esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Ricardo Wave Engine Modelling
### GT1548 Turbocharger Matching to Engine
#### 4.2.2 Compressor and Turbine Map Scaling
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Compressor Target** & **Turbine Target** \\ \hline T\({}_{\text{targ}}\) & 411 K & 1170 K \\ P\({}_{\text{targ}}\) & 2.14 bar & 2.65 bar \\ m\({}_{\text{targ}}\) & 0.118 kg/s & 0.127 kg/s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Target conditions for turbocharger. Figure 8: Turbine map for different dimensionless speeds. Figure 7: Compressor dimensionless map. With the target parameters obtained, the diameters can be calculated using the dimensionless relationship, assuming that the mass flow coefficient is constant for the unscaled and scaled turbocharger maps. Equations (6) and (7) can be equated and rearranged using the current upstream parameters and the target upstream parameters, resulting in two new equations, Equations (13) and (14), in which the \"ref\" subscript denotes the current parameter values and \"targ\" denotes the target parameter values [11]. Similarly, Equations (8) and (9) are used to scale the turbocharger speed by taking the average results obtained and is shown in Equation (15). As the aim of this is to also achieve the correct compressor outlet condition, these were used instead of the inlet conditions to obtain the compressor diameter. The reason this method worked is due to the assumption of the efficiency not changing, and for the target conditions, the power of the compressor absorbed will be the same for the scaled unscaled maps. \[D_{C,\;new}=\sqrt{\frac{D_{C,ref}^{2}P_{d,\;targ}\dot{m}_{C,\;ref}\sqrt{RT_{d, \;ref}}}{P_{d,ref}\dot{m}_{C,\;targ}\sqrt{RT_{d,\;targ}}}} \tag{13}\]  
\[D_{T,\;new}=\sqrt{\frac{D_{T,ref}^{2}P_{u,\;targ}\dot{m}_{T,\;ref}\sqrt{RT_{u, \;ref}}}{P_{u,ref}\dot{m}_{T,\;targ}\sqrt{RT_{u,\;targ}}}} \tag{14}\]  
\[N_{TC}=\;\frac{1}{2}\left[\frac{N_{T,\;ref}D_{T,\;ref}\sqrt{\gamma_{E}RT_{T, \;u,\;targ}}}{D_{T,\;new}\sqrt{\gamma_{E}RT_{T,\;u,\;ref}}}+\;\frac{N_{C,\;ref} D_{C,\;ref}\sqrt{\gamma_{a}RT_{C,\;d,\;targ}}}{D_{C,\;new}\sqrt{\gamma_{a}RT_{C,\;d,\;ref}}}\right] \tag{15}\]  
Running this calculation once yielded better performance results; however, the maps were still not matched to the engine, and the target conditions were not achieved. It was decided to continue iterating the calculation using Equations (13)-(15) and updating the reference parameters with the current values. The final results were then used for the turbocharger maps shown in Figures 9 and 10. These maps were then used to identify the correct compressor speeds for the rest of the engine model at full load. It should be noted that comparing the literature, it was not necessary to scale the efficiency of the compressor as the scale factor was relatively low (2.1%). However, for the turbine with a scale factor of 29%, it may not have been ideal to assume that the efficiency would not change. Therefore, it is possible that the turbine would, in theory, have a higher efficiency and thus a greater power output. Figure 9: Scaled compressor map.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 4 Experiments
### Analysis of Model Efficiency  
In addition to evaluating the registration accuracy, we assessed the computational efficiency of our method in comparison with that of a representative voxel-based approach, VoxelMorph++ [48]. Table 8 summarizes the training memory consumption, inference memory usage, floating point operations (FLOPS), and inference time per scan pair for both methods.  
