esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results
### Energy Consumption
#### 7.1.1 Etr Calculations  
As a hardware implementation is absent in this context, the energy consumption calculations are derived through initialization with predefined values. This fixed value is determined by considering the energy consumption for transferring one bit of data at the data centre level, estimated to be approximately 0.2 mJ, as referenced in [24]. Consequently, the energy consumption for transferring a single byte of data amounts to 0.2 \(\times\) 8 mJ, equalling 1.6 mJ.  
The data transfer takes place across three tiers: Mist, Fog, and Cloud. Consequently, the total energy consumption for transmitting a byte of data aggregates to 1.6 \(\times\) 3 mJ, which is equivalent to 4.8 mJ or approximately 0.0048 joule, and can be approximated as 0.005 joule. With the data packet size fixed at 1500 bytes, a value that optimizes TCP connection performance as outlined in [25], the energy consumption (Et) required to transmit a 1500-byte packet amounts to 0.005 \(\times\) 1500 joule, which translates to 7.5 joule.  
Extending this computation to encompass multiple packet transfers, the energy consumption for transmitting 20 packets (Et for 20 packet transfer) is determined as 20 \(\times\) 7.5 joule, which equals 150 joule or approximately 0.15 KJ. Analogously, for 40, 60, 80, and 100 packet transfers, the corresponding energy consumptions (ET) amount to 300 joule (0.3 KJ), 450 joule (0.45 KJ), 600 joule (0.60 KJ), and 750 joule (0.75 KJ), respectively.
# New Space Engineering Design: Characterization of Key Drivers
###### Abstract  
The recent evolution of the space industry, commonly referred to as New Space, has changed the way space missions are conceived, developed, and executed. In contrast to traditional approaches, the current paradigm emphasizes accessibility, commercial competitiveness, and rapid and sustainable innovation. This study proposes a research methodology for selecting relevant literature to identify the key design drivers and associated enablers that characterize the New Space context from an engineering design perspective. These elements are then organized into three categories: the evolution of traditional drivers, emerging manufacturing and integration practices, and sustainability and technology independence. This categorization highlights their role and relevance, providing a baseline for the development of systems for New Space missions. The results are further contextualized within three major application domains, namely Low Earth Orbit (LEO) small satellite constellations, operations and servicing in space, and space exploration, to illustrate their practical role in engineering space systems. By linking high-level industry trends to concrete design choices, this work aims to support the early design phases of New Space innovative systems and promote a more integrated approach between strategic objectives and technical development.  
space systems; new space economy; engineering design; design drivers; design enablers +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Probiotic Potential of Lactococcus Strains A13 and A14
#### 3.5.3 Autoaggregation/Coaggregation  
The ability of bacteria to aggregate and co-aggregate determines its ability to create aggregates with possible pathogens present in the intestine. A good probiotic is believed to have these abilities higher than pathogens [40]. Strains A13 and A14, however, showed quite poor aggregation and co-aggregation abilities and needed 24 h to reach aggregation levels of 28 and 38%, respectively, and co-aggregation in the range of 28.9-45.14% depending on the combination (Table 6). _L. lactis_ KC24 isolated from kimchi showed 36.15% aggregation and 29.28-74.11% co-aggregation capacities with pathogens _L. monocytogenes_ and _S. aureus_ after 5 h of incubation [40]. [PERSON] et al. [41] in their study on this phenomenon proved that the ability to autoaggregate and co-aggregate depends on cell surface proteins and some ions.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Bacteria**} & \multicolumn{2}{c}{**Autoaggregation (\%)**} & \multicolumn{2}{c}{**Co-Aggregation (\%)**} \\ \cline{2-4}  & **5 h** & **24 h** & **5 h** & **24 h** \\ \hline _L. lactis_ A13 & 8.52 \(\pm\) 1.43 1  & 28.15 \(\pm\) 0.36 & - & - \\ _L. lactis_ A14 & 7.49 \(\pm\) 2.79 & 38.46 \(\pm\) 3.33 & - & - \\ _S. aureus_ & 12.03 \(\pm\) 3.84 & 45.74 \(\pm\) 5.73 & - & - \\ _S. anatum_ & 31.81 \(\pm\) 1.89 & 66.91 \(\pm\) 0.77 & - & - \\ A13 + _S. aureus_ & - & - & 13.70 \(\pm\) 0.01 & 28.91 \(\pm\) 0.06 \\ A13 + _S. anatum_ & - & - & 27.80 \(\pm\) 0.01 & 38.57 \(\pm\) 0.02 \\ A14 + _S. aureus_ & - & - & 15.76 \(\pm\) 0.02 & 44.57 \(\pm\) 0.01 \\ A14 + _S. anatum_ & - & - & 29.39 \(\pm\) 0.02 & 45.14 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Autoaggregation and co-aggregation abilities of the _L. lactis_ A13 and A14 isolates.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Pathogens**} & \multicolumn{2}{c}{**Antibacterial Activity (Inhibition Zone [mm])**} \\ \cline{2-4}  & _L. lactis_ A13 & _L. lactis_ A14 \\ \hline _Salmonella anatum_ & 12 \(\pm\) 1.1 1d  & 8 \(\pm\) 1.3 d  \\ _Salmonella enterica_ & 13 \(\pm\) 1.0 e  \\ _Escherichia coli_ & 4 \(\pm\) 2.3 a  & 4 \(\pm\) 1.5 a  \\ _Basilla cereus_ & - & - \\ _S. aureus_ & 7 \(\pm\) 2.0 b  & 5 \(\pm\) 2.5 b [ENDFOOTNOTE] \\ _Enterococcus faecalis_ & 11 \(\pm\) 1.2 c  \\ \hline \hline \end{tabular}
\end{table}
Table 5: Antibacterial activity of _L. lactis_ strains against different pathogens.
