esa-sceva/satcom-chunk-collection huggingface dataset:
# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Performance Evaluation Using Different Classifiers
#### 3.5.3 Classification Using ELM  
We also designed an experiment to measure shot classification performance using the ELM classifier. For this purpose, we extracted the LBP and LTrP features in the similar manner as discussed in Section 3.1. For ELM classification, we obtained an average accuracy of 73.45%, precision of 72.67%, recall of 76.39%, and error rate of 26.55% using LBP descriptor. Similarly, an average accuracy of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SVM Kernels** & **Crowd Shots** & **Close-Up Shots** & **Long Shots** & **Medium Shots** \\ \hline RBF & 72.27\% & 71.97\% & 70.27\% & 73.37\% \\ Quadratic & 72.34\% & 78.34\% & 73.67\% & 73.45\% \\ MLP & 59.67\% & 59.23\% & 60.78\% & 62.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracy using SVM (Quadratic, MLP and RBF kernels) with LTrP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SVM Kernels** & **Crowd Shots** & **Close-Up Shots** & **Long Shots** & **Medium Shots** \\ \hline RBF & 72.13\% & 68.73\% & 65.34\% & 67.65\% \\ Quadratic & 77.56\% & 76.24\% & 73.21\% & 72.34\% \\ MLP & 58.67\% & 60.98\% & 59.53\% & 61.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy using SVM (Quadratic, MLP, and RBF kernels) with LBP.  
75.56%, precision of 76.43%, recall of 77.89%, and error rate of 24.44% and 75.50% using LTrP descriptor was achieved. The results of LBP and LTrP descriptors with the ELM classifier are provided in Tables 7 and 8, respectively.  
From the results presented in Tables 7 and 8, it can be clearly observed that the combination of LTrP with ELM provides better performance as compared to LBP with the ELM. This illustrates that LTrP descriptors are more effective in comparison of LBP for shot classification because it includes magnitude and direction of the neighboring pixels, whereas in case of LBP only magnitude of the vertical and horizontal neighbor is concerned. It is to be noted that crowd shots classification results are far better than the remaining types of shot classification. One significant reason is the absence of playfield in crowd shots, whereas playfield exists for all the in-field shots.
## Materials and Methods
### Characterization of SPs and Cu NPs  
For Fourier Transform Infrared (FTIR) spectroscopic analysis, samples of SPs and Cu NPs were combined with potassium bromide to form pellets of 1 mm. The analysis was performed using a Nicolet 380 Thermogravimetric Analysis/Fourier Transform Infrared (TGA/FTIR) spectrometer for a range of 500 to 4000 cm\({}^{-1}\) wavenumbers. Additionally, an X-ray diffractometer with Cu K\(\alpha\) radiation (Bruker D8 Discover, Karlsruhe, Germany) at a wavelength of 1.54 A\({}^{\circ}\) was used to examine the phase and constitution of the prepared nanoparticle powders which were precipitated on a silicon holder. To study the morphology of Cu NPs, SEM (ZEISS- LEO SUPRA 55, Jena, Germany) was used to examine the surface structure of the synthesized nanoparticles. A fine gold coat was deposited on the samples under vacuum in a JFC-1100 sputter coater (JEOL) for 3 min at 15 mA. TEM (Type JEOL-JEM-2100, Software: Gatan Digital Micrograph, Akishima, Tokyo, Japan) was also used to examine the nanosize of the particles. Specimens of several milligrams of powdered samples were suspended in distilled water and spread well for 15 min in an ultrasonic bath. The zeta potential of the synthesized nanoparticles was determined using Dynamic Light Scattering (DLS) measurements (Malvern Zeta Sizer, Nano ZS, Malvern, UK), with a helium-neon laser operating at a 90\({}^{\circ}\) scattering angle and 633 nm wavelength at 25 \({}^{\circ}\)C. The chemical composition of the synthesized nanoparticles was measured using a Bench Top SEM + EDX, JEOL, JCM 6000 plus (Akhishima, Tokyo, Japan). The Energy-dispersive X-ray (EDX) spectroscopic conditions were 15 KV with a working distance of 19 mm.
