esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
For the study, some conclusions have been summarized as follows:  
* Assessing the degradation of bulk biomass materials in pneumatic conveyors can be challenging because the particle velocity in pipe bends can be significantly different, subject to the type of the materials.
* The degradation level of solid particles increases with an increased particle velocity in pneumatic conveyors because of an increased air mass flow rate. Degradation of the materials can decrease with an increased solid feed rate or particle concentration in the pipeline.
* Degradation of wood pellets shows that the degradation can be different at the same operation conditions because different moisture contents and surface properties of the material lead to different particle accelerations. The degradation in a pneumatic conveying pipeline is sensitive to particle impact velocity, although particle concentration has little influence.
* Degradation of dry roasted coffee beans shows a significantly different level at an identical operation condition. It is believed that the conveying characteristics of coffee beans must be different from the wood pellets, which may be due to a low bulk density.
* The study shows the degradation of biomass materials in a pneumatic conveyor strongly depends upon the particle velocities, which can be subject to the conveying characteristics of the material.
* If an assessment is required to evaluate the degradation in pneumatic conveyors, it is hard to take the assessment on an industrial scale equipment. A laboratory scale test can be a solution for advanced degradation assessments in pneumatic conveying systems. However, impact conditions such as particle velocities can be critical for an accurate evaluation, which needs to be determined in advance.  
Conceptualization, T.D., M.S.A.B. and R.E.; methodology, T.D. and G.S.; validation, M.S.A.B. and R.E.; formal analysis, G.S.; investigation, G.S., M.S.A.B. and R.E.; data curation, G.S.; writing--original draft preparation, G.S. and T.D.; writing--review and editing, T.D., M.S.A.B. and R.E.; supervision, T.D., M.S.A.B. and R.E.; funding acquisition, M.S.A.B. and R.E. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Schenck Process UK and the Wolfson Centre for Bulk Solids Handling Technology, University of Greenwich, UK, for a Ph.D. study.  
Data Availability StatementData was collected during PhD study and will be available in the form of PhD thesis.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [PERSON] is an employee of Schenck Process UK, who provided funding and technical support for the work. The funder had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# Performance of Solar Control Films on Building Glazing: A Literature Review
## 4 Existing Studies on the Performance of Solar Control Films  
A web-based search of existing studies was carried out covering scientific engineering databases such as Science Direct, Scopus and Google Scholar. Very scarce studies have investigated the impact of solar control films (SCFs) on the performance of buildings using numerical [34; 35; 36; 37; 38; 39; 40], experimental [19; 50; 51] or computer simulation [35; 43; 44; 45; 46; 48; 49; 52; 53; 54; 55; 56; 57] approaches, singly or in combination.  
Table 2 resents a brief description of the existing studies that assess the performance of SCFs installed in building glazing organized by the type of climate (Koppen-Geiger climate classification [58]: Aw, tropical monsoon; Bsk, cold arid steppe; Bwh, hot desert; Cfa, humid subtropical with a hot summer; Cfb, temperate oceanic; Csa, hot-summer Mediterranean; Cwa, monsoon-influenced humid subtropical; Dfb, warm-summer humid continental). These studies generally conclude that the presence of SCFs increases the energy efficiency of buildings and improves the thermal and visual comfort of occupants due to the reduction in extreme solar gains and control of potential glare.  
Figure 7: Ranges of values of the main thermal and solar optical properties of clear double glazing with and without different types of SCFs applied to the internal (**a**) and external (**b**) glass surfaces.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Climate\({}^{\mathbf{1}}\)** & **Research Studies** & **Glazing** & \multicolumn{4}{c}{**Research Approach\({}^{\mathbf{2}}\)**} & \multicolumn{4}{c}{**Performance Assessment\({}^{\mathbf{3}}\)**} & \multicolumn{4}{c}{**Confort Assessment\({}^{\mathbf{4}}\)**} \\ \cline{2-13}
[SS] & **Single** & **Laminated** & **Double** & **Num.** & **Exp.** & **Sim.** & **Therm.** & **Lum.** & **Energy** & **Econ.** &  
This section comprises the main findings of previous research studies regarding the thermal, luminous, energy, economic and environmental performance and comfort assessment of SCFs when installed in building glazing systems.
