esa-sceva/satcom-chunk-collection huggingface dataset:
# ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
## 4 Methodology
### Communication Reduction  
When sending updates to the servers, the workers can choose to compress the message using an arbitrary compressor \(\mathcal{C}\) and use the technique of error reset to handle the residual error of the compression. In the algorithm, we compress the locally accumulated update \(u_{i}\) and maintain the local residual error \(e_{i}\) of worker \(i\), in Line 9 in Algorithm 3 (in the worker process). The local residual error will then be applied to the model parameters pulled from the servers in the next time. Similar to CSER in [15], we assume \(\delta\)-approximate compressors: \(\|\mathcal{C}(v)-v\|^{2}\leq(1-\delta)\|v\|^{2},\forall v\in\mathbb{R}^{d}\), where \(\delta\in[0,1]\). The choice of the expected compression ratio \(\delta\) depends on the sensitivity of the deep neural network and the networking environment. When the number of workers increases, the network tends to become congested and requires a larger compression ratio to maintain the efficiency of training. However, larger compression ratios usually incur larger noise. Thus, case-by-case hyperparameter tuning is typically required to find the optimal trade-offs.
## 3 Results and Discussion
Figure 4b is the pulse retrieved from Figure 3 (b) (red), (d) (black), and (f) (blue). The pulses duration (full-width half maximum (FWHM)) is 6.06 fs for five sidebands (blue), 6.10 for four sidebands (black) and 7.37 for three sidebands (red). In principle, the five sidebands AS3, AS4, AS5, AS6, and AS7 span the spectral range from 490 nm to 597 nm and, thereby, it is possible to obtain a pulse whose full-width half-maximum (FWHM) is around 4 fs with five sidebands (5 fs with four sidebands (AS3, AS4, AS5, and AS6) and 6 fs with three sidebands (AS3, AS4, and AS5)). However, in the experiment, the spectral phases of the sidebands were distorted due to the nonlinear effect and the pulse duration (FWHM) retrieved from the numerical result (Figure 4a) is larger than that of the Fourier-transform limited pulse fs. Notably, our results showed that the pulse duration with five sidebands (AS3-7) is close to that of four sidebands (AS3-6). This is due to the fact that the spectral phase of AS7 is distorted too much and, thereby, when synthesizing the waveform using AS7, the pulse duration barely changes. Figure 3: _Cont._  
Figure 4: **(a)** The spectral phases retrieved from the numerical simulation (black) and the rms deviation of the simulation _vs._ experiment at different wavelength (rms is evaluated between the normalized simulated and original spectrograms by summing over the scanning of time steps). **(b)** The pulse retrieved from the interferogram of three sidebands AS3, AS4, and AS5 (red line), four sidebands AS3, AS4, AS5, and AS6 (black line), and five sidebands AS3, AS4, AS5, AS6, and AS7 (blue line). Figure 3: **(a)** Theoretical interferogram of AS3, AS4 and AS5; **(b)** Interferogram of AS3, AS4 and AS5, which is reconstructed with 2D bilinear interpolation from experimental data; **(c)** Theoretical interferogram of AS3, AS4, AS5, and AS6; **(d)** Interferogram of AS3, AS4, AS5, and AS6 reconstructed with 2D bilinear interpolation from experimental data; **(e)** Theoretical interferogram of AS3, AS4, AS5, AS6, and AS7; **(f)** Interferogram of AS3, AS4, AS5, AS6, and AS7 reconstructed with 2D bilinear interpolation from experimental result.
