esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Analysis and Hypothesis Testing
### Usage Scenario and Perceived Information Volume  
This research explored the impacts of a video-displayed product usage scenario on consumers' perceived information volume by independent-samples \(t\)-tests and a variance analysis. The experimental data show that the influence of the product usage scenario information in the video (F\({}_{\text{PIV}}\)(1,61) = 23.230, p \(<\) 0.001) significantly affected consumers' perceived information volume, as shown in Figure 8. Specifically, compared with products without usage scenario information, products with usage scenario information included in the video were associated with an increase in consumers' perceived information volume (M\({}_{\text{without usage scenario}}\) = 2.8, M\({}_{\text{with usage scenario}}\) = 4.42). Hypothesis 1 was verified. Displaying product usage scenario information positively affects the perceived information of consumers.
## 4 Literature Review: Other Research on CWSN
### Sensing Techniques
#### 4.1.2 Cooperative Sensing  
A few cooperative sensing schemes are available in the literature. Some of them are discussed here. [PERSON] and [PERSON] [74] proposed a censor-based cooperative spectrum sensing scheme using fuzzy logic for CR-WSNs. They proposed a Takagi-Sugeno's fuzzy system to make a decision on the presence of the PU's signal based on the observed energy at each CR wireless sensor. In this scheme, the local spectrum sensing results aggregated at the fusion center after being censored to reduce the transmission energy and reporting time to make a final sensing decision. [PERSON] _et al._[75] also proposed a similar cooperative spectrum sensing scheme based on fuzzy logic for CR-WSNs. In this scheme, the CR wireless sensors use T-S fuzzy logic to make local decisions on the presence or absence of the PU's signal, and use a censoring method to allow only relatively reliable decisions sent to the fusion center.  
A problem in cooperative sensing is that selfish wireless sensors have the option to choose cooperative spectrum sensing or local spectrum sensing. A selfish wireless sensor selects whatever it determines to be more profitable. How to achieve a desired decision outcome that maximizes spectrum utilization under the requirement of self-interest maximization and PU protection is discussed in reference [76]. The authors propose a revenue function that evaluates the gain and cost of a wireless sensor in choosing cooperative sensing and local sensing. The gain comes from data transmission and the cost comes from the delay and energy consumption. The interactive decision-making of wireless sensors is formulated as a noncooperative game, and the Nash equilibrium corresponds to a stable decision outcome in the sense that no wireless sensor is willing to unilaterally deviate from it.  
[PERSON] and [PERSON] [77] proposed a hybrid cooperative detection scheme that associates the Eigen value-based spectrum sensing with an energy detector. This can be implemented for a range of signal detection applications without knowledge of the signal, channel and noise power. [PERSON] _et al._[78] proposed a cooperative spectrum sensing scheme for CR-WSNs. In this scheme, CR wireless sensors detect the channels using an energy detection technique. The sensing results of each CR wireless sensor are collected at the fusion center, which makes a global decision on the presence of the PU on the channel using a fusion rule. This scheme employs hard decision based spectrum sensing, in which the sensor nodes send one bit per decision, unlike a soft decision scheme that sends multiple bits per decision. They also proposed a sleeping and censoring scheme for energy efficiency.
## 2 Materials and Methods
### Finite Element Modelling
#### 2.4.2 Material Properties  
Experimental compression tests were carried out to determine the elastic modulus of the additively manufactured insole. The force-displacement curves of three cylindrical specimens (height 8 mm, diameter 60 mm) were measured using the ZwickRoell testing machine (ZwickRoell AG, Ulm, Germany) and its testing software, testXpert 2 (ZwickRoell AG, Ulm, Germany). The compression test was based on the compression test standard ASTM D 3574-17 [50], with a compression speed of 1 mm/min; the trial ended at 25% of the initial height. The recording frequency was 25 Hz. The mean value curve was calculated from the force-displacement curves of the three specimens. These were converted into  
Figure 3: Finite element model of the reconstructed foot with the calcaneus coordinate system [48], the origin of which is located at the inter-malleolar point (midway between medial and lateral malleolus).  
stress-strain curves, and the slope was calculated in the linear-elastic range to determine the elastic modulus [51].  
The bone material, insole, and shoe were defined as linear elastic materials in the FE model. The material properties are summarized in Table 1.  
