esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Methods  
Statistica 13.1 was used for the statistical analysis. All results are presented as mean \(\pm\) standard deviation. In order to verify the normality, homogeneity, and sphericity of the sample data variances, the Shapiro-Wilk, [PERSON], and [PERSON]'s tests were used, respectively. A repeated two-way ANOVA (2 conditions \(\times\) 2 times) was used to examine the differences between the GR-HCT5 min and GR-HCT10 min conditions. Furthermore, \(t\)-test comparisons for delta values (pre-post) were made between GR_HCT5 min and GR_HCT10 min conditions. Effect sizes for main effects and interactions were determined with partial eta squared (np2). Partial eta squared values were classified as small (0.01 to 0.059), moderate (0.06 to 0.137), and large (>0.137). Post hoc comparisons using [PERSON]'s test were conducted to show the differences between mean values when a main effect or an interaction was found. For pairwise comparisons, the effect size was determined by parametric effect sizes (Cohen's d), which were defined as large (d \(>\) 0.8); moderate (d between 0.8 and 0.5); small (d between 0.49 and 0.20); and trivial (d \(<\) 0.2). [PERSON]'s ANOVA was applied and pairwise comparisons for variables lacking normal distributions. The effect size for the Friedman ANOVA was calculated as [PERSON]'s W. [PERSON]'s W values were classified as W \(<\) 0.1 very small; W 0.1-0.3 small; 0.3-0.5 medium; W \(>\) 0.5 large. For pairwise comparisons for [PERSON]'s ANOVA, the effect size was determined by nonparametric effect sizes (r\({}_{\rm g}\)) which were defined as large (r\({}_{\rm g}\) \(>\) 0.5); moderate (r\({}_{\rm g}\) between 0.5 and 0.3); small (r\({}_{\rm g}\) between 0.3 and 0.1); and trivial (r\({}_{\rm g}\) \(<\) 0.1). Percent changes with 95% confidence intervals (95 CI) were calculated. Statistical significance was set at \(p<\) 0.05. The repeated measure ANOVA between interactions with an effect size of at least 0.25, \(\alpha\) = 0.05, and 1-\(\beta\) = 0.95 gave a statistical power of 97.37% and the sample size of a minimum of 15  subjects (calculation performed with G*Power software (version 3.1.9.7) according to [PERSON] et al. [55].
## 3 Results and Discussion
### Analysis of the Statistical Design of Experiments
#### 3.6.1 Numerical Analysis
Specifically, the conditions that result in maximum elimination of chrysoidine (98.9%) using the lowest concentration of hydrogen peroxide are obtained by decoding the values in Table 3 for optimum 1, which are as follows: [Fe\({}^{2+}\)] = 4.17 \(\times\) 10\({}^{-4}\) M; [Fe\({}^{3+}\)] = 5.89 \(\times\) 10\({}^{-4}\) M; [H\({}_{2}\)O\({}_{2}\)] = 3.01 \(\times\) 10\({}^{-3}\) M; and [Polyphenols] = 120 mg/L (gallic acid equivalents). It is worth noting that these conditions lead to a double cost reduction in the process. Firstly, the molar ratio of Fe(II) to Fe(III) is decreased to values below even 1:1. Specifically, a Fe(II):Fe(III) ratio of 0.71:1 is used, while the literature suggests ratios between 1:1 and 2:1. Since Fe(III) salts have a lower price compared to Fe(II) salts, this results in lower reagent costs. Secondly, the molar ratio of total iron (Fe, including Fe\({}^{2+}\) and Fe\({}^{3+}\)) to hydrogen peroxide is also reduced. The H\({}_{2}\)O\({}_{2}\):(Fe\({}^{2+}\) + Fe\({}^{3+}\)) ratio is around 3, while many sources in the literature use much higher ratios, reaching 9:1 or 10:1 (see [84] and references therein). Considering that hydrogen peroxide is the most expensive reagent in the process, a decrease in the required amount leads to a significant reduction in operating costs. For the removal of methylene blue, the real values corresponding to a maximum removal efficiency are [Fe\({}^{2+}\)] = 5.39 \(\times\) 10\({}^{-4}\) M; [Fe\({}^{3+}\)] = 3.17 \(\times\) 10\({}^{-4}\) M; [H\({}_{2}\)O\({}_{2}\)] = 3.04 \(\times\) 10\({}^{-3}\) M; and [Polyphenols] = 77 mg/L (gallic acid equivalents). Again, the model predicts that operating under these conditions, total removal of the dye should be achieved. An experiment was conducted under those optimal conditions, resulting in a dye elimination percentage of 99.1%, which is very close to the total elimination predicted by the model. In this case, the molar ratio of Fe(II) to Fe(III) equals 1.7, in line with the results previously reported in the literature [84]. Furthermore, as per the removal of chrysoidine, the H\({}_{2}\)O\({}_{2}\):(Fe\({}^{2+}\) + Fe\({}^{3+}\)) ratio is around 3, which also involves a significant reduction in operating costs.
