esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conclusions  
The analysis of the recent literature determines the amount of evidence regarding the improvement of the bone-healing process in patients treated with PEMFs. The effectiveness of these therapies is now consolidated with cure rates that largely support the cost-benefit ratio. In the literature review we conducted, no validated scientific studies were identified that reported side effects of these therapies. All the works considered reiterate the fundamental importance regarding the correct diagnosis of use, methods, and times of application, and above all compliance with the treatment by the patient. The dominant opinion that emerges is in favor of the use of stimulation with electromagnetic fields in the sectors taken into consideration. The studies in which there was no evidence of statistically significant results presented, as declared by the authors, bias with potential impact on the interpretation of the result.  
As observed in the studies reviewed in this discussion, there are different devices that deliver different combinations of physical parameters, but all of them give a positive therapeutic result.  
Our goal is not to compare the results obtained with different biophysical stimulation devices and identify the best one, but it is to be able to recommend to our patients a biophysical stimulation device, free of side effects or contraindications, based on scientific support and documented clinical validation.  
In the presence of osteoarthritis or osteonecrosis, the choice of the moment of application of the stimulation plays a fundamental role; better results have in fact been reported in recent forms of OA with a predominance for the inflammatory component, while the results are less evident in advanced and structured forms of joint damage. The effectiveness on the joint inflammatory phase makes the application interesting after joint replacement and in the field of arthroscopic surgery due to promising results regarding the inhibition of catabolic processes. We also found positive impacts on clinical symptoms and patient compliance with rehabilitation therapies. For this reason, notable applications can be expected in the fields of prosthetic surgery and sports medicine. In the coming years, we will observe a strong increase in applications in these areas. In fact, the valid results on joint inflammatory pathology demonstrate a strong indication for use both in the field of joint replacement and joint preservation. In this last sector, and even more so in sports medicine, it will be extremely interesting to evaluate the results emerging from preclinical studies on devices dedicated to the regeneration of cartilage tissue. In fact, unlike bone tissue, there is currently no dominant opinion on the regeneration of cartilage tissue; however, the preliminary results mentioned above suggest an optimistic attitude in this field of application.  
We, therefore, conclude by stating that biophysical therapy can provide valuable aid in clinical practice if the correct indications and methods of administration are respected.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
###### Abstract  
Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy.  
GPP; up-scaling; machine learning; validation Article
## 2 Wireless Communication Technologies
#### Smart Wearable Devices
Key characteristics of the flexible and stretchable sensors created in this manner include their biocompatibility, scalability, lightweight design, flexibility, and wearability. Additionally, connection with a triboelectric source has made it possible to develop a self-powered, wearable, portable sensor. In conclusion, a wearable sensor for healthcare systems that runs on piezoelectric and triboelectric nanogenerators has been demonstrated. Smart sensors are useful because they can be created rapidly and affordably, worn conveniently, have a long lifespan, react swiftly, and may be used anywhere and at any time. They are also resistant to water and other factors [2; 3]. However, SAR, which is regulated to prevent dangerous exposure, assesses the rate at which the human body absorbs energy from electromagnetic fields (EMF) in wearable devices. However, millimetre waves, which are now increasingly used in technologies like 5G, raise additional concerns [31]. Table 2 summarises various physiological signal-sensing devices. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Various Research Areas That Used Wearable Devices in the Literature** \\ \hline Healthcare/medical technologies for physiological signals monitoring systems & (1) Technologies (RF and microwave), (2) materials, and (3) applications and research directions \\ \hline
2017 to 2022 [113; 114] & Wearable design and systems: healthcare, clinic, and hospital applications \\ \hline
2018 to 2022 [115; 116] & Wearable design and systems: Energy harvesting, holistic systems, and self-healing composite \\ \hline
2021 to 2022 [117; 118; 119] & Next enabled device systems: Backscattering \\ \hline
2013 to 2022 [120; 121; 122; 123; 124; 125; 126; 127; 128] & Enabled device systems: RFID and RF sensing devices, far-field, and RF rectenna systems \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Various available wearable devices in literature.
# Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens
###### Abstract  
This paper explores the integration of adaptive neuro-fuzzy inference systems (ANFIS) with additive manufacturing (AM) to enhance the prediction of mechanical properties in 3D-printed components. Despite AM's versatility in producing complex geometries, achieving consistent mechanical performance remains challenging due to various process parameters and the anisotropic behavior of printed parts. The proposed approach combines the learning capabilities of neural networks with the decision-making strengths of fuzzy logic, enabling the ANFIS to refine printing parameters to improve part quality. Experimental data collected from AM processes are used to train the ANFIS model, allowing it to predict outputs such as stress, strain, and Young's modulus under various printing parameters values. The predictive performance of the model was assessed with the root mean square error (RMSE) and coefficient of determination (R\({}^{2}\)) as evaluation metrics. The study initially examined the impact of key parameters on model performance and subsequently compared two fuzzy partitioning techniques--grid partitioning and subtractive clustering--to identify the most effective configuration. The experimental results and analysis demonstrated that ANFIS could dynamically adjust key printing parameters, leading to significant improvements in the prediction accuracy of stress, strain, and Young's modulus, showcasing its potential to address the inherent complexities of additive manufacturing processes.  
additive manufacturing; adaptive neuro-fuzzy inference system (ANFIS); 3D printing; mechanical properties prediction; intelligent manufacturing systems Article
## 4 Applications of \({}^{31}\)P qNMR for Phosphorus-Containing Compounds
### Others  
\({}^{31}\)P NMR spectroscopy is also a convenient and widely used analytical method for investigating phosphorus-containing organic compounds or metal complexes. [PERSON] et al. [58] studied the degradation behavior and mechanism of black phosphorus crystals in an atmospheric environment using phosphorus nuclear magnetic resonance spectroscopy. [PERSON] et al. [59] developed a high-resolution \({}^{31}\)P qNMR for the detection, identification, and quantification of non-phosphorus markers for toxic nerve agents, foaming agents, and incapacitation agents. [PERSON] et al. [60] demonstrated a novel approach combining \({}^{1}\)H NMR and \({}^{31}\)P NMR for the first time to determine the level of purity in the assessment of tripropyl phosphate using NMR.  
Chiral recognition of phosphines was reported with metal-based sensors by utilizing the metal chelation ability of phosphines. [PERSON] et al. [61] developed an efficient chiral recognition of phosphine oxides formed via in situ oxidation of phosphines by optimizing the chiral metal complexes for the efficient \({}^{31}\)P NMR analysis of in situ-prepared phosphine oxides (Figure 9). This chiral \({}^{31}\)P NMR analysis can be used as a universal tool to measure the optical purity of chiral phosphines. The use of \({}^{31}\)P NMR spectroscopy for chiral analysis, such as the determination of enantiomeric excess or absolute chirality, can be a direct, complementary, and convenient analytical protocol.
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 6 Performance Evaluation
### Metrics  
Our aim is to assess the performance of the proposal, showing that C-V2X communications match the strict latency and reliability requirements of platooning, and evaluating their efficiency in terms of resource usage. In order to do this, we refer to the following set of metrics:  
* Probability of successful reception of the PL-to-PM CAM against the distance from the PL. It is derived as the number of successfully received CAMs over the number of transmitted CAMs by the PL.
* Allocated RBs per TTI. It represents the number of RBs per TTI which are allocated to accommodate the transmission of CAMs of all platoon vehicles.
* CAM cycle completion time. It refers to the time required to accommodate all the CAM transmissions (PL to PMs and PM to PM) within a platoon at a given update cycle.
* Ratio of successfully completed CAM cycles. It is computed as the ratio between the number of CAM cycles for which _all_ CAMs have been correctly delivered and the total number of CAM cycles.
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 5 Conclusions  
GIMAD has been developed and verified. The verification results have shown that GIMAD is capable of detecting, characterizing, and localizing RFI both in-band and out-band. The GIMAD prototype uses algorithms based not only on GNSS observables but also on IQ samples. The combination of the selected algorithms maximizes the detection probability, as they emphasize different metrics. The capability of detecting very short interference signals (125 \(\upmu\)s) has been verified, in line with EGNOS v3/ICAO GBAS RFI masks. Thus, it targets not only the aeronautical segment but also safety-critical environments.  
Figure 11: AoA estimation for 1575.42 MHz RFI in the anechoic chamber.  
Figure 10: AoA estimation for 827.5 MHz RFI in the anechoic chamber.  
