esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Water Absorption Variations  
The water absorption of concrete is expressed as a percentage of the initial weight of the sample. The lower the water absorption, the more water-resistant and durable the concrete is considered to be. Figure 8 shows the results of water absorption tests.  
Before evaluating the results of the tests, the necessity of these tests should be briefly introduced. Concrete's water absorption is reported as a percentage of the sample's initial weight. Concrete is said to be more water-resistant and long-lasting when the water absorption rate is lower. The water absorption test is a crucial technique used by engineers and contractors to assess the water resistance and durability of concrete. Test results are useful in determining the uniformity and quality of the concrete mix and in confirming that the concrete satisfies the criteria and requirements needed for a certain application. In addition to helping choose the best type of concrete for a given project, the results of the water absorption test can also be used to guarantee that the concrete will function as planned for the duration of its anticipated service life.  
For concrete paves, the test procedure involves drying a specimen to a constant weight, weighing it, immersing it in water for a specified amount of time and weighing it again. The increase in weight as a percentage of the original weight is expressed as its absorption (in percent). For normal concrete, the water absorption is typically in the range of 10-15%. For high-performance concrete, the water absorption is typically lower, in the range of 5-10%. For lightweight concrete, the water absorption is typically higher, in the range of 20-25%. The critical value is accepted as 30%.  
Figure 7: Compressive strength with respect to exclay ratios: (a) 2–4 mm, (b) 4–6 mm.  
In Figure 8, the water absorption ratio increases as the exclay ratio increases. The increase in water absorption ratio in the specimens is lower than the critical value of 30%. Thus, it is possible to use such concretes in humid environments. Furthermore, such structure materials may be used as materials in places having direct contact with water, in internal plasters and sandwich walls and as roof sheeting plaster material.
# POL-MUX System for Noncoherent Optical Networks
## 2 Temperature Influence on the POL-MUX Signal  
The angle and state of polarization are changed by mechanical stress and/or temperature. If the POL-MUX is deployed on an existing optical network, where the optical cables are fixed, then the temperature mainly rotates with the polarization angle, which is critical for the PER. For this reason, we measured the temperature influence on the change in the polarization angle of the optical network. To change the temperature of the testbed, a Votch VC3 7018 thermo chamber was used. The aim of the experiment was to determine the influence of temperature on birefringence and the change in the angle of polarization planes, e.g., how the power aligned with each polarization plane changes due to the rotation of polarization.
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
## 2 Materials and Methods
### Characterization of the Biowaste Samples  
The biowaste samples after collection (on a wet basis) were used to determine the total moisture content. Next, the prepared samples were completely dried in a laboratory dryer at 105 \({}^{\circ}\)C for 24 h. After drying, the biowastes were ground in an IKA-WERKE M20 mill (manufacturer: IKA\({}^{\oplus}\)-Werke GmbH & Co. KG, Staufen, Germany), which is used for dry grinding hard and friable substances.  
Proximate analysis involves determining the moisture content, volatile matter, fixed carbon, and ash content in biowaste. To assess the total moisture content, wet biowaste samples were analyzed following the PN-EN ISO 18134-1:2023-02 standard [37]. This method involves drying the sample at 105 \({}^{\circ}\)C. All other parameters were determined using dry samples. The volatile matter content was measured according to PN-EN ISO 18123:2016-01 [38], which requires heating the sample at 850 \({}^{\circ}\)C under anaerobic conditions for 3 min. The ash content was determined based on the PN-Z-15008-03 standard [39], where samples are calculated to a constant weight at a temperature of 815 \(\pm\) 10 \({}^{\circ}\)C. Fixed carbon was not measured directly, but instead calculated by subtracting the percentages of moisture, volatile matter, and ash from the total mass [40], using formulas from [12; 16].  
The ultimate analysis determines the elemental composition of biowaste, including carbon, hydrogen, oxygen, nitrogen, sulfur, and chlorine. All measurements were conducted on dry samples. The carbon and hydrogen contents were determined in accordance with ISO 609:1996 [41], which involves combusting the sample at 950 \({}^{\circ}\)C in an oxygen-rich environment. The total nitrogen was measured using the Kjeldahl method, with prior mineralization in concentrated sulfuric acid, as specified in PN-G/-40523 [42]. The sulfur content was determined based on PN-ISO 351:1999 [43], involving combustion at 950 \({}^{\circ}\)C in the presence of oxygen. The chlorine content was measured following PN-ISO 587:2000 [44], where the biowaste sample, mixed with Eschka reagent, is combusted at 675 \({}^{\circ}\)C in an oxidizing environment. The oxygen content was calculated by difference, subtracting the sum of the measured percentages of moisture, ash, carbon, hydrogen, nitrogen, sulfur, and chlorine from 100% [40].  