## 3 Results and Discussion
### Numerical Simulation Results
As expected a feedback enhancement point re-appears in the exact same position but this time, the coupling efficiency \(r_{ext}\) in the  
Figure 4: 2D simulation contour profiles in 96 μm \(\times\) 96 μm DF channel: (**a**) The illumination irradiance profile through the channel; (**b**) The feedback light power re-entering-cavity coupling efficiency \(r_{ext}\) profile of 4.89 μm PS particle in the channel without any coating; (**c**) \(r_{ext}\) profile of 552 nm PS particle in the channel without gold coating; (**d**) \(r_{ext}\) profile of 4.89 μm PS in the channel with gold coating on the rear interface. enhancement region is 300 times higher than in the non-coated channel of Figure 4b. These simulation results provide strong evidence for our mentioned hypothesis that the feedback light enhancement resulting from the forward scattering light reflection on the rear channel interface. Based on the coupling efficiency and parabolic particle velocity distribution in laminar fluid, one can simulate the OFI signal spectrum by Equations (3) and (7). A noise level is added in each simulated spectrum to make the results more realistic.
# Progress in Topological Mechanics
## 2 Mechanical Topological Edge Sates  
The discovery of the integer quantum Hall effect opened a new research field in condensed-matter physics [1]. It was later discovered that the existence of the topological invariant (non-zero Chern number) is the root cause of quantization, which is independent of symmetry breaking [2]. If the Chern number is zero, the phase of the structure is ordinary; otherwise, it is topological. The Chern number in 2D systems [32] is given by:  
\[C=\frac{1}{2\pi}\int\
abla_{\mathbf{k}}\times A(\mathbf{k})\cdot ds, \tag{1}\]  
where the Berry connection \(A(\mathbf{k})=\langle\mu_{n}(\mathbf{k})|i\
abla_{k}|\mu_{n}(\mathbf{k})\rangle\), \(\mu_{n}(\mathbf{k})\) is the Bloch state on the \(n\)th energy band. It has been proven that achieving the QHE requires an externally reinforced magnetic field to break the time-reversal symmetry. However, it is difficult to apply a magnetic field in a real environment.  
Soon after that, [PERSON] and [PERSON] et al. proposed the QSHE around 2005 [4, 6]. This new state of matter exhibits two topologically protected spin-up and spin-down edge states. The implementation of the QSHE is different from QHE in that it does not need to break the time-reversal symmetry and depends on spin-orbit interactions. The topological states can be classified by a \(Z_{2}\) topological invariant or the spin Chern number. The spin Chern number [32] is given by:  
\[C_{\pm}=\frac{1}{2\pi}\int\
abla_{\mathbf{k}}\times A_{\pm}(\mathbf{k})\cdot ds, \tag{2}\]  
With the development of TIs, QVHE was proposed in 2007 [10]. Compared to the QHE and QSHE, realizing the QVHE is simpler and only needs to break the space symmetry, such as inverse symmetry or mirror symmetry, which leads to the Dirac degeneracies being opened in momentum space. The integral over the full Brillouin zone is zero because the Berry curvature is opposite at two valleys. The integral for each valley is a half-integer. The QVHE is characterized by valley Chern number \(C_{K/K^{\prime}}=\pm\frac{1}{2}\text{sgn}(m)\), \(m\) is the effective mass.  
Although implementing QHE, QSHE and QVHE require structures to satisfy different conditions, they have the common feature that the gapless edge states span the band gap when the topological material is nontrivial. With the rapid development of topological phases in quantum systems, they were quickly transferred to photonics [33, 34, 35, 36, 37, 38, 39, 40, 41], acoustics [42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53] and mechanical metamaterials [54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]. Next, we will introduce the achievements of elastic topological edge states in three categories which include QHE, QSHE and QVHE.
## 3 DoS Attacks on the AC/DC Multi-Area LFC System with Virtual Inertia  
In this section, we study the effects of data availability attacks on the LFC system developed in the previous section. The DoS attack is mainly considered as it is one of the major threats against the availability of data [10]. In a DoS attack, it typically causes periods of time at which the communication is not possible, thus preventing measurements or control data from reaching the respective destinations [35]. To launch DoS attacks, there are many strategies that an attacker can exploit. For instance, the attacker can jam the communication channels, compromise devices and prevent them from sending data, attack the routing protocols, and flood the network traffic [36]. To be illustrative, one can consider a man-in-the-middle (MITM) attack scenario on the communication channels between the substations and the control center. Then, the attacker is capable of interfering with the transmitted measurements using the MITM tool to block the measurements, either by attacking the routing scheme or flooding the network traffic [37]. In this article, we aim to model DoS attacks on the developed LFC mathematical model in Section 2 such that the behavior of data absence caused by DoS attacks is considered in the modeling process. We refer to the work in [10] for the specific strategies for launching such DoS attacks.
