esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 5 Conclusions  
In this paper, a literature review on the erosion mechanism and the flow dynamics in cavitating jets is provided. The focus was on both experimental and numerical investigations in cavitating jets and their industrial applications.  
It was shown by many authors that the erosion potential of cavitating jets can be enhanced using specific jet nozzle geometries that result in self-excited flow regimes and thus, the amplification of the pressure amplitude at the impinging wall. It was illustrated that the incipient cavitation numbers for the self-excited structured jet can be two to six times higher than with a conventional jet.  
It was also shown that the nozzle material and its surface finish in addition to the standoff distance, the jet angle, the fluid temperature, and the flow regime play a crucial role for effective cavitating jet performance.  
When selecting a self-resonating nozzle jet, both the flow regime and the nozzle geometry should be considered for a proper nozzle design. The critical Strouhal number can be varied by changing the nozzle geometry, so the circulation strength of the large-scale vortices is maximized.  
When properly designed, self-resonating jets showed their superior performance as compared to standard jet nozzles for different industrial applications, ranging from cleaning applications to rock cutting, oil-shale mining, and bitumen separation.  
The main challenge facing the implementation of the cavitating jets in the drilling industry is the shorter jet core, resulting in a pressure amplitude peak that occurs at a few jet diameter downstream from the jet exit. Therefore, careful design of these jets should be conducted depending on the specific drilling configuration, and further development of the nozzle geometries would be of high benefit for their successful industrial implementation.  
[PERSON] conceptualized the paper; [PERSON] and [PERSON] reviewed the paper; [PERSON]-[PERSON] contributed to the analysis; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This study was financially support by an internal research grant from the Prince Mohammad Bin Fahd University.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Findings and Discussion
### ITS/IoV Intrusion Detection Systems (IDS)

Table 4: \(Cont.\)AI-enabled IDSs are an effective security measure to combat invasions. Conventional IDSs include rule-based, signature-based, flow-based, and traffic-based techniques. Because most connections and traffic in IoV networks were previously specified, these IDS detect aberrant activity. For example, when the intruder establishes new connections to the victim or delivers a different type of traffic, the network will see anomalous data flows [113; 114]. Unfortunately, due to the frequent network, which results in constant topology changes, conventional IDSs do not perform adequately. In addition, intelligent IDSs are necessary to counter the emergence of new forms of attacks or wisely planned intrusions such as man-in-the-middle (MiM) attacks. AI-based IDSs are often helpful if an intruder disrupts network traffic communication. It is difficult to detect the breach if the attacker does not connect with any network components. To compromise network operations, the intruder must disrupt the network somehow. AI techniques can detect anomalies that are difficult to detect by a human. The capacity of AI techniques to detect even minor anomalies distinguishes them from other types of IDSs. AI-based IDSs can be constructed with a shifting target to create a secure network. Because attacks are continually developing and new vulnerabilities occur daily, the capacity of AI-based models to adapt and grow is precious. It is why signature-based IDSs are becoming outdated, thereby giving way to anomaly-based ML IDS as an emerging trend [113]. AI-based techniques are apt for provisioning authentication and authorization of security control and network traffic patterns, which require high learning sensitivity and robustness. They have been helpful as detection and mitigation tools against data integrity threats and snooping intrusion by targeting the availability of specific attack features. Their  
Figure 5: Classification of Intrusion Detection Systems. superiority to network analyzers is in automation and disinclination to human error [115]. Below are some AI-based IDS frameworks for ITS/IoV:
# Burst Detection in Water Distribution Systems:
## 1 Introduction
This model simulates each component of the water request with a proper pressure-demand relationship and suitable demand scheme: distributed along pipes for the water demand and background leakages and concentrated at nodes for bursts. In addition, DFPDM enables randomly generating different types of pipe bursts by varying the position along the networks, the amount of water losses, and the frequency and duration according to a defined occurrence probability. Therefore, DFPDM that allows for obtaining extended period simulations includes both the hydraulic and mechanical reliability of WDS through a proper simulation of the satisfied water request and the pipe breaks [27; 28]. The results highlight the generation of a suitable hydraulic dataset of a lacking labels time series, which are closed to real data in both mean and variance. Eventually, the SHtsG is able to provide a massive amount of data that is crucial in the development of machine learning algorithms for anomaly detection. The rest of the paper is organized as follows: Section 2 presents the framework of the proposed methodology; Section 3 depicts two test cases aimed at validating the methodology as well as enlightening its advantages and strengths; and Section 4 draws conclusions and final remarks.
