esa-sceva/satcom-chunk-collection huggingface dataset:
# A Thermogravimetric Analysis of Biomass Conversion to Biochar: Experimental and Kinetic Modeling
## 3 Results and Discussion
### Effect of Temperature on Biomass Waste Surface Structure  
#### 3.3.1 FTIR Analysis  
Analysis of the FTIR spectra of food waste biomass, biochar, and active coal samples has demonstrated the presence of vibrations from certain specific groups such as hydroxyl, aliphatics, carboxyl and carbonyl, ester and ether, aromatic, etc. The representative peak of the hydroxyl zone is around 3300 cm\({}^{-1}\), which indicates the existence of water, carboxylic acids, phenolics, and alcohols. The intensity of these signals decreases with increasing temperature, as can be seen in Figure 8 [72]. As can be seen in Figure 8, the peak around 3233 cm\({}^{-1}\) is considerably reduced for the potato-250 and apple-250 samples. The bands assigned to stretching modes of C-H groups were observed in the range of approximately 3100-2800 cm\({}^{-1}\) of the FTIR spectrum, and the peak at 1373 cm\({}^{-1}\) was ascribed to C-H groups, indicating the presence of methylene and methyl groups from hemicellulose and cellulose. The peak at a wavenumber of 1730 cm\({}^{-1}\) was ascribed to C=O groups, signifying the existence of ketones from hemicellulose [73]. In the area of aliphatic bonds, it can be observed that they are preserved even at 250 \({}^{\circ}\)C but disappear at 800 \({}^{\circ}\)C. The absorption peak observed at 1040 cm\({}^{-1}\) corresponds to the C-O stretching vibration mode (symmetric and asymmetric). This specific peak also indicates some carbohydrate functional groups (C-OH and C-OR groups) [74]. Also, the peak at 1242 cm\({}^{-1}\) corresponds to the C-O bond from ester or ether, while the one at 820 cm\({}^{-1}\) corresponds to C-H from aromatics [47].  
Figure 7: Coats–Redfren parity diagrams for potato peel thermal degradation.  
Components such as the C-O bond from ester or ether (i.e., 1247, 1240, 1246, and 1242 cm\({}^{-1}\)) lost their strength in the samples produced at high temperatures (800 \({}^{\circ}\)C). The band at 1610 cm\({}^{-1}\) indicates the occurrence of C=C vibrations of the aromatic skeleton [75]. The presence of intense stretching vibrations of the C-O bonds, C=O groups, C=C, C-H, O-H, in the case of the raw material as well as the one at 250 \({}^{\circ}\)C may suggest their use as sorbent materials for organic and inorganic compounds [76].
## 3 Results and Discussion  
### Coloured Glazes  
All the white glazes analysed non-invasively in situ showed a lead-based composition. In the XRF spectra the presence of lead (Pb) signals, typical of Della Robbia glazes, and the presence of tin (Sn) used as an opacifier, was registered [10; 31; 32]. On glazes with a greyish tone, higher levels of iron (Fe) and manganese (Mn) were also detected. In this case, we can assume that the addition of iron and manganese-based pigments was used to darken the mixture.  
Analyses of the yellow glazes indicated that they contain principally lead (Pb) and antimony (Sb) with a consistent amount of iron (Fe) in the dark yellow. The colour of green glazed tiles is due to copper (Cu) the quantity of which increases with the colour's tone.  
The colouring agent responsible for blue glazes is cobalt (Co). This was confirmed by FORS, revealing the typical signals of Co\({}^{2+}\) in the spectra (Figure 3) [8; 17; 18; 19; 20; 22; 33; 34; 35].  
As expected, the spectral features are comparable, and the percentage of reflectance is higher for the area with the lightest tone. Iron (Fe) signals are also present in all XRF spectra, highlighting the frequent combination Fe-Ni-Co reported in the literature [6,8,36]. The composition of the blue glazes, revealed by XRF, is characterized by the presence of a cobalt (Co) pigment as expected and on the absence of arsenic (As), generally associated with cobalt (Co) in most cobalt-minerals. As reported by [PERSON] 2004 [6], arsenic (As) is indeed above the detection limit in sculptures dated after 1520. Opposing this, arsenic (As) is absent in the entire production of [PERSON]. In order to confirm these hypotheses, a micro-sample (FED_06) was taken from a blue flower (cf. Figure 2). Microscopic examination and micro-chemical analysis revealed that this sample has a variable distribution of tin-oxide opacifying particles and the cobalt (Co) blue colourant throughout the thickness of the glaze.  
