esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)
## 3 Results
### Case Study 2: Relative Feature Importance of the Real Dataset
#### 3.2.1 ANN Training Using the Real Dataset  
During the ANN model training process, the real dataset with 429 data points was randomly divided into two subsets: 70% of the data (287 data points) for training the model and the remaining 30% (142 data points) for testing the model's accuracy.  
As with the synthetic dataset, the hidden layer neuron number setting was also determined through comparing the model errors given by the five optimization methods mentioned above (including Bayesian optimization method, AIC method, BIC method, Hebb's rule, and the OBD rule) to minimize the model error and maximize the computational efficiency. Figure 11 presents the optimal neuron settings for the hidden layer as determined by each method, along with their corresponding model errors in test data over 70 modeling cycles. The results showed that Bayesian optimization achieved the lowest model errors with two hidden layers, each containing 103 neurons. The OBD rule produced the second-lowest model, with one hidden layer and 34 neurons. The error difference between these two methods was no more than 0.0191, considering their 95% confidence intervals (0.101 \(\pm\) 0.0048 and 0.091 \(\pm\) 0.0043). This indicates that the more complex configuration suggested by Bayesian optimization was not necessary. In contrast, the simple configuration of 34 provides nearly the same accuracy but is significantly more efficient in terms of computational resources, making it the optimal choice for balancing model prediction accuracy and computational efficiency. Consequently, the neuron configuration proposed by the OBD rule was applied throughout the study for this dataset. Namely, the ANN model developed in this real dataset includes a single hidden layer with 34 neurons.  
The number of epochs was determined based on the model loss (error)-epoch plot (Figure 12), which showed that model loss (error) reduction plateaued after 40 epochs. To balance high prediction accuracy with computational efficiency while achieving a conservative result, the number of epochs was set to 50.  
Figure 10: Theoretical BCS feature importance calculated using the improved McKee formula.  
The feature importance of 10 modeling cycles was averaged to achieve a reliable average feature importance.
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 4 Methods for Volumetric Assessment
### Computed Tomography (CT)  
CT is a widely used imaging technique that has found applications in the volumetric assessment of upper-limb lymphhedema. This method utilizes X-rays and advanced computer algorithms to create detailed cross-sectional images of the limb, providing valuable information about tissue volumes and morphology [67; 68].  
CT offers several advantages in the volumetric assessment of upper-limb lymphedema. It provides high-resolution images with excellent tissue contrast, enabling precise delineation of anatomical structures and accurate measurement of tissue volumes [69]. This capability allows for the detailed evaluation of edematous tissue, muscle, and fat distribution, aiding in assessing lymphedema severity and response to treatment [67].  
Moreover, CT can provide valuable insights into the presence of other coexisting conditions or complications, such as fibrosis or lymphatic vessel abnormalities, which may impact the management of lymphedema. The ability to visualize the limb in three dimensions enhances the understanding of anatomical relationships and facilitates treatment planning [70; 71].  
However, it is important to consider certain considerations when using CT for volumetric assessment. In particular, CT scans involve exposure to ionizing radiation in a wide area, and precautions should be taken to ensure appropriate radiation doses, especially when repeated scans are necessary for monitoring purposes [68]. Moreover, the availability and cost of CT scanners may also limit their widespread use, particularly in resource-limited settings [72].  
Altogether, these findings underline the utility of CT in assessing limb volume and tissue composition in lymphedema. Despite the ability to provide detailed anatomical information, excellent tissue contrast, and accurate volumetric measurements, different barriers affect its comprehensive integration into the routine rehabilitation management of upper-limb lymphedema.
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 3 Sharing of Optical Access Networks
### Time Division Multiplexing Passive Optical Network (TDM-PON)
The Slice Scheduler is an interface located between the network operators and the PON transmission convergence layer to enable several operators to control their share of bandwidth resources in a TDM manner. The second stage is the frame scheduling stage, which lets each operator employ their customized DBA to serve their ONTs. As mentioned by the authors, there is a trade-off between the isolation and efficiency of bandwidth allocation. A completely isolated scheduling--referred to as static bandwidth resource sharing (static BRS)--would waste the excess bandwidth of lightly loaded operators. The authors solve the issue by also proposing a dynamic BRS. This achieves better PON utilization but at the expense of less isolation between the tenants (e.g., in terms of latency dependence). In addition, separating operators by assigning different frames increases the latency seen by each operator, which also becomes dependent on the number of operators sharing the PON. The concept of DBA virtualization was introduced in [38]. Figure 3, shows its principle (drawn on the right-hand side), compared to the default physical DBA (left-hand side). The objective of virtual DBAs is to allow VNOs to implement their own version of DBA, independently from the infrastructure provider. The procedure of the bandwidth allocation starts with the Merging Engine forwarding incoming buffer occupancy messages from the ONTs to each virtual DBA (vDBA). Next, each vDBA issues a (virtual) bandwidth map for their own ONTs, using customized DBA algorithms to fulfil their requirements. All virtual bandwidth maps are then collected by the Merging Engine, which merges them into a single physical bandwidth map, which is broadcast to all ONTs. The reported results show that this approach can enable true multi-tenancy, while not imposing any additional delay to the PON scheduling, thus achieving adequate isolation across operators. The study of PON sharing is in its early stages, and more research is currently being carried out on this topic. The recent progress in NFV architectures and frameworks represent an excellent opportunity to boost the virtualization and slicing ability for PONs, which are essential for improving isolation. Along with NFV, SDN can provide a suitable framework for the orchestration of the virtualized functions of the PON.