# Multipath Error Fusion Modeling Methods for Multi-GNSS
## 3 Results
### Use the Observations from Multi-Day for Testing
It can be seen from the figure that the RMS of the double-differenced residuals further decreased with multipath error correction strategy M (10). An average improvement of 13.1% in the RMS of the double-differenced residuals could be achieved compared with M (1). The statistical results of M (1) had a relatively low improvement over a few days compared with M (10), which was especially obvious in GPS. Therefore, the right part of Figure 6 shows the change of RMS improvement of the double-differenced residuals in the verification days with different strategies, and the total number of observations involved in modeling with M (1) of the corresponding days. It was found that there was a certain correlation between the total number of observations and the improvement of error correction in M (1). This also explains that the low improvement of DOVs 226-230 in 2018 of GPS was mainly due to the decrease of observations for MGHM modeling, which led to a reduction in model accuracy, and the integrated modeling of multi-day data could solve this problem and keep the improvement of error correction with MGHM at a relatively stable level. The following statistics were also gathered regarding the average value of the double-differenced residuals with multipath error correction strategy M (10) in the verification days. Compared with the statistical results in Figures 5 and 7, the data distribution with error correction strategy M (10) was more concentrated than M (1) in Figure 5.  
In this section, the one-hour static positioning results of the verification day with error correction strategy M (10) were also counted to show the influence of the model correction on the positioning results. Compared with the uncorrected solutions in the strong multipath environment, the 3D positioning accuracy of Station A with M (10) correction increased by 84.1%, and that of station B in a normal environment reached 63.5%, which were both better than those of M (1). Therefore, the modeling of multi-day could further improve the accuracy of MHGMs and avoid the phenomenon where the lack of modeling observations in M (1) led to a poor effect of multipath error correction.
# Three-Band Spectral Camera Structure Design Based on the Topology Optimization Method
## 2 Design of Three-Band Structural Components
### Initial Structural Design  
The stability of the structural components plays a decisive role in maintaining the relative positions of the optical components and ensuring the overall imaging quality. Based on the requirements of the initial optical system, an initial structural design was developed, as shown in Figure 2a. The system is divided into five submodule systems, with all five modules connected to the housing by screws, as depicted in Figure 2b. Thesemodules are then secured to the housing through five support legs, as shown in Figure 2c. In Figure 2d, the structural components are labeled as follows: structure D supports the mid-wave infrared system; structures C and B support the beam-splitting system; and structures A and E provide support for the visible light system. The structural material chosen is hard aluminum. The initial weight for the housing is 8.84 kg, and the mass for the support is 0.72 kg, as shown in Figure 2d. The total weight of the initial structure before optimization is 9.56 kg.  
Figure 1: Three-band optical system.  
Figure 2: Initial structural design: (**a**) preliminary overall structural diagram; (**b**) preliminary overall housing structure; (**c**) preliminary optimization of support with opti-lens; (**d**) preliminary optimized support.  
Given the small size of the lenses, their mounting method is based on common aerospace practices. Each individual lens is fixed using its own lens barrel with a protruding base on the end face and a retaining ring. During lens installation, an adhesive is applied around the circumference to bond the lens to the frame. The retaining ring is then screwed in place to secure the lens. Each lens is individually mounted as a single lens assembly. To ensure the alignment of the optical axis with the barrel's axis, centering machining is performed for all lens assemblies. The axial spacing between the optical components is maintained using spacers. Inside the lens barrel, a 0.5-mm-deep adhesive groove is added to ensure that the spatial positions of the optical components remain stable in complex mechanical environments.
## 7 Open Research Issues  
Accurate path loss is central to the success of 5G deployments, a critical factor that directly impacts network planning, optimization, and resource allocation. While considerable research has been devoted to path loss modeling, a comprehensive survey revealed significant research gaps and outlined promising directions for future work in understanding the performance of path loss models across the diverse frequency bands of 5G, including mid-band and high-band channels.
## 4 Results and Discussion
### Discussion
#### 4.4.2 Computational Complexity Analysis  
From the perspective of computational efficiency, the proposed framework maintains a favorable balance between model expressiveness and runtime feasibility. The transformer-based policy-learning module, composed of three stacked encoder layers, introduces a self-attention mechanism with a complexity of \(\mathcal{O}(T^{2}d)\), where \(T\) denotes the input sequence length and \(d\) is the feature embedding dimension. In our setting, \(T=10\) and \(d=256\), which constrains the quadratic growth and allows practical training on modern GPUs. Multi-head attention introduces a constant multiplicative overhead proportional to the number of heads (set to four in our implementation), but parallelization across heads mitigates latency. The reward estimation module adopts a 1D convolutional architecture with a total of two convolutional layers and one pooling operation. Its time complexity is \(\mathcal{O}(Tkd)\), where \(k\) is the kernel size. This design is significantly more efficient than a transformer- or RNN-based critic while still capturing local dependencies through sliding receptive fields. The entire training pipeline runs on a single Tesla V100 GPU (manufactured by NVIDIA Corporation, headquartered in Santa Clara, CA, USA) with approximately 24 min per epoch and peak memory usage below 12 GB, as profiled on the full-scale dataset. Furthermore, the sparse-feedback-enhancement module operates during training only and introduces minimal inference-time overhead. Clustering-based reward smoothing has a complexity of \(\mathcal{O}(Nk)\) for \(k\)-nearest neighbor search, where \(N\) is the batch size. However, this process is offloaded to auxiliary GPU streams and does not impede forward or backward propagation.  
