esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Microorganisms producing lacases may be used for the pretreatment of lignocellulosic biomass to recover fermentable sugar. Very few fungi and other microbes growing in high altitudes have been tested for this purpose. As part of this study, we have collected soil samples from different parts of the Kathmandu Valley and the Rautahat at district of Nepal (1600 to 2303 m above sea level) and successfully cultured 53 different isolates of microorganisms. Among the 53 isolates obtained 30 were _Actinomycetes_, 20 were _Streptomyces_, and three were fungi). These isolates were tested for lacase expression using guaiacol, tannic acid, and 1-naphthol as substrates. Twelve of the 53 isolates tested positive for the expression of lacase. Among the lacase- positive isolates, a fungal species designated as CDBT-F-G1 was found to produce high levels of lacase. This isolate was identified as _Pestalotiopsis_ species based on 18S rRNA sequencing. _Pestalotiopsis_ spp. CDBT-F-G1 isolate grows efficiently in PDB media containing 1% Kraft lignin at pH 5 and 30 \({}^{\circ}\)C and secretes 20 \(\pm\) 2 U/mL increase in culture medium. Further optimization of growth conditions revealed that addition of (i) metal salts, e.g., 1 mM magnesium sulfate (51 \(\pm\) 25 U/mL); (ii) agitation of cultures at 200 rpm (51 \(\pm\) 9 U/mL); (iii) surfactants, e.g., 0.75 mM Tween 80 (54 \(\pm\) 14 U/mL); (iv) 40% dissolved O\({}_{2}\) (57 \(\pm\) 2 U/mL) and inducers, e.g., 1 mM gallic acid (69 \(\pm\) 11 U/mL), further promote lacase production by _Pestalotiopsis_ spp. CDBT-F-G1 isolate. On the other hand, 0.1 mM cysteine inhibited lacase production. The secretory lacase obtained from fermentation broth of CDBT-F-G1 was partially purified by ammonium sulfate (13-fold purification with specific activity 26,200 U/mg) and acetone (14-fold purification with specific activity 31,700 U/mg) precipitation methods. The enzyme has an approximate molecular mass of 43 kDa, pH and temperature optima were pH 6 and 60 \({}^{\circ}\)C, respectively. V\({}_{\text{max}}\) and K\({}_{\text{m}}\) were 100 umol/min and 0.10 mM, respectively, with ABTS as the substrate. Given the above characteristics, we believe _Pestalotiopsis_ spp. CDBT-F-G1 strain native to high altitudes of Nepal could be used to pretreat lignocellulosic biomass to efficiently recover fermentable sugars.  
**Keywords:** lacase; lignin; _Pestalotiopsis_ spp. CDBT-F-G1; guaiacol; tannic acid and 1 naphthol
## 3 Experimental Results and Discussions
### GA-FL Simulation Results  
The proposed GA-FL model was trained with a randomly chosen training set and tested data for a specific period, such as a monthly or weekly dataset. The results showed that the GA-FL model gives satisfying results for the specific part of the testing data, but couldn't have full coverage. In other words, the final rule base set that was created with the optimization model didn't fit for properly testing the entire dataset.  
In order to reach the best rule base set, four different rule base sets consisting of 625 random rules were defined as a binary system to the optimization system in the initial phase. The performance value of each rule base was measured separately for the same training set for each step of the iteration. The results of this measurement are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Hours**} & **Last Day** & **Last Week** & **Consumption** & **Weighted Average** \\  & **Consumption** & **Consumption** & **Trend** & **Trend Temperature** \\  & **(MWh)** & **(MWh)** & **(MWh)** & **(\({}^{\circ}\)C)** \\ \hline
00–01 & 28.700 & 28.459 & 28.872 & 2.72 \\
01–02 & 26.856 & 26.740 & 27.0  
After each step of the iteration, selection, crossover and mutation operations that were designed in compliance with genetic algorithm methods were used, and a new rule base population was produced. The performance values of each rule base set and the optimization process are given in Figure 18.  
