esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations, Challenges, and Possible Future Works  
However, it is crucial to acknowledge the limitations and challenges associated with this study. Data collection for environmental factors and vegetation indices introduces the possibility of errors and variability. Ensuring the accuracy and consistency of data collection methods becomes paramount to the reliability of the study findings. Furthermore, while the study identifies correlations between factors and seed weight, it does not imply causality. Other unmeasured variables can contribute to seed weight, which requires caution when drawing causal relationships from correlations alone. Maize varieties are also one of the factors that can affect grain yield [67; 68]. This study used the CP303 seed type to plant due to the high percentage of the kernel set and being suitable for planting in flat and sloped areas [69]. The exclusion of certain weather factors, such as the daily rain and rain rate, from the correlation analysis due to negligible values presents a limitation. Although seemingly insignificant, these factors could still influence crop yield, and their omission could affect the comprehensiveness of the analysis. Furthermore, the study findings are specific to the study area and conditions, and generalizing these results to different regions or crops may require additional validation. Climate conditions are one of the challenges farmers face when managing plants. Maize grows in temperatures between 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C, and the irrigation system must offer approximately 450-600 mm coverage during the growing season [70]. Future work will employ enhanced techniques for water crop management [71], pest control [28], and disease control [72; 73], such as early detection of emerging plants [74], and precision fertilizer application based on plant needs [75] that use deep learning techniques to mitigate pests and diseases. In conclusion, this study provides valuable information on the intricate relationships between environmental factors, vegetation indices, and maize seed weight. These insights can inform more precise decision making in agriculture, especially in the context of precision agriculture. However, it is imperative to consider the study's limitations and the data's potential variability when applying these results to real-world farming scenarios. Further research and validation efforts may be necessary to establish causation and extend the applicability of these findings to broader agricultural contexts. Future efforts will further enhance the model's capabilities,extending it to other areas of applicability to foster more resilient and efficient agricultural practices amidst evolving environmental and societal challenges.
## 2 Materials and Methods
### Structural, Morphological, Optical and Electrical Analysis  
Structural analysis was acquired via X-ray diffraction (XRD) within a 20 range of 35-90\({}^{\circ}\), on a \"Philips X'Pert\" diffractometer fitted with a CuK\(\alpha\) (\(\lambda\) = 1.541838 A) radiation source. We have taken into consideration the \(\beta\) broadening due to the instrument, and the factor is included in our calculations. Raman spectra were recorded with an excitation at 473 nm, from 100 to 1000 cm\({}^{-1}\), on a \"Horiba Scientific\" Raman spectrometer. Electrical investigation of the films was conducted by adopting the four-point method with a \"JANDEL\" model connected to a \"Keithley 2400 SourceMeter\". A \"Shimadzu 1700\" double-beam spectrophotometer was used for recording the optical transmission spectra between 200 and 900 nm. Film thickness was accurately determined using an \"Alpha-Step D-500\" stylus profilometer, which provided the results of the film's evolution with respect to the deposition conditions. The resulting films were dense, smooth and homogeneous. Thus, the CuO films were investigated from four different perspectives: their structural composition (1); their morphological structure (2); their physical and chemical properties (3); and of course their electrical and optical properties (4). We show that the films were polycrystalline, exhibiting a pure CuO monoclinic structure, with no other secondary phase present. X-ray diffraction and Raman spectroscopy completed the investigations, revealing that a pure phase was obtained.
## 2 Materials and Methods
### Manual Segmentation  
Manual RPS segmentations were performed in consensus by two radiologists ([PERSON] and [PERSON]), both with 3 years of experience in abdominal CT. CT portal-venous phase images were used for manual segmentation. Each CT portal-venous examination was anonymized and imported into an open-source DICOM (Digital Imaging and Communication in Medicine), viewer equipped with Horos (LGPL license at Horosproject.org [21]) in order to obtain volume lesion by manual segmentation. Lesion boundaries were manually traced with a contouring tool (pencil), slice by slice. Afterward, the \"compute volume\" tool was used to obtain the volume rendering of the entire RPS with the volume measurement. The time required by the entire process was dependent on the lesion's dimension. Manual RPS segmentation volumes were used as a reference standard.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 2 WPT System Coupling Circuit Model
### Effect of Phase Difference on the System  
Figure 5 shows the transmitter side's current and voltage waveforms under the system's capacitive state. In the figure, when the system operates in a capacitive state, the inverter output voltage will produce large spikes and have reversed polarity, increasing the possibility of damaging the switching tubes. In order to avoid the above phenomenon, the system works at ZVS by using frequency-tracking technology.  
