esa-sceva/satcom-chunk-collection huggingface dataset:
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 4 Evaluation
### Area and Power Analysis  
We implement the proposed router in RTLand synthesized it using Synopsys Design Compiler in TSMC 45-nm technology. This five-port router has 16 buffer slots in each input port, and the flat width is 128 bits. The operating frequency is set to be 2 GHz. We present the area and power of the adaptive router with and without the bypass datapath in Table 3. Bypassing increases the router's area by 3.66% and power by 1.40%, mainly due to the larger crossbar used.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency** & **1 GHz** \\ \hline ISA & x86-64 \\ Core & 64 cores, Two-way out-of-order, 8 memory controller \\ L1 ICache & 32 KB, private, 4-way \\ L1 DCache & 32 KB, private, 4-way \\ L2 Cache & 512 KB, shared, 16-way \\ Cache coherence & MESI \\ Main memory latency & 80 cycles \\ Flit width & 128 bits \\ Network Size & 8\(\times\)8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation setup for benchmarks.  
Figure 12: Normalized execution time of SPLASH-2 workloads.  
We also examine the power overhead of sideband lookahead signals using DSENT [48]. In SWIFT, the flit width is 64-bit, and it requires a 14-bit lookahead flit for each data flit. Table 4 shows the link power differences with and without lookaheads at an injection rate of 0.1. The lookahead signals increase SWIFT's link power consumption by 21.9%. SlideAcross can avoid such power overhead introduced by lookahead signals.
# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
## 3 State of the Art on Ontologies
### Semantic Technologies and Ontologies for Predictive Maintenance  
A PdM platform should be based on the continuous monitoring of the behavior of manufacturing assets and the utilization of several data sources across different levels of a manufacturing system (e.g., component, machinery, production system, product). As state-of-the-art ontologies model these problems independently, [PERSON] et al. (2021) investigated existing ontologies in the domain of PdM and proposed extensions to bridge the gaps with machine learning [39]. [PERSON] et al. (2019) addressed the issue of data management and integration in PdM and proposed a new method to effectively integrate data for Industry 4.0 applications via semantic technologies [19]. [PERSON] et al. (2019) provided an approach based on ontologies to support and foster PdM applications in the manufacturing industry using a combination of semantic technologies and fuzzy clustering [40]. In a later work, [PERSON] et al. (2020) introduced another method: a combination of semantics and data mining to predict failures in industrial machines [41]. They developed a PdM ontology for manufacturing, including a rule-based extension to create a formal representation of the predictive results. One of the latest research studies by [PERSON] et al. (2021) highlighted the importance of cross-functional knowledge management [34]. They also stressed the need for and significance of both process- and product-related information for joint decision-making in maintenance and production and demonstrate the role and importance of ontologies for this purpose in smart factories.
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
###### Abstract  
Chemoselective hydrogenation of aldehydes with heterogeneous catalysts under mild conditions is of great importance but remains a major challenge. Herein, an efficient strategy was developed for low temperature chemoselective hydrogenation of aldehydes with broad substrate scope over a magnetic material supported palladium catalyst (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd). Aldehydes bearing various reducible functional groups readily underwent hydrogenation to give the corresponding primary alcohols with moderate to excellent yield at room temperature in aqueous solutions. The Hammett equation revealed that the hydrogenation of aromatic aldehydes proceeded via an anionic intermediate. Additionally, when the temperature increased to 70 \({}^{\circ}\)C, toluene was obtained by the deoxygenation of benzaldehyde in excellent yield. Furthermore, the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd could be recycled up to six times without loss of activity and metal leaching.  
selective hydrogenation; aldehydes; Pd catalysts; mild conditions +
Footnote †: journal: Applied Physics Letters
# Mental Workload Classification Method Based on EEG Independent Component Features
## 2 Materials and Methods
### Mental Workload Classification Method Based on EEG ICs Features
#### 2.2.4 Principle of Mental Workload Classification Method Based on EEG ICs Features  
The mental workload classification based on EEG ICs is mainly composed of four steps: EEG signal filtering; ICs separation of EEG with ICA; feature extraction of ICs; and mental workload classification. The presented method is shown in Figure 4.  
The above steps are as follows:  
* The 30-channel mixed EEG signals were filtered with 1 Hz high-pass filter and 30 Hz low-pass filter, respectively. The filtered EEG signals were \(X\), \(m\) indicates the number of channels, and here \(m\) was 30.