## 2 Training of the ANN Flow Law
### Neural Network Governing Equations  
According to Figure 2, the proposed neural network has 3 inputs (referred as the input vector \(\overrightarrow{x}\)) corresponding to the plastic strain \(\varepsilon^{p}\), the strain rate \(\dot{\varepsilon}\) and the temperature \(T\), respectively. These inputs are first normalized within the range \([0,1]\) to avoid an ill-conditioning of the system as presented by many other authors in the literature [40,44] since these three variables represent different physical data with very different amplitudes (0.7 for the plastic strain, 100 s\({}^{-1}\) for the strain rate and 550 \({}^{\circ}\)C for the temperature in the case of the training data reported in Figure 1). Therefore, the three components of the input vector \(\overrightarrow{x}\) are coming from the plastic strain \(\varepsilon^{p}\), the strain rate \(\dot{\varepsilon}\) and the temperature \(T\) using the following expressions:  
\[\overrightarrow{x}= \begin{cases}x_{1}=\frac{\varepsilon^{p}-[\varepsilon^{p}]_ {min}}{[\varepsilon^{p}]_{max}-[\varepsilon^{p}]_{min}}\\ x_{2}=\frac{\ln[\varepsilon/\varepsilon_{0})-[\ln(\varepsilon/\varepsilon_{0}) ]_{min}}{[\ln(\varepsilon/\varepsilon_{0})]_{max}-[\ln(\varepsilon/\varepsilon _{0})]_{min}}\\ x_{3}=\frac{T-[\varepsilon]_{min}}{[\varepsilon]_{max}-[\varepsilon]_{min}} \end{cases} , \tag{5}\]  
where \([\ ]_{min}\) and \([\ ]_{max}\) are the boundaries of the range of the corresponding field. Concerning the strain rate \(\dot{\varepsilon}\), and considering that its amplitude in a real case can reach \(10^{5}\) s\({}^{-1}\), as proposed in [PERSON] et al. [31], we chose initially to substitute \(\ln(\dot{\varepsilon}/\dot{\varepsilon}_{0})\), with \(\dot{\varepsilon}_{0}\) equal to the lowest strain rate test, for the value of \(\dot{\varepsilon}\). After normalization, these three input variables are introduced into the neural network and are propagated within it by the feed-forward propagation mechanism. Conforming to the structure of the ANN reported in Figure 2 any hidden layer \(k\), containing \(n\) neurons, takes a weighted sum of the outputs \(\overrightarrow{y}^{(k-1)}\) of the immediately previous layer \((k-1)\), containing \(m\) neurons, given by the following equation:  
\[y_{i}^{(k)}=\sum_{j=1}^{m}w_{ij}^{(k)}\dot{y}_{j}^{(k-1)}+b_{i}^{(k)}, \tag{6}\]  
where \(y_{i}^{(k)}\) is the entry of the \(i\)th neuron of layer \(k\), \(\dot{y}_{j}^{(k-1)}\) is the output of the \(j\)th neuron of layer \((k-1)\), \(w_{ij}^{(k)}\) is the associated weight parameter between the \(i\)th neuron of layer \(k\) and the \(j\)th neuron of layer \((k-1)\) and \(b_{i}^{(k)}\) is the associated bias of the \(i\)th neuron of layer \(k\). Those weights \(w_{ij}\) and bias \(b_{i}\), for each layer, are the training parameters of the ANN that we have to adjust during the training process.
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
## 1 Introduction
[23; 24], where a Hamiltonian representation is used for fault isolation in a planar vertical take-off and landing (PVTOL) of an aircraft model. This work considers a general approach to fault diagnosis using a generalized Hamiltonian system representation. Sensor and actuator additive faults are analyzed in a whole frame and then decoupling techniques are used to obtain a set of subsystems, so that each subsystem is sensible only for one fault (or to a specific set of coupled faults). Additionally, an approach to residual design is considered. The proposed approach is applied to a case study: a two-degree of freedom (DOF) helicopter, a laboratory model of the brand Quanser(r). The scheme is tested by both simulations and by the implementation of a laboratory facility test. Experimental results confirm the obtained results. There are other methods applied to the problem of fault detection, such as the one presented in [25] where the authors use an observed-based fault-detection schema for Takagi-Sugeno (T-S) fuzzy systems, which consists of approximates a non-linear system by convex combinations of locally linearized systems, using fuzzy rules type \"if-then\". This method is suitable to be applied in processes represented by non-linear models in general. However, it is important to mention that the purpose of the present paper is to provide a novel methodology for fault detection in mechanical systems, which can be characterized using a generalized Hamiltonian structure, with an application to the case of a 2-DOF Quanser Helicopter. It is worth remarking that, even if the class of generalized Hamiltonian systems seems to be a bit restrictive at first glance, we can realize that most mechanical systems can be rewritten in the generalized Hamiltonian form. Besides, a larger class of dynamic systems with twice continuously differentiable right-hand sides and affine control input can be transformed into generalized Hamiltonian systems for a well-suited design of the generalized Hamiltonian function. Therefore, even if a study of systems that can be transformed into generalized Hamiltonian systems is of great interest, such a study is beyond the present paper's scope. This paper is organized as follows: in the next section, preliminaries such as a review of Hamiltonian system representation and some general concepts on fault diagnosis are discussed. In Section 3 the fault isolation based on the Hamiltonian structure is shown. Actually, this proposal represents a more general result that the one presented by in ref. [23], see also in ref. [24]. In Section 4 the study case is presented by means of a model of a two degree of freedom helicopter with its corresponding Hamiltonian representation. Fault modeling, fault decoupling and residual generation are also discussed in this section. Simulations and experimental results, as well as a discussion of the obtained results compared to different cases, are presented in Section 5. Finally, conclusions are provided in Section 6.
