esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determining Kinematic Variables  
The recordings were subjected to kinematic analysis using the Kinovea program (version 0.8.15, [PERSON] & Contrib, Bordeaux, France). First, the time to cover the 5 m sections (T\({}_{\rm SM}\)) was determined: 15-20 m; 40-45 m; and, for 100 m races, additionally 65-70 m and 90-95 m. The sections were measured on the basis of the line connecting the markings on the side wall of the swimming pool. The time to cover these sections was estimated when the competitor's head passed the lines. The time to perform three complete movement cycles in the 5 m zone (T\({}_{\rm 3C}\)) was also determined. In this case, the limits of one cycle were consecutive, successive submersion of the same hand in the water. Using MS  
Figure 1: Arrangement of cameras in relation to the swimming pool.  
Excel (Microsoft Corporation, Redmond, WA, USA), mean values were calculated for each 5 m section:  
- velocity: v = 10/T\({}_{5\mathrm{M}}\) [m/s];  
- stroke rate: SR = 60 \(\times\) 3/T\({}_{3\mathrm{C}}\) [cycle/min];  
- stroke length: SL = v \(\times\) 3/T\({}_{3\mathrm{C}}\) [m];  
- stroke index: SI = v \(\times\) SL [m\({}^{2}\)/s].  
On the basis of each 5 m section, the average values of the indexes were determined for both races because they were used in these sections and not in measurements based on data from the literature:  
- velocity: v\({}_{50}\) and v\({}_{100}\);  
- stroke rate: SR\({}_{50}\) and SR\({}_{100}\);  
- stroke length: SL\({}_{50}\) and SL\({}_{100}\);  
- stroke index: SI\({}_{50}\) and SI\({}_{100}\).  
The above-mentioned variables were used to determine the differences in values of indicators for both distances called Dv (differences in velocity), DSR (difference in stroke rate), DSL (difference in stroke length), and DSI (difference in stroke index):  
\[\mathrm{Dv}=\mathrm{v}_{50}-\mathrm{v}_{100} \tag{3}\]  
\[\mathrm{DSR}=\mathrm{SR}_{50}-\mathrm{SR}_{100} \tag{4}\]  
\[\mathrm{DSL}=\mathrm{SL}_{100}-\mathrm{SL}_{50} \tag{5}\]  
\[\mathrm{DSI}=\mathrm{SI}_{50}-\mathrm{SI}_{100} \tag{6}\]  
where v\({}_{50}\)--mean velocity of fullstroke swimming zones for the 50 m freestyle race, v\({}_{100}\)--mean velocity of fullstroke swimming zones for the 100 m freestyle race, SR\({}_{50}\)--mean stroke rate for the 50 m freestyle race, SR\({}_{100}\)--mean stroke rate for the 100 m freestyle race, SL\({}_{50}\)--mean stroke length for the 50 m freestyle race, SI\({}_{100}\)--mean stroke length for the 100 m freestyle race, SI\({}_{50}\)--mean stroke index for the 50 m freestyle race, and SI\({}_{100}\)--mean stroke index for the 100 m freestyle race.
# Candidate Waveforms for ARoF in Beyond 5G
## 2 Requirements to Waveforms for Beyond 5G
### Requirements in ARoF  
ARoF combines optical and RF transmission. An example of a simple ARoF scheme is shown in Figure 3, where \(f_{RF}\) is the RF carrier frequency and \(f_{L}\) the optical carrier frequency. The characteristic of the spectrum form for each step of the ARoF system can also be observed in Figure 3. The appropriate requirements for this type of scheme are listed below.  
* Robustness to phase noise: ARoF is limited by phase noise when phase modulations are used. In the optical part, one of the most prominent impairments of the optical fiber is the chromatic dispersion. This dispersion produces phase rotation and ISI. Furthermore, in the mmWave tone generation, phase noise is introduced. The impact of this phase noise depends on the used technique to produce the mmWave tones in the optical domain [20]. Therefore, high robustness to phase noise is a relevant requirement for a waveform format in an ARoF system.