# Effect of Biochar Application Depth on Crop Productivity Under Tropical Rainfed Conditions
## 3 Results and Analysis
### Crop Weight Dynamics  
The weights of the various yield components of the NERICA-4 rice plant were taken at harvest, dried, reweighed, and the dry weight plotted in Figure 5. Note that the trend line in Figure 5a is the sum for the yield components (which is the same as the value for the plant), and that in the bottom plate (of Figure 5) is the average across the treatments. Based on the plot in Figure 5, yield weight was smallest at the root scale (1302.56 kg/ha) and highest at the plant scale (16,673.07 kg/ha). At the treatment scale, yield weight was highest under TB (17,799.57 kg/ha) and lowest under TC (14,521.25 kg/ha). Grain weight, which is among the most important yield components of rice, was highest under TB (5704.45 kg/ha), followed by CK (5222.32 kg/ha) and TA (4791.87 kg/ha), and it was lowest under TC (3910.07 kg/ha). For NERICA-4 rice cultivar, average grain weight is 5000 kg/ha [32,50]. This suggested that the plant performed above average for TB and CK, but below average for TA and TC. While the below-average performance of TA could be attributed to the shallow biochar application depth (10 cm, far below rice root penetration depth), that for TC was likely due to pest damage and mounting effect  
Figure 4: Length of NERICA-4 rice plant along the different treatments (**a**) and yield components (**b**). Treatments A, B, and C respectively denote the 10, 20, and 30 cm biochar application depths and then CK is the control.  
Figure 3: Number of tillers from the 6 th to the 14 th week (plots **a** & **b**) and plant growth from the 4 th to the 14 th week (plots **c** & **d**) of planting of New Rice for Africa (NERICA)-4 rice plant in 3 biochar application depths plus the control under tropical rainfed conditions. Treatments A, B, and C respectively denote the 10, 20, and 30 cm biochar application depths and then non-biochar treatment (CK) is the control.  
on the yield. In terms of dry weight, root accounted for 7.81%, grain 29.43% and then shoot 62.76% of the NERICA-4 rice plant.  
Despite the good correlation (\(r>0.74\)), 2-tailed T-test analysis showed significant differences (at \(p=0.05\)) among all paired combinations of the parameters along crop yield components. However, there was strong correlation (\(r>0.99\)) and no significant differences (at \(p=0.05\)) among paired combinations of the treatments for the yield weights. Based on ANOVA analysis, the treatments did not have any significant effect on yield weight, but the yield components were indeed significantly different in terms of weight. It is good to note, however, that yield component is partly a genetic factor that controls root, shoot and grain ratio, irrespective of the treatment.
# Improvement of Mechanical Properties of 3D Bioprinted Structures through Cellular Overgrowth
## 4 Conclusions  
A number of biomedical applications require significant mechanical strength in the manufactured implants. In the case of bioprinted structures, it poses a significant challenge. Currently proposed solutions rely on polymers or metals to achieve such mechanical strength. In soft tissue engineering, the preferred solutions are those that do not introduce rigid elements and simultaneously provide a high level of flexibility and strength. This effect can be achieved by relying on hydrogel bioinks and the growth of cells in their volume, where the required shape is obtained through the bioprinting process.  