Our point-cloud-based model demonstrates substantial efficiency advantages. Specifically, the training memory usage is reduced from 5.70 GB to 2.78 GB, and the inference memory usage is decreased from 4.97 GB to 0.82 GB. In terms of the computational complex  
\begin{table}
\begin{tabular}{l l|c c} \hline
**Ref.** & \(\mathbf{N_{h}}\) & **TRE**\(\downarrow\) & **DSC**\(\uparrow\) \\ \hline (a) & 1 & 2.34 & 91.1 \\ (b) & 2 & 2.31 & 90.8 \\ Ours & 4 & 2.24 & 91.4 \\ (c) & 8 & 2.25 & 91.0 \\ \hline \end{tabular}
\end{table}
Table 6: Analysis of the # of attention heads \(N_{h}\).  
\begin{table}
\begin{tabular}{l l|c c c c|c} \hline
**Dataset** & & **TRE**\(\downarrow\) & **25\%** & **50\%** & **75\%** & **DSC**\(\uparrow\) \\ \hline \multirow{2}{*}{L2R-LungCT [7]} & initial & 26.04 & 21.59 & 25.90 & 31.05 & 65.2 \\  & result & 4.53 & 2.63 & 3.79 & 5.73 & 84.8 \\ \hline \multirow{2}{*}{TCIA-Ventilation [4; 6]} & initial & 15.82 & 11.17 & 16.03 & 20.69 & 71.4 \\  & result & 4.33 & 2.44 & 3.26 & 4.50 & 88.8 \\ \hline \multirow{2}{*}{DIR-LAB COPDgene [1]} & initial & 16.25 & 10.14 & 15.94 & 21.76 & 75.8 \\  & result & 3.53 & 1.89 & 2.92 & 4.59 & 89.3 \\ \hline \end{tabular}
\end{table}
Table 7: Generalizability experiment results on various datasets. The model was trained on the Empire10 [3] and TCIA-NLST [4; 45] datasets and evaluated on L2R-LungCT [7], TCIA-Ventilation [4; 6], and DIR-LAB COPDgene [1]. We refer the readers to Table 1 for further dataset details.  
ity, our method achieves a five-fold reduction in FLOPS (318.72 G vs. 56.89 G). Furthermore, the inference time per scan pair is significantly shortened from 266.88 ms to 74.55 ms, indicating a substantial gain in the processing speed.  
These results highlight that by leveraging the sparsity and flexibility of point cloud representations, our method achieves a superior computational efficiency compared to that of voxel-based models while maintaining a competitive registration performance. This efficiency is particularly advantageous for potential clinical applications where fast and memory-efficient processing is critical.
## 1 Introduction
For this purpose, we target the following methodological improvements: (1) increasing spatial resolution, (2) capturing temporal variability, (3) improving the data quality (primarily by reducing uncertainties and increasing consistency), and (4) developing better algorithms to enable the simultaneous use of multiple relevant parameters and different spatial and temporal scales.
## 2 Experimental Study
### Test Instruments and Setup  
A load cell was installed in the center of the steel load distribution plate to capture the loading details during the static and fatigue tests (Figure 3). The relative slip between the H-shaped steel column and concrete slabs of the specimen was measured using two linear variable differential transformers (LVDTs). The two LVDTs were vertically installed on the top of concrete slabs. The LVDTs were mounted on the concrete slabs, and the measuring tips were attached to the L-shaped steel plate fixed to the flange of the steel column (Figure 3). Additionally, unidirectional strain gauges were applied to allow for local strain measurements. A total of nine strain gauges were set on the H-shaped steel column of the specimen. They were mainly arranged on the surface of the web and flange plates (Figure 3). One strain gauge was arranged in the middle of the web plate to observe the global deformation, with a vertical distance 250 mm to the bottom of the steel column. The remaining eight strain gauges were arranged on the inner side of the flange plate at the headed shear studs location to measure shear stresses transferred through the studs. The strain gauges were named Sr1 to Sr8 in order corresponding to the stud number. Figure 3 shows the detailed layout of test instruments, including the load cell, LVDTs and strain gauges.  
Figure 2: Geometric details of push-out specimens (unit: mm): (**a**) front view; (**b**) side view; (**c**) A–A view; (**d**) 3D drawing of specimens.  
The push-out tests were carried out on a computer-controlled electro hydraulic servo fatigue testing machine, as shown in Figure 4. The loading frequency range of the fatigue testing machine was 1 Hz to 10 Hz. The maximum loading capacity of the actuator was 500 kN, both of which could realize force control and displacement control. The H-shaped steel column was loaded by an actuator through a steel drum (Figure 4). All the specimens were subjected to unidirectional loading during the static and fatigue tests.
