esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Identification of Development Status of MASS Technologies and Directions of Improvement
## 5 Hazard Analysis for MASS
While the number of studies that apply or discuss FRAM for autonomous ship hazard analysis is comparatively lower than that of STPA, these studies support that FRAM can also be a possible candidate method to analyze the hazards of autonomous ships. In addition to STPA and FRAM, there have been several studies that discuss applying a new method or model to analyze the hazards of autonomous ships. [PERSON] et al. [76] explored the applicability of system modeling language (SysML) and object process methodology (OPM) to autonomous ships. [PERSON] et al. [15] investigated risk-informed design, goal-based safety case approach, and STPA for the risk assessment of autonomous ships. [PERSON] et al. [16] analyzed 64 risk models for ship collision and groundings to examine their applicability to autonomous ships, and [PERSON] [77] reviewed the Bayesian belief network (BBN), the human factors analysis and classification system (HFACS) framework, and STPA, and discussed the future of risk assessments of autonomous ships. All studies explored above support the fact that traditional hazard analysis methods and accident models are not sufficient to analyze hazards and ensure the safe operation of autonomous ships. We need to apply a new hazard analysis method or use new methods along with traditional methods, and further studies must be carried out to identify optimal method(s) for hazard analysis of autonomous ships and to standardize the hazard analysis procedure of autonomous ships in the maritime industry.
## 2 Materials and Methods  
### Pyrolysis Test and Char Characterization  
The cork powder used came from the smoothing phase in the production of agglomerated cork caps and was directly collected through a cyclonic air-filtering system. The average diameter of the powder particles was in the range 0.063 mm \(<\) d \(<\) 1 mm. The ratio of virgin cork and additives (polyurethane glue, paraffin and others) was 83/17.  
The prototype pyrolyser used consisted of a small cylindrical chamber made of stainless steel and operated in a retort configuration [13; 14]. The pyrolyser was heated by means of two electric resistors (nozzle heater) connected to a thermocouple for the measurement of the temperature inside the cylinder (Figure 1). Multiple tests were carried out in order to calibrate the pyrolysis process. The various tests were performed by modifying the parameters of the temperature control strategy implemented in an Arduino relay board; the control algorithm allowed the temperature trend to be as linear as possible. The residence time at the target temperature (400 \({}^{\circ}\)C or 600 \({}^{\circ}\)C) was set to 30 min. These operating conditions were consistent with slow pyrolysis; that is, the pyrolysis mode in which char yield is maximized at the expense of liquid and gas production [15].  
Tar and particulate contents in the pyrolysis gas were estimated with a simplified procedure deriving from the Guideline for Sampling and Analysis of Tar and Particles in Biomass Producer Gases [16]. The gas produced flowed through a series of impinger bottles filled with acetone, after which distillation was performed [17]. Particulate matter larger than 7 \(\upmu\)m was measured by filtering the acetone with Whatman quantitative filter paper 1452-150, separating the particles from the solvent.  
In order to determine if flushing with an inert gas would create a better distribution of the temperature in the pyrolysis chamber, a flushing test with CO\({}_{2}\) was carried out (Figure 2).  
Figure 1: Pyrolyser setup: (**a**) picture; (**b**) scheme.  
Figure 2: Fluxed pyrolyser setup: (**a**) picture; (**b**) scheme.  
In this test, one of the two resistances was moved from the pyrolyser and placed on a spiral pipe in order to heat up the flow of CO2 that flowed inside. Two flow meters were positioned to measure the input and the output flows of CO2 together with the produced gases. To avoid the cork, which was of fine size, being transported away by the CO2 flow and moved up to the flasks, a metal mesh was placed in the closing section of the pyrolyser.  
Elemental analysis (using a Flash 2000 Organic Elemental Analyzer) and ash analysis were performed on both the dried cork powder and the obtained char in order to establish the percentages of C, H, N, S and ash.
