esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Stage-Wise Role of SiL and HiL Across Development Phases
### Test Coverage Across Development Stages
#### 5.4.1 Phase I--Definition and Design (G10-G70)  
G10-G30 focused on system requirements and early functional discovery. At this stage, conceptual validation (L0) is the dominant approach. In G40-G50, SiL is introduced to test control logic and virtual ECU behavior, independent of hardware. This phase enables the rapid iteration of software functions. G60 supports requirement traceability, connecting SiL outcomes to functional and safety requirements in preparation for physical validation.
## 5 Conclusions  
This paper proposes the ARPD-RTK/INS algorithm, which searches the integer ambiguities in the position domain and correct positioning states by carrier-phase measurements. The purpose is to provide a continuous and accurate positioning result under harsh GNSS signal conditions. Compared with the conventional tightly coupled RTK/INS integrations, the new algorithm has three distinct advantages. Since we search the integer ambiguity solution in the position domain, the ARPD-RTK/INS is insensitive to frequent cycle slips. The weight of the candidate position is only related to the fractional part of the carrier-phase measurements, so the second advantage is that our algorithm is not interfered by pseudorange errors. Last but not least, the new method can provide relatively accurate ranging information steadily, because it uses weighted average ambiguity results to avoid the risk of failure.  
Our future work aims to improve the efficiency of the ARPD-RTK/INS algorithm. One direction is to use pre-integration during the INS process. Another direction is to use the sequential importance sampling (SIS) technique instead of uniform sampling. If the amount of computation is effectively reduced, the ARPD-RTK/INS algorithm can be applied more widely in the future.  
[PERSON]. ([PERSON]) designed the detailed realization of the new RTK/INS algorithm, and implemented it in Matlab. She also processed and analyzed the real as well as simulated data. [PERSON]. ([PERSON]) accomplished a robust RTK using adaptive point mass filter, which provides inspiration for the later research. [PERSON] proposed the idea of combination between RTK and inertial information. He also devised the integrated navigation equipment, and collected data. [PERSON] proposed the research direction of this work, and contributed analysis tools. [PERSON]. ([PERSON]) wrote the original manuscript. [PERSON] revised it and provided instructions on the design of experiments. [PERSON] reviewed the manuscript and made meaningful modifications.  
All sources of funding of the study should be disclosed. Please clearly indicate grants that you have received in support of your research work. Clearly state if you received funds for covering the costs to publish in open access.  
The authors declare no conflict of interest.
# An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
## 3 Ontology Development for RiceMan
### Ontology Design and Development
#### 3.2.2 RiceDO v2: Revised Design  
RiceDO v2 is redesigned to address the aforementioned issues and is enhanced to support interoperability with related ontologies. Three reliable knowledge sources are used to formulate axioms in RiceDO: (1) Rice Knowledge Bank Thai version (Thai-RKB) [41] maintained by the Rice Department, Thailand; (2) Rice Knowledge Bank (RKB) from IRRI [26]; and (3) Rice Ontology (CO_320) [10]. Here, we consider Thai-RKB as the primary knowledge source for modeling our ontology, while the other two are used in absence of the information in Thai-RKB.  
We adopt the design principle of TBox-based knowledge representation from a well-known medical ontology called SNOMED CT ([[https://www.snomed.org/](https://www.snomed.org/)]([https://www.snomed.org/](https://www.snomed.org/)), accessed on 1 November 2021). Following this design principle, RiceDO v2 ([[http://purl.org/ricedo](http://purl.org/ricedo)]([http://purl.org/ricedo](http://purl.org/ricedo)), accessed on 1 November 2021) [42] consists of nine top-level classes under the RiceDTop and 11 object properties in the object property hierarchy, as shown in Figure 1a,b, respectively. RiceDO v2 uses the special-purpose property called abnormalityGroup to axiomatize a group of various characteristics that can occur together on a certain disease. We illustrate this in Figure 1c for the rice brown spot fungal disease, in which the property abnormalityGroup is used to group characteristics 'light yellow halo spot on leaf during the tillering growth stage'; that is, abnormalityGroup some (SpotOnLeaf and (hasColor some LightYellow) and (hasGrowthStage some Tillering) and (hasShape some Halo)).  