The frequency-tracking method usually tracks the transmitter's current to achieve the same frequency and phase voltage of the inverter output. In practice, the signal delays are due to the acquisition circuit, the over-zero comparison circuit, the dead zone, and the inverter diver module. As a result, the generated voltage will lag behind the current, so the phase angle of the collected current signal needs to be compensated for. Furthermore, different coils, coil distances, and load variations will have some effect on the phase angle, so using a fixed phase angle compensation is not reliable.  
In order to solve the impact of the phase lag on the frequency tracking and the system in the above problems, this paper proposes a frequency-tracking method based on FPGA to track the phase difference accurately. This method fixes the phase difference between voltage and current through feedback control of the phase difference. The system always works under the optimal ZVS state, increasing the system's output power and transmission frequency.
# Designing Audio Equalization Filters by Deep Neural Networks
###### Abstract  
Audio equalization is an active research topic aiming at improving the audio quality of a loudspeaker system by correcting the overall frequency response using linear filters. The estimation of their coefficients is not an easy task, especially in binaural and multipoint scenarios, due to the contribution of multiple impulse responses to each listening point. This paper presents a deep learning approach for tuning filter coefficients employing three different neural networks architectures--the Multilayer Perceptron, the Convolutional Neural Network, and the Convolutional Autoencoder. Suitable loss functions are proposed for each architecture, and are formulated in terms of spectral Euclidean distance. The experiments were conducted in the automotive scenario, considering several loudspeakers and microphones. The obtained results show that deep learning techniques give superior performance compared to baseline methods, achieving almost flat magnitude frequency response.  
deep neural networks; FIR filter design; audio equalization; automotive audio 2020
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 4 Case Study
### Fault Scenario II  
Baseline eigenvalues are estimated as seen in Figure 18:  
Clusters are generated using \(k\)-means clustering and the E  
Figure 17: 1000 \(\Omega\) fault identified.  
Figure 18: Baseline eigenvalues.  
\begin{table} \end{table}
Table 5: 1000 \(\Omega\) fault statistics.  
Statistics of this case study are presented in Table 7:  
\begin{tabular}{c c c} \hline
**Euclidean Distance Statistics** & **Normal Conditions** & **Fault Conditions** \\ \hline Mean & 109.4366 & 0.0215 \\ Standard Deviation & 90.7864 & 0.0145 \\ \hline \end{tabular}  
The solution successfully detects an C-phase to ground fault with a fault resistance of 1000 \(\Omega\), as illustrated in Figure 22:  
\begin{tabular}{c c c} \hline
**Euclidean Distance Statistics** & **Normal Conditions** & **Fault Conditions** \\ \hline Mean & 109.4366 & 169.0397 \\ Standard Deviation & 90.7864 & 152.1032 \\ \hline \end{tabular}  
Statistics for this case study are presented in Table 8:  
\begin{tabular}{c  
As expected, during faults of lower impedance the profile of the estimated system is dominated by the fault. This pushes the eigenvalues far away from their normal zones as they congregate around a single point determined by the fault. The mean and standard deviation values seen during the 10 \(\Omega\) and 100 \(\Omega\) faults are evidence of this, as the values are almost the same for both fault conditions, and far from the normal values. In this context, a fault with a resistance of 100 \(\Omega\) is considered a HIF, this illustrates the robustness of this framework.  
At 1000 \(\Omega\), the eigenvalue drift is no longer dominated by the fault, however, the increase in resistance experienced by the system moves the Eigenvalues to new locations in the Eigenvalue Space. This deviation is still large enough for this solution to observe the change and issue an alarm.