* Dynamic range (DR): ARoF is restricted by dynamic range too. The DR determines the minimum and maximum amplitude of the signal received to recover the information correctly. Then, the maximum DR value is directly related to the highest signal peaks (PAPR). In the optical part, the noise floor is increased by relative intensity noise (RIN) from the laser, amplifier spontaneous emission (ASE) from the amplifiers, and thermal and shot noises from the photodiode [21]. In its part, each RF device adds noise that can be quantified by the noise figure. All these additive noise contributions increase the noise floor. On the other hand, a distortion region is created and increased by the non-linearity of the optical fiber and the RF amplifier [14]. This region is also incremented by the intermodulation products and spurious of the RF amplifiers and MZMs, respectively [22; 23]. Thus, the distortion region and the noise floor, which suffer from ARoF systems, limit the DR extremely. Therefore, the DR of ARoF systems determines the type of waveform format that will be used and is related indirectly with the PAPR of the waveform.
## 6 Experimental Setup
### Evaluation Metrics  
To evaluate the intent detection results, the accuracy, precision, recall and F1 scores were calculated as given in the Equations (8)-(11):  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}\;+\;\text{FP}} \tag{8}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}\;+\;\text{FN}} \tag{9}\]  
\[\text{F1}-\text{Score}=\frac{2\times\text{Precision}\;\times\;\text{Recall}}{ \text{Precision}\;+\;\text{Recall}} \tag{10}\]  
\[\text{Accuracy}=\frac{\text{TP}\;+\;\text{TN}}{(\text{TP}\;+\;\text{FP})+( \text{FN}\;+\;\text{TN})} \tag{11}\]  
In Equations (8)-(11), true positive (TP) represents the correctly predicted samples, false positive (FP) represents the incorrectly predicted samples, false negative (FN) is the number of samples that are incorrectly predicted of the other categories, and true negative (TN) is the number of samples that are correctly predicted of other categories.
# Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools
## 4 Optimization Strategy
### TO Constraints  
Introducing optimization constraints in TO process improves the efficiency of the DfAM workflow by reducing the number of design iterations to verify the product simulation and printing evaluation. Major structural constrains include: physical limits as maximum stress and displacements and AM geometrical limitations based on fixed printing direction.  
In structural applications, maximum stress measure takes relevance to verify material resistance. Solving TO with compliance minimization as objective function takes into account preserving material in regions with elevated strain energy, providing results with elevated stress concentrations inducing several design iterations until the prescribed safety factor. One feasible solution is to include stress constraints in the TO formulation. However, this implies computational problems related to physical phenomena such as stress singularity, local nature and highly non-linear stress behavior [78]. Singularity problem appears in degenerated regions where the design variables tend to zero: for these elements, nonzero values of stress are found promoting the selection of a local minima solution. Stress is a local measure in the design domain, the use of local stress constraints for each element needs a high number of variables, thus increasing the computational effort. Highly non-linear stress behavior is related to stress gradient and design domain, as for reentrant corners where density changes in neighboring regions.  
A common approach is to convert the local stress-constrained for each discretization element into a single global stress measure by relaxing methods with p-norm or Kreisselmeier-Steinhauser (KS) functions [79]. This technique lacks physical interpretation, since it provides an approximation of the maximum reached stress by penalization of the local stress measure for all domain elements.  
Since stress-constrained TO is well suited to solve the shape optimization problem with parametrized domain boundaries, another approach solves the traditional unconstrained TO problem at first for then applying a design refinement on critical regions by shape optimization technique [80].  
To obtain TO designs that are suitable to AM with minimum support generation, manufacturing constraints can be included into the algorithm process by imposing geometrical printing limitations such as minimal angle of unsupported faces related to material and printing technology; and minimum size thickness related to machine printing capabilities [81]. AM constraints limit the algorithm freedom of searching the optimal layout distribution obtaining a solution with reducing mechanical properties concerning the unconstrained one. Therefore, a trade-off between support generation and performance must be set.
# Performance Analysis of Post-Quantum Cryptography Algorithms for Digital Signature
## 2 Modern Cryptography  
Cryptography has become an integral part of modern communication. It has significantly evolved over the centuries, resulting in the development of two primary categories: symmetric and asymmetric cryptography. Symmetric cryptography uses only one key, with which both encryption and decryption are executed. It is a simple and remarkably effective method, but it is not without its weaknesses. Among these, the biggest is the way of exchanging or establishing the key between the parties. Fortunately, other methods can be entrusted with this task. For example, asymmetric cryptography or quantum key distribution can be used to transfer the key [7]. Under the condition of key confidentiality and integrity, symmetric cryptography can offer reasonable speed of operations and security.  
