esa-sceva/satcom-chunk-collection huggingface dataset:
# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 4 Approach
### Model Design  
The SAE described in Section 2 was used in the operating mode classification model. To improve the performance of the SAE model, this study considered two aspects: the structure of the model and the size of the latent layer. Here, the model structure refers to the number of hidden layers within the encoder and decoder and the number of neurons used in each hidden layer. The size of the latent layer refers to the number of neurons in the latent layer. Figure 4 shows the basic structure of the model. The encoder and decoder were arranged in a symmetric form, and a softmax layer [32] was added to the output part of the decoder for classification.  
The softmax layer uses a softmax activation function, which is employed in a deep learning-based model, to produce a classification model that classifies data into three or more classes [33]. If the input vector is \(z\), the softmax activation function can be expressed as follows:  
\[softmax(z)=\frac{\exp(a_{i})}{\sum_{j=i}^{k}\exp(a_{j})}\]  
Figure 4: SAE model structure.  
Here, \(k\) is the number of classes that should be output by the multi-class classifier, and \(\exp(a_{i})\) is the standard exponential function of the \(i\)th input vector. Lastly, \(\exp(a_{j})\) is the standard exponential function of the \(j\)th output vector.
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 4 Discussion  
The accuracy of the reconstruction is vital to robotics tasks when using depth information to generate trajectories for a robot in a large scene. The usage of depth stream to reconstruct the scene, as in [PERSON] et al. [42], may not be appropriate if the system is time-sensitive. It is faster to move the robotic arm to positions at a high speed and then capture a frame than it is to cycle through at a slower speed to capture the data.  
The algorithms currently available for point cloud registration, such as iterative closest point (ICP) [16; 43] and its variants, as well as other methods that try to find the transformation between point clouds, use overlapping, which in this case is minimal due to the fact that the observed scene is large. Moreover, they are too slow to be used in a time-sensitive system.  
The ABB IRB 4600 has a preset maximum speed of 7000 mm/s, and the axis speed varies from 175\({}^{\circ}\)/s (axis 1) to 500 \({}^{\circ}\)/s (axis 6). With these high speeds, the data acquisition and reconstruction can be sped up through a straightforward mathematical approach, such as the TCP transformation method. The idea is to have a fast and reliable transformation for the camera positions.  
Further investigations could involve the study of how the trajectory generated for the TCP is affected by the errors in the reconstructions. Understanding this impact could help in developing a faster method without having repercussions on the desired output.  
This work will assist in developing new designs and understanding one's options when responding to reconstruction challenges using multi-camera views and a robotic arm. It casts light on two different approaches and how to evaluate the reconstruction of the 3D scene. However, it is not an exhaustive study on the topic.
# Adaptive Damping Log-Domain Message-Passing Algorithm for FTN-OTFS in V2X Communications
## 2 System Model
### Transmitter  
The information symbols in the DD domain are represented as \(\mathbf{X}_{\text{DD}}\in\mathbb{C}^{M\times N}\). Transmitter processing occurs in three main stages, namely OTFS modulation, SEFDM modulation, and FTN pulse shaping, which occur in sequence.  
First, the time-domain signals are generated through OTFS modulation. To convert \(\mathbf{X}_{\text{DD}}\) into the TF domain, the inverse symplectic finite Fourier transform (ISFFT) is applied. Specifically, the ISFFT performs an \(M\)-point fast Fourier transform (FFT) along the delay dimension and an \(N\)-point IFFT along the Doppler dimension, resulting in the TF-domain transmit symbols. Subsequently, an \(M\)-point inverse fast Fourier transform (IFFT) is applied to the TF-domain symbols to obtain the time-domain signal, which can be expressed as follows:  
\[\mathbf{X}_{\text{T}}=\mathbf{F}_{M}^{H}\left(\mathbf{F}_{M}\mathbf{X}_{\text{ DD}}\mathbf{F}_{N}^{H}\right), \tag{1}\]  
where \(\mathbf{F}_{M}\) represents the \(M\)-point discrete Fourier transform (DFT) matrix, while \(\mathbf{F}_{N}^{\text{H}}\) and \(\mathbf{F}_{M}^{\text{H}}\) denote the \(N\)-point and \(M\)-point inverse discrete Fourier transform (IDFT) matrices, respectively.  