# A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection
###### Abstract  
Damage detection of structures based on swarm intelligence optimization algorithms is an effective method for structural damage detection and key parts of the field of structural health monitoring. Based on the chimp optimization algorithm (ChoOA) and the whale optimization algorithm, this paper proposes a novel hybrid whale-chimp optimization algorithm (W-ChoOA) for structural damage detection. To improve the identification accuracy of the ChoOA, the Sobol sequence is adopted in the population initialization stage to make the population evenly fill the entire solution space. In addition, to improve the local search ability of the traditional ChoA, the bubble-net hunting mechanism and the random search mechanism of the whale optimization algorithm are introduced into the position update process of the ChoA. In this paper, the validity and applicability of the proposed method are illustrated by a two-story rigid frame model and a simply supported beam model. Simulations show that the presented method has much better performance than the ChoA, especially in dealing with multiple damage detection cases. The W-ChoOA has good performance in both overcoming misjudgment and improving computational efficiency, which should be a preferred choice in adoption for structural damage detection.  
structural damage detection; chimp optimization algorithm; whale optimization algorithm; school sequence; bubble-net hunting mechanism; identification accuracy
## 3 Results and Discussion
### Results for Other Face Datasets
#### 3.2.2 Results for BeautyREC Face Dataset
Table 5 (Figures S7 and S8) displays the results of the confusion matrix for this dataset. Table 5 shows that overall, our proposed method performed better if the data were balanced and used only five images per class/individual. The accuracy difference between the imbalanced and balanced datasets was around 24.51% to 32.16%. We also observed that the imbalanced dataset had a higher recall. However, the balanced dataset had the same recall or lower recall. High recall in a face recognition system means correctly identifying most faces it is supposed to recognize (fewer false negatives). On the other hand, low recall means the system misses more faces than it should recognize (higher false negatives). In the case of financial transactions using face recognition, generally, the system may prefer lower recall but balanced with higher precision, so the financial transactions can be stricter in their matching. The accuracy, precision, and recall results varied vastly (Figure S7). The accuracy range was from 3.3% to 100%, the precision range was from 0% to 100%, and the recall range was from 0.2% to 75%. Meanwhile, a balanced dataset showed a narrower range for accuracy, precision, recall, and lowered misclassification results (Figure S8). Overall, after studying the results and understanding the distribution of the original imbalanced BeautyREC Face Dataset more deeply, a balanced dataset was much preferred for better performance in the recognition system.