Figure 1: The structure of RiceDO ontology is divided into three parts: (**a**) the class hierarchy of RiceDO; (**b**) the object properties of RiceDO; and (**c**) an example of axioms representing abnormalities of the rice brown spot fungal disease.  
Another radical change in RiceDO v2 is to redefine Insect as a kind of Pest instead of RiceDisease as in the first version. In addition, the class PestDamage is redefined to characterize damages caused by pests. Furthermore, the class PlantDisease subsumes 18 diseases, which are categorized under four classes, namely PlantBacterialDisease, PlantFungalDisease, PlantFhydoplasmaDisease, and PlantViralDisease.  
Moreover, RiceDO v2 complies with good modeling practices of W3C. Regarding the interoperability, RiceDO v2 reuses terms and handles potential mappings with existing ontologies: PDO, PO, and PATO. Rice disease names are imported from PDO. Besides, terms of plant parts and growth stages are mapped to existing equivalent classes in PO by using ow:equivalentClass. Similarly, terms of colors and shapes are mapped to the existing ones in PATO. Mapping terms to PO and PATO are used instead of importing in order to maintain the meaning and simplicity of RiceDO v2's class hierarchy.
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
## 2 Algorithm Principle
### \(t\)-Distributed Stochastic Neighbor Embedding (t-SNE)  
The t-SNE algorithm is an information-theoretic, nonlinear, unsupervised manifold learning method [19]. Its core concept involves replacing a high-dimensional space with a low-dimensional space using probability. It aims to preserve the geometric shapes of data points as much as possible in the low-dimensional space, thus achieving data dimensionality reduction and visualization.  
Assuming high-dimensional data \(Y=\{y_{1},y_{2},\ldots,y_{N}\}\), we calculate the probability distribution between high-dimensional data points \(x_{n}\) and \(x_{k}\), and it does not include an exponent term with Lyapunov parameters:  
\[p_{k|n}=\frac{\exp\Bigl{(}-\left\|y_{n}-y_{k}\right\|^{2}/2\sigma_{n}^{2} \Bigr{)}}{\sum\limits_{m\
eq n}\exp\Bigl{(}-\left\|y_{n}-y_{m}\right\|^{2}/2 \sigma_{n}^{2}\Bigr{)}} \tag{14}\]  
where \(\sigma_{n}\) is the Gaussian variance in data points \(y_{n}\) that is determined through a given perplexity and binary search.  
Calculate the joint probability \(p_{nk}=\frac{p_{k|n}+p_{n|k}}{2N}\) to obtain the initial solution \(f^{(0)}=\{f_{1},f_{2},\ldots,f_{N}\}\).  
Compute the joint probability \(q_{nk}\) and the gradient \(\frac{\partial C}{\partial f_{n}}\) for the high-dimensional data corresponding to the low-dimensional data.  
\[q_{nk}=\frac{\Bigl{(}\left\|f_{n}-f_{k}\right\|^{2}+1\Bigr{)}^{-1}}{\sum \limits_{m\
eq l}\Bigl{(}\left\|f_{m}-f_{l}\right\|^{2}+1\Bigr{)}^{-1}} \tag{15}\]  
\[\frac{\partial C}{\partial f_{n}}=4\sum\limits_{k}(p_{nk}-q_{nk})(f_{n}-f_{k} )\Bigl{(}\left\|f_{n}-f_{k}\right\|^{2}+1\Bigr{)}^{-1} \tag{16}\]  
Calculate the low-dimensional data output \(f^{(t)}\).  
\[f^{(t)}=f^{(t-1)}+\mu\frac{\partial C}{\partial f}+\phi(t)\Bigl{(}f^{(t-1)}-f^ {(t-2)}\Bigr{)} \tag{17}\]  
where \(\mu\), \(\phi(t)\), \(t\) represent the learning rate, momentum factor, and number of iterations, respectively.  
When the number of iterations reaches \(t\), stop the iteration and output the obtained low-dimensional feature data; otherwise, repeat the above steps.
