esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Compressive Sensing Framework
### Using CS in WSNs  
We employ CS to encode and decode environmental data monitored by a WSN. Figure 1 depicts the basics of the coding/decoding process:  
* **Sensing--**The WSN node \(S_{i}\) makes measurements (from the environment), and stores them in a vector \(\mathbf{x}_{i}\in\mathbb{R}^{N}\) \[\mathbf{x}_{i}=\left[x_{i}[1],x_{i}[2],\ldots,x_{i}[N]\right].\] (10)
* **Compression--**The compressed sensing vector \(\mathbf{y}_{i}\in\mathbb{R}^{M}\) (\(M<N\)), \[\mathbf{y}_{i}=\left[y_{i}[1],y_{i}[2],\ldots,y_{i}[M]\right],\] (11) is obtained from \(\mathbf{x}_{i}\) using (2).
* **Quantization--**For transmission, the compressive sensed coefficients are quantized using a uniform scalar quantizer of bit-depth \(B\) (that is, with \(2^{B}\) reconstruction levels), this produces the quantized coefficients vector \(\mathbf{y}_{q_{i}}\).
* **Transmission--**Each node forwards its quantized coefficients vector \(\mathbf{y}_{q_{i}}\) to the sink node.
* **Reception--**Due to possible packet loss, the received coefficients vector at the sink node is \[\mathbf{y}_{r_{i}}=\left[y_{r_{i}}[1],y_{r_{i}}[2],\ldots,y_{r_{i}}[L]\right]\] (12) with \(L\leq M\).
* **Reconstruction--**The \(\mathbf{y}_{r_{i}}\)is used at the sink to reconstruct a version \(\hat{\mathbf{x}_{i}}\) of the original signal.
# Deposition of 3 YSZ-TiC PVD Coatings with High-Power Impulse Magnetron Sputtering (HiPTMS)
## 3 Results  
### Gas Flow  
The maximum table current had two high peaks in the low gas flow mix. One peak was observed at 150 sccm argon, 0 sccm krypton (150 Ar0 Kr) with a maximum table current of 49.1 A, and another was observed at 50 sccm argon and 50 sccm krypton (50 Ar50 Kr) with a maximum table current of 44.8 A. The 150 Ar0 Kr showed a stable plasma, while 50 Ar50 Kr showed arcs on the cathode surface and an unstable (flickering) plasma. It should be noted that while the process was stable at a low gas mix, it was impossible to ignite plasma by directly starting with a low gas mix. Plasma ignition was only possible with a higher gas mix (e.g., 650 Ar0 Kr). After ignition, the gas mix could be changed to any stable measuring point shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Values** \\ \hline Frequency & Hz & 800 \\ On-time cathode & us & 40 \\ On-time bias & us & 40 \\ Offset bias & us & 20 \\ Cathode power & W & 4000 \\ Bias voltage & V & 70 \\ Gas mix & Sccm & 150 Ar/0 Kr \\ Coating time & s & 18,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of parameters used for the coating of tungsten carbide with cobalt (WC-Co) specimens.  
In the parameter field displayed by results of the gas mix measurements (Figure 1), one could not observe any DAE phenomena.
# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
### Leveraging Synthetic Data  
In line with recent trends of using data augmentation techniques to train more robust machine learning algorithms when data collection is costly (for example [26; 27]), we investigated the possibility of generating synthetic data to train machine learning algorithms that usually perform well in operating conditions.  
Synthetic data is data that is generated on a computer by an algorithm, as opposed to captured by a sensor. This type of data can have various purposes. For example, in a remote-sensing context, [PERSON] et al. [28] simulate the reflectance of the top of the atmosphere in clear-sky conditions. Their goal is to then use these simulations, together with real data to estimate cloud coverage. [PERSON] and [PERSON] [29] generate synthetic data to train machine learning algorithms to detect offshore wind farms from Sentinel-1. They choose to go down this path after identifying the same main issue as us, namely the scarcity of well-validated data. They develop an ontology approach to generate random images based on expert knowledge. [PERSON] et al. [30] use a 3D urban environment modeling system to generate synthetic Sentinel-1 images to train machine learning algorithms for building segmentation. We take a different perspective based on the direct simulation of selected characteristics of the physical object that constitutes an image.  
