esa-sceva/satcom-chunk-collection huggingface dataset:
Multislice spiral computed tomography (MST) is a crucial volumetric imaging technique to correctly assess the clinical TNM staging, which is different between the HPV-related and classically tobacco/alcohol-related HPV-negative OPC [7]. Pre-irradiation dental-periodontal screening is usually carried out by means of clinical assessment and a panoramic radiography (PAN) so as to examine oral mucosa, bone tissue, and tooth inflammations. The ultimate aim is to minimize complications both during or immediately after RT, e.g., mucositis, xerostomia, and oral mucosa keratinization, and in the long term, atrophy, masticatory muscle fibrosis, trismus, and osteeradionecrosis [8]. PAN plays a key role in the diagnosis of oral infectious foci taking advantage from its panoramic view, low radiation dose, and low cost [9]. Nevertheless, a volumetric imaging technique such as MST has better diagnostic accuracy than PAN in the identification of dentoalveolar structures and especially in the detection of periapical bone lesions [10]. Post-RT complications are notably reduced by extracting untreatable dental elements and teeth with an uncertain prognosis before the radiation treatment begins [11]. However, there are no guidelines that indicate what criteria should be used to determine whether a tooth must be extracted or fixed before a RT treatment [12], nor are there any papers on the diagnostic accuracy of each available imaging techniques in the identification of pre-existing dental alterations. The aim of this retrospective study was to evaluate the diagnostic accuracy of PAN in the detection of deep caries, root remnants, stage III periodontal disease, and apical periodontitis (AP) in a cohort of OPC patients that will have to be treated by RT, using MST imaging as the reference standard.
## 3 Results and Discussion  
### Flow Curves of Tested Film-Forming Solutions  
#### 3.1.1 _Gelatine_ Solutions with Pumpkin Puree  
Figure 1A shows the flow curves of composite solutions with gelatine concentrations of 4 (PGEL_4), 8 (PGEL_8), and 12% (PGEL_12) and with the addition of pumpkin puree. It  
was found that the flow curves of the tested composite solutions were non-linear, regardless of the concentration of gelatine in the sample. However, compared to gelatine solutions without the addition of pumpkin puree [27], it was found that the addition of pumpkin puree changed the flow tendencies of the tested gelatine solutions, which allowed the curves to be described using the Ostwald de Waele model (1) taking into account the value of parameter R\({}^{2}\) for the described experimental data (Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Gelatin (PGEL)**} \\ \hline Concentration [\%] & \(k\) & \(n\) & R\({}^{2}\) & Apparent viscosity [Pa-s] \\ \hline
4 & 0.1101 & 0.6544 & 1 & 0.0285 \(\pm\) 0.0000 \\
8 & 0.1098 & 0.7524 & 0.999 & 0.0417 \(\pm\) 0.0000 \\
12 & 0.2096 & 0.7733 & 0.999 & 0.0860 \(\pm\) 0.0001 \\ \hline \hline \end{tabular} Statistical analysis of the effect of pumpkin puree concentration and addition on the apparent viscosity of gelatine solutions  
\end{table} 
Table 2: Rheological parameters and results of correlation analysis of tested solutions of selected hydrocolloids with pumpkin puree. Figure 1: Flow curves of composite solutions with concentrations of protein and polysaccharide film-forming ingredients with the addition of pumpkin puree: (**A**) gelatine concentrations of 4 (PGEL_4), 8 (PGEL_8) and 12% (PGEL_12); (**B**) soy protein isolate concentrations of 4 (SPL_4), 8 (SPL_8), and 12% (SPL_12); (**C**) sodium alginate concentrations of 1 (SA_1), 1.5 (SA_1.5) and 2% (SA_2); (**D**) highly methylated apple pectin concentrations of 1 (HMAP_1), 1.5 (HMAP_1.5) and 2% (HMAP_2).
## 2 Materials and Methods
### Measurements of Canopy and Soil CO\({}_{2}\) Fluxes  
Canopy fluxes of CO\({}_{2}\) were measured using a closed transparent Plexiglas chamber with similar dimension as the opaque chamber used to measure N\({}_{2}\)O flux. The chamber included sensors for temperature and photosynthetically active radiation (PAR), and a fan to mix headspace air. Concentrations of CO\({}_{2}\) and H\({}_{2}\)O was determined by a portable infrared gas analyser (EGM-5; PP Systems, Amesbury, MA, USA) that was connected to the chamber through 3 mm inlet and outlet tubing. The gas analyser recorded all the measurements at 1 s intervals. On each measurement date, fluxes were measured consecutively at all collars under both ambient light and darkened conditions. At first, net ecosystem exchange (NEE) of CO\({}_{2}\) was measured at ambient light for 1 min with the fully transparent chamber. Then the chamber was covered with a white cloth to block 100% PAR for ecosystem respiration (ER) flux measurements for 1 min after plant adaptation to dark conditions for 1 min.  