###### Abstract  
This work aims to define the microstructure and to study the mechanical properties of an Al matrix incorporated with various amounts of Fe\({}_{2}\)O\({}_{3}\) (3, 6, 9, 12 and 15 wt.%) with a constant amount of Ag at 1 wt.%. Al/Fe\({}_{2}\)O\({}_{3}\) + Ag hybrid nano-composite samples are manufactured using powder metallurgy. An aluminum matrix is considered an important alloy, owing to its properties such as being lightweight, strong and corrosion and wear resistant, which enable it to be used in many applications, such as electronics, aerospace and automotive purposes. Various examinations have been performed for the samples of this work, such as Field Emission Scanning Electron Microscopy (FESEM) and X-ray Diffraction (XRD) analysis to estimate the microstructure and phases of manufactured nano-composites. Mechanical testing is also carried out, such as micro-hardness testing, compressive testing and wear testing, to estimate the mechanical properties of the hybrid nano-composites. The results of FESEM and XRD demonstrate that Fe\({}_{2}\)O\({}_{3}\) and Ag nanoparticles are uniformly distributed and dispersed into the Al matrix, whereas the mechanical tests show that enhancement t micro-hardness, compressive strength of 12 wt.% Fe\({}_{2}\)O\({}_{3}\) + 1 Ag and wear rate decrease to a minimum value of 12 wt.% of Fe\({}_{2}\)O\({}_{3}\) + 1 Ag.  
hybrid nano-composites; powder metallurgy; Fe\({}_{2}\)O\({}_{3}\); Ag microstructure; mechanical properties; wear
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 2 Related Works
### Consumer-Side Congestion Control  
The congestion control on the consumer side in NDN inherits the scheme from Transmission Control Protocol (TCP). [PERSON] et al. proposed the Interest Control Protocol (ICP) [25], in which the consumer uses the Additive Increase Multiplicative Decrease (AIMD) algorithm to control the sending rate of Interests. Since Data in NDN are no longer obtained along a single path, [PERSON] et al. proposed Content-Centric TCP (CCTCP) [26] to maintain a Retransmission Timeout (RTO) timer for each content producer. The Remote Adaptive Active Queue Management (RAAQM) [27], also proposed by [PERSON] et al., maintains the RTO value for each path separately. These methods can be used for multi-source and multi-path scenarios to a certain extent. However, in the case of request aggregation and Data multicast, calculating an accurate RTO is still challenging [1]. To address the issues mentioned above, the Q-NDN congestion control method [28] was proposed. It not only considers RTT but also incorporates various state information. Consequently, consumers can dynamically adjust the congestion window (cwnd) through real-time monitoring of network status, leveraging the Q-learning algorithm for this adjustment.  
This type of method treats consumers as agents and adjusts the sending rate of Interests based on the estimation of congestion status. However, due to delays in obtaining network information such as timeouts and NACKs within NDN, consumers are unable to promptly access the current network congestion status using this information.
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 3 Distribution Grid Consumption Strategy with Distributed PV
#### 3.1.2 Constraints  
(1) Current constraints  
\[\Delta P=P_{Gi}-V_{i}{\sum\limits_{j\in i}{V_{j}(G_{ij}\cos\theta_{ij}+B_{ij} \sin\theta_{ij})}}=0 \tag{4}\]  
\[\Delta Q=Q_{Gi}-V_{i}{\sum\limits_{j\in i}{V_{j}(G_{ij}\sin\theta_{ij}-B_{ij} \cos\theta_{ij})}}=0 \tag{5}\]  
where P\({}_{\text{Gi}}\) and Q\({}_{\text{Gi}}\) denote the active and reactive power injected by the distributed power source at the ith node, respectively; V\({}_{\text{i}}\) denotes the voltage magnitude at the ith node, and G\({}_{\text{ij}}\), B\({}_{\text{ij}}\), and \(\theta_{\text{ij}}\) denote the conductance, conductivity, and voltage phase angle difference between two nodes i and j, respectively.  
(2) Node voltage constraints  
\[U_{\text{imin}}\leq U_{i}\leq U_{\text{imax}} \tag{6}\]where \(\text{U}_{\text{imin}}\) and \(\text{U}_{\text{max}}\) are the minimum and maximum voltage amplitude allowed at node i, respectively; \(\text{U}_{\text{i}}\) is the node i voltage value.  
(3) Branch current constraints  
\[I_{\text{l}}\leq I_{\text{max}} \tag{7}\]  
where \(\text{I}_{\text{max}}\) is the upper limit of the branch l-carrying capacity.  
The above constraints are also more typical constraints, and as many constraints as possible can be added when dealing with multi-objective problems to ensure that the model is more realistic to achieve the most stable access state.