The elemental composition of Della Robbia blue reported by [PERSON] [6] is in agreement with our findings. At the body-glaze interface the formation of wollastonite (CaSiO\({}_{3}\)) is also clearly present. SEM images of the internal glaze structure and the tin oxide opacifying particles are shown in Figure 4 with the reflected light image of the cross section.  
Figure 3: FORS spectra of the blue glazes analysed and the corresponding high magnification image.
## 4 Discussion  
### Main Interfering Factors  
Eliminating the effects of stand age, phenological rhythm, and meteorological resources: During the process of tree growth, GPP increases with the tree's growth, and this trend will tends toward zero, resulting in a dynamic balance of the whole ecosystem [71]. Additionally, GPP is affected by meteorological conditions and will vary from year to year [4]. Thus, considering the two points mentioned above, it cannot be denied that the high GPP in 2015 was not caused by the advantages of meteorological resources, and the increase in GPP from 2016 to 2018 may also be due to the growth of trees (increases in stand age). To confirm that the reduction from 2015 to 2016 and the uplift from 2016 to 2018 were caused by TC disturbance, we used Global Mangrove Watch to obtain the distribution of mangroves from 1996 to 2020 (Figure S7) and judge the age of the mangroves. We found that the mangroves were mostly older than 20 years. The stand age that GPP reached to the stable period across tree species is different, accounting for 55.56% within 20 years [72]. Temperate tree species generally reach the balance in about 30 years, based on the relationship between the GPP balance period of the boreal and temperate forest [71] and the annual growth period in the tropics leading to faster tree growth; thus, we could deduce that 20-year-old tropical forest could basically reach the GPP equilibrium state. Secondly, we compared the monthly median value composited VIs (including EVI, NDVI, kNDVI, and NIRv) of mangroves before SuperTY Sarika attacked (2013-2015) and after recovery from damage (2018-2020) to eliminate the effect of meteorological factors. In comparing the monthly VIs during the two periods, there was no significant increase or decrease trend (Figures S8-S10).  
This study did not compare the images of two scenes before and after SuperTY Sarika attacked to determine the direct damage (e.g., uprooting, broken branches, and scattered leaves), unlike most studies on the vulnerability, loss, and assessment [24; 41].  
Comparing temporally adjacent images is the common method used in short-term disasters such as fires and typhoons and can complete disaster damage assessment in near real time [41]. Considering that the mangroves were in the growing season when SuperTY Sarika attacked, the inherent phenological rhythm would overestimate the loss; of course, if SuperTY Sarika had occurred in March or April, the inherent phenological rhythm would have underestimated the GPP loss (the monthly loss GPP can be seen in Figures S11 and S12). Therefore, the annual scale GPP was selected as the proxy to determine the legacy effect duration of the TC and quantify the recovery process. It can be judged that the impairment of GPP obtained in this study is mainly caused by the influence of the TC.
## 4 Discussion  
This paper builds an experimental platform based on the mixed reality evacuation LVC simulation system and conducts experimental research on various factors that affect pedestrian evacuation decision-making and path trajectory during the evacuation process.  
The evacuation distance and numbers of people had a significant impact on the exit choice of the participant, and the participant was more inclined to choose the exit with a closer distance or a smaller number of people. It also can be concluded that the angle between the exits and the participant would have a certain impact on the evacuation behavior of the participant.  
In the two-factor coupling influence experiment, it was found that the number of exits had a greater impact on the exit choice of the experimenter, and the participant would give priority to the number of exiting virtual pedestrians compared with the distance. At the same time, the large difference in the number of people made the decision of pedestrians more decisive, and the walking track of evacuees was close to a straight line.  
