esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety-Aware Optimal Attitude Pointing for Low-Thrust Satellites
## 4 Discussion and Conclusions
cotangent bundle of any manifold \(G\) is endowed with a canonical symplectic two-form \(\chi_{\xi}\) that associates to each function \(H\) on \(T^{*}G\) a vector field \(\overrightarrow{H}\) defined through the relation:  
\[dH_{\xi}(v)=\chi_{\xi}(\overrightarrow{H}_{\xi},v)\]  
and each tangent vector \(v\in T_{\xi}(T^{*}G)\). Since in the case of \(SO(3)\), \(\mathfrak{so}(3)^{*}\) can be identified with \(\mathfrak{so}(3)\)[15], in this case, the Hamiltonian function is a function on \(SO(3)\times\mathfrak{so}(3)\). It follows that the Hamiltonian vector fields are given by the equation \(\overrightarrow{H}[.,. ]=\{.,.\}\), where \(\{.,.\}\) denotes the right Poisson bracket. Let \(\hat{p}\) denote an element of \(\mathfrak{so}(3)^{*}\). Then, denoting the lift of the basis \(A_{1},A_{2},A_{3}\) as \(M_{i}=\hat{p}(A_{i})\), an optimal trajectory \(R(. ):[0,t]\to SO(3)\) is a projection of an integral curve \((R(.),M(. ))\) of the time-varying Hamiltonian vector field \(\overrightarrow{H}\) that satisfies the boundary conditions given in (1) and (10). This is called the projection \"downstairs\", and \(M(. )\) is the extremal curve, or the projection \"upstairs\" of \((R(.),M(.))\). The projection downstairs satisfies the differential equation:  
\[R^{-1}(t)\bigg{(}\frac{dR}{dt}\bigg{)}=dH, \tag{10}\]  
(where \(dH=\sum_{i=1}^{3}\frac{\partial H}{\partial t_{i}}A_{i}\), the unique vector field satisfying \(dH(v)=\chi(\overrightarrow{H},v)\)), while the projection upstairs can be determined by solving the extremal equations given in the following theorem. Theorem [16]: The non-canonical Hamiltonian vector fields that are necessary conditions for optimality under the PMP are defined by:  
\[ \begin{array}{rcl}\frac{dM_{1}}{dt}&=&-\frac{M_{2}M_{3}}{c_{2}}+\frac{M_{2} M_{3}}{c_{3}},\\ \frac{dM_{2}}{dt}&=&\frac{M_{1}M_{3}}{c_{1}}-\frac{M_{1}M_{3}}{c_{3}},\\ \frac{dM_{3}}{dt}&=&-\frac{M_{1}M_{2}}{c_{1}}+\frac{M_{1}M_{2}}{c_{2}}.\end{array}  \tag{11}\]  
While in our particular case, optimality was used to produce a certain class of useful trajectories, we note that from [16], when the weights \(c_{i}\) are fixed to the values \(c_{i}=I_{i}\) where \(I_{i}\) represents inertia about the \(i\)-th axis, the trajectory \((R(.),M(. ))\) is an energy-minimal trajectory. Here, however, we used the optimization process to access a class of smooth trajectories described using a global representation of the attitude on the matrix Lie group \(SO(3)\).