Soil respiration (SR) fluxes were measured using a plant and root exclusion method [21] on cylindrical PVC cores (diameter, 0.10 m; height, 0.30 m) inserted to 0.28 m soil depth. A core was inserted in all plots at about 1 m distance from the collar installed for measurements of canopy CO\({}_{2}\) and N\({}_{2}\)O fluxes. The cores were inserted between two rows of winter wheat after germination. Growth of weeds was rarely seen inside the cores, and those encountered were removed by hand before SR measurements. Fluxes were measured using an EGM-5 gas analyzer connected to a cylindrical (height, 0.15 m; diameter, 0.10 m) chamber (model SRC-2; PP Systems). During the flux measurements, the chamber was enclosed for 60 seconds and CO\({}_{2}\) concentrations were recorded at 1 s intervals with the first 10 s of measurements discarded as a dead-band.  
Fluxes of CO\({}_{2}\) were measured at midday between 9:30 and 13:00. Measurements were generally taken at regular intervals of two weeks. For measurements of canopy CO\({}_{2}\) fluxes, a Plexiglas chamber extension with similar dimensions as the top chamber was used when the height of crops exceeded chamber height. Fluxes of CO\({}_{2}\) were calculated by linear regression using the MATLAB(r) routine by [PERSON] et al. [20]. The first 10 s of recordings after chamber deployment were discarded as a dead-band.
## 2 System Architecture and Components  
The system architecture considered in this work is shown in Figure 1. It consists of an educational building with laboratories (engineering building 2-7), a smart parking lot with 100 charging/discharging stations, PV panels, and an ESS. The power grid is connected to the system through a point of common coupling (PCC). The power grid supports the electric power for both the building and the parking lot. The university energy management system (UEMS) is a local controller responsible for serving both the building's energy demands as well as EV demands. The UEMS receives monitoring data from different sub-systems through communication networks. The major roles of the UEMS are:  
* To receive information about the real-time price from the power grid and the building's power consumption.
* To receive information from PV and ESS to decide on their participation.
* To receive information from newly arrived EVs and the priority determination of each EV.
* To allocate time slots for EV charging with a low cost and discharging with a high cost by considering their priority and load profile.
* To update the building load profile.
## 2 FANET Architecture and Routing Protocol
### Routing Protocols
#### 2.2.3 Swarm-Based  
A routing protocol based on swarm draws heavily on the collective behavior, collaboration, and self-organization mechanisms found in swarm intelligence [39]. It treats the nodes in a UAV network as a group and implements routing decisions by means of collective intelligence. In recent years, many scholars have studied and improved routing protocols based on swarm intelligence.  
[PERSON] et al. [40] proposed a dynamic UAV network routing protocol based on the ant colony algorithm, which improved the convergence speed and stability of the routing protocol by optimizing the pheromone update mechanism in the ant colony algorithm. Similarly, [PERSON] et al. [41] proposed a dynamic source routing protocol based on ACO to address the problems of low data transfer rate and high routing overhead in FANETs with conventional routing. In contrast to the studies by [PERSON] and [PERSON] et al., [PERSON] [42] abstracts the routing process of the FANET as the colony behavior of bees. The routing process is divided into two parts: bees exploring the pistil (available nodes) and bees carrying food (transmitting data packets). The special point of this protocol is that the relay drone node is not involved in the decision-making process, and all decisions are given by the source node.  
Despite the achievements of swarm-intelligence-based routing protocols in FANETs, there are still some problems and challenges. Firstly, many swarm-intelligence-based routing protocols may face slow convergence and high computational complexity [43] when dealing with large-scale FANETs. Secondly, most of the swarm intelligence algorithms used in current research are based on heuristic strategies and do not fully consider the special characteristics of the FANET and practical application scenarios. Meanwhile, there is complementarity between swarm intelligence algorithms that can be fused with each other to optimize routing protocols.
# Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising
## 4 Proposed Methodology: Convolved Feature Vector Based Fuzzy Filter  
Generally, in noise removal techniques, two key conventions are considered. Firstly, the image that is noise-free usually comprises of smooth regions separated by edges and lines. Secondly, smooth regions, edges and lines in a noisy image do not disclose compatibility with neighboring pixels and intensity transformations are also very abrupt.  
Figure 1: Shows a Barbara image in which different regions are artificially corrupted with (a) random value impulse, (b) salt like impulse and (c) pepper like impulse.  
Furthermore, kernel size is very important for feature extraction and analysis. Small size kernel i.e., 3 \(\times\) 3 is considered very effective in terms of computation but gives inadequate performance at high noise. Also, the edge and texture information is limited in small size kernel. Large size kernels, i.e., 5 \(\times\) 5 and above adds more textures and edges information around a certain pixel but they are cost effective in terms of computation. Furthermore, median drifting problems usually occur in large size kernels causing blurry effects on restored images.  
Keeping in view the above assumptions and limitations, a convolved feature vector (CFV) based fuzzy mechanism is introduced which consists of two steps, noise detection followed by noise filtering. In noise detection phase, noisy pixels are judged based on its neighboring pixels as well as fuzzy rules-based mechanism. Pixel having traces of noise are filtered out in filtering phase based on fuzzy membership degrees.
## Sensor Bus Standards
### Serial Peripheral Interface
#### IEEE P1451.3  
IEEE P1451.3 is a proposed standard which attempts to define a set of specifications that will allow a transducer manufacturer to build transducers that have a wide range of price and performance but can interoperate within a system [7]. It is a multi-drop transducer bus standard, defining a digital interface for connecting multiple, physically separated sensors. Figure 4 is a representation of what is expected in the physical realm. A single transmission line will be used to supply power to the transducers and to provide the communications between the bus controller and the Transducer Bus Interface Modules (TBIM). A bus is expected to have one bus controller and many TBIMs. As in IEEE P1451.2, the Network Capable Application Processor (NCAP) contains the sensor bus controller as well as interface to the broader network. A TBIM may contain one or more different transducers.  
All TBIMs contains five communication functions, including network communications, TBIM communications, data transfer, synchronization, and triggering. These communication functions will utilize as few as two communications channels on one physical transmission medium. The transmission medium can be a coaxial cable or twisted-pair cable, to five feet or more. The communication channel will share the same signal line as the power supply. For high power transducers, the current that can be supplied over the communications cable may be inadequate, so it will be acceptable to power the transducers with an external power supply. The interface for multiple  
Figure 4: Physical representation of IEEE1451.3.  
physically separated modules will support TEDS, as well as channel identification, hot swapping, and time-synchronization protocols.  
IEEE1451.3 is promising to achieve a universal connection and transducer-to-network interoperability [8]. It provides two-way communications with sensors in process control loops. It supports interruption, hot swapping and time-synchronization. Additionally, wireless techniques can be included in the system for some applications. The multi-drop transducer bus standard is a minibus implementation that is sufficiently small and inexpensive to integrate into a transducer. The amount of overhead for the proposed 1451.3 is reportedly considerably less than that for existing field buses. On the other hand, two-way communication requires much more complicated protocol and therefore increases the complexity of the bus controller. To support five communication functions, multiplexing techniques are used to implement various communication channels on one physical medium. The interface hardware for this bus must be complex and will therefore require considerable power. In summary, IEEE P1451.3 is for more complex systems which need a variety of sensors with a wide range of bandwidth requirement but do not have severe constraints on power consumption.
## 2 Methods
### Radiometric Consistency Assessment  
Radiometric consistency was assessed by computing, pixel by pixel, differences for the co-registered reflectance maps in the overlapping area of photogrammetric blocks 3 and 4. Respective statistics were also analyzed. Considering that Sequoia is a sensor mainly dedicated to agricultural applications, assessment was conducted also for some vegetation index (VI) maps, since they commonly represent a proxy of vegetation parameters to be used for agronomy purposes. VI maps were computed in Matlab, by applying an index formula to proper reflectance maps (Table 1).  
For Ig/Ir and Jg/Ir scenarios, maps derived from blocks 3 and 4 were directly compared, while Ig/Ir scenario was checked with respect to the single blocks of Ig/Ir scenario (see Section 2.3 for more details about scenario characteristics). From here on, maps are identified with the names \"3 Ig/Ir\", \"4 Ig/Ir\", \"3 Jg/Ir\", \"4 Jg/Ir\", \"Ig/Ir\", where \"3\" stands for block 3, and \"4\" for block 4. DG scenario was not considered for radiometric assessment.  