## 3 Results and Discussion
### Extraction Kinetics  
Unlike the previous experiments in which extraction was performed immediately after exposure to PEF, this section concerns the analysis of the extraction kinetics of each compound during the 24-h maceration period. The results plotted in Figure 6 were obtained with the optimal values obtained previously (T = 39 us, n = 356, and [C] = 60%). The effect of PEF treatment on the phenol compound was significant, and the effect was more pronounced on total phenols.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Pulse with** & **Pulses Number** & **Ethanol Concentration** & **Phenol** & **Flavonoid** & **Tannis** & **Iter** & **Log(D)** \\ \hline
1 & 31.7183 & 373.281 & 20 & 518.11 & 308.153 & 570.436 & 5001 & 0.9413 \\ \hline
2 & 25.0017 & 370.821 & 20 & 520.402 & 323.891 & 528.312 & 5000 & 0.9515 \\ \hline
3 & 33.7269 & 355.863 & 60 & 597.531 & 348.11 & 536.394 & 5000 & 0.4018 \\ \hline
4 & 74.9998 & 400 & 60 & 438.3 & 324.987 & 575.681 & 5000 & 1.2152 \\ \hline
5 & 38.7139 & 360.684 & 60 & 581.963 & 340.647 & 568.918 & 5000 & 0.2352 \\ \hline
6 & 32.7857 & 369.125 & 60 & 596.869 & 348.717 & 532.862 & 5000 & 0.4419 \\ \hline
7 & 74.9998 & 400 & 60 & 438.3 & 324.987 & 575.681 & 5000 & 1.2152 \\ \hline
8 & 38.9804 & 356.259 & 60 & 582.463 & 340.447 & 568.946 & 5000 & 0.2336 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the optimisation routine.  
Figure 6: Extraction kinetics of the three compounds.(**a**)Total phenols; (**b**)Total flavonoids; (**c**) Condensed tannins.  
The PEF treatment allowed the extraction of approximately 680 mg of total phenol in 30 min, whereas for the control sample, the maximum amount of approximately 520 mg was obtained after 24 h. Regarding flavonoids and tannins, the PEF treatment produced a greater amount of extracted product during the first 4 h of maceration. However, after the 24-h maceration period, the yields from the treated and control samples were almost similar.  
The influence of PEF was significant in these experiments for all three phenolic compounds compared with that observed in the preliminary experiments. This difference was observed because extraction was performed after a maceration period of 30 min (for the first measurement point), whereas extraction in the preliminary experiments was performed immediately after PEF treatment. On the other hand, the _Eucalyptus_ leaves used in these experiments were harvested 2 months after those used in the preliminary experiments.
## 3 RUL Prediction Methodology
### Bayesian RUL Prediction Based on Extended Kalman Filter
#### 3.4.5 Uncertainty Estimation
**Output:** RUL estimates at the prediction time \(\widetilde{rul}_{t_{PT}}\) with its credible interval. On the above basis, the overall procedure for the proposed methodology for the RUL prediction of rolling bearingss can be summarized in Figure 4.
## 4 Bridge Degradation Identification and Model Update Results
### Model Updating Results and Structural Health Assessment  
For continuous rigid frame curved bridges, factors such as the vertical settlement at the pier base, the change in the inclination angle at the pier base, the effects of overall temperature rise and fall, solar radiation temperature, shrinkage and creep, stiffness reduction, and the transverse offset at the pier top are all primary factors that lead to cracks and spatial shape changes during the service life of the bridge. In the modeling process, for Hedong Bridge, this study employs the solid95 element to simulate concrete and the link8 element to simulate prestressed steel. Additionally, the local column coordinate system is utilized for constraint settings and node displacement readings, with the positive x-axis oriented radially outward and the positive z-axis oriented vertically upwards. For different types of bridge piers, constraints are set on the horizontal, longitudinal, and vertical displacements (i.e., x, y, and z directions) of the nodes at the pier base and the corresponding nodes on the bottom surface of the main girder. Furthermore, this paper references relevant static parameters involving bridge material strength parameters, concrete strength, steel strength, geometric parameters, stiffness parameters, load parameters, and boundary conditions to establish the model updating matrix. Firstly, the value range of each static parameter is defined to represent the possible variation interval of the parameter, such as concrete compressive strength \(\sigma_{\text{comp}}\in\left[30,\quad 50\right]\) Mpa; steel yield strength \(\sigma_{\text{yield}}\in\left[400,\quad 600\right]\) Mpa; and elastic modulus \(E_{\text{concrete}}\in\left[25,\quad 35\right]\) Gpa, \(E_{\text{steel}}\in\left[190,\quad 210\right]\) Gpa.  
Furthermore, a finite element model parameter matrix of the bridge before and after service is established, consisting of parameters and their value ranges. This matrix is then used in combination with the \"cracks + deformation\" fused data to update the parameters of the bridge finite element model:  
\[\text{P}=\begin{bmatrix}\text{Parameter}&\text{MinValue}&\text{MaxValue}\\ \sigma_{\text{comp}}&30&50\\ \sigma_{\text{yield}}&400&600\\ E_{\text{concrete}}&25&35\\ E_{\text{steel}}&190&210\\ \vdots&\vdots&\vdots\end{bmatrix} \tag{15}\]
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 3 Experiment and Results
### Results
In extremely low SNR environments, the proposed system significantly outperformed the compared methods by utilizing the complementarity of PR and IRM-BF and made a trade-off to get much better results over individual enhancements. In the case of 7 dB, the proposed system caused performance degradation in terms of fwSegSNR and STOI, possibly due to signal distortions. Specifically, when the original noisy speech was of relatively high quality, the PR model failed to show its superiority in improving the SNR of the desired signal, while the fusion operations introduced extra noise. For IRM estimation, the improvement of fwSegSNR and STOI achieved by each application of the mask was much higher than that of DSB beamformers. The CIRM-F method led to larger improvements in terms of fwSegSNR but suffered from lower PESQ and STOI improvement.
