esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Three-Phase Inverters
### Grid Supporting Inverters
 \tag{154}\]  
and based on the DC power flow approximation, the active power is \(P=\frac{|E|^{2}}{\omega_{g}L}\delta\). The resulting dynamic model is  
\[ \begin{split}\delta&=\theta-\omega_{g}t,\quad P=\frac{|E|^{2}}{ \omega_{g}L}\delta,\\ \frac{\mathrm{d}}{\mathrm{d}t}\theta&=\omega^{*}= \omega_{s}+3D\left(P_{ref}-P\right),\end{split}  \tag{155}\]  
and several substitutions yield the differential equation  
\[\frac{\mathrm{d}}{\mathrm{d}t}\delta=\omega_{s}-\omega_{g}+3D\left(P_{ref}- \frac{|E|^{2}}{\omega_{g}L}\delta\right). \tag{156}\]  
Due to the negative feedback, this differential equation is stable for any \(D>0\). The steady state is computed by solving \(\frac{\mathrm{d}}{\mathrm{d}t}\delta=0\), which yields  
\[\delta=\frac{\omega_{g}L}{|E|^{2}}\left(P_{ref}+\frac{\omega_{s}-\omega_{g}}{3 D}\right). \tag{157}\]  
In addition, according to (155),  
\[\delta=\theta-\omega_{g}t\quad\Rightarrow\quad\frac{\mathrm{d}}{\mathrm{d}t }\delta=\omega^{*}-\omega_{g}=0\quad\Rightarrow\quad\omega^{*}=\omega_{g}. \tag{158}\]  
So in the steady state \(\omega^{*}=\omega_{g}\), and the inverter is synchronized to the grid. Note that the system is not stable if \(D=0\), since in this case the inverter operates as a grid forming inverter, and cannot be connected in parallel to an infinite bus.
## 2 Materials and Methods
### In Silico Typing and Characterization  
Genome assembly statistics were assessed with QUAST v5.2.0 [21]. Species identification was performed with the Kraken2 v2.1.2 taxonomic classifier [22] and the Type Strain Genome Server (TYGS) [23]. The genomes were annotated using PROKKA v1.14.5 [24] while further functional annotation and subsystem analysis of predicted open reading frames (ORFs) was done via the COG database (March 2022 version) [25]. Moreover, the presence of clustered regularly interspaced short palindromic repeats (CRISPRs) was evaluated with CRISPRCasFinder v4.2.20 [26], while integrated prophages were identified with PHASTER (December 2020 version) [27]. Abricate v1.0.0 [28] was used to determine the presence of resistance genes (RGs), virulence genes (VGs) and mobile genetic elements (MGEs) using the Resfinder (May 2022 version) [29], VFDB (August 2022 version) [30] and MobileElementFinder (June 2020 version) [31] databases, respectively. Additionally, the presence of bacteriocins was determined with BAGEL4 v2.0-115 [32], whereas plasmids were detected, reconstructed, and genotyped with MOB-suite v.3.1.0 [33]. Lastly, we used the PathogenFinder v.1.1 [34] classifier to predict the pathogenicity of the isolates in our collection.
# Optimization Method for Stiffened-Plate Layout in Box Structures Based on Load Paths
## 5 Conclusions  
Aiming at the difficult problem of designing the layout of the internal stiffened plate of large box structures, a method for optimizing the layout of the internal stiffened plate of large box structures based on force-flow guidance is proposed. Initially, an envelope is constructed for analysis, and functional section decomposition is carried out based on load distribution to achieve dimension reduction, transforming the 3D problem into a  
Figure 16: The natural frequency of optimized gantry. (**a**) First order. (**b**) Second order. (**c**) Third order. (**d**) Fourth order. (**e**) Fifth order. (**f**) Sixth order.  
