esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-Compact Digital Metasurface Polarization Beam Splitter via Physics-Constrained Inverse Design
## 2 Structure and Method
Therefore, a gradient index distribution is set as the initial value of the inverse design method and provides physical constraints for the subsequent optimization algorithm. By arranging the topographic structure of the metasurface layer, the initial index profile is set to be approximately centrosymmetric, while the effective refractive index only varies with the distance from the center, as shown in Figure 2c. We use the objective-first inverse design method to optimize this structure, as the figure of merit (FOM) of the device is defined as:  
\[\text{FOM}=\frac{1}{4}[(1-\text{t}1)+\text{t}2+\text{t}3+(1-\text{t}4)] \tag{3}\]  
where t1 and t2 denote the transmission coefficients from the input TE mode to the output TM and TE ports; and t3 and t4 denote the transmission coefficients from the input TM mode to the output TM and TE ports. In general, the higher value of the FOM, the better the device performance is. As a result, this problem can be regarded as a multi-objective optimization task. In this work, we use a physically constrained direct binary search (DBS) algorithm [41] to approach this goal. Different from traditional DBS methods, here, physical prior provides a better initial distribution and, meanwhile, limits the search routine of the algorithm, as shown in Figure 3. This enables a more rapid convergence as the algorithms avoid the solutions being trapped in local optimums and get rid of the uncertainty resulting from the sensitivity to stochastic initial values. Specifically, we divide the semicircular subsurface layer into 11 different regions along the radius, where the central most region is filled with cells of mode 0, the outermost region is filled with cells of mode 9, and the remaining regions are sequentially doped with two adjacent modes as shown in Figure 1a. In the mode-mixing regions, each unit in it switches between two adjacent modes, while the structural parameters with better FOM are preserved. The same actions are repeated pixel-by-pixel until the whole region is covered, namely one iteration. The pre-calibrated physical constraints enable the DBS algorithm to reach the convergence more rapidly. Next, the precise correction step is employed to further adjust the internal structure of the units. After determining the density pattern of each unit, the arrangement in the unit is further adjusted, and the interaction is repeated until the FOM converges. Through multiple physically restricted direct-binary searches, the topographical arrangement of the two-dimensional code on the metasurface layer is finally obtained, whose index profile can be seen in Figure 2d.
# Hydrogen-Rock Interactions in Carbonate and Siliceous Reservoirs: A Petrophysical Perspective
###### Abstract  
Underground hydrogen storage (UHS) in carbonate and siliceous formations presents a promising solution for managing intermittent renewable energy. However, experimental data on hydrogen-rock interactions under representative subsurface conditions remain limited. This study systematically investigates mineralogical and petrophysical alterations in dolomite, calcite-rich limestone, and quartz-dominant siliceous cores subjected to high-pressure hydrogen (100 bar, 70 \({}^{\circ}\)C, 100 days). Distinct from prior research focused on diffraction peak shifts, our analysis prioritizes quantitative changes in mineral concentration (%) as a direct metric of reactivity and structural integrity, offering more robust insights into long-term storage viability. Hydrogen exposure induced significant dolomite dissolution, evidenced by reduced crystalline content (from 12.20% to 10.53%) and accessory phase loss, indicative of partial decarbonation and ankerite-like formation via cation exchange. Conversely, limestone exhibited more pronounced carbonate reduction (vaterite from 6.05% to 4.82% and calcite from 2.35% to 0%), signaling high reactivity, mineral instability, and potential pore clogging from secondary precipitation. In contrast, quartz-rich cores demonstrated exceptional chemical inertness, maintaining consistent mineral concentrations. Furthermore, Brunauer-Emmett-Teller (BET) surface area and Barrett-Joyner-Halenda (BJH) pore distribution analyses revealed enhanced porosity and permeability in dolomite (pore volume increased >10\(\times\)), while calcite showed declining properties and quartz showed negligible changes. SEM-EDS supported these trends, detailing Fe migration and textural evolution in dolomite, microfissuring in calcite, and structural preservation in quartz. This research establishes a unique experimental framework for understanding hydrogen-rock interactions under reservoir-relevant conditions. It provides crucial insights into mineralogical compatibility and structural resilience for UHS, identifying dolomite as a highly promising host and highlighting calcite rocks' limitations for long-term hydrogen containment.  