To further characterize the tested biowaste samples, the higher heating value (HHV) and lower heating value (LHV) were additionally determined in a calorimetric bomb [45] according to PN-ISO 1928:2020-05 [46]. In addition, the total organic matter content, total organic carbon content, and pH of the water extract of the biowaste samples were determined following the laboratory guidelines [47]. In addition, a surface analysis of the studied biowaste was performed using scanning electron microscopy (SEM) (JEOL, model JSM-7001F, manufacturer: JEOL Ltd., Tokyo, Japan) [48].  
Figure 3: Autumn garden biowaste: (**a**) before drying; (**b**) after drying; and (**c**) after grinding.
## 4 Results and Discussion
### Cr(VI) Content in Soils before and after Stabilization  
The Cr(VI) contents of the stabilized soils are shown in Figure 5. For the untreated soil, the Cr(VI) content was approximately 971.3 mg/kg. After FeSO\({}_{4}\) and CaS\({}_{5}\) stabilization, the Cr(VI) content in the stabilized soils was reduced significantly with the increase of reductant dosage. This phenomenon can be attributed to the fact that Cr(VI) was reduced as the FeSO\({}_{4}\) and CaS\({}_{5}\) dosage increased. Figure 5 also shows that the Cr(VI) content of the FeSO\({}_{4}\)-stabilized soil was higher than that of the CaS\({}_{5}\)-stabilized soil with the same reductant dosage. When the reductant dosage increased from 1% to 3%, the Cr(VI) content in the FeSO\({}_{4}\)-stabilized soil was decreased from 168 mg/kg to 58 mg/kg, whereas that in the CaS\({}_{5}\)-stabilized soil was decreased from 94 mg/kg to 22 mg/kg. The Cr(VI) content in the CaS\({}_{5}\)-stabilized soil with 3% dosage was below the threshold allowed by China's Environmental Regulations for industrial reuse (<30 mg/kg) [35]. Similarly, the Cr(VI) content in the FeSO\({}_{4}\)-stabilized soil with 5% dosage was 21 mg/kg higher than that in the CaS\({}_{5}\)-stabilized soil (4.2 mg/kg). The Cr(VI) content in the CaS\({}_{5}\)-stabilized soil with 5% dosage was below the threshold of civil reuse (<5 mg/kg) [35]. CaS\({}_{5}\) presented a better effect than FeSO\({}_{4}\) in the stabilization of Cr(VI).  
Figure 4: Effect of reductant types and dosage on Cr/Cr(VI) concentration in the simplified bioaccessibility extraction test (SBET) leachate.
# Parameter Study for the Ice Adhesion Centrifuge Test
###### Abstract  
In this study, we assessed the effects of ice types, test parameters, and surface properties on measurement data of the ice adhesion centrifuge test. This method is often used for the evaluation of low ice adhesion surfaces, although no test standard has been defined yet. The aim of this paper is to improve the understanding of the relevant test parameter and identify crucial criteria to be considered in harmonization and standardization efforts. Results clearly indicate that the ice type (static vs. impact ice) has the greatest impact on the test results, with static ice delivering higher values in a broader data span. This is beneficial for material developers as it eases the evaluation process, but it contradicts the technical efforts to design tests that are as close as possible to realistic technical environments. Additionally, the selected ice type has a significant impact on the relevance of the surface properties (roughness, wettability). Despite the complexity of interactions, a trend was observed that the roughness is the determining surface parameter for high impact velocity ice (95 m/s). In contrast, for tests with static ice, the wettability of the test surface is of higher relevance, leading to the risk of overestimating the icephobic performance of structured surfaces. The results of this paper contribute to the demanding future tasks of defining well-founded test standards and support the development of icephobic surfaces.  
ice adhesion; centrifuge test; static ice; impact ice; wettability; roughness Article
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 4 Results
### Evaluation Metrics  
To objectively evaluate the detection framework's performance, we use precision (P), recall (R), mean average precision (mAP), giga floating-point operations (GFLOPs), frames per second (FPS), and parameters as evaluation metrics.  
