esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Flow-diverting stents (FDSs) show considerable promise for the treatment of cerebral aneurysms by diverting blood flow away from the aneurysmal sacs, however, post-treatment complications such as failure of occlusion and subarachnoid haemorrhage remain and vary with the FDS used. Based on computational fluid dynamics (CFD), this study aimed to investigate the performance of a new biodegradable stent as compared to two metallic commercially available FDSs. CFD models were developed for an idealized cerebral artery with a sidewall aneurysmal sac treated by deploying the aforementioned stents of different porosities (90, 80, and 70%) respectively. By using these models, the simulation and analysis were performed, with a focus on comparing the local hemodynamics or the blood flow in the stented arteries as compared to the one without the stent deployment. For the comparison, we computed and compared the flow velocity, wall shear stress (WSS) and pressure distributions, as well as the WSS related indices, all of which are of important parameters for studying the occlusion and potential rupture of the aneurysm. Our results illustrate that the WSS decreases within the aneurysmal sac on the treated arteries, which is more significant for the stents with lower porosity or finer mesh. Our results also show that the maximum WSS near the aneurysmal neck increases regardless of the stents used. In addition, the WSS related indices including the time-average WSS, oscillatory shear index and relative residence time show different distributions, depending on the FDSs. Together, we found that the finer mesh stents provide more flow reduction and smaller region characterized by high oscillatory shear index, while the new stent has a higher relative residence time.  
cerebral aneurysm; CFD; flow diverting stent; mass flow reduction; hemodynamics
# Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things
## 3 Design of the DCO
### Reliable Burst Transmission  
In Wi-Fi backscatter uplink communication, the Wi-Fi backscatter tag transmits its tag information comprised of multiple bits to the Wi-Fi reader by reflecting a series of packets, since one packet reflected by the Wi-Fi backscatter tag is decoded as one bit by the Wi-Fi reader. Accordingly, to guarantee the reliability of Wi-Fi backscatter uplink communication, the Wi-Fi backscatter devices should have the ability to transmit multiple packets without any interruption. In other words, reliable burst transmission is indispensable for guaranteeing the communication between Wi-Fi backscatter devices. For reliable burst transmission, the DCO uses SIFS between consecutive packets. Note that the legacy Wi-Fi uses the CSMA/CA mechanism whenever the packet is transmitted, which causes DIFS and backoff delay. The use of SIFS between burst packets can provide dominant channel occupancy to Wi-Fi backscatter devices, since it enables the Wi-Fi backscatter devices to start packet transmission before the legacy Wi-Fi devices perform carrier sensing (i.e., backoff). The legacy Wi-Fi devices defer their transmission until the end of the burst transmission of Wi-Fi backscatter devices upon detecting the busy channel through carrier sensing.  
Figure 3 shows an operational example of the burst transmission of Wi-Fi backscatter devices running DCO. In the example, a pair of Wi-Fi backscatter devices and two pairs of neighboring legacy Wi-Fi devices are assumed. The Wi-Fi backscatter devices employ DCO and have three packets for burst transmission. On the other hand, the legacy Wi-Fi devices use the CSMA/CA mechanism and have one packet to transmit. In the figure, one of the Wi-Fi backscatter devices transmits the packet earlier than the others since it uses \"CSMA/CA starting with SIFS\" and \"reduced CW\({}_{\text{min}}\)\". Due to the transmission of the Wi-Fi backscatter devices, the legacy Wi-Fi devices detect a busy channel after DIFS and defer their transmission until the end of the transmission. After the successful transmission, the Wi-Fi backscatter devices and legacy Wi-Fi devices wait for SIFS and DIFS, respectively. Then, the Wi-Fi backscatter devices occupy the channel and the others defer their transmission again, since SIFS is shorter than DIFS. All the devices repeat their previous operation until the end of the burst transmission. In this example, all the packets of Wi-Fi backscatter can be transmitted successively without any interruption.  