underground hydrogen storage; hydrogen-rock interactions; dolomite; petrophysical properties; geochemical reactivity Article
## 3 Results and Discussion
### Electrode Wear Ratio (EWR)  
The electrode wear ratio (EWR) is defined by the removal volume of the electrode due to erosion wear to the removal volume of the workpiece in the machining process, as defined in Equation (2) [50]. The obtained results found that the electrode wear ratio of the step cylindrical electrode (SCE) was lower than the ratio for the conventional electrode (CE) for electrodes of any size as shown in Figure 7. We found that both SCE and CE showed the nonlinear electrode wear ratio for the electrode diameter of 3 mm. This is because the poor removal of machined debris with the flushing system led to debris blending in the dielectric [52] and deposited on the machining surface under the action of gravity and formed a protective film [51]. In addition, the electrode wear ratio for the step cylindrical electrode decreased by approximately 35.0%, 47.2%, 63.1%, and 37.3% compared to the conventional electrode for the diameters of 3, 6, 9, and 12 mm, respectively. This is because suitable flushing as a result of the electrode design led to improved flushing performance to eliminate debris particles and resulted in a shorter machining time due to the reduction in secondary sparks, which led to a high energy concentration in the sparking area. From these results, we concluded that electrode wear was improved by self-flushing in the machining process with the step cylindrical electrode:  
\[\text{EWR}=\frac{(M_{t1}-M_{t2})(\rho_{t})}{(M_{w1}-M_{w2})(\rho_{w})}\times 100, \tag{2}\]  
where the EWR is the electrode wear ratio (%); \(M_{t1}\) and \(M_{t2}\) are the electrode weight (\(M_{w1}\) and \(M_{w2}\) are the workpiece weight (g) before and after machining); and \(\rho_{t}\) is the density of the copper electrode (8.96 g/cm\({}^{3}\)).
# A Novel Slicing Strategy to Print Overhangs without Support Material
## 1 Introduction
### Limitations of Conventional FDM Printing  
Apart from its enormous advantages like the simplicity and the widespread of printers and materials, FDM printing has at the same time major disadvantages ([4, 5]).  
Overhangs with more than 45-60\({}^{\circ}\) are not printable without support structures, which is one of the main disadvantages. This problem does not only occur in the field of FDM printing, but also in the related 3D printing technology of Directed Energy Deposition (DED). The challenge of printing overhangs for metal DE are mostly solved with 5-axis printers, where the path planning as well as the kinematics are similar solved as well known CNC solutions.The topic of support structures in general is discussed in [6] more extensively. The influence of support on the accuracy of the part is shown in [7].  
A common approach for supportless printing are multi-axis printers, where more than 3 axis are driven to allow additional movements. Beside the complicated mechanics, the main difficulty here is the path planning for the printhead. Reference [8] shows new optimization and path planning methods for multi-axis additive manufacturing in general.  
4-axis printers with an additional rotational axis for parts with a rotational symmetry are discussed in [9; 10]. A \(3+2\) axis approach is proposed in [11; 12], where the part is split into sections, that are printable with 3-axis without support structures. After printing one section, the printing platform is tilted with respect to two axis for the alignment of the part for the next section. Different research has been done on slicing strategies for 5-axis printing for DED ([13; 14]) and FDM ([15]). The multi-axis approach, which offers the most degrees of freedom is FDM printing with a robot arm. This process and possibilities for path generation are discussed in [16; 17].  
An alternative way to reduce support structures with two different nonplanar slicing strategies is presented in [18].  
The lack of a simple and generic multi-axis solutions to avoid support structures lead to the development of a totally new printer kinematic. It is a 4-axis kinematic, called \"RotBot\" with a 45\({}^{\circ}\) tilted nozzle and an additional rotational axis around the z-axis. This approach allows overhangs of up to 100\({}^{\circ}\) without support material. This system is briefly described in the next section. Before, also another concept like the 6-axis printer \"MaxBot\", where the build platform can tilt around two axes and overhangs of up to 80\({}^{\circ}\) are printable has been developed [19].
