting with user apps.
## 2 Materials and Methods
### Supervised Classification Algorithms
#### 2.3.2 Artificial Neural Network (ANN)  
The ANN classifier is an artificial intelligence (AI) algorithm that mimics how humans classify patterns, learn tasks, and solve problems [61]. The basic architecture of an ANN consists of networks of primitive functions that can receive numerous weighted inputs and are rated on how well they discriminate between the classes in training data (Figure 2).  
Models differ depending on the primitive functions and network configurations used [32; 62]. The network connection weights are changed, and convergence continues until the error reduction between iterations has reached a decay threshold [26; 63]. The important hypermeters that affect the performance of ANN are the activation function, loss function, optimizer, hidden layer, number of nodes, and regularization layers [32]. A deep neural network (DNN) represents machine learning when the system has some level of complexity and uses many layers of nodes to derive high-level functions from input information [64].  
An ANN can extract underlying patterns in a data collection without prior information (e.g., a deposit model) and operate with sufficient accuracy even when the data are noisy [25]. The features of Google Cloud and TensorFlow Keras API in Google Colab were used to create an ANN model since GEE had no provision to apply an ANN model. First, we authenticated GEE with Google Colab; the training data were imported from GEE to Colab by uploading them onto the cloud in TF file format to enable working in the TensorFlow ML platform. Then, we created an ANN model by calling TensorFlow Keras API. Keras is a Python-based deep learning API that runs on top of TensorFlow [31]. It is used to create a deep learning model to allow quick experimentation and results generation [31]. A Keras sequential model with one input, four hidden, and an output layer (1-4-1) was created. After each dense layer, a normalization layer was used to prevent model overfitting. A normalization technique used was layer normalization, which finds normalized values in each layer of the ANN model. The normalization statistics of the summed inputs to the neurons within a hidden layer are directly estimated with this method [65]. An ReLU (rectified linear unit) activation function was used to produce input for the next neuron, which is defined as \(f(x)=\text{max}\ (0,x)\) for fast computing and learning of the model. We used the softmax activation function at the output layer, a more generalized form of the sigmoid function, which works well with multiclassification problems [66]. To check the performance of the ANN model, we used the categorical cross-entropy error/loss function for faster training while computing the error [66] and better generalization since it generalizes discrete classification problems efficiently [67]. A comparative study of different optimizers used in ANN is discussed by [PERSON] et al. [54]; the most widely used Adam and SGD were used to train the model.
## 5 Conclusions  
The seismic performance of RC frame structures with different heights of shear walls was studied numerically under the nonlinear dynamic analysis.  
First, four sets of previous experimental one-directional test models by [PERSON] [28] comprising RC frames with different wall height coverages were modeled and analyzed by the STERA_3D. The simulation results matched well with the test results.  
Then, 10-story and 20-story RC frames with 30%, 50%, and 70% wall coverages were generated and analyzed using the STERA_3D. The results show that the upper region where the wall coverage stops has a significant response of inter-story drift. In order to decrease the seismic response at the upper region of the frame without wall coverage, the installation of BRBs was proposed.  
The genetic algorithm is applied to determine the optimum locations of BRBs by considering three main parameters in the fitness function: the inter-story drifts, the damage index of the beams, and the total number of BRBs. Under ten selected earthquake motions, the optimum locations of BRBs were found to be different for each motion. Thus, a simple probabilistic method is employed to select the final locations of BRBs. The method successfully reduced the average inter-story drifts from 1/75 to 1/100 and gave a better seismic response at the upper region.  