Next, the time-domain signal \(\mathbf{X}_{\text{T}}\) undergoes modulation using SEFDM. In SEFDM, the subcarrier spacing is compressed to \(\alpha\Delta f\), where \(\alpha\) represents the SEFDM compression factor, satisfying \(0<\alpha<1\). The transmitted signal in SEFDM can be expressed as [23]  
\[\mathbf{S}=\mathbf{F}_{\alpha}^{-1}\mathbf{X}_{\text{T}}=\mathbf{F}_{\alpha}^ {-1}\mathbf{X}_{\text{DD}}\mathbf{F}_{N}^{H}, \tag{2}\]  
where \(\mathbf{F}_{\alpha}^{-1}\) represents the SEFDM modulation matrix, which is the inverse fractional Fourier transform (IFrFT) matrix and can be expressed as  
\[\mathbf{F}_{\alpha}^{-1}=\left[\begin{array}{cccc}1&1&\cdots&1\\ 1&\varepsilon^{\alpha}&\cdots&\varepsilon^{\alpha(M-1)}\\ \vdots&\vdots&\ddots&\vdots\\ 1&\varepsilon^{\alpha(M-1)}&\cdots&\varepsilon^{\alpha(M-1)(M-1)}\end{array} \right], \tag{3}\]  
where \(\varepsilon=e^{i2\pi/M}\). It is worth noting that when \(\alpha=1\), the IFrFT becomes equivalent to the conventional IFFT. Then, \(\mathbf{S}\) can be vectorized as  
\[\mathbf{s}=\text{vec}(\mathbf{S})=\left(\mathbf{F}_{N}^{H}\otimes\mathbf{F}_ {\alpha}^{-1}\right)\mathbf{x}_{\text{DD}}, \tag{4}\]  
where \(\otimes\) is the Kronecker product.  
Subsequently, the transmitted signal \(\mathbf{s}\) is then passed through the FTN pulse-shaping filter \(p(t)\). We assume that the pulse-shaping filter has unit energy; i.e., \(\int_{-\infty}^{+\infty}|p(t)|^{2}dt=1\). The FTN-OTFS transmitted signal is given by  
\[s(t)=\sum_{n=0}^{MN-1}s_{n}p(t-n\beta T_{0}), \tag{5}\]  
where \(\beta T_{0}=T/M\), \(\beta\) is the FTN compression factor, and \(T_{0}\) is the symbol interval defined by the ISI-free Nyquist criterion.
# Experimental Characterization of RGB LED Transceiver in Low-Complexity LED-to-LED Link
## 3 Results and Analysis
### Response of RGB LED Photodetector  
The aim of this experiment is to deduce the response of the RGB LED as photodetector to incoming light at different energy levels and to relate the signal decay response to a well-known function. The obtained energy levels at each distance are shown in Figure 3a. The graph shows a significant but expected drop up to a transmission distance of 1 cm and proves that the LED photodetector can be described by the inverse square law of light \(1/r^{2}\) at distances longer than 0.5 cm [10]. The measured signal energy also shows an overlap with the negative exponential function. Therefore, the response can be best described as a two-term exponential function as follows:  
\[E=1.9\times 10^{-5}\times e^{-2.8d}+9.1\times 10^{-7}\times e^{-0.89d} \tag{2}\]  
where \(d\) is the transmission distance.
## 1 Introduction  
Performance specifications are the starting point for selecting an instrument or technology for areal surface topography measurement. The precise measurement of surface texture requires sensitivity to small differences in surface height \(z\) as a function of position in the \(x\)-\(y\) plane, as illustrated in the topography map of Figure 1.  
Figure 1: An example of optical surface topography measurement requiring nm-level sensitivity to surface heights.  
The most frequently cited parameter related to surface height measurement sensitivity is the _vertical resolution_, qualitatively understood to mean the smallest surface height variation that we can detect. A review of instrument brochures, technical articles and international standards reveals a lack of consensus regarding how to determine the vertical resolution, with the result that, today, its numerical specification is of questionable value [1]. This is in contrast to the steady progress in understanding and quantifying sources of uncertainty in areal surface topography measurement [2; 3; 4]. Although there is no formal definition for vertical resolution, instrument makers, researchers and users are asked to specify its value quantitatively. This can lead to confusion and even commercial disputes. It is therefore important to re-evaluate the meaning of vertical resolution and perhaps identify a better way to characterize instrument performance.
## 3 Results
### Effects of Convergence Semi-Angle and Imaging Modes  
Figure 4 demonstrates the results of the investigation into the effects of convergence semi-angle (\(\alpha\)) and imaging mode via beam focal depth on beam width through silicon dioxide. Beam width represented by R\({}_{0.68}\) collected within 10 mrad at 10 \(\upmu\)m depth was calculated and compared:  
1. When the beam is focused on the top surface, a larger convergence semi-angle produces wider beams, thus larger beam width: top-focused (at 0 \(\upmu\)m) with \(\alpha\) = 1 mrad was 55.0 nm, and top-focused with \(\alpha\) = 10 mrad was 63.1 nm (pink and cyan curves in Figure 4).