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 4 Simulation Setup for TSN Synchronization over 5G Scenarios
### OMNeT+ Based Simulation Framework  
Our simulation studies were carried out with the discrete event simulator OMNeT++ [15]. OMNeT++ has various frameworks, which provide different functionalities--for example, the Simu5G [16] framework comprises basic 5G user plane functions. INET [17], another major OMNeT++ framework, includes a OSI layer model and multiple protocol options for these layers. INET also contains Time-Sensitive Networking functionality at the Data Link Layer. Therefore, for the simulation studies presented in this paper, both the Simu5G and the INET framework are used as the base. Using these frameworks, new nodes are created which contain both 5G NIC and Ethernet NIC for TSN. The already existing 5G UE model in Simu5G is updated to include time synchronization functionality, and a TSN Bridge model, from the INET framework, is updated with a modified 802.1 AS functionality (sync message encapsulation in UDP/IP packets) so that time synchronization messages can be forwarded through the 5 GS (which in Simu5G only supports the transmission of IP PDUs).
# Assessment of the Structural State of Dissimilar Welded Joints by the Acoustic Emission Method
###### Abstract  
In this study, we investigated defect detection in dissimilar welded joints by the acoustic emission (AE) method. The study objects were carbide and decarburized interlayers, which are formed at the fusion boundary between austenitic and pearitic steels. Diffusion interlayers, as a structural defect, usually have microscopic dimensions and cannot be detected using conventional non-destructive testing (NDT) methods. In this regard, the AE method is a promising approach to diagnose metal objects with a complex structure and to detect microscopic defects. In this paper, the AE signatures obtained from testing defect-free specimens and specimens with diffusion interlayers are analyzed. We found that the AE signature for defective and defect-free welded joints has significant differences, which makes it possible to identify descriptors corresponding to the presence of diffusion interlayers in dissimilar welded joints.  
dissimilar welded joints; acoustic emission; defects; cyclic loading; NDT; pearlitic steel; austenitic steel; lack of penetration Article
## 4 The Effect of the Measurement Error over the First-Passage Time Distributions  
It is expected that the measurement error affects the behavior of the first-passage time distributions of the observed degradation and the true degradation. If the measurement error is not considered in the modeling, the reliability assessment may be underestimated. For these reasons, it is important to study the effect of the measurement error over the first-passage time distributions, such that a maximum level of error in the measurement system can be determined to obtain a desired reliability assessment. The analysis in this section is focused on determining the differences between the PDF of \(T_{o}\) and \(T_{s}\). [PERSON] et al. [4] proposed to compare two first-passage time distributions via the coefficient of variation (CV) and the variation \((Var(T))\) of two distributions. They implemented such approach in a Wiener model with measurement error. The CV can describe the amount of variability in any random variable, thus it is expected that the difference between the CV of \(T_{o}\) and \(T_{s}\) is relatively small if the effect of measurement error is small. The same approach can be considered if the corresponding variations \(Var(T)\) and means \(E(T)\) are compared. In this  
Figure 1: Comparison of degradation paths for observed and true degradation. (**a**) True non-monotone degradation paths in red dotted lines when \(\Delta Z_{i}\big{(}t_{j}\big{)}<e_{ij}\), (**b**) true monotone degradation paths in red dotted lines when \(\Delta Z_{i}\big{(}t_{j}\big{)}>e_{ij}\).  
article, the indices proposed by [PERSON] et al. [4] are considered and described in (12) and (13) for the CV and variances, respectively. In addition, an index considering the means is proposed in (14). In fact, any percentile \(\big{(}z_{q}\big{)}\) of interest of the first-passage time distributions can be compared as described in (15).  
\[I_{CV}(T_{s},T_{o})=\frac{\left|CV(T_{s})-CV(T_{o})\right|}{CV(T_{o})}. \tag{12}\]  
\[I_{Var}(T_{s},T_{o})=\frac{\left|Var(T_{s})-Var(T_{o})\right|}{Var(T_{o})}. \tag{13}\]  
\[I_{E}(T_{s},T_{o})=\frac{\left|E(T_{s})-E(T_{o})\right|}{E(T_{o})}. \tag{14}\]  
\[I_{z_{q}}(T_{s},T_{o})=\frac{\left|z_{q}(T_{s})-z_{q}(T_{o})\right|}{z_{q}(T_{ o})}. \tag{15}\]  
The four indices are considered to describe the differences between the first-passage time distributions. In this way, it is expected that the four indices do not exceed critical values \(\left(C_{CV},C_{var},C_{E},C_{z_{q}}\right)\), which means that the estimated lifetime obtained from the distribution \(F_{T_{s}}(t)\) can approach to the estimation of the distribution \(F_{T_{s}}(t)\) under certain permissible level of measurement error described in the four critical levels \(\left(C_{CV},C_{var},C_{E},C_{z_{q}}\right)\). The flow chart in Figure 2 is followed to optimize \(\sigma\) in order to establish a control over the measurement system for a certain accuracy of the reliability assessment of interest.  