## 3 Results  
### FTIR Analysis Results  
FTIR analysis was performed to identify and characterize the molecular vibrations and functional groups present in the calcined and uncalcined clam shell samples. The main objective was to obtain key information about their chemical structure and to observe possible changes in their composition after specific treatments, such as calcination, which affects the structure of the material. The FTIR analysis result is presented in Figure 6a for the uncalcined clam shell sample and in Figure 6b for the calcined clam sample.  
The band initially observed in the uncalcined clam powder spectra at 709 cm\({}^{-1}\) were found to persist in the calcined clam powder with a slight shift in wavenumbers. These bands are typical characteristics of (CO\({}_{3}\))\({}^{2-}\) functional group and the formed new carbonates in the activated systems [30; 50; 51]. The 1080 cm\({}^{-1}\) band in the uncalcined clam shell confirms the presence of calcite, and its disappearance in the calcined clam shell can be explained by the structural transformation of the unreacted CaCO\({}_{3}\) as well as the formation of new carbonates [51]. The peak around 1482 cm\({}^{-1}\) arises from the asymmetric stretching vibrations and out-of-plane vibrations of CO\({}_{3}\)\({}^{2-}\) in calcium carbonate [50; 51]. The 1787 cm\({}^{-1}\) and 1805 cm\({}^{-1}\) peaks of both samples indicate symmetric CO\({}_{3}\) stretching plus symmetric CO\({}_{3}\) deformation [50]. The bands at 2518 cm\({}^{-1}\) and 2522 cm\({}^{-1}\) are attributed to the C-O asymmetric stretching mode in the CO\({}_{2}\) molecule [30]. The bands at 2920 cm\({}^{-1}\) and 2978 cm\({}^{-1}\) are attributed to the asymmetric stretching in the CO\({}_{3}\) molecule [50]. In addition, the bands 3642 cm\({}^{-1}\) in the calcined clam shell and 3744 cm\({}^{-1}\) in the uncalcined clam shell represent a stretching vibrational band of -OH in silicates that have more intensity at the calcined clam shell [52; 53].  
Figure 5: Mechanical characterization: (**a**) tensile flexural test and (**b**) compression test.
# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 3 Proposed Method
### Loss Function  
Our network is an end-to-end deep learning system. The image segmentation task turns into a classification task for each pixel in the image eventually. Compared with the natural image segmentation, the medical image segmentation task is relatively simple, and it is mostly a two-class problem. It only needs to segment the background and target pixels. However, the proportion of target pixels in the whole image may be much less than that of background pixels, thus causing a class imbalance problem. The most commonly used loss function is cross-entropy, but it may not be the best choice for class imbalance problems. Dice loss can alleviate the class-imbalance problem because it is insensitive to the number of foreground or background pixels. However, dice loss will affect back propagation adversely and can make training unstable. In this paper, binary cross-entropy and dice loss are adopted to jointly optimize the training learning. Under the constraint of improved loss function, our network updates the neuron parameters through gradient descent, and the parameters of the network are optimized through continuous forward propagation and back propagation. Our loss function is formulated as follows:  
\[L(p,g)=L_{bce}(p,\ g)+L_{dice}(p,g), \tag{10}\]  
where \(p\in\mathbb{R}^{H\times W}\) denotes the predicted image and \(g\in\mathbb{R}^{H\times W}\) denotes the corresponding ground-truth. \(L_{bce}\) is the binary cross-entropy loss and \(L_{dice}\) is the dice loss. \(L_{dice}\) is given as follows:  
\[L_{dice}(p,g)=1-\frac{\sum_{i}^{H\times W}2p^{i}g^{i}+\theta}{\sum_{i}^{H \times W}\left(p^{i}\right)^{2}+\sum_{i}^{H\times W}\left(g^{i}\right)^{2}+ \theta}, \tag{11}\]  
where \(\theta\) is a Laplace smoothing item to avoid the case where the denominator is 0 during division. In this paper, we set \(\theta=1\). \(i\) is the position of the \(ith\) pixel.
# Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept
## 2 Relaxed Separate-and-Conquer Rule Learning approach
\begin{table}
\begin{tabular}{|c|l|c|} \hline
**Alarm** & \multicolumn{1}{c|}{**Rules**} & **Precision** \\ \hline \multirow{3}{*}{1017} & \((\textit{TEMP\_E}_{1}\)\(\in\)[166.75,193.43)\(\land\)\(\textit{TEMP\_E}_{4}\)\(\in\)[180.43,194.98])\(\wedge\)\(\textit{TEMP\_P}\)\(\in\)[1015,17.34]) & 93\% \\ \cline{2-3}  & \((\textit{\%\_UGC}\)\(\in\)[0.9.9,13]\(\land\)\(\textit{TEMP\_E}_{1}\)\(\in\)[166.12,196.57]) \(\wedge\)\(\textit{TEMP\_C}\)\(\in\)[44.76,52.21]) \(\wedge\)\(\textit{TEMP\_P}\)\(\in\)[11.69,18.51]) & 89\% \\ \hline \multirow{3}{*}{3042} & \((\textit{TEMP\_C}\)\(\in\)[45.63,57.21])\(\wedge\)\((\textit{TEMP\_E}_{2}\)\(\in\)[174.96,6)) \(\wedge\)\(\textit{TEMP\_E}\)\(\in\)[8.79,8]) \(\wedge\)\((\textit{\%\_E}\)\(\in\)[-12.61,17.54]) & 90\% \\ \cline{2-3}  & \((\textit{TEMP\_C}\)\(\in\)[48.21,54.66])\(\wedge\)\((\textit{\%\_E}\)\(\in\)[-10.26,18.04]) \(\wedge\)\(\textit{\%\_UGC}\)\(\in\)[0.98,10.15]) \(\wedge\)\(\textit{\%\_E}\)[1.13,10.65]) & 92\% \\ \hline \multirow{3}{*}{3167} & \((\textit{Abs\_HmiVis\_CCM\_CORSA\_AP}\)\(\in\)[114.76,712.39]) \(\wedge\)\((\textit{TEMP\_C}\)\(\in\)[37.18,43.76]) & 91\% \\ \cline{2-3}  & \((\textit{Abs\_HmiVis\_CENTR\_PRES\_B}\)\(\in\)[32.20,23]) \(\wedge\)\((\textit{TEMP\_E}\)\(\in\)[173.74,199.32]) & \((\textit{TEMP\_E}\)\(\in\)[173.74,199.32]) & 91\% \\ \cline{2-3}  & \((\textit{TEMP\_E}\)\(\in\)[174.59,192.8]) \(\wedge\)\((\textit{\%\_E}\)\(\in\)[-10.72,18.65]) \(\wedge\)\((\textit{TEMP\_P}\)\(\in\)[22.18,33.78]) \(\wedge\)\((\textit{TEMP\_E}\)\(\in\)[168.76,189.25]) & 88\% \\ \hline \multirow{3}{*}{3197} & \((\textit{Abs\_HmiVis\_CENTR\_PRES\_A}\)\(\in\)[107.72,28.86]) \(\wedge\)\((\textit{Abs\_HmiVis\_EXTR\_PRES\_I}\)\(\in\)[149.76,162.83]) & \multirow{3}{*}{90\%} \\ \cline{2-3}  & \((\textit{TEMP\_P}\)\(\in\)[30.35,39.71]) \(\wedge\)\((\textit{TEMP\_P}\)\(\in\)[31.73,35.12]) \(\wedge\)\((\textit{TEMP\_C}\)\(\in\)[40.81,85.84])\(\wedge\)\((\textit{\%\_E}\)\(\in\)[31.72,73.18]) & 90\% \\ \cline{2-3}  & \((\textit{Abs\_HmiVis\_EXTR\_PRES\_I}\)\(\in\)[15.14,178.52]) \(\wedge\)\((\textit{TEMP\_P}\)\(\in\)[108.62,28.6]) & 91\% \\ \hline \multirow{3}{*}{3099} & \((\textit{TEMP\_C}\)\(\in\)[198.41,415]) \(\wedge\)\((\textit{Abs\_HmiVis\_EXTR\_PRES\_I}\)\(\in\)[178.96,657.1])\(\wedge\)\((\textit{TEMP\_ST}\)\(\in\)[50.07,82.13]) & 92\% \\ \cline{2-3}  & \((\textit{\%\_P}\)\(\in\)[78.13,80.54]) \(\wedge\)\((\textit{Abs\_HmiVis\_EXTR\_PRES\_O}\)\(\in\)[48.133]) \(\wedge\)\((\textit{\%\_ZF}\)\(\in\)[102.1,200]) & 93\% \\ \hline \end{tabular}
\end{table} 
