esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles and Methods
### The Label Switching-Based ISTNs
#### 2.2.2 Principles of CCSDS AOS Frame Label Switching  
CCSDS AOS allows the coexistence of ISTNs and multiple forms of data access. CCSDS AOS is able to transmit different types of data, such as voice, video, and images, in the form of a unified data flow over a spatial physical channel based on dynamic scheduling management of different virtual channels in the physical channel. Meanwhile, the flexible reuse mechanism and effective error correction/detection measures in AOS guarantee that the spatial physical channel has a high channel capacity, channel quality, and reliability to meet the needs of spacecraft data transmission and processing. The CCSDS AOS space data link protocol specifies the recommended procedures for transmitting various types of data using fixed-length packet units, i.e., transmission frames as shown in Figure 2a. However, in the intermediate node, AOS frames could not be switched directly, and all the traffic flows will be unpacked and forwarded to the upper-layer for the further process. As a result, the energy-resource-consuming frame unpackaging-packing-forwarding process cannot meet the growing demand for space data transmission and QoS routing performance. To address those issues, an extended AOS frame with **extended control field** is proposed to realize label-based frame switching directly in routers, thus avoiding the unnecessary unpacking-repacking process for bypassing traffic data, as shown in Figure 2b,c, respectively. In Figure 3, there is an explicit comparison between the open system interconnect (OSI) and extended CCSDS reference models, where the label switching sublayer is added between the data link protocol sublayer and the synchronization and channel coding sublayer, and an extended label-based AOS switching protocol is applied in the CCSDS protocol stack as follows:  
* The **label** field is added in an AOS frame to forward traffic flows based on labels instead of IP-address-based forwarding;
* The **data communication network** field is used as an overhead for forwarding management and controlling messages for the control plane and the management plane;
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 3 Comparison of Calculation Results
### Radial Stiffness Coefficient  
Due to the irregular surface of the steel pipe and the grouted material, any slip between them will cause radial deformation of the inner and outer steel pipes and the grouted material. Increased pressure between the contact surface of the steel pipe and the grouted material increases the friction between the contact surfaces, which will lead to improved axial strength of the grouted connection. Therefore, radial stiffness coefficient \(K\) has a more significant influence on the grouted connection. The test results of Aritenang [18] confirmed a similar view, that increased radial stiffness can help to improve the axial strength of grouted connections. The relationship between \(f_{\rm bk}\) obtained by different codes and radial stiffness K is shown in Figure 6. It can be concluded that the API code holds that there is no direct relationship between \(f_{\rm bk}\) and \(K\), that is, the change of radial stiffness of the grouted connection section will not affect the shear strength of the grouted joint. In contrast, the HSE and DNV codes assume that \(f_{\rm bk}\) of the grouted junction is proportional to radial stiffness \(K\). The DNV-GL code assumes that \(f_{\rm bk}\) is proportional to the 0.6 power of radial stiffness coefficient \(K\), which reduces the effect of \(K\) on the shear strength of the grouted connection.  
Formulas in the DNV-GL and HSE codes adopt the same radial stiffness \(K\), and believe that it is related to the diameter-to-thickness ratio of steel pipe pile \(D_{\rm p}/t_{\rm p}\), the diameter-to-thickness ratio of steel sleeve \(D_{\rm s}/t_{\rm s}\), the \(D_{\rm g}/t_{\rm g}\) ratio, and the ratio of elastic modulus of steel pipe to grouted material \(E/E_{\rm g}\). Hence, the analysis results of radial stiffness \(K\) are presented in Figure 7 by changing the parameters above. It can be found that a \(D_{\rm g}/t_{\rm g}\) ratio between 0 and 15 shows a significant effect on radial stiffness \(K\), but this effect gradually decreases when the ratio is more than 15. The \(D_{\rm p}/t_{\rm p}\) and \(D_{\rm s}/t_{\rm s}\) ratios have less effect on radial stiffness \(K\). The \(E/E_{\rm g}\) ratio can only achieve a relatively small change range because the value only relates to the material properties, but it can also effectively enhance radial stiffness \(K\) in the variation range.  
Figure 6: Relationship between \(f_{\rm bk}\) and radial stiffness factor (\(k\)).
