esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Infrared Spectra  
To identify the intercalated anions, an infrared spectrum of Co-Si LDHs after the insertion of zirconium is displayed in Figure 8a. Cyanate anions (N\(\equiv\)C-O-) were observed by the appearance of a clear band at 2203 cm\({}^{-1}\) corresponding to the vibrational mode of cyanate. The nitrate anions were also detected by observing two bands at 1384 cm\({}^{-1}\) and 1495 cm\({}^{-1}\)[28]. Strong and sharp bands were observed around 2856 cm\({}^{-1}\). These bands are attributed to the stretching mode of O-H belonging to the molecules of interlayered water that hydrogen-bonded to interlayered anions [28]. The sharpness of these bands suggests that there is a large number of water molecules hydrogen-bonded with the cyanate anions inside the interlayered space. This speculation was confirmed by the bending mode band of water molecules that was observed clearly close to 1637 cm\({}^{-1}\)[29].  
Figure 8: Infrared spectra of Co–Si LDHs: (**a**) after zirconium inclusion and (**b**) after zirconium and titanium inclusion.  
Figure 7: Thermal analyses of Co–Si LDHs after zirconium and titanium inclusion: (**a**) TG, (**b**) DTG, and (**c**) DTA.  
After inserting both zirconium and titanium into Co-Si LDHs, the absorption peak at 2204 cm\({}^{-1}\) shifts to 2217 cm\({}^{-1}\), indicating that the intercalated anions became isocyanate anions (~N=C=O), as shown in Figure 8b [30]. It means that cyanate anions transformed to isocyanate anions, according to the balance (N\(\equiv\)C-O\({}^{-}\)\(\leftrightarrow\)~N=C=O).  
As can be noted in Figure 8b, traces of nitrate anions were also detected by a weak peak at 1382 cm\({}^{-1}\)[28]. There is no peak shift for the vibrational mode of NO\({}_{3}\)-, suggesting that nitrate anions adopt a flat-lying configuration parallel to the hydroxide layer [31].  
The effect of the presence of isocyanate and nitrate anions inside the interlayer space beside the hydroxide layers of the LDH structure showed a broad OH vibration band around 3500 cm\({}^{-1}\)[28; 29; 30; 31]. This effect was extending by increasing the hydrogen bonds between the water molecules and the interlayered anions producing clear peaks around 2923 cm\({}^{-1}\)[32]. The presence of water molecules was confirmed by observing a clear band at 1637 cm\({}^{-1}\) corresponding to its bending mode [28].
# Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs
## 4 The Proposed Strategy
### The Proposed SPT-EBR Strategy  
In order to prolong the lifecycle of WSNs with node energy efficiency and energy balance, this paper proposes two novel schemes by using forward awareness factors as path cost to select the next node for packet transmission. One is the power consumption scheme, and the other is the energy harvesting scheme. The power consumption scheme considers the packet forwarding rate as the forward awareness factor for each node to alter the desired routing path. The packet forwarding rate is proportional to the power consumption rate and it is considered to keep the sensors using the shortest path, thus making the packets transmission efficient. Simultaneously, the power consumption between the hotspots is also balanced, achieving the effect of both packet transmission efficiency and load balancing design goal.  