2D solution. Then, in conjunction with the load path method, load path visualization operations are conducted to obtain the distribution of load paths across various functional sections. Finally, through the analysis of load paths, ineffective paths are eliminated, and the optimized stiffener layout is obtained by distributing materials along the remaining paths. Taking the gantry of a certain aluminum ingot composite processing unit as an example for validation, the simulation results indicate that with a reduction of 14.9% in maximum stress and 32.95% in displacement, the overall weight of the structure has decreased by 14.4%. While the natural frequency of the structure is improved to reduce the possibility of resonance. This demonstrates that the method proposed in this paper exhibits good practicality and effectiveness for the stiffener layout design of box structures.  
The \"outer walls + inner stiffened plates\" structure is widely present in fields such as aircraft, spacecraft, automotive, marine, and architecture. The method proposed in this paper can significantly reduce structural mass while ensuring structural performance. This effectively achieves cost reduction and revenue enhancement and demonstrates considerable practical value. On the other hand, the research methodology of this paper can be further explored to develop optimization models based on the force path method tailored to different target requirements. This offers a new perspective for optimizing problems related to this type of structure, holding certain research significance.  
Data curation, C.W.; Formal analysis, C.W.; Funding acquisition, F.W.; Methodology, H.Z. and H.L.; Supervision, F.W.; Validation, H.L.; Writing--original draft, H.Z.; Writing--review and editing, Z.W. All authors have read and agreed to the published version of the manuscript.  
This study is funded by the National Nature Science Foundation of China (92266203), Key projects of the Shijiazhuang basic research program (241791077A), and the Central Guide Local Science and Technology Development Fund Project of Hebei Province (246Z1022G).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
###### Abstract  
Polar codes are closer to the Shannon limit with lower complexity in coding and decoding. As traditional decoding techniques suffer from high latency and low throughput, with the development of deep learning technology, some deep learning-based decoding methods have been proposed to solve these problems. Usually, the deep neural network is treated as a black box and learns to map the polar codes with noise to the original information code directly. In fact, it is difficult for the network to distinguish between valid and interfering information, which leads to limited BER performance. In this paper, a deep residual network based on information refinement (DIR-NET) is proposed for decoding polar-coded short packets. The proposed method works to fully distinguish the effective and interference information in the codewords, thus obtaining a lower bit error rate. To achieve this goal, we design a two-stage decoding network, including a denoising subnetwork and decoding subnetwork. This structure can further improve the accuracy of the decoding method. Furthermore, we construct the whole network solely on the basis of the attention mechanism. It has a stronger information extraction ability than the traditional neural network structure. Benefiting from cascaded attention modules, information can be filtered and refined step-by-step, thus obtaining a low bit error rate. The simulation results show that DIR-Net outperforms existing decoding methods in terms of BER performance under both AWGN channels and flat fading channels.  
Keywords:polar codes; deep learning; DIR-Net; denoising subnetwork; decoding subnetwork; attention mechanism +
Footnote †: journal:
## 4 Discussion
### Conclusion and Future Perspectives
In addition to EEG, which provides high temporal resolution for the study of electrical signals in the brain, the use of functional near-infrared spectroscopy (fNIRS) may be useful for monitoring changes in cortical blood oxygenation in a non-invasive manner and in more natural contexts to promote a better understanding of decision making and related strategies. Alongside this neuroimaging tool, moreover, it would also be interesting to investigate any emotional processes related to different decision-making strategies, adopting for example a biofeedback technique. Finally, future studies could also integrate self-report data on preferences for decision-making strategies, such as the General Decision-Making Style Questionnaire (GDMS; [49]), or on the role of individual differences or personality traits, to understand possible effects on decision-making strategies. Additionally, longitudinal studies could examine how exposure to different decision-making contexts over time shapes individuals' strategic preferences. A.D.: formal analysis, resources, software, writing--original draft, and writing--review and editing. C.A. : data curation, formal analysis, investigation, resources, software, writing--original draft, writing--review and editing, and visualization. M.B. : conceptualization, funding acquisition, methodology, software, validation, resources, writing--review and editing, supervision, and project administration. All authors have read and agreed to the published version of the manuscript. Universita Cattolica del Sacro Cuore contributed to the funding of this research project and its publication (grant D3.2 2023: \"Comunicare la scienza. Mediazione e mediatori del sapere scientifico nella societa complessa.\"--\"Comunicating science. Mediation and mediators of scientific knowledge in complex society\"). This study involving human participants was conducted in accordance with the Declaration of Helsinki (2013) and reviewed and approved by the Ethics Committee of the Catholic University of the Sacred Heart, Milan, Italy (approval code: 125/24--Valutare il Decision-Making: consapevolezza e metacognizione decisionale; approval date: 23 July 2024). The participants provided their written informed consent to participate in this study. The data presented in this study are available on request from the corresponding author due to ethical reasons for sensitive personal data protection (requests will be evaluated according to the GDPR-Reg. UE 2016/679 and its ethical guidelines). The authors declare no conflicts of interest.