## 2 Methods
### Weighted Distance Transform  
We proposed a weighted distance transform to convert user interactions into distance maps. An image can be regarded as an undirected graph with weight. Each pixel is a node and the grayscale difference between neighboring pixels is the weights of the edge. In graph theory, the geodesic distance is the distance of the shortest path between two nodes in a graph, so the geodesic distance map can reflect the grayscale texture information of the original image. The Euclidean distance is the shortest distance between two points in geometric space.  
Let \(i\) and \(j\) be two different pixels in an image I, then the unsigned geodesic distance between \(i\) and \(j\) is:  
\[D_{G\omega}(i,\,j,\,\mathbf{I})=\min_{p\in\mathcal{P}_{i,\,j}}\int_{0}^{1}\, \parallel\,\
abla\mathbf{I}(p(s)\cdot\boldsymbol{u}(s))\parallel ds, \tag{2}\]  
where \(\mathcal{P}_{i,\,j}\) is the set of all paths between pixel \(i\) and \(j\). \(p(s)\) is one feasible path and it is parameterized by \(\text{s}\in[0,\,1]\). \(\boldsymbol{u}(s)\) is a unit vector that is tangent to the direction of \(p(s)\)[7; 13; 14].  
The unsigned Euclidean distance between \(i\) and \(j\) is:  
\[D_{Euc}(i,\,j,\,\mathbf{I})=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{ j}\right)^{2}}, \tag{3}\]When we combine the two distances, let the weighted distance between pixel \(i\) and \(j\) be:  
\[D_{w}(i,\,j,\,\,\textbf{I})=\lambda\cdot D_{Geo}(i,\,j,\,\textbf{I})+(1-\lambda) \cdot D_{Euc}(i,\,j,\,\,\textbf{I}), \tag{4}\]  
where \(\lambda\) is a hyperparameter that requires experimental verification. Weighted distance turns into Euclidean distance when \(\lambda=0\), and geodesic distance when \(\lambda=1\), respectively.  
Suppose \(S_{f}\) and \(S_{b}\) represent the foreground points set and the background points set, respectively. Then the unsigned weighted distance from \(i\) to the points set \(S\) (\(S\in\left\{S_{f},\,S_{b}\right\}\) is:  
\[G(i,\,S,\,\,\textbf{I})=\underset{j\in S}{\min}D_{w}(i,\,j,\,\textbf{I}), \tag{5}\]  
There are already many algorithms in computer science for solving optimization Equation (3), such as the [PERSON]'s algorithm, the [PERSON]'s algorithm Dijkstra (1996), and the fast marching algorithm Dijkstra (1996). Here we use fast marching for its speediness. The geodesic distance map was set to all zeros if no points in the foreground region and background region were clicked. Figure 2 shows an example of weighted distance maps when \(\lambda\) in (4) takes different values.
# A State of the Art Review of Systems of Linear Inequalities and Related Observability Problems
## 4 Observability  
In this section, the observability problem of the different variables will be discussed. By observability we refer to the information we can obtain from the general solution of a system of linear inequalities. Observability ranges from full observability to no observability at all, passing by partial observability, in which only some unknowns are uniquely determined and bounds for the remaining variables can be obtained.
# Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia
###### Abstract
From that perspective, we aimed to figure out the main determinants behind the distinct spatial footprints of road traffic crashes in different weather conditions in Slovenia. In order to do so, we sought answers to the following research questions: (1) Where in Slovenia was the road traffic crash frequency trend (major injury or death) positive or negative in regard to adverse weather conditions? (2) Were there significant hot or cold spots for road traffic crashes on the municipal level? (3) Could the geographically weighted regression approach or machine learning techniques explain the existing spatial pattern of road traffic crashes in different weather conditions?