The present work represents an innovative approach to improve the mechanical properties of printed hydrogel structures as an effect of their cellular overgrowth. Directly after the 3D printing process, the tubular bioprinted hydrogel structures are characterized by an extremely low tensile strength. The cultivation of fibroblast cells of the NIH/3T3 lineage in 2A9G biopprinted 3D tubular structures for 6 weeks resulted in an almost ninefold increase in tensile strength. A prolonged incubation period of up to 2 months led to a reduction in the mechanical properties of hydrogel printouts. This reduction in mechanical properties may result from unbalanced matrix degradation processes, cell growth, and perforation of their cell membrane, leading to the disappearance of the mechanical properties provided by a coherent network of cell matrices.  
The research results presented in this paper are preliminary findings for further analysis of the mutual advanced intercellular interactions in 3D bioprinted hydrogel structures.  
Conceptualization, D.B.; methodology, A.W., M.B., J.G. and D.B.; software, M.A.; validation, A.W., M.B., J.G. and D.B.; formal analysis, D.B.; investigation, A.W., M.B., J.G., N.B., M.A., T.W. and D.B.; data curation, A.W., M.B., J.G., N.B., M.A., T.W. and D.B.; writing--original draft preparation, [PERSON], N.B., M.A., T.W. and D.B.; writing--review and editing, [PERSON] and D.B.; visualization, [PERSON], N.B., M.A., T.W. and D.B.; supervision, D.B.; project administration, D.B.; funding acquisition, D.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Centre for Research and Development under grant no. TECHATSTRATEG2/407770/2/NCBR/2020.  
Not applicable.  
Data Availability Statement: The data that support the findings of the current study are listed within the article.  
This work was completed while the first author was a Doctoral Candidate in the Interdisciplinary Doctoral School at the Lodz University of Technology, Poland.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Entropy-Based Feature Engineering Framework  
Model performance strongly correlates with data quality, where missing data may result in poor performance. Hence, we proposed a novel entropy-based feature engineering framework which is suitable for vital signs, based on their frequency of occurrence considering clinical availability. This framework was constructed through the following steps:  
1. Step 1: We evaluated the setting time \(T_{ST}\), which is the time interval between the patient's first admission time and their first data entry. This step is important, as the setting time dramatically affects the portion of missing data. In another word, we only consider data collected after setting time \(T_{ST}\).
2. Step 2: The Shannon entropy was used to evaluate all vital signs \(V\). This measures the probability distribution that characterizes the amount of missing information and data quality.
3. Step 3: We conducted missing value imputation on \(V\). Both Steps 1 and 2 are critical for the data quality, as it is not measured on a frequent and consistent basis; yet, vital signs are crucial for AKI evaluation and indication. An overall workflow is shown in Figure 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Definition** \\ \hline \(y=f(F)\) & Target function \\ \(F=(I,J,T,V,L)\) & Set of features \\ \(I\) & Set of patients \(I=\{i_{1},...,i_{n}\}\) \\ \(J\) & Set of admission ID \(J=\{j_{1},...,j_{m}\}\) \\ \(T\) & Set of time stamps \\ \(T_{TP}\) & Time gap where \(T_{TP}=[0,48]\) h before AKI onset \\ \(T_{ST}\) & Setting time \\ \(T_{FW}\) & Feature window where \(T_{F}W=[24,48]\) h before AKI onset \\ \(V\) & Set of vital signs \(V_{k,t}\in V\) \\ \(n\) & Total number of patients \\ \(m\) & Total number of admission IDs \\ \(k\) & Total number of vital signs \\ \(t\) & Time stamp \\ \(H_{q}(V)\) & Entropy for Vital sign V at the \(q^{\text{th}}\) time \\ \(H\) & Target entropy \\ \(h\) & Total number of possible states \\ \(D0\) & AKI onset \\ \(D1\) & 24 h before AKI onset \\ \(D2\) & 48 h before AKI onset \\ \(D3\) & 72 h before AKI onset \\ \(V_{general}\) & Mean and variance of vital signs \\ \(V_{entropy}\) & Entropy of vital signs \\ \(V_{merge}\) & The combination of both \(V_{general}\) and \(V_{entropy}\) \\ \(V_{mean}\) & Mean of vital signs \\ \(V_{variance}\) & Variance of vital signs \\ \(fw\) & Feature window length \\ \(q\) & q is an iterative counter denoting the \(q^{\text{th}}\) time, where \(q\in[1,150]\) \\ \(N\) & Number of computation times \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 4 BSM Reuse Model: Our Proposal  
### Problem Specification  
BSMs frequently generated from multiple connected vehicles can play a primordial role in providing transport data and see the credible and reliable information they contain. Otherwise, given the way that BSMs are considered and treated, multiple deficiencies prevent the latter to be capable of constituting a precious connected vehicle data source. Therefore, the following features can be listed.  