The material assignment for the soft tissue was carried out with a hyperelastic material due to the non-linear behaviour [55]. Here, an isotropic, incompressible, second-order hyperelastic polynomial formula is used according to [PERSON] et al. [56]; Equation (1):  
\[U=\sum_{i+j=1}^{N}C_{ij}\big{(}\overline{I}_{1}-3\big{)}^{i}\big{(}\overline{I} _{2}-3\big{)}^{j}+\sum_{i=1}^{N}\frac{1}{D_{i}}(J_{el}-1)^{2i} \tag{1}\]  
where \(U\) is the strain energy, \(J_{el}\) is the elastic volume ratio, \(\overline{I}_{1}\) and \(\overline{I}_{2}\) are the deviatoric strain invariants, \(N\) is the order, and \(C_{ij}\) and \(D_{i}\) are material parameters. The corresponding coefficients can be obtained from Table 2. The stress-strain relationship of this nonlinear function relates to an in vivo ultrasound study by [PERSON] et al. [55] on subjects with healthy plantar tissue.
## 2 Materials and Methods
### Specimen Preparation  
A series of undrained triaxial compression tests was conducted on reconstituted base sand and binary test specimens to study their shear responses as a function of NS content. Double-split-type molds, 36-mm diameter and 80 mm high, were used to prepare the sand specimens. A rubber membrane was first placed (stretched) over the inner wall surface of the mold and secured against it by applying vacuum pressure. Air-pluviation was used to form the initially metastable loose to medium-dense sand specimens. This method has been successfully used to minimize grain segregation and leads to formation of homogeneous metastable random packing [29; 30; 31]. The air-dried sand was poured from a constant height (~10 mm drop) into the specimen mold. Through tapping of the mold's outer wall surface, identical uniform specimens were formed to various targeted relative density values. When set up in the triaxial cell apparatus, full saturation was achieved by flushing gaseous carbon dioxide and de-aired water upwards through the membrane-enclosed sand specimens via the triaxial base pedestal. For practical purposes, a pore pressure coefficient \(B\) value of 0.96 was deemed enough to represent the specimen state of full saturation. With respect to the treated specimens, the NS solution was allowed to permeate upwards through the saturated specimen via the bottom drainage  
\begin{table}
\begin{tabular}{c c} \hline
**Component** & **Fraction: wt.\%** \\ \hline SiO\({}_{2}\) & 33.96 \\ Fe\({}_{2}\)O\({}_{3}\) & 0.07 \\ Al\({}_{2}\)O\({}_{3}\) & 0.27 \\ Na\({}_{2}\)O & 5.02 \\ Cl & 0.35 \\ P\({}_{2}\)O\({}_{5}\) & 0.11 \\ CaO & 0.07 \\ MgO & 0.046 \\ SO\({}_{3}\) & 0.097 \\ K\({}_{2}\)O & 0.12 \\ LOI & 59.89 \\ \hline \end{tabular}
\end{table}
Table 3: Chemical composition of colloidal NS.  
line of the base pedestal. A small backpressure of 10 kPa was applied to facilitate the grout movement. The specimens were retained within the double-split mold to avoid disintegration during the grouting process. A grout volume equivalent to three times the volume of the specimen pore voids was flushed into each specimen to maximize grout reach. When the solution was exposed to air, the grout gel time was approx. 60-90 min; nevertheless, to ensure the formation of cementation bonds, grouted specimens were allowed to cure for a 24-h period at constant temperature of 25 \({}^{\circ}\)C. Following curing, the split mold and rubber membrane were removed, and the post-treatment specimen height and mass were recorded. Treated specimens were then carefully placed back on the base pedestal, resealed inside the rubber membrane, and a 20 kPa confinement pressure applied. Figure 3 shows the general triaxial apparatus set-up used in this study.
