esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 3 Proposed Adaptive Reconfigurable Cache Scheme Based on Decision Tree
### Associativity Reconfigurable Cache  
By changing the indexing policy, cache blocks that originally belonged to two different ways are concatenated to tune the associativity from four to two. Figure 7 illustrates the associativity reconfigurable cache with fixed cache size.  
Cache sets are indexed using the index field in the address. When cache size is constant, higher associativity will bring fewer cache sets and fewer bits required to index the cache sets. The width of the address is fixed, so bits of the tag array increase. The address decoding unit is designed to process indexes with different bits. For an 8 MB cache with 64 bytes cache block size, the number of sets reaches the maximum value when the cache is direct-mapped (1-way set-associative) and a 17-bit index is required. Therefore, the row decoding unit can process up to a 17-bit address.  
Figure 6: False hit caused by associativity decrease.  
At the same time, the tag comparator is designed to compare inputs with different widths. To ensure that there are enough tag bits under all associativity, the tag array should be designed according to the associativity with the maximum value of tag width. The maximum value of the tag width is 13 bits when it is a 16-way set-associative cache. With the valid bit and the dirty bit, the total width of the tag array is 15 bits.  
Figure 8 illustrates the hardware architecture of associativity-reconfigurable cache. The basic structure of the cache is designed according to the maximum associativity. The cache array is divided into 16 equal-capacity ways, each of which has a comparator to determine whether a cache hit has occurred. The adaptive controller controls the row decoder and comparator to tune the cache associativity based on the decision of the trained decision tree model. Taking a 16-way set-associative cache as an example, the row decoder processes a 13-bit index, and the comparator compares two 13-bit tags. For an 8-way set-associative cache, the 19 th bit of the address is used to select the cache way from the original 16-way set-associative cache sets. The comparator inputs a 12-bit tag for comparison.  
Figure 8: Hardware architecture of associativity-reconfigurable cache.  
Figure 7: Associativity reconfigurable cache with a fixed size.
## 2 Bibliographic Study
### Classification Algorithms
#### 2.1.3 Random Forest Algorithm  
RF is an ensemble learning method used in classification that creates multiple decision trees during the process of training. Algorithm A3 represents the creation of bootstrap (random replacement subsets) samples for each decision tree for the forest. While creatinga decision tree for each bootstrap, the algorithm selects a random subset of features and votes on the best split while it grows to the maximal depth. This random selection of features works better with imbalanced data because it reduces the overfitting of the majority class [20].  
[PERSON] and [PERSON] [10] used Lasso LR, RF, and XGBoost classifiers with random under- and oversampling methods and varied the target imbalance ratio. The study used four large health databases--three U.S. claims databases and one German EHR database, all mapped to the OMOP Common Data Model to investigate outcomes in patients with treated depression, where the initial sample was 100,000. After applying 58 prediction tasks on electronic health data, the results showed that only the models for random oversampling with RF showed more variation in the Receiver Operating Characteristic--Area Under the Curve (ROC-AUC) difference (which compares the performance of two classification models by quantifying how much better a model is able to separate positive and negative classes compared to another model).  
Other works collected data through surveys, as demonstrated by [PERSON] and [PERSON] [21], whose main goal was to predict depression among women, who may experience anxiety due to their life roles and physiological differences in Malaysia's environment whereby women may face issues with money, family, health, and work. In this research, the authors used the SMOTE oversampling method with RF and concluded that the imbalance ratio affected the sensitivity of the RF model, which predicted disease accurately. RF can be applied in identifying longitudinal predictors of health and was able to discriminate poor from good self-perceived health outcomes in a 30-year cohort study [22].  
According to the bibliographic study, in the medical field, LR is mostly used in cases of smaller datasets, while KNN and ensemble RF algorithms are applied in complex datasets with multiple attributes. The difference between them lies in their methodological approaches. The simple form of bLR's parameters can be tuned easily, which works well with a lower number of features, while KNN and RF can capture interactions among features when patient data in medical research convey different types of information.
## 3 System Architecture, Software Services and Deployment
### Data Engineering on the Fog Layer  
All data messages that are accumulated in SAL are then circulated within EPL-A and EPL-B through a dedicated, highly customized message bus system that essentially distributes all obtained information to the various subsystems responsible for data processing, data storage or alert generation. The existence of a dedicated message bus submodule introduces a certain degree of flexibility allowing the integration of data transformation and processing mechanisms. These mechanisms stretch over several different hierarchical layers and accommodate all end-user services and functions. When viewed from the inside, the message bus delivers a fully autonomous stream processing pipeline compatible with all interconnected sensors and last-mile devices. Each data stream is provided with a unique tag/name resembling the network addresses of the sensors where data are originated from and extended with the unique identifier of the EPL-A device that received it.  