Asymmetric cryptography solves the biggest problem of symmetric cryptography, namely secure key exchange or key establishment. For encryption and decryption, a key pair, public and private, is used. The private key is obligatorily kept confidential, as only with this key can the owner decrypt communications addressed to him. However, the public key is shared to give senders the ability to encrypt messages. The security of asymmetric cryptography is fundamentally based on the difficulty of mathematical problems. Among these, we can distinguish the factorisation of large prime numbers, which is the basis of RSA, and systems based on elliptic curves, for example, ECC. Asymmetric cryptography also underlies digital signatures.  
Digital signatures are one of the fundamental applications of asymmetric cryptography. Digital signatures ensure the non-repudiation of data and its integrity. In order to create a signature, several elements are necessary: a private key, a signature algorithm, and usually, a hash function. A scheme of digital signature is presented in Figure 1. Firstly, the hash is computed (1) to sign a file--this process is not obligatory but supports efficiency of signature process. Subsequently, the signature algorithm is used. Its inputs are the hash of the file to be signed and the private key of the signer (2). The result of this action is a signature that proves the undeniable authorship of the file. This collection--original data and signature--is sent to the recipient (3).  
The person verifying the signature, apart from the file and its signature, also needs to have information on what hashing function was used and what signature algorithm was applied. The verification process begins by applying the signature algorithm together with the signer's public key to the received signature (4). The result of this operation is the hash of the original data. The next step is to separately calculate the hash of the signed file using a known hash function (5). Then, these two hashes must be compared (6). If they are equal, this confirms the non-repudiation and integrity of the signed data. Verification process is also presented in Figure 1.
## 3 Methodology
### Vulnerability Verification  
Building upon the network simulation and emulation framework and its capabilities for collecting a variety of metrics under a wide range of scenarios, we then endeavored to leverage this system for vulnerability identification and validation. In developing this approach, we generated attack traffic in a manner consistent with our preceding research [24]. The crafted attack traffic was categorized into three distinct subsets, each designed to test specific aspects of device resilience. Traffic directed at the device's IP address featured intentionally malformed packet headers specific to the Modbus application protocol, simulating invalid protocol header attacks. Additionally, a subset of traffic contained syntactically correct headers but bore incorrect data, allowing for assessing device behavior in response to valid yet erroneous information. Furthermore, duplicated packets from previous transmissions were used to simulate replay attack scenarios. To assess the impact on device performance, this crafted attack traffic was subjected to incremental frequency testing, starting at a low frequency of once per second and escalating to 5, 10, 20, 30, 40, 50, 60, and 70 packets per second. Prior experiments demonstrated that such attacks can precipitate various detrimental effects, from elevated latency to complete operational failure in affected devices. The fine-grain control of packet generation also allows for vulnerability verification that may not be performance-based, such as responding to incorrect packets.
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 2 All-in-Focused Image Combination
### Discrete Cosine Transform (DCT)  
Each stack image (\(I_{t}\)) is transformed into the frequency domain by DCT. The source image is partitioned into blocks of \(8\times 8\) pixels and DCT coefficients of each block are computed by  
\[D(u,v)\ =\ \sum_{x=0}^{n-1}\sum_{y=0}^{n-1}\cos\biggl{(}\frac{\pi u}{2n}(2x+1) \biggr{)}\cos\biggl{(}\frac{\pi v}{2n}(2y+1)\biggr{)}I(x,y) \tag{1}\]  
where \(D(u,\,v)\) represents the DCT coefficient at the position (\(u,\,v\)) in the DCT domain. The DCT coefficients consist of the DC coefficient \(D(0,0)\) and AC coefficients. The AC coefficients are used for focus value calculation.