2. When the beam convergence semi-angle is set at 1 mrad, a deeper focus produces a wider beam in the top region of the sample, but the beam width is similar at locations deeper than 3 \(\upmu\)m (pink and red curves in Figure 4). This observation is similar to what has been observed for a thinner sample [15].
3. When the beam width is fixed at the top surface by either focusing the \(\alpha\) = 10 mrad beam at 1 \(\upmu\)m or focusing the \(\alpha\) = 1 mrad beam at 10 \(\upmu\)m (blue and red curves in Figure 4), the more diverged beam produces a narrower beam up to 2 \(\upmu\)m in depth. This might be because the \(\alpha\) = 10 mrad beam continues to focus after entering the sample. After that, the more diverged beam becomes wider than the less diverged beam as expected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Collection Angle** & **Collection Angle** \\  & **(0-10 mrad)** & **(10-50 mrad)** \\ \hline Ice & 41.43\% & 53.18\% \\ Carbon (a) & 27.87\% & 58.96\% \\ Silicon Dioxide & 15.67\% & 51.14\% \\ Silicon & 14.08\% & 48.49\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electron beam intensity expressed as a percentage of the incident beam detected at 10 \(\upmu\)m. Simulations used a bottom-focused 3 MeV beam and a convergence semi-angle of 1 mrad.  
Figure 4: Electron beam width R\({}_{0.68}\) as a function of sample depth in silicon dioxide. Imaging mode and convergence semi-angle were varied. Geometric beam broadening is shown as a dashed line for reference. A 10 mrad collection angle and incident energy of 3 MeV were used in all plots. The electron beam focuses on the position indicated by the first number in the legend.  
Table 2 lists the number of electrons collected within a 10 mrad collection angle for each combination of imaging mode and convergence semi-angle in Figure 4, evaluated at various depths for reference. The number of collected electrons is significantly fewer for the more diverged beam (\(\alpha\) = 10 mrad).
# Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability
## 1 Introduction
### Contributions and Organization of the Paper  
A mathematical model for reliability indices improvement in power system architecture including on-site PV generation and efficient DR strategies is proposed in this study. The main objective of the propounded concept is to increase system reliability performances by reducing the impact of failures on end-users' power supply during the critical day periods. The contributions of the study can be detailed as follows:  
* Both the management of DR and DG penetration are crucial tasks for the improvement of distribution system reliability; hence, this paper presents the contribution of both methods in terms of reliability level of the system.
* The results of this study by modeling the distribution system and the PV generation unit with real data may provide guidance for more effective integration of PV generation from reliability enhancement perspective.  
The rest of paper is organized as follows: the relevant basic mathematical background of reliability indices is detailed in Section 2. Afterwards, Section 3 provides the declarations of case studies and the discussion of results. Finally, concluding remarks and future work are presented in Section 4.
## 2 State of the Art
### Main Functionality of the System  
#### 3.2.1 Ethereum Authentication  
In order to describe how user authentication is performed, it must be first indicated that the state of an Ethereum blockchain consists of accounts and contracts. User accounts are represented by private (SK) and public keys (PK), which are necessary to interact with the blockchain. Wallets [84,85] allow users to easily generate accounts to sign messages, hold assets or perform transactions in the network.  
To access an AR, it is necessary to log in into the DApp. Among the different techniques to log in users into Web3 applications, the developed application implements a standardized login system that involves a signature verification using an ECDSA [86] to determine that the PK/SK holds the legitimacy of the user. For such a purpose, the user needs to sign a string with his/her SK, resulting in a ciphered string. Then, to make sure that the user is the legitimate holder of the SK, he/she needs to introduce the aforementioned string, the cipher string and his/her PK. With these three values, the DApp can determine whether the user used his/her SK to sign the message, without actually knowing or storing the SK.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 4 Simulation Model
### HCC Architecture Computing Resource Allocation Module  
The second simulation module is used to determine optimal computing resource allocation between individual edge clouds and the central cloud within the HCC architecture. The module is constructed on the basis of queueing theory.  