This overall approach renders the framework capable of storing data in a scalable manner across several different architectural layers. Data can be formatted for imminent retrieval in which case time-consuming queries or aggregations are avoided, while at the same time, caching mechanisms are integrated to store recent data retrieved from specific sensors or series of sensors temporarily. Moreover, the framework also stores historical data in a more permanent repository via a dedicated process that identifies obsolete measurements and forwards them accordingly. The decoupling of data generation and storage entities facilitates the implementation of a wide variety of services in a more independent way since the necessity of optimizing code for meeting vastly different performance requirements no longer exists.
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 3 Performance Analysis  
Let \(h_{ab}\) denote the channel coefficient of the \(a\to b\) link and \(X_{ab}=|h_{ab}|^{2}\) denote the channel gain power, where \(a,b\in\{S_{1},S_{2},R\}\) and \(a\
eq b\). With channel reciprocity assumption, we have \(h_{ab}=h_{ba}\) and \(X_{ab}=X_{ba}\). Moreover, assume that (i) all channels (i.e., \(h_{S_{1}S_{2}},h_{S_{1}R}\), and \(h_{S_{2}R}\)) are independent with distribution \(\mathcal{CN}(0,\Omega_{0})\), \(\mathcal{CN}(0,\Omega_{1})\), and \(\mathcal{CN}(0,\Omega_{2})\), respectively; (ii) all the noises at the corresponding receiving nodes are independently and identically distributed (i.i.d.) complex additive white Gaussian noises with distribution \(\mathcal{CN}(0,1)\); (iii) all nodes transmit with power \(\rho\); (iv) each codeword of the two sources bears \(M\) bits information and then the target data rate for each source is \(C=\frac{M}{W(1-a)T}\) bits/s/Hz, where \(W\) is the transmission bandwidth of the system; and (v) ideal capacity approaching rateless coding is adopted.  
With the above assumptions, the transmission length of the first or second time slot of DTDBC-RC is expressed as \(T_{DTDBC-RC}=\min\left(\frac{M}{W\log\left(1+\rho\left|h_{S_{1}S_{2}}\right|^{ 2}\right)},\alpha T\right)\), a random variable depending on the channel quality of the direct link \(S_{1}\to S_{2}\) (with \(\rho\) being the transmit signal-to-noise ratio (SNR). (The transmit SNR is defined as the ratio between the transmit power and the channel noise power, and it is usually used in performance analysis although it is not an important metric in practice.)).
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 4 Implementation Criteria
### Implementation Comparison  
A comparison between the two implemented versions is carried out in terms of estimated overall chain sensitivity. Figure 8 reports the values for \(P_{sens}\) with the use of the PTAT block employing versions 1 and 2, again assuming pad parasitics to be around 1.3 pF. The nominal sensitivity trend across temperature looks similar in the two cases, but sensitivity is better for version 2, i.e., with minimal diodes, as the corresponding curve is shifted downwards by an amount of 2-3 dBs. We carried out 100-run Monte Carlo simulations of versions 1 and 2 of the ED at 120 \({}^{\circ}\)C, which for both designs is the worst-case scenario in terms of sensitivity. They demonstrated a 1-\(\sigma\) sensitivity spread of 0.44 dB  
Figure 6: Version 1 simulated \(R_{in}\) with and without the use of the PTAT block.  
Figure 7: Version 1 simulated \(P_{sens}\) with and without the use of the PTAT block.  
and 1.09 dB for versions 1 and 2, respectively, indicating the overall superior sensitivity performance of version 2.  
Table 3 summarizes the main results for the two versions.
# Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network
## 2 Related Work
### LF Image Enhancement
#### 2.2.3 Contrast Enhancement  
The decoded LF image also has low quality. Researchers hope to heighten the image information expression by enhancing the image contrast. The research on the contrast of LF images is limited, so we expanded the contrast enhancement algorithm to ordinary images. Among the numerous contrast enhancement algorithms, HE is the basic strategy for most enhancement techniques in image processing. On account of its effectiveness and simplicity, histogram equalization is one of the most widely used techniques to achieve contrast enhancement [35]. HE modifies the histogram to improve the limited dynamic range of the image. The original image histogram can be transformed into a uniform distribution (equilibrium) form, and the dynamic range of the gray value difference between pixels can be increased to heighten the contrast of the image by using HE. Many attempts [12; 36; 37; 38] have been based on this method to improve the visual quality. However, HE enhances the details excessively, which augments background interference and reduces the detailed data. In this paper, to the best of our knowledge, we apply adaptive image enhancement for the first time and resolve the inherent problems of the HE method for LF images.