## 2 Methodology
### Extract Multimodal Social Sensing Disaster Information
#### 2.3.2 ERNIE-Based Extraction of Location and Damage Information  
In this study, the ERNIE model [45] was used to extract information from text, including the time of disaster occurrence, the geographical location of affected areas, the type of disaster damage, and rescue and aid requests. For the same Weibo post, when both the text and images contain disaster damage information, they can complement each other in providing a more comprehensive view of the situation. On one hand, textual descriptions of severely affected areas may be insufficient, while on the other hand, images struggle to convey social impacts such as water and power outages.  
Geographic information from social media related to disaster damage can provide detailed insights into affected areas for disaster response and management. Early methods such as text semantic classification, topic modeling, and sentiment analysis are insufficient to meet these needs. Weibo's check-in location is only available when the location feature is enabled, and disaster response and aid efforts require highly accurate geographic information. Text can facilitate fine-grained location extraction, including roads, intersections, and points of interest (POI), which help in quickly locating disaster-affected areas and providing aid. Therefore, we extracted disaster-related information by identifying key terms such as full geographic locations, disaster damage types, and time from the text. We matched the recognized flood images with the corresponding posts to filter the textual data associated with typhoon disaster damage. Geographic location extraction typically involves two key steps: identification and geolocation. The first step identifies location descriptions from the text content of social media messages, and the second step aims to find appropriate geographic coordinates and spatial representations for these identified locations. We used the ERNIE model to extract full addresses in the format of \"province-city-district (county)-specific location\". Subsequently, a map application API was used for geocoding, accurately converting the addresses into WGS1984 latitude and longitude coordinates for spatial visualization.
## 2 Results and Discussion
### Mo-Catalyzed Epoxidation with TBHP
Quantification using NMR analysis, however, indicated that the total yield (**2A**, **2B** and **2C**) was around 70% (calculated from the ratio of integrals in the \({}^{1}\)H spectrum). This difference may be explained by the fact that signal intensity in the total ion chromatogram is not fully quantitative, due to a different degree of ionization for the products. As each product was not quantitatively calibrated on GC, quantification using NMR seemed to be more reliable. Furthermore, the NMR spectra showed that the ratio of products **2A**, **2B** and **2C** was roughly 6:2:1. The variation in product distribution between GC-MS and NMR may be partially explained by transformation of **2A** to **2B** during the derivatization step or upon injection to the GC-MS. Those types of transformations have been reported for other lignin structures as well [25]. For substrate **3** (containing OH functions), the Mo-catalyzed reactions gave a mixture of mono- and oligomeric products. Only traces of the expected ring closed products (**3A** and **3B**) were detected using NMR and GC-MS. NMR analyses of the reaction mixtures showed that the double bond had reacted. Here, again, the high temperature in these reactions may result in unselective reactions of the epoxide intermediate. With lower equivalents of TBHP, no conversion occurred even with higher equivalents of pre-catalysts (Entries 8 and 10). Substrate **4** reacted slower compared to the other norlignans. NMR showed the conversion to one major product (**4B**) in 14% or 19% for [MoO\({}_{2}\)(SAP)]\({}_{2}\) or [MoO\({}_{2}\)(SATP)]\({}_{2}\) respectively, as well as other mono- (**4C**, **4D** and other unknown products) and oligomers. Analysis using HPSEC gave a rough estimation of 40% monomeric structures, 35% di- and trimers, and 25% small molecular structures including molybdenum complexes (Figure 6). GC-MS revealed that the main degradation product was 3,4-dimethoxybenzaldehyde issued from the C=C oxidative cleavage. Figure 4: Dimethyl-dihydroperanene, used as internal standard in the kinetic studies. Figure 5: Kinetic studies on the reaction rate of **2** in toluene with 0.5 mol% dimeric precatalyst ([MoO\({}_{2}\)(SAP)]\({}_{2}\) to the left and [MoO\({}_{2}\)(SATP)]\({}_{2}\) to the right, and 5 equivalents of TBHP (70 wt% in water) at 80 \({}^{\circ}\)C. The trend lines (stacked lines) for [MoO\({}_{2}\)(SAP)]\({}_{2}\) suggested that the reaction had a quantitative conversion after 570 min.