## 2 Materials and Methods
### GC-IMS Data Acquisition  
A FlavourSpec from G.A.S. (Dortmund, Germany) and gas chromatography (Agilent Technologies, Agilent, CA, USA) were used to obtain HS-GC-IMS data. First, 100 \(\upmu\)L of SAEO was automatically injected in the splitless mode at 85 \({}^{\circ}\)C after hatching for 5 min. The injection needle temperature was 85 \({}^{\circ}\)C and the IMS temperature was 45 \({}^{\circ}\)C. The separation was carried out on an FS-SE-54-CB-1 (15 m \(\times\) 0.53 mm ID: 0.53 mm) column at 40 \({}^{\circ}\)C. Nitrogen (99.99% purity) was used as the carrier gas and the linear pressure program was as follows: 2 mL/min for 2 min, ramped up to 20 mL/min for 8 min, ramped up to 100 mL/min for 10 min, then, ramped up to 150 mL/min for 10 min, and held for 10 min. Nitrogen was used as a drift gas at a flow rate of 150 mL/min. LAV software version 2.2.1 (Gesellschaft fur Analytische Sensorsysteme mbH, Dortmund, Germany) was used to collect the data.
# Tip Vortex Cavitation and Induced Noise Characteristics of Hydrofoils
## 4 Conclusions  
Experimental observations in relation to the inception of tip vortex cavitation and the induced noise characteristics from hydrofoils of elliptic planform (NACA 16-020 and NACA 66(2)-415) have been presented. Using a high-speed imaging, we identified the cavitation inception number for a given hydrofoil once the sporadic streaks of water vapour pockets appear in the vortex core. Upon the decrease of the cavitation number, the cavitation in the  
Figure 11: Comparison of noise characteristics of TVC attached to the tip of the hydrofoils.  
Figure 10: Cavitation pattern for NACA 66(2)-415 at \(\alpha=8^{\circ}\).  
vortex core became fully developed and got attached to the tip and further formed as sheet cavity and cloud cavity at the leading edge of the hydrofoil. The development process of tip vortex cavitation including inception is shown to occur in six different stages. Noise due to the cavitation was measured by unsteady pressure sensors. It was found that the sound pressure levels associated with the cavitating case was found to be considerably higher than the non-cavitating case. It was also found that there was an intense noise with a discrete frequency as the cavitating vortex grows and gets attached to the tip of the hydrofoil.  
Conceptualization and methodology, S.S. and B.-K.A.; investigation, S.S., J.-W.H. and D.N.; writing--S.S.; writing--review and editing, supervision, B.-K.A.; project administration and funding acquisition, S.-G.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019 RIA2C1084306) and Daewoo Shipbuilding & Marine Engineering Co., Ltd.  
Not applicable.  
The study did not involve humans.  
The data presented in this study are available on request from the corresponding author.  
We thank National Research Foundation of Korea (NRF) of the Korean government and Daewoo Shipbuilding & Marine Engineering Co., Ltd. for the funding.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TVC & Tip Vortex Cavitation \\ SPL & Sound Pressure Level \\ LDV & Laser Doppler Velocimetry \\ SPIV & Stereo Particle Imaging Velocimetry \\ CMOS & Complementary metal-oxide-semiconductor \\ FFT & Fast Fourier Transform \\ \end{tabular}
## 1 Introduction
TRFC performance is highly affected by throughput and packet loss due to unfair RTT and RTO calculations. Further TFRC requires a sophisticated mechanism for calculating accurate pocket loss rate. To achieve Quality of Service for transferring online multimedia streaming applications, more sophisticated mechanisms are needed for TFRC. This encourages the researchers to propose  
Figure 1: Functionality of TCP Friendly Rate Control mechanism. various strategies like Tuning RTT and RTO dynamically by calculating them using a better approach, introducing mean and confidence interval into average loss interval method and modification to TTL computation method to reduce packet dropping using RSA to prevent TTL modification to improve the performance of TFRC in terms of throughput, delay and packet loss. This paper is organized as follows. Section 2 describes related work done so far on TFRC. Section 3 presents the various proposed strategies for enhancing the performance of TFRC. In Section 4. Simulation environment is described. Metrics, simulation results and discussions of using the proposed technique to improve the throughput of TFRC are included in Section 5. Section 6 presents the conclusion.
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 5 Discussion  
We focused on two main components in our approach. The first is a multiscale point cloud feature extraction module integrated with a self-attention mechanism, which enhances the representation of local features and mitigates information loss caused by partial occlusion. The second is a lightweight image feature extraction module, designed to reduce computational complexity while maintaining high-precision pose estimation. These two components complement each other to improve the performance in complex and low-texture environments.  
