esa-sceva/satcom-chunk-collection huggingface dataset:
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 4 Assessment of the Adaptive Fault Tolerance Mechanism
### Injection Experiments  
We have scheduled a number of injection experiments on the models based on different fault scenarios:  
* Single transient faults;
* Double and triple random (in the same memory word) transient faults;
* Double and triple adjacent (in the same memory word) transient faults;
* Single intermittent faults;
* Single intermittent faults combined with single and multiple transient faults in the same memory word. These experiments simulate the occurrence of a transient fault in the same memory word after an intermittent error has been detected, the system has been reconfigured with a new ECC, and the memory has been scrubbed. The objective of these experiments is to check the behavior of the system after the reconfiguration.  
The multiple transient faults include double and triple random, double and triple adjacent, double pseudorandom, double pseudo-adjacent and triple pseudo-adjacent. The \"pseudo-\" refers to the fact that one of the faulty bits must be the bit labeled as EPB during the detection of the intermittent fault. Hence, if a bit is marked as EPB upon the intermittent fault injection, this bit will be mandatorily injected in the following pseudo-multiple transient fault injection after the ECC is swapped.  
In the experiments, the values of the parameters defined with ranges have been calculated using a uniform distribution function.
# Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
## 3 Methodology
### Dataset  
The publicly available Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset was chosen for this research, as it provides a wide range of emotional expressions in both audio and video formats [3]. RAVDESS, collected between 2015 and 2018, includes 1440 audio files from 24 professional actors (12 male and 12 female) expressing eight emotions: neutral, calm, happy, sad, angry, fearful, disgust, and surprise. Each emotion is recorded at two intensity levels (normal and strong), except for neutral, enhancing its suitability for SER. Its advantages include high-quality recordings and balanced gender representation, though it is limited to English and controlled settings, potentially reducing real-world variability. For this study, only audio data were used, which were extracted by isolating the speech component from the multimodal dataset, ignoring video elements. Figure 1 shows samples for each class.
# A Novel Optimization Design Method of Form Ginding Wheel for Screw Rotor
## 3 Installation Parameter Optimization Design Method
### Installation Angle Optimization Design Method  
In order to obtain a precise installation angle, the optimal design procedures of the installation angle are designed as shown in Figure 6. In industrial applications, the diameter of the form grinding wheel usually becomes increasingly smaller with an increased grinding time. In order to make full use of the whole grinding wheel, the minimum installation center distance can be used as the initial center distance. The installation angle is usually the value near the lead angle (pitch circle) of the screw rotor. In this study, the best installation angle was found within \(\pm 5^{\circ}\) away from the lead angle. A female screw rotor is used as an example in Table 1.  
The steps involved in the optimized design procedure are described as follows:  
Step 1. Obtain the parameter inputs, including rotor profile parameters, lead angle, center distance, screw parameter, and cycle index.  
Step 2. Preprocess the input parameters.  
Step 3. Obtain the profile of the grinding wheel.  
Step 4. Obtain the simulation profile of the screw rotor.  
Step 5. If the cycle index \(n\leq 3\), then perform the next step; otherwise, return to Step 1.  
Step 6. Evaluate the profile error between the simulation profile and the theoretical profile. Once the profile error \(\leq\) threshold \(\Delta\), record the installation angle; otherwise, return to Step 2 and continue to search for the installation angle.  
During the calculation of the first cycle, the installation angle is searched in the interval \((\lambda-5,\lambda+5)\) that satisfies the condition \(E_{\max}\leq\Delta\). Through the calculation of three cycles, the precise installation angle range can be obtained.