# A Method for Estimating Source Depth Based on the Adjacent Mode Group Acoustic Pressure Field
## 1 Introduction
In MFP, the underwater acoustic channel needs to be modeled; whether the model can accurately describe the sound field will directly determine the accuracy of the location result. Recently, scholars have put forward more new methods and theories. In 2020, [PERSON] et al. [15] proposed a method based on a convolution neural network (CNN) and conventional beamforming (CBF), which overcame the limitation of real sound-speed profiles and source bandwidths on the accuracy of multispectral transformation for depth estimation (MSTDE). Although a neural network can estimate the source depth, it requires a large number of data sets and training of these data sets. The randomness of the distribution of ambient noise in the frequency domain also greatly affects the output results. In 2022, [PERSON] et al. [16] used different cost functions and proposed three methods to estimate the source depth by matching the normalized cross-spectral density, temporal envelope and time delay of the smooth-averaged cross-correlation function for the case of two horizontally separated hydrophones or arrays. However, the average absolute error in the source depth of the proposed method was still large and the proposed method was more suitable for the case of a low-frequency band and an environment with large negative sound speed gradient. In 2023, [PERSON] et al. [17] proposed an efficient two-step depth estimation scheme, using narrowband and broadband constructive and deconstructive striation patterns due to interference between the direct and sea-surface-reflected arrivals at a horizontal linear array at the bottom of deep water. Due to the limited source range spans, the distortion of the beam intensity striations and range estimation errors, the performance of the algorithm will be degraded. On the basis of previous research, this study simulated and analyzed how to determine the AMG acoustic pressure field and related it to the depth of the source. We studied the method of estimating the depth of the source based on the AMG acoustic pressure field and ultimately applied it to the experimental data. Without the need for accurate modeling of underwater acoustic channels, the proposed method achieved higher localization accuracy. In addition, the proposed method has low sensitivity to environmental mismatch, and vertical array data collection is closer to the specific experiment, which makes it applicable in more seas and increases flexibility. The main content of this paper is as follows: Section 2 derives AMG acoustic pressure field, obtains mode information through singular value decomposition and explains the design idea of the algorithm. Section 3 analyzes the influence of different parameters on the simulated annealing algorithm. Section 4 conducts experiments using SACLANT data and obtains experimental results. In addition, the advantages and disadvantages of the proposed algorithm are analyzed, and the proposed algorithm is compared with MFP. Finally, Section 5 concludes this paper.
# Conjugate Heat Transfer Analysis and Heat Dissipation Design of Nucleic Acid Detector Instrument
## 1 Introduction  
Quantitative real-time polymerase chain reaction (RT-PCR) is a vital technique in the diagnostic industry as it facilitates rapid detection, the importance of which was emphasized by the COVID-19 outbreak in December 2019. In particular, the use of PCR technology in combination with nucleic acid detectors has attracted considerable attention from researchers [1, 2, 3]. These instruments provide the specific temperatures required for each stage of the PCR process: (1) DNA separation at temperatures greater than 363.15 K, (2) primer annealing at 323.15-348.15 K, and (3) optimal extension at 345.15-351.15 K [4, 5]. The precise temperature depends on the reagent system. The stages are repeated until the DNA is sufficiently amplified for fluorescence detection.  