The main goal of our study is to showcase a proof of concept of the transferability of marine debris detection from synthetic data to real data. We propose a general methodology that includes a physical model of debris patches at sea and of satellite data acquisition. We implemented this model into a simulator that can generate large datasets of synthetic images. These are then used to train algorithms that learn to discriminate debris and water. We illustrate this method with a case study of macro-plastic detection from S2 with validation on existing S2 images provided by the PLP.
## 4 SMART DShot: ML-Enhanced Architecture
### Integration and Scalability  
The design supports 1 to 8 channels with linear resource scaling that requires 687 LUTs and 723 FFs per additional channel, while sharing common resources, including the APB interface, clock generation, and performance monitoring across all channels. Independent operation ensures no inter-channel timing dependencies with synthesis validation confirming timing closure up to 8 channels.  
The memory-mapped register interface provides comprehensive system control through channel configuration registers (0x00-0x7F) for enable, mode, and threshold settings, ML parameter registers (0x80-0 xBF) for algorithm weights and selection criteria, security setting registers (0 xC0-0 xDF) for attack detection thresholds and response modes, and system control registers (0 xF0-0 xFF) for global enable, interrupts, and status reporting.  
This integrated architecture successfully balances sophisticated ML processing capabilities with stringent real-time motor control requirements, enabling practical deployment in resource-constrained embedded systems while providing robust security enhancement.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Module** & **LUTs** & **FFs** & **Power (mW)** & **Function** \\ \hline APB Interface & 312 & 256 & 8.2 & Configuration \\ DShot Core & 1824 & 2048 & 42.1 & 8-channel protocol \\ Timing Analyzer & 1256 & 1432 & 28.7 & Error measurement \\ ML Engine & 892 & 1024 & 23.4 & Algorithm execution \\ Neural Network & 756 & 645 & 18.9 & Anomaly detection \\ Feature Extractor & 462 & 382 & 11.3 & Real-time features \\ \hline Total & 5502 & 5787 & 132.6 & Complete system \\ Utilization & 2.16\% & 1.14\% & 16.6 mW/ch & 8 channels \\ Performance & 156.8 MHz & 3.1\(\times\) margin & 9.1 \(\upmu\)s latency & PolarFire SoC \\ \hline \end{tabular}
\end{table}
Table 4: PolarFire SoC FPGA implementation results.  
Figure 6: Hardware architecture of ML-powered DShot timing correction engine.
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 5 System Overview  
### System Requirements  
In order to construct a decentralized SSL certificate system, a set of fundamental requirements was identified. These requirements serve as the guiding principles for the proposed system and are outlined as follows:  
1. **User Validation:** Ensures that only legitimate domain owners request SSL certificates by authenticating their identity to create an X.509 certificate. It is crucial for introducers to verify and confirm that only authorized domain owners are granted digital certificates.
2. **Decentralization:** Involves multiple ring members, also referred to as introducers, to avoid a single point of failure. Allows authorized introducers to authenticate certificate information and add individual signatures, resolving dependency on the existing CA hierarchy.
3. **Transparency:** Publicly accessible certificate creation and verification data are stored on the blockchain network. This facilitates public visibility of certificate issuances and revocations, enabling the detection of certificates that have been tampered with or compromised introducers.
4. **Interoperability:** Requesters are provided with X.509v3 format certificates to ensure privacy, legitimacy, and secure connections during TLS handshakes. Ensuring compatibility with the existing browser verification mechanism is essential, necessitating minor adjustments.
5. **Revocation Efficiency:** The certificate revocation process should be expedited. The system will automatically recognize and respond to revocation requests from authentic users or introducers. Additionally, it is pivotal to engage multiple introducers to thoroughly authenticate the revocation request.