* The filtered EEG signals were separated by the ICA with EEGLAB in order to obtain the EEG ICs [39]. The input, (\(X_{1}\), \(X_{2}\),..., \(X_{m}\)), represents 30-channel filtered EEG signals. The optimal EEG ICs, (\(S_{1}\), \(S_{2}\),..., \(S_{n}\)), were found by matrix decomposition and optimization. Here \(n\) represents the number of ICs and \(n\) = 30. \(a_{ij}\) (\(l\) = 1,2,..., \(m\); \(j\) = 1,2,..., \(n\)) represents the mixing parameters. The dimension of mixing matrix was 30\(\times\)30. Because the relationship between the measured electrode points and the independent EEG signal source is fixed, each participant had a fixed mixing matrix, A.
* From the data preprocessing in Section 2.1.2, there were 300 EEG segments for each mental workload. The number of EEG segments became 900 for three mental workloads. Each segment had 30 EEG ICs, and each EEG IC can extract four types of power spectra energy, so 120-dimensinal energy features were finally obtained.
* All the energy features of EEG ICs can be obtained for three mental workloads of each participant. The number of segments for each participant was 900 and the dimension of each segment was 120. This was used as the input of the SVM classifier. The optimal parameters of the classifier were found through grid searching method. The cross-validation was performed to realize the three mental workload classifications.
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 3 System Model and Threat Model
### System Model  
Our proposed system model is represented in Figure 1 and has three entities:  
* **Data Owner (DO)**: \(DO\) is an entity that independently manages data as an owner of data and information without TTP. When data are requested from \(DU\), \(DO\) encrypts data and related keywords and stores them in the cloud server, delivering a single aggregate key of a fixed size. \(DO\) encrypts the group identity \(GID\) for delegation of authority of delegate and delegator to define delegation of authority.
* **Data User (DU)**: \(DU\) receives aggregate key when requesting data from the user. \(DU\) generates a trap door to retrieve data from \(CS\) using aggregate key and keyword, receives encrypted data through authentication with \(CS\), and then decrypts to receive data.
* **Cloud Server (CS)**: Since \(CS\) is an honest but curious entity, it may legitimately try to learn all the information from a received message. \(CS\) provides \(DO\) with storage and computing power. In addition, \(CS\) searches data through the trapdoor received from \(DU\) and performs keyword verification.  
\(DO\) creates public parameters to be used in the system and publishes them to entities. Then, \(DO\) creates a bloom filter for keyword verification, encrypts data, and uploads it to \(CS\). \(DU\) sends a data request to \(DO\), and \(DO\) returns a single aggregate key for the data received from \(DU\), an authentication credential for authentication with \(CS\), and a \(GID\) for verifying authorization. The authentication credential is delivered it to \(CS\) at this time. Subsequently, \(DU\) creates a single trapdoor using aggregate and keywords from \(CS\) and requests a search query. After \(CS\) authenticates with \(DU\), \(CS\) searches the data and confirms the keyword using the trap door. After that, \(CS\) generates a data search result and  
Figure 1: Proposed system model.  
proof set for decryption and sends it to \(DU\). \(DU\) uses the bloom filter to decrypt the data after verification. In addition, if a \(DU\) wants to delegate authority to another \(DU\), mutual authentication is performed. If the authentication is valid, \(DU\) can delegate the aggregate key and some of the keywords he/she has.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 4 Simulation and Results: Comprehensive Analysis
### Comparative Analysis of the Simulated EEG-B3V57C2 with the Cases 1, 2, 3  
As depicted in Table 2, with regard to CMRR, the proposed circuit is in alignment with the specification that demands values greater than 90 in the area of interest of the alpha, beta and theta wavebands. The maximum CMRR in the passband of our output signal is 97 dB and 98 dB at 50 Hz and 60 Hz, respectively. It must be stated that this proposed circuit has been designed in order to focus on meeting the requirements of protocols with compound muscle movement. The satisfaction of these requirements necessitates a filtering of the noise artifacts before the first amplification in order to protect the amplifier from reaching a state of saturation by amplifying undesired muscle artifacts that are present in the raw EEG signal. The introduction of filters at the initial stage limits the possibility of increasing the CMRR in any EEG analog front-end. The only technical obligation of the proposed circuit with respect to CMRR is to be higher than 90 dB in the frequencies of interest, so any values higher than this lower limit can be considered acceptable. Thus, even though our CMRR is lower than the ones of the other Case \"1\" [47], Case \"2\" [48] and Case \"3\" [49] (110 dB up