Moreover, since no ground truth was available, the reliability of reflectance and VI maps was evaluated by comparing maps with the one obtained from Sentinel-2 (S2) imagery. Indeed, a Sentinel-2 acquisition two days after the survey (September 19, 2017) was available. Maps derived from the photogrammetric blocks (having a GSD equal to 0.10 m) were upscaled with a nearest-neighbor resampling to 10 m spatial resolution, to match Sentinel-2 imagery resolution. Correlation analysis was applied and statistics were performed on differences in terms of single bands and radiometric indices.  
Although a comparison with ground truths calculated with a spectroradiometer would have been more effective, a test on compatibility between Sequoia and S2 data is also of scientific relevance, given the growing interest in the integration of data acquired from satellite and UAV platforms [48] for environmental applications [49; 50], including PA [51; 52; 53], both from research and applied points of view.
# Carbazoles: Role and Functions in Fighting Diabetes
## 2 Carbazole Derivatives in the Pathogenesis of Diabetes  
Over the years, the academic community has delved into the chemical and pharmacological properties of small heterocyclic molecules, including those with a carbazole-based structure [23; 24; 25; 26]. Growing interest in these derivatives has resulted in the development of various synthetic routes for their preparation as well as their extraction from various plants. Several carbazole-containing molecules have found application in the pharmaceutical field. In particular, the studies included in this review showed that certain derivatives are promising antidiabetic agents or may otherwise play an important role in the pathogenesis of diabetes. Data reported in the literature suggest that carbazoles are able to modulate glucose metabolism, block adrenergic hyperactivation, prevent damage to \(\beta\) cells of the pancreas, inhibit inflammatory and oxidative mediators, control the cryptochrome or inhibit \(\alpha\)-glucosidase (Table 2). Therefore, carbazole represents a versatile scaffold for the preparation of biologically active derivatives, useful in the treatment of diabetes [6; 7; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. \begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Compound** & **Name** & **Biological Activity** & **References** \\ \hline
**1** (Synthetic compound) & Carvediol (1-(9\(H\)-carbazol-4-yloxy)-3-[2-(2-methoxyphenoxyoxy)ethylamine)propanan 2-ol) & Reduction of insulin resistance by sensitizing insulin receptors and inhibiting the sympathetic nervous system Beneficial effects on left ventricular function, resting and exercise hemodynamics and clinical conditions Stimulation of endothelial NO production Beneficial effects on endothelial dysfunction caused by oxidative stress Long-term benefits on glucose metabolism Blockade of adrenergic hyperactivation Prevention Prevention of pancreatic \(\beta\)-cell damage Inhibition of inflammatory and oxidative mediators & [7; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. \\ \hline
**2** & Hydroxyphenyl-carvediol (OHC) & Metabolite of carvediol & [30] \\  & 4-[2-[[3-(9\(H\)-Carbaz-ol-4-yloxy)-2-hydroxyprop-pyl]amino]ethoxy]-3-methoxy & & \\ \hline
**3** & \(O\)-Desmethylcarvediol (DMC) & Metabolite of carvediol & [30] \\  & 2-[2-[[3-(9\(H\)-Carbaz-ol-4-yloxy)-2-hydroxyprop-pyl]amino]ethoxy]phenol & & \\ \hline
**4** & 1-((5,6-Di(furan-2-yl)-1,2,4-triazin-3-yl))thio-3-(3,6-dibromo-9\(H\)-carbazol-9-yl)propanan-2-ol & \(\alpha\)-Glucosidase inhibition & [6; 34] \\ \hline
**5** & 2-(3-(9\(H\)-Carbazol-9-yl)-2-hydroxypropyl)isothiazolline-1,1-dioxide & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Carbazole derivatives in the pathogenesis of diabetes.
## 2 Adaptive Beam Alignment Using RL
These specific phases were derived through the utilization of the pertinent Q-tables depicted in Figure 4. It can be noted that the phase summation of the symmetric angles of the beamforming steering angles at \(\pm\)45\({}^{\circ}\), \(\pm\)30\({}^{\circ}\), and \(\pm\)15\({}^{\circ}\) are 360\({}^{\circ}\), 345\({}^{\circ}\), and 360\({}^{\circ}\), respectively, which are close to the optimal phase angle of 0\({}^{\circ}\) (which is also equal to 360\({}^{\circ}\)) for a 0\({}^{\circ}\) steering angle (no steering). Furthermore, although EVM values below 12.5% of the 3 GPP EVM standard for 16-QAM signals can be attainable at all of these beamforming steering angles, the EVMs obtained deteriorated significantly when the steering angle was aligned with the edge of the sector, as described previously. Figure 4: Mapping of Q-tables with different steering angles. The steering angles are as follows: (a) 45\({}^{\circ}\), (b) 30\({}^{\circ}\), (c) 15\({}^{\circ}\), (d) 0\({}^{\circ}\), (e) \(-\)15\({}^{\circ}\), (f) \(-\)30\({}^{\circ}\), and (g) \(-\)45\({}^{\circ}\).