## 2 Materials and Methods
### Data Analysis
#### 2.5.2 NS Data Treatment  
After PTR-ToF-MS spectra processing, the unfiltered dataset consisted of 468 mass peaks recorded over 45 NS sessions. The dataset was filtered (or pre-processed) according to the procedure fully described in [10], the key steps of which are the selection of peak-like features to highlight those mass peaks related to release profiles and the elimination of NaN-containing columns to rule out low-quality data. Overall, the final nosepiece dataset contained 35 mass peaks, each associated with up to six parameters. The parameters were the release maximum (maximum), the area under the curve (area), the median intensity (median), the final intensity (final), the time required to reach maximum intensity (tmax), and the slope of the descending part of the curve (slope). This provided a data matrix consisting of 207 parameters. After this step, ANOVA and post-hoc analysis were carried out by setting a \(p\)-value threshold of 0.01 (after false discovery rate correction). Partial Least Square Discriminant Analysis (PLS-DA) was carried out with the aim of assessing PTR-ToF-MS discrimination capabilities based on NS data and pinpointing key discriminating variables through the employment of Variable Importance in Projection (VIP) values [19]. The PLS-DA prediction model was built by applying leave-group-out (LGO) cross-validation, where each group consisted of an NS session that was performed on different days.
## 3 Simulated Results and Discussions
### Equivalent Circuit Model (ECM)  
An equivalent circuit model of the proposed FSS shield designed in ADS is illustrated in Figure 3b. The ECM of the FSS is a series combination of lumped components (\(L_{1}\), \(C_{1}\), and \(C_{2}\)). The metal/trace mainly contributes to inductance, whereas the inter-element spacing and etched slots on the FSS surface are modeled as capacitance. The employed dielectric is modeled as a transmission line. Moreover, \(Z_{\rm o}=377\,\Omega\) is the characteristic impedance of free space on either side of the FSS. The surface impedance of the FSS can be expressed as  
\[Z_{\rm FSS}=j\omega L_{1}+\frac{1}{j\omega C_{1}}+\frac{1}{j\omega C_{2}}, \tag{2}\]  
\[Z_{\rm FSS}=\frac{\left(j\omega L_{1}\right)\left(\omega^{2}C_{1}C_{2}\right) -\left(j\omega\right)\left(C_{1}+C_{2}\right)}{\omega^{2}C_{1}C_{2}}, \tag{3}\]  
Several simulations are conducted in ADS to obtain the desired response of the ECM at the operating frequency. The optimized lumped circuit parameters, obtained through the curve fitting method, are listed in Table 2. However, alternative combinations of the lumped values may also yield a similar response at 10 GHz. Figure 3d presents a comparison of the SE curves of the circuit and EM models.  
Figure 3: (**a**) The surface current distribution at FSS surface; (**b**) equivalent circuit model of the FSS shield; (**c**) ADS simulations, conducted to achieve an optimized response; (**d**) a comparison of SE of ECM and EM models.
# Experimental Study on the Effect of Different Parameters on Rotor Blade Icing in a Cold Chamber
## 2 Experimental Environment and Equipment  
In this paper, an icing cold chamber was built on the ground, and a rotor icing experimental platform was installed in the cold chamber to simulate rotor blade icing. In order to ensure the smooth operation of the experiment and that the related parameters can be measured, it was necessary to equip the refrigeration system, spray system, rotor system, and related measuring equipment in the cold chamber.
# Detection of Gas-Solid Two-Phase Flow Based on CFD and Capacitance Method
## 1 Introduction
[18] studied interfacial friction in upward annular gas-liquid two-phase flow in pipes. The experiments were carried out in a large diameter flow loop of 101.6 mm internal diameter with the superficial gas and liquid ranges of 11-29 m/s and 0.1-1.0 m/s respectively. Significant discrepancies were found between many of the published correlations and the large pipe data, primarily in the thick film region at low interfacial shear stress. [PERSON] [19] studied the effect of the fluid properties on the wave velocity and wave frequency in annular pipe. He used water and glycerol solution to analyze the influence of the viscosity, and water and butanol solution to analyze the influence of the surface tension. The experiments summarized the influence of fluid property like viscosity and surface tension to the gas-liquid flow in pipes. Although the above has laid a good foundation for the studies in gas-solid pipe flow, the flow conditions in annular pipes have not been thoroughly investigated. Particularly, the flow in the region of the drill bit, where the carrier medium air exits the inner pipe and flows into the annular channel is still not clear. Moreover, with the solids added into the flow, the condition becomes complicated. Therefore, in this study, we intend to describe the flow characteristics of the solids in the annulus section to a certain degree of detail. This is to be carried out by both CFD simulations and Electrical Capacitance Tomography (ECT) measurement. Such information could be valuable for the industry. Also, we wish to verify the simulation methods and results by ECT measurement, which could establish a certain degree of guide or experience for future design of the trilling process and equipment development.
