esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary Concepts  
### Hopfield Networks  
HNNs are an essential type of recurrent artificial neural networks used to implement associative memory. Their value lies in their ability to pattern recognition, optimization or image segmentation [41]. HNN's origin is the Ising model, a mathematical model employed in the well-known statistical mechanics to explain the ferromagnetism phenomenon. This model use variables to represent magnetic dipole moments of atomic spins. Two states are possible (+1 or -1). This feature is employed in the Hopfield neural networks where each neuron only takes values on two different states (+1 or -1) depending on the input value and if it exceeds their threshold \(U_{i}\). In particular, Discrete Hopfield neural networks establish a relationship between each pair of neurons \((i,j)\) with \(1,2,\ldots i,j,\ldots N\)[42].  
In a particular instant of time, a vector \(V\) of \(N\) bits is employed to represent the state of the network. Each vector \(V\) stores information about which neurons are firing. The interactions between neurons are represented using weights \(w_{ij}\) whose values are usually 1 or -1 (We employ this convention in this paper. Other literature employs neurons that take values of 0 and 1.). In this kind of neural networks the [PERSON]'s law of association is employed to learn these interactions, a neuron cannot be connected with itself and the connections between neurons are symmetric. More formally, for a certain state \(V^{\text{s}}\), a weight \((i,j)\) is defined as follows:  
\[w_{ij}=(2V_{i}^{\text{s}}-1)(2V_{j}^{\text{s}}-1)\]  
Then \(\forall i\) we have that \(w_{ii}=0\) and \(\forall i,j\) we have that \(w_{ij}=w_{ji}\). Then, the values for each vector are computed as follows:  
1. \(V_{i}\to 1\) if \(\sum_{j}w_{ij}V_{j}>U_{i}\)  
Figure 4: A general methodology for transforming HNN’s execution in an explainer video using four phases: trace, frames generation/local NLG, storytelling/global NLG and video generation.  
\(V_{i}\to 0\) if \(\sum_{j}w_{ij}V_{j}<U_{i}\)  
The main feature of HNNs is their capability of remembering states stored in the vectors (interaction matrix), i.e., each neuron is going to change until it matches the original state \(V^{s}\) if a new state \(V^{s^{\prime}}\) is subjected to the interaction matrix. A more comprehensive study on the HNNs can be found in [43].  
**Example 1**.: _This is a hypothetical example, suppose we want to use an HNN to recognize a happy face in a picture. The first step is to provide the system with a representative example. Once the system learns this pattern, it can recognize any example similar to it. The Figure 3 shows the three options that can occur: (a) training an HNN with an example; (b) recognizing two noise examples and; (c) when the HNN is not capable of identifying an example given._
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 2 Materials and Methods
### System Setup
#### 2.4.2 Training Procedure  
Our training framework employs a multiscale approach. It starts at a base scale and progressively refines through larger scales. This progression is parameter-controlled, which defines the number of scales and when to stop refining. The Discriminator is trained with both real and synthetically generated images, incorporating traditional adversarial training steps and a gradient penalty for stability. The Generator is then trained to produce images that increasingly challenge the Discriminator's detection capabilities. Additional loss functions, such as topological and reconstruction losses, are applied conditionally based on the scale and training parameters. Coefficients \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) in the loss function (Equation (1)) were set to 10, 5, and 0.145, respectively, in our experiments. These values were determined through extensive hyperparameter tuning, carefully balancing adversarial, topological, and reconstruction components to preserve topological features and maintain overall image quality, as reflected in improved NTC scores discussed in Section 3.2. In Section 3.5, we provide a more detailed discussion on the hyperparameter tuning process for \(\lambda_{3}\).