The proposed GA-FL load forecasting model was trained with a monthly weekday dataset between 2011 and 2013, and tested with a monthly weekday dataset of 2014. Each rule base that was obtained at the last iteration of the optimization process was tested with the training dataset, and some of the forecasting results of the proposed forecasting model for the training and testing processes are shown in Figures 19 and 20.  
The proposed GA-FL model consisted of four individuals, and the mutation constant was 1%. The script was executed for more than 20 different runs with 100 iterations in order to have statistically meaningful results.  
Each genetic rule base set after the optimization process was tested and compared with the actual data in Figure 19 for the same consumption period. The optimal rule base set was then tested with actual data, and the results are seen in Figure 20.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Iteration**} & \multicolumn{4}{c}{**MAPE (\%)**} \\ \cline{2-5}  & **rb1** & **rb2** & **rb3** & **rb4** \\ \hline
1 & 0.189 & 0.066 & 0.155 & 0.194 \\
2 & 0.164 & 0.066 & 0.155 & 0.062 \\
3 & 0.070 & 0.066 & 0.082 & 0.062 \\ - & - & - & - & - \\
96 & 0.041 & 0.078 & 0.039 & 0.083 \\
97 & 0.041 & 0.042 & 0.039 & 0.054 \\
98 & 0.041 & 0.077 & 0.039 & 0.070 \\
99 & 0.041 & 0.042 & 0.039 & 0.053 \\
100 & 0.041 & 0.043 & 0.039 & 0.059 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MAPE (%) values for each GA–FL rule base set. GA: genetic algorithm, FL: fuzzy logic.  
Figure 18: MAPE values of GA–FL rule base sets during the optimization process.
## Analysis of Urban Flood Inundation Patterns
###### Abstract  
An urban flood in the Sadang area located in South Korea was reproduced using a rainfall simulator. The rainfall simulator was developed to be able to demonstrate the rainfall intensity in range of 80-200 mm/h, and the artificial rainfall was created using 42 full cone type nozzles in the urban model. The uniformity coefficient of the rainfall distribution was 89.5%, which indicates the rainfall simulator achieved the high requirements for spatial uniformity. The flood experiments in the 1/200 scale model of the Sadang area were conducted using the rainfall simulator, and the flood patterns were investigated by changing the rainfall intensity. The rainwater mainly accumulated in the lowland of the crossroad where the entrances to the subway station are located. The flow velocity and the inundation depth were sharply increased until the rainfall intensity became 160 mm/h. Furthermore, the unstable human activities based on the moment and the friction instabilities also occurred from 160 mm/h. These results suggest that the study area requires flood damage mitigation facilities considering a rainfall intensity exceeding 160 mm/h.  
urban flood; rainfall simulator; rainfall intensity; runoff velocity; inundation depth 2020
## 3 Technology, Hardware and Software of Edge Computing
### The Technology of Edge Computing
#### 3.1.1 Data Processing of Edge Computing
Figure 3 illustrates the data processing flow in EC, where each edge node handles a specific IoT device region, processing data locally and optimizing storage and bandwidth usage. IoT data are categorized as short-term or long-term, with short-term data being discarded once obsolete, ensuring efficient resource utilization. In efforts to address high transmission delays and limited bandwidth, several studies propose hybrid network architectures that combine CC and EC. For example, ref. [79] introduced a three-layer network architecture designed to reduce communication delays by optimizing device cooperation at the edge layer using [PERSON]'s algorithm to compute a minimum spanning tree. Additionally, ref. [80] examines performance metrics such as message transmission delays and response times, focusing on large dataset processing within MEC environments. Another notable proposal for smart grid environments [81] integrates P2P technology with EC to enhance data processing and transmission efficiency through distributed data collection, computation, and storage. Data management in IoT also benefits from EC by enabling the categorization of data into distinct segments for more efficient processing. Ref. [82] introduces such a framework, while [83] proposes a statistical analysis scheme of multidimensional smart grid data, ensuring user privacy preservation. Moreover, ref. [84] presents an EC-based platform for distributed networks, where computational tasks are distributed across edge nodes prior to central data aggregation. The importance of data security in EC is also evident in studies focused on smart grids. For example, ref. [85] introduces a monitoring model for EC-based grids, which facilitates the timely detection of network failures and supports real-time responses. The transformational impact of EC on operational efficiency, data security, and business optimization is increasingly acknowledged. By reducing latency and improving autonomous decision-making and analytics capabilities [86], EC is reshaping industries. Research on hardware accelerators for EC underscores their role in achieving low latency, low power consumption, and high reliability. Ref. [87] explores the use of the CORDIC algorithm to pre-calculate micro-rotation directions, proposing a multi-stage structure to  
Figure 3: Localized data processing architecture in EC [78]. enhance EC performance. Overall, these developments demonstrate how EC is reshaping traditional data-handling paradigms by enabling localized, secure, and real-time processing capabilities, particularly in data-intensive and latency-sensitive environments like smart cities and grids.