Precision (P) is defined as the ratio of correctly detected objects to the total number of detections. In the context of ship detection tasks, it is calculated as the number of accurately identified ships divided by the total number of predicted bounding boxes. Recall (R) measures the ratio of detected objects to the actual number of objects, calculated as the number of correctly detected ships divided by the ground-truth ship count. A higher precision indicates more accurate detections, while a higher recall indicates fewer missed targets. The exact computations are provided in Equations (7) and (8).  
\[P=\frac{TP}{TP+FP} \tag{7}\]  
\[R=\frac{TP}{FP+FN} \tag{8}\]  
Mean average precision (mAP) represents the area under the precision-recall curve. It combines precision and recall into a single metric and is the most widely used measure in object detection. A higher mAP indicates better detection accuracy. mAP@50 denotes the average precision over all classes at an IoU threshold of 0.5. mAP@50:95 represents the average precision at IoU thresholds from 0.5 to 0.95 in increments of 0.05. These two metrics evaluate performance at different strictness levels, with higher values indicating superior detection capabilities. Detailed formulations of these metrics can be found in Equations (9) and (10).  
\[AP=\int_{0}^{1}P(R)\mathrm{d}R \tag{9}\]  
\[mAP=\frac{1}{n}\sum_{i=1}^{n}AP_{i} \tag{10}\]
## 6 Conclusions and Further Studies  
This study proposes a feed-forward NN-based DR estimation method to estimate the process mean and standard deviation functions for RSM and RD modeling. The proposed method can avoid basic error assumptions of the conventional LSM approach in this particular situation. Further, integrating the DFs into the NN structures in the proposed method results in a general and flexible transfer function family for transforming data based on the proposed DFs. The optimal weight parameters of the DF family can be determined using the MLE and the confidence intervals of the optimal weight parameters using the proposed asymptotic distribution of maximum-likelihood estimators. The best transfer function in the NN structure was achieved using the proposed method. The proposed NN-based DR estimation method provides better solutions for EQL than those of the conventional LSM approach and the NN-based estimation method that uses the conventional log-sigmoid transfer function in the particular case study example. In order to improve the reliability of the proposed methods, different types of experimental data (i.e., small and large data sets, different DoE results, and small and large sets of replications) should be considered.  
In further studies, the assumption that the error follows a normal distribution and whether the DF values can follow any distribution will be investigated. Additionally, the optimal weight parameters of the DF-based transfer functions were estimated using a Bayesian approach or the Newton-Raphson algorithm. We then plan to determine the confidence intervals of the optimal weight parameters using a t-distribution. In addition, a more comprehensive comparative study between the proposed NN models and higher-order models of conventional RSM can be a significant further research issue. Based on this comparative study and a Bayesian approach, we may develop a new optimal estimation system by integrating the proposed NN models and conventional RSM models.  
Conceptualization, T-HL. and S.S.; Methodology, T-HL. and S.S.; Modeling, T-HL.; Validation T-HL. and S.S.; Writing--original draft preparation, T-HL. and S.S.; Writing--Review and Editing, H.J. and S.S.; Funding Acquisition, H.J. and S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) (No. NRF-2019R1F1A1060067 and No. NRF-2019R1G1A1010335).  
The authors declare no conflict of interest.
## 3 Results and Discussion
They have reported a decrease in the costs of media from US$4/L for commercial medium to US$1.16/L for medium developed during optimization. Likewise, the cost analysis of media in our study showed that the cost of medium developed under the optimization is 5 times lower compared to the commercial medium, considering that both offer similar biomass densities. The last stage of our research was the comparison between the biomass yield obtained during the cultivation on the optimized medium in different volume cultures: from 0.5 L to 7 L on a laboratory scale to 42 L on a pilot scale, and 300 L on an industrial scale. Regarding the scale-up, the highest number of microorganisms was observed in the 0.5 L fermenters (2.16 \(\times\) 10\({}^{9}\) CFU/mL-1.78 \(\times\) 10\({}^{10}\) CFU/mL, depending on the strain) (Table 4), but there were no statistical differences observed in the microorganism number for three strains (_B. megaterium_,  
Figure 1: Final scatter light values for standard media and media optimized on the basis of Tauguchi experiments. TSB—reference/control medium in the concentration recommended by the manufacturer; 3 TSB—reference medium in concentration three times higher than what is recommended by the manufacturer; G—variant of the optimization of the medium containing glucose; S—variant of the optimization of the medium containing sucrose. _E. ficeium_ and _L. mesenteroides_). For the other strains (_B. subtilis, P. fluorescens_ and _L. plantarum_) the analysis showed statistically significant differences only between the cultures of 0.5 L and 300 L. Likewise, other authors performing scale-up experiments did not report any significant changes in the process efficiency e.g., synthesis of 1,3-propanediol by _Clostridium butylericum_[24] or biofluorescent synthesis by _Halomonans_ sp. [25]. This study allowed for developing culture medium with a composition based on components of plant origin, for the production of biomass containing six bacterial strains. These six bacterial strains are constituents of a biopreparation that is applied for deodorization of livestock premises. Furthermore, similar biomass yields were achieved in both laboratory, pilot- and industrial-scale experiments. This research was financially supported by the National Centre for Research and Development grant No. PBS2/BS/14/2014 \"Innovative deodorizing biopreparation for poultry production premises\". The authors would like to thank JJH Sp. z. o. o., especially Mr [PERSON] for the possibility to conduct research on an industrial scale. [PERSON] planned, supervised and conducted the experiments, collected data and wrote the manuscript, [PERSON] conducted the experiments and collected data, [PERSON], [PERSON] and [PERSON] conducted the experiments, [PERSON], [PERSON], [PERSON] and [PERSON] wrote the manuscript. The authors declare no conflict of interest.