## 2 Simulation Scenario and Models
### Scenario  
In simulation, a local coordinate system as shown in Figure 1 is developed, in which (1) the coordinate origin is at earth's core; (2) the YOZ plane is in the incident plane; (3) Z axis has the same direction with the normal of the tangent plane of the specular point; (4) and it is assumed that earth,the orbits of GNSS and LEO satellite are circular. The gain pattern of receiver antenna is assumed as Gaussian function as  
\[G_{r}(\mathbf{r})=G_{0}\exp\left\{-\left(\frac{\sqrt{\ln 2}\phi(\mathbf{r})}{ \varphi}\right)^{2}\right\} \tag{1}\]  
where \(G_{0}\) and \(\varphi\) are the maximum gain and 3 dB beam width, respectively; \(\phi(\mathbf{r})\) is the included angle between the direction of receiver antenna pointing and scattering unit \(\mathbf{r}\). The parameters of simulation scenario are shown in Table 1.
## 6 Cellular Basis of Laser-driven Radiation Therapy
### Laser-Acceleration Studies at JAEA in Japan  
Electron and proton acceleration with lasers has been an active area of investigation at the Kansai Photon Science Institute (KPSI) of JAEA for several years. Researchers at KPSI explore candidate laser-driven proton sources and the potential for various applications. For example KPSI has demonstrated focusing and spectral selectivity of 1.9 MeV protons at 1 Hz [73]. The RBE of 2.3 MeV laser-accelerated protons irradiating _in vitro_ cells of a human salivary gland tumor has been measured with 0.2 Gy single bunch doses delivered at 1 Hz [25,120]. In recent laser experiments protons have been accelerated to energies as high as \(\sim\)43 MeV (cut-off value) with an on-target laser intensity (power) of 10\({}^{21}\) W\(\cdot\)cm\({}^{-2}\) (200 TW) and an intensity contrast at the 10\({}^{-10}\) level [121].  
Experiments have been supported historically by strong laser-plasma simulation efforts **(**see for example [43; 44; 45; 46; 47; 48; 49; 50]). Furthermore, in collaboration with the University of Bologna, the University of Milano and INFN the use of multi-layered targets as sources combined with candidate charge collection/transport optics have been investigated in simulations of a hybrid ILDIAS (_i.e._, HILDIAS) in which final acceleration to a desired operating proton energy is accomplished using a conventional compact side-coupled RF linac) [122].  
Currently, the J-KAREN laser at KPSI is being upgraded to higher pulse power and repetition-rate capability (with the upgraded result to be renamed as J-KAREN-P). J-KAREN-P will deliver an on-target peak power of \(\sim\)PW at 0.1 Hz with improved focusing and intensity contrast. This will enable extended secondary source studies. With higher intensity on target KPSI aims to make notable progress toward reaching \(\sim\)200 MeV proton energies as well as KeV X-rays.
## 3 Results
### Architecture of Aviation Health Management System
* Regulatory compliance modules automatically check maintenance activities and operational data against regulatory standards and requirements, ensuring compliance and simplifying audit processes. 5. Actionable insights and interface layer: * User dashboards and alerts as customizable interfaces display real-time and historical data, health indicators, and predictive alerts. These dashboards are designed for different user roles, from maintenance technicians to fleet managers, providing relevant information that supports informed decision-making. * Maintenance planning and scheduling tools that incorporate predictive maintenance recommendations, allowing maintenance teams to schedule work orders, assign resources, and manage parts inventory in an efficient manner. 6. Feedback and continuous improvement layer: * Performance monitoring tools track the outcomes of maintenance actions and operational decisions to assess their effectiveness. This includes monitoring aircraft performance, maintenance costs, and downtime, providing data for continuous improvement. * Machine learning feedback loops. As new data are collected, they are fed back into the machine learning models, allowing them to learn from the outcomes of previous predictions and decisions. This iterative learning process continually improves the accuracy and reliability of predictive insights. Figure 3: Architecture of aviation health management system. This architecture is underpinned by several core characteristics common to all the layers:
* Modularity is provided by design with interchangeable components, allowing for upgrades or changes without disrupting the entire system. * Security and privacy incorporate encryption, secure data transmission protocols, and access controls to protect sensitive data and ensure privacy. * Interoperability is provided by the capability to integrate with existing aviation systems and standards, facilitating data exchange and collaboration across different platforms and stakeholders. * User-centric design adapts interfaces and functionalities to meet the needs of various users, from pilots and maintenance crews to operations managers, enhancing usability and adoption. By integrating these layers and characteristics, the HMGT system architecture provides a robust framework for enhancing aircraft maintenance, safety, and operational efficiency through data-driven insights and proactive management strategies.