## 2 Experimental Procedures
### Rheological Measurements  
It is essential to comprehend the flow behavior of the feedstock to produce defect-free green parts using the MIM process. The most critical flow property is viscosity, a proportional function between shear stress and shear strain rate. The pressure stability of the feedstock at different shear rates is a further trait that might characterize the flow behavior of the feedstock. Therefore, the high-pressure capillary rheometer Rheograph 2002 (Figure 3) (Gottfert Werkstoff-Prufmaschinen GmbH, Buchen, Germany) was used to evaluate the rheological properties of the feedstocks.  
The apparent shear viscosity based on ISO 11443 [63] was measured at 160 \({}^{\circ}\)C at 100 and 1000 s\({}^{-1}\) shear rates for the feedstocks with different binder formulations and a 100-2000 s\({}^{-1}\) shear rate range for the powder content optimization. The tests used a round die with a diameter and length of 1 mm and 30 mm, respectively. The relation between  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feedstock** & **LLDPE (vol.\%)** & **PW (vol.\%)** & **SA (vol.\%)** \\ \hline A & 52.5 & 41.25 & 6.25 \\ B & 45 & 47.5 & 7.5 \\ C & 37.5 & 53.75 & 8.75 \\ D & 35 & 60 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different binder system formulations investigated in this study.  
Figure 3: Capillary rheometer used in the study.  
viscosity and shear rate can be described using Equation (1) for pseudo-plastic behavior at a particular temperature:  
\[\eta=K\dot{\gamma}^{n-1} \tag{1}\]  
where \(\eta\) represents the viscosity, \(n\) is the flow behavior index, \(K\) is the consistency constant and \(\dot{\gamma}\) represents the shear rate [64]. The n index indicates the feedstock viscosity dependence on shear rate, which should generally fall within an acceptable range; higher values of n are preferable for the rheological behavior of MIM feedstocks because they indicate a less drastic viscosity decrease with increasing shear rate. However, it is challenging or impossible to process feedstocks with an extreme value of \(n=1\) (or close to 1) because the high viscosity is constant and requires very high pressure, even at high shear rates. On the contrary, it was claimed that very high sensitivity (low n value) is undesirable since it might result in flashing during injection molding [26; 65].
## 4 Conclusions  
In conclusion, we demonstrate a programmable zoom lens system where the magnification and sense of the image can be controlled without moving any parts. We use two programmable SLMs onto which we can encode the required focal length lenses to achieve these results. We note that the physical size of this system is extremely large primarily because of the small sizes of the lenses (diameters of 14 mm and 12 mm respectively) and the Nyquist limits on the focal lengths that we can encode onto our SLMs.  
However, there are new high resolution 4 KUHD(Ultra-High Definition) SLM models having a pixel size as small as 3.74 microns in an array of 3840 \(\times\) 2160 pixels (data from the Exulus-4K1 model [(23)] offered by Thorlabs), or 4160 \(\times\) 2464 (data from the the GAEA-2 model [(24)], offered by Holoeye). The pixel spacing of these SLMs lead to a Nyquist focal length limit of about 50 mm. These devices would allow a much more compact zoom lens system.  
Another strategy for reducing the size of the system is to combine the lenses displayed on the SLMs with other glass lenses that add a constant optical power. However, for simplicity, here we analyze the system where all the optical power is encoded only on the SLMs.  
Nevertheless, we obtain excellent results and show a proof-of-concept of a zoom system without moving elements based on SLMs, and showing negative magnifications ranging from \(-2\) to \(-1/2\) and negative magnifications from \(+2\) to \(+1/2\). The use of SLMs to produce the zoom system can be very promising since other functionalities of SLMs like wavefront aberration correction, optical processing functions, or polarization control can be incorporated into the system.  
Conceptualization, J.A.D. and I.M.; Methodology, J.A.D., I.M. and D.M.C.; Software, D.M.C.; Validation, J.A.D., T.I.H., and J.P.S.; All authors; Writing-Review & Editing, J.A.D.; Supervision, J.A.D.  
The participation of I.M. in this investigation was funded by Ministerio de Economia y Competitividad from Spain and FEDER Funds (ref. FIS2015-66328-C3-3-R) and from Conselleria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (ref. PROMETEO-2017-154).  