# Transmission Reduction for UV and IR Radiation with Dyed Lyocell Knitted Textiles
## 3 Results
### Usability of the Dyed Knitted Fabrics
#### 3.3.3 Ultraviolet Protection Factor (UPF)  
The textiles examined here are intended to be used for the production of clothing with a special protective function against UV and IR radiation. The ultraviolet protection factor (UPF) is used to assess UV-protective textiles. The UPF values for the different textiles are shown in Table 4.  
Except for the pure Lyocell material, all textiles have a very good to excellent protective effect against UV radiation. Both the addition of TiO\({}_{2}\) and dyeing strongly enhance the protection. Washing reduces the UV protection effect, but it remains at a high level. It can be stated, that developed dyed Lyocell fabrics are suitable for the production of clothing with UV protection. Depending on the application, it can be considered to do without the addition of the TiO\({}_{2}\) in the fiber and instead, just dye it. Avoiding spin-doping can be reasonable from an economic point of view.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 4 Experiments
### Datasets and Experiment Setup  
#### 4.1.1 Datasets  
To verify the effectiveness of the proposed method, experiments, and evaluations were conducted on three large publicly available human video action-recognition datasets.  
Something-Something V1 is a richly annotated video action dataset that contains basic interactions between humans and various objects. It includes a wide range of subtle actions characterized by rapid changes over short periods, such as slight hand movements or brief interactions with objects. The dataset comprises 174 action categories and 108,499 videos, where 86,017 are used for training, 11,522 are used for validation, and 10,960 for testing.  
Something-Something V2 is an expanded action datasets, based on Something-Something V1, including 174 action categories and total 220,847 videos, with 168,913 training videos, 24,777 validating videos, and 27,157 testing videos. Figure 8 illustrates sample frames from three action categories from Something-Something V2.  
Kinetics-400 is a dataset collected from the YouTube video containing 400 action classes, with each class featuring at least 400 clips of around 10 s. Kinetics-400 presents a significant challenge due to background clutter, complex motion tempos, and long-range or short-range temporal dependencies. Figure 9 shows sampled frames from three action categories within Kinetics-400.  
Figure 8: Video frames of Something-Something V2 datasets, from categories “Putting something on a surface” (**top**), “Putting something and something on the table” (**middle**), and “Plugging something into something but pulling it right out as you remove your hand” (**bottom**). These actions include sub-actions with subtle variances, characterized by rapid changes over short periods and brief interactions with objects.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 5 Implementation and Evaluation of a Commercial Vehicle Operation System
### Experimental Results and Discussions
#### 5.2.2 Evaluation and Discussions  
The practical results of using the proposed mobile positioning method are illustrated and evaluated in this section. In this study, the CVO data from October 2013 to January 2014 was collected for evaluation. The data of October 2013 was used as training data, and the data from November 2013 to January 2014 was used as testing data.  
For the analyses of OBU traces, Table 1 shows the practical results of location determination with different positioning methods (_i.e._, GPS, cell-ID-based method, and the proposed method) on 23 November 2013. The location information obtained from a GPS receiver was defined as the baseline data. In this case, the availability of GPS was only approximately 6.31%, so 93.69% of total records would lack GPS location information. This study compared location information using mobile positioning methods and GPS when GPS was available. The cell-ID-based positioning method [9] was considered to be implemented and evaluated, and the errors in location determination and availability were approximately 521.2 m and 99.51%, respectively. Finally, this study implemented and evaluated the proposed mobile positioning method, and the errors of location determination and availability were approximately 163.7 m and 99.58%, respectively. The CDFs of location errors in this case are shown in Figure 9. In another case, Table 2 and Figure 10 compare different positioning methods on 4 December 2013. The results indicate that the availabilities of the cell-ID-based method and the proposed method are 72.50% and 99.49%, respectively. These results show that the proposed mobile positioning method can provide the precise location information and is suitable for CVO.  