## 2 Experimental Section
### LC-MSMS Analysis of Digests  
Reversed-phase chromatography was performed in-line prior to MSMS using a Dionex U3000 nano HPLC system (Thermo, Waltham, MA, USA). For soluble proteins, 1 \(\mu\)L was injected from each well on to a 200 \(\mu\)m \(\times\) 5 cm _Pepswift_ PS-DVB monolithic column (Thermo, Waltham, MA, USA) housed in a column oven set at 60 \({}^{\circ}\)C, and equipped with a 1:97 flow splitter. For IMPs, 5 \(\mu\)L was injected. The solvent system used consisted of 2% (v/v) acetonitrile, 0.1% (v/v) formic acid in LC-MS grade water (solvent A) and 80% (v/v) acetonitrile, 0.1% (v/v) formic acid in LC-MS grade water (solvent B). Chromatography was performed as follows: Initial conditions were 0% B and a post-splitter flow rate of 2.5 \(\mu\)L/min. A linear gradient was developed over 5 min to 15% B and a further 2 min to 40% B. Isocratic elution at 90% B proceeded for 1 min, followed by isocratic elution at 0% B for 6 min to equilibrate the column at the initial conditions. MSMS was performed using a Bruker _Esquire_ HTC ESI-ion trap mass spectrometer fitted with a standard ion source in positive ion mode. Nebulizer pressure was 16 psi, drying gas flow was 5 L/min and drying gas temperature was 300 \({}^{\circ}\)C. Voltages were capillary -4 k V, skimmer 40 V, capillary exit 145 V. Scan range was 200-2000 _m/z_. Data-dependent peptide fragmentation was achieved in auto MS\({}^{\mathrm{n}}\) mode. Three precursor ions were fragmented per MS1 scan. Precursors were actively excluded after 2 spectra, and released after 20 s. Each LC-MS sample run was preceded by a blank run to identify whether significant sample carryover had occurred. The blank was spiked with 1 fmol BSA tryptic digest. This was sufficient to obtain a Mascot hit for BSA reliably without interfering with the blank's original purpose. This standard/blank injection enabled the LC-MSMS performance to bemonitored continuously throughout the batch. If no signal was observed because of low amounts of peptides, the digests were concentrated and re-analyzed.
## 3 Results
### Physical Test Analysis
#### 3.1.2 Study on Liquid Limit and Plastic Limit of Heavy Metal-Contaminated Soil  
In Figure 6, the change curves of liquid limit, plastic limit, and plastic index of Zn- and Pb-contaminated soils with different concentrations are shown. The test results show that heavy metal ions have significant effects on the liquid limit and plastic index of the sample soil. With the increase in heavy metal ion concentration, the liquid limit and plasticity index showed a decreasing trend, which indicates that heavy metal pollution would cause the weakening of soil fluidity and plasticity.  
The reason for this change may be that with the increase in heavy metal ion concentration, the surface double layer of soil particles becomes thinner [17]. This results in a polymerization effect between particles, resulting in a decrease in the surface area and the water retention capacity between particles, which, in turn, leads to a decrease in both the liquid limit water content and the plasticity index [18]. Metal ions further enhance this effect by reducing the repulsive forces between particles and allowing particles to move more freely at lower water content. This process leads to the decrease in both the liquid limit and the plastic index of heavy metal-contaminated soil [19].
# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 2 Background  
### Lithium Battery Pack  
Lithium batteries are widely used as major power sources for electric vehicles, owing to their high energy density, high power density, long life cycle, and environmentally friendly characteristics [11]. Battery cells exhibiting high energy density are currently manufactured so that a large amount of energy can be stored in the same volume. Battery modules, which include a certain number, protect the battery cell from external shock and both high and low temperatures. A battery pack is composed of the BMS, and various battery control devices, installed in the battery module. The decline in the battery cell capacity and the occurrence of failures, due to external shock may cause a breakdown of the battery module composed of multiple battery cells. In addition, a battery pack that contains multiple battery modules can also cause failure. Even if the same battery is used for the battery pack, the battery cell becomes unbalanced when continuously charged and discharged, due to the difference in electrochemical characteristics [12]. Therefore, an accurate SOC estimation of a battery cell makes it possible to safely use the battery pack. Figure 1 is a diagram showing the relationship between battery cell, module and pack.  
Figure 1: Correlation among the cell, module, and pack of the lithium battery.
# Assessing the Role of Program Suspend Operation in 3D NAND Flash Based Solid State Drives
## 5 SSD Simulations to Understand the Role of Program Suspend
### SSD Power Consumption  
To understand the role of the program suspension on the SSD's power consumption, we investigated two different contributions in the drive: (i) the sole 3D NAND Flash memory modules constituting the storage media sub-system; (ii) the internal SSD I/O bus on which both the write and the read flows to and from the memories. As we considered a TLC memory in this study, we collected different power traces for each page type (i.e., lower, central, and upper pages) using our experimental setup applied on a TLC 3D NAND Flash product.  
The assumption behind the calculation of the overall memory power consumption in drive is the following: when multiple memory chips are accessed in parallel on an SSD, the Kirchoff's current law (KCL) holds on the power supply of the drive so that the memory sub-system power consumption is the sum of the single power contributions [8]. The results in Figure 10(a) show one of the negative sides of the program suspension. When the 3D NAND Flash memories in the SSD allows for preempting read operations, it means that the memory chips are available to sustain more operations per second (as shown by the results in the previous section concerning the bandwidth), hence, being in a sort of \"active\" state drawing current from the power supply for a longer period.  
The larger the number of operations served is, the higher is the power consumption (up to 400 mW increase in the QD = 64 25W-75R condition). A similar result can be appreciated in Figure 10(b). Indeed, more suspensions in the program operation means that more read data are to be requested from the memory chips requiring the activation of the internal I/O bus of the drive for a longer period (up to 17 mW in the QD = 16 75W-25R condition).  
The only counter-intuitive result is related to the power consumption extracted from simulations with a write intensive workload and QD = 64, in which the former metric displays an inverted trend with respect to all the other cases. We interpret this result as a combination of several factors. The case of QD = 64 represents a situation in which all the SSD resources (i.e., channels) are allocated to serve write/read transactions.  
Write (program) operations in 3D NAND Flash devices consume more power than the read one, and, since the former operation is longer, there is a large probability, especially in the high QD scenario, that multiple 3D NAND Flash chips concurrently serve a write function, and therefore the total power consumption is the sum of each operation. By including program suspensions, we are deliberately reducing that probability and allowing a power consumption reduction of almost 1.5 W.
## 2 Problem Description
### Gradient Problem  
The training target minimizes the error function by adjusting the parameters of weight matrices. Derivative-based methods are widely used in this move. Although the second derivative methods, such as Hessian optimization [28] and Levenberg-Marquardt algorithm [29], can get optimum estimation accurately, the first derivative methods are more efficient in practice because of the balance between calculation complication and iteration effect.  
During RNNs training process, a stochastic gradient descent (SGD) algorithm is used with the theory of back-propagation through time (BPTT) [30], because of its recurrent temporal connections. The well-known BPTT algorithm is used to the calculate weight derivative for RNNs, with simple conception and efficient computation. It is simple to obtain the BPTT via unfolding RNNs in time, as shown in Figure 2.  