# Structural Monitoring of a Drawbridge in Operation: Signal Analysis
## 5 Conclusions  
The developed monitoring system has been in operation for an extended period of time, during which it was already able to detect some anomalous situations and provided valuable insight regarding what happened, helping to identify their causes.  
The proposed signal processing methods were developed considering the target basic bridge in particular, making use of the expected waveforms to align and identify events.  
The monitoring system has been in full operation since October 2023 and has analyzed, on average, 200 open and 200 close events per month. During this time, maintenance of the sensing system was also of utmost importance to ensure the availability of data when required. The next steps in this development include the continued maintenance of this  
Figure 8: Example of an abnormal event detected by the monitoring system, as seen by the (**a**) pressure sensors and (**b**) rosettes.  
system, as well as the analysis of future events. Another logical step, considering the increasing amount of available data, would be the implementation of predictive algorithms.  
Conceptualization, S.D., P.J.S.C.P.S., N.V.R., M.A.P.V. and P.M.G.P.M.; methodology, P.J.S.C.P.S., N.V.R. and S.D.; software, S.D. and P.J.S.C.P.S.; formal analysis, P.J.S.C.P.S. and N.V.R.; investigation, N.V.R. and J.S.S.; resources, P.M.G.P.M.; data curation, S.D. and P.J.S.C.P.S.; writing--original draft preparation, P.J.S.C.P.S. and S.D.; writing--review and editing, P.J.S.C.P.S., S.D., N.V.R., P.J.T., J.S.S., M.A.P.V. and P.M.G.P.M.; visualization, P.J.S.C.P.S. and S.D.; supervision, P.J.S.C.P.S. and N.V.R.; project administration, P.J.T. and P.M.G.P.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets presented in this article are not readily available because the data are part of an ongoing study and are the property of APDL. Requests to access the datasets should be directed to INEGI and APDL.  
The authors gratefully acknowledge APDL--Administracao dos Portos do Douro, Leixoes e Viana do Castelo, SA, for their openness to the publication of this manuscript, as well as for the solid collaboration that this work has generated.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
### Volatile Organic Compounds Analysis  
VOCs were extracted using the SPME method described in [38], with slight modifications. Briefly, 4 g of sample pasta cooked at OCT was placed in 20 mL headspace vials closed with an aluminium crimp cap and a silicone/PTFE septum. Extraction was manually performed using Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fibre (Supelco, Bellefonte, PA, USA) and a thermomixer (MultiTherm shaker, Benchmark Scientific, Edison, NJ, USA). Samples were equilibrated for 5 min at 60 \({}^{\circ}\)C at 500 rpm, and the fibre was exposed for 30 min at 60 \({}^{\circ}\)C at 500 rpm. The VOCs were desorbed for 10 min with the injector port set at 250 \({}^{\circ}\)C in a splitless mode in a 7890 A gas chromatograph coupled with a 5975 C mass-selective detector (Agilent Technologies, Santa Clara, CA, USA). VOCs were separated using a capillary SupelcoWAX 10 column (30 m length, 250 \(\upmu\)m internal diameter, 0.25 \(\upmu\)m film thickness, Supelco, Bellefonte, PA, USA). The oven temperature was initially set at 40 \({}^{\circ}\)C for 2 min, then increased to 240 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min and held for 2 min. Helium was used as carrier gas at a constant flow rate of 1 mL/min. The detector was operated in electron impact (EI) ionization mode at 70 eV. The scan range was from 35 to 550. The ion source temperature was at 240 \({}^{\circ}\)C.  
Compounds were tentatively identified by comparison of the mass spectra with the NIST/EPA/NIH Mass Spectral Library (version 2.2, 2014, Gaithersburg, MD, USA) for records with a matching score above 70% and confirmed by comparing the Kovats retention indices. For the detected compounds, the odour descriptions were assigned based on the online database [37].