Figure 2: Proposed scheme for the optimal reliability analysis of degradation processes with measurement error.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 2 Background and Motivation
### Significance of On-Device AI  
Integrating AI directly into constrained devices offers several compelling benefits that align with modern technological and societal needs [50]. One advantage is reduced inference latency, as local processing eliminates the round-trip delay of sending data to a central server, which is critical in real-time systems like drones or industrial robots [18]. Another benefit is improved privacy, as raw data can remain on the device, thereby minimizing exposure to potential eavesdropping or data breaches. This aspect is particularly important in sectors handling sensitive information, such as healthcare and finance, where data protection regulations and ethical considerations are paramount [25].  
Additionally, bandwidth conservation emerges as an important incentive for on-device processing. As the volume of IoT data grows, transmitting these data continuously to a remote server can strain networks, particularly in rural or remote settings where connectivity is sporadic or expensive [11]. By performing key inferences locally, the system drastically reduces the volume of data needed to be sent. In scenarios such as environment monitoring or agricultural sensing, local AI can differentiate between normal and exceptional conditions, ensuring that only relevant data trigger an alert or a database update [12]. Beyond these technical motivations, on-device AI also paves the way for large-scale, distributed intelligence architectures, where billions of devices can each contribute to localized analysis, collectively forming a robust, decentralized network of computing resources [51].
# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 5 Results
### Performance Comparison Using Different Detection Networks  
Table 5 provides a comprehensive comparison of the object detection accuracy between our proposed algorithm and other detection methods. Evidently, our algorithm outperformed the other existing methods with respect to AP50. Furthermore, our analysis revealed that the YOLOv8 model exhibited enhanced performance at higher IoU thresholds.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Neck**} & \multirow{2}{*}{**LF**} & **AP50** & **AP50:95** & **P** & **R** & **Latency** & **FLOPs** & **Params** \\  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(ms)** & **(G)** & **(M)** \\ \hline YOLOv5-s & CSPDarkNet & PAN & CIoU & 46.4 & 23.0 & 49.7 & 49.8 & 8.7 & 15.8 & 7.02 \\ YOLOv5-s & CSPDarkNet & PAN & SIoU & 47.9 & 24.4 & 51.9 & 48.6 & 8.8 & 15.8 & 7.02 \\ YOLOv5-s & ShuffleNet & PAN & SIoU & 44.3 & 21.9 & 49.3 & 46.7 & 8.2 & 1.8 & 0.84 \\ YOLOv5-s & PP-LCNet & PAN & SIoU & 43.9 & 20.8 & 50.3 & 46.1 & 9.4 & 5.8 & 2.96 \\ YOLOv5-s & EfficientNetB0 & PAN & SIoU & 45.7 & 22.8 & 50.2 & 48.7 & 10.5 & 7.1 & 3.51 \\ YOLOv5-s & MobileNetV3 & PAN & SIoU & 42.0 & 20.5 & 48.8 & 45.0 & 10.8 & 2.5 & 1.38 \\ YOLOv5-s & repVGG & PAN & SIoU & 44.5 & 21.5 & 50.1 & 47.2 & 9.1 & 7.0 & 3.4 \\ YOLOv5-s & GhostNet & PAN & SIoU & 45.6 & 22.3 & 50.9 & 49.5 & 12.0 & 7.6 & 4.75 \\ LP-YOLO(ours) & PartitionNet & PAN & SIoU & **46.8** & **24.6** & 52.1 & 50.0 & 11.5 & 6.9 & 4.52 \\  & PartitionNet & & & & & & & & & \\ LP-YOLO(ours) & + VH block + & PAN & SIoU & **48.4** & **25.1** & 53.6 & 49.0 & 13.9 & 7.0 & 4.52 \\  & \multicolumn{2}{c}{} & & & & & & & & & \\ LP-YOLO(ours) & + VH block + & NewPAN & SIoU & **49.0** & **25.5** & **53.7** & 49.8 & 13.5 & 7.4 & **4.32** \\  & CBAM & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection performance comparison of different backbone networks. LF denotes loss function.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 3 Techniques for Explainable AI
### Model-Based Interpretation Techniques
#### 3.1.2 Bayesian Rule Lists (BRLs)  
[PERSON] et al. [25] were the first to propose a method for learning Bayesian rule sets. This method uses Bayesian model averaging to learn a set of rules that jointly maximize the posterior probability of the class labels. They demonstrated the effectiveness of their method on classification tasks such as predicting adverse drug events and detecting fraudulent insurance claims.  