## 1 Introduction  
In 1954 [PERSON] proposed that cooperative spontaneous emission could occur among a collection of dipoles through interaction with a common electromagnetic radiation field [1]. He named the phenomenon \"superradiance,\" which was subsequently observed in atomic/molecular and even spin systems [2; 3]. One would logically deduce that superradiance may also occur in quantum dot (QD) systems as QDs are sometimes considered as artificial \"atoms\". Indeed, there were numerous theoretical investigations regarding this subject [4; 5; 6]. To be specific, when a QD array is properly prepared, the longitudinal relaxation time of the population T\({}_{1}\) may be limited by the lifetime of inversion of a two-energy-level QD which is in the order of 1 ns, and the homogeneous (T\({}_{2}\)) and inhomogeneous broadening lifetime (T\({}_{2}\)*) of the array is in the range of T\({}_{2}\)*\(\sim\)T\({}_{2}\)*-0.1-1 ps (i.e., the dephasing time of the polarization). If the QD array is pumped by a short and strong external pulse, the resulting spontaneous emission from the inversion can become superradiant [6]. However, experimental evidence is rare, and only in the past decade or so has there been confirmation of radiation-field-induced cooperative behavior amongst quantum dots [7]. Here we first present measurements of THz emission from an antenna-coupled QD array pumped by an ultrashort 1550 nm laser. Then we provide evidence suggesting that the THz emission is a consequence of cooperative spontaneous emission from the QD optical dipoles. This can also help explain why these devices have displayed such high efficiency, \(\sim\)0.2% [8], when used as pulsed THz emitters.
# Detecting and Mitigating Attacks on GPS Devices
## 5 Detection Techniques and Their Comparison  
The necessary first step in effectively mitigating performance degradation is reliably detecting that degradation mechanism. This section provides an overview of the detection mechanisms found in the literature for different types of GPS disruptions. We did not focus on propagation-induced GPS disruption; instead, we focused on detecting intentional attacks on GPS receivers, GPS jamming, and GPS spoofing. Some of the existing research efforts can apply to multipath-induced GPS disruption and unintentional interference scenarios.
# Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels
## 3 Experimental Results and Analysis
### Load Resistance and Failure Modes  
Typical deformation of a NCLT test specimen is shown in Figure 8. The mid-span displacement of specimen 2 was the smallest, yet exceeded 100 mm; specimen 5 had the largest displacement at mid-span, exceeding 130 mm, far beyond the floor deformation limit of L/200 according to the Chinese Standard GB 50010-2002 [19] under normal use conditions, indicating that NCLT panels have very large deformation capacity. The failure mode of specimens was primarily through tensile failure of the panel in the extreme tension zone, while the horizontally pressed wooden panels did not undergo failure. The test specimens exhibited overall ductile behavior. Damage of specimens originated from weak links in the tension zone, such as wood knots and defects, which caused stress concentration, leading to crack initiation and growth. Such cracks further developed into diagonal shape, which eventually caused the veneer to be broken. Some specimens warped in the width direction during processing, which resulted in larger gap between plates. As a result, the depth at which the nails were nailed into the wood decreased, and the nail force between the layers weakened. It was also observed that threaded nails in NCLT panels pulled out. Damage then occurred right at the part where the nails were pulled out, indicating that the pullout force of the threaded nails needed to be strengthened, for instance by increasing the nail diameter. When using veneers, previous experience with conventional CLT panels showed that they exhibited slippage and rolling shear. However, NCLT panels in this study experienced no slippage or rolling shear, demonstrating that the interlaminar combination and integrity can be largely improved by using nail connections rather than glue.
# NDN Content Store and Caching Policies: Performance Evaluation
## 2 The NDN Architecture Overview
In the case of failure in forwarding an interest due to path congestion, insufficient information in FIB about the prefix, or any other error, a _negative acknowledgment_ (NACK) is sent back (downstream) with information indicating the possible cause of failure.
# Video Error-Resilience Research Based on Error-Resilient Screen Content
## 4 Experiment Results
### Experimental Results  
Figure 3(a),b, respectively exhibits the PSNR value distribution with the change of the web browsing and sideshow screen content sequences at the 256 kbps bit rate. According to the experiment results, the proposed scheme is able to achieve a higher overall PSNR score, which illustrates the better video encoding effect. By judging the mutation frame, the abrupt frame is regarded as a more important frame and more redundancy is assigned to it for adaptive frame-level video error-resilient coding. Additional code stream or redundant information is considered to improve error-resilient performance with redundant coding and acceptable video quality is obtained in the case of frame transmission error as a consequence.  
Figure 4: _Cont_.  