Although the LVC simulation system provides an experience that combines virtual and natural environments, it still cannot restore the emotions and pressure of pedestrians in the real environment, as well as the complexity of conditions such as noise and light. The control of experimental variables and the representativeness of the experimenters are also tricky. It has certain limitations. Future research should improve the complexity and authenticity of the simulation environment, expand the diversity of participants, comprehensively analyze multiple influencing factors, and further improve the effectiveness and applicability of the evacuation plan.  
Conceptualization, Z.T. and Y.L.; methodology, Y.L.; software, Z.T. and Y.L.; validation, X.H. and Y.W.; formal analysis, Y.L.; investigation, M.C.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Z.T., Y.L. and X.H.; writing--review and editing, Y.L.; visualization, Y.L.; supervision, R.Y.; project administration, Z.T. and Y.L.; funding acquisition, Z.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52304273), the Opening Fund of Key Laboratory of Civil Aviation Emergency Science & Technology (CAAC) (NJ202022), and the Fundamental Research Funds for the Central Universities (2023 ZKPYAQ07).  
This study is not applicable to human or animal research.  
We have obtained informed consent forms from all participants in this study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Figure 7: Comparison of exit choices and trajectory of the participants for different starting positions and virtual pedestrian ratio, they should be listed as: (**a**) pedestrian ratio 1:9; (**b**) distance ratio 1:1.6.
## 8 Retrieval Performance  
To compare the retrieval performance of sea surface height and wind speed using retracked and non-retracked waveform, the following simulation steps are implemented as:  
1. generating randomly 1000 sets of wind speed, incident angle, the moving direction of LEO and GNSS satellites as the input parameters of the models in Section 2;
2. using scattering scenario and models in Section 2 to produce 1000 delay waveform corrupted by the noises and the dynamic of GNSS-R geometry;
3. retracking above delay waveforms to obtain pure ones through proposed methods in Section 6 and estimating the retracked and non-retracked waveforms' features defined by (11)\(\sim\)(14);
4. developing retrieval approaches and evaluating the root mean square error (RMSE) of sea surface height and wind speed measured using retracked and non-retracked waveforms.
## 4 Discussion
### Limitations of the Study and Future Studies  
This study aimed to evaluate the antibacterial efficacy and cytotoxicity of extracts of nine selected plant species. Test bacteria included drug-resistant strains of _S. aureus_ and NAS isolated from clinical cases of bovine mastitis. The cytotoxic effects of extracts of three plants selected according to their promising antibacterial activities were also determined. In vitro studies have limitations insofar as biological activity in the laboratory cannot replicate conditions in vivo; therefore, biological activity and toxicity studies need to be conducted, preferably in dairy cows. Future work will involve developing and testing formulations containing active extracts for topical application to udders of dairy cows in order to assess their ability to prevent and treat mastitis. Additionally, isolation and identification of bioactive compounds from the most antibacteriality active and least cytotoxic plant extracts will be conducted to identify potential marker compounds. Furthermore, synergistic activity of different plant extracts and their components will be explored.