# Comparative Analysis of the Methods for Fiber Bragg Structures Spectrum Modeling
## 3 Results and Discussion  
### Modeling Parameters of Homogeneous FBG  
In order to verify the implementation of the layer sweep (LS) and transfer matrix (TM) methods, a homogeneous non-apodized FBG was modeled and the results were compared with the spectrum obtained using specialized software package Optiwave OptiGrating 4.2.2. The spectra simulated via the OptiGrating were taken as reference, since it is a commercial software widely used in industry.  
To compare the simulation results, we chose a homogeneous non-apodized FBG \(6\times 10^{-3}\) m long with a Bragg wavelength of 1550 nm, a refractive index \(n_{0}=1.4682\), and an induced refractive index \(\Delta n=2\times 10^{-4}\). The chosen Bragg wavelength corresponds to the commonly used C-band wavelength range [20]. The refractive index of fiber core \(n_{0}\) is chosen according to the specifications of the common optical fiber SMF-28 at 1550 nm [21], while the induced refractive index lies within the typical range for the FBG [12; 22; 23]. The effective refractive index of the FBG model is defined as the average value of the refractive index over the fiber section containing the grating:  
\[n_{\mathrm{eff}}=n_{0}+\frac{\Delta n}{2}\, \tag{25}\]  
which gives the average value of the effective refractive index of the FBG equal to \(n_{\mathrm{eff}}\approx 1.4683\). The FBG period is determined by the Wulff-Bragg relation:  
\[\Lambda=\frac{\lambda_{\mathrm{Br}}}{2\cdot n_{\mathrm{eff}}}=\frac{\lambda_{ \mathrm{Br}}}{2\cdot(n_{0}+\Delta n/2)}\, \tag{26}\]  
which is equal to \(\Lambda=5.278213\times 10^{-7}\) m. The defined FBG parameters are listed in Table 1.
# Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection
###### Abstract  
Sugarcane is a vital crop, accounting for approximately 75% of the global sugar production. Ensuring its health through the early detection and classification of diseases is essential in maximizing crop yields and productivity. While recent deep learning advancements, such as Vision Transformers, have shown promise in sugarcane disease classification, these methods often rely on resource-intensive models, limiting their practical applicability. This study introduces a novel stacking-based ensemble framework that combines embeddings from multiple state-of-the-art deep learning methods. It offers a lightweight and accurate approach for sugarcane disease classification. Leveraging the publicly available sugarcane leaf dataset, which includes 7134 high-resolution images across 11 classes (nine diseases, healthy leaves, and dried leaves), the proposed framework integrates embeddings from InceptionV3, SqueezeNet, and DeepLoc models with stacked ensemble classifiers. This approach addresses the challenges posed by imbalanced datasets and significantly enhances the classification performance. In binary classification, the model accuracy is 98.89% and the weighted F1-score is 98.92%, while the multi-classification approach attains accuracy of 95.64% and a weighted F1-score of 95.62%. The stacking-based framework is superior to Transformer models, reducing the training time by 75% and demonstrating superior generalization across diverse and imbalanced classes. These findings directly contribute to the sustainability goals of zero hunger and responsible consumption and production by improving agricultural productivity and promoting resource-efficient farming practices.  