# Sensitivity Analysis of a Numerical Model for Percutaneous Auxicular Vagus Nerve Stimulation
## 5 Conclusions  
A sensitivity analysis of a realistic numerical model for a pVNS application was performed. The numerical model was used to investigate the excitation threshold in single and bundled axons with respect to the axon fiber diameter, axon number, SENN temperature, electrodes' penetration depth and position, and the ear conductivity. Results show that the stimulation thresholds increase with an increasing number of axons and a decreasing axon fiber diameter. The SENN model temperature and the ear conductivity effect are negligible compared to the effect of the axon fiber diameter and the electrodes' penetration depth. The electrodes' penetration depth is the most influential parameter with maximum sensitivity of 38 mV for each 0.1 mm and bundled axons. The sensitivity analysis results show the high importance of the electrodes' position to prevent opposite physiological reactions and an even adverse therapeutic outcome by unfavorable stimulation of pain-related fibers or under-stimulation. The proposed model does not consider any network between the nerves and relies on the SENN model for transmembrane mechanisms and axon activation. Future works will further evaluate the numerical model by comparing numerical results with experimental data using clinical studies. Additional stimulation patterns (pulses with equal energy and triphasic pulses) and bursted stimulation will also be investigated to perform both experimental validation and mutual optimization of the model and the experimental setup.  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], E.K., and [PERSON] conceived and designed the simulations; [PERSON], [PERSON], E.K., and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The research was supported by COST Action BM1309 (COST EMF-MED) and the FWO G003415N project. [PERSON]: is a postdoctoral fellow of the Research Foundation-Flanders (FWO-V).  
Conflicts of Interest: [PERSON], [PERSON], and [PERSON] are shareholders and receive remunerations from SzeleSTIM GmbH. The authors declare no conflict of interest.
## 4 Conclusions  
This study proposes a multiphysics-coupled analysis method to simulate the entire induction hardening process for 3D geometries, examining its potential to improve product quality and minimize defects.  
A electromagnetic-thermal coupled analysis was conducted to implement a process that considers electrical and thermal interactions, including the skin effect. Based on this analysis, the temperature distribution in the CTBA was evaluated. The results indicate that an imbalance in the gap between the induction coil and the product could lead to a non-uniform temperature distribution. Therefore, to minimize deformation in the CTBA, future research should focus on optimizing coil design to eliminate such imbalances.  
Additionally, the thermal-structural coupled analysis incorporated the effects of temperature variations and phase transformations on structural deformation. The simulation results predicted the occurrence of structural deformation in the final product, confirming that such deformation could significantly affect product integrity.  
A comparison between the simulation results and actual measurements demonstrated an accuracy of approximately 96%, validating the feasibility of the proposed approach for industrial applications. However, some discrepancies were attributed to modeling simplifications introduced to enhance computational convergence.  
This study confirms that multiphysics-coupled simulations of induction hardening can contribute to improving product performance and production efficiency. However, the phase transformation itself was not experimentally validated, and specific solutions for mitigating deformation caused by imbalance were not provided. Therefore, future research should focus on experimental validation of the phase transformation effects and  
Figure 13: Comparison of CTBA \(z\)-direction displacement between experimental and FE analysis, (a) 3D geometry, and (b) A–B section.  
optimizations of the coil design to improve localized hardness distribution and reduce structural deformation.  
S.H. ([PERSON]): Model design, Methodology, Writing--original draft. S.Y. and J.P.: Writing--modification of draft, Writing--review and editing. S.H. ([PERSON]): Supervision, Writing--modification of draft, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (2024-0913-01) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea). This work was partly supported by the Technology development Program of MSS [RS-2024-00446241] and the Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) [(2021 RIS-004)].  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company Hwashin Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Related Works
In summary, the related works can be classified in two categories: (a) works that try to solve a similar problem, but present a different approach or modeling assumption; and (b) works that consider a similar system, but focus on different problems. It is important to highlight the differences (advantages) of our proposal in relation to the analyzed works in the first category. An important advantage of our proposal in relation to those described above is that it does not have any dependency on predefined conditions to work, such as the predefined preferred locations to send alarm messages in AWARE [16] or specific movement patterns of the mobile nodes as in [22, 23].