Despite these contributions, several limitations remain. Although the proposed method improves the pose estimation accuracy under moderate occlusion, challenges still persist in cases of severe occlusion or highly symmetric objects. In these scenarios, pose ambiguity arises due to insufficient visual cues. However, as observed in the Occlusion LineMOD dataset, our method still shows considerable potential in such cases, outperforming several strong baselines on difficult categories such as duck and holepuncher. This suggests that with further refinement--such as improved feature alignment and occlusion-aware learning--the method could be extended to better handle these challenging conditions.  
Another limitation is that the reliance on depth and point cloud data makes the model sensitive to sensor quality; poor or missing depth maps significantly affect estimation results, especially in dynamic or low-light environments.  
While our lightweight design improves efficiency, we did not perform extensive comparisons with other lightweight 6D pose estimation frameworks such as PVN3D-Lite or EfficientPose. Future work will include a more systematic evaluation of accuracy-efficiency trade-offs across representative models.  
Furthermore, the current study focuses primarily on known-object pose estimation, and its generalization ability to unseen objects is not explicitly addressed. This limits the model's applicability in zero- or few-shot scenarios, which are commonly encountered in real-world robotics. One possible direction is to explore meta-learning techniques or feature-space adaptation to allow generalization to novel object categories with minimal training samples.  
In summary, future work will focus on (1) enhancing robustness to occlusions and symmetry by incorporating synthetic and augmented training data; (2) integrating multi-viewfusion and temporal consistency modules; (3) improving the generalization capability to unseen objects by investigating zero- or few-shot learning extensions of the current framework; and (4) further optimizing the model for deployment on edge devices, especially under real-world constraints such as low-power and limited bandwidth conditions.
## Nomenclature
### Assumptions  
The following listing summarizes the assumptions used throughout this study.  
1. Dynamic model is a rigid-body, i.e., there is no relative movement/velocity between two points on the body.
2. Quasi-stationary mass distribution, i.e., \(\dot{m}\approx 0\).
3. Reference frame is a navigation frame \(N\) (e.g., North-East-Down) on a fixed-flat earth (i.e., no influence of earth rotation and ellipsoidal earth shape are considered).
4. Reference point is the center of gravity.  
It should be noted that these assumptions are fairly standard in, e.g., aviation or robotics [1], and are only used in this paper for the sake of simplicity. Indeed, the specified assumptions are generally not required for the derivation as well as for the application of the proposed methodology and can thus be relaxed based on the required model fidelity. Thus, they do not imply any loss of generality.
# Hybrid Dual-Polarization Synthetic Aperture Radar
## 4 Performance in Earth Applications
### Methodologies to Avoid  
Recall that circularly-polarized transmissions (either R or L) are comprised of simultaneous H and V components, 90-degrees out of phase. In short-hand notation, the data observed through the H and V receiver channels could be described as (CH and CV) in general, or (RH and RV) in response to right-circularly polarized transmissions. It follows that in the post-detection radar image domain, following that notation the corresponding normalized backscatter coefficient \(\sigma^{0}\) terms would appear as \(\sigma^{0}{}_{CH}\) and \(\sigma^{0}{}_{CV}\) (or \(\sigma^{0}{}_{RH}\) and \(\sigma^{0}{}_{RV}\) in the event that the transmission was R-circular). Such representations are misleading. A more transparent short-hand way to describe the data product from the H channel is \((\text{H}+\text{V})\times\text{H}\Rightarrow\) (HH + VH). Similarly, data from the V channel are comprised of two terms (HV + VV). Respectively, the corresponding \(\sigma^{0}\) terms are proportional to \(|\text{HH}+\text{VH}|^{2}\) and \(|\text{HV}+\text{VV}|^{2}\). In this expanded short-hand form, it is evident that the like- and cross-polarized contributions cannot be separated. CH and CV (or RH and RV) \(\sigma^{0}\) terms are self-contaminated. This unwanted but unavoidable attribute compromises all studies whose hybrid dual-polarization classifications are based on \(\sigma^{0}{}_{CH}\) and \(\sigma^{0}{}_{CV}\) (or equivalently, \(\sigma^{0}{}_{RH}\) and \(\sigma^{0}{}_{RV}\)). Advice would be: _Do not use this method_.  