In general, hotspot nodes are usually spatially distributed. With the assistance of the power consumption scheme, hotspot nodes can be transferred from the irregular location to the same intra-layer, and this benefit makes the implementation of efficient wireless power charging over a large area is possible. In the energy harvesting scheme, it is assumed that the network nodes have the capability to capture energy. The role of the charging station can be deployed by a nearby data sink or other RF base stations, and the sensing node can be charged in a random model or a constant model. The power charging rate in each node is combined with the packet forwarding rate as the forward awareness factor to calculate the path cost for next node selection, achieving the design goal of energy balance and permanent utilization WSNs.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 2 Technology of Indoor Localization
### RFID Module  
The RFID-RC522 module is a versatile tool for real-time localization and tracking, operating at a frequency of 13.56 MHz. In Figure 1, this module consists of an antenna coil that generates an electromagnetic field, a microcontroller that processes the signals, and pins for interfacing with external systems like Arduino or Raspberry Pi [43]. When an RFID tag which contains an embedded antenna and a microchip storing unique identification data comes into proximity (within 2-5 cm) to the module, the module's antenna powers the tag. The tag then transmits its data back to the reader via radio waves. These data are captured, processed, and sent to a connected system, triggering actions like updating a database, logging asset information, or controlling access to secure areas [44]. RFID technology is widely used across various sectors. In healthcare, RFID modules track patients, staff, and medical equipment, improving workflow efficiency and patient safety by reducing equipment misplacement. In industrial environments, RFID facilitates inventory management, process automation, and environmental monitoring, such as detecting metal corrosion in harsh conditions [43]. The military sector benefits from RFID for tracking logistics and ensuring mission readiness in GNSS-denied environments like urban warfare zones. In retail and logistics, RFID supports inventory control, enhances the customer experience, and ensures real-time shipment tracking. Emergency response operations use RFID to improve coordination by tracking first responders and equipment during rescue missions. These applications demonstrate the adaptability and effectiveness of RFID in improving efficiency, safety, and resource management across diverse industries [44].  
RFID technology combined with a random forest classifier improves patient localization accuracy in hospital emergency units. Patients wear RFID tags that emit signals, which are detected by strategically placed antennas. A hierarchical random forest-based classifier groups rooms into macro-regions for efficient room-level classification, achieving an 83% success rate for correct room identification. This system enhances scalability and ensures reliable, real-time tracking in high-density areas like emergency departments, reducing the risk of lost patients and improving safety. Additionally, a distributed indoor localization algorithm using received signal strength (RSS) from RF beacons leverages existing nurse call networks to minimize hardware costs. Wearable devices communicate with nearby beacons, and a decision-tree algorithm processes the data independently, achieving room-level accuracy. This approach enhances scalability, improves response times, and ensures  
Figure 1: RFID-RC522 module with key fob and RFID card.  
operational efficiency by accurately associating patient calls with their real-time locations, seamlessly integrating into the existing hospital infrastructure [45; 46].
# Porous Organic Polymers-Supported Metallocene Catalysts for Ethylene/1-Hexene Copolymerization
## 2 Results and Discussion
### Polymerization Activity and Active Sites on the POPs-Supported Metallocene Catalysts  
To evaluate the molecular structure of the ethylene/1-hexene copolymer, ethylene polymerizations were conducted using the POPs-supported (_n_-BuCp)\({}_{2}\)ZrCl\({}_{2}\)/MAO catalysts in a slurry process. The supported metallocene catalysts displayed excellent \(\alpha\)-olefin incorporation ability and high ethylene/1-hexene copolymerization activity, and ethylene/1-hexene copolymerization results are shown in Table 2.  
Surprisingly, sharply different loading amounts of zirconium and aluminum of the four supported metallocene catalysts from ICP (Inductive Coupled Plasma Emission Spectrometer) analysis were observed through the same (_n_-BuCp)\({}_{2}\)ZrCl\({}_{2}\)/MAO/Supports immobilization formula. Thus, the same active species were obtained in the foursupported metallocene catalysts. From Table 2, we can see that the highest zirconium and aluminum contents (Zr = 29.6 \(\upmu\)mol g\({}^{-1}\), Al/Zr = 172) were achieved from the silica gel-supported metallocene catalyst, which could be caused by strong interaction of MAO with the highly hydrophilic surface of the silica gel, and relatively low zirconium and aluminum content on the POPs-supported metallocene catalysts were obtained due to the less-hydrophilic surface of the poly(DVB-_co_-HEMA) porous polymers. Furthermore, even in the three POPs-supported catalysts, varied zirconium and aluminum contents were noticed with similar chemical component of the supports. This discrepancy might be caused by the different pore structure of the porous polymers--especially the specific surface area.  
As for the ethylene/1-hexene polymerization results, the highest polymerization activity (10,583 Kg PE/molZr bar h) was not obtained from highest Zr loading catalyst, but from the catalyst (BCZ/MAO@POP-2) with the highest Al/Zr ratio. This proves that not all loading zirconium in the supported metallocene catalysts are effective active sites, and the MAO/Zr ratio is another key factor in ethylene polymerization. In this regard, the BCZ/MAO@POP-2 catalyst is commercially beneficial for up-scaling.
## 5 Experimental Characterization of the Cohesive Law
Under these hypotheses, the energy release rate, G, may be expressed as:  
\[G(s)=\frac{P(s)^{2}}{2\;b_{\mathrm{f}}}\frac{dC(a)}{da}, \tag{11}\]  
where the \(P\) is the applied load, \(a\) is the current crack length, and \(C\) is the compliance of the system given by the following expression:  
\[C(a)=\frac{w(a)}{P}=\frac{l(a)}{E_{\mathrm{f}}\;A_{\mathrm{f}}}=\frac{l_{0}+a }{E_{\mathrm{f}}\;A_{\mathrm{f}}}, \tag{12}\]  
where \(w\), \(l\), and \(l_{0}\) are the longitudinal extension, the current length and the initial length of the unbounded portion of the composite, respectively. The energy release rate, \(G\), corresponds to the \(J\)-integral, \(J\), within the field of linear elasticity:  
\[J(s)=G(s)=\frac{P(s)^{2}}{2\;b_{\mathrm{f}}\;A_{\mathrm{f}}\;E_{\mathrm{f}}}. \tag{13}\]  
Due to the energy release rate failure criterion, it is well known that a crack will grow when the available energy release rate, \(G(s)\), is greater than or equal to the critical value, \(Gc\). The parameter \(Gc\) represents the fracture energy, and is evaluated as:  
\[\mathrm{{}_{F}}=\frac{P_{\mathrm{max}}^{2}}{2b_{\mathrm{f}}\;A_{\mathrm{f}}\; E_{\mathrm{f}}}. \tag{14}\]  
where \(P_{\mathrm{max}}\) is the maximum force recorded during the test. The corresponding cohesive shear may be evaluated as:  
\[\tau(s)=\frac{\partial J(s)}{\partial s}. \tag{15}\]  
The debonding failure modes may be overcome if anchoring devices are utilized. Some anchoring systems mentioned in the literature [29; 30; 31; 32; 33; 34; 35; 36; 37] and, to a limited extend, available on the market, are based on steel devices or composite elements. These systems require further investigation in order to assess and validate their reliability and durability over time. Bonded joints may be also utilized in severe conditions of temperature and humidity, which may degrade their bonding strength. Numerous researchers have investigated the durability of bonded joints in aging environments [38; 39; 40; 41; 42]. These investigations demonstrated a non-negligible reduction in the peak load of FRP reinforcements and the ultimate displacement of conditioned specimens, even though the trends described by the interfacial law typically do not change. By comparison, FRCM-based strengthening systems are less affected by environmental conditions. This very different response is mainly due to the different behaviors of the two adhesives: the epoxy adhesive used in FRP laminate may become plastic as it is immersed in water, whereas the mechanical properties of FRCM's cementitious matrix tend to improve when it is immersed in water, due to the positive effects of the additional hydration. Finally, it can be observed that the debonding failure modes occurring in brittle substrates (concrete or masonry) are different from those seen in ductile substrates (e.g.,FRP-FRP, FRP-steel elements, honeycomb components). For this reason, the results of studies performed within the context of aeronautics or mechanical engineering [43; 44] may not be extended to assess traditional materials used in the field of civil engineering.