The real implementation of DCO additionally requires the low MAC development of the Wi-Fi helper, for which the developer can modify the MAC parameter configurations within the device driver source code of the embedded operating system. Note that current Wi-Fi open source device drivers such as OpenWrt, DD-WRT, Tomato, MadWifi are available for the hands-on development of DCO [25; 26; 27; 28].  
Figure 3: Example of burst transmission of DCO.
## 3 Network Background
### Ultra-Dense 5G/6G Cellular Networks  
UDNs are characterized by having a higher number of cells than active users [10; 29; 30]. Specifically, a UDN is defined as when the density of the base stations (\(\lambda\)b) significantly exceeds the density of users (\(\lambda\)u). Another definition of a UDN is based solely on cell density, independent of user density, where a network is considered ultra-dense when it reaches a density of \(\geq\)10\({}^{3}\) cells/km\({}^{2}\)[11]. In a UDN, high-power macrocells coexist with numerous low-power small cells, which may include picocells, femtocells, RRHs, and relay nodes [15]. Small cells in UDNs are generally categorized into fully functional BSs, such as femtocells and picocells, and macro-extension access points, including RRHs and relays [31]. A fully functional BS operates at lower power over a smaller coverage area while retaining all macrocell capabilities and executing the full protocol stack [32]. Conversely, a macro-extension access node enhances the macrocell coverage while performing some or all of the physical (PHY) layer functions [33; 34]. UDNs play a critical role in future network evolution, enabling wireless backhaul deployment, fostering a new industry ecosystem for 6G services, and supporting both licensed and unlicensed spectrum usage. Their adaptability makes them an optimal solution for enhancing spectrum efficiency and improving the user experience in high-density environments [35].
## 5 Conclusions  
We present the detailed fabrication process of a Bragg grating-based FP sensor within the core of the biconical fiber taper (tapered adiabatically) using fs-laser. The sensing characteristics of the fabricated sensor were investigated for strain and temperature measurands, and compared with the response of an FBG, which is a widely used fiber sensor for continuous monitoring applications. The fabricated sensor attains a high strain sensitivity of 2.32 pm/\(\upomega\) in comparison to the stain sensitivity experienced by the FBG in standard fiber (0.73 pm/\(\upomega\)). In contrast, both sensors demonstrate similar sensitivity to temperature (about 8.85 pm/\({}^{\circ}\)C). The sensor can be deployed in a high-temperature environment (after having a suitable outer coating depending on application scenario), as the fabrication is performed using fs-laser irradiation that makes the sensor more robust compared to conventional UV-written sensors. Such sensors would be highly desirable in structural health monitoring applications where precise strain detection is crucial. The advantages of this simple fabrication process enhance the scalability, ease of mounting, etc. during deployment. For future work, we will investigate the sensing characteristics and fabrication feasibility of sensors with fs-laser in further reduced waist diameter fibers or microfibers, and some specialty fibers, to monitor parameters enabling structural health monitoring applications.  
Conceptualization, A.M., W.C., P.P.S., and J.H.; Methodology, A.M., S.H.K.Y., W.C., and P.P.S.; Investigation, A.M. and V.P.; Resources, A.M., S.H.K.Y., V.P., P.P.S., and J.H.; Data curation, A.M., and V.P.; Writing--original draft, A.M.; Writing--review and editing, A.M., S.H.K.Y., V.P., W.C., P.P.S., and J.H.; Visualization, A.M., W.C., P.P.S., and J.H.; Supervision, W.C., P.P.S., and J.H.; Project administration, W.C., P.P.S., and J.H.; Funding acquisition, P.P.S. and J.H. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank [PERSON], Technica Advanced Technology Center Singapore, for providing his valuable suggestions and access to the design and fabrication facility.  
The authors declare no conflict of interest.