# High Resolution STEM Images of the Human Tooth Enamel Crystals
## 1 Introduction
### The Central Dark Line
The electron diffraction patterns (EDPs) of human tooth enamel crystals indicated a hexagonal unit cell in agreement with the HAP data of a PDF 09-0432 X-ray diffraction card (hexagonal unit cell with lattice parameters (a = 9.418 A and c = 6.884 A) and space group P63/m (No. 176)). Figure 4a shows the electron diffraction pattern of a human tooth enamel crystal along the [10-10] direction (the diffraction patterns shown in Figure 4 are in fact nano-diffraction patterns (nano-EDP), since the crystals had nanometric dimensions and the minimum cross-section of the incident electron beam used to obtain EDPs in TEM was greater than 10 nm and the nanometric region of the crystals from which the EDPs came was practically the whole crystal of the human tooth enamel). The CDL plane was perpendicular to the [10-10] direction and parallel to the plane formed by the [0001] and [1-210] directions (Figure 4b). Therefore, for the observation of the CDL, the crystals must be oriented in directions perpendicular to the [10-10] axis, such as the [0001], [10-11], and [1-210] directions, and these were the directions in which the crystals were observed to obtain the STEM images that we presented in this work. If the [0001] axis was designated as 0\({}^{\circ}\), the [10-11] and [1-210] axes were at 54\({}^{\circ}\) and 90\({}^{\circ}\), respectively. Figure 2: (**a**) Graphic representation of the dissolution of the enamel crystals of human teeth by acid attack. This starts from the center of the crystal. (**b**) HRTEM image of enamel crystals in step 2 of an enamel sample after treated with orthophosphoric acid for simulating acid attacks. Figure 4: (**a**) Electron diffraction pattern of a human tooth enamel crystal along the [10-10] axis. It was indexed with the hexagonal hydroxyapatite (HAP) unit cell. (**b**) The [1000], [10-11], and [1-210] diffraction patterns, which are perpendicular to the [10-10] axis. The color of the line indicates the zone axis of the diffraction pattern of the squares with the same color. Figure 3: Scanning transmission electron microscopy (TEM) images of the human tooth enamel crystals. (**a**) STEM-bright-field (BF) image; (**b**) STEM-angular dark-field (ADF) image; (**c**) STEM-high-angle angular dark-field (HAADF) image. Note the CDL contrast in each case; (**d**) magnified ADF-STEM image of one of the human tooth enamel crystals where the dark contrast around the CDL indicates a region of strain. The arrows indicate the CDL positions. Figure 5 shows a three-dimensional (3D) representation of the CDL plane in the hexagonal HAP unit cell. The CDL plane coincided with the plane formed by [0001] and [1-210], i.e., the plane (10-10). Figure 5 also includes the views of the HAP unit cell along the [0001] (Figure 5b) and [10-11] (Figure 5b) axes. As observed in Figure 5b, the HAP unit cell was symmetric on both sides of the CDL plane, which became a mirror plane m.
## 3 Modeling of Coupled Hysteresis
### Inherent Hysteresis Submodel  
Among many kinds of available inherent hysteresis models, the generalized P-I hysteresis (GPI) model [36] is applied for its simplicity, good generalization ability for symmetric and asymmetric hysteresis, and the availability of its analytical inverse model. It should be noted that many other asymmetric hysteresis models for the PAM can also be used, such as the EUPI model [37], MPI model, etc.  
The GPI model is defined by using weighted generalized play operators [33] to establish asymmetric and saturated hysteresis. The generalized play operator can be derived by a discontinuous function switching between envelop functions of rising and falling edges. The expression of the generalized play operator is shown in Equation (1):  
\[\left\{\begin{matrix}w(0)=F_{r,\ \gamma}\ [p_{k}](0)=f_{r,\ \gamma}(  
worth noting that envelop functions can be defined as other function forms, and we only chose one form to discuss in this paper.  