## 2 Related Works
### Dependability Assessment  
Quality of Monitoring and occlusion can negatively affect a system's dependability, being important the using of a method to assess their impact. In case of distributed sensor-systems (Cyber-Physical Systems, Internet of Things (IoT), WSN), dependability assessment has been more seriously addressed in recent years, generally considering aspects involving hardware components of sensor/actuator nodes, network communication elements and network lifetime. The work in [PERSON] and [PERSON] (2018) proposes a dependability evaluation approach based on stochastic Petri nets (SPN). That approach analyses disaster recovery solutions for IoT infrastructures, evaluating system reliability and availability, considering energy consumption and network communication. In Reference (2019), a methodology for automatic generation of analytical dependability models based on fault trees is proposed for industrial applications. That methodology is integrated with the SHARPE tool and evaluates the behavior of a scalar wireless sensor networks with regard to some attributes, such as reliability and availability. That methodology was later improved in References (2010,20). The authors of Reference (2020) perform availability assessments in wireless visual sensor networks. That evaluation is associated to target monitoring, considering hardware, communication and coverage failures, which were modeled as \"loss of view\" over targets. However, the proposal presents some limitations, such as not considering the effects of routing protocols or ignoring different importance degrees of sensor nodes on network failure conditions generation. The aforementioned limitations are circumvented in Reference (2010), which considered different routing strategies in the context of area coverage monitoring. That way, it is possible to specify network failure conditions as a function of a coverage objective. However, none of these methodologies considered the effect of visual coverage failures by occlusion and loss of Quality of Monitoring. Actually, although several papers have addressed issues such as visual occlusion, Quality of Monitoring and dependability assessment on WVSN, such issues have been considered separately. In a different way, this article integrates all those issues in a unified dependability evaluation model that has not been proposed before, to the best of our knowledge. Table 1 summarizes the discussed related works in areas related to QoM, occlusion, WSN dependability evaluation and modelling of visual coverage failures.
## 3 Results
### Advances in SOEC beyond MOXIE (2017-2023)  
In its major role in MOXIE, OxEon Energy LLC delivered eleven final \"flight equivalent\" electrolysis stacks to JPL in 2017. After that, their role in MOXIE was advisory, playing a significant part in planning tests and analyzing data. In the post-2017 period, independent of MOXIE, OxEon Energy LLC continued work on several development tasks in SOEC with support from the DOE, Air Force Research Laboratory (AFRL), and NASA. Most of the terrestrial-oriented tasks overlapped to a considerable degree with Mars ISRU needs. While the stacks for MOXIE were devoted to electrolysis of pure CO\({}_{2}\) (with 2% CO recirculated to prevent oxidation of cathode electrodes), further research was successfully conducted in pure CO\({}_{2}\), co-electrolysis of CO\({}_{2}\) and H\({}_{2}\)O, and electrolysis of H\({}_{2}\)O, all with the same multi-cell stacks. The stacks developed for MOXIE and extended by OxEon Energy LLC are CO\({}_{2}\)-conversion devices. The same SOXE stack can electrolyze dry CO\({}_{2}\), co-electrolyte CO\({}_{2}\)-steam, or steam by itself. Higher current densities and operating voltages are possible with co-electrolysis, and higher still for straight steam electrolysis (it is necessary to limit voltages in pure CO\({}_{2}\) to avoid CO reduction to carbon when electrolyzing pure CO\({}_{2}\)). However, OxEon Energy LLC made progress in reducing the probability of carbon formation [42; 43; 44; 45].  
The advances made by OxEon Energy LLC from 2017 through 2023 were not incorporated in MOXIE. Some of these advances are summarized below:  
* Scaled up the active area of cells from 22.7 cm\({}^{2}\) to 100 cm\({}^{2}\);
* Assembled a 65-cell stack (MOXIE stacks were limited to 10 cells);
* Demonstrated use of the stack for electrolysis of CO\({}_{2}\), CO\({}_{2}\) + H\({}_{2}\)O, or H\({}_{2}\)O.  
OxEon Energy LLC tested advanced electrodes that better resist oxidation by CO\({}_{2}\), or carbon formation by dissociation of CO. They also carried out a long-term steady state test up to 1000 h.  
The field of electrolysis via SOEC is far greater than Mars ISRU. Advances in electrode technology are being achieved continually. One example is exsolution, that has emerged as a powerful new method for decorating oxide supports with uniformly dispersed nanoparticles leading to high-performance, versatile, and easily manufactured devices. This opens the possibility of simple, almost instantaneous production of highly active nanostructures for reinvigorating SOCs during operation [46; 47].