## 1 Introduction
It showcases a wide range of techniques and applications that aid in identifying potential structural weaknesses, and enhancing durability, reliability, and performance. Specifically, the presented article introduces an analytical modeling approach to enhance structural analysis by understanding acoustic emission signals in thin-walled objects [20]. It proposes a novel intelligent method for bearing fault diagnosis that employs EEMD permutation entropy and GG clustering to improve accuracy and efficiency [21]. Furthermore, it introduces a new online operational modal analysis method for vibration control in linear time-varying structures, offering real-time monitoring and adjustment capabilities [22]. Additionally, the article investigates the application of empirical mode decomposition and artificial neural networks for automatic bearing fault diagnosis based on vibration signals, aiming to achieve reliable and efficient fault detection [23]. Understanding the modal behavior of a car class is crucial for analyzing its dynamic behavior. The vibrational characteristics of a vehicle depend on its stiffness and mass distribution. Global bending and torsional vibration mode frequencies are often used to evaluate the structural performance of a vehicle. Bending and torsional stiffness are critical for the vibrational behavior of the structure, especially its first natural frequency. Therefore, to enhance the performance and durability of the entire CBF, a combined simulation and test-based method is used for conducting dynamics analysis of the CBF, based on simulation analysis and vibration test results. This paper is structured as follows: Section 2 presents the research methodology and materials used. Section 3 analyzes the results and provides validation for both simulation and experimental performance analysis. Section 4 interprets the differences found in the requirement expression characteristic analysis. Lastly, Section 5 presents the conclusions derived from the proposed technique.
## 4 Applications of WSN in Urban Areas
### Transportation Applications
Each method employs different sensors, such as GPS devices, multimedia mobile nodes, light, temperature, acoustic sensors, RFID, and magneto-resistive sensors, within centralized or decentralized networks featuring mesh, cluster-based, star, and grid topologies. Objectives include assessing policy alignment with citizen needs, analyzing discrete choices, studying land-use and travel behavior, integrating urban planning with public transport, connecting transportation with health, and enhancing travel standards. While these methods effectively address aspects like EC, NL, E, Delay, R, and Cost, they each have specific limitations. These include not reducing computational complexity, failing to minimize energy consumption, not reducing delays, failing to improve network lifetime, and not increasing the efficiency of transportation-health connections. Despite their varied goals and approaches, each methodology encounters challenges in optimizing its respective systems. The \(\swarrow\) in Table 5 shows that the method increases the performance of the particular metric, whereas the blank space indicates that the method does not consider the performance of the particular metric. The summary conclusions of the transportation applications are listed below. * The studies highlight the importance of sensor networks and integrated methodologies in addressing urban transportation challenges like policy alignment, transportation choice analysis, and energy consumption, focusing on efficiency, safety, and sustainability. * Different network topologies, such as centralized mesh and grid and decentralized cluster-based and star, optimize transportation systems for policy assessment, health impact analysis, and transportation mode integration. * Mesh network topology offers scalability and efficiency for policy alignment and public transport integration. Grid topology is robust for linking transportation with health outcomes. * Star network topology is effective for integrating various transportation modes and improving travel standards, while cluster-based topology excels in detailed transportation choice analysis. * Decentralized architectures enhance urban transportation systems by providing insights into user preferences, policy effectiveness, and system efficiency, each topology contributing to resilience and responsiveness.
## 4 Discussion
### Contributions and Limitations of the Study  
This study analyzes the land use carbon emission risk in the Yanshan-Taihang Mountain region of Hebei Province at the county level, providing insights into the temporal and spatial differences in carbon emission risk associated with land use. This helps fill the gap in understanding county-level spatial carbon dynamics. By classifying carbon emission risk, this study proposes specific measures for reducing land use carbon emission risk tailored to different levels of counties, contributing to the coordinated development of regional ecology and economy, as well as the steady advancement of the \"dual carbon\" goals.  
However, there are some limitations in this study. Firstly, the land use carbon emission coefficients used were derived from previous research and may overlook the temporal changes in land productivity and carbon absorption rates due to their fixed nature. Therefore, future research could adopt time-varying land use carbon emission coefficients to enhance the accuracy of the findings. Additionally, this study conducted its analysis at the county level. In future research, it may be beneficial to examine land use carbon emission risk at smaller scales, such as using a grid-based approach, to facilitate a more precise analysis of the spatial heterogeneity and local characteristics of carbon emission risk.
## 2 Materials and Methods
### Image Acquisition and Processing
#### 2.2.2 Image Preprocessing  
Image preprocessing includes image denoising, image binarisation, and image hole filling. Firstly, the original WSP fog droplet image was denoised using high-pass filtering to lay the foundation for subsequent droplet extraction and segmentation. Then, in order to effectively binarise the image, automatic thresholding was performed using the maximum inter-class variance method [19]. Next, diffuse water filling was performed on the binarised image to fill the small holes and gaps in the image to make the target region more complete and coherent, as shown in Figure 3.  
The above preprocessing of the overlapping-droplet image can make the edges of the overlapping droplets clearer, reduce the interference of factors such as edge blurring and noise on the overlapping-droplet detection, and make the position and shape of the overlapping droplets more accurate, thus improving the accuracy and robustness of the overlapping-droplet detection, making it suitable for more complex environments, which plays a key role in the subsequent overlapping-droplet-segmentation process.