BRLs are a type of ML model that combines the interpretability of rule-based models with the probabilistic reasoning of Bayesian models. The basic idea behind BRLs is to use a set of logical rules for making predictions and to allow for uncertainty in the rules and their weights. The objective of BRLs is to learn a list of if-then rules that define decision boundaries between different classes or regression targets. Each rule in the list consists of a condition and a class or regression target. The conditions are defined in terms of features or attributes of the data, and the class or regression target is assigned a probability based on the conditions of the rules that are satisfied. The weights and probabilities assigned to each rule can be learned from training data or based on expert knowledge. BRLs are often used in natural language processing and other areas where the decision-making process involves multiple competing rules or factors. For example, [PERSON] et al. [26] use BRLs for interpretable classifications of consumer behaviors and [PERSON] et al. [27] apply BRLs to improve the explainability for stroke predictions.
# Image Representation Using Stacked Colour Histogram
## 4 Experimental Evaluation  
In this section, we demonstrate the performance of the SCH method using four datasets (Batik, Coil100, Outext, and Corel10K). The Batik, Coil100 and Outext datasets are used to evaluate SCH in differentiating texture images. The Corel10K dataset is used to evaluate the heterogeneous image recognition capabilities of the proposed method.  
Below is a concise description of datasets.  
* Batik [30] dataset contains 300 images in total. There are 50 classes and each class contains six images. The images are of 128x128 pixels sizes in JPEG format.
* Coil100 [31] dataset contains 7200 colour images categorised into 100 classes. There are 72 images of each object in different poses in each class.
* Outext [32] dataset contains 11,484 total images. The image database is categorised into 29 classes.
* Corel10K [33] dataset contains 10,000 images in total. There are 100 classes and each class contains 100 images. The images are sized 128 \(\times\) 192 pixels in JPEG format.  
Sample images from each dataset are illustrated in Figure 2.  
The initial experiment evaluated the accuracy of window sizes and recurrent transformations using different classifiers and different dataset features. Window sizes of 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, 9 \(\times\) 9 and 11 \(\times\) 11 and the numbers of recurrent transformations of 5, 10, 15, 20 and 25 were used for extracting features for this experiment [34].
## 3 Model Comparisons
### Data Analysis and Model Comparison with Real Application
#### 3.2.4 Comparison of Models' Predictive Power  
For the predictive power comparison, we divide the data set into two parts, 80% and the remaining 20%, respectively. We use the first 80% of data points to estimate the models' parameters, then use the remaining data points to compare the models' predictive power. The SSE values for the prediction are 37.0169, 236.0253 and 23.0348, accordingly shown in Table 9. For DS-1, comparing all models, we find that the new model has the smallest SSE value of 37.0169, which are smaller than the values of other models, e.g., others' SSE values can be 1.87-times (P-Z coverage model's 69.0496) and even 22.24-times (Yamada imperfect (1) model's 823.0770) larger than the value of the proposed model. For DS-2, comparing all models, we find that the new model has the smallest SSE value of 236.0253, which are smaller than the values of other models, e.g., others' SSE values can be 11.75 times (Inflection S-shaped model's 2.7742 \(\times\) 10\({}^{3}\)), even 1468.78 times (Yamada Rayleigh model's 3.4667 \(\times\) 10\({}^{5}\)) larger than the value of the proposed model. For DS-3, though SSE = 23.0348 for the proposed model is not the best result, it is only a little larger than the best result, for the smallest SSE value is 6.9655 (given by the delayed S-shaped model), and comparing to other models, we find that the new model's SSE value is much smaller than the values of other models, e.g., others' SSE values can be 3.16-times (Yamada Rayleigh model's 73.4246) and even 555.33-times (SRGM-3 model's 1.2792 \(\times\) 10\({}^{4}\)) larger than the value of the proposed model. Additionally, for the delayed S-shaped model, it only provides the best result for DS-3, but does not provide the best results for DS-1 and DS-2. This may indicate that this proposed model gives a better predictive power.