The authors declare no conflict of interest.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 2 System Model
### Channel Model Description  
A spatial scattering MIMO channel is used, modeling a LoS multipath propagation channel where the transmitted signals are reflected from multiple scatterers to the receiver [10]. This channel model applies free-space path loss and specifies the locations for both the BS and UE as coordinates in a Cartesian system, consisting of both a uniform rectangular array (URA) and a uniform linear array (ULA) with isotropic antenna elements at the gNB and UE, respectively. Moreover, to simulate the noise in the actual propagation medium, distributed and uncorrelated additive white Gaussian noise (AWGN) is added to the transmitted signal.  
The spatial scene illustrating a combined view of the channel scatterers along with the gNB and UE determined beams is presented in Figure 2:
## 4 Discussion  
Ensuring robust and well-documented training of ML models in healthcare is pivotal for their clinical utility, as described in Figure 1 (especially the middle square highlighted in red). The model training phase, which encompasses proper learning methodologies, the prevention of overfitting or underfitting, and uthe se of appropriate performance metrics, is foundational to achieving clinically actionable outcomes. Without rigorous training protocols, high accuracy metrics may be deceptive, creating a false sense of reliability in a model's clinical applicability.  
A model trained inadequately or with methodological flaws, such as overfitting to training data or insufficient validation, can yield inflated accuracy metrics that fail to generalize to real-world scenarios [46]. For instance, as demonstrated in the case study, a model achieving 70% accuracy on its training dataset catastrophically failed during cross-dataset evaluation, with accuracy plummeting to under 2% for a critical grade 2 osteoarthritis classification. This highlights a systemic issue where models validated solely on in-domain data appear promising but ultimately lack the robustness necessary for diverse clinical environments. Robust training ensures that the model learns meaningful patterns from data rather than memorizing noise or artifacts. Overfitting, characterized by exceptional performance on training data but poor generalization to unseen data, can render a model clinically useless. Similarly, underfitting, where a model fails to capture the underlying data structure, results in subpar performance across all datasets. Both scenarios undermine the validity of reported performance metrics, making them clinically irrelevant.  
Properly trained models are better equipped to handle variability in patient data, imaging protocols, and disease presentations, ensuring they are reliable tools for clinical decision-making. Incorporating standardized training methodologies, as outlined in regulatory frameworks like the FDA's guidance on AI credibility, bridges the gap between research prototypes and bedside applications. By adhering to these practices, healthcare ML models can transition from deceptively high accuracy metrics to robust, actionable tools that prioritize patient safety and ethical deployment.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
###### Abstract  
Node mobility in multi-hop communication environments is an important feature of Wireless Sensor Network (WSN)-based monitoring systems. It allows nodes to have freedom of movement, without being restricted to a single-hop communication range. In IEEE 802.15.4 WSNs, nodes are only able to transfer data messages after completing a connection with a coordinator through an association mechanism. Within this context, a handover procedure needs to be executed by a mobile node whenever there is a disconnection from a coordinator and the establishment of a connection to another one. Many applications, such as those found in health monitoring systems, strongly need support for node mobility without loss of data during the handover. However, it has been observed that the time required to execute the handover procedure is one of the main reasons why IEEE 802.15.4 cannot fully support mobility. This paper proposes an improvement to this procedure using a set of combined strategies, such as anticipation of both the handover mechanism and the scan phase enhancement. Simulations show that it is possible to reduce latency during the association and re-association processes, making it feasible to develop WSN-based distributed monitoring systems with mobile nodes and stringent time constraints.  