## 3 Results
### Molecular Weight (MW)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Starch** & \begin{tabular}{c} **Average Molecular** \\ **Weight (kDa)** \\ \end{tabular} & \begin{tabular}{c} **Polydispersity** \\ **Index (PDI)** \\ \end{tabular} & \begin{tabular}{c} **Retention Time of** \\ **Main Peak (min)** \\ \end{tabular} & **Observations** \\ \hline Cassava, natural & 500 & 1.5 & 12.5 & \begin{tabular}{c} High amylopectin content; \\ large molecules indicated by \\ an early peak. \\ \end{tabular} \\ \hline Cassava, modified & 480 & 1.4 & 12.3 & \begin{tabular}{c} Stable molecular structure with \\ improved humidity resistance. \\ \end{tabular} \\ \hline Potato, natural & 450 & 1.4 & 13.0 & \begin{tabular}{c} Medium molecular weight \\ with dense distribution. \\ \end{tabular} \\ \hline Potato, modified & 430 & 1.3 & 13.2 & \begin{tabular}{c} Lower PDI enhances stability \\ and mechanical properties. \\ \end{tabular} \\ \hline Wheat, natural & 320 & 2.0 & 15.2 & \begin{tabular}{c} Broad distribution, likely \\ related to the ratio of amylose \\ and amylopeetin. \\ \end{tabular} \\ \hline Wheat, modified & 310 & 1.8 & 15.0 & \begin{tabular}{c} Moderate reduction in PDI \\ improves stability and \\ biodegradability. \\ \end{tabular} \\ \hline Corn, natural & 470 & 1.3 & 12.8 & \begin{tabular}{c} Consistent molecular size with \\ low PDI, suitable for strong \\ film formation. \\ \end{tabular} \\ \hline Corn, modified & 455 & 1.2 & 12.6 & \begin{tabular}{c} Low PDI and high molecular \\ weight provide excellent \\ mechanical properties. \\ \end{tabular} \\ \hline Pea, natural & 350 & 1.7 & 14.5 & \begin{tabular}{c} Medium molecular size with \\ moderate polydispersity. \\ \end{tabular} \\ \hline Pea, modified & 340 & 1.6 & 14.3 & \begin{tabular}{c} Lower PDI enhances resistance \\ to external factors. \\ \end{tabular} \\ \hline Rice, natural & 300 & 2.1 & 15.5 & \
\begin{tabular}{c} High polydispersity; smaller \\ molecules ideal for \\ rapid degradation. \\ \end{tabular} \\ \hline Rice, modified & 290 & 1.9 & 15.4 &
\begin{tabular}{c} Moderate polydispersity \\ supports high \\ biodegradation rates. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of molecular characteristics and observations of different starch samples for biodegradable films.
## 6 Application of the Optimization Techniques: Routing Protocols
### Multipath Routing Protocols
#### 6.4.3 Energy Aware Routing in Wireless Sensor Networks  
Once multiple paths are discovered, this algorithm associates a probability of use to each route [27]. This probability is related to the residual energy of the nodes that form the route but it is also considers the cost of transmitting through that route.
## Materials and Methods
### Calculation Scheme and Assumption  
The LCA and the carbon emission factors method are used to calculate the carbon emissions of the case building. As it is in the constructive stage, the data of materials and the construction process were collected from architectural design and budget documents. Related standards and databases are referred to as supplements. The energy consumption during the operational phase is simulated using DesignBuilder, in which a virtual model is established.  
In addition, specific assumptions and instructions to be made in the calculation process are as follows.  
1. This paper uses kgCO\({}_{2}\)e and tCO\({}_{2}\)e as the units to calculate the whole life cycle carbon emissions.
2. The area to be used in calculating of carbon emissions per unit area is 717 m\({}^{2}\), which is the sum of building area and landscape area.
3. The carbon emission factors of construction materials and energy consumption come from the \"Building Carbon Emission Calculation Standard\", which includes the equivalent environmental impact caused by the other greenhouse gases.
4. The electricity consumption carbon emission factor used in this paper is 4.2 tCO\({}_{2}\)/10\({}^{4}\) kWh, according to the notice of Shanghai Municipal Bureau of Ecology and Environment on the \"Adjustment of the Values of Emission Factors Related to the City's Greenhouse Gas Emission Accounting Guidelines\", issued in February 2022 [32].
5. The new building has the same operation carbon emission as the refurbished building.
6. It is assumed that the service life of the building is 50 years, and the constructive materials are the same as the whole building [28]. To simplify the calculation, the total energy consumption and carbon emission of 50 years are based on the one-year situation result in the software.