# An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming
## 4 Experimental Evaluation  
### Experimental Setup  
To make the experiment more convincing, a high-quality defect dataset is needed to evaluate the effectiveness of automated defect repair methods. Defects4 [20] has been one of the most popular defect datasets for evaluating Java systems, as it extracts datasets from large-scale Java open-source projects in reality and covers almost all types of defects. Due to its ease of use and reliable framework, Defects4 provides corresponding test cases for each defect, making it a benchmark dataset for most automated defect repair methods in experimental evaluations. In this section, we adopt the Defects4 defect dataset for the experimental evaluation phase, which allows for a good comparison of the repair effect with other repair methods. As shown in Table 3, we have selected six of the most frequently used projects: Chart, Closure, Lang, Math, Mockito, and Time, with a total of 395 defects. This section will verify the performance of the repair method through experiments in terms of the number of repaired defects, the time required to repair defects, and other factors. We have chosen several popular repair methods such as Genprog, Simfix, ARJA, and Nopol to compare with MGPRepair.  
Figure 5: Test Case Filtering Framework Diagram.  
To keep the experiment within acceptable limits, in this method, the initial population size N is set to 40, the maximum number of population evolution iterations is set to 50, the probability of crossover is 1, and the mutation probability is set to 0.1. The maximum number of modifications per variant is set to n = 100. After numerous experiments, it was found that when the minimum suspicion threshold is set to 0.2, it can effectively detect defects while better balancing the situations of false positives and false negatives. If this threshold is lowered, it may lead to more false positives, that is, some locations that are unlikely to be defects are also marked as suspicious, thereby increasing the workload of subsequent processing. If the threshold is increased, some real defects may be missed, affecting the repair effect. Setting \(\omega\) to 0.5 can better balance the importance of the two objective functions in Formula (4). If the value of \(\omega\) changes, it will change the weights of the two objective functions in the optimization process, thereby affecting the decision-making of the NSGA-II algorithm in multi-objective optimization.
## Materials and Methods
### Data Source and Processing  
Landsat 8 OLI imageries with 30 m spatial resolution and \(<\)10% cloud cover from January to December in 2013 were downloaded from the Geospatial Data Cloud. In particular, although the OLI 2013-12-06 image presented 49.93% cloud coverage, the study area was clear and without cloud cover for all image data. Other image data in the same month from adjacent years (2014-2016) were selected to replace certain months that exhibited insufficient image data or substantial cloud cover. Table 1 shows the classified parameters of these remote sensing images. Radiometric calibration and atmospheric correction through the tools of Radiometric Correction in ENVI 5.1 were used to handle the data. One remote image of resource satellite three-01 A with 2.1 m spatial resolution (November 28, 2013) was interpreted into five land-use types through supervised classification in the ENVI 5.1 tool (a method of SVM classification based on fuzzy recognition) with an overall accuracy of 90.27% and a kappa coefficient of 0.827. The land-use types were cultivated, forest, and construction land, waters, and other land (Figure 1). For Landsat 8 OLI, the best representations of vegetation growth and variable, visible red band 4 (630-680 nm) and near-infrared band 5 (845-885 nm), respectively, were used to construct an NDVI model. This index was calculated as follows:  
\[\mathrm{NDVI}=\frac{(\mathrm{Band5-Band4})}{(\mathrm{Band5+Band4})}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Num.** & **Date** & **Path/Row** & **Cloud Cover (\%)** & **ID** \\ \hline
1 & 2014-01-23 & 123/39 & 19.72 & LC81230392014023 LGN00 \\
2 & 2016-03-01 & 123/39 & 3.87 & LC81230392016061 LGN00 \\
3 & 2015-03-31 & 123/39 & 8.99 & LC81230392015090 LGN00 \\
4 & 2013-04-26 & 123/39 & 2.25 & LC8123039201316 LGN01 \\
5 & 2013-05-28 & 123/39 & 1.49 & LC81230392013148 LGN01 \\
6 & 2013-06-13 & 123/39 & 0.28 & LC81230392013164 LGN00 \\
7 & 2013-07-31 & 123/39 & 1.22 & LC81230392013212 LGN00 \\
8 & 2013-08-16 & 123/39 & 13.88 & LC81230392013228 LGN00 \\
9 & 2013-09-17 & 123/39 & 0.12 & LC81230392013286 LGN00 \\
10 & 2015-10-25 & 123/39 & 0.203 & LC81230392015298 LGN00 \\
11 & 2015-11-26 & 123/39 & 6.70 & LC81230392015330 LGN01 \\
12 & 2013-12-06 & 123/39 & 49.93 & LC81230392013340 LGN00 \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of remote sensing images.  