**Author Contributions:** Conceptualization, software, validation: E.O.-B., L.T.-R., A.C.-B., A.G.-N., F.F. and G.S.-G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by CDTI and it was articulated through the program NAVISP EL2-034 (ESA contract 4000128898/19/NL/MP).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The GIMAD project has been carried out in the frame of the NAVISP EL2-034 Program with the invaluable collaboration of CDTI, ESA, and ENAIRE.  
**Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON], and [PERSON] was employed by INDRA Sistemas. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 4 Performance Analysis  
There are some evaluation criteria for the GNSS code tracking discriminators, like sensitivity, precision, stability, etc. Here, the multipath error envelope and the tracking jitter of the proposed discriminators are evaluated, since they are mostly concerned during tracking. The multipath envelopes of these discriminators are analyzed and compared to the CCRW W2 and the double-delta correlator.
## 5 An SOA-Based Platform Example and Case Study Results
### Additional Details on the Results from the Arrowhead Tools Case (Comprising More Than 30 Use Cases)  
The Arrowhead Tools project continues to add necessary tools to the Arrowhead Framework to enable more companies and organizations to use the Arrowhead Framework (platform) while building and operating automation solutions of various kinds where security, integrations, and interoperability between systems-of-systems are key for success. During the project, comprising more than 30 relevant use cases, the focus has been to use the Arrowhead Framework to build various tools to increase the usability and enable more organizations and companies to use the platform. The areas of interest have been mainly the 4 development-related phases (i.e., \"engineering work\") and the operations phase later on. The measurements and estimated improvements have been recorded in two project deliverables and show, overall, very promising results. However, to be able to achieve the results, there is an investment required in skills and competencies to be able to understand the concept, how to further develop it, and how to best use it during the whole lifecycle. Nevertheless, if the investment is made, it seems like the ones who have started do not want to go back to the old traditional way of development of automation solutions or to integrate various types of systems. As the use cases have spanned a number of manufacturing and process industries, e.g., manufacturing and production of vehicles, bearings, semiconductors, and consumer products, the generalizability level of the Arrowhead Framework (platform) with the new tools is deemed as high.  
Figure 1: Outline of general architecture—Arrowhead Framework.
## 3 Results and Discussion
### Cyclic Voltammetry  
Electrolytes in lithium-ion batteries need to retain sufficiently wide reversible electrochemical stability against Li/Li\({}^{+}\). The electrochemical stability of the SPE membranes was determined by cyclic voltammetry (CV) using a Solartron potentiostat/galvanostat impedance/gain-phase analyzer (SI 1260) coupled with a Solartron electrochemical interface (SI 1287). A testing cell was assembled to determine the oxidation potential of stainless steel as the working electrode and lithium foil serving as the counter and reference electrode. All scans were conducted at 25 \({}^{\circ}\)C at a scan rate of 10 mV s\({}^{-1}\) with the voltage range of 0 V to 5.0 V versus Li/Li\({}^{+}\)[33].  
For practical applications, an electrolyte system should exhibit excellent electrochemical stability in excess of 4.5 V since secondary lithium-ion batteries typically operate in the voltage range of 2.8-4.35 V [34]. The characteristic CV properties for each of the three best electrolyte membranes are depicted in Figure 14. According to the CV curves, both PhSI-PPL and DTTO-PPL films at the weight ratio of 1:3:4:3 displayed electrochemical stability up to 4.2 V. The PhSI-PPL membrane showed an anodic peak at 1.26 V, while the DTTO-PPL membrane exhibited another anodic peak at 1.31 V [35]. The TFSI-PPL electrolyte at the weight ratio of 1:3:4:3 displayed the best electrochemical stability as no significant electrochemical activity appeared below 4.8 V versus Li/Li\({}^{+}\).  
Figure 13: Ionic conductivity versus temperature of various weight ratios of PSPhSILi–PEO–PEGDM–LiTFSI (DTTO–PPL) electrolytes.  
The rationale for designing PSTFSILi is the striking similarity with LiTFSI (as illustrated in Figure 15) with regard to the negative charge on the nitrogen atom being delocalized by two sulfone groups and one trifluoromethane group. This creates less interionic attractions between the cation and anion, commonly described as a low lattice energy salt [36]. Therefore, this TFSI-PPL solid polymer electrolyte also shows potential use with high-voltage cathode materials, which cannot be safely tested with liquid electrolytes due to their lower electrochemical stability.  