## 4 CES-KOS vs. RANS, DES, WMLES, and WRLES
### CES-KOS vs. RANS
The numerical predictions of SA, SST, and k-kL models indicate lower pressure levels in the post-separation region (\(0.7\leq x/c\leq 0.938\)) compared to experimental data. In addition, all RANS models underpredict the surface pressure within the separation region (\(x/c=0.938\)) [possibly due to an inadequate flow resolution and turbulent mixing], whereas CES-KOS marginally overestimates the profile. In the post-separation region, the CES-KOS and SA models exhibit similar skin-friction coefficient profiles. However, after reattachment, the SA simulation fails to capture the secondary \(C_{f}\) peak at about \(x/c=1.2\) and the distinct skin-friction distribution in this region, while CES-KOS accurately captures this feature. CES-KOS provides the most accurate skin-friction results compared to the RANS models considered. Figure 16: CES-KOS vs. RANS-type (WA, SA) [50] and (SST, \(k-kL\)) [51] models: Profiles of pressure and skin-friction coefficients. Figure 15: CES-KOS vs. RANS-type (WA, SA) [50] and (SST, \(k-kL\)) [51] models: Profiles of the normalized streamwise velocity \(\left\langle U\right\rangle/U_{ref}\) and normalized Reynolds stress \(\left\langle uv\right\rangle/U_{ref}^{2}\) at different axial locations.
# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 1 Introduction
In recent years, Quantum Machine Learning (QML) approaches have been applied to various tasks, such as image classification [17], forecasting [18], anomaly detection [19], and text classification [20; 21; 22; 23]. Additionally, a growing body of work has explored the use of quantum and classical convolutional neural networks, highlighting the potential of quantum-enhanced architectures in deep learning contexts [24; 25; 26]. In recent years, hybrid neural architectures that combine classical and quantum components have attracted significant attention due to their potential to enhance representational capacity. Recent studies have also explored hybrid neural architectures that incorporate physics-inspired mechanisms to enhance representation learning, such as physics-informed GCNs for modeling information cascades in complex networks [27]. In this study, we propose a novel hybrid architecture, the Hybrid Quantum--Deep Neural Network (HQDNN), for the task of fake news detection, which seamlessly integrates classical deep learning components with parameterized quantum circuits. The main contributions of this work are as follows:  
* We present one of the first hybrid quantum-classical neural models specifically tailored for binary fake news classification, leveraging both classical and quantum processing within a unified framework. * We provide a comparative evaluation between traditional TF-IDF representations and modern transformer-based embeddings (DistilBERT), demonstrating the superior semantic capability of contextualized features in text classification tasks. * We show that a strong recall performance (94.40%) can be obtained using a quantum circuit with only two qubits, highlighting the model's ability to generalize well with low-level parameter complexity. * We conduct an ablation experiment to isolate the impact of the quantum layer and show its role in minimizing false negatives, which is crucial in safety-critical applications such as misinformation detection. * We benchmark the HQDNN against classical baselines (Logistic Regression, SVM, and MLP) and a quantum-free variant, offering a transparent comparison that validates the practical advantage of the hybrid design. * The entire model is implemented using PyTorch version 2.0.1 and PennyLane, and the source code will be made publicly available upon publication, facilitating reproducibility and future work on hybrid quantum NLP. These contributions provide strong empirical evidence for the potential of hybrid quantum-classical architectures in natural language processing, especially in domains where accurate recall and robustness to false negatives are paramount. Furthermore, to assess the relative performance of the HQDNN against new transformer-based few-shot models, we benchmark it against SetFit. The results show that while SetFit achieves higher precision, the HQDNN consistently outperforms it in recall, further confirming its suitability for applications where minimizing false negatives is critical.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols
### Wifi  
WiFi (IEEE 802.11) is one of the most widely adopted IP-based wireless technologies in buildings worldwide, which is attributed to its key features such as high data rates, scalability, and existing global support, i.e., nearly all modern-day electronic devices (laptops, mobile phones, game consoles, etc.) contain WiFi communication capabilities. The technology is based on five main IEEE technology standards: 802.11a (54 Mbps), 802.11b (11 Mbps), 802.11g (54 Mbps), 802.11n (300 Mbps), 802.11 ac (>1 Gbps), and can operate in three different unlicensed ISM frequency bands: 2.4 GHz, 3.5 GHz and 5 GHz. It is worth highlighting that channel assignment in WiFi varies in different countries around the world, e.g., the number of assigned channels in Europe is 13 whereas in the US it is 11, and therefore, only a limited number of devices can be connected in a WiFi network (due to the limited number of channels that can be used without an overlap) [93; 99]. The maximum limit on the number of nodes in a WiFi network is 255 and the maximum coverage range for WiFi is typically up to 50-70 m, which, along with high power consumptions and interference challenges (as it operates on the same 2.4 GHz frequency band as other technologies such as Bluetooth and Zigbee), are some of the limitations of this technology. However, WiFi finds its applications in key areas such as building automation systems, meter readings, security systems, and communications between various electronic devices, such as laptops, televisions, mobile phones, etc. Moreover, a new WiFi standard called WiFi Direct has also been introduced in the last few years which enables devices to connect to each other without the need for a wireless access point (i.e., it essentially embeds a software access point into any device that supports this standard and, as a result, it bypasses the need to connect to a home, office or hotspot network) [100].