## 4 Results and Analysis
### Comparative Studies
#### 4.4.3 Performance of Available Works in the Literature  
We also compared the performance of the proposed system with other existing works performed in the Bangla language. The experiments were performed following their frameworks and tested on our dataset. [PERSON] et al. [19] used semantic-based and stylistic-based features for the identification of poems from four genres based on a support vector machine and achieved 92.3% accuracy. [PERSON] and [PERSON] [20] proposed a hybrid model by combining n-gram and naive Bayes (NB) algorithm and the results showed encouraging performance of the system with a 95% accuracy. The obtained accuracies using our dataset are presented in Table 12. The performance of the proposed model was better than all other existing algorithms when accuracy was concerned. The dataset used in this study was larger when compared to all the other research.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Approach** & **Accuracy (\%)** \\ \hline [PERSON] et al. & Semantic and stylistic features + SVM & 90.67 \\ [PERSON] and [PERSON] & N-gram + NB & 84.28 \\ Our approach & Image-based features + CNN & 93.58 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The obtained accuracies of the existing systems on our dataset.  
Figure 15: Performance of different popular classifiers.
## 1 Introduction
Formally, according to the definition of AND/OR nodes, the completion times at the nodes of the attack path satisfy the following conditions:  
\[t_{j}\geq t_{S},\forall j\in V \tag{1}\]  
\[t_{j}\geq max_{k\in\mathbb{P}\texttt{red}\left(j\right)}\Big{(}t_{k}+\tau_{kj }+\pi_{j}\Big{)},\forall j\in\texttt{AND} \tag{2}\]  
\[t_{j}\geq min_{k\in\mathbb{P}\texttt{red}\left(j\right)}\Big{(}t_{k}+\tau_{kj }+\pi_{j}\Big{)},\forall j\in\texttt{OR}, \tag{3}\]  
where \(\mathbb{P}\texttt{red}\left(j\right)\) is the set of nodes immediately preceding node \(j\). The model in (1)-(3) is applicable for describing and solving path extraction problems on unweighted graphs. In this case, it is sufficient to set in (1)-(3) \(\pi_{j}=0,\) for all nodes \(j\in V\) and \(\tau_{kj}=1\), and for all edges \((k,j)\in E\); then, obviously, the objective function will represent the total number of edges in the extracted path. This problem turns into the standard critical path problem (PERT-CPM) if the set \(\mathbb{OR}\) is empty and into the shortest path problem if the set \(\texttt{AND}\) is empty [28; 29]. The main contributions of this paper are as follows:  
(i) We present and explore a new flexible and efficient approach to assessing hostile attack paths and extracting the fastest and, hence, most dangerous vulnerable paths of adversarial attacks in computer networks;  
(ii) The attack-path extraction problem is formulated as an optimization problem on AND/OR attack graphs, where the timing characteristics of adversarial actions are injected into the nodes and edges of the graph;  
(iii) A new exact polynomial-time path extraction algorithm is developed, which complements previous approximation and heuristic graph algorithms;  
(iv) From the defender's perspective, extraction of the attack path will reveal the most vulnerable places in the cyber network, thus facilitating efficient network hardening;  
(v) The proposed algorithm is proven to successfully handle directed cycles of positive length, solving the cyclic graph problem in \(O(nm)\) time, where \(n\) is the number of nodes and \(m\) is the number of edges in the underlying attack graph. The algorithm's complexity is verified on synthetic and real network examples. The remainder of this paper is organized as follows. Section 2 briefly overviews the earlier related algorithms and their complexity. Section 3 describes a new fast algorithm and highlights its advantages compared to known related solution algorithms. Sections 4 and 5 contain illustrative numerical examples and a case study, respectively. Section 6 briefly describes the importance of extracting attack paths for developing countermeasures and cyber-defense. Section 7 concludes the paper.
# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 2 Background  
Smart home systems, underpinned by the Internet of Things (IoT), have traditionally relied on rule-based or semi-automated solutions, allowing users to configure device behaviours in controlled environments [1]. While these early approaches significantly improved convenience and reduced manual oversight, they often lacked the flexibility to accommodate changing user requirements, device heterogeneity, and evolving environmental conditions. As the proliferation of connected sensors, actuators, and appliances continues to accelerate, the need for more adaptive and context-aware orchestration mechanisms has become increasingly evident [3]. Placing our proposed approach within this evolving landscape calls for examining several foundational concepts. Firstly, understanding how different IoT devices, protocols, and services can be orchestrated into a cohesive environment sheds light on the fundamental challenges of managing interoperability and responsiveness. Building on this, recent advances in generative AI--particularly in code generation using large language models--highlight new possibilities for automating the creation and revision of system logic in response to changing user needs. However, these models do not operate in isolation, and thus, the technique of Retrieval-Augmented Generation (RAG) provides a means to inject real-time context from domain-specific repositories into the generative process, ensuring that the solutions proposed remain accurate and current. To integrate and manage these dynamically generated or updated services without disrupting the broader system, a robust software architecture is imperative, and the OSGi framework has emerged as a widely accepted technology for ensuring modular and seamless service deployment. Finally, despite the strengths of these approaches, unresolved issues--ranging from security and privacy concerns to device heterogeneity--underscore the need for further exploration. In the subsections that follow, we discuss these pillars of smart home orchestration in detail, thereby laying the groundwork for the design and validation of our proposed system.
## 3 Verifications
### Discussion on Concrete Parameters Affecting Fatigue Degradation
#### 3.3.2 Size Effect  
The static strength varies with the size of concrete specimens, and specimen size may also affect the dynamic behavior of the material. Scholars have found that fatigue life generally increases with increasing specimen size [24], while discussion on the influence of specimen size on fatigue residual strength is scarce.  
This section focuses on the influence of specimen size on fatigue behavior. For the axial compression fatigue test, the size effect is analyzed by comparing specimens' fatigue damage evolution behavior from the literature [31; 40; 42]; the information on selected fatigue deformation data is shown in Table 5.  
Since loading frequency in the literature [31] and [42] are different, these data are used to analyze the size effect at lower stress levels. As discussed in the previous section, loading frequency has a more significant impact at higher stress levels. Therefore, the data from reference [40] is supplemented for comparative verification under \(S_{\text{max}}=0.85\). The comparison results are shown in Figure 20. For a given stress level, the smaller the specimen size, the higher the accumulated damage under the same cycle ratio. The damage development of smaller-sized concrete is relatively higher than that of larger specimens, resulting in a faster degradation rate of fatigue residual strength and, thus, a lower theoretical fatigue residual strength. However, this difference induced by the size effect is not particularly significant. Comparing the damage evolution curves in Figure 20, it is evident that stress level is more critical for fatigue behavior degradation than the size effect, which is also consistent with the findings in the literature [24].  
Figure 19: Compressive fatigue damage evolution curves under different maximum stress levels, obtained from fatigue test results in the literature [39; 40] with varying loading frequency: (**a**) \(S_{\text{max}}=0.75\); (**b**) \(S_{\text{max}}=0.80\); (**c**) \(S_{\text{max}}=0.85\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Source** & **Specimen Size** & **Loading Frequency** \\ \hline Literature [31] & \(\Phi 100\) mm \(\times\) H200 mm & 1 Hz \\ Literature [42] & \(\Phi 150\) mm \(\times\) H250 mm & 5 Hz \\ Literature [40] & \(100\) mm \(\times\) 100 mm \(\times\) 100 mm & 1 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data information for fatigue tests under axial compression from the literature [31; 40; 42] for which the effect of specimen size on fatigue damage evolution is studied. The loading frequency varies slightly between 1 and 5 Hz, a relatively standard value during fatigue loading.