Figure 14: Cyclic voltammetry (CV) curves of the selected electrolyte membranes: (**a**) TFSI–PPL = 1:3:4:3; (**b**) PhSI–PPL = 1:3:4:3; (**c**) DTTO–PPL = 1:3:4:3.  
Figure 15: Structural comparisons of PSTFSILi with LiTFSI.
To test the ability of the method, two warning criteria are assumed in the paper and, accordingly, two monitoring strategies are applied to a dataset of MT-InSAR timeseries relevant to a large urban area. It is to note, however, that the method can be applied under different warning criteria and monitoring strategies, with those considered in the present study being just two of the many conceivable. It is also to note that the considered dataset is only used to show the ability of the method to generate early-stage warnings accordingly to the assumed warning criterion and monitoring strategy. Anyconsideration about the geological phenomena detected while testing the efficacy of the method in the considered area is beyond the purpose of the present study. Lastly, it should be mentioned that several early-warning systems have been developed in the last decades to forecast various kinds of hazards. In civil engineering, optimized structural health monitoring technologies and data processing techniques have been adopted for early warning in seismic-prone zones, [45,46]. The method presented in this paper fits into this line of research, its novelty lying in the capability to forecast events related to early-stage ground settlements in urban areas by analyzing InSAR data through an unsupervised learning paradigm.
## 3 Results
### Fault Diagnosis
#### 3.2.2 Power Spectral Density (PSD)  
Starting again from the raw signal, the Power Spectral Density technique has been applied, obtaining information about the frequency distribution of the signal power; its formulation implies the computation of the signal Fourier transform, the corresponding square (power), the division of the result by the sampling frequency \(f_{s}\) and the number of  
Figure 19: STFT applied to the signal from sensor 3 on bearing 2 (last 49 s).  
Figure 17: STFT applied to the signal from sensor 6 on bearing 3 (first 49 s).  
Figure 18: STFT applied to the signal from sensor 7 on bearing 4 (last 49 s).  
sampled values \(N\) (density), a chain of operations that may be equivalently replaced by the evaluation of the periodogram:  
\[PSD(f)=\frac{1}{f_{\text{s}}\cdot N}\bigg{|}\sum_{n=0}^{N-1}x_{n}e^{-j2\pi f\Delta t \ n}\bigg{|}^{2}\quad with\quad-\frac{1}{2}\Delta t\leq f\leq\frac{1}{2}\Delta t,\quad\Delta t=\frac{1}{f_{\text{s}}} \tag{23}\]  
In the graphs, shown in linear scale for purposes of clarity, some harmonics of the BPFI for bearing 3 (Figure 20, with harmonics highlighted in magenta) and of the BPFO for bearing 4 (Figure 21, with harmonics highlighted in red as multiples of 238 Hz instead of 236 Hz, as stated before) are already visible, but their detection is strongly affected by the emergence of other frequency lines related to the test rig mechanical structure and hence still present at the beginning of the experiment (Figure 22); specifically, they are located at 160 Hz and their harmonic frequencies (320 Hz and 480 Hz) and at 505 Hz and its striking double (1011 Hz). There are also peaks at very high frequencies, around 4000-5000 Hz and 9000 Hz, probably related to the system natural resonances. It is curious to observe that the first harmonic of the BPFO is more clearly visible in the spectrum of bearing 3 than in that of bearing 4 due to the cross-talk among contiguous sensors.
## 1 Introduction  
Along with the widespread adoption of the Internet of Things (IoT), the application of IoT has been extended to infrastructure-lacking environments without continuous end-to-end connectivity, such as rural and post-disastrous areas. Disruption Tolerant Networking (DTN) bridges the gap between intermittently connected environments and helps the expansion of the IoT applications in these stressed and unstable environments. Mobile Social Network (MSN) is a special type of DTN that has become increasingly important due to the proliferation of portable devices with higher computing power at a lower cost. The social behaviors of humans have a significant impact on the overall network performance, especially in networks with disruptions and long delays. Furthermore, IoT applications require efficient and reliable multicast services to facilitate data dissemination to the group of participating nodes. The following subsections briefly describe the concept, background, and interrelation of IoT, DTN, MSN, and multicasting in DTN.