Keywords:IEEE 802.15.4; handover; node-mobility; health monitoring; cluster-tree +
Footnote †: journal: Accepted: 1 October 2020; Published: 6 October 2020
###### Abstract  
Pre-processing associated with massive multiple input-multiple output (MIMO) systems can lead to signals with high envelope fluctuations, which are very prone to nonlinear effects, especially when massive MIMO schemes are combined with orthogonal transform multiplexing (OFDM) modulations. If the nonlinear characteristics that affect a given system are known, we can design appropriate receivers that take into account the nonlinear effects introduced by the transmitter. Cubic systems are particularly important, not only because they can approximate many nonlinear effects (e.g., due to the power amplifier or clipping effects), but also because many more complex nonlinear characteristics in communication schemes can be replaced by equivalent lower-order nonlinear characteristics in general, and cubic characteristics in particular. To compensate the effects at the receiver side (e.g., by using the so-called Bussgang receivers), we need to estimate the nonlinear operation that was introduced at the transmitter, and this should be done blindly, without the need of training symbols. The paper contains a description of a mathematical approach for modeling and identification of nonlinear kernels in cubic systems. Based on theoretical tools of HOC in cubic systems, we build a new formula which relates the second- and fifth-order cumulants. Our performance results indicate that the proposed approach allows an accurate identification, yielding the desired kernels via fifth-order cumulants, and ensures a very good convergence, outperforming existing adaptive methods. This is achieved blindly, by exploiting the maximum information of the output system, making it suitable for many practical nonlinear effects.  
nonlinear distortion effects; cubic system identification; fifth-order cumulants; polyspectra; massive MIMO; OFDM +
Footnote †: journal: Applied  
1
Footnote 1: email: [EMAIL_ADDRESS]
# Characterisation of Dust Particles Deposited on Photovoltaic Panels in the United Arab Emirates
## 3 Results and Discussion
### Dust Particle Morphology (SEM Characterisation)
#### 3.3.3 Dust R-10  
Figure 6 presents the SEM micrographs of dust particles identified as R-10, which reveal a distribution of irregularly shaped particles with few closely packed particles and some irregular-shaped cavities. The majority of the particles exhibit an irregular shape resembling spheres, ovals, or ellipses, and the surfaces of the dust particles can be characterised as rough. The dust particles exhibit a wide range of sizes and morphologies, with the majority of the particle size falling within the 0.01-30 \(\upmu\)m range, and a Gaussian distribution centred around 10 \(\upmu\)m.  
Further analysis of the micrographs at a magnification of 10 \(\upmu\)m reveals that the larger particles are typically surrounded by several small features measuring less than 5 \(\upmu\)m in size. This observation is noteworthy, as it suggests that the small features may play a role in the physical behaviour and properties of the dust particles.
# Biodiesel and Biolubricant Production from Waste Cooking Oil: Transesterification Reactor Modeling
## 2 Materials and Methods
### Biodiesel and Biolubricant Production  
Biodiesel was obtained from WCO at a lab scale, following a transesterification process described elsewhere [39]. Thus, about 450 g of WCO was allowed to react with methanol (pure, pharma grade, Panreac Applichem, Barcelona, Spain) in the presence of sodium methoxide (30% in methanol, Merck, Darmstadt, Germany) as a catalyst. The process conditions were as follows: methanol/WCO mass ratio = 6; catalyst concentration = 1% \(w/w\); reaction time = 90 min; reaction temperature = 60 \({}^{\circ}\)C; stirring rate = 350 rpm. After the programmed reaction time, several washing steps were carried out with distilled water until a neutral pH was obtained for the spent distilled water. Thus, a biodiesel sample was obtained as a mixture of fatty acid methyl esters (FAMEs). Finally, biodiesel samples were dried at 110 \({}^{\circ}\)C and stored in opaque containers for further characterization or processing. These samples were labeled as WCO-FAMEs. For biolubricant production, around 250 g of the WCO-FAME samples were added to a thermoregulated batch reactor coupled to a vacuum pump and a methanol collector. Trimethylolpropane (TMP, pure, analytical grade, Merck, Darmstadt, Germany) and sodium methoxide (i.e., the catalyst) were added to the reactor to start the transesterification of the WCO-FAMEs to the biolubricant. Table 1 summarizes the process conditions used for biolubricant production.  
Once the reaction time had elapsed, the resulting biolubricant was filtered through filter paper to remove unreacted TMP. The final biolubricant (labeled as WCO-TMP) was stored in opaque containers for further characterization.  
Different experiments were carried out to assess the effects of the pressure (210, 360, 510, and 760 mmHg), temperature (80, 100, 120, and 140 \({}^{\circ}\)C), and catalyst concentration (0.3, 0.5, 0.7, and 0.9% \(w/w\)) on the conversion of the FAMEs.