Table 6: Separate-and-Conquer (SeCo) (RUL = 60 s).
# Storing Set Families More Compactly with Top ZDDs
## 2 Preliminaries
### Succinct Data Structures
#### 2.2.2 Trees  
Consider a rooted ordered tree with \(n\) nodes. The information-theoretic lower bound on the size of such trees is \(2n-\Theta(\log n)\) bits. When we traverse a tree by depth-first search  
Figure 3: Example of how ZDDs are utilized to obtain a new ZDD from given ZDDs via a set operation.  
Figure 2: The ZDD obtained from the binary tree in Figure 1 by merging equivalent nodes and deleting redundant nodes. Terminal nodes and branching nodes are depicted by squares and circles, respectively, and \(0\)-edges and \(1\)-edges are depicted by dashed and solid lines, respectively. The family of sets represented by each node is indicated beside it.  
and number each node in the order of the first visit, the number is called the preorder id of the node. When nodes are given ids, we may refer to each node as its number. We want to support the following operations:  
* \(parent(x)\): returns the parent of node \(x\).
* \(firstchild(x),lastchild(x)\): returns the first/last child of node \(x\).
* \(nextsibling(x),previsible(x)\): returns the next/previous sibling of node \(x\).
* \(isleaf(x)\): returns if node \(x\) is a leaf or not.
* \(preorder\_rank(x)\): returns the preorder id of node \(x\).
* \(preorder\_select(i)\): returns the node with preorder id \(i\).
* \(leaf\_rank(x)\): returns the number of leaves whose preorder ids are smaller than that of node \(x\).
* \(leaf\_select(i)\): returns the \(i\)-th leaf in preorder.
* \(depth(x)\): returns the depth of node \(x\), that is, the distance from the root to \(x\).
* \(subtreesize(x)\): returns the number of nodes in the subtree rooted at node \(x\).
* \(lca(x,y)\): returns the lowest common ancestor (LCA) between nodes \(x\) and \(y\).  
**Theorem 3**.: ([14]) _On the word-RAM with word length \(\Omega(\log n)\), the above operations are done in constant time using a \(2n+\mathrm{o}(n)\)-bits data structure._  
We call this the balanced parenthesis (BP) representation in this paper.