Context: presently, the basic safety messages are particularly even exclusively used for the safety purpose. In contrast, a big bundle of its data elements are necessary for a considerable collection of applications not related to safety. At least, the BSM Part 1 data elements can conveniently provide basic vehicle information required by several applications. Arguably helpful, safety, mobility, and efficiency are not a separated aspects. Obviously, traffic accidents can breed Traffic congestion and increase CO2 emission.  
Validity: BSMs as safety data are overall regarded as snapshot data that give an idea of the state of the system at a definite time. From the first use, the BSM becomes outdated, useless, and is deleted.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Num** & **Designation** \\ \hline
1 & DSRCmsgID \\ \hline
2 & MsgCount \\ \hline
3 & Common Safety Request \\ \hline
4 & TemporaryID \\ \hline
5 & DSecond \\ \hline
6 & Latitude \\ \hline
7 & Positional Accuracy \\ \hline
8 & Transmission \& Speed \\ \hline
9 & Heading \\ \hline
10 & Steering Wheel Angle \\ \hline
11 & AccelerationSet 4 Way \\ \hline
12 & Brake System Status \\ \hline
13 & Vehicle Size \\ \hline \hline \end{tabular}
\end{table}
Table 2: SAE J2735 basic safety message, Part I.  
Figure 3: SAE J2735 basic Safety Message Format.  
Undoubtedly, BSMs collected from multiple connected vehicles can be cached, grouped and stored to construct continuous data streams that can supply almost real-time metrics, while they evolve over space and time. Indeed, data are then exploited in a better-connected form to improve safety, decrease fuel consumption, reduce traffic jams, as well as facilitate people's travel overall.  
Range: as is well known, safety applications between vehicles require local broadcast of BSM within the limits of the DSRC. Even though, connected vehicles using applications are required in order to enable an extra-vehicular data exchange to permit collaborative sensing and action at scale. Thus, if transmitted according to different technologies other than DSRC, such as Cellular V2X, continuous data obtained through BSMs processing can widely feed applications with requested vehicle information.
# FirepanIF: High Performance Host-Side Flash Cache Warm-Up Method in Cloud Computing
###### Abstract  
In cloud computing, a shared storage server, which provides a network-attached storage device, is usually used for centralized data management. However, when multiple virtual machines (VMs) concurrently access the storage server through the network, the performance of each VM may decrease due to limited bandwidth. To address this issue, a flash-based storage device such as a solid state drive (SSD) is often employed as a cache in the host server. This host-side flash cache saves remote data, which are frequently accessed by the VM, locally in the cache. However, frequent VM migration in the data center can weaken the effectiveness of a host-side flash cache as the migrated VM needs to warm up its flash cache again on the destination machine. This study proposes Cachemior, Firepan, and FirepanIF for rapid flash-cache migration in cloud computing. Cachemior warms up the flash cache with a data preloading approach using the shared storage server after VM migration. However, it does not achieve a satisfactory level of performance. Firepan and FirepanIF use the source node's flash cache as the data source for flash cache warm-up. They can migrate the flash-cache more quickly than conventional methods as they can avoid storage and network congestion on the shared storage server. Firepan incurs downtime of the VM during flash cache migration for data consistency. FirepanIF minimizes the VM downtime with the invalidation filter, which traces the I/O activity of the migrated VM during flash cache migration in order to invalidate inconsistent cache blocks. We implement and evaluate the three flash cache migration techniques in a realistic virtualized environment. FirepanIF demonstrates that it can improve the performance of the I/O workload by up to 21.87% compared to conventional methods.  
cloud computing; flash cache migration; flash cache warm-up +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
Accurate prediction of the coiled tubing erosion rate plays an important role in prolonging and improving the effective service life of coiled tubing. In this study, the influence of different factors on the erosion rate of coiled tubing was studied from the experiment, and the input index of the model was determined by GRA. At the same time, the SSA is innovatively proposed to optimize the BPNN, and a prediction model of coiled tubing erosion rate is constructed. There are five main conclusions.  