For those who are familiar with the analysis of quad-pol SAR data, a natural first step taken by such practitioners could be to expand HCP's native \(2\times 2\) covariance matrix into a \(3\times 3\)_pseudo-covariance matrix_. Such an expansion entails invoking certain symmetry and similarity arguments [14]. The resulting \(3\times 3\) matrix is a familiar form, hence investigators are able to apply standard quad-pol decomposition algorithms [38]. Unfortunately, that scheme does not turn out very well. Expanding a \(2\times 2\) matrix into a \(3\times 3\) matrix through the artifice of assumptions, no matter how clever, cannot add valid information. The \(3\times 3\) form may be more familiar, but at its best it is no better than its \(2\times 2\) foundation. At its worst, assumptions introduce biases that can lead to major classification errors [39]. There is no way for a user to know if or when to trust the results from the pseudo-covariance approach. Advice would be: _Do not use this method_.
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## Virtual Infrastructure: Discussion and Challenges  
There are several challenges to implement Digital as a Service:  
1. The key risk, as Digitalization and Big Data becomes more relevant and fundamental, CyberSecurity threats and attacks will generate major impacts. There are currently Cybersecurity guidelines [70] that cover the seven layers of the OSI model. The use of proxy servers, firewalls, cryptography, authentication, authorization and accounting (AAA) servers reduces the risk of Cyber attacks. In addition, the block chain will add a layer of security with a decentralized model where key information is distributed in a shared network therefore removing single or double points of failure.
2. As Digitalization enables services to become more integrated, companies may not allow access from manufacturer independent protocols or interactions with third party software in order to protect their intellectual property, assets or economic interests.
3. System virtualization will be another challenge where business and economic interests between competing companies shall be managed by regulators. Internet Service Providers (ISPs) or Mobile Network Operators (MNOs) will not be willing to make their services virtual because virtualization will remove the need for competition where service providers offer similar and reductant coverage in the same area with duplicated equipment.
4. Digitalization and virtualization will also raise ownership and maintenance issues; it shall be clearly determined and assigned what each stakeholder owns including its responsibilities Aspects to agree are the procurement of the equipment and software and the payment for its maintenance such as energy bills and software upgrades. Lack of proactive maintenance will increase the risk of a cyber-attacks.
5. The reduction of cost (CAPEX and OPEX) will be the main driver for Digital as a Service; the success of DaaS will depend on successful economic business cases that include stakeholder and user requirements in addition to the whole life cycle cost: design, procurement, installation and operational stages.
6. There will be an increasing level of dependency to technology: Big Data; Artificial Intelligence and Machine Learning Algorithms. However this dependency will be balanced with an increment of reliability, performance and redundancy on services and equipment.
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 4 Numerical Simulation
### Four Radars under Attack  
In this part scene with four radars under attack was considered to test the estimation accuracy when half the radars in the network were under attack. Radar 1 and radar 3 were attacked starting from simulation time \(t=5\), and radar 5 and radar 7 were attacked starting from simulation time \(t=10\). Figure 6 shows the injected data detection results for all the radars. It can be seen that the attacks could be detected in a timely manner.  
Figure 4: RMSE of the attack parameter estimations for radars 5–8.  
Figure 5: RMSE of the position and velocity over time for different algorithms.  
Figures 7 and 8 show the RMSE of the estimated range, elevation and azimuth APE for radars 1-4 and radars 5-8 over time, respectively. It can be seen that the attack parameters could be estimated as soon as the attacks occurred.  
Figure 6: Injected data detection results.  
Figure 7: RMSE of the attack parameter estimations for radars 1–4.  
To illustrate the superiority of the proposed DSE-FDI method, a comparison with other methods is given. The methods include (1) the traditional hybrid consensus-based DSE method [12] (denoted as DSE); (2) the traditional hybrid consensus-based DSE method with injected data detection, in which the attacked measurements are discarded directly (denoted as DSE-D); and (3) the CM-based method [10] with FDI compensation proposed in this paper (denoted as CM-FDI).  
Figure 9 shows that DSE-D diverged as time went on, which was caused by information loss. The performance of DSE decreased as the number of attacked radars increased, and the performance of DSE-FDI was almost unaffected by FDI attacks. Moreover, DSE-FDI performed better than CM-FDI, which verifies the superiority of hybrid consensus over consensus in the measurements.