Figure 1: Location of the study area and spatial distribution of the calibration (407) and validation (271) datasets.  
Many studies have shown that crop production is related to temperature and precipitation [54; 55; 56]. Figure 2 shows the small variation of temperature and precipitation in the study area from different years (2013-2016). Thus, the monthly data are close to the long-term average, and the data in Table 1 could be considered an artificial time series.
## 3 Methodology
### Histogram Equalization  
Histogram equalization is a technique used in image processing to enhance the contrast and improve the overall appearance of an image by redistributing the intensity values across the image. Dark and light regions in an image might not have optimal contrast, making details hard to perceive. Histogram equalization spreads out the intensity levels, making darker areas darker and brighter areas brighter.  
The histogram of a digital image with intensity levels in the range [0, L \(-\) 1] is a discrete function \(h(r_{k})=n_{k}\), where L is the number of the level, \(r_{k}\) is the \(k\)th intensity value, and \(n_{k}\) is the number of pixels in the image with intensity \(r_{k}\). It is common practice to normalize a histogram by dividing each of its components by the total number of pixels in the image, which is denoted by M \(\times\) N, where M and N are the row and column dimensions of the image. A normalized histogram is given by  
\[p(r_{k})=\frac{n_{k}}{MN}\text{ for }k=0,1,2,...L-1 \tag{1}\]where \(p(r_{k})\) can be seen as an estimate of the probability of the occurrence of intensity level \(r_{k}\), in an image. Then,  
\[\sum_{k=1}^{L-1}p(r_{k})=1 \tag{2}\]  
Consider the continuous intensity values and let the variable \(r\) denote the intensities of an image. We assume that r is in the range [0, L \(-\) 1]. We focus on transformations (intensity mappings) of the form s = T(r), where \(0\leq r\leq(L-1)\), which produces an output intensity level \(s\) for every pixel in the input image with intensity [74].  
Figure 2 shows an image after histogram equalization. Figure 2A displays the original image from the KDEF dataset. Figure 2B depicts the corresponding histogram, illustrating the distribution of pixel intensities. Subsequently, Figure 2C exhibits the image post-histogram equalization, a process aimed at enhancing contrast and brightness. Finally, Figure 2D shows the resulting histogram, reflecting the altered intensity distribution after equalization. In Figure 2B,D, the X-axis denotes the intensity values, while the Y-axis represents the pixel counts.
## 4 Efficient Band Ordering
### Band Ordering  
The proposed approach can be sub-divided into three step:  
* _Graph_ creation
* _Minimum Spanning Tree_ computation  
Figure 9: Lunar Lake Scene 01, Band 160.  
* _Depth-First Search visit_  
The first step consists of making a dependence graph \(\mathrm{G}=\mathrm{(V,\,E)}\), where each vertex denotes a band and each vertex \(i\) is connected, by a weighted edge, with each other vertex \(j\) (where \(i\
eq j\) ).  
An edge \((i,j)\in\mathrm{E}\) is weighted as:  
\[w(i,j)=\mathrm{distance}\text{ between the band }i\text{ to the band }j\]  
Now, we are able to compute the Minimum Spanning Tree (MST) on the graph \(\mathrm{G}\).  
MST allows us to associate each band \(i\) (vertex \(i\) in the graph \(\mathrm{G}\)) to its minimum distance band \(j\), therefore to the band where the distance between the band \(i\) and the band \(j\) is minimal.  
The last step consists into computing a Depth First Search (DFS) visit on M. This gives a band ordering, where M is a MST on graph \(\mathrm{G}\).  
In some cases DFS does not work correctly for our purposes.  
In the simple example below we see a case in which DFS does not work correctly for band ordering.  