## 6 Alternative Methods of Articular Cartilage Diagnosis
### Raman Spectroscopy  
A promising technique for investigating OA is Raman spectroscopy, which can provide information on the subtle molecular changes in joint tissues as the disease progresses [355]. An important advantage of this method is its non-invasiveness, as well as the direct analysis of well-hydrated tissues, such as AC, in an almost in vivo state [355]. Raman spectroscopy has high spatial resolution and requires little sample processing, making it a suitable method for use in ex vivo AC experiments. [PERSON] et al. [356] described that the analysis of AC samples using this method showed a significant decrease in GAG- and proteoglycan-related signals, with a concomitant increase in the collagen disintegration rate as degenerative changes increased. At the same time, there was also a decrease in the ratio of bands corresponding to GAG versus hydroxyapatite, with an increase in the hydroxyapatite/collagen ratio with OA progression. Correlations of the spectral markers with the biochemical content of GAG and collagen in the tissue were confirmed, proposing them as optical biomarkers of OA. [PERSON] et al. [357] used Raman multivariate curve resolution (MCR) to analyse the cartilage surface, and the results showed that Raman MCR allows accurate quantification of the distribution of AC subcomponents across the surface, down to a depth of 0.5 mm. [PERSON] et al. [358] demonstrated that polarised Raman spectroscopy can distinguish the different orientations of collagen fibres in different AC layers. The practical potential of the method has been confirmed by screening and classification studies. [PERSON] et al. [36] described that machine-learning models based on Raman spectra enable healthy AC to be distinguished from damaged AC with high accuracy, and also allow structural, biochemical and biomechanical properties to be estimated. The use of Raman spectroscopy could complement classical imaging methods as well as clinical examination, influencing the early detection of degenerative changes and tracing the course of OA at the molecular level [36,355].
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 2 AIoT in PV Systems: Background
### Artificial Intelligence Techniques  
Artificial Intelligence (AI) refers to the development of systems capable of performing tasks that typically require human intelligence, such as reasoning, learning, problem-solving, and language understanding [14]. AI encompasses a broad range of techniques and methodologies designed to enable machines to replicate or enhance human capabilities.  
According to the literature, machine learning (ML) is especially effective for addressing complex problems where traditional techniques either fail or are difficult to apply. ML excels in dynamic environments, as it can easily adapt to new data. Unlike traditional methods, ML is particularly advantageous in situations where there is no clear or direct relationship between inputs and outputs, making it ideal for problems that involve long and complex rules.  
Deep learning (DL) builds on machine learning by providing enhanced accuracy in classification and prediction tasks, particularly when trained on large datasets. DL algorithms can process raw data directly and automatically extract relevant features, eliminating the need for manual feature engineering. This makes DL especially suitable for solving complex problems that go beyond the capabilities of traditional ML approaches. However, when data are limited, ML algorithms often outperform DL methods due to their lower data requirements.  
Recently, Generative AI (GenAI), a cutting-edge field within AI, has garnered significant attention. Unlike traditional AI models, which perform deterministic tasks based on predefined rules, GenAI models learn patterns and structures from large datasets to generate text, images, audio, and even synthetic data that are virtually indistinguishable from real data [15].  
Importantly, there are no universal rules for selecting the most suitable AI technique for a given application [16]. The choice depends on several factors: the nature of the problem, the volume of available data, the complexity of the algorithm, the implementation difficulty, and the desired accuracy and generalization capability.  
This variability underscores the importance of understanding both the problem and the available tools when selecting the most appropriate technique  
The Venn diagram in Figure 3 illustrates the relationships and overlaps among AI, ML, DL, and GenAI, highlighting both their unique features and shared capabilities. This representation emphasizes the hierarchical structure and interrelated nature of these technologies.  
Figure 3: Venn diagram representing AI, ML, DL, and GenAI.