The gradients are rewritten as a sum-of-products form in the timeline, in the light of [18],  
\[\begin{array}{l}\frac{\partial\epsilon}{\partial\theta}=\sum\limits_{t=1}^{ \mathsf{T}}\frac{\partial\epsilon_{t}}{\partial\theta}\\ \frac{\partial\epsilon_{t}}{\partial\theta}=\sum\limits_{k=1}^{\mathsf{T}} \left(\frac{\partial\epsilon_{t}}{\partial\theta_{t}},\frac{\partial\epsilon _{t}}{\partial\theta_{k}},\frac{\partial\epsilon_{k}}{\partial\theta}\right) \end{array} \tag{9}\]  
where \(\frac{\partial\epsilon_{t}}{\partial\theta}\) is the sum of temporal components, and indicates the effect of transmitting from the \(t\)-th time-step back to the \(k\)-th (\(k\leq t\)is only suitable for forward-RNNs).  
One can see that the gradient will vanish, i.e., if the constant \(\|\frac{\partial\eta_{k+1}}{\partial\eta_{k}}\|<1\), the norm of \(\frac{\partial\eta_{k}}{\partial\eta_{k}}\) will go rapidly to 0 exponentially. Conversely, the gradient will explode to infinity, if \(\|\frac{\partial\eta_{k+1}}{\partial\eta_{k}}\|>1\). Note that it is unnecessary and impossible to keep \(\|\frac{\partial\eta_{k+1}}{\partial\eta_{k}}\|=1\) at all time-steps, because the input series and the internal dynamics determine the activations of neurons in networks [31]. Faced with the correlated relationship in a long time-step span, the standard RNNs structure is awkward with regards to the gradient problem. There are some important branches of RNNs that appear to solve the long-term problem, such as LSTM [5; 20; 21] and Gated Recurrent Unit (GRU) [32] structures. However, these redesigned complex structures require specific training algorithms. Comparatively, we intend to attain a more concise structure with more general training methods, in order to achieve superb performance in various specific applications. For example, ECG signal classification using our proposed RNNs may have a promising performance in screening and preliminary diagnosis. Our reconstructed RNNs can analyze an infrared radiation intensity sequence of an object, in order to monitor changes in temperature. In addition, the method proposed in this paper can also be developed for image sequences or video classification, because of its excellent ability to learn correlations in information in the time domain.
## 5 Simulation Results and Discussion
### Baseline Model Performance and Comparative Analysis with the DE-ACSW-R Model  
The DE-ACSW-R model fuses two predictive engines: LR for low-latency near-linear mobility, and curvature-adaptive SVR for handling non-linear trajectories and abrupt jumps. Unlike traditional single-model or reactive switching schemes, DE-ACSW-R dynamically balances the influence of LR and SVR based on real-time prediction error and model confidence. When an abrupt change (\"jump\") is detected within the sliding AoA history window, the framework automatically clusters the window to focus on the most recent relevant samples, resets outliers, and re-weights model contributions. This enables the system to rapidly \"forget\" outdated trajectory data, avoid model lag, and achieve fast realignment with the user's actual movement.  
Figure 4 illustrates this process using a representative segment of AoA evolution with both smooth movement and sudden jumps.  
As shown in the figure, both predictors closely follow the Estimated AoA during steady movement (indices 6-26), but their differences become pronounced around abrupt mobility changes. At index 28, the user's AoA drops sharply from 79.5\({}^{\circ}\) to 46.9\({}^{\circ}\). The baseline predictor, which lacks jump detection and adaptive fusion, produces a value of 62.7\({}^{\circ}\), demonstrating significant lag and prediction error. In contrast, the DE-ACSW-R model immediately detects the jump, clusters the history, and produces a much more accurate estimate of 49.9\({}^{\circ}\). This advantage persists in subsequent indices (29-30), where the new model's outlier logic and window clustering rapidly stabilise its predictions near the ground truth, whereas the baseline remains slow to recover and displays greater oscillation.  
Across the full trajectory, DE-ACSW-R consistently demonstrates the following:  
* Faster adaptation after abrupt mobility changes due to window clustering and dominance-enforced fusion.
* Reduced prediction error and variance during both linear and non-linear segments, owing to smooth confidence-weighted transitions between models.
* Increased robustness to outliers and history contamination by actively re-centring on relevant trajectory clusters after a jump.  
Figure 4: Comparison of baseline predictor and DE-ACSW-R model during AoA trajectory with abrupt mobility changes.