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 7 Discussion and Future Work  
Our experimental results confirm that the developed application is capable of accurately distinguishing between walking, running, and squatting states using data from Android smartphone sensors. However, it is important to acknowledge potential limitations of our approach. Under extreme noise conditions, such as highly dynamic environments with significant electromagnetic interference or sensor disturbances, the recognition accuracy may decrease due to the reduced efficacy of filtering techniques. Additionally, variations in sensor configurations across different smartphone models or placements (e.g., orientation, mounting positions, etc.) might lead to discrepancies in recognition performance. Future work will address these limitations by exploring adaptive filtering methods, robust feature extraction algorithms, and techniques that enable the system to generalize across diverse sensor setups and challenging operational environments.  
The contributions of our work extend beyond technical performance. The system provides practical tools for health monitoring, sports training, and rehabilitation therapy by enabling real-time tracking and analysis of motion patterns. Our approach also demonstrates the potential for personalized activity recognition by adapting to diverse user profiles through further data collection and advanced machine learning techniques. Future work will focus on refining feature extraction algorithms, enhancing model generalization across varied environments and users and improving user interface design to offer real-time feedback and comprehensive progress tracking. Additionally, addressing data security and privacy through robust measures and adherence to regulatory standards will be pivotal as the application scales to broader usage scenarios, such as fall detection for the elderly, child activity monitoring, and behavioral analysis in security fields.
## 4 Experiments and Results
### Dataset  
Data quality is a crucial factor that directly affects the performance of supervised learning algorithms. The utilization of a representative and high-quality dataset is critical for achieving optimal accuracy and performance [47]. In this study, we obtained the dataset from the DFUC2021 challenge organized by the Medical Image Computing and Computer-Assisted Intervention (MICCAI) society [48]. The proper licensing was also secured for this research, ensuring that all ethical and legal requirements were met. We further added an addition class called healthy, which is available on Kaggle platform [17; 18; 19]. Upon initial preprocessing, we observed that the dataset's class distribution was imbalanced, with 621, 2555, 227, and 2552 instances belonging to the classes both, infection, ischemia, and none, respectively. The number of images of normal/healthy classes we obtained from Kaggle was 543. The reason to introduce the healthy class by collecting additional images from Kaggle is that the none class represents ulcers without any infection or ischemia [48], therefore a healthy condition anchor was still required to assess ulcer evolution towards recovery. Sample images from the different classes are shown in Figure 10 and give a better idea. From the class distribution, we can conclude that the dataset is imbalanced. Such an imbalance poses a challenge to the performance of supervised learning algorithms, as they tend to be biased towards the majority class. To address imbalanced data, we applied geometric data augmentation techniques.  
To test the proposed system, it was mandatory to have the proper hardware and software setup. The next section focuses on the experimental set-up.  
Figure 9: Example 3: Using the same Healthy Image as an anchor, we feed Image B into our SNN and obtain a similarity score of 0.7. This high similarity score indicates a significant improvement compared to the previous score of 0.02, with respect to the Healthy Anchor. This provides a clear insight that the treatment protocol is yielding the expected outcome, as the similarity between Image B and the Healthy Anchor has increased substantially.
# Shear Strength Improvement of Clay Soil Stabilized by Coffee Husk Ash
## 1 Introduction
Even though coffee husk, for example, has a variety of commercial uses, it is often discarded in surrounding landfills due to a lack of appropriate rules and technical specifications for the use in underdeveloped countries. There is no equivalent market need for the coffee husk that is now in use, which results in disposal issues. The majority of coffee husks are merely landfilled or burnt, leading to a waste of resources and degradation of the environment [36]. This improper disposal of coffee husks has a negative effect on the environment, such as land degradation and water bodies contamination, which harms human health [37]. As a result, it is critical to develop sustainable alternative solutions for utilizing this waste to increase cleaner production in the coffee industry. Few efforts have been conducted recently to evaluate coffee husk ash (CHA), a byproduct of the coffee husk burning process, for its possible application as a geomaterial [22; 38; 39]. Accordingly, the general aims of this current research are to clarify the effect of CHA on the geotechnical behavior of clay soil and to study the interacting process. Theoretically, the high concentration of potassium and silica in CHA may contribute to the pozzolanic reactions that occur during hydration and result in the formation of cementitious composites, as mentioned in previous research [22]. These chemicals are responsible for enhancing the soil engineering properties, which improve with the period as the pozzolanic process continues [40]. The CHA revolution in civil engineering has garnered considerable interest, owing to its availability and cheap commercial value. In general, research on the stabilization of clay soils with CHA is rather limited. In the last five years, only black cotton soil has been stabilized using coffee husk ashes [22; 38; 40]. Hence, the novelty of the study is the use of high plasticity tropical clay (CH), which has never been studied before. The specific objective of this study is to determine the effect of CHA application as an agricultural and industrial waste on the clay soil geotechnical properties. Various geotechnical laboratory experiments were conducted to establish the physical properties (Atterberg limits and standard Proctor compaction test), and the mechanical properties (UCS and direct shear tests) of the CHA-soil mixture. Insights are established concerning the effect of CHA content on clay soil as well as the practicability of waste material for use as a geomaterial for construction.