## 4 Experiments and Analysis
### Ablation Experiments  
To thoroughly evaluate the practical impact of the modules in improving the performance of the baseline method in real-world scenarios, this study conducted ablation experiments.  
Two ablation experiments were performed: (1) Comparison between the improved cross-entropy loss function (ICELF) and the regular cross-entropy loss function, assessing the accuracy of driver intent recognition results under these two scenarios. (2) Comparison of the accuracy of driver intent recognition results obtained by adding the CSAM and MCFM against directly using CSAM4. as shown in Table 1.  
We systematically tested the impacts of CSAM and MCFM and the improved cross-entropy loss function on the accuracy of driver intent recognition by modifying one component at a time while keeping the other unchanged. The experiments utilized both in-cabin and external camera views. According to the results in Table 1, CSAM and MCFM increased the ACC score by 7.32% compared to the baseline model. Simultaneously, the improved cross-entropy loss function raised the ACC score by 0.81%. These results indicate that these two components complement each other in terms of performance and variance, playing crucial roles in enhancing the accuracy of driver intent recognition. When used together, their accuracy is 90.97%, with an F1 score of 89.37%.
# Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports
## 3 Case Studies
### Swimming  
The swimming video was selected to show the movements of a male swimmer, extended horizontally while swimming in \"front crawl\" style, from a perpendicular point of view. Manual digitization of 10 points was performed: Left (L) toe, L ankle, L knee, L hip, L shoulder, L elbow, L wrist, L finger, chin, and forehead; and since this case of study was obtained from an online video, the following assumptions were made. A height of 1.5 m, and a weight of 78 kg were assumed. The approximated area of the hand was estimated as \(A_{hand}=0.017161256\) m\({}^{2}\)[97], and the drag coefficient was considered as \(C_{d}=1.8262\). In this case study, we were interested in estimating instantaneous force and torque (in a specific videoframe), as well as the average swimming power. Due to the water imposing drag forces onto the swimmer, the equations proposed in [98] were exploited to improve the force estimation made by SkillSpector that doesn't take into account drag forces. The velocity of the hand in the \(x\) axis was estimated via the manual digitization in SkillSpector. A specific frame (\(\sim\)2.5 s) of Figure 2 was selected to estimate and observe the generated force, when the swimmer was pushing the water with the arm flexed (close to finishing the stroke). At that point, the estimated force (\(F_{free\, swim}\)) measured at the finger, despite the active drag related to free swimming was calculated using the following formula [98]:  
\[F_{free\, swim}=\frac{1}{2}\rho V_{free\, swim}^{2}\,A_{hand}\,C_{d} \tag{1}\]  
where \(\rho\) is the density of water of \(\frac{997\text{kg}}{\text{m}^{3}}\), and considering the instantaneous velocity at 2.5 s (see Figure 2), \(V_{free\, swim}^{2}=(2.3\,\text{m}/\text{s})^{2}\), the estimated average force is \(F_{free\, swim}=82.65\,\text{N}\). The maximum distance from the normal position to the hand was obtained with another graph from SkillSpector, subtracting the distance in the \(y\) axis of the shoulder minus the distance in the indicated position of the hand. The distance was 0.63 m in the position where the arm was practically extended and perpendicular to the body, so for the torque in the shoulder we multiplied the force with the distance:  
\[\tau=F_{free\, swim}*d=82.65\,\text{N}(0.63\,\text{m})=52.07\,\text{Nm} \tag{2}\]  
Such results indicate that the dimensions of the estimated torque are consistent with the ones obtained in different biomechanical studies of athletes during crawl swimming, which have reported values of maximal torques of 69 and 91 and Nm [99]. For the calculations that we found on the clinical method [98], we wanted to obtain the average swimming power, which was given by the following equation:  
\[\textit{Swimming Power}=(\textit{Towing Force})(\textit{Swimming Velocity}) \tag{3}\]  
The average swimming velocity was obtained from the video we are analyzing, which indicates that the athlete swam 50 m (length of an olympic pool) in 38.8 s, and therefore, an average velocity (\(V_{swim}\)) of 1.2886 m/s was considered.
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
### Contribution  
A successful approach to designing a new lightweight cryptographic algorithm must use the modality of a trusted, well-known cryptographic algorithm applicable in a heavyweight cryptographic approach under the constraints of a lightweight one. This approach was successfully used to design the DESL [12] based on DES, and the DESXL [12] based on DESX. This paper investigates the same approach by proposing a lightweight cryptographic algorithm based on the Advanced Encryption Standard (AES), which we call Lightweight Advanced Encryption Standard (LAES). This paper extends the design and evaluation of [13], and makes the following main contributions.  