## 5 Experiments and Results
### Metrics  
In our study, the confusion matrix is constructed to evaluate the performance of the multi-class classification model across four distinct classes: both, infection, ischemia, and none. Here is how we define each term within our confusion matrix for each class:  
True positives (TPs): These are cases where the model correctly identifies the presence of a condition. For instance, if a case is actually 'both' (meaning it has both infection and ischemia), and our model also predicts 'Both', it is counted as a true positive for that class. Similarly, we count TPs for each of the other classes (infection, ischemia, and none) when the model's prediction matches the actual label.  
True negatives (TNs): These are the cases where the model correctly identifies that a condition is not present. For example, for the class 'infection', a true negative is when the model predicts any class other than 'infection' (be it ischemia, both, or none), and the actual class is indeed not 'infection'. This logic applies similarly for the other classes.  
False positives (FPs): These occur when the model incorrectly predicts the presence of a condition. Taking 'ischemia' as an example, if the model predicts 'ischemia' when the actual class is 'none', 'infection', or 'both', it would be considered a false positive for 'ischemia'.  
False negatives (FNs): These occur when the model incorrectly predicts the absence of a condition. Using the class 'none' as an example, a false negative would be when the model predicts either 'infection', 'ischemia', or 'both', but the actual condition is 'none'.  
\begin{table}
\begin{tabular}{c c} \hline \hline Learning rate & \(10^{-5}\) \\ \hline Number of Epochs & 10 \\ \hline steps per epoch & 3 \\ \hline Original image Size & [224; 224; 3] \\ \hline Cropped Image Size & [200; 200; 3] \\ \hline Activation Function & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of model trained.  
The output of the confusion matrix is used to calculate the f1-score as shown in Equation (4):  
\[F1\text{-}Score=2\times\frac{Precision\times Recall}{Precision+Recall}=\frac{2 \times TP}{2\times TP+FP+FN} \tag{4}\]  
For multi-class classification with imbalanced data, the main consideration is the Macro f1-score. The formula is illustrated with the following Formula (5), where \(n\) represents the number of class involved. In the DFU classification, \(n\) is equal to 4:  
\[Macro\ F1\ Score=\frac{\sum_{i=1}^{n}F1\ score}{n} \tag{5}\]
## 2 Background
### Word Embeddings  
A more recent approach in feature extraction is using word embeddings. Word embeddings acquire the meaning of words or phrases mapped to vector representations, enabling similar text grouping in a new vector space. Hence, word embeddings might be more efficient than the BOW models. In the BOW models, the broadness of document collection and tagging at the index position causes data sparsity problems. However, word embeddings take the token's surrounding words into account to solve the data sparsity problem. The given text's information is transferred to the model to end up with dense vectors. In this continuous vector space representation, semantically alike words are close to each other. This deduction can either be ensured by utilizing neural networks for language modeling, predicting a word in a sentence given the nearby words as input, or capturing the training corpus's statistical properties. When predicting words in similar context inputs, neural networks generate similar predicted word outputs, resulting in a semantic representation space with the desired property.  
The most common word embeddings libraries used in the literature are Word2 Vec [7], Doc2 Vec [8], GloVe [9], and FastText [10].  
FastText treats each word as composed of character n-grams contrary to Word2 Vec and GloVe. For rare words, FastText results in better word embeddings. Furthermore, it can generate a vector for an out of vocabulary word that does not exist in the training corpus. This feature is not possible with either Word2 Vec or Glove.
## 4 Use Case and Simulation of the Proposed System
### PMIPv6/AAND  
_Analysis and Simulation Results Based on Handover_  
The simulator NS-3 was used for the concerned work. The subsequent subsections clarify MIPV6, PMIPv6, F-PMIPv6 and PMIPv6/AAND handover. This segment explains the MN transfer execution with multiple interfaces. The simulation arrangement begins with its interface 1 (if_1) i.e., WLAN at 0 seconds, the second interface2 (if_2), i.e., Wi-Max, comes into the simulation at 11 seconds, and then the third interface 3 (if_3), i.e., 3G, comes into the simulation at 14.5 seconds. The following results are occupied at the 30 th second of the simulation. The speed of mobility is 100 Mbps. Table 8 represents the concerned simulation parameters.