###### Abstract  
With the rapid development of heterogeneous network technologies, such as mobile edge computing, satellite communications, self-organizing networks, and the wired Internet, satisfying users' increasingly diversified and complex communication needs in dynamic and evolving network environments has become a critical research topic. Ensuring secure and reliable information transmission is essential for stable network operation in these complex environments. Addressing this challenge, this study proposed a secure and reliable multi-objective optimized multipath transmission algorithm for heterogeneous networks to enhance security and reliability during data transmission. The core principle of this algorithm was that multipath transmission can provide additional protection through redundant paths. This redundancy ensured that even if one path is attacked or fails, alternative paths can maintain data integrity and reachability. In this study, we employed the Optimized Non-dominated Sorting Genetic Algorithm II (ONSAG-II) to determine the range of the initial population and filter suitable paths by optimizing them according to different demand objectives. In the path selection process, we introduced an innovative deletion graph method, which ensures that redundant paths do not share any common links with the original paths, except when there are unique links. This approach enhances the independence of transmission paths and improves the security of the transmission process. It effectively protects against security threats such as single points of failure and link attacks. We have verified the effectiveness of the algorithm through a series of experiments, and the proposed algorithm can provide decision-makers with high-reliability and low-latency transmission paths in heterogeneous network environments. At the same time, we verified the performance of the algorithm when encountering attacks, which is superior to other classical algorithms. Even in the face of network failures and attacks, it can maintain a high level of data integrity and security.  
heterogeneous network; multi-objective optimization; multipath transmission; privacy protection; security; reliability +
Footnote †: journal: Electronics Letters
# Ionospheric Remote Sensing with GNSS
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GNSS & Global navigation satellite system \\ PNT & Positioning, Navigation and Timing \\ GPS & Global Positioning System \\ GLONASS & Global\({}^{\prime}\)naya Navigatsionnaya Sputnikovaya Sistema \\ BDS & BeiDou Navigation Satellite System \\ FOC & Full Operational Capability \\ MEO & medium Earth orbit \\ LOS & line-of-sight \\ RF & radio-frequency \\ CDMA & Code Division Multiple Access \\ FDMA & Frequency Division Multiple Access \\ BOC & Binary Offset Carrier \\ GEO & geostationary orbit \\ IGSO & inclined geosynchronous orbits \\ DOP & dilution of precision \\ RINEX & Receiver Independent Exchange Format \\ P & Pseudorange \\ CP & Carrier-phase \\ ESF & Equatorial Spread F \\ EPB & Equatorial plasma bubbles \\ SAPS & sub-auroral polarization streams \\ TOI & tongues of ionization \\ TEC & total electron content \\ DCB & differential code bias \\ MIT & Massachusetts Institute of Technology \\ AAL-PIP & Autonomous Adaptive Low-Power Instrument Platforms \\ ULF & ultra-low frequency \\ COSMIC & Constellation Observing System for Meteorology, Ionosphere,and Climate \\ \end{tabular}
# Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
## 2 Non-Invasive Monitoring Techniques
### Eddy Current Detection  
Eddy current testing (ECT) is based on the interaction between the main magnetic field and the tested material to generate eddy currents within the specimen [67; 68]. Discontinuity features such as corrosion and material loss can be detected by monitoring changes in coil impedance or measuring induced magnetic fields [36; 67; 69]. The significant advantage of ECT is that it can detect defects under high-temperature conditions, and its probes can be made into various shapes to adapt to different objects. However, the ECT detection of deeper defects within the material substrate still faces issues of lower penetration depth and skin effects of ferromagnetic materials [35]. Traditional ECT works at a single frequency, where a sine wave drives the excitation coil, of which the application, in the case of non-conductive composite materials, may be limited by penetration depth due to the impact of delamination on the signal-to-noise ratio [70; 71]. Typical eddy current probes include impedance change probes and excitation detection probes. Several commercial probes, such as absolute and differential tube probes, electric rotary probe coils, and array probes, have been widely used for pipeline detection [72]. ECT has several advantages: (1) non-contact detection can penetrate the coating layer without coupling medium; (2) defects can be detected under the condition of high temperatures, and the probe of ECT can be made into various shapes to adapt to different objects; (3) it has a higher sensitivity to near-surface defects [35].  