## 3 Methodology
### Literature Search  
Literature search was carried out by consulting three databases of Scopus, Web of Science, and Google Scholar for systematic review of the applications and methodologies on subspace-based SHM. Defining keywords for a systematic review and meta-analysis is more than just important. Selecting keywords from subject heading is of the best tools for efficient retrieval and survey of data from database [150]. Hence, in the first step, the following combinations were used in the keyword search: (\"subspace system identification\" AND (\"structural health monitoring\" OR \"damage detection\" OR \"fault detection\" OR \"modal\")). Duplicates and unrelated articles; assessed from title screening; were excluded from the study. Following the database searches and title screening, eligibility of the retrieved records were assessed through abstract screening. The search process was iterative, and the studies that met the inclusion and exclusion criteria were continuously extracted till the end of the study. Moreover, the search terms were refined in the process of becoming familiar with literature. Other search keyword were also added in the course of the review process such as a combination of (\"subspace system identification\" AND (\"output-only\" OR \"ambient excitation\" OR \"civil\" OR \"stochastic\")).  
It is now about 25 years or more since subspace system identification was linked as an approach to the dynamic identification and SHM of civil structures. The literature search and eligibility assessment study shows that the time period 1995-2019 can be divided into two time intervals. The 1995-2008 can be characterized to development of the theoretical foundation and conceptualization of the framework that is discussed in introduction section. Hence, to deal with application and application-related topics more specifically, the scope of the literature search was limited to the papers published in the time frame of 2008-2019. An evaluation process was conducted to determine whether a publication must be retained in the final list.  
The literature search was confined to the English language journal papers and the relevant works in the form of book chapters, non-indexed conference papers, editorial notes, master dissertations, doctoral theses, and textbooks were excluded from the review. Abstract review is the first screening of the papers for inclusion or exclusion that is conducted based on the pass/fail criteria. Using this criteria a total of 90 scholarly papers were identified. The duplicated records with redundant information were removed from the final search results. In this stage, 67 papers remained. All the above identifiedarticles were thoroughly read based on topics and abstracts while unrelated studies were removed. Totally, 69 potentially related studies qualified, as shown in Figure 4.
# An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks
## 5 Simulations  
In this section, we present some simulation results to verify the effectiveness and validation of EBLE. The performance evaluation is done using the data from NS-3 simulator, which is a discrete event simulator for network simulation [32]. In this section, we conduct simulations on two kinds of node distribution as shown in Figure 5. Figure 5a is a regular node distribution case. In this case, the network is divided into \(n\) concentric circular rings \(S1,S2,S3\dots Sn\) and each ring's radius is \(R\). A sink node is deployed in the center of the network. The sensor nodes are deployed with equal spacing and each ring has the same number of sensor nodes. It is obviously seen that the node density is higher when the concentric circular rings are closer to the sink node. The sensor nodes that are closer to the sink have to transmit more data in multi-hop transmission mode as these nodes are required to relay more data loads to the sink. Figure 5b is a universal case in which the sensor nodes are deployed randomly in a circle with radius \(nR\). The node density in each area of the network is a random value and the sensor nodes may not have more data loads when they are closer to the sink in multi-hop transmission mode. If the routing paths remain unchanged, some nodes may only need to send data packets that are created by themselves even when they are closer to the sink node. The relative simulation parameters are shown in Table 2. We value our proposed EBLE protocol against BEAR [16], BTM [17] and one-hop direct transmission in terms of energy consumption, average residual energy, average end-to-end delay and network lifetime. Here we define that a node is considered to be dead when it consumes 90% battery power of itself and the network lifetime is defined in two versions: the \(FirstNodeDead\) means the time when a first node is dead and the 50%_Dead_ means the time when 50% of the sensor nodes are dead. When a node is dead, it broadcasts an energy depletion notice packet to its neighborhood nodes and the dead node cannot transmit any packets any more. In the following simulations, we first conduct simulations in the regular node distribution case and then in the randomly deployed case.  
Figure 5: (**a**) regular node distribution, (**b**) random node distribution.