1. Design and construction: Our approach utilizes the modality of a trusted cryptographic algorithm in proposing a reduced version of AES, LAES, which is more amenable to lightweight applications germane to the IoT.
2. Evaluation of randomness and performance: This paper evaluates LAES performance with existing well-known lightweight cryptographic algorithms (Present, Celfia, and AES). The four algorithms were challenged in identical scenarios to encrypt a range of images of varying sizes. The randomness level of each algorithm was measured using three metrics (i.e., frequency, block, and run tests), while the remaining metrics were used to determine their time efficiency.
3. Evaluation based on the microcontroller: The performance of LAES was tested on a low-power, 8-bit microcontroller using an Arduino Uno board. The evaluation outcomes of LAES and its counterparts were compared and analyzed using six measures [14]. These measures aimed to evaluate the proposed LAES from a different perspective: The average required encryption time for block, round and entire process, average CPU cycle required for encrypting block and round, as well as measuring the average power and battery charge consumption required for encrypting a block.
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 1 Introduction
Launched in 2006, COSMIC satellites have been in operation for over 15 years, which has provided sufficient amounts of space-borne observations that are valuable for statistical analysis of space-LEO satellite signal propagations under ionospheric scintillation. It was discovered that the spread E irregularities led to the U shape of the received occultation signals. The U shape was closely correlated with the inclination of E irregularities [35]. [PERSON] et al. further investigated the occultation signal features under ionospheric scintillations and statistically analyzed the loss of COSMIC satellite signal locks [36]. The COSMIC occultation signals are greatly affected by ionospheric irregularities, representing prominent scintillation characteristics. The long-term scintillation distributions on a global scale were then discussed with amplitude scintillation indices from the COSMIC received signals [37]. It was found that space-borne signal scintillation was partially correlated with the geophysical and seasonal variations in ionospheric irregularities [38]. However, there are few studies on scintillation signal distribution and models under space-LEO occultation propagation channels. To address this problem, the COSMIC scintillation observations in solar cycle 24 are processed and analyzed in this work, and several models are discussed and evaluated to fit the space-LEO satellite propagation characteristics. The COSMIC Level-2 data are used to extract the amplitude scintillation index S4 and signal-to-noise ratio (SNR) for GPS L1 frequency scintillation observations. The Nakagami-\(m\), \(\alpha\)-\(\mu\) and \(\kappa\)-\(\mu\) distributions are tested and fitted. The entropy function is computed to evaluate the fitting confidence levels under different scintillation intensities and in different solar activity phases. To investigate the statistical distribution of amplitude scintillation indices, the normalized SNRs of the received GPS-LEO occultation signals are fitted with the three distribution models. The fitting performances are evaluated by standard deviation, kurtosis, information entropy and fitting interval.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 5 Discussion
### Limitations of Deep Learning for Medical Image Cryptography and Future Prospects
#### 5.2.2 Adversarial Attack Vulnerability  
Deep learning models are vulnerable to adversarial attacks, in which dishonest individuals tamper with the input data to falsify the model's predictions. The security and integrity of the encrypted data can be jeopardized by adversarial attacks in medical imaging cryptography. To develop deep learning models more resistant to such attacks, future research might investigate reliable training techniques and protection mechanisms.
# Progress in Phase Calibration for Liquid Crystal Spatial Light Modulators
## 4 Diffractive Phase Calibration Method  
The diffractive phase calibration method uses the SLM uploaded with virtual optical elements or coded grayscale patterns to transform the phase distribution into the spectrum or unique intensity features. There are also other methods based on the polarization technique. Compared to the interferometric methods, some of the diffractive calibration systems are more compact with less optical elements, and some distortion induced by the reference beam could be avoided. Besides, some diffractive calibration methods could be carried out without replacing the on-duty setup [39], which makes it suitable for satisfying extremely high-precision requirements.  
Figure 5: Schematic of the SLM calibration method based on digital holographic microscopy. MO: microscope objective; PH: pinhole filter; L: collimating lens; P: polarizer; BS: beam splitter; M: mirror; SLM: spatial light modulator; CAM: camera. [33].