## 3 Results and Discussion
### Results of the Two-Level System of Multi-Objective GAs
#### 3.2.2 Results of Level 2: Multi-Objective GA for Measuring the Forecasting Accuracy
The value for \(fitness(MASE)\) is 1.99, while the values for \(fitness(RMSPE)\) and \(fitness(MAPE)\) are 3.7 and 6.84, respectively. With regard to the third population, \(fitness(MdAPE)\) and \(fitness(RMSPE)\) show the same value, 0.13, which is the lowest fitness value for this population. The values for \(fitness(RMdSPE)\) and \(fitness(MASE)\) are 0.82 and 0.55, respectively. The value for \(fitness(MAPE)\) is 7.42, which is the highest value for the third population. Concerning the fourth population, \(fitness(MdAPE)\) and \(fitness(RMdSPE)\) have the same value, 0.52, which is the lowest fitness value of this population. The value for \(fitness(RMSPE)\) is 0.8, while the values for \(fitness(MASE)\) and \(fitness(MAPE)\) are 0.61 and 3.87, respectively; 3.87 is the highest value for this population. The fitness functions are variants. The curve of \(fitness(MAPE)\) is the highest of all the curves of the four populations. \(fitness(MdAPE)\) and \(fitness(RMdSPE)\) have almost equal values over the four populations. The \(fitness(RMSPE)\) and \(fitness(MASE)\) curves show sensitivity to the data according to the populations. Selecting a proper population for the material cost data is also quite difficult due to the variety of fitness values. In this study, we considered the population that was selected by more than two of the fitness functions to have a low forecasting error rate. The tenth generation for the third population was selected as having the lowest forecasting error rate with a suitable selection of input data. The material cost data were selected using three fitness functions, \(fitness(MdAPE)\), \(fitness(RMSPE)\) and \(fitness(MASE)\), with fitness values of 0.13, 0.13 and 0.55, respectively. The multiple fitness functions used in the second level helped in evaluating the forecasted data and in making a judgement on the forecasting method for each object. These models have different sensitivity to the data depending on the calculation method. Therefore, considering all of them is important to find a proper population for forecasting and then a proper generation of data.
# Redefining Archaeological Research: Digital Tools, Challenges, and Integration in Advancing Methods
## 4 Trends in Digital Tools in Archaeology  
A good indicator of trends in archaeological studies using digital tools is the analysis of peer-reviewed publications. Such an analysis synthesizes the current state of the field, helping to identify key trends in the adoption and application of digital tools within archaeology. This article aims to provide a systematic review of the academic landscape concerning the use of these technologies in the field.  
To achieve this, we examined four major scientific databases: ScienceDirect, MDPI, Springer Nature, and Elsevier/Scopus. These are well-respected sources within the academic community, where a significant portion of archaeological research is published. Our search focused on peer-reviewed articles from the last five years (2019-2024), using the keyword \"archaeological studies\" in combination with terms related to digital tools such as GIS, GPS, remote sensing, 3D scanning, photogrammetry, drones, UAVs, aerial photography, and mobile mapping. We chose to exclude other types of publications, such as books, conference proceedings, and reviews, to maintain a focus on peer-reviewed articles. The results of this systematic review are illustrated in Figure 19.  
Figure 19: _Cont._Figure 19: _Cont._  
The analysis results reveal a clear preference for publications in archaeological studies utilizing remote sensing and GISs across all four databases in the last five years. The use of drones has also shown a remarkable increase, with the ScienceDirect database showing 57 publications featuring drones in 2019, rising to 104 publications by 2024. It is important to note that the 2024 figures only account for publications up to September, so the numbers are expected to increase by the year's end.  
Conversely, there has been a noticeable decline in publications related to laser scanning from 2019 to 2024, with a similar downward trend observed for GPS-focused publications during the same period. Meanwhile, papers focusing on photogrammetric tools, aerial photographs, and GPSs have maintained a consistent presence in archaeological studies.  
A significant trend is the increase in publications using mobile mapping, particularly in ScienceDirect, which recorded an increase from 76 in 2019 to 96 publications in 2024. Similarly, the Springer Nature database showed growth in mobile-mapping-related publications, rising from 7 in 2019 to 17 publications in 2024.  