# Optimization of Multilayer Absorbers Using the Bald Eagle Optimization Algorithm
## 4 Discussion  
BESOA is a recently introduced optimization algorithm used to optimize the sequences of materials and thickness of the layers to obtain optimum MMAs with less overall thickness and lower RC for a wide FR and angle of incidence. Its performance is studied in the design of wideband MMAs; the results are compared to similar designs obtained by other well-developed algorithms, such as DE, PSO, GSA, CFO, LSA, and DS-ABC. In this study, for consistency, 16 dielectric and magnetic materials, which have been used to design MMAs in the literature for the last 25 years, are preferred. The optimized MMAs are thinner and have a lower RC than the designs presented in the literature and the results of BESOA are comparable to those obtained by other algorithms. The BESOA converges to the minimum with less than 100 iterations.
## 5 Conclusions and Future Work  
In this paper, we propose a ship classification method named MFELCM which is suitable for space-based AIS data worldwide. MFELCM integrates four base classifiers, i.e., Radom Forest, 1D-CNN, Bi-GRU, and XGBoost. The dynamic and static data are firstly preprocessed and four datasets are constructed (i.e., the static feature dataset \(SF\), the dynamic feature distribution dataset \(DFD\), the time-series dataset \(TS\), and the time-series feature dataset \(TSF\)), after which the datasets are split by MMSI to avoid the data leakage problem. Finally, the base classifiers are integrated by another Random Forest. Experiments  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline max\_depth & 37 \\ max\_leaf\_nodes & 9 \\ min\_samples\_leaf & 61 \\ n\_estimators & 2000 \\ \hline RF\_0 & 37 \\ RF\_1 & 9 \\ RF\_3 & 61 \\ RF\_2 & 20 \\ RF\_0 & 3 \\ RF\_1 & 10336 \\ RF\_2 & 20 \\ RF\_3 & 20 \\ RF\_4  
show that MFELCM performs better than the four base classifiers, and MFELCM can effectively integrate the static and dynamic information of ships. Moreover, in the case of one base classifier being missing, the degraded MFELCM--which integrates the remaining base classifiers--still outperforms the base classifiers. As MFELCM integrates multiple features, it can achieve near real-time online classification, which can be applied to ship behavior anomaly detection as well as enhancing the supervision of maritime activities.  
The methods used to generate the dynamic features are an important factor for classification performance. In addition, the parameters of the classifiers are obtained by experiments in this paper. In the future, to further improve the performance of MFELCM, we plan to refine the methods of dynamic features generation as well as develop an automatic classifier parameter optimization method.  
Conceptualization, Y.W.; methodology, Y.W.; software, Y.W.; validation, L.Y., X.S. and Q.C.; resources, L.Y.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, L.Y., Q.C., X.S. and Z.Y.; visualization, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Restrictions apply to the availability of these data. Data was obtained from National Satellite Oceanic Application Center and are available at China Ocean Satellite Data Service Center (nsoas.org.cn) with the permission of National Satellite Oceanic Application Center.  
HY-1C/D and HY-2B/C data were obtained from [[https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)]([https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)) (accessed on 5 March 2021). The authors would like to thank NSOAS for providing the data free of charge.  
The authors declare no conflict of interest.
# Numerical Analysis of Vibroacoustic Response of Timber Floor Panels Damped with Porous Materials
## 1 Introduction
Furthermore, finite element models are developed to detect material parameters, ensuring accurate alignment with experimental force-displacement curves. Moving on, the second section focuses on the finite element formulation of sound radiation in the panel incorporating the porous-material layer. The porous material is characterized using Biot theory, with the solid-phase displacement and the fluid-phase pressure being key parameters. Predictions of radiated sound power and sound-transmission loss are made using the Rayleigh integral and variational formulations. In the last section, the paper implements the proposed finite element approach to analyze the timber floor panel's vibroacoustic response. Different loading scenarios, including walking time history and washing machine time history, are considered. The fundamental frequencies of the panel and the effect of boundary conditions on resonance peaks are examined. The parametric study demonstrates the positive impact of the porous-material layer on noise reduction and vibration control.