In Figure 10 the band ordering given by DFS on MST M is \(a\), \(b\), \(c\). _i.e._, SLSQ predicts the band \(b\) from the band \(a\) and the band \(c\) from the band \(b\), but the band \(b\) and the band \(c\) are very low related with respect to band \(b\) and band \(a\). Therefore it is evident that this band ordering is incorrect.  
For these cases we need to modify our approach and introduce band ordering based on DFS and _pairs_. A pair is defined as:  
\[<\]  
_previous band in the ordering_, _current band in the ordering_ \[>\]  
In the previous example the band ordering given by a modified DFS visit is therefore \[<\]  
\[a\]  
, \[b\]  
\[>\]  
, \[<\]  
\[a\]  
, \[c\]  
, this means that SLSQ predicts the band \(b\) from the band \(a\) and the band \(c\) also from the band \(a\), and this is now a correct band ordering.  
Figure 11 shows an example in which the DFS visit on the MST M gives a correct band ordering.  
Figure 10: Incorrect band ordering given by DFS on MST M.  
As we can see in Figure 11, the band ordering given by the DFS on MST M is correct and the band ordering given by a modified DFS on MST M is \(<\)_a_, \(b\)\(>\), \(<\)_b_, \(c\)\(>\) that is also correct.  
Others approaches of band ordering may be found in [21].
###### Abstract  
This study explored feeding recycled food waste-based diets for gut health, nutrient digestibility, bone morphology, and bone mineral level in laying hens. Hy-Line Brown laying hens (\(n=150\)) were randomly allocated to three experimental treatments consisting of a commercial control diet, a recycled food waste-based diet, and a 50:50 blend of the control and food waste-based diets, with 50 replicates of a single bird per cage per treatment from 24 to 63 weeks of age. Egg production was recorded daily and feed intake was measured weekly. The gut pH, jejun and ileal morphology, nutrient digestibility, bone morphology, and mineral composition were measured at 63 weeks of age. Hens on the food waste-based treatment had similar egg production but lower feed conversion ratio (FCR, 1.948 vs. 2.172 kg feed/kg egg, \(p<0.001\)) and higher ileal pH (\(p<0.001\)) and bone ash content (\(p<0.001\)) compared to birds on the control treatment. Moreover, hens fed the food waste-based diets had higher ileal digestible energy (\(p<0.001\)); ileal energy digestibility (\(p<0.01\)); tibia S, Fe, Mn, and Zn levels (\(p<0.05\)); and Mg, K, S, Mn, and Mo digestibility (\(p<0.05\)) compared to hens fed the control diets. Hens offered the 50:50 blend diets had higher tibia P, Mg, and Mo levels (\(p<0.05\)) and higher Ca digestibility (\(p<0.05\)) compared to those fed the control diets. Thus, feeding recycled food waste-based diets is effective to improve laying performance, nutrient digestibility, and bone mineralization in laying hens.  
chicken; digestibility; poultry nutrition; food waste; mineral Article
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 3 Proposed Method
### Proposed Network Training
As mentioned earlier, we aimed to develop a rough architecture for an optimized design by utilizing a selected subset of the dataset. The optimization analysis, based on cost function calculations, showed that the impact of variations in the num head and transformer stages did not significantly increase as the number of epochs increased. Additionally, when considering training time and complexity, the number of selectable stages may be limited. On the other hand, variations in transformer layers and embedding dimensions resulted in significant performance changes. Among these, the number of layers was found to have the most significant impact on performance. Figure 9 presents the results of this analysis. The increase in the number of layers and dimensions is directly related to the number of network parameters and the complexity of modeling. It is presumed that this contributes to the extraction of key information from time-series data, leading to improved accuracy. Based on this information, network design was carried out by additionally considering training and processing time, incorporating the values of stage and head. The network parameters obtained through this optimization process are presented in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Initial learning rate & 0.0002 \\ Transformer stage & 2 \\ Transformer layer & 8 \\ Embedding dimension & 80 \\ Num head & 6, 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the selected network. Subsequently, the selected network structure was used to train a model for predicting battery voltage using the training dataset. After the training phase, the transformed values were restored to their original scale via normalization adjustments. Based on the restored values, the UKF algorithm was applied to estimate the SOC and \(\rm{R_{i}}\). Finally, prediction performance was evaluated, and continuous learning was performed as the quantity of training data increased over the satellite's operational period.