However, the internal temperature of the instrument increases as the operation time increases, which increases the failure rate of the components; therefore, heat dissipation is an important consideration in the design  
design of PCR instruments. For example, [PERSON] et al. [14] simulated the 3D heat transfer of a continuous-flow PCR model and used ANSYS CFX to investigate the effect of the fluids on the temperature distribution in the canals. [PERSON] et al. [15] used a numerical simulation of a fully automated PCR system to improve the structure of the operation unit and reduce the operation time. Subsequently, they optimized the time consumption by applying a mixed convective heat transfer mechanism. [PERSON] et al. [16] developed an innovative design in which multilayer microchannels were built in a single tube; thus, droplet generation and PCR could be accomplished in the same tube. Their experiments showed that this design had high heating and cooling rates. [PERSON] et al. [17] analyzed the pedestal heat transfer of a PCR device and proposed a mathematical model to describe the pedestal temperature field, which was simplified into a simple 2D heat transfer model. Thus, they analyzed the effects of various parameters on the temperature field of the pedestal during uneven heating. Finally, [PERSON] et al. [18] designed a well-shaped sample block with a volume of 50 \(\upmu\)L and demonstrated that the fluid sample exhibited a higher temperature response rate and greater uniformity. This pedestal can be manufactured without any special process, paving the way for low-cost PCR.  
However, there has been little research on the design of nucleic acid detectors concerning heat dissipation. Therefore, this study aimed to design an instrument ventilation scheme that could provide stable and safe ambient temperatures. This was achieved by using the finite-volume method to simulate the temperature distribution and flow, validating the simulation using experimental data, and using the simulation to design a new instrument casing. Finally, the experimental results prove that the designed heat-dissipation scheme demonstrates high heat-dissipation capability.
## 4 Experimental Results
### Zero-Shot Retrieval Results  
For the sake of fairness, we follow the zero-shot retrieval protocol proposed in SDGZSL [10]. First, the backbone network (ResNet-101) extracts the visual features from all unseen images. Then, the disentanglement encoder extracts semantic-consistent features in the unseen visual features. Third, the semantic decoder further extracts the semantic-consistent features in the unseen visual features. Finally, these two semantic-consistent features are transformed into enhanced semantic-consistent features and the centroid point is computed as a retrieval query, which is further used to retrieve the nearest samples. To evaluate the performance on the retrieved samples, the mean average precision (mAP) score is adopted. In Figure 3, we compare our propose ESTD method with CVAE and SDGZSL when retrieving 100%, 50%, and 25% of the images from all unseen classes on APY, AwA2, CUB, and FLO. It can be seen that the proposed method can significantly boost the retrieval performance among all settings, which can also demonstrate the effectiveness of the proposes method from the retrieval perspective.
# Fast Minimum Error Entropy for Linear Regression
## 5 Conclusions  
This study introduces a rapid minimum error entropy algorithm tailored for linear regression. Several notable conclusions emerge from our investigation. Firstly, the proposed FMEE exhibits significantly faster convergence, requiring fewer iterations compared to MEE. Secondly, while FMEE does not surpass traditional MEE in performance for Gaussian noise and yields comparable results for non-Gaussian noise as measured by mean squared error, its computational efficiency is strikingly superior, consuming less than 1% of the time required by MEE. Thirdly, despite being derived under the assumption of noise proximity to Gaussian distribution, FMEE demonstrates efficacy in handling non-Gaussian, heavy-tailed noise distributions. Fourthly, owing to its exceptional time efficiency, FMEE holds promise for real-time applications in scenarios featuring non-Gaussian noise. Moving forward, further analysis of FMEE's convergence conditions and exploration of noise approximation methods for arbitrary distributions represent avenues for future research.  
Conceptualization, Q.L. and X.L.; methodology, Q.L.; software, W.C.; validation, Q.L., X.L. and W.C.; formal analysis, Y.W.; investigation, X.L.; resources, Q.L.; data curation, Y.W.; writing--original draft preparation, Q.G.; writing--review and editing, H.C.; visualization, Q.G.; supervision, H.C.; project administration, W.C.; funding acquisition, Q.L. and H.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the State Grid Information and Telecommunication Group scientific and technological innovation projects \"Research on Power Digital Space Technology System and Key Technologies\" (Grant no: SGIT000 XXMJS2310456).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the special significance of the electrical power system used in the experimental process, which involve privacy in some research fields, it is not convenient to fully disclose their data.  
Authors ([PERSON], [PERSON], [PERSON], and [PERSON]) were employed by the State Grid Information & Telecommunication Group 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.
## 4 Discussion  
### Benefits and Trade-Offs  
As mentioned in Section 1, the quality of CPES testing platforms can be assessed regarding different aspects and measures. Therefore, each platform has a different design goal and fills a different niche. The mosaik co-simulation framework is designed to include a broad range of simulation models from different domains (flexibility). Furthermore, the system should be usable by CPES researchers that are not necessarily co-simulation experts (usability).  
Usability of co-simulation is partly achieved here through a small number of software dependencies in the framework since this leads to an easier deployment process. It has to be noted, however, that the upcoming concept of simulation as a service (SaaS) presents an alternative solution to this challenge. In SaaS solutions, simulation software is executed on a cloud platform and is only accessible to researchers via a user interface. This has the benefit of eliminating the deployment process and additionally allows the utilization of more powerful, dedicated computation hardware. In the future, mosaik will likely also be provided in a SaaS context, but for now a focus is put on local deployment to allow for shorter testing an debug cycles of new co-simulation setups.  