Conceptualization, T.S., P.A.d.F.F. and T.I.M.; methodology, P.A.d.F.F. and T.I.M.; software, T.S.; writing--original draft preparation, P.A.d.F.F. and T.I.M.; writing--review and editing, T.S.; supervision, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Application of 3D Imaging for Analyzing the Chip Groove Shapes of Cutting Inserts
###### Abstract  
An effective chip formation process is significant for an efficient metal-cutting process. Long continuous chips can lead to scratches on the machined surface, increasing the risk to operator safety and stability of the machining process. The use of chip grooves on cutting inserts allows for control of the chip formation and breaking process during machining. The shape of the rake surface and the design of the chip groove also affect the efficiency of the machining process. The article presents the use of 3D imaging to analyze changes in the selected chip groove shapes depending on the cutting depth \(a_{p}=0.10\), 0.25, and 0.50 mm and the angular location of the cutting insert relative to the machined surface of the workpiece (i.e., major cutting-edge angle \(K=60^{\circ}\) and \(K=90^{\circ}\)). The analysis methodology was based on the use of 3D image registration and surface shape modeling. In the analysis based on the 3D imaging presented, the novelty was the adaptation of methods typically used to map and model the terrain surface, which have not been used previously in cutting processes. The evaluation of the shape of the chip groove surface was carried out using, e.g., watershed maps and 3D surface maps. The obtained results indicated a significant influence of the cutting depth and major cutting-edge angle on the surface shape, profile, and length of the chip former; chip groove volume; and the theoretical contact area of the formed chip with the cutting insert. It was observed that for small depths of cut, i.e., \(a_{p}<0.25\) mm, the chip-curling process may be difficult due to the flattened shape of the rake surface. In addition, the influence of the convexity of the rake surface of the cutting insert on the chip formation process was demonstrated. The results of the experimental research that verified the conclusions are presented. The developed results may be useful in the process of selecting the parameters and conditions of the metal finishing through use of tools with a shaped rake surface.  
3D imaging; cutting insert; shape of the rake surface; chip groove Article Application of 3D Imaging for Analyzing the Chip Groove Shapes of Cutting Inserts.
## 3 Experimental Results and Analysis  
### Damage Process and Morphology  
It can be found that through the damage process the failure modes of specimens under different parameters are nearly the same. The typical failure modes of the specimens are as follows: (a) the specimen is subjected to a small bending moment during the period from the beginning of the test of formal loading to 10% of the peak load. The cross-section is not yet cracked, so there is no significant change on the outer surface. At this time, the deflection growth in the span is approximately linear, and the specimen exhibits linear elastic deformation characteristics. (b) Near the time of cracking, the specimen exhibits certain plastic properties, and the deflection growth rate is greater than that of the first stage. (c) When the load cell monitor readings reach 13% to 27% of the peak load, the appearance of significant cracks can be observed in the purely bending section of the span of the beam, with the extension height. (d) As the loading proceeds, the mid-span cracks gradually increase and the height degree of crack development further deepens. Continuing to load, oblique cracks appear in the bending shear section along the line between the loading point and the corresponding side of the support. By the time the concrete is crushed in the compression zone, the loading ends, and it can be observed that fewer new cracks are produced and are basically the deepening extension of the original cracks. The specimen shows a typical bending damage of suitable reinforcement.  
The failure pattern and crack distribution (excluding crush zone) of each specimen are shown in Figure 4A-H. From the comparison of each figure, it can be seen that for the reinforced sea sand concrete beam specimens with different variation parameters,  
Figure 3: Fine Aggregate Grading Curve and Concrete Slump.  
the damage pattern and final damage morphology are similar, and they all exhibit the proper reinforcement damage in the positive section damage. The above indicates that the variation parameters (sea sand replacement rate, longitudinal reinforcement rate, type of mixing water and duration of service) do not have significant effects on the damage mode and final damage morphology of the specimens under the tidal environment of time duration of 270 d.
## 3 Experimental Results and Analysis
### Hydrodynamic Pressure
#### 3.3.4 Frequency Domain Characteristics  
The power spectrum is an important parameter that represents the frequency domain characteristics of fluctuating pressure, given by  
\[S(f)=4\int_{0}^{\infty}R(t)\cos\ 2\pi ftdt \tag{5}\]  
where \(f\) is the frequency of fluctuating pressure, \(t\) is the test time, and _S(f)_ is the power spectrum of the fluctuating pressure. The maximum fluctuating pressure on the slab of the plunge pool is also a key characteristic for studying the frequency domain characteristics. Figure 11 depicts the relationships of the power spectrum and frequency for an upstream water head of 1.90 m at different sediment concentrations. The calculations show that the turbulence energy was most concentrated at frequencies of 0-0.2 Hz and that the frequency bands of the fluctuating energy widen with increasing sediment concentrations. The dominant frequency trended to a lower frequency with increasing sediment concentration. The results demonstrate that the higher the sediment concentration, the larger the viscosity of the flow and the lower the turbulent kinetic energy of the small-scale vortexes, which decrease gradually, causing the turbulent kinetic energy to become more concentrated in large-scale energetic vortexes. Notably, under these conditions, the turbulence eddies are longer. These results are similar to the results of [PERSON] and [PERSON] [37] who undertook experiments to study turbulent structure of open channel flow with plastic particles. Figure 12 depicts the relationships of spectral density and frequency for four concentrations (0%, 1.4%, 5.6% and 10.4%) of particles with a mean diameter of 0.266 mm. They reveal that the turbulent frequency decreases with the increase  
Figure 10: Probability densities for peak pressure at 1.20 m from the upstream tank at different sediment concentrations and an upstream water head of 1.90 m.  