to 140 dB), this was a deliberate trade-off. The high CMRR of Case \"1\" [47], Case \"2\" [48] and Case \"3\" [49] is practically useless for the case of muscle movement artifact removal, because the aforementioned circuit has neither been tested for this use case nor designed with the accommodation needs of such protocols in mind. As far as circuit bandwidth is concerned, the proposed circuit design covers all the frequencies from 0.1 Hz to 20 kHz, and the bibliographies state that an EEG signal may appear for performing raw EEG data acquisition and artifact removal. More specifically, the proposed circuit is tested to be more than sufficient to process the EEG signal in the wavebands of particular interest in the zone of 4 Hz to 30 Hz. On the one hand, Cases \"1\" [47] and \"3\" [49] are designed to collect signals with frequencies from the alpha, beta, delta and theta wavebands, and they do so efficiently. From the previous two, Case \"1\" [47] has a greater margin for errors, in terms of the higher cut-off frequency of the low-pass filters. On the other hand, Case \"2\" [48] has such a large bandwidth that it is of no use for the accurate collection of the EEG signal, thus resulting in the collection of unnecessary high frequency noise that would possibly be present in its output signal (the authors refer to higher frequencies in the output of amplifier that fall within its operation bandwidth, but can be categorized from the perspective of EEG signal acquisition). It must be noted that even though Case \"2\" [48] is designed only with an amplification and an inverting stage, all real-world amplifiers function as amplifiers only in a certain bandwidth. Beyond this
###### Abstract  
For high-power magnetic resonance imaging (MRI) radio frequency (RF) combiners operating in the frequency range from 60 MHz to 300 MHz, the primary challenges lie in achieving high-power transmission capability while minimizing the insertion loss (IL), reducing the physical dimensions, and meeting application bandwidth requirements. This paper presents a high-performance RF power combiner based on capacitor-loaded microstrip technology for 3.0T MRI radio frequency power amplifier (RFPA) systems. The proposed combiner features low loss, high integration, and miniaturization, and it comprises multiple branches, each employing microstrip lines and capacitors in a series-parallel arrangement to achieve an impedance transformation of 50 \(\Omega\) to 100 \(\Omega\). Each branch was designed through theoretical analysis and electromagnetic simulations to achieve a line length 30% shorter than \(\lambda/4\), a 6.2 mm line width, and 0.08 dB IL at the 3.0T MRI operation frequency band. A two-way to one-way combiner was further designed using this branch structure to achieve 0.2 dB IL through simulation optimization. A four-way to one-way combiner was then constructed by cascading two-way combiners and optimized via ADS-HFSS software(ADS2014 HFSS19) co-simulation. The fabricated combiner module uses an FR4 substrate and achieves a 0.4 dB insertion loss, \(-\)25 dB return loss, and 25 dB port isolation at 128 MHz \(\pm\) 1 MHz, with compact dimensions (320 \(\times\) 200 \(\times\) 10 mm). To ensure high power capability, thermal analysis was performed to confirm that the module's power-handling capacity exceeded 8 kW, and experimental validation with the 8 kW 3.0T RFPA demonstrated a stable temperature rise of approximately 2 \({}^{\circ}\)C. In this study, the innovative single-branch topology and the RF high-power four-to-one combiner for 3.0T MRI systems were used, resolving the trade-offs between power-handling capability, insertion loss, structural compactness, and operating bandwidth in MRI power combiners. The combiner was successfully integrated into the 3.0T MRI RFPA system, reducing the overall dimensions of the RFPA system and simplifying its installation, thereby enabling high-quality imaging validation. This solution demonstrates the scalable potential of the design for other high-field MRI systems operating in the MHz range (from tens to hundreds of MHz), including in 1.5T and 7.0T MRI systems.  
low-loss power combiner; MRI RFPA; miniaturization; high power; capacitor loading +
Footnote †: journal: Applied Sciences
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 2 Materials and Methods
### Algorithms in Comparison  
In Section 2.3, we exploited the strong power of _CNN_. After that, we compared its performance with some traditional classification approaches, which extracted hand craft features for the classifiers. In this section, three types of hand craft features were consider: color histogram, local binary pattern histogram, and vegetation index. The reasons for feature selection are that for: (1) the color histogram, the color difference of healthy and diseased wheat can be visually distinguished, which may result in the difference of the color histogram; (2) the LBP feature, the disease infection may cause texture variation in UAV imagery, and (3) the vegetation index, it has the potential to detect crop stress [9].