# Effects of Manure Waste Biochars in Mining Soils
###### Abstract  
Land degradation by old mining activities is a concern worldwide. However, many known technologies are expensive and cannot be considered for mining soil restoration. Biochar amendment of mining soils is becoming an interesting alternative to traditional technologies due to an improvement in soil properties and metal mobility reduction. Biochar effects depend on soil and biochar properties, which in turn vary with pyrolysis conversion parameters and the feedstock used. The objective of this study is to evaluate the effect of four biochars prepared from poultry and rabbit manure at two pyrolysis temperatures (450 and 600 \({}^{\circ}\)C) in the trace metal mobility, CO\({}_{2}\) emissions, and enzymatic activity of 10 mining soils located in three historical mining areas of Spain (Zarandas-Andalusia, Mijarojos-Cantabria, and Portman-Murcia). For this reason, soils were amended with biochars at a rate of 10% (_w/w_), and different treatments were incubated for 180 days. For acid soils of the Zarandas-Andalusia area, biochar addition reduced the mobility of Ni, Zn, Cd, Pb, and Cr, respectively, by 91%, 81%, 29%, 67%, and 70%. Nevertheless, biochars did not exhibit the same efficiency in the other two areas where alkaline soils were predominant. CO\({}_{2}\) emissions generally increased in the treated soils. The application of biochars produced at 600 \({}^{\circ}\)C reduced CO\({}_{2}\) emissions, in some cases by more than 28%, being an adequate strategy for C sequestration in soil. The results showed that application of manure biochars can be an effective technique to reduce the mobility of metals in multi-contaminated acid soils, while reducing metal toxicity for soil microorganisms.  
biochar; mobile metal content; CO\({}_{2}\) emissions; enzyme activities +
Footnote †: journal: Applied Physics Letters
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
## 3 Results
### HRV Successive Differences  
Statistically significant differences in the data from E4 data were found among the phases in both RMSSD (F(2, 38) = 44.822, \(p<0.001\)) and pNN50 (F(1.411, 26.816) = 57.508, \(p<0.001\)) (Figures 10 and 11). Bonferroni post-hoc test showed that the values increased statistically significantly _between the baseline and the easy driving phase_ (\(p<0.001\)) and _between the baseline and the demanding driving phase_ (\(p<0.001\)), for both variables, RMSSD and pNN50. The test showed no statistically significant differences between easy driving and demanding driving (\(p=0.964\) (RMSSD), \(p=0.530\) (pNN50)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SDANN** & **SDNN Index** \\ \hline E4 & \begin{tabular}{c} RMANOVA: \\ F(2, 38) = 27.721, \(p<0.001\) \\ \end{tabular} & \begin{tabular}{c} RMANOVA with Greenhouse-Geisser correction: \\ F(1.586, 33.303) = 3.923, \(p=0.0381\)1 \\ \end{tabular} \\ \hline \multirow{2}{*}{Faros 360} & \
\begin{tabular}{c} RMANOVA: \\ F(2, 42) = 13.312, \(p<0.001\) \\ \end{tabular} &
\begin{tabular}{c} RMANOVA with Greenhouse-Geisser correction: \\ F(1.398, 29.364) = 3.405, \(p=0.062\) \\ \end{tabular} \\ \hline \hline \end{tabular}  
* [PERSON] post-hoc test showed no statistically significant differences between phases, \(p\)(1,2) = 0.166, \(p\)(2,3) = 0.641, \(p\)(1,3) = 0.091.  
\end{table}
Table 2: Statistical tests’ results for HRV variables for shorter timeframes.  
Figure 9: HRV SDANN for 10-s timeframes for each participant in each phase for E4 (**left**) and Faros 360 (**right**).  
In the data, captured with Faroos 360, [PERSON]'s non-parametric tests did not reveal any statistically significant differences in RMSSD (\(\chi^{2}(2)=3.545\), \(p=0.170\)) or pNN50 (\(\chi^{2}(2)=0.364\), \(p=0.834\)).