These trends highlight a growing emphasis on technology's transformative role in uncovering, analyzing, and preserving historical artifacts and sites. The focus has shifted toward the development of more user-friendly and accessible tools such as remote sensing, GIS, and mobile mapping, making technology an integral component of modern archaeology.  
Figure 19: Graphs depicting numbers of peer-reviewed publication articles using digital tools in archaeology from 2019 to 2024: (**a**) in ScienceDirect, (**b**) in MDPI, (**c**) in Springer Nature, (**d**) in Elsevier/Scopus.
## 5 Chitosan Nanocarriers in Brain Targeting
### Liposomes
The reduced mucociliary clearance and increased drug retention in the nasal cavity is due to the mucoadhesive properties of chitosan coated liposomes. A 5-fold increase of AUC with a sustained drug release after intranasal administration in vivo was observed when compared to the oral administration of the coated formulation [119]. [PERSON] et al. developed curcumin loaded liposomes coated with chitosan. The entrapment efficiency of the liposomes was 87% with 2.3% drug-loading efficiency [120]. The particle size of the coated liposomes increased to 656 nm when compared to the uncoated liposomes, which were 221.4 nm. The pharmacokinetic parameters and bioavailability of the coated liposomes were (C\({}_{\text{max}}\) = 46 \(\mu\)g/L, t\({}_{1/2}\) = 12 h, AUC = 417 \(\mu\)g/L-h) when compared to the uncoated liposomes, which were (C\({}_{\text{max}}\) = 32 \(\mu\)g/L, t\({}_{1/2}\) = 9.8 h, AUC = 264 \(\mu\)g/L-h) and the curcumin suspension, which was (C\({}_{\text{max}}\) = 35 \(\mu\)g/L, t\({}_{1/2}\) = 3.9 h, AUC = 245 \(\mu\)g/L-h) [120]. The results from researchers indicate the potential of coating liposomes with chitosan in brain targeting.
## 3 Results and Discussion
### Effect of Working Medium  
Since tocopherols and tocotrienols are lipophilic compounds, they are soluble in polar organic solvents (DMF, PC, EtOH, and MeCN). The above-mentioned solvents and their aqueous/organic mixtures containing 0.1 mol L\({}^{-1}\) LiClO\({}_{4}\) were examined to find a compatible working medium in which the anodic peaks will be well-defined. According to the results shown in Figure 3A, two broad anodic peaks with low current responses were observed in DMF and PC. In contrast, three overlapping peaks were observed in the case of using EtOH and MeCN, where the latter represents the best choice due for high resolution of individual peaks. Figure 3B points to the fact that the presence of water in organic solvents caused a shift of anodic peaks to more negative potentials and significantly decreased their current responses. This phenomenon can be attributed to easier deprotonation of hydroxyl group in aqueous environment than in pure organic solvents [29].  
Generally, it is known that the presence of surfactants increases the solubility of non-polar substances in polar organic solvents and facilitates the charge transfer at the working electrode due to their ability to reduce surface tension. For that reason, the effect of different contents (0.1, 0.01, and 0.001 mol L\({}^{-1}\)) of nonionic (Triton X-100), anionic (SDS), and cationic (CPC and CTAB) surfactants on peaks separation was investigated. It was observed that presence of surfactant did not improve overlapping of adjacent signals and rather caused a deterioration in peak current responses (see Figure S1 in the Supplementary Materials). Finally, the effect of electrode material and composition of working medium on anodic peak potentials of tocopherols are included in Table 1.  
Figure 3: SWVs of 50 \(\mu\)mol L\({}^{-1}\) tocopherols obtained at GCE in different organic solvents (**A**) and different content of water (**B**) containing 0.1 mol L\({}^{-1}\) LiClO\({}_{4}\) at \(E_{\text{step}}\) = 1 mV, \(E_{\text{ampl}}\) = 25 mV and \(f\) = 20 Hz, the potential was scanned from 0.0 to 1.0 V.