Figure 9: Relationship between pressures and sediment concentrations for different upstream water heads.  
in concentration and the turbulence energy is transmitted to the eddy with low frequency and large size. However, the different values of frequencies by the two experiments are the result of different turbulence and flow regimes.
# Respiration Rate Extraction of Moving Subject Using Velocity Change in FMCW Radar
## 2 Methods
### Comparison of the Conventional Method and the Proposed Method  
In the conventional method, even when the subject is standing still while leaning against a wall, a distorted signal is generated, owing to the microscopic movements of the human body, which combine with the displacement caused by the movement of the chest, according to Equation (3). Thus, if the human body is not entirely stationary, the respiration extraction is inaccurate.  
In the conventional method, \(M(t,\,r_{k})\), inhalation, and exhalation appear in one cycle. However, in the proposed method, \(D_{p}(t)\) exists separately in one cycle each of inhalation and exhalation, according to Equation (5). As depicted in Figure 7, in environment (a), in which the human body is in a stationary state, the outputs of the proposed and conventional methods are accurate respiration signals with a constant cycle. In experimental environments (b), (c), and (d), which include movements other than respiration, the correct respiration signal is extracted from \(D_{p}(t)\). In contrast, when using \(M(t,\,r_{k})\), a distorted signal is extracted, owing to the addition of displacement caused by movement other than respiration.
# Analysis of Distributed Measurement Method for Array Antenna Position
## 2 Scheme and Principle
### Error Analysis of Distributed POS Technology
#### 2.3.2 Navigation Equation of Strapdown Inertial Navigation System  
The velocity of the vehicle relative to the earth is expressed as \(v_{e}^{n}\) in the navigation coordinate system, and its navigation equation is shown as follows.  
\[\dot{v}_{e}^{n}=C_{b}^{n}f^{b}-[2\omega_{i\epsilon}^{n}+\omega_{en}^{n}]\times v _{e}^{n}+g_{l}^{n} \tag{18}\]  
where, \(C_{b}^{n}\) is the direction cosine matrix from carrier coordinate system to navigation coordinate system, \(f^{b}\) is the specific force of the carrier measured by the accelerometer, \(\omega_{i\epsilon}^{n}\) is the angular velocity of rotation of the geocentric fixed coordinate system \(\epsilon\) relative to the geocentric inertial coordinate system \(i\) under the navigation coordinate system \(n\), \(\omega_{cn}^{n}\) is the rotation angular velocity of the navigation coordinate system relative to the geocentric and fixed coordinate system in the navigation coordinate system, \(g_{l}^{n}\) is the gravity of the carrier.
## 2 Related Work
### Multilingual Sentiment Analysis  
Sentiment analysis research has predominantly been applied on English. Approaches to improve sentiment analysis in a resource-poor focus language in a multilingual manner include either: (a) the translation of resources, such as sentiment labeled corpora and sentiment lexicons, from English into the focus language, and use them as additional resources in the focus-language sentiment analysis system; or (b) the translation of the focus language text into a resource-rich language, such as English, and apply a powerful sentiment analysis system on the translation.  
Initial study on multilingual sentiment analysis has primarily addressed mapping sentiment resources from English into morphologically complex languages, i.e., the former direction (a). [PERSON] et al. [42] used English resources to automatically generate a Romanian subjectivity lexicon using an English-Romanian dictionary. The work of [43] investigated both (a) and (b) for Arabic. Morespecifically, they conducted several experiments by either using translated English polarity lexica into Arabic texts or freely-available machine translation engines and manual translations for converting Arabic to English in order to subsequently apply sentiment analysis.  