This study evaluates the proposed method for the six road types defined in [38] (national highway, provincial highway, urban road, county road, village road, and alley). From November 2013 to January 2014, 6,571,550 records were collected, and the ratio of records for each road type was calculated in Table 3. Because commercial vehicles are usually driven on national highways and urban roads, the ratios of national highways and urban roads are 36.470% and 42.639%, respectively. Tables 4 and 5 show the analyses of average location error and availability rate using various location determination methods. The practical experimental results indicate that the average location error using the proposed method is lower than using the cell-ID-based positioning method (Figure 11). The proposed method  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GPS** & **Cell ID Based Method** & **The Proposed Method** \\ \hline Average Location Error (m) & Baseline & 521.2 & 163.7 \\ Availability & 6.31\% & 99.51\% & 99.58\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of different positioning methods on 23 November 2013.  
Figure 8: The results of the proposed positioning method on 23 November 2013.
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 4 Discussion  
Four reasonable triple-frequency combinations are selected for the experiments, namely, B1I/B2a/B3I, B1C/B2a/B3I, B1I/B2b/B3I and B1C/B2a/B3I.  
The TGD/DCB correction significantly improves the positioning accuracy of SPP, especially for the IF123 and IF1213 models, because the errors are amplified, including the UCDs by the signal combinations. When the satellite TGD/DCB is corrected, the positioning accuracies of UC123 in the E and U components reaches nearly the same as those of the IF123 and IF1213 modes but slightly worse in the N component.  
The DCB correction improves the accuracy of the initial epochs of PPP, but the improvement effect gradually diminishes with time. Since the high-precision carrier phase contributes more and more in subsequent epochs, the positioning accuracies of the four combinations of IF123, IF1213 and UC123 models are nearly the same after the filtering  
Figure 16: Differences of floating ambiguity of IF123-0 model at AREG.  
Figure 14: STEC of UC123-0 model at AREG.  
Figure 15: Differences of tropospheric delay of UC123-0 model at AREG.  
convergence. However, the convergence time of UC123 is longer than that of IF123 and IF1213 because there are more parameters to be estimated.  
DCB not only affects the PPP convergence time but also affects other parameters. The pseudorange residuals without DCB have a systematic bias of several meters, and the bias value varies quite differently for different satellites. When the DCB is corrected, the pseudorange residuals are significantly more reasonably distributed, approaching zero-mean random noise.  
The IFB needs to be estimated for IF1213 and UC123 due to satellite and receiver UCDs. When the satellite DCB is corrected, the IFB fluctuates less during the day and has short-term stability. The DCB also affected the estimated STEC. If DCB is not taken into account, the estimated STEC will show anomalies or even negative numbers. The floating ambiguity of each satellite is affected differently by the DCB at the earliest epoch but converges to nearly the same value after some time. Tropospheric delay is nearly unaffected by DCB, and the differences between the estimated tropospheric parameters with or without DCB correction is less than 5 mm after convergence.  
In summary, the DCB correction significantly improves the positioning accuracy of BDS-3 triple-frequency SPP and reduces the convergence time of PPP. Therefore, the correction should not be neglected in BDS-3 triple-frequency positioning.