## 3 Applications with J-KAREN Laser System
### Soft X-ray Harmonic Comb Generation  
Bright coherent X-ray sources are necessary for fundamental research and many applications in life sciences, material sciences, nanotechnology, _etc._ Using the J-KAREN laser system [63], we have discovered [89] a new regime of high-order harmonic generation by multi-TW relativistic-irradiance (I \(>\) 10\({}^{18}\) W/cm\({}^{2}\)) [90] femtosecond lasers focused to gas jet targets, Figure 18a. Our further experiments [25] with the J-KAREN and Astra Gemini (CLF, RAL, Didcot, UK) [91] lasers show that comb-like spectra with hundreds of comparably intense even & odd harmonic orders, Figure 18b,c reaching the photon energy of 500 eV, including the \"water window\" spectral range, Figure 18d, are generated by either linearly or circularly polarized pulses. Importantly, the photon energy and number of photons are scalable with the laser power, Figure 18c.  
Figure 17: Energy spectrum of the X-ray radiation generated by laser-cluster interaction.  
Figure 18: High-order harmonics generation by multi-TW femtosecond lasers in gas jets [89,25]. **(a)** Experimental setup; **(b)** Typical single-shot raw data; **(c)** Harmonics spectra in absolute units and scalability with the laser power; **(d)** “Water window” radiation; **(e)** 3D PIC simulations demonstrating electron density spike and location of high harmonics source; **(d)** Harmonics spectrum from high-resolution 2D PIC simulation.  
Using particle-in-cell (PIC) simulations with the code REMP [92] and employing mathematical catastrophe theory [93; 94], we introduce a new mechanism of harmonic generation by sharp, structurally stable, oscillating electron spikes, Figure 18e,f. The spikes appear at the joint of the wake wave and bow wave [95] boundaries. The structural stability of the cusp singularity ensures that the spike formation is robust and insensitive to perturbations.  
Our results open the way to a compact bright coherent X-ray source which can be built using a university-lab-scale repetitive laser and accessible, replenishable, and debris-free gas jet target. Our discovery will benefit many areas of fundamental research and applications requiring a bright X-ray/XUV source for pumping, probing, imaging, and attosecond science.
## 3 Analysis Method and Assumptions
### Method for Solving Potential Failure Surfaces  
When the rock mass is in a state of limit stress and meets the Mohr-Coulomb strength criterion, if a point within the rock mass is under compression, it will exhibit the weakest resistance to sliding in two symmetrical directions, indicating potential fracture planes. The angle formed by the fracture plane and the direction of the first principal stress is \(\psi=\pi/4-\phi/2(or\phi/2-\pi/4)\), where \(\phi\) is the internal angle of friction. When the point is under tensile stress and the minimum principal stress reaches the tensile strength of the rock-soil mass, the fracture plane will be parallel to the plane of tensile stress application. Taking a two-dimensional model as an example, the potential fracture surface can be represented as a curve. Assuming this curve, \(S\), is parameterized by the arc length s, the expressions for the curve can be written as  
\[\begin{cases}x=x(s)\\ y=y(s)\end{cases}, \tag{1}\]  
Then, \(S\) is one of the integral curves of the following system of ordinary differential equations:  
\[\begin{cases}\dfrac{\mathrm{d}x}{\mathrm{d}s}=\cos(\beta+\Psi)\\ \dfrac{\mathrm{d}y}{\mathrm{d}s}=\sin(\beta+\Psi)\end{cases}, \tag{2}\]  
where \(\beta\) is the is the direction angle of the first principal stress. Write the above equation in vector form as  
\[\begin{cases}\frac{\mathrm{d}\mathbf{r}}{\mathrm{d}S}=f(\mathbf{r})\\ \mathbf{r}(s_{0})=r_{0}\end{cases}, \tag{3}\]  
where \(\mathbf{r}=(x,y)^{T}\), and \(f(\mathbf{r})=\{\cos(\beta+\Psi),\sin(\beta+\Psi)\}^{T}\). Equation (3) represents an initial value problem (IVF) for an ordinary differential equation (ODE), which can be solved using methods such as [PERSON]'s method, among others. [PERSON]'s method provides a straightforward numerical approach to approximate the solution of the ODE by incrementally advancing along the curve, using the derivative information at each step to estimate the curve's future values. Therefore, by inputting the starting point of the fracture surface, the entire potential fracture curve can be determined. For three-dimensional models, by inputting a series of  
Figure 5: Schematic diagram of the computational model: (**a**) tunnel-type; (**b**) silo-type.  
starting points of fracture surfaces, the entire potential fracture surface can be spanned. The solution schematic is shown in Figure 6. The algorithm can be found in Algorithm A1.