sugarcane disease classification; stacking ensemble methods; convolutional neural networks; sustainable development goals; imbalanced datasets +
Footnote †: journal: Applied sciences  
1
## 2 Background and Motivation
The app workload involves a large amount of data; hence, reducing data transfers via cellular links is beneficial, due to latency, data access fees, and energy usage. During the drive, [PERSON]'s smartphone suffers intermittent data connectivity, and so a considerable amount of work is being supported by the local collective resource, instead of the other servers. How much work is performed by the two smartphones (the local collective resource) and the conventional edge, fog, and cloud servers can depend on their availability and latency, as well as the task scheduling algorithm in [PERSON]'s collaborative resource sharing middleware. When the cellular data connectivity drops, this can increase the latency of conventional edge, fog, and cloud servers, impacting their performance. In this case, the two smartphones should be able to pick up most of the workload. When [PERSON] is at the medical clinic (rightmost situation in Figure 2), her smartphone forms a local collective resource with her doctors tablet via D2D and with the clinic edge server via WiFi. She further shares her work with fog and cloud servers because the doctor's diagnostic process requires the app to provide faster performance. As [PERSON] uses the clinic's high-speed WiFi to connect to the remote servers, there is no concern of intermittent data  
Figure 2: Scenario showing how different contexts require collaboration amongst resource nodes at edge, fog, and cloud tiers. connectivity or data access fees. In this situation, the fog and cloud servers should be able to pick up most of the workload due to the availability of high-speed connectivity. In the three aforementioned contexts described in the scenarios in Figure 2, [PERSON] shared her work with resource nodes at the device level, edge, fog, and cloud levels in various degrees. The three situations had different constraints in terms of connectivity, trust and security, and amount of available resources, yet the application QoS requirements remained the same. Hence, to continue to meet the QoS requirements, the collaboration across edge, fog, and cloud needs to adapt dynamically depending on the context.
# Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment
## 1 Introduction
The core contribution of this paper can be summarized as follows:  
* A scalable and low-delay data delivery scheme is investigated for microservices-based IoT applications. * An Edge-Cloud Pub/Sub brokers model is proposed to support collaboration and data exchange among microservices on large-scale IoT applications. * A method is devised to construct a broker-based utility matrix with constraints (for example, event channel similarities, nearby geolocations, and upper limit node degrees) to cluster the pub/sub brokers efficiently. Moreover, in the proposed model the edge brokers are grouped together according to the similarities in their event channels in order to reduce data delivery latency. * Finally, extensive experiments were conducted to verify the performance of the proposed scheme with respect to latency, relay traffic, and scalability. The remainder of this paper is structured as follows. Section 2 discusses previous research on pub/sub systems to facilitate microservice-based IoT applications. The proposed system model and the edge broker clustering procedure are described in Section 3. Section 4 presents the comprehensive simulation results, and the article is summarized in Section 5.
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 3 Roadside and Cloud Architecture  
A vehicular communications framework was developed as part of the design and implementation of mobility testbeds in Portugal, namely those deployed under the scope of STEROID research project [5] and A-to-Be's participation in the pan-European C-Roads initiative [22]. This framework consists of a roadside and cloud architecture to support C-ITS services along some of the nation's motorways. The main objective of this architecture is to support road safety use cases in networks of vehicles with infrastructure assistance. Figure 1 presents a representation of the developed and implemented architecture.  
The roadside infrastructure consists of ITS-G5 RSUs connected to the backhauling network by fiber optics or cellular (3G/4G/5G) networks. To provide the C-ITS framework with more information, such as the presence and dynamics of legacy (i.e., non-connected) vehicles or Vulnerable Road Users (VRUs) on the road, some additional road sensors are attached to the RSUs, such as traffic radars and IP cameras.  
A cloud-based MQTT Broker, used by all RSUs, facilitates information exchange and connects them to the central C-ITS Platform (MOBICS--Mobility Intelligent Cooperative Systems [23]). This platform enables the handling of all data exchanges between C-ITS devices, the current road infrastructure, and the road operator's traffic control technologies. For example, it enables the continuous monitoring of all road activity. It allows a human operator to signal a traffic event, such as roadworks or traffic accidents, at a specific geographical location, triggering the C-ITS platform to generate the corresponding C-ITS  
Figure 1: Roadside and cloud-based C-ITS architecture.  
message automatically. This C-ITS message is typically either a DENM in the case of specific events, or an IVIM in the case of advisory or mandatory contextual speed limits or road signage information. This message is then delivered to the relevant location served by the local RSUs.  