## 3 Results
### Seismogram Conversion--R\({}_{Q}\)-Envelope
#### 3.2.1 R/R\({}_{\text{Q}}\)-Envelope and Seismogram Variability  
Figure 14 shows a real example of an R\({}_{\text{Q}}\)-envelope. It is obvious how the R\({}_{\text{Q}}\)-envelope reduces the intrinsic variability existing in seismograms. Variability is another name for randomness. Rearrangement of random SFs is actually a smoothing, averaging operation.  
What one can see in Figure 14 is a gradually decreasing (monotonic) 'tail' and irregular 'head'. This is natural since the tail is (usually) composed of many small SFs (i.e., \(A_{\text{max}}\)s), while the head is composed of several large SFs (i.e., \(A_{\text{max}}\)s). Therefore, it is more probable  
Figure 12: Absolute bigraph of rectified seismogram from Figure 2, shown in Figure 11, which mesh of edges is similar to combined mesh of twin bigraph from Figure 6.  
Figure 13: When the down bigraph of the twin bigraph from Figure 6 is rotated (inverted), overlapped, and slightly shifted over the top one to the right (to make both bigraphs from the twin graph visually distinguishable), one gets a fused twin bigraph, very similar to the absolute bigraph, formerly produced by seismogram rectification. Close similarity between meshes of edges in these graphs can be noticed. Indexing of input and output nodes of the down bigraph is given in red and blue integers, respectively, to distinguish each other.  
to find an SF among many other SFs whose peak amplitude is close enough to the peak amplitude of currently considered SFs. On the other hand, such probability is low for bigger or big SFs among a small number of big SFs. Because of that, one sees gradual change in the R\({}_{\rm Q}\)-envelope along the tail and indented change along the head.  
This means that the variability across the seismogram population is expected to be more prominent for heads of their R\({}_{\rm Q}\)-envelopes, while the tails'stick together'.  
Taking into account the heavy variability of seismogram duration, either expressed in time units (seconds) or by the number of SFs, namely the slight variability of the R\({}_{\rm Q}\) tail corresponding to less significant SFs, gives the basis for applying the weighting function on the R\({}_{\rm Q}\)-envelope to trim it to the prescribed length, so that the whole population of R\({}_{\rm Q}\)-envelopes, i.e., the whole population of seismograms, can have the same size. The same can be said in terms of bigraph nodes, which will be explained later.
## Appendix A  
### Naming Convention  
The framework adopts a consistent naming convention and folder structure, as shown in Figure A1. Sentinel-1 and Sentinel-2 data are stored in the S1 and S2 folder, respectively, and contain the ROI in the Clipped folder and the corresponding patches in the Patches folder. S1_id and S2_id are the universally unique identifiers (UUIDs) for Sentinel-1 and Sentinel-2, respectively. ROI subsets are numbered in descending order to their overlapped area with the region of interest, i.e., ROI1 is the Sentinel-2 tile that has the largest intersection with the ROI and so on. start_row_px and start_col_px are the top left corner row and column pixel numbers for the cropped patches of the ROI, and row_size and col_size are the number of pixels in the patch for the rows and columns, respectively. The clipped Sentinel-1 tiles are stored in the following manner:  
\[\text{S1\_roi}\text{<ROI\_no>}\text{\_<S1\_id>}.\text{if}\]  
For instance,  
\[\text{S1\_roi1\_2c522712-e4a5-4 bec-a828-4c8d5c0930f4.tif}\]  
The generated Sentinel-1 patches are stored in the following manner:  
\[\text{S1\_<S1\_id>}\text{\_<start\_row\_px>}\text{\_<start\_col\_px>}\text{ \_<row\_size>}\text{\_<col\_size>}.\text{if}\]  
For instance,  
\[\text{S1\_2c522712-e4a5-4 bec-a828-4c8d5c0930f4\_0\_0\_256x256.tif}\]  
The generated Sentinel-2 patches are stored following similar naming conventions as Sentinel-1. For collocated Sentinel-1 and Sentinel-2, the patches follow the naming convention shown below.  