# Neural Network-Assisted DPD of Wideband PA Nonlinearity for Sub-Nyquist Sampling Systems
## 4 Simulation Results
In contrast, the proposed NN-assisted DPD method delivers outstanding performance across all SNR levels, achieving an EVM of 1.014% at 35 dB. Figure 6: Constellation diagrams at \(SNR=15\) dB: (**a**) ARVTDNN and (**b**) the proposed method. Figure 7: The EVM performance of various DPD methods versus different SNRs. Figure 8 illustrates the bit error rate (BER) of various DPD methods across different SNRs. Specifically, both the proposed method and the ARVTDNN outperform the TDMPR-based method at low SNRs, validating the effectiveness of virtual training in noisy environments. Moreover, the proposed method demonstrates good nonlinearity correction performance at both low and high SNRs. Additionally, the BER performance of the system without DPD saturates easily around \(2\times 10^{-2}\), which significantly affects the communication performance of the sub-Nyquist sampling system. Considering that the nonlinearity of the amplifier is affected by temperature and humidity, three different PA models are simulated in this paper to simulate the nonlinearity of the PA under different temperatures and humidity conditions. We still use the memory polynomial model to model the PA, which is shown in (1). As in [23], from experiments, we drew the conclusion that the higher humidity and temperature generally increase the nonlinear behavior of a PA. Therefore, by modifying the MP coefficients \([h_{10},h_{11},h_{12},h_{30},h_{31},h_{32},h_{50},h_{51},h_{52}]\), we can simulate various PAs with different levels of nonlinearity: normal nonlinearity for low temperature and low humidity, medium nonlinearity for mild temperature and mild humidity, and severe nonlinearity for high temperature and high humidity. Table 2 demonstrates the model parameters used at different temperatures and humidities. Figure 9 shows the PSD performance of the proposed DPD with three different power amplifiers. It can be seen that the proposed DPD provides excellent performance even with high nonlinearity. In contrast, the performance without considering DPD (denoted as \"W/o DPD\" in the legends) degrades with the temperature and humidity change. We also simulated the constellation diagrams for these three different power amplifiers under nonlinear conditions. As can be seen in Figure 10a-c, the constellation points are well separated, which demonstrates the robustness and adaptability of the proposed NN-based DPD method. Figure 8: The BER performance of various DPD methods versus different SNRs. Figure 10: _Cont._  
Figure 9: PSD of PA output with the proposed NN-assisted DPD with different levels of temperature and humidity.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 3 Results and Discussion
### Self-Organizing Control of Two Satellites  
The two-satellite case is simulated, where one satellite, named the chief, performs semi-major axis control only, and the other, named the deputy, performs self-organizing control to keep the relative motion bound within the expected range.  
The simulation parameters are listed in Table 2.  
The relative motion-bound changing with time is shown in Figure 7, where the blue, black, red, and green curves represent the maximum bound under control, the minimum bound under control, the expected maximum bound, and the expected minimum bound, respectively. It can be found that both the maximum bound and the minimum bound are constrained within the expected range.  
Due to the existence of atmospheric drag, the mean semi-major axis drops by 10 m every day without semi-major control, shown in Figure 8. It can be seen that the relative  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Simulation Time & 60 days \\ \hline Step Length & 600 s \\ \hline Initial Conditions & \([a_{c},\,\,e_{c},\,\,i_{c},\,\,\Omega_{c},\,\,\omega_{c},M_{c}]=[R_{\text{e}}+550 \text{ km},\,0,\,53^{\circ},\,\,30^{\circ},\,\,40^{\circ},\,\,50^{\circ}]\) \\ \([a_{d},\,\,e_{d},\,\,i_{d},\,\,\Omega_{d},\,\,\omega_{d},\,\,M_{d}]=[a_{c},\, \,\,\epsilon_{c}+0.01,\,\,i_{c},\,\,\Omega_{c}+1^{\circ},\,\,\omega_{c}+1^{ \circ},\,\,M_{c}+10^{\circ}]\) \\ \hline Perturbations & \(J_{2}\), atmospheric drag, lunar gravity, solar gravity \\ \hline Control Coefficients & \(k_{\text{bound}}=2\times 10^{-8},k_{a}=2\times 10^{-8}\) \\ \hline Ballistic Coefficient & \(C_{D}A/m=0.01\) \\ \hline Expected Range & \(d_{\text{max}}=d_{\text{max}}-1\text{ km},d_{\text{min}}=d_{\text{min}}+1 \text{ km},a_{t}=R_{e}+500\text{ km}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters for the two-satellites case.  
Figure 7: Relative motion-bound changing with time.  
motion-bound control makes the semi-major axis of the deputy converge to that of the chief as well. That is because the expected bound values are fixed, and the fixed bounds exist only if the semi-major axis difference takes zero. Figure 9 shows the semi-major axes of the chief satellite and the deputy under semi-major axis control. The results indicate that the semi-major axes of the two satellites have been maintained around the expected value.  
The control effect of the deputy is shown in Figure 10. The accumulated velocity increment is 1.98 m/s, and the single-step velocity increment is less than 0.02 m/s, which is minor enough for mega constellation control.  
Figure 8: Semi-major axis time histories without semi-major axis control.  
Figure 9: Semi-major axis time histories under semi-major axis control.