Assume that \(\lambda\) is the arrival rate for tasks into the HCC network, and \(\mu\) is the service rate. Then, by using the Little's Law, the queuing time (\(t^{queue}\)) for each edge cloud or central cloud can be expressed as  
\[t^{queue}=\frac{\lambda/\mu}{\mu-\lambda} \tag{6}\]  
Here, if we assume a simple network in which the edge cloud and central cloud are connected in a 1:1 manner, then the queuing time for the edge cloud and the central cloud are expressed, respectively, as follows:  
\[t^{queue}_{EC}=\frac{\lambda_{EC}/R_{EC}}{R_{EC}-\lambda_{EC}} \tag{7}\]  
\[t^{queue}_{CC}=\frac{\lambda_{CC}/R_{CC}}{R_{CC}-\lambda_{CC}} \tag{8}\]  
The arrival rate for edge cloud (\(\lambda_{EC}\)) and central cloud (\(\lambda_{CC}\)) can alternatively be expressed as the ratio of tasks offloaded from edge cloud to the central cloud (\(p\)), which can then be stated as \(\lambda_{EC}=\lambda(1-p)\) and \(\lambda_{CC}=\lambda p\).  
To prevent queuing time divergence for a specific edge cloud or central cloud, the HCC offloading decision must be made to minimize the difference, \(\Delta\), between \(t^{queue}_{EC}\) and \(t^{queue}_{CC}\), which can then be expressed as  
\[\Delta=\left|\frac{\lambda(1-p)/R_{EC}}{R_{EC}-\lambda(1-p)}-\frac{\lambda_{p }/R_{CC}}{R_{CC}-\lambda_{p}}\right| \tag{9}\]  
Taking the assumption one step further, a more realistic network structure is considered, where edge clouds and central cloud are connected in N:1 star topology configuration. Here, \(\Delta\) can be expressed as  
\[\Delta=\left|\frac{1}{N}\sum\
olimits_{i=1}^{N}\frac{\lambda_{i}(1-p_{i})/R_{ EC,i}}{R_{EC,i}-\lambda_{i}(1-p_{i})}-\frac{\sum\
olimits_{i=1}^{N}\lambda_{p,i}/R_{CC}}{R_{CC}-\sum\
olimits_{i=1}^{N}\lambda_{p}}\right| \tag{10}\]  
In the simulation model, \(R_{EC,i}\) and \(R_{CC}\) are set as the design variables, and \(t_{HC}\) and \(\Delta\) are measured through the simulation. Additionally, variable \(\lambda\) is represented by the data traffic and \(\mu\) is represented by the computing resource.
## 4 Experiments
### Heuristic EDD  
The heuristic EDD sequences the jobs according to the due date. This heuristic favors the highest priority jobs; however, it does not take advantage of reducing the setup times when successively processing jobs from the same family [43].  
The first step of the heuristic calculates the average delivery time of each job \(j\,d_{jM}\) by averaging the lower limit of the time window \(d_{jA}\) and the upper limit of the time window \(d_{jB}\), then the jobs are ranked from lowest to highest according to this value. In Step 2, the jobs are sequenced according to the ranking obtained in Step 1, forming a chromosome as in the genetic algorithm, with the difference that all the operations of each job \(j\) are located according to their average delivery time. Figure 11 shows an example of a solution (chromosome) sequencing five jobs (1, 2, 3, 4, and 5), which have 4, 4, 3, 3, and 3 operations, respectively. The order of the jobs according to their average delivery time is 3, 5, 2, 1, and 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Elitism rate & 0.1 \\ Mutation rate & 0.1 \\ Crossover rate & 0.9 \\ Population & 50 \\ Iterations & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Genetic algorithm parameter values.  
Figure 10: Fabric finishing production system.  
In Step 3, the machine with the shortest time to complete the operation is assigned, allowing it to complete the operation in the shortest time. For this, it is necessary to defuzzify the processing times of the operation in each machine and the sequence-dependent setup times (for triangular functions, average the lower value, the mode value, and the upper value). Equation (31) shows that this result is added to the maximum between the available start time of the evaluated machine and the completion time of the previous operation of the processed job. After assigning the machines, the EDD heuristic solution shows the sequence of jobs, operations, and assigned machines (see Figure 11). In Step 4, the fitness of the chromosome constructed with the EDD heuristic is evaluated using the same procedure proposed to assess a beta chromosome generated by the proposed genetic algorithm.  