\[\text{S1\_<S1\_id>}\text{\_S2\_<S2\_id>}\text{\_<start\_row\_px>}\text{ \_<start\_col\_px>}\text{\_<row\_size>}\text{\_<col\_size>}.\text{if}\]
# Factors Affecting Implementation of Radiological Protection Aspects of Imaging in Radiotherapy
## 3 Results
### Imaging during the Treatment Cycle
#### 3.2.4 Imaging Field of View  
The volume of normal tissue surrounding a tumour target that is irradiated during imaging is determined by the size of the X-ray field used. This should be restricted to the minimum to achieve the required accuracy of target positioning with the need to ensure that appropriate markers are visible on the images. All centres in Cyprus, Malaysia and Egypt used standard adult protocols provided by the vendors, with limited adjustment of field size for individual patients (Table 3). Between 38% and 60% of the radiotherapy centres surveyed in other countries adjusted the field size for individual adult patients [14]. A slightly greater proportion of the centres performing paediatric treatments adjusted field sizes for individual patients.
## 4 Methodologies and Implementation
### Comparison with Other Segmentation Methods
#### 4.2.3 Adaptive and Dynamic Segmentation  
Deep learning models are static once trained and may struggle with variations in image characteristics. They lack adaptability during runtime, which can limit their performance in handling diverse medical imaging scenarios [78,79].  
DeepSegDDPM introduces adaptability through DDPM, which dynamically adjusts its parameters during pre-processing based on image characteristics. This adaptive na  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research** & **Purpose** & **Method** & **Database** & **Accuracy (\%)** & **Advantages** \\ \hline
[71] 2023 & High efficient 3D MRI volumes segmentation & PatchDDM & 3D brain MRI & 89.9 &
\begin{tabular}{c} Reduce the resource consumption for 3D diffusion \\ models, applied to the total \\ volume during inference while \\ the training is performed only \\ on patches \\ \end{tabular} \\ \hline
[72] 2023 & The need for accurate and consistent segmentation in medical imaging & MedSegDiff-V2 & \
\begin{tabular}{c} Abdominal CT \\ images, Fundus \\ images, Brain MRI \\ images, Thyroid \\ module ultrasound \\ images \\ \end{tabular} & 90.1 &
\begin{tabular}{c} Propose an anchor condition to \\ ensure model stability and \\ introduce the Spectrum-Space \\ Transformer (SS-Fomer) to \\ enhance the interaction between \\ noise and semantic features \\ \end{tabular} \\ \hline
[73] 2023 & \
\begin{tabular}{c} Histopathological image segmentation is a laborious \\ and time-intensive task \\ \end{tabular} & GenSelfDiff-HIS & Head and neck (HN) cancer & 92.65 &
\begin{tabular}{c} Use of self-supervised learning \\ and generative diffusion \\ models \\ \end{tabular} \\ \hline
[74] 2023 & \
\begin{tabular}{c} The scarcity of expert \\ annotations in medical \\ image analysis \\ \end{tabular} & Semi-supervised learning and Diffusion models & Brain MRI images & 75.86 &
\begin{tabular}{c} The method showcases \\ promising performance in brain \\ tumor segmentation, even with \\ a small number of training \\ samples \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._ture allows the algorithm to handle variations and complexities in medical images effectively [80; 81].
## 1 Introduction
However, certain environmental conditions can adversely affect the performance of MICP-treated soils. In regions with hot and humid climates, such as Guangxi and Yunnan provinces in China, cyclic wet-dry conditions can lead to a decline in the bearing capacity of MICP-treated expansive soil bases [24]. This phenomenon poses challenges for the long-term stability and effectiveness of MICP applications in these regions [25]. Additionally, it is important to note that the natural concentration of calcium ions in expansive soils is often low, necessitating the addition of an appropriate calcium source to enhance the production of calcium carbonate precipitates [26]. Despite the promising results of MICP, there is currently limited research focused on determining the optimal concentration of the cementation solution required for the effective MICP treatment of expansive soils. This study aimed to investigate the influence of different cementation solution concentrations on the physical, swelling, and mechanical properties of MICP-treated expansive soils. Furthermore, a series of wet-dry cycling experiments were conducted to analyze the performance degradation of the MICP-treated expansive soils under cyclic environmental conditions. The findings of this study contribute to the development of sustainable and eco-friendly soil stabilization techniques for expansive soil-prone regions, providing insights into the long-term performance of MICP-treated soils under varying environmental conditions.