## 3 Results and Discussion
### Accuracy Rate for Inference Model
#### 3.3.1 Caffe Model  
The classification results with the trained Caffe model developed using NVIDIA Jetson are shown in Table 6 below. As discussed above, results were aggregated to two classes: Healthy, which means a patient does not have diabetic retinopathy (No DR), and Unhealthy, which implies that a patient has diabetic retinopathy (Mild, Moderate, Severe, or Proliferate DR). The input image types to the model are indicated in the table. As discussed previously, the diabetic retinopathy generally progresses from mild to proliferating over time. The result shows that the model accurately predicts healthy images 98% (specificity or true negative) of the time. The best result for images with DR was observed for the Proliferate DR. The model accurately classified 40% (sensitivity or true positive) of images as unhealthy. The least accurate results were with Mild DR images. The models inaccurately classified 97% of Mild DR images as healthy. We observed that the sensitivity increases as the severity of the disease increases. The combined sensitivity for severe and proliferate DR was 35%. The overall accuracy of the model is 75.6%. We can see that it is highly influenced by the higher specificity value of the model and a large number of No DR images compared to other classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Resources** & **LMDB** & **HDF5** \\  & **[NVIDIA Jetson TX2]** & **[Google Engine]** \\ \hline RAM & 8 GB & 16 GB \\ CPU & 2 & 1 \\ Cores & 4 & 1 \\ Processing Time & 2 Hours 37 Minutes & 2 Hours 33 Minutes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data preprocessing resource comparison between lightning memory-mapped database (LMDB) and hierarchical data format version 5 (HDF5).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Tested Value Being Used** \\ \hline Training Epochs & 100 \\ Random Seed & 100 \\ Batch Size & 32 \\ Solver Options & Gradient Descent \\ Base Learning Rate & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimized hyperparameters.
# Leveraging Static and Dynamic Wear Leveling to Prolong the Lifespan of Solid-State Drives
## 2 Background and Related Work
### Wear Leveling Policies
Therefore, the PWL policy performs migration when the erase count of a victim block exceeds the average of the average erase count of all blocks and the maximum allowable erase count. Consequently, when the overall erase count of blocks is low initially, migration is rarely performed, reducing migration overhead. Conversely, as the overall erase count of blocks increases, migration is performed more frequently, delaying the transition to bad blocks and extending the lifespan of the SSD. The primary drawback of static wear leveling policies lies in the potential inaccuracy of identifying cold data. If non-cold data are migrated to a worn-out block, the invalidation ratio of the block increases, making it more likely to be selected as the victim block for garbage collection again. Consequently, the block may transition into a bad block earlier compared to other blocks. Both static and dynamic wear leveling techniques play crucial roles in delaying the occurrence of bad blocks, thereby extending the lifespan of SSDs. Dynamic wear leveling actively manages wear distribution during normal operations by selecting blocks with lower erase counts for garbage collection. This ensures that wear is balanced across all blocks, reducing the likelihood that any single block reaches its maximum erase limit too quickly. Static wear leveling complements this by intervening when specific blocks show signs of excessive wear. By relocating cold data--data that are infrequently modified--to these at-risk blocks, static wear leveling prevents them from being selected for garbage collection too often, thereby delaying their transition into bad blocks. When combined, these techniques work synergistically to ensure a more even distribution of wear across all blocks, maximizing the delay in bad block formation and enhancing the overall reliability and longevity of SSDs.
## 3 Results and Discussion
### Scenario 2 (Cylinder)
At the end of the simulation, \(t=0.1\) (Figure 5d), the situation is similar to that shown in Scenario 1. The pressure near the lateral sides of the domain has dropped to a relatively low value, while high-pressure regions are formed around the corners of the computational domain. The time history of the pressure at Points 1 and 2 are also shown (Figure 6). It is found that the initial shock wave along the horizontal direction arrives at the side boundary earlier than that along the vertical direction, at the time of approximately 0.076. The peak value of the pressure detected at the side boundary is 1416. In contrast to that, on the top of the boundary (Point 2), the increase of the pressure is retarded. However, the maximum value of the pressure recorded at the top is higher than that at the lateral boundary (Point 1) as mentioned above. In Figure 6, it is also interesting to note that the pressure drop at Point 2 is more rapid than that at Point 1. It is possibly because the horizontal thickness of the initial cylindrical bubble (\(2r\)) is larger than the vertical thickness (\(4r/3\)). As a result, more energy is transmitted along the horizontal direction after the first shock wave, compared with the situation in the vertical direction. The energy following the first shock wave is able to replenish the pressure after the reflection of the shock wave, thus slowing down the decrease of the pressure at the side boundaries of the computational domain.