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Stochastic Models
However, incorporating this process into stochastic models poses a notable mathematical challenge. Additionally, stochastic tools, much like screening models, rely on simplified flow conditions, such as a uniform and steady mean flow and permeability characterized by a multi-Gaussian distribution with low variance. Consequently, this approach appears to fall short in accurately describing the intricate hydrogeological formations and modelling the long-term fate of pollutants. But from another perspective, the advantages of stochastic approaches are the following:  
* Dealing with large and small data sets. For large data sets, the law of large numbers and the central limit theorem state that a large number of samples converge to the expected value/mean and that this sample mean tends to the standard normal distribution. This justifies the use of the Gaussian normal distribution and the mean value in the stochastic models. Depending on the specific modelling approach, the type of data, and the research question, stochastic models can be applied to small data sets when the researcher is faced with the greatest uncertainty [438, 439, 440, 441]. * Dealing with multiple uncertainties at different levels requires a complex approach and the involvement of experts from the fields of statistics, computer science, and domain-specific knowledge. This is an emerging and challenging field of research [442, 443, 445]. * The probabilistic approach offers a range of equally probable solutions instead of a single (approximate) solution, and can provide better information for risk managers and policy makers [444, 445, 446, 428]. * Dealing with the plume is possible, but can be more difficult to calculate using travel time and breakthrough curves in a given plane at a given distance from the source. However, with increasing distance, the travel time moments become less sensitive to the variability of the parameters responsible for transport, and can be expressed by simple statistical moments such as mean, variance, and correlation function [447, 448, 449]. * Rapid progress is being made through the general development of computational methods, and this is likely to become increasingly important, supported by the recognition of population inference from big data and data validation [450, 451]. * Simplifications are justified in stochastic models, as local heterogeneities have only limited long-range effects. Furthermore, simplifications can make the simulations more manageable and still capture the essential behaviour of the system [452, 453, 454]. * Conceptualisation: Since many stochastic models have a solid mathematical basis, it is common to start modelling on the basis of detailed mathematical and theoretical knowledge and then transfer the results of the conceptual work into one or more mathematical models. For deterministic models with GUI, it is easier to forget the initial modelling phase [455].
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 1 Introduction
The S-LMB-GOM filter applying the proposed method can significantly reduce the probability of violating the separable likelihood assumption and drastically reduce the computational complexity without loss of performance compared with the G-LMB-GOM filter. The rest of the paper is structured as follows. We review the basic definitions of LMB RFS and introduce the generic observation model for radar sensor in Section 2. In Section 3, the S-LMB-GOM filter is introduced, and then a one-to-one correspondence method and an approximate method to calculate point spread function are proposed. Section 4 is the simulation results of radar target detection and tracking under nonlinear motion model. The conclusion is given in Section 5.
## 3 Materials and Methods
### Eigenvector Filter Identification and Explanation  
The eigenvectors were created applying a maximum distance connectivity estimation (A number of connectivity criterion algorithms were investigated: the distance criterion (0 < d > 1), the minimum spanning tree, relative neighbourhood, Gabriel criterion, and Delaunay triangulation connections to determine the sampling units), with the spatial filter selection determined by a pre-selection criteria (Threshold set at \(p\) < 0.05), as the number of filters appointed tends to increase with both level of linear regression residual spatial autocorrelation and the number of areal units. The spatial filters are subsequently examined with the extraction of the filters to be utilised in the regression modelling undertaken using a filter selection criteria with minimisation of the residuals is achieved based on a local Moran's \(I\) statistic. Overall, 301 (Note that only six spatial filters are demonstrated) spatial eigenvectors filters were determined with the filter selection process applying the Akaike Information Criterion corrected (AICc) and \(R^{2}\) improvement employed to retain those spatial filters where it reduced the AICc statistic. This step therefore minimises the residual short-distance spatial autocorrelation and reduces the level of residual autocorrelation, ensuring model optimality and model stability whilst further encompassing the assessment of each spatial filters spatial correlogram and the variance of the log-price estimation.  
This produced 62 spatial filters to be included as independent predictors within the modelling to mitigate spatial autocorrelation and error bias, with a filters showing a coefficient of determination of 47.7 percent (AICc: 78485.929; \(p\) < 0.001). Further inspection of Figure 3 shows a sample of the extracted spatial filters. Notably, each filter extracted presents a detailed representation of the spatial patterns which can have a different degree of spatial structure, smoothness and geographically varying relationship with house prices. For example, spatial filters one and two capture the initial pronounced structure of market clustering of the eigenvectors which tend to correlate with the underpinning high/low price clusters observed in Figure 1. Notably the spatial structure becomes more 'localised' when displaying the filters (such as Filters 6 and 12) with smaller eigenvalues culminating in more localized parameter surfaces (for example: Filters 25 and 78) given the reduced truncation distances.
## 3 Experimental Results and Discussion
### Engineering Problems
#### 3.7.3. Tersoff Potential Function Minimization Problem (TPF)
Similar to the Lennard-Jones potential problem, the first decision variable due to the second atom, i.e., \(x_{1}\in[0,4]\), then the second and third variables are such that \(x_{2}\in[0,4]\) and \(x_{3}\in[0,\pi]\). The coordinates \(x_{i}\) for any other atom is taken to be bound in the range; \(\left[-4-\frac{1}{4}\left[\frac{i-4}{3}\right],4+\frac{1}{4}\frac{i-4}{3} \right]\right]\). Table 11 of the test results for the TPF demonstrates that GSLEO-AEFA outperformed other optimizers and was able to offer a significantly superior solution. Additionally, it has performed effectively as engineering issues have become more constrained.