In addition to the slim code base, the centralized scheduling concept of mosaik is supporting its usability. In a centralized setup, all data exchanged is treated in the same way so that no additional configuration is needed. More distributed systems, like those based on HLA, typically require the user to provide more specification on how one simulator interacts with the others. This obviously has the benefit of supporting a wider range of application cases and allows some performance boosts, but users may at times be overwhelmed by the number of options. mosaik, thus, displays a trade-off between usability on the one side and higher performance and flexibility on the other side. Practical experience has shown, however, that the scheduling capabilities provided by mosaik are sufficient for a wide range of application cases (see Section 4.2).  
All in all, it is important to note that the structure of a co-simulation framework alone cannot satisfy all the usability or performance needs of CPES researchers. Therefore, an extended environment with tools and processes is suggested here that adds functionalities, e.g., in the selection of simulation components or in the development of MAS-based CPES solutions. Further planned extensions are discussed in Section 4.3.
# Comprehensive Evaluation of a Sparse Dataset, Assessment and Selection of Competing Models
## 5 Results
### Hurdle Model Results  
As the hurdle model with negative binomial was identified as the best identified model, this section briefly discusses the results obtained from Table 2. The results of the hurdle model include two components. The first layer includes the impact of various predictors on the frequency of EPDO. First, it was found that the impact of barrier height and shoulder width on EPDO should not be considered separately, but rather should be considered as interactive terms that impact crash severity. Non-compliance with the speed limit and being unrestrained were two of the factors that were found to increase the risk of EPDO crashes, with the impact of the restraint condition being much higher than the impact of the other variable, 2.482 versus 0.89.  
Moving to the next layer of the hurdle model, as a crash did not occur in this section, only predictors related to the road and barrier geometric characteristics were considered. Barrier length and traffic are two factors that were considered to account for exposure. The coefficient estimates with positive signs indicated the contributory impacts of those predictors in predicting a crash compared with no crash. For instance, as can be expected, higher barrier length and higher traffic (AADT) increase the odds of having a crash. Additionally, the interaction between shoulder width and barrier height were found to  
Figure 2: Rootograms for the negative binomial and Poisson hurdle models.  
impact the odds of having a crash. However, the differences across magnitudes and signs between these two layers should be noted.
## 5 Overview of 5G-Enabling Technologies
### Integration of Non-Terrestrial Networks (NTNs) with 5G  
This section will discuss the NTN-5G integration overview, standardization, architectures, and use cases.
# Relationships Among Origin, Genotype, and Oenological Traits of _Brettanomyces_ Yeasts
## 4 Materials and Methods
### Molecular Identification and Botyping  
_Brettanomyces_ strains were identified by molecular analysis through sequence analysis of the 26S-D1/D2 rDNA region, which identified species-specific nucleotide sequences [45].  
The DNA extracted from the _B. bruxellensis_ and _B. anomalous_ strains underwent biotyping using the RAPD-PCR primers M13 (5'-GAG GGT GGCGGT TCT-3'); M14 (5'-GAGGGT GGG GCC GTT-3'); OPC20 (5'-ACTTCGCCAC-3'); and OPK03 (5'-CCAGCTTAGG-3') and the minisatellite primers PIR1 (forward: 5'-GCCACTACTGCTTCCTCCA-3'); reverse: 5'-TG GACCAACCAGCAGCATAG-3') and PIR3 (forward: 5'-TCCT CCGTCCATCTAAG-3'); reverse: 5'-GGCACTGAGAACA ATGTGC-3') [43; 46]. For molecular typing, the strains from the University of Perugia collection DBVPG 6706 and 6710 were also included. The conditions for the amplification protocols were the ones described by [PERSON] et al. [16] and [PERSON] et al. [46] (reported in Figure S2 Supplementary Materials). Different genetic profiles using the RAPD-PCR and minisatellite primers described above define the biotype (strain). Samples of 15 \(\mu\)L of PCR products were loaded onto 2.5% (\(w/v\)) agarose gels, and the electrophoresis was carried out at 70 V for 2 h in 0.5\(\times\) TBE buffer. The DNA bands on the gel were visualized by staining with ethidium bromide, and acquisition of the images was performed under a UV lamp (UV source GelDoc 1000; Bio-Rad, Milan, Italy). The length of the PCR products was estimated by comparing them with a 100 bp marker DNA standards (GeneRuler 100 bp DNA Ladder; AB Fermentas Inc., Waltham, MD, USA).