In summary, the overall framework achieves a complexity-efficient configuration that supports deployment in real-time decision-making systems. Compared with other DRL-based pricing models, the proposed approach introduces only moderate computational overhead while yielding consistent gains in stability, generalization, and long-horizon value estimation.
## 4 Discussion on Results  
The results show that the Long-Term and Short-Term HRV analyses are comparable in terms of predictive power on the detected parameters, when the target is identifying if patients are healthy or diseased (Heart Failure). The Short-Term HRV method is highly preferable, since it is much less invasive for the patient (five minutes for ECG acquisition, compared to a 24 h Holter ECG acquisition). It is also very suitable for tele-monitoring scenarios, such as those described in [29].  
These results are comparable with the literature. In [23] similar results are obtained--using a static (non cloud) Classification And Regression Tree (CART) approach on MatLab--in terms of overall  
Figure 14: Results for Short Term HRV, using LF/HF + SDNN as multiple predictors.  
Figure 13: Results for Short Term HRV, using LF_PWR as single predictor.  
accuracy (>90%) and most predictive parameters (SDNN, SDANN and TOT_PWR). In [24] is described a Short-Term approach; the obtained results are similar to ours, both in terms of overall accuracy and of most effective predictors (LF/HF).  
We can therefore assert that the results obtained using a cloud approach on IBM WA are comparable to the results obtained on ad hoc custom desktop platforms. The results are shown in a clear and friendly way, easily understandable also by non experts.  
The main advantage of the proposed approach for the researcher is the possibility of being quickly operative, focusing only on the experiment, without taking care of hardware requirements (high computational power is needed for these analyses) or machine learning algorithms development.  
From a medical point of view, the results of this study can be interpreted as the possibility to perform a preliminary and early diagnosis of HF, basing solely on the analysis of the ECG signal (accepting a certain level of uncertainty, as shown by the accuracy values).  
These findings are not meant to replace the diagnostic procedures for an exhaustive diagnosis, explained in the ESC guidelines, but can be very helpful in many scenarios such as home telemonitoring for the daily monitoring of patient status.  
As shown, even short term analysis has a strong predictive power: this means that the patient will benefit of the proposed approach, having to stay connected to an electrocardiograph for only 5 min (instead of 24 h).  
It is very important to note that HRV analysis is based only on the progress of heart rate without any further analysis of the ECG wave form. This means that for the proposed system it is only needed a device for high quality detection of the heartbeat (for example, a 2-lead ECG measured from hands) instead of a costly and less practical 12-lead electrocardiograph. This aspect is particularly important for enabling mobile applications.
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 8 Discussion
### Findings
#### 8.1.4 Free-to-Play Task  
In the _Free-to-Play_ task, some behaviour is visible in the data of Patients 1, 2 and 3. Specifically, the more towards the neglected side it is, the more likely a key will be left unplayed for longer. This behaviour is unmistakable evidence of USN. This task has no limited exploration phase as with the _Memory_ task; the cues appear continuously and thus exploration is required continuously. An action also did not lead to a predictable next stimulus as with the _Scales_ task. Indeed, for the _Free-to-Play_ task, stimuli could appear at random times and in random order. Furthermore, the stimuli are only visual (the ball turning green and then red while resting on a key). Therefore, the user must continuously scan the playing area for visual cues, and the incentive to do so depends entirely on the user's ability. This can explain why the centre of the playing area is explored the most while the peripheral area is the least. Therefore, it can also explain why the keys in the centre are left unplayed for the shortest time on average. Moreover, also for this task, a noteworthy evolution is visible in the pattern of the average head direction of the different sessions. The patients appear to be exploring more as sessions in VR progress, as is the case for the _Memory_ task. This can also indicate that some behaviour is taught to explore the neglected side. This behaviour provides additional evidence that the presented VR system contributes to the rehabilitation of patients with USN. Moreover, for this task, minimal mistakes were made by the test users, meaning that they rarely let a ball touch a key, and therefore, not a lot of data are available. This makes a comparison infeasible. This task aims explicitly to encourage patients to explore their neglected peripheral area. This was the reason for placing most keys on the neglected side instead of distributing them evenly as for the _Assessment_ task. Comparable data from the _Assessment_ would provide more insights into the behaviour of the patients and the impact of the VR tasks. Unfortunately, these data are not available from this study.