The general schematic of the pulse eddy current (PEC) system is depicted in Figure 5a, by which [PERSON] et al. [73] investigated the corrosion problem of soft steel (S275). The PEC response caused by corrosion is a complex combination of many factors. The two time-domain features extracted from the PEC response, representing the changes in conductivity and permeability of the corrosion layer or actual corrosion area, are generally used to characterize corrosion behavior. Additionally, the relationship between PEC characteristics and exposure time, which can be used for measuring corrosion rate and early corrosion assessment and prediction, has been derived. The experimentally obtained values and fitting lines of PV (Bnorm) for uncoated and coated samples are shown in Figure 5b [73]. The measurements at different exposure times were performed under the same excitation, taking the average measured results as the experimentally obtained values.
## 3 Results
### Selection of Charge Amount per Hole  
A new free surface formed at 64.5 ms after the detonators of the first and second delays initiated. Since its action was still not stabilized until 91.375 ms, for safety reasons, we needed to superpose the single-hole waves of the first three blast holes to optimize the single-hole charge amount.  
We conducted single-shot tests in the field to select the optimal charge to achieve safety and a high advance. The Monte Carlo method was used to select 1000 initiation time sequences at random at first to simulate the detonations' randomness. Then, 1000 different superposed waves were superposed by single-hole waves according to the sequences. Each superposed wave had a peak velocity. After comparison, we could obtain the maximum value of the peak velocity for the different single-hole charges. This was the most unfavorable situation for vibration control. Therefore, it was used to judge whether the charge amount in a single hole would cause the superposed vibration to exceed the limit in blasting practice. Tables 1 and 2 shows the calculation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & \(\delta_{2}\)**/ms** & \(\delta_{3}\)**/ms** & **Peak of \(S^{\prime\prime}(t)\)**/cm**-\(\mathbf{s^{-1}}\) \\ \hline
1 & 44.60 & 90.48 & 0.629 \\
2 & 47.90 & 90.90 & 0.638 \\... &... &... &... \\
712 & 37.77 & 88.90 & 0.775 \\... &... &... &... \\
999 & 44.62 & 87.19 & 0.639 \\
1000 & 38.06 & 89.70 & 0.769 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of solving for the maximum value of vibration velocity when the charge amount was 1.0 kg in a single hole.  
Figure 11: Linear correlation \(\rho\) of \(E(t)\) and \(E^{\prime}(t)\).  
Figure 12 shows the single-hole waves for 1.0 kg and 1.2 kg and their superposition process. For each hole with 1.0 kg of explosive, the wave of the first delay was \(f_{1.0}(t)\). If the detonators of the second and the third delays initiated at 37.77 ms and 88.90 ms, respectively, the superposed wave was \(f_{1.0}(t)+f_{1.0}(t-37.77)+f_{1.0}(t-88.90)\). The maximum vibration peak of the superposed wave was 0.775 cm\(\cdot\)s\({}^{-1}\). For each hole with 1.2 kg of explosive, the wave of the first delay was \(f_{1.2}(t)\). If the detonators of the second and third delays initiated at 41.37 ms and 91.94 ms, respectively, the superposed wave was \(f_{1.2}(t)+f_{1.2}(t-41.37)+f_{1.2}(t-91.94)\). The maximum vibration peak of the superposed wave was 0.868 cm\(\cdot\)s\({}^{-1}\). Thus, in theory, 1.0 kg or 1.2 kg explosive charged in each hole could ensure that the vibration peak did not exceed the safety threshold before the formation of the new free surface.