## 6 Conclusions and Future Work
### Future Work  
The OMF-enabled test environment presents several opportunities for future research to enhance its configurability, functionality, and scalability. A key area of focus is the integration of additional measurement tools to address further network metrics, such as packet duplication, reordering, and buffer utilization. These enhancements would extend the environment's utility to scenarios requiring precise evaluations of network behavior under controlled conditions, such as congestion control mechanisms and traffic prioritization strategies. Furthermore, support for more diverse configurations, such as asymmetric links and variable network delays, would broaden the testbed's scope, particularly for applications in IoT, smart city deployments, and latency-sensitive systems.  
Incorporating machine learning (ML) and artificial intelligence (AI) techniques could optimize experiment configuration and data analysis. For example, ML-driven anomaly detection could identify irregularities in real time, while adaptive experiment scheduling could dynamically adjust parameters based on network conditions. These advancements would enhance the accuracy and efficiency of network simulations.  
Federated testbeds, which connect independently managed OMF-enabled networks, could enable large-scale, geographically distributed experiments. This approach would support studies on wide-area networks, latency, and resource synchronization while fostering cross-organizational collaboration.  
Lastly, integrating security tools, such as intrusion detection systems and encryption protocols, alongside privacy-preserving data handling, would enable the testbed to address critical network security challenges. Continuous feedback from the research community will ensure the platform adapts to emerging research trends and technologies.  
Conceptualization, F.A.; methodology, F.A. and M.K.; formal analysis, M.K.; investigation, F.A. and M.K.; project administration, F.A.; supervision, F.A.; writing--original draft, F.A.; writing--review and editing, F.A. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company SABIS Bilgi Teknologileri ve San. Tic. Ltd. Sti. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Methodology
### Determinations of Experimental Data  
#### 2.1.1 Determination of \(R_{A}\) and \(K_{G}a\)  
In a bubble-column scrubber, a gas mixture containing CO\({}_{2}\) (A) and N\({}_{2}\) (B) flowing into a bubble column from the bottom comes into continuous contact with the liquid solvent flowing into the column from the top. The gas mixture and liquid simultaneously come into contact within the column counter. If a binary A-B system is assumed, then \(R_{A}\) and \(K_{G}a\) can be obtained using the mass balance with two-film models at a steady-state condition [2]. Therefore, \(R_{A}\) can be written as follows:  
\[R_{A}(\text{mols}^{-1}\text{L}^{-1})=\frac{F_{A1}(\text{mol}/\text{s})}{V_{L}( \text{L})}[1-(\frac{1-y_{A1}}{y_{A1}})(\frac{y_{A2}}{1-y_{A2}})] \tag{7}\]  
where \(F_{A1}\) is the CO\({}_{2}\) gas molar flow rate, which can be evaluated when \(Q_{g}\) (gas flow rate), \(P_{A1}\) (partial pressure of CO\({}_{2}\) at inlet), and \(T\) (liquid temperature in the bubble column) are available. In addition, \(V_{L}\) is the volume of liquid in the scrubber, \(y_{A1}\) is the CO\({}_{2}\) gas molar fraction at the inlet, and y\({}_{A2}\) is the CO\({}_{2}\) gas molar fraction at the outlet. On the other hand, \(K_{G}a\) becomes the following:  
\[K_{G}a(\text{s}^{-1})=\frac{Q_{g}(\text{L}/\text{s})}{V_{L}(\text{L})}\ln\frac {F_{A1}(\text{mol}/\text{s})}{F_{A2}(\text{mol}/\text{s})} \tag{8}\]  
Figure 1: Concept of research framework used in this work.  
Considering the ideal gas law for the inlet and outlet gases, \(F_{A1}/F_{A2}\) could be replaced with \((P_{1}/P_{2})\) (\(T_{2}/T_{1}\)) (\(y_{A1}/y_{A2}\)).