# Investigating Load-Bearing Capabilities and Failure Mechanisms of Inflatable Air Ribs
## 2 The Load-Bearing Performance of Air Ribs with Different Rise-Span Ratios
### The Analysis of the Stiffness with Different Rise-Span Ratios  
Stiffness is a measure of a structure's resistance to deformation. Therefore, the stiffness of the air rib can be determined by calculating the slope of the displacement-force curve of the rigid plate. Figure 3 illustrates the trend of the displacement with the resisted load and stiffness. All the curves exhibit the same trend that the resisted force increases with the downward movement of the rigid plate. For the equal-spans group, the bearing capacity is weakened with the increase in the rise under the same deformation conditions. For the equal-rises group, the bearing capacity is enhanced with the increasing of the span under the same deformation conditions. At the same displacement, the air rib named 0.5-S/R provides the least support, while the rib named 0.333-S provides the best support. Additionally, proportionally scaling the air ribs named 0.5-mini can improve their support performance. The maximum supporting load of the air rib named 0.5-S/R is 2968.85 N, while the air rib named 0.417-S can support a maximum load of 3915.85 N. To describe the ability to resist deformation, the stiffness of the air ribs is calculated. The stiffness of the air rib decreases nonlinearly with the increase in the displacement. In the early stage, the stiffness of air ribs is larger than 10,000 N/m, reflecting a robust resistance to deformation. The rib named 0.333-S has the largest curvature, and the load-bearing performance is the best. The stiffness of model 0.333-S is the largest and decreases quickly. It means that the rib named 0.333-S can resist more load at first and is hard to deform. When the rise-span ratio is 0.5, the air rib is the longest, and the stiffness is the lowest.
## 3 Results and Discussions
The SHELL type of FE is recommended for modelling the concrete layer, and compliance of the bonds should be considered in cases where mechanical fasteners are present between the layers of the considered structure. Comparison of the results obtained by the simplified design method, FE model analyses, and experiments indicated that the simplified method accurately predicted the maximum vertical displacement for the investigated structure fragment, including posi-joisted beams, with precision values of 11.6% and 23.10% with and without a concrete layer, respectively. The difference in normal stresses in the chords of the beams obtained by the simplified method and the FEM model was 7.69%. This suggests that the generalized simplified method can be considered for the preliminary design and determination of structural parameters for developing FE models. It can be concluded that the addition of a concrete layer with a thickness of 50 mm, possessing mechanical properties close to C25/30 strength class concrete, increases the load-carrying capacity of a structure with posi-joisted beams and OSB sheets joined with screws by 32.28%.
# Ten Years of TerraSAR-X Operations
## 6 Satellite Operations
Both spacecraft are still fully functional; SAR operations do not suffer any restrictions due to on-board hardware failures. Furthermore, the state of depletion of consumables gives reason to expect many more years of satellite operations. For example, the measured capacity degradation of the batteries is much less than predicted for the age that the spacecraft have now reached. Figure 13 details the hydrazine consumption of TSX and TDX. In both cases, approximately half of hydrazine budget is still available for future use. So far, the phases directly after launch (2007 for TSX and 2010 for TDX) were expensive in terms of hydrazine usage for attitude control after separation and for orbit control to adjust the target orbit. In 2008, a safe mode drop on TSX caused an unusually high yearly budget. In those days, the safe mode still used thrusters primarily for attitude control, as described above. Generally, TDX uses more hydrazine because of its helix formation flight relative to TSX. The consumption was remarkably high in the years 2014 and 2015 due to the specific formations that were required in the TanDEM-X science phase. Beside the primary payload, the SAR instrument, TSX carries secondary payloads, namely a laser communication terminal (LCT) by Tesat-Spacecom GmbH & Co. KG (Backnang, Germany) and a two frequency GPS receiver IGOR, as supplied by the Deutsches GeoForschungsZentrum (GFZ). The latter is also mounted on TDX, and is classified in this case, as primary payload due to the strengthened requirements of the TanDEM-X mission on absolute and relative orbit determination. Both additional payloads showed a very good performance in orbit. On 21 February 2008, the LCT on TSX communicated with a counterpart of the NFIRE satellite, the very first inter-satellite communication with laser technology in Low Earth Orbit. Figure 12: Typical acquisition and downlink activities for TanDEM-X Satellite (TSX) and TanDEM-X Satellite (TDX) for about one day. DT rows: Data acquisition activities (number of tele-commands is given on the left hand side) SSMM rows: The on-board memory utilization (TDX’s memory has twice the size of TSX’s one, as indicated on the left hand side). Replay rows: The X-Band downlink contacts used (number of replays is given on the left hand side). S-Band rows: TT&C contacts (indicated by light color).