\[C_{j}^{\textit{desf}}=\max\,(C_{j}^{\textit{defu}},\,\,TM_{i}^{\textit{defu} })+P_{jio}^{\textit{defu}}+A_{ilj}^{\textit{defu}} \tag{31}\]
## 3 Performance Analysis Framework
### Calculation of SER  
The SER of the considered HSTS for M-PSK modulation is given by [22] as:  
\[P_{MPSK}=\frac{1}{\pi}\int\limits_{0}^{\theta_{M}}M_{\gamma}\left(\frac{g_{MPSK }}{\sin^{2}\theta}\right)d\theta \tag{27}\]  
where \(\theta_{M}=\frac{\pi(M-1)}{M}\) and \(g_{MPSK}=\sin^{2}\left(\frac{\pi}{M}\right)\). Alternatively, one can use the following accurate approximation of Equation (27) developed by [27]:  
\[P_{MPSK}=\sum\limits_{p=1}^{3}b_{p}M_{\gamma}\left(a_{p}\right) \tag{28}\]  
where \(b_{1}=\frac{\theta_{M}}{2\pi}-\frac{1}{6}\), \(b_{2}=\frac{1}{4}\), \(b_{3}=\frac{\theta_{M}}{2\pi}-\frac{1}{4}\), \(a_{1}=g_{MPSK}\), \(a_{2}=\frac{4}{3}g_{MPSK}\) and \(a_{3}=\frac{g_{MPSK}}{\sin^{2}(\theta_{M})}\). The approximate average SER of the considered HSTS is finally obtained from Equations (21) and (28). Similarly to M-PSK, the approximate average SER of M-PAM signal can be calculated as:  
\[P_{MPAM}=\left(1-\frac{1}{M}\right)\left(\frac{1}{6}M_{\gamma}\left(\frac{3}{ M^{2}-1}\right)+\frac{1}{2}M_{\gamma}\left(\frac{4}{M^{2}-1}\right)\right)\]  
Similarly to M-PSK, the approximate average SER of the M-QAM signal can be calculated as:  
\[P_{MQAM} = \left(1-\frac{1}{\sqrt{M}}\right)\left(\frac{1}{3}M_{\gamma} \left(\frac{3}{M-1}\right)+M_{\gamma}\left(\frac{4}{M-1}\right)\right)\] \[-\frac{1}{6}\left(1-\frac{1}{\sqrt{M}}\right)^{2}\left(3M_{\gamma }\left(\frac{6}{M-1}\right)+M_{\gamma}\left(\frac{3}{M-1}\right)\right)\]
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
### Task-Agnostic Architecture  
In the last decade, neural networks have been extensively used in language modeling tasks, starting from shallow feed-forward networks, RNNs, LSTMs, and deep neural networks to self-attention-based transformer networks. The shallow feed-forward networks deduced single-layer representation called 'word vectors', which were learned and then transferred to task-specific architectures. Then, RNNs with deep or multiple layers (DNNs) were used to generate context-sensitive and more robust (deep) representations that were transferred or applied to task-specific architectures. To overcome task-specific architectures, transformer-based pretrained language models (PLMs) came into play. Recent work using transformers has focused on a task-independent approach, where transfer and finetuning of the self-attention block are sufficient. These transformer-based language models are flexible and task-agnostic. They can be finetuned on different downstream tasks without requiring architecture modifications. They have also led to significant improvement boosts, especially in capturing long-range dependencies.  
As shown in Figure 1, the phases in these transformer-based LLMs can broadly be classified into pretraining, transfer learning, and/or in-context learning. In the sections to come, we explore in detail different attention mechanism masks, architectures, objectives used during pretraining, transfer and in-context learning techniques, scalability factors, and challenges regarding LLMs.  
The outline of this survey paper is as follows. In Section 2, we look at the language model definition and the attention layer mechanism in detail. In Section 3, we describe the types of architectures and attention masks used in transformers. Section 4 elaborates on the pretraining objectives and different learning strategies used by the LLMs. Section 5 discusses transfer learning strategies, followed by in-context learning in Section 6. Section 7 describes different scale factors, such as model width, depth, datasets, architecture, and how they affect the performance of LLMs. Section 8 enumerates the challenges encountered by LLMs, followed by future directions and development trends in Section 9. Section 10 concludes the paper.
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 2 Materials and Methods
### Dynamic Time Warping (DTW)
The result is a DTW distance image **DI** that assesses the scores of similarity between the given query and the evolutions for each pixel location. As already mentioned, the DTW distance is expected to be small for evolutions that are similar to the query and large for non-similar evolutions. This is due to the fact that, if the spatio-temporal evolutions are similar, the DTW algorithm finds an alignment with a smaller cost between the corresponding temporal sequences and the given query than in the case of non-similar evolutions. Therefore, the separation of similar from non-similar evolution patterns is equivalent to finding an optimal threshold that can be applied over DTW distances in order to retrieve the specific pattern queried by the end-user.