## 1 Introduction
The SSFXG framework introduces several advanced features designed to revolutionize wireless communication networks by addressing critical challenges such as scalability, privacy, and user autonomy. One of the key innovations of SSFXG is its distributed storage mechanism, which employs decentralized storage capsules (SCs) to securely manage user credentials. This approach ensures enhanced privacy during identity verification processes by reducing reliance on centralized storage systems, which are often vulnerable to breaches. Another notable aspect of SSFXG is its integration of blockchain technology, which enables immutable and transparent record-keeping, fostering trust and accountability in federated network operations. Additionally, the framework incorporates sophisticated mechanisms for untraceability, allowing subscribers to access wireless services, such as voice calls and other interactions, without revealing their identity or enabling tracking. These features collectively address current limitations in wireless communication networks while advancing scalability, enhancing privacy, and empowering users with greater control over their data and interactions. The SSFXG framework opens the door to a wide range of transformative applications across multiple domains. For instance, in disaster recovery scenarios, SSFXG can facilitate rapid and secure communication by enabling resource-sharing among different operators, ensuring uninterrupted service for affected regions. Similarly, in global travel, the framework allows seamless connectivity and secure identity verification across international borders without reliance on roaming agreements. Additionally, SSFXG can empower next-generation smart cities by supporting decentralized management of IoT devices, ensuring scalable and secure interconnectivity. By enabling privacy-preserving telehealth services, the framework can also enhance healthcare delivery in remote areas, ensuring data security and patient autonomy. These diverse use cases underscore the practical relevance of SSFXG in addressing real-world challenges while meeting the demands of an increasingly interconnected future. To the best of our knowledge, SSFXG is the first decentralized SSI-enabled wireless communication framework that integrates untraceability features and decentralized trust layers. SSFXG introduces a novel approach to managing identity credentials by employing distributed storage capsules and blockchain technology, ensuring privacy, scalability, and anonymous communication. Therefore, the key contributions of this research can be summarized as follows:  
* Introducing the SSFXG framework, a decentralized wireless communication model incorporating SSI principles for autonomous user identity management. * Proposing a secure and anonymous communication system within SSFXG, including untraceable voice call services enabled by cascaded VPN-like tunnels. * Developing a distributed storage capsule mechanism to securely manage user identity credentials, ensuring scalable and anonymous verification processes.
## 5 Analysis of Results and Field Testing
### Ablation Experiments
#### 5.4.2 Influence of Encoder Composition Structure  
The architecture of the encoder in a neural network model is crucial as it directly impacts the performance of the model. Therefore, designing a robust encoder is necessary to provide better feature information encoding capability to the neural network model. To achieve this, the paper presents a redesigned encoder architecture that integrates convolution and Transformer modules by summarizing previous excellent encoder architectures. This integration enables the neural network model to fully exploit the advantages of both modules. To determine the best organization structure to achieve optimal model performance, we analyzed the impacts of the combined convolution and Transformer module model on model performance magnitude, as shown in Table 4. In the table, CA represents the multi-scale convolutional attention block, and TA represents the Transformer block. From the data in the table, it is evident that the model performance is optimal on the dataset used in this paper when using the CA-CA-TA-TA architecture adopted by the encoder in this paper.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Backbone** & **Params (M)** & **GFLOPs (G)** & **mIoU (\%)** & **mPA (\%)** \\ \hline SegFormer-UNet & B0 & 5.54 & 14.49 & 96.76 & 98.2 \\ SegFormer & B0 & 3.72 & 6.77 & 93.03 & 96.04 \\ \hline SegFormer-UNet & B1 & 15.78 & 23.50 & 97.14 & 98.46 \\ SegFormer & B1 & 13.28 & 26.48 & 95.30 & 97.55 \\ \hline SegFormer-UNet & B2 & 26.00 & 32.04 & 97.43 & 98.62 \\ SegFormer & B2 & 27.35 & 56.71 & 96.35 & 98.12 \\ \hline SegFormer-UNet & B3 & 46.56 & 49.43 & 98.34 & 99.10 \\ SegFormer & B3 & 47.22 & 71.36 & 96.43 & 98.10 \\ \hline SegFormer & B4 & 63.99 & 85.43 & 96.30 & 98.00 \\ SegFormer & B5 & 84.60 & 99.76 & 96.66 & 98.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation studies related to Encoder Size.  