The work of [PERSON] and [PERSON] (2017) was the first to introduce the idea of machine-translated aid towards sentiment analysis of Greek texts. In their approach, they translated the whole document, which led to significant deterioration of the original meaning, probably due to the noisy content (the instances were collected by a social media platform) and the early versions of the Greek-to-English machine translation engines. [PERSON] and [PERSON] (2014) investigated the performance of statistical sentiment analysis on machine-translated texts. Opinion-bearing English phrases from the New York Times dataset were fed into an English sentiment analysis system that portrayed a prediction accuracy of approximately 68%. Following, the dataset was automatically translated into German, Spanish and French using publicly available machine-translation APIs, such as Google, Bing and Moses. The translated test sets were subsequently manually corrected for errors. Upon corrections for German, Spanish and French, a sentiment analysis system was trained on the translated training set for that language and tested on the translated-and-corrected test set. The authors observed that these German, Spanish and French sentiment analysis systems performed similar to the initial English sentiment classifier. There also exists research on using sentiment analysis to improve machine translation, such as the work by [PERSON] and [PERSON] (2015), but that is beyond the scope of the proposed work.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 6 Further Insights and Outlooks
### Comparison with Existing Literature
Therefore, w.r.t. [28], the calculations in this article went a little further: exploiting the Fourier transform properties over the hermitian product it is possible to jump from the PSD back to the baseband signal samples, as it is done in Appendix A and in [16]. From this remark, it is possible to express the CRB for the estimation of the altitude \(h\) of a receiver with the baseband signal samples, in the case where the signal is real and the path difference is large enough, so that the interference terms can be neglected,  
\[\mathrm{CRB}_{h|e}=\frac{\sigma_{n}^{2}c^{2}\left(1+\left(\rho_{1}/\rho_{0} \right)^{2}\right)}{8\rho_{1}^{2}\sin^{2}(e)F_{s}W_{3,3}} \tag{33}\]  
where \(c\) is the speed of light in vacuum and \(e\) is the elevation angle of the transmitter seen from the specular point (see Figure 1). \(W_{3,3}\) expression is recalled in (12). Note that this expression covers a broader area since it was obtained without any assumption on the Doppler shifts.
## 2 Methods and Materials
### Detail of Dataset and Analysis Method  
#### 2.1.1 Analyzed Materials  
Five types of solid polyurethane foams were prepared as artificial bones, which represented the vertebrae used for the measurement data. Each type represented a different density and was cut to a size of 60 \(\times\) 40 \(\times\) 60 mm, with a screw placed at the center of the material on a 60 \(\times\) 40 mm surface, after which 27 measurements were conducted. Unfrozen cadaveric bone was used after receiving approval from the Ethical Review Committee of Keio University School of Medicine (approval number 20150385), and written informed consent was obtained from each donor in accordance with the guidelines. Nine sections of the two human lumbar spines (L1-L5; 10 vertebrae) were used in this study, excluding one vertebra that was damaged during preparation. Two pedicle screws were placed per vertebra for a total of 17 conditions measured, excluding one missed placement.  
The screws to be placed in the artificial and cadaveric bones were monoaxial titanium alloy (Ti-6 Al-4V, ASTM F136) screws (catalog no. CMS05135, Kyocera, Corp., Kyoto, Japan). The pedicle screw was inserted at a length of 40 mm, with 5 mm remaining at the base of the screw. [PERSON] et al. reported that when a screw head is inserted until it contacts the bone, it causes bone fracture and reduces the fixation force [21]. Therefore, the technique used in this study did not allow the screw heads to contact the specimen in a manner similar to that used in clinical practice. All screws were inserted at the same depth (40 mm) using a depth gauge.  
The torque at the time of insertion was measured using a digital torque gauge (HTGA-5N, IMADA Co., Ltd., Aichi, Japan) to evaluate the insertion torque (peak torque) at a 40 mm insertion. This method of measuring insertion torque is identical to that of the conventional method [16; 22; 23; 24].