## 2 Outlook for Smart Grid Integration
### Knowledge of Power System Operation  
The central function of power grids is to transport real power to feed loads. To achieve that, strong, stable, and reliable power systems should be built on a foundation that consists of equipment and grid services. Traditional power plants are equipped with synchronous generators (SG) to extract high-density bulk energy (coal, hydro, nuclear) for electricity. SG, as independent voltage sources, can either feed isolated loads or easily be integrated into groups that form a more reliable power grid through their distributed management of the grid. When the generator is operating, large kinetic power is stored in machine rotors while reactive and harmonic currents are fed through stator impedance. The droop characteristics of SG possess and stabilize the dynamic electro-magnetic-mechanical energy conversion through regulating and coordinating the output power according to the rated frequencies and voltage. According to the operation status and load profile, transformer taps can be modified to raise/lower the voltage of a specific feed line to change its power flow. Capacitor banks are commonly used to supply reactive power and eject harmonic currents to enhance voltage regulation, power quality, and reduce network losses. System-level grid services then are able to enhance the stability, reliability and economic efficiency of the equipment in both steady and transient states. Power/demand forecasting, which is the foundation of this high-level system operation, includes optimizing both mid-term and long-term operation through scheduling, dispatching, and marketing. The operation of a traditional power system is shown is Figure 1 on a time-scale.
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 3 Solution-Phase Ligand Exchange  
Even though solid-state ligand exchange procedures have been widely used to fabricate PbS CQD PVs [7; 23; 28; 29; 30; 31; 32; 33], several limitations still remain. For example, partial loss of a ligand can take place during the solid-state ligand exchange process by a protic solvent effect. Additionally, to produce a thick CQD film using solid-state ligand exchanges for sufficient light harvesting, layer-by-layer approaches requiring a long fabrication time and a large number of materials should be carried out. Solution-phase ligand exchange using aprotic solvents has been recently developed to address these issues (Figure 5) [34]. This approach relies on creating a colloidal dispersion in a polar solvent after ligand exchange resulting from the replacement of long-chain organic ligands to short halide ligands. The formation of surface defects on CQDs during ligand exchange can be suppressed by this method, allowing for the elimination of multiple deposition steps, which are necessary to the previous layer-by-layer method. The best device performances in CQD PVs reported so far have also been achieved by the solution-phase ligand exchange approach. Some recent impressive achievements in PbS CQD PVs fabricated using the solution-phase ligand exchange are described in the sections below.  
Figure 4: Characterization of PbS CQDs with different treatments. (**a**) Absorption spectra of PbS CQDs depending on methylammonium iodide (MAI) treatment. (**b**) Photoluminescence spectra of PbS CQDs depending on MAI treatment. (**c**) High-resolution transmission electron microscopy (HR-TEM) image of MAI-treated PbS CQDs showing no fusion. (**d**) Quantitative measurements by XPS. I 3D peak of both \(\mathrm{I}_{2}\)-treated and MAI-treated PbS CQDs, showing large enhancement of I-incorporation for the MAI case. Reprinted from reference [27]. Copyright 2016 American Chemical Society.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 1 Introduction
Among all dynamic facility location problems, the temporal dimension constitutes a main aspect of the dynamics [12]. In particular, multi-period models are considered the most effective approach in addressing dynamic challenges such as the incremental increase in facility numbers and phased policy changes [13, 14, 15]. This study aims to provide a modeling and operating framework for facility location, which includes constructing a new community-facility location model, specifying the methods for data acquisition and processing, and defining the algorithm for the model. The core task is to propose a dynamic model that improves service provision efficiency and equity by considering facility number variations and inter-community competition. To address the complex issue of community-facility location, our research explores the facility location decision-making process from both temporal and spatial dimensions, integrating dynamic strategies. Temporally, we simulate the gradual development of the community-facility network through a multi-period incremental strategy. Spatially, we introduce community boundaries as a spatial constraint to simulate inter-community competition for facilities. Furthermore, we establish corresponding \"facility-communityallocation\" strategies that adapt based on the cumulative number of facilities within the planning area. In addition, taking the cumulative facility number, incremental strategy, and facility-community allocation strategies as constraints, as well as the efficiency and equity of facility location as its two fundamental objectives, we construct a bi-objective multi-scenario dynamic model (BOMSDM) for community-facility location problems. Finally, an empirical case is presented to evaluate the effectiveness and superiority of the newly developed model. The remainder of this paper is organized as follows: Section 2 reviews the literature on community-facility location and dynamic facility location problems and highlights the contributions of this study. Section 3 details the construction and solution methods of the BOMSDM and introduces three comparative models. Section 4 provides an overview of the study area, along with the data sources and preprocessing methods for each dataset used. Section 5 presents the simulation results of the BOMSDM and compares them with real-world solutions and the outcomes of the three comparative models. Additionally, the sensitivity testing of the model's optimization capability was conducted under different levels of facility redundancy. In Section 6, we discuss the performance, applicability, and limitations of BOMSDM. Finally, Section 7 concludes the study.