Figure 5 gives the average PSNR comparison without and with the proposed scheme. From Figure 5, the average PSNR score with the proposed scheme is higher than that without the proposed scheme [22]. These results accord with the analysis in Section 3, which validates the effectiveness of the proposed scheme in this paper.  
Figure 4: PSNR-Y comparison without and with the proposed scheme.  
Figure 5: _Cont._  
To further verify the performance of the proposed scheme, the average PSNR is compared with three classical video error-resilience coding schemes [11; 13; 15], as it is shown in Figure 6. Obviously, the average PSNR score of the proposed scheme is the highest, which indicates the proposed adaptive frame-level video error prevention coding algorithm can improve the transmission quality of video on an error-prone network channel by judging the abrupt frames and allocating more redundancy to them.  
Figure 5: Average PSNR comparison without and with the proposed scheme.  
Figure 6: Cont.  
Figure 6: Average PSNR comparisons among multiple schemes.  
According to the simulations in Figures 4-6, the proposed algorithm improves bit rate error and encoding efficiency, which is very helpful for video encoding transmission in multimedia applications, especially in the case of poor network conditions like the situation of packet loss. At the same time, since the proposed method does not perform redundant coding for each frame, the bit rate is small. During redundant embedding, more redundant slices are embedded in the abrupt frame and the region of interest is considered to achieve a better error-resilience and ensure the performance of video R-D performance.
# A Robust Adversarial Example Attack Based on Video Augmentation
## 4 Experimental Results  
### Experimental Methodology  
**Dataset.** We evaluate the performance of our method v3a on the most widely used video classification dataset, UCF-101 [32]. UCF-101 is a dataset containing 13,320 videos including 101 action classes for action recognition. During the evaluation, we randomly select one video from each class and sample 40 frames for each video as the input stream of the target video classification model.  
**Target model.** We use a long-term recurrent convolutional network (LRCN) [33] as the victim model, for instance, SA [21]. The LRCN model consists of two parts: (1) a CNN part for feature maps extracting from the original frames, and (2) a recurrent neural network (RNN) part for prediction making.  
**Metrics.** We evaluate our approach with two metrics: (1) Fooling rate (F). The percentage of success adversarial examples [34]. In this paper, the fooling rate is evaluated under video transformations. We use the fooling rate to evaluate the ability of v3a that can bypass video-augmentation-based transformations defense. (2) Mean absolute perturbation (MAP). The mean absolute perturbation size of each pixel throughout the video. We use the mean absolute perturbation to evaluate the impact of v3a on the perturbation sizes, which also represents the human imperceptibility of different attack methods.  
**Attack settings.** We consider both untargeted attacks and targeted attacks under white-box conditions and black-box conditions. Note that we have three parameter weight \(\gamma_{1}\gamma_{2}\gamma_{3}\) in transformation function. The parameters balance the effect of each transformation on the perturbations. Thus, we determined the appropriate values for \(\gamma_{1}\gamma_{2}\gamma_{3}\) through a grid search as \((0.5,0.25,0.25)\), respectively.
# Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization
## 2 Data and Methodology
### Electrofacies Prediction  
The various clustering methods are applied to the well-log data to predict three electrofacies. Three clusters are identified with each method; based on the geological characteristics of the identified clusters, they correspond to shale facies (Facies 1), sand facies (Facies 2, green), and mixed shaly sand facies (Facies 3, blue). Figure 3 shows the clusters derived for two input wells. By examining the geological characteristics of the logs, we observe that Facies 2 (green in Figure 3) represents a high-porosity clean sand formation with porosity distribution ranging from 13% to 20% and is likely to be the best target electrofacies for production. The committee machine is implemented to identify consensus predictions between the different methods and significantly improves on the separation of clusters as illustrated using silhouette plots (Figure 4). The electrofacies are upscaled to a 3D discretized grid to generate a 3D electrofacies model for each method. The model consists of a grid of 81 \(\times\) 60 \(\times\) 11 blocks, where each block has dimensions of 200 feet in both horizontal directions. The electrofacies are distributed in the 3D structure geostatically using kriging. Kriging is an advanced method used for estimating the values of a variable over a continuous spatial field, relying on a limited number of sampled data points. This technique incorporates the spatial correlation between sampled points to facilitate interpolation. The interpolation process is informed by the spatial arrangement of empirical observations. We boost performance by employing a static committee machine that combines the output from the different machine learning algorithms discussed above to identify grid cells where there is consensus on the vertical and lateral distribution of a
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 5 Scalability and Application Potentials
### Application Potential in Wireless Communication Systems  
The application of OBFNs in practical wireless communication system may not be realized in a short time, suffering from the relatively low energy efficiency of electro-optic conversion at the modulator/laser and optic-electric conversion at the PD. Nevertheless, one approach that combines the advantages of analog radio over fiber (A-RoF) and OBFNs has demonstrated application potential in high-frequency wireless communications such as mmWave wireless communication [32; 33; 34; 35; 38; 150]. This approach implements A-RoF in the mobile fronthaul and OBFNs as beamformers for PAAs. A-RoF technology is a promising alternative for mobile fronthaul, thanks to its high bandwidth efficiency, carrying RF signals directly on the optical signals [7; 151]. Recently, A-RoF fronthaul linked with RF signals of high-level modulation formats such as 16 QAM, 32 QAM and 64 QAM have experimentally succeeded and achieved a data rate larger than 1 Gb/s for each beam [152; 153; 154]. One issue that may hinder the application of the A-RoF with OBFN is its nonlinearity, which results from the laser nonlinear effect, four-wave mixing in optical amplifier and fibers, the nonlinear transfer function of modulators, and the nonlinearity of PDs, as well as the power amplifier [155; 155]. OBFNs with high-linearity modulators and high-power PDs provide a solution for this issue. The convergence of an A-RoF fronthaul and OBFN-based PAAs can not only eliminate mixers and digital-analog convertors/analog-digital convertors [32; 35], but also remove the lasers and modulators required for OBFNs in antenna sites. This is because OBFNs can be deployed at the central office, which simplifies the antenna units and improves the cost effectiveness and installation flexibility [38; 114; 152].  
Figure 23: (**a**) A MRR-assisted MZM (reprinted with permission from [134] © The Optical Society), and (**b**) band diagram of UTC-PD [136] (Copyright 2017, AIP Publishing, #5443550099755).
## 5 Experiments
### Setting
#### 5.1.2 Metrics  
Spatiotemporal Linear Combine Distance (STLC) [32]. The STLC measures the similarity between two trajectories by separately calculating the distances between their spatial sequences and temporal sequences and then combining these distances with a weighted sum. A larger STLC value indicates higher accuracy, as it signifies a closer match between the reconstructed trajectory and the ground truth.  
Spatiotemporal Longest Common Sub-Sequence (STLCSS) [33]. The STLCSS extends the Longest Common Sub-Sequence (LCSS) metric by considering both spatial and temporal dimensions. It matches trajectory points only when their spatial and temporal errors are within predefined thresholds. A lower STLCSS value reflects greater accuracy, as it indicates more overlap between the reconstructed trajectory and the ground truth when accounting for spatial and temporal constraints.