## 3 Discussion of the Noise Results
### Effect of the Spanwise Spacing  
Figure 19 presents \(\Delta\)SPL as a function of free-stream velocity and frequency for structured porous trailing edges with a spanwise spacing of \(s_{h}=20.0\) mm, \(s_{h}=10.0\) mm, and \(s_{h}=5.0\) mm (from left to right), respectively. Figures 20 and 21 plot the corresponding typical noise spectra and \(\Delta\)OASPL, respectively. It is shown in these figures that a medium-size spanwise spacing of \(s_{h}=10.0\) mm generally provides better noise reduction at a zero angle of attack of \(\alpha=0^{\circ}\), while the smallest spanwise spacing of \(s_{h}=5.0\) mm typically provides the best noise reduction at \(\alpha=10^{\circ}\).
## 2 Fundamental Principles
### Signal Strength  
An additional factor limiting a systems ability to measure over large distances is signal strength resulting from the amount of light backreflected or backscattered into the interferometer. The surface which is being measured will also have a large impact on the signal magnitude and quality along with the orientation of the surface in relation to the axis of measurement. Environmental impacts may also degrade the signal, from attenuation due to operating medium, or the effects of vibration and particulates in solution causing scattering of the light source.  
Some improvement can be achieved using optics, focussing the light on a constrained small region to improve re-coupling of back-reflected light into the system from the region of interest. However, for in situ measurements or for integration into many manufacturing regions, tight geometric constraints along with the influence of non-ideal environmental factors may limit the ability to add such optical elements into the system.
# Study of Register Value Branch Predictor Based on CNN
## 7 Conclusions and Future Works  
This paper addresses the problem of how to improve the prediction accuracy of data-dependent H2P branches. Based on the previously proposed CNN branch predictor BranchNet, we added the register-value-processing sub-network and modified it to adapt to its more complex recognition needs. And the effectiveness of the work was evaluated in the top five H2P branches filtered by the TAGE-SC-L predictor on each integer benchmark of SPEC2017. Through experiments, the H2P branch MPKI was reduced for more than half of the benchmarks. Further research on related aspects can be carried out in the future in the following areas:  
* In response to the phenomenon that the addition of register values negatively affects the prediction of a few irrelevant branches, it can be introduced as an additional independent predictor, and mechanisms for determining the register values of relevant branches can be further studied to minimize the negative impact.
* Very few H2P branches that depend on register values are difficult to predict with both slices used in this paper, and there exists room for further optimization of these networks. Given the speed of development of neural networks, the next step could be to choose a more efficient network to handle register values or to continue optimizing the existing CNN branch predictor.
* In terms of how to introduce register values into the branch predictor in a real processor design, additional data paths can be designed to introduce register values into the branch predictor, or another register table can be maintained in the branch predictor,  
Figure 8: Prediction accuracy for gcc’s different inputs with BranchNet and improved slice with 32 and 8 bits.  
or other plausible methods can be investigated. It also needs to be investigated how to deploy the neural network branch predictor into the processor.  
Conceptualization, Y.T.; methodology, Y.T.; software, Y.T.; validation, Z.C. and W.F.; writing, Y.T.; review and editing, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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.  
The following abbreviations are used in this manuscript: