esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 5 Analyzing the Challenges and Advantages of Protocols in Underwater Communication
### Transport Layer Protocol  
In this section, the transport layer protocols designed and developed for underwater communication are described and their advantages and disadvantages are noted.  
Multi path and network coding (MPNC) was developed in 2015. It is considered a reliable protocol for underwater acoustic communication [47]. Twin path and network coding (TPNC) were developed in 2015. The ratio of data delivery is the same as that of MPNC, and power consumption is low in comparison [47]. Th erasure code based multi-hop reliable data transfer scheme (ECRDT) was developed in 2017 for underwater wireless sensor networks. This approach uses a packet level forward error correction method in end to end codes [48]. The adaptive RTT-driven transport layer flow and error control protocol (ARTFEC) was developed in 2014. Data flow control techniques are used here to apply various characteristics to the acoustic channel. In this approach, the reliability and data transmission rate are high [49]. Segmented data reliable transfer (SDRT) was developed in 2010. This protocol transfers packets in blocks, and it improves the utilization of underwater channels. Power consumption is also moderate in this approach [50].
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 2 Materials and Methods  
The metal corrosion area detection model proposed in this paper, MCD-Net, consists of three parts: a convolutional encoder, a Transformer encoder, and a decoder, as shown in Figure 2.  
The convolutional encoder includes multiple convolutional block operations, with each block containing several convolutional layers. Through down-sampling and convolution operations, the feature map size is reduced while the number of channels increases. The final output feature map has a size of 32 \(\times\) 32 with 768 channels. The Transformer encoder first serializes the feature map, converting it into a sequence of length 1024. After serialization, it performs sequential encoding. Once the encoding is completed, the sequence is restored to the original image feature map of 32 \(\times\) 32 \(\times\) 768. The decoder includes an attention-based feature fusion module that merges features from both the convolutional encoder and the Transformer encoder, restoring the feature map to its original size of 256 \(\times\) 256 \(\times\) 64. After processing through the multi-scale feature enhancement unit, the number of feature channels is finally adjusted to complete the feature map output.  
During the training phase, the input to the model consists of metal corrosion images and their corresponding corrosion segmentation labels. The original image features are extracted by the convolutional encoder, and the Transformer encoder captures the dependencies between the global features of the image. The decoder further refines the feature extraction, and the final output is the metal corrosion segmentation result. Using the loss function to calculate the discrepancy between the predicted segmentation result and the ground truth, the model progressively learns to fully represent the metal corrosion features through multiple iterations of loss minimization. During the testing phase, the model input is metal corrosion images, and the final output is the segmentation result.  
Figure 2: MCD-Net metal corrosion detection model architecture.
## 5 Conclusions  
A comprehensive study of how the working frequency band (915 MHz or 2400 MHz) affects IoT networks operating in SGs has been conducted. To do so, we have proposed a dual-band propagation model based on the well-established LNSPL model. Our model implementation has been geared towards IoT-based SG networks and with a special focus on radio propagation visibility conditions (LOS/OLOS/NLOS). Accordingly, we have extended the TOSSIM simulator to incorporate this new propagation model and developed a set of tools to efficiently extract data from a series of simulations.  
By defining specific KPI (PRR, MND, and PCPT), we have quantified the performance of the IoT network under different scenarios. In general terms, we can unexpectedly affirm that in SG scenarios where the 2400 MHz band is used by other devices, the 915 MHz band is always the best option in terms of the three analyzed KPI, regardless of the packet length, and \(\overline{D}_{in}\) (mean inter-node distance). However, when both bands are equally affected by a similar noise level, there are particular values  
Figure 6: Results of Scenario 2 for different packet sizes and resizing factors. (**a**) PRR, (**b**) MND, (**c**) Zoom of MND, (**d**) PCPT, and (**e**) Zoom of PCPT.  
of \(\overline{D}_{in}\) for which each band is better suited: the 2400 MHz band is the preferred option for small size networks, whereas for large size networks the 915 MHz band is the best choice.  
Although the exact values of when to use one or another band will depend on the specific environment and network layout, the models and figures presented here seek to raise awareness and set ground rules for when to consider the proper standardized ISM band in SG environments as the best option.  
This research was supported by the MINECO/FEDER project grants TEC2013-47016-C2-2-R (COINS) and TEC2016-76465-C2-1-R (AIM). The authors would like to thank [PERSON] and [PERSON] (part of the Iberdrola staff) for the support provided during the realization of this work. [PERSON] also thanks the Spanish MICINN for an FPU (REF FPU14/03424) pre-doctoral fellowship.  
[PERSON] and [PERSON] conceived the idea, proposed the scientific methodology and performed the measurements campaigns. [PERSON] conducted the simulations. [PERSON] and [PERSON] contributed to the technical improvements. All authors participated in the review of the related works, analysis of the results, elaboration of the manuscript and its revision.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Optimization
In other words, compared to that line, the optimum mixes were those with higher MPR-to cost-ratio (e.g., equivalent costs and superior mechanical behaviour, or equivalent mechanical behaviour but lower cost). For example, from the cost-efficient concrete mixes, the following ones can be considered as optimum: M1 sp, M5 sp, M14 sp, M4 and M13, which were mostly made by incorporating FA, RCA, and SP simultaneously. Furthermore, it is possible to consider M14 sp (100% coarse RCA, 50% fine RCA, 30% FA, 1% SP) as the best mix among the optimum mixes, because its strength is higher than that of the reference mix (M1) and its cost is lower. In addition, M4 (30% FA) is considered to be the second-best due the same reason. These results agree with the literature (Section 1) that shows that it is wrong to optimize concrete mixes based on their strength (Figure 3) or cost (Figure 4) individually. In order to confirm these findings, this study presents a relationship between the \"MPR-to-cost ratio\" and the compressive strength (\(f_{\text{cm,cube}}\)) of the concrete mixes (Figure 10). The compressive strength (\(x\)-axis) is considered because it is the most demanded characteristic of concrete in the construction industry. Since the optimization of concrete depends on its application, this chart can be useful when a minimum compressive strength is considered to select the optimum mix. For example, high-strength concrete may not be required for residential houses. As concluded before, the following mixes (M1 sp, M5 sp, M14 sp, M4 and M13) can be considered as optimum because their \"MPR-to-cost ratio\" values is higher compared to other mixes. Again, apart from the fact that the SP increases the cost of concrete, most of the mixes made with SP are above the reference line and are therefore more cost-efficient than M1 (M1 sp, M5 sp, M7 sp, M9 sp, M10 sp, M14 sp and M16 sp). Thus, the use of SP is highly cost-efficient. In other words, mixes with high-strength are more cost-efficient than low-strength  
Figure 9: Relationship between relative cost and mechanical performances ratio of mixes classified based on the incorporation ratio of (**a**) FA (regardless of the type and ratio of the aggregates) and (**b**) RCA (regardless of the type and ratio of the binders). concrete. In that case, it is worth paying more to increase the strength of concrete, since this investment has a higher return. In addition, the cost efficiency of the coarse RCA concrete is significantly higher than that of the fine RCA concrete (e.g., M13 versus M6; M16 sp versus M16 sp). Furthermore, concrete with an incorporation of FA can be considered as cost-efficient, and the high incorporation of FA are even more cost-efficient when SP used.
## 3 Data Source
### Physical-Property Data  
The differences in the physical properties of rocks form the foundation of geophysical exploration. A comprehensive understanding of the physical-property parameters of various rocks in the study area is a prerequisite for inversion and interpretation. We incorporated highly reliable data spanning multiple years and different areas, including both drilling and outcrop observations. The data employ rock magnetism measurements and research from the coastal lands and islands around the northern SCS [31], as well as the summary report of the comprehensive geophysical survey of the northern SCS continental slope area [32], to statistically analyze the magnetism values for igneous rocks, sedimentary strata, and lower crust materials in the area. Geological bodies with similar magnetism parameters or minimal physical property differences are classified into a single magnetic susceptibility unit. Based on the magnetic susceptibility and corresponding geological types, the study area is divided into five geological units with distinct magnetic susceptibility differences, as follows: (1) seawater layer (no magnetism), (2) sedimentary strata (low or no magnetism), (3) intermediate to acidic intrusions (with a wide range of magnetic variations and strong magnetism), (4) mafic and ultramafic intrusions (highly magnetism), and (5) crust (no magnetism). The statistical results are presented in Table 1.
# Fully Superconducting Josephson Bolometers for Gigahertz Astronomy
## 2 Sensors Structure and Operating Principle
Furthermore, Equation (3) shows that \(\delta U\) is suppressed faster by rising \(I\) than by suppressing \(I_{C}\) (see Figure 2c). As a consequence, the transition to the dissipative state can be more efficiently induced by rising the bias current than by decreasing the Josephson energy. In addition, we note that \(E_{J}\) (and thus \(I_{C}\)) can be suppressed by applying an out-of-plane magnetic field. Since a magnetic field can be problematic for several applications and broadens the superconductor-to-dissipative transition [41], we will focus on the impact of \(I\) on the transport properties of the 1D-JJJ. Since the nanowire normal-state resistance is very low, the 1D-JJJ can be efficiently described by the RSJ model in the overdamped limit. Within this model, the resulting voltage (\(V\)) versus temperature (\(T\)) characteristics of a 1D-JJ for different values of \(I\) and \(E_{J}\) is given by [42]  
\[V(I,E_{J},T)=R_{N}\left[I-I_{C,0}\operatorname{Im}\frac{\mathcal{I}_{1-iZ} \left(\frac{E_{J}}{k_{B}T}\right)}{\mathcal{I}_{-iZ}\left(\frac{E_{J}}{k_{B}T} \right)}\right], \tag{4}\]  
where \(I_{C,0}\) is the zero-temperature critical current and \(\mathcal{I}_{\mu}(x)\) is the modified Bessel function with imaginary argument \(\mu\) with \(z=\frac{E_{J}}{k_{B}T}\frac{I}{I_{C}}\). Thus, the current dependent resistance versus temperature characteristics (\(R(T)\)) of the 1D-JJJ can be evaluated by calculating the current derivative of the voltage drop  
\[R(I,E_{J},T)=\frac{\mathrm{d}V(I,E_{J},T)}{\mathrm{d}I}. \tag{5}\]  
Figure 2d shows that \(I\) has a twofold effect on the \(R(T)\) characteristics. On the one hand, an increase of \(I\) lowers the temperature of the superconducting-to-dissipative state transition. On the other hand, the width of the transition narrows by rising \(I\). This behavior is also highlighted by the temperature derivative of \(R(T)\) (see the inset of Figure 2d). From each \(R(T)\) curve we can define two temperatures related to the superconductor-to-normal-state transition: the effective critical temperature (\(T_{C}\)) and the escape temperature (\(T_{e}\)), as shown in Figure 2e. In particular, \(T_{C}\) is the temperature corresponding to half of the normal-state resistance (\(R(T_{C})=R_{N}/2\)), while \(T_{e}(I)\) is the maximum value of temperature providing a zero resistance of the nanowire (\(R(T_{e})=0\)). These two temperatures define two distinct operating conditions for the 1D-JJJ: the nano-TES operates at \(T_{C}\), i.e., at the middle of the superconductor-to-normal-state transition, and the JES operates at \(T_{e}\), i.e., deeply in the superconducting state. Indeed, \(T_{C}\) and \(T_{e}\) strongly depend on the bias current, as shown in Figure 2f. As a consequence, both nano-TES and JES are in situ current-tunable radiation sensors.
# Exploring Effective Bio-Cover Materials for Mitigating Methane Emission at a Tropical Landfill
## 1 Introduction
Nevertheless, there was a wide fluctuation of reported methane oxidation capacities observed in the field varying from 27.5% to 52.5% depending on the waste disposal and climatic conditions [16; 17; 18]. The discrepancy in methane oxidation capacities between different scales could be attributed to differences in soil structure, water content, and temperature of landfill cover soils between laboratory- and field-scale operations [19]. Therefore, this study was conducted to evaluate actual methane emission and oxidation in bio-cover operated at a tropical municipal landfill in Thailand. Sandy loam (SL), compost (C), and stabilized waste (SW) were employed as alternative materials to clay, which is typically used as landfill cover. Enhanced methane oxidation in those media was anticipated as oxygen supply would be improved through their high porosity properties. Moreover, organic and nutrient supplements from alternative materials as well as leachate application would also help promote their methanotrophic activities. However, their performance would be affected by environmental condition changes during wet and dry seasons of tropical climate.
# The Running Performance Decrement in Elite Hurling
###### Abstract  
Since the COVID-19 restrictions, hurling games have been played in four quarters instead of two halves. This study described the decrement in running performance between quarters in elite hurling. GPS (10 Hz) were used to collect data from 48 players over 20 games during 2018-2020 season. Total distance (TD), high-speed running (HSR), high-metabolic-load distance (HMLD), sprint-distance, and number of sprints (N-sprints) between quarters were assessed. Greater TD was covered in Q1 than Q3 ([PERSON]'s \(d\) = 0.26). HSR and HMLD was greater in Q1 than Q2 (\(d\) = 0.45 and \(d\) = 0.55, respectively), Q3 (\(d\) = 0.34 and \(d\) = 44, respectively) and Q4 (\(d\) = 0.38 and \(d\) = 0.48, respectively). Sprint-distance and N-sprints were similar (\(p\) > 0.05) across quarters. All positions experienced a drop-off in at least one quarter (\(d\) = 0.43-1.46) in all metrics except midfielders' TD and full-forwards' HSR and HMLD. Sprint-distance and N-sprints were similar (\(p\) > 0.05) in each quarter for all positions. Following Q1, players were able to maintain their running performance. The current results can aid the timing of substitutions especially in positions where temporal decrements were observed.  
team sport; performance; match-play demands; high-speed running; sprint distance; quarters +
Footnote †: journal: Applied Sciences
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.2 Vegetation Data  
The characteristics of vegetation growth in the study area were analyzed using three remote sensing vegetation products, including GPP, LAI, and NDVI. Considering that Xinjiang is an arid to semi-arid region with a complex soil background, these three indices were more suitable, which could help reflect changes in vegetation cover, biomass accumulation, and leaf area, aligning well with the objectives. The monthly-scale GPP data were simulated from NIRv (Normalized Difference Vegetation Index and near-infrared reflectance), with a spatial resolution of 5 km and units of 0.0001 g C/m\({}^{2}\) d. The data are sourced from the Big Data Platform for Spatiotemporal Environmental Studies in the Three Poles ([[https://figshare.com/articles/dataset/Longterm_1982-2018_global_gross_primary_production_dataset_based_on_NIRv/12981977/2](https://figshare.com/articles/dataset/Longterm_1982-2018_global_gross_primary_production_dataset_based_on_NIRv/12981977/2)]([https://figshare.com/articles/dataset/Longterm_1982-2018_global_gross_primary_production_dataset_based_on_NIRv/12981977/2](https://figshare.com/articles/dataset/Longterm_1982-2018_global_gross_primary_production_dataset_based_on_NIRv/12981977/2)); accessed on 2 September  
Figure 2: Location and land use of the research area.  
2024) [44]. The LAI data were generated by combining AVHRR and MOD09A1 C6 datasets ([[https://zenodo.org/records/4700264](https://zenodo.org/records/4700264)]([https://zenodo.org/records/4700264](https://zenodo.org/records/4700264)); accessed on 2 September 2024) [45], with an 8 day temporal resolution, a spatial resolution of 5 km, and units of 0.01 m\({}^{2}\)/m\({}^{2}\).  
NDVI data were derived from the 16 day composite product from the Advanced Very High Resolution Radiometer (AVHRR) ([[https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2187](https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2187)]([https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2187](https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2187)); accessed on 2 September 2024) [46], with a spatial resolution of 5 km.  
All climate and vegetation data were reprojected using the Albers Equal Area Conic Projection. For further analysis and calculations, all data were processed to monthly averages at the pixel scale, and spatial matching was achieved through resampling. The processed climate and vegetation data have a spatial resolution of 5 km and a temporal monthly scale, covering the period from 1991 to 2018.
## 2 Materials and Methods
### Biological Activities in CSC
#### 2.5.2 Antioxidant Activity  
The radical scavenging activity of CuO NP treated and non-treated CSC extracts were assessed using an established 2, 2-diphenyl-1-picrylhydrazyl (DPPH) assay [25; 32]. CSC extracts (100 \(\mu\)L) were added to 1.4 mL DPPH solution and incubated in the dark at room temperature for 45 min. The absorbance (A) was read at 517 nm in UV-visible spectrophotometer (UV-2120 Optizen, Mecasys, Korea). Inhibition (%) was calculated as = [(A\({}_{blank}-\)A\({}_{sample}\))/A\({}_{blank}\)] \(\times\) 100.  
Reductive Potential  
The reducing potential of _G. sylvestre_ cell extracts was determined as previously described [25; 33]. Each CSC extract (100 \(\mu\)L) was mixed with 2.5 mL sodium phosphate buffer (200 mM), followed by 2.5 mL potassium ferricyanide (1%) and incubation at 50 \({}^{\circ}\)C for 20 min. Then, 2.5 mL trichloroacetic acid (10%) was added and the mixture was centrifuged for 10 min at 650 g. The supernatant (2.5 mL) was recovered and 2.5 mL distilled water and 0.5 mL ferric chloride (0.1%) was added. After thorough vortexing, the absorbance was measured at 700 nm.  
The total antioxidant potential of extracts was assessed using the phosphomolybdenum method [25]. Each CSC extract (100 \(\mu\)L, 1 mg/mL) was mixed with 1 mL of phosphomolybdenum reagent (4 mM ammonium molybdate, 0.6 M sulfuric acid, and 28 mM sodium phosphate) and incubated at 95 \({}^{\circ}\)C for 90 min in a thermal block. The sample was cooled to 23 \({}^{\circ}\)C and the optical density (OD) was determined at 695 nm. Total antioxidant activity was expressed as equivalents of alpha-tocopherol (\(\mu\)g/g of extract).
# Microwave-Assisted Extraction of Fatty Acids from Cultured and Commercial Phytoplankton Species
## 3 Results  
### Lipids and Fatty Acids  
The composition of lipids from microalgae and cyanobacteria can vary according to the fatty acids that compose them, the species, and even the lipid extraction method. Therefore, to prove good efficiency and possible advantages of microwave-assisted extraction (MAE), the traditional extraction (TE) was also carried out for comparative purposes. The lipid content of _Chloerlla_ sp., _Arthospira_ sp., and _S. torques-reginase_ ranged between 11% and 23%. As shown in Figure 1, and according to percentage obtained from GC-MS analysis of triplicate extractions and statistical analyses, TE was more efficient than MAE in extracting total lipids from _Chloerlla_ sp. and _S. torques-reginase_, but there was no statistical difference between the results of these methods for _Arthospira_ sp. As expected, the microalga _Chloerlla_ sp. had a higher concentration of lipids than the cyanobacteria. Nevertheless, it is possible to observe that the use of cyanobacterial biomass is also a clean and sustainable alternative to produce biodiesel. Given the abundance of _Sphaerospernopsis_ sp. in bloom episodes and the satisfactory percentage of lipids detected in _S. torques-reginase_ to produce biodiesel, this strain was also chosen to compare the MAE and TE methods for fatty acid extraction. Several fatty acids were identified in the _S. torques-reginase_ extract obtained by TE, most of them being saturated fatty acids (SAFAs) such as 16:0 and 18:0, while polyunsaturated fatty acids (PUFAs) such as 18:2 and 18:3 were present in intermediate contents, and monounsaturated fatty acids (MUFAs) such as 16:1 and 18:1 in the lowest ones (Table 1). Analyzing the fatty acid profile of the strains was essential, as it determined the quality of the biofuel. Although the fatty acid profile of the oil obtained by TE showed higher concentrations of fatty acids, the MAE method was able to extract more types of fatty acids.
## 2 Techniques Used for Adaptive Equalization
### Recursive Least Squares  
RLS is another popular adaptive filtering algorithm used for adaptive equalization [30]. It recursively updates the filter coefficients based on the instantaneous estimation error and the input signal [31]. RLS utilizes a matrix inversion technique to achieve optimal filter updates [32]. In the context of adaptive equalization, RLS offers fast convergence and provides accurate filter estimation [33]. However, RLS has higher computational complexity and memory requirements compared to LMS [34]. Despite these limitations, RLS is preferred in applications that require rapid convergence and optimal filter updates [35].