## 3 Data Analysis Setup
### The Mathematical Framework
Data transformation in this way gives a new insight into features spread and constitution, where they can be worked out, i.e., reorganized, sorted, etc., and finally linked with the inputs. To elaborate further, while the direct noise observed in the accelerometer of the inertia measurement unit (IMU) exhibits a random walk behavior [30] and can be effectively filtered in the time series data collected from sensors, its influence on feature  
Figure 7: (**a**). Representation of features for wave response in the form of greyscale scalograms for two different segments of data (wavelet coefficients). (**b**). Manifestation of features after operation given in Equations (5)-(7) in new form with respect to scalograms with green dots for left image and red dots for right scalogram. distribution remains uncertain. This influence becomes more pronounced, especially in the context of feature clustering concerning the classification of wave classes (labels), and the limitation of current work can be the scope of further studies. The transformation reorganizes new features, presenting different approaches with unique benefits and challenges. This will be explored in the next paper, part B, focusing on how feature reorganization impacts computational efficiency and classification. For the current analysis, features on the diagonal plane have been projected into two planes based on the pixel's intensity stratification. Using Equations (5) and (6), higher amplitudes in the sequence of \(y_{d}[k]\) correspond to higher pixel intensities, approaching white (or 1) in the scalogram. A threshold value of 0.5 splits the features for projection: those below 0.5 are projected onto the plane called \"black\", while those above are projected onto the \"white\" plane. This sieving not only provides insight into the spectral elements but also their contribution within the vector. More accurately, vector k is reconstructed in two vectors of the same size A, B, where the vectors A\({}_{\text{a,b}}\) and B\({}_{\text{a,b}}\) are composed of dummy values of 0.5 in arguments and K \(\leq\) 0.5 for A and K \(>\) 0.5 for B. Figure 8 shows the projection of features into black and white planes with the color bar representing pixel intensity. Figure 8: _Cont._  
Engineered data possess twofold advantages: Firstly, this facilitates monitoring features spread not only from different exciting forces but also different data generation processes. For example, it shows how the features from the model test and synthesized data differ in distribution, and this insight can be further utilized for feature transformation between different fidelity level modeling that is the focal point of the present study. Secondly, engineered features can be reorganized as new unique attributes, ranging between 0 and 1, which can positively enhance classification and reduce training costs, as the second outcome of feature engineering in Figure 6, which will be discussed in part B of the current issue.
## 3 Materials and Methods
### Model Design
#### 3.6.2 Long Short-Term Memory (LSTM)  
LSTM [41] is used in this research to address short-term and long-term dependency issues in sequential data. As a recurrent neural network (RNN) type, the LSTM workflow uses gates and memory cells to retain information over extended periods. There are three types of gates in LSTM: the input gate, the forget gate, and the output gate. In the context of this research, the LSTM method is employed to create a model based on the dataset obtained from sensor data.  
The modeling process to recognize data patterns begins by determining the appropriate number of layers, neurons, and activation functions. The model's performance is evaluated using test data to measure its effectiveness. The mathematical calculations depict the operations within LSTM that enable it to recognize patterns in sequential data and maintain and manipulate information over extended periods through Equations (3)-(10). The architectural design of the LSTM is illustrated in Figure 7 [33].  
\[F_{t}=\sigma\Big{(}([H_{t-1},X_{t}]\cdot W_{f})+b_{f}\Big{)} \tag{3}\]  
Figure 6: Preprocessing flow diagram.  
\[I_{t}=\sigma(([H_{t-1},X_{t}]\cdot W_{i})+b_{i}) \tag{4}\]  
\[\widetilde{C}_{t}=\tanh(([H_{t-1},X_{t}]\cdot W_{c})+b_{c}) \tag{5}\]  
\[C_{t}=(F_{t}\cdot C_{t-1})+(I_{t}\cdot\widetilde{C}_{t}) \tag{6}\]  
\[O_{t}=\sigma((W_{o}\cdot[H_{t-1},X_{t}])+b_{o}) \tag{7}\]  
\[H_{t}=O_{t}\cdot\tanh(C_{t}) \tag{8}\]  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{9}\]  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{10}\]  
Based on the structure and equations, LSTM explains that the input values of weights are represented by \(W_{f}\), \(W_{i}\), \(W_{c}\), and \(W_{o}\). The variable \(t\) indicates the current time, while \(t-1\) represents the previous time. Furthermore, \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) are biases. The symbol \(X\) refers to the input, \(H\) to the output, and \(C\) to the cell state. The notation \(\sigma\) describes the sigmoid function, producing inputs between 0 and 1. A value of 0 indicates that no values are allowed to pass to the next stage, while a value of 1 signifies that the output can fully enter the next stage. Additionally, the hyperbolic tangent function (tan h) is used to address gradient loss during the training process.