\[\begin{cases}\gamma_{R}(p_{k}(t))=\tanh(a_{i}^{k}p_{k}(t)+a_{2}^{k})+a_{3}^{k},\\ \gamma_{L}(p_{k}(t))=\tanh(b_{1}^{k}p_{k}(t)+b_{2}^{k})+b_{3}^{k}.\end{cases} \tag{4}\]  
Then, the output of the GPI model can be defined as:  
\[GPI[p_{k}](t)=\int_{0}^{R}x^{k}(r^{k})F_{r,\;\gamma}[p_{k}](t)dr \tag{5}\]  
where \(x^{k}(r^{k})\) is the weight coefficient of the \(GPI[p_{k}](t)\). In practical application, a finite number of GPI operators are sufficient to characterize the hysteresis; thus, the GPI model in Equation (5) can be discretized as Equation (6):  
\[GPI[p_{k}](t)=\sum_{i=0}^{N}x^{k}(r_{i}^{k})F_{r_{i},\;\gamma}[p_{k}](k) \tag{6}\]  
In Equation (6), \(N\) denotes the number of operators. \(x^{k}(r_{i}^{k})\) denotes the weight coefficient of the \(i\)th generalized play operator, and the value of \(r_{i}^{k}\) is given as  
\[r_{i}^{k}=\frac{i}{N}p_{k\max}(i=0,1,2,\cdots,N) \tag{7}\]  
In Equation (7), \(p_{k\max}\) represents the maximum input value. Parameters \(x_{i}^{k}(r_{i}^{k})\), \([a_{1}^{k},a_{2}^{k},a_{3}^{k}]\), and \([b_{1}^{k},b_{2}^{k},b_{3}^{k}]\) should be identified based on experimental data.
# Depth-Extrapolation-Based True-Amplitude Full-Wave-Equation Migration from Topography
## 1 Introduction
Moreover, compared with RTM, depth-extrapolation-based full wave equation migration has some potential advantages, such as less storage requirements and possible cleaner imaging results [10]. However, there are some difficulties in implementing the full-wave-equation depth extrapolation. Solving the full wave equation in depth requires two initial conditions: the wavefield value of the surface and its vertical partial derivative. The traditional seismic acquisition system can only provide the wavefield value of the surface. The lack of another initial condition is the first problem faced by full-wave-equation depth extrapolation. To solve this problem, scholars [12; 13; 15] estimated the vertical partial derivative of the wavefield from the wavefield value by assuming constant-velocity near the surface. On land, this assumption is often not satisfied. To move beyond this assumption, [PERSON] et al. [14] proposed a dual-sensor seismic acquisition system. This acquisition system collects two sets of wavefield data at different depths, so the vertical partial derivative of the wavefield at the surface can be provided. However, it is very difficult to use such a complex acquisition system in complex terrain. Moreover, the economic cost of this acquisition system is very high. These solutions that provide the vertical partial derivative, limit the application scenarios of the migration methods based on full-wave-equation depth extrapolation. In this paper, we propose a new vertical partial derivative calculation method. This new method is not limited by the topographic surface with lateral velocity variation, and makes it possible to apply the full-wave-equation depth extrapolation in irregular topography scenarios. Based on this, we implement a depth-extrapolation-based full-wave-equation migration from topography by using direct downward continuation. Numerical experiments prove the structural imaging performance and the amplitude-preserving performance of our proposed method.
## 5 Performance Evaluation
### Experiment 4  
In this experiment, both flows have the same transmission rate. The rate of both flows varies from 400 kbps to 550 kbps with step of 10 kbps. Figure 10 shows the jitter performance of specific real-time flow, network throughput, average end-to-end delay and packet loss ratio due to both mechanisms _versus_ offered load.  
From Figure 10a, it is seen that BLJCAR can improve the jitter performance as compared with the ONC policy because it considers the queue state of coding node in packet coding algorithm and could better optimize the trade-off of effect of packet waiting delay and network coding on jitter, resulting in improved long-term jitter benefits.  
From Figure 10b,d, it is observed that BLJCAR can improve the network throughput and packet loss rate as compared with the ONC policy because it delays packets to wait for the arrival of a future coding opportunity, which allows more data transmissions in each time unit.  
From Figure 10c, it also shows that BLJCAR scheme outperforms the ONC policy in average end-to-end delay because the effective queue management mechanism of BLJCAR can result in improved jitter benefits, the transmission competition is greatly reduced and the queuing delay of packets is reduced.  
Figure 10 shows that the capability of BLJCAR to cope with the longer communication paths is also fine for the single rate case.  
Figure 9: **(a)** Average packet jitter _versus_ offered load; **(b)** Network throughput _versus_ offered load; **(c)** Average end-to-end delay _versus_ offered load; **(d)** Packet loss ratio _versus_ offered load.