1. Based on the experimental results, it is evident that the maximum erosion rate of the coiled tubing occurs at a 45\({}^{\circ}\) impact angle.
2. According to the GRA results, the impact velocity, impact angle, and sand concentration of the sand-carrying fluid have a strong influence on the erosion rate of coiled tubing, and the impact velocity has the greatest influence on the erosion rate of coiled tubing.
3. The SSA-BPNN model is reliable in predicting the erosion rate of coiled tubing, which can provide guidance for coiled tubing hydraulic sand blasting operation.
4. This method can provide a reference for the erosion rate of coiled tubing. In the future, influence factors such as the defect size and working conditions of coiled tubing can be considered.
5. The feature selection of the model is limited, because only five influencing parameters are selected as input parameters to predict the erosion rate of coiled tubing. This indicates that the influence of other parameters on the erosion rate may not be considered, which makes the model unable to fully explain the complexity of the erosion rate. In future model optimization, it is recommended to expand the scope of the dataset and consider a wider range of impact parameters.  
Conceptualization, Y.C.; Methodology, Y.C.; Software, F.F. and G.W.; Validation, G.W.; Formal analysis, G.W. and Y.H.; Investigation, W.Z.; Resources, W.Z.; Data curation, F.F.; Writing--original draft, Y.C.; Writing--review & editing, F.F.; Visualization, F.F.; Supervision, W.Z. and Y.H.; Project administration, Y.C.; Funding acquisition, Y.C. 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, under grant number 52374039 and 52274006, the Natural Science Basic Research Program of Shaanxi, under grant number 2023-JC-YB-337. And the APC was funded by Yinping Cao.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Intra-Reliability of a Wearable Near-Infrared Sensor for Monitoring the Intensity of Exercise
## 4 Discussion
[14] found even higher CV values for intensities of 1.0 W/kg, 2.5 W/kg, and 4.0 W/kg, at 11.0, 22.9, and 43.8%, respectively. On the other hand, [PERSON] et al. [15] compared a healthy group of cyclists and patients with FLA; however, they revealed comparable values of the CV, 6-10% and 5-11%, respectively, for cyclists and patients measured at baseline, post-exercise recovery, and maximal task tolerance. It is important to note that we demonstrate lower values of the SEM (2.2-3.4 vs. 5-7) and MDC (6.1-9.7 vs. 12-18) for SmO\({}_{2}\) than previously published [14]. Both parameters are important and useful for sport practitioners, because when considering daily variation, absolute reliability is quantified by the SEM, while the MDC is used for interpreting the results of a longitudinal training intervention. Changes within a range of \(\pm\) SEM may be meaningful for an individual person but should not be considered to represent a significant difference [15], whereas regular training programs can be considered to have a significant effect on SmO\({}_{2}\) if the values measured at a given absolute workload change by more than the MDC [15]. In our research, in the Bland-Altman plots, the limits of agreement were slightly lower than those reported by [PERSON] et al. [15] for both examined groups. According to our best knowledge, this is the first study to report the sensitivity for SmO\({}_{2}\) analysis for a wearable NIRS sensor. The SWCs were below the SEM for all analyzed SIET intensities and indicated marginal sensitivity to detection changes. The cohort in the current study was physically active males but those not experienced at cycle training or training for a specific sport. Therefore, they may have demonstrated sufficiently large within-participant performance variation, which did lead to larger within-participant variability (SEM value), with resulting impacting on test sensitivity. Research with a larger sample size would have been interesting to analyze \"high variability\" and \"low variability\" cohorts [33]. In summary, the present results suggest that SmO\({}_{2}\) derived from the Q-LAC sensor is capable of assessing differences between subjects and may have the potential to monitor training responses, but the sensitivity of this device should be improved.