## 2 Material and Methods
### Antioxidant Properties Evaluation Using DPPH and Ferric ReducingAntioxidant Power (FRAP) Methods  
The antiradical activity was measured using the synthetic radical DPPH (1,1-diphenyl-2-picrylhydrazyl) using the method of [PERSON] et al. [21]. Approximately 10 mg of sample was suspended in 0.5 mL of distilled water. After making up the volume to 1.2 mL with 0.5 mL of absolute ethanol and 0.2 mL of DPPH (50 uM in ethanol), the mixture was incubated for 30 min in the dark at room temperature. The absorbance was measured at 517 nm using the T70 UV-visible spectrophotometer (PG Instruments Ltd., Beijing, China). The control was done in the same manner, except that distilled water was used instead of sample.  
From the absorbance, % inhibition or % scavenging activity is calculated using the formula  
\[\text{DPPH scavenging activity}=(\text{OD control}-\text{OD sample})/\text{OD control}\times 100 \tag{6}\]  
The ferric reducing antioxidant power (FRAP) of burger samples was determined using the method of [PERSON] et al. [22]. Fish burgers were cut into small pieces (10 mg), immersed in 0.1 mL of distilled water, and mixed with 2.5 mL of sodium phosphate buffer (NaHPO4, 0.2 M, pH 6.6) and 2.5 mL of potassium ferricyanide (K3 Fe (CN)6) (1% _w/v_). The mixtures were incubated for 30 min at 50 \({}^{\circ}\)C. After incubation, 2.5 mL of 10% trichloroacetic acid (TCA) was added and the reaction mixtures were centrifuged at 10,000 \(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C. Finally, 2.5 mL of the upper layer solution was taken and mixed with 2.5 mL of distilled water and 0.5 mL of 0.1% ferric chloride. The procedure was carried out in triplicate, and absorbance was measured at 700 nm. Increased concentrations of the extracts showed higher absorbance, which in turn indicates better reducing capacity.
## 2 Materials and Methods  
### Dataset and Pro-Precessing  
The Herlev dataset [6] was collected at Herlev University Hospital, the department of Pathology and department of Automation on Technical University of Denmark. These cervical cells images were collected and manually annotated into 7 classes by skilled cyto-technicians. The Herlev dataset is consist of 917 cell images, and each of the cell images only contains one single nuclei. The distribution of the Herlev pap-smear dataset is illustrated in Table 1 and some cell examples are provided in Figure 1.  
The data pre-processing method presented in [4] is adopted in this paper for creating the training samples. To simplify the procedure, we only followed these steps: extract several image patches in size \(128\times 128\) by translating the centroid of the ground-truth nucleus mask. After that, in order to build a balanced training set, different rotation rates are set to abnormal cell images and normal ones, respectively. The rotation rate is set to 36 degree for individual abnormal cell images, and for each normal cell image, 18 degree is performed. These image patches are then resized to \(224\times 224\times 3\) for facilitating the transfer learning phase. Zero padding is adopted to void regions that lie outside of the image boundary. The data augmentation step is crucial to the training of the deep CNN models,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cell Type** & **Numbers** \\ \hline \multirow{3}{*}{Normal} & Superficial squamous epithelial & 74 \\ \cline{2-3}  & Intermediate squamous epithelial & 70 \\ \cline{2-3}  & Columnar epithelial & 98 \\ \hline \multirow{3}{*}{Abnormal} & Mild squamous non-keratinizing dysplasia & 182 \\ \cline{2-3}  & Moderate squamous non-keratinizing dysplasia & 146 \\ \cline{2-3}  & Severe squamous non-keratinizing dysplasia & 197 \\ \cline{2-3}  & Squamous cell carcinoma in situ intermediate & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The distribution of [PERSON] dataset.  
which improves the accuracy and convergence when training deep CNN models. Figure 2 shows the two sets of cervical cell image patches by cropping and rotation.
## 5 Performance Evaluations
### Evaluation of Accuracy of Local Updates
In Figure 13, we evaluate ERs of local and global updating when we increase mobility speed. ERs of local updating with different \(k\) values first slightly decrease when we increase the speed of mobile nodes up to \(9\ m/s\), then ERs slightly increase or stay unchanged for the speeds higher than \(9\ m/s\). For higher speeds of mobile nodes, MN_BEACON_INTERVAL gets shorter and mobile nodes become the leaf nodes of the trees more quickly since they send leave messages to their child nodes quickly after they start to move. In Figure 14, we explore the influence of speed of mobile nodes on COs of local and global updates. When we increase the speed of mobile nodes, COs of local updates with different \(k\) values increase. This is due to the fact that higher speed means shorter MN_BEACON_INTERVAL; thus, more MN beacon and convex hull update messages for local updating mechanism. On the other hand, CO of global updating slightly increase with increasing speed. CO of local updating with \(k\ =\ 0.20\) is always smaller than CO of global updating; however, when we assign \(k\ =\ 0.10\), the COs of local and global updating are very similar for the speed of 15 \(m/s\) (_i.e._, around \(55\ km/h\)).