## 4 Discussion
### Parameter Impact Analysis
#### 4.2.1 Discussion of Sample Selection  
In deep learning, the selection structure of the sample and the universality of the sample are necessary for the trained model. Our sample selection method is different from the sample selection method for convolutional neural networks for PolSAR image classification. Generally, in the classification of PolSAR features by CNN, it is mainly to use the real surface map to select M% samples for each type of ground object for training and the remaining \((1-\text{M})\%\) as the test sample set. The final evaluation in this method is similar to the test sample set. The number of buildings in the real object distribution is significantly less than that of other non-building objects, and the proportion of similar buildings in the PolSAR image is less than that of the non-building objects. We propose to randomly select the building elements using the buildings in the real surface map and select the training samples of N and the test samples of N. Then, for the non-building area, we select a specific non-building area instead of all non-building areas, and randomly select a number N of non-building training samples and a number N of test samples in the non-building area.  
Under this method, we use fewer training samples than under other CNN classification methods. It increases the proportion of non-building land types with similar buildings in the training sample in all non-buildings. By increasing the training frequency of the object samples that are easily misclassified, the convolutional neural network model can learn a useful PolSAR building identification model at a relatively small cost. The building extraction effect under different sample selection methods is shown in Figure 11, and the results are not superpixel-constrained. Through the graphical representation of the two samples, we can see that the trained model is more focused on distinguishing non-buildings similar to buildings by increasing the utilization of non-building samples in the training sample. This process improves the accuracy of building extraction, and the description of the boundary of the building is more accurate than before.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**E-SAR**} & \multicolumn{3}{c}{**GF-3**} & \multicolumn{3}{c}{**RADASAT-2**} \\ \hline  & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** \\ \hline Non-superpixel & **88.05** & 25.01 & 80.99 & **94.37** & 21.76 & 85.55 & **95.56** & 15.45 & 89.71 \\ \hline Superpixel & 86.14 & **17.61** & **84.22** & 93.64 & **17.89** & **87.49** & 94.97 & **12.2** & **94.12** \\ \hline \end{tabular}
\end{table}
Table 6: Building extraction accuracy table using the superpixel methods.
# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 4 Results
### Performance with Measurement Biasing
Our new optimal gating network, however, is able to detect statistically significant mis-modeling. Beyond this, its outputs may also be used to isolate the possible root causes behind detected anomalies, which can greatly aid in operational analysis. Moving forward, we will investigate how this new optimal gating network performs in the presence of dynamic mis-modeling.
###### Abstract  
In this study, effects of pretreatment of corn stover (CS) with sodium hydroxide (NaOH) combined with calcium oxide (CaO) on anaerobic co-digestion of swine manure and CS for biogas production were investigated. Different pretreated-CSs were prepared by adding different doses of NaOH and CaO to CS: Treat-CSA (0.10 g NaOH/g CS), Treat-CSB ((0.075 g NaOH + 0.05 g CaO)/g CS), Treat-CSC ((0.05 g NaOH + 0.05 g CaO)/g CS), and Treat-CSD ((0.025 g NaOH + 0.1 g CaO)/g CS). Lignin removal rate, biomass recovery, reduced sugar, methane yield, DT\({}_{80}\) (digestion time when biogas achieved 80% of the total biogas), composition of residues, and cost-efficiency were measured to characterize CS after pretreatment and to evaluate the performance of co-digestors fed with swine manure and differently-pretreated CS. The results showed that Treat-CSB showed an excellent lignin removal efficiency and biomass recovery, resulting in the highest methane yield in its co-digestion with swine manure. Since the net benefit of Treat-CSB was calculated to be the highest (i.e., $1.89/ton total solids), therefore, we believe the co-digestion of Treat-CSB and swine manure for biogas production be an effective valorization option for the wastes.  
corn stover; digestion; calcium oxide; sodium hydroxide; cost-efficiency Article
## 4 Discussion  
In natural environments, diverse microbial communities colonize in the rhizosphere of plants, and affect plant growth and fitness [1; 2; 3]. PGPR form mutually beneficial associations with plants by a complicated exchange of chemical metabolites [4; 5; 6]. Depending on bacterial relationships with plants, PGPR can be classified into two groups: symbiotic and free-living bacteria species. PGPR can regulate multiple physiological processes in plants by different mechanisms, such as synthesizing plant growth-regulating compounds, increasing the solubility of soil nutrients, and activating a plant's defense system [4; 5; 6; 7; 8]. Several free-living PGPR strains can facilitate plants' acquisition of nutrients from their surroundings, and help them resist soil-borne pathogens [1; 2]. Ample evidence has indicated that the microbial release of VOCs triggers plant growth without physical contact with the roots [9; 10; 11]. The shift of soil microbes to belowground space is physically constrained by the soil, whereas mVOCs can easily diffuse into the rhizosphere and reach the aboveground tissues of plants [9; 10]. Rhizospheric conditions are beneficial to VOCs-mediated communication in plant-microbe interactions. Importantly, mVOCs have been demonstrated to play an important role in modulating nutrient absorption by plants [22]. Herein, we demonstrated that the release of VOCs by BF06 induced the expression of some Fe uptake-related genes, thereby enhancing plants' Fe acquisition machinery. Such regulation of Fe acquisition mechanisms by microbial VOCs was previously shown to improve plant growth under Fe deficiency. Additionally, BF06 VOCs also markedly enhanced Se accumulation in plants via the upregulation of sulfate transporter genes.
###### Abstract  
Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to the SPOT/VEGETATION (SPOT VGT) base products archive. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle, a continuous series of data products from the combined data archives of SPOT VGT (1998-2014), PROBA-V (2013-2020) and Sentinel-3 SYN VGT (from 2018 onwards) are available to users, the consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. In past years, important changes have been implemented in the SYN VGT processing baseline. The archive of SYN VGT products is therefore intrinsically inconsistent, leading to different consistency levels with SPOT VGT and PROBA-V throughout the years. A spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and an intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for three distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have indeed resulted in better-quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive; a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.  
Sentinel-3; SPOT/VEGETATION; PROBA-V; surface reflectance; NDVI; long-term data record; consistency 2024
# Numerical Study of Tympanosclerosis Including Its Effect on Human Hearing
## 1 Introduction
If this process continues to the middle ear, creating larger plates adherent to the ossicles, deafness occurs, the severity of which depends on the middle ear contribution and on how the ossicular chain is affected. Surgery comprises excision of the tympanosclerosis plaques and reconstruction of the ossicular chain. This surgery frequently results in significant hearing improvement, but occasionally the problem can return, and the best solution for hearing loss correction will be the usage of hearing aids. When tympanosclerosis fixes the incus and malleus, a reconstruction of the ossicular chain is necessary, using manubrio-stapedioplasty surgery [11]. If only the malleus becomes fixed, remotion of the sclerotic plaques is needed by performing a canaloplasty [12]. A biomechanical study of myringosclerosis [13] showed that the transmission of sound to the stapes footplate depends on the calcification degree of the myringosclerosis plaques, as well as the localization and dimensions. Other results indicate that the dimension and localization of these plaques are more relevant for hearing loss than the degree of calcification [14]. Eardrum perforations occur following acute or chronic otitis media, where consequent myringosclerosis and tympanosclerosis can develop. A previous work [15] studding the mechanical behavior of the tympano-ossicular chain, comparing the effect of myringosclerosis and eardrum perforations on the mechanical properties of the ossicular chain. By using the finite element method, it is possible to verify that when a perforation of 0.6 mm is present in the eardrum, no differences occur relative to the displacement of the umbo and stapes footplate, when compared with a model that represents a normal eardrum. The same study concluded that from a hearing point of view, the differences between a normal and a myringosclerosis ear are reduced, which is in accordance with clinical practice. The largest differences occur when a large perforation in the inferior two quadrants of the eardrum (7.0 mm) is present, leading to a large decrease in the displacements for the low and middle frequencies (Figure 1). In the present study, the finite element method was used to compare a model representing a healthy ear with three different models representing different tympanosclerosis cases (only in the eardrum; affecting the eardrum and malleus; and affecting the eardrum and the three ossicles).