Additionally, the C-ITS Platform is continually connected to the National Access Point (NAP) for the exchange of high-level transport-related data in DATEX-II format and to the Portuguese Public Key Infrastructure (PKI) for the implementation of the security measures outlined in ETSI standards. The management of certificates and credentials between the Root Certificate Authority, the Enrolment Authority, and the Authorization Authority in the PKI and the RSUs and the On-Board Units (OBUs) on the road is made possible by this PKI interconnection.  
All the entities of the roadside and cloud frameworks are described in more detail below, focusing on the main entities that act as a bridge between the local vehicular network and the road manager, that is, the RSU and the cloud MQTT broker.
# Extrinsic Bayesian Optimization on Manifolds
## 4 Examples and Applications
### Estimation of Frechet Means  
We will first apply the eBO method to the estimation of sample Frechet means on the sphere. Let \(x_{1},\ldots,x_{n}\) be \(n\) points on the manifold, such as the sphere. The sample Frechet function is defined as  
\[f_{n}(x)=\frac{1}{n}\sum_{i=1}^{n}\rho^{2}(x,x_{i}), \tag{13}\]  
and the minimization of \(f_{n}(x)\) leads to the estimation of sample Frechet mean \(\mu_{n}\), where  
\[\mu_{n}=\arg\min_{x\in M}\frac{1}{n}\sum_{i=1}^{n}\rho^{2}(x,x_{i}). \tag{14}\]  
The evaluation of \(f_{n}(x)\) at the data point \(x_{1},\ldots,x_{n}\) will be our data points for guiding the optimization to find the Frechet mean. The choice of distance \(\rho\) leads to different notions of means on manifolds. In the extrinsic setting, we can define an extrinsic mean via some embedding of the manifold onto some Euclidean space. Specifically, let \(J:M\to\mathbb{R}^{D}\) be some embedding of the manifold onto some higher-dimensional Euclidean space \(\mathbb{R}^{D}\). Then one can define the extrinsic distance as  
\[\rho(x,z)=\|J(x)-J(z)\|, \tag{15}\]  
where \(\|\cdot\|\) is the Euclidean distance on \(\mathbb{R}^{D}\). This leads to the extrinsic Frechet mean. Fortunately, the extrinsic mean has a close form expression, namely \(\mu_{n}=\mathcal{P}_{\tilde{M}}\left(\frac{\sum_{i=1}^{n}J(x_{i})}{n}\right)\), where \(\mathcal{P}\) stands for the projection map onto the image \(\tilde{M}=J(M)\).  
In our simulation study, we estimate the extrinsic mean on the sphere \(S^{2}\) and evaluate the performance of our eBO method by comparing it to the gradient descent (GD) algorithm in terms of convergence to the ground truth (the true minimizer given above). The embedding map \(J\) is the identity map \(I:\mathbb{S}^{2}\to\mathbb{R}^{3}\). As shown in Figure 2, we simulate some data on the sphere, and the goal is to find the extrinsic mean by solving Equation (14). Specifically, those \(x_{i}\)\((i=1,\ldots,n)\) points in black are sampled on the circle of latitude, which leads to the south pole \((0,0,-1)\) as the true extrinsic Frechet mean. In terms of our BO Algorithm 1, we first sample those blue points randomly on the sphere as initial points and initialize the covariance matrix by evaluating the covariance kernel at these points. Then, we randomly select the direction on the sphere to maximize the acquisition function based on the eGP. In each iteration, we mark the minimizer as the stepping point in Figure 2 and add it to the data for the next iteration. Not surprisingly, those stepping points in red converge to the ground truth (south pole) after a few steps. We also compare our eBO method with the gradient descent (GD) method on the sphere under the same initialization. As illustrated in Figure 3, although our BO method converges slightly slower than GD at the first four steps, the BO method achieves better accuracy with a few more steps. It confirms the quick convergence and high accuracy as the advantages of the eBO method.