Figure 10: Different network segmentation results.
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## Appendix C The QTB for a Double-Well Potential in One Dimension  
As the handling of an energy barrier is crucial in many applications, we consider a generic 1D double-well potential, written as a two-center Morse potential (Figure 11):  
\[V(x)=V_{0}\Big{[}\mathrm{e}\mathrm{-}\frac{x-x_{0}+a/2}{d}\Big{(}\mathrm{e} \mathrm{-}\frac{x-x_{0}+a/2}{d}-2\Big{)}+\mathrm{e}\mathrm{-}\frac{x-x_{0}+a/ 2}{d}\Big{(}\mathrm{e}\mathrm{-}\frac{x-x_{0}+a/2}{d}-2\Big{)}+1\Big{]} \tag{11}\]  
In this example \(V_{0}=3.0\) eV, \(d=0.2\) A while \(a\) is the distance between the two centers located at \(-a/2\) and \(+a/2\) (typically describing an H atom interacting with two heavier atoms in a hydrogen bond configuration). When \(a\) is increased, the wells become deeper and more separate. In this case, anharmonicity cannot be considered as a perturbation, as it is inherent to the barrier handling.  
Figure 12 shows how the QTB corrects the classical probability distributions to include quantum effects. At 300 K, the classical distribution remains strongly concentrated at the bottom of the wells and at the simulation time-scale (20 ns), the classical particle hardly crosses the energy barrier while the QTB-driven particle does. The QTB distribution shape, however, shows sharper peaks than the fully quantum result. The QTB tends to underestimates the probability in between the two peaks when the wells are close, while the peak positions are shifted away from the midpoint, with respect to the exact quantum solution. For large inter-well distances \(a=2.5\) A and 2.6 A, the features obtained in the single-well problem (Section 4.2) can be recognized: the QTB distribution maxima remain at the classical location, while tails in between extend further.
## 4 Architecture
the _HealthTracker Upload Server_, whose implementation is discussed in Section 6.2, which exposes an API that allows the Android app to upload the health data collected, which, after validation, are sent to the Time series database;
* the _Time-series Database_, whose implementation is discussed in Section 6.3, which stores the health data making them easily and efficiently accessible to the rest of the system;
* the _HealthTracker Dashboard_, whose implementation is discussed in Section 6.5, which is a web application enabling physicians to plot the time series recorded in each time frame;
* the _HealthTracker Admin Server_, whose implementation is discussed in Section 6.4, which fulfills the task of authorizing users before enabling the _Dashboard_ to retrieve time-series data on their behalf. * the _Authenticator_, whose main purpose is to provide a fully functioning authentication and authorization mechanism (based on the _OAuth_2 standard [16]) with a series of ancillary services useful to speed up the development. Its integration with the other components of the architecture is discussed in Section 6. An important issue in systems storing and processing health data is protecting the confidentiality of these data. To adequately protect confidentiality, data needs to be protected both while in transit and while at rest. In HealthTracker, we deal with this issue by incorporating into its design encryption mechanisms both for data transmission and data storage, in the form of encrypted communication protocols between the Android smartphone and the _Upload Server_, and an encrypted DBMS for the _time-series database_. As will be discussed in Section 6, in the current implementation, we have adopted the _HTTPS_ protocol for communication between the Android smartphone and the _Upload Server_, and the _MongoDB_ DBMS to store time-series data, both of which provide data encryption. Furthermore, HealthTracker requires that all user operations be authenticated before being actually carried out by means of the authentication functionalities provided by the _Authenticator_, thus providing access to unencrypted data only to legitimate users. Jointly, encryption and strong user authentication mechanisms provide the necessary level of protection for the health data stored in and processed by HealthTracker.