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 2 Related Work
### FFT-Spectrum-Based  
In recent years, heatmap-based methods have gained increasing attention for their ability to exploit the rich spatial and temporal information embedded in radar signals. Within spectrogram-based representations, mmPose, proposed by [PERSON] et al. [20], introduces a novel transformation that converts sparse point cloud data into customized three-channel RGB images encoding horizontal (range-azimuth-intensity) and vertical (range-elevation-intensity) features. Compared to the earlier RF-Pose system [21], mmPose reduces estimation errors by 1.0 cm and 1.3 cm along the x and y axes, respectively. However, it incurs a 2.6 cm increase in z-axis error, highlighting the persistent challenge of accurate elevation estimation.  
To address the challenge of multi-dimensional feature fusion, [PERSON] et al. [22] proposed the RPM framework, which integrates dual-path heatmap inputs with a spatiotemporal attention mechanism and a multi-dimensional fusion module. Cross-environment evaluations show that RPM consistently outperforms both RF-Pose and mmPose in pose estimation accuracy. The HuPR framework, introduced by [PERSON] et al. [23], further advances the field by releasing the first public benchmark dataset for mmWave FMCW radar-based pose estimation. HuPR employs four-dimensional heatmaps (range-Doppler-azimuth-elevation) and incorporates a graph neural network within a cross-modal learning architecture to optimize skeletal predictions, achieving significantly improved average accuracy comparedto RF-Pose. Notably, [PERSON] et al. [24] address the issue of kinematic inconsistency by introducing a tri-modal heatmap input processed via a CNN-LSTM hybrid network. Their system achieves an average estimation error of 6 cm across 13 keypoints, highlighting the potential of mmWave FMCW radar-based pose estimation in clinical applications such as medical monitoring.  
A systematic review of existing literature reveals several persistent challenges in mmWave FMCW radar-based human skeleton estimation. First, the lack of comprehensive robustness evaluation limits the model's adaptability to complex and dynamic environments. Second, most existing architectures fail to incorporate explicit modeling of human anatomical priors or kinematic constraints, often resulting in physically implausible or anatomically inconsistent pose estimations. These limitations collectively hinder the deployment of mmWave-based HSE systems in real-world applications, particularly in scenarios that require high reliability, spatial coherence, and interpretability.  
To address these limitations, we propose mmHSE, as illustrated in Figure 1. The mmHSE framework consists of three primary components: (1) a dual-radar data acquisition platform (detailed in Section 3.2), (2) a signal processing pipeline that converts raw ADC data into structured range-angle maps (Section 3.3), and (3) a two-stage neural architecture for skeletal keypoint estimation and anatomical refinement (Section 4).
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.2 Kinetic Studies and Isotherms  
The results concerning kinetic behavior (Table 4) revealed that the adsorption of CBZ onto the biochars was better described by a pseudo-first-order reaction (it was not possible to adjust the pseudo-second-order model to kinetic results of the biochar TN < 75 \(\upmu\)m). An analysis of the k\({}_{1}\) values (Table 4) showed that the k\({}_{1}\) value for the CBZ/biochar TN < 75 \(\upmu\)m was higher than that for biochar 1-2 mm. Thus, the adsorption rate of CBZ on  
Figure 6: Effect of the initial CBZ concentration on CBZ removal from aqueous solutions (operational conditions: j = 6.67 mA/cm\({}^{2}\); NaCl = 0.1 M; pH = 7; reaction time = 10 min; cathode = STS; anode = MMO).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **SARATECH®-102282** & **Biochar TN** & **Biochar TN** \\ \hline Ash content (\%) & 0.2 & 5.90 & 5.90 \\ \hline Moisture content (\%) & 0.1 & 4.32 & 4.32 \\ \hline Particle size (mm) & 0.457 & \textless{}0.075 & 1.0–2.0 \\ \hline Surface area (m\({}^{2}\)/g) & & & \\ BET & 1736 & 62 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the adsorbents.  
the smaller biochar was faster than for 1-2 mm. The activated carbon from SARATECH(r) showed almost instant adsorption of CBZ, therefore it was not possible to adjust the results to kinetic models.  
The equilibrium adsorption isotherm is fundamental to describe the interactive behavior between solutes and adsorbents, and is the basic requirement in the design of adsorption systems [49].  
The calculated constants for the Freundlich and Langmuir equilibrium models are presented in Table 4. These isotherms showed a characteristic L-type behavior according to the Giles classification, thus representing a system in which the adsorbate was strongly attracted by the adsorbent [49]. Although all models described the experimental results well, taking into consideration the parameters obtained, the Freundlich equation gave a better representation of the experimental results for all the tested adsorbents.  
Considering the adsorption capacity, the SARATECH(r) and the biochar TN < 75 \(\upmu\)m were chosen for further optimization studies.