## 6 Discussion
### Discussing and Comparing the Results
In fact, the global GPPs that are used for calibration and bias adjustment of other GPPs suffer from a lack of spatial density consistency, and such limitations could lead to inconsistent results [107; 108]. However, based on the MBE metric, a notable trend was observed for GSMaP, indicating bias correction improvements over the last two decades. Moving toward monthly observations, the CC values of all GPPs improved considerably; however, this was not true for two other metrics. The increase of CC values, while other metrics were not generally enhanced, probably suggests that CC would not be a reliable metric to present the best performing GPP (see Tables 3 and 4), which was also stated by [PERSON] et al. [96] for discharge modeling. In fact, the obtained MBE and RMSE values were relatively higher when compared to daily values multiplied by the number of days. This suggests applying more robust bias correction procedures since this amount of bias and RMSE value could lead to unreliable results. Although, one alternative solution could be the use of monthly estimates of GPPs instead of generating monthly estimates from daily estimations, in case their monthly estimations are available and had been generated separately. For instance, the PERSIANN daily estimates are the disaggregation of monthly estimates [16], and thus, the original monthly estimates might have better accuracies. Furthermore, the average monthly data were found to be more accurate than the monthly accumulated data, suggesting the usage of monthly average data instead of accumulated data for different applications if applicable. Finally, the validation of GPPs based on yearly (12 months) observations, either in average or accumulation, showed that the CC values for some GPPs were improved, while some remained relatively the same with respect to the daily comparison. However, even for GPPs whose CC values had improved, other metrics, i.e., MBE and RMSE, generally showed reverse trends. High MBE and RMSE values, along with their high SD values, suggest using yearly accumulated data with high caution. The yearly observations also implied that CHIRPS data had the lowest RMSE values and, in this term, were the most accurate GPP, and GSMaP was second to that. In summary, the comparison in different time scales indicated that in longer time intervals (monthly or yearly), the accumulated estimates had larger errors than the average estimates and may lead to higher uncertainties in any task. For instance, it can be suggested that yearly average data could provide more accurate results (i.e., than yearly accumulated) for applications in which annual precipitation data are required, such as trend analysis. This is because yearly accumulated data are associated with more significant errors, and consequently, these errors would propagate and amplify into the models [91], making the final result unreliable. Consequently, the results could not present the real-world situation properly and may bias the final understandings and conclusions.
## 2 Materials and Methods
### Operation of the Inspection System  
The inspection algorithm includes the following image processing steps. The first stage is carried out on color images in an HSV space (the model for describing the color space containing Hue, Saturation, and Value components). Based on the pixel values, areas that may contain blue stain are pre-selected. The second stage is the filtration of the highlighted areas based on their geometric properties such as length, width, their proportions, and surface area. The third stage is preparing the set for the neural network. The input images are in the shape of a square with dimensions of \(32\times 32\) pixels. The images partially overlap, i.e., their margins contain common parts of the source image. The fourth stage is classification using a neural network. The system operates a set of neural networks dedicated to detecting individual wood defects. This article discusses a neural network designed to detect blue stains; other defects are not cross-detected. The algorithm's operation diagram is shown in Figure 3.  
The fifth, equally important stage is training the neural network while the system is operating under production conditions. Training the system involves completing the training set. New images are collected as it works. When replenishing the training set, its original size is preserved. For this purpose, images are randomly removed from the existing set. Then, the learning and testing procedures are started. Replacing data in the training set is limited. The network is not learned from scratch, but only trained. The weights are not randomized, but retained because they contain the current learning outcomes. The purpose  
Figure 3: Algorithm for detecting blue stain in sawmill wood.  
of this practice is to accelerate the learning process. When implemented, the system is adapted to the individual requirements of the customer, which include the type of wood, surface, roughness, smoothness, and any defects in their quality classification. The neural network connection procedure used in the system speeds up the process of training the network and enriching its knowledge with new cases.  
If an employee notices boards that have not been classified correctly, the boards are removed from the production line. Then, the operator passes them through the scanner again, this time, marking the areas covered by the blue stain on the computer. These data go to the training set, and the training of the network does not take place automatically, but on demand.  
The stages of network improvement are discussed below with examples.