esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Batch Studies  
#### 3.2.1 Adsorption pH Effect for MB and IBF Removal  
Figure 5 shows the pH effect at pH 2, 4, 6, 8 and 10. The pH of the solution is one of the most vital parameters in adsorption because it influences the pollutant and adsorbent materials' speciation and charge densities [36]. A similar trend was observed for both pollutants. Higher adsorption representing the maximum capacity was observed for MB and IBF at pH 6. Lower sorption capacities were observed at lower pH due to the repulsion force between the adsorbent and pollutants and competition for active sites [37]. As pH increased, greater sorption capacities were observed, and the maximum sorption capacity for MB and IBF were 60.96 and 71.37 mg/g, respectively.
## 2 Materials and Methods
### Microalgae Biomass  
Seven species of microalgae--_Limnospira_ sp., _Dunaliella_ sp., _Lobosphaera_ sp., _Nanochloropsis_ sp., _Odontella_ sp., _Porphyridium_ sp., and _Tetraselmis_ sp.--were selected for this study. All microalgae used in the present work were cultivated by A4F (Lisbon, Portugal) under non-limiting nutrient conditions. Each species was cultured under species-specific conditions using proprietary media developed and optimized by A4F. Although the detailed composition of these media is confidential due to industrial protection, all cultivation procedures followed standard microalgal culture protocols as described by [PERSON] [26], ensuring comparability with widely accepted methods. Only solar irradiance was employed, except for _Dunaliella_ sp., which was cultivated with supplemental artificial light to enhance biomass productivity. _Limnospira_ sp., _Dunaliella_ sp., _Lobosphaera_ sp., _Nannochloropsis_ sp., _Porphyridium_ sp. were cultivated under a semi-continuous regime, whereas _Odontella_ sp. and _Tetraselmis_ sp. were cultivated in a batch regime. The cultivation conditions of pH, temperature, and light are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MicroaIga Species** & **pH** & **Temperature** & \begin{tabular}{c} **Photosynthetically** \\ **Active Radiation** \\ **(PAR, mol m\({}^{-2}\) d\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Industrial Medium** \\ **(A4F)** \\ \end{tabular} & **Culttivation Technology** \\ \hline _Limnospira_ sp. & 9–11 & \(<\)32 & 24.6 \(\pm\) 8.9 & Alkaline & Open cascade raceway \\ _Dunaliella_ sp. & 7–8 & \(<\)26 & 35.8 \(\pm\) 13.1 & Hypersaline & Flat-panel photobioreactor \\ _Lobosphaera_ sp. & 7–8 & \(<\)33 & 46.0 \(\pm\) 6.89 & Freshwater & \
\begin{tabular}{c} Multilayer horizontal tubular \\ photobioreactor (MHT-PBR) \\ \end{tabular} \\ _Nannochloropsis_ sp. & 7–8 & \(<\)30 & 44.0 \(\pm\) 4.64 & Artificial saltwater & Unilateral horizontal tubular \\ _Odontella_ sp. & 7–8 & \(<\)20 & 10.8 \(\pm\) 3.15 & Artificial saltwater &
\begin{tabular}{c} FP-PBR \\ \end{tabular} \\ _Porphyridium_ sp. & 7–8 & \(<\)25 & 37.8 \(\pm\) 7.91 & Artificial saltwater & FP-PBR \\ _Tetraselmis_ sp. & 7–8 & \(<\)30 & 36.7 \(\pm\) 10.9 & Artificial saltwater & FP-PBR \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cultivation conditions of the microalgae biomass.  
The biomass of each strain was harvested by centrifugation, frozen, and subsequently freeze-dried. All biomass samples were stored at \(-20\)\({}^{\circ}\)C before freeze-drying.
# Edge-Nodes Representation Neural Machine for Link Prediction
## 4 Experiments and Results
### Time and Computational Complexity  
We compare the computational complexity and time needed in the process of link representation of the methods WLNM, Node2 vec and ENRNM in Table 5. Since our method combines the edge and nodes' representations so we need to spend a little more time to build link's representation, and our method is a bit more complex than WLNM and Node2 vec. Actually, the time needed of ENRNM to encode link pattern is less than 1 min. Fortunately, our method achieves the best batch accuracy and the smallest batch loss quicker (shown in Figure 7) in the process of training due to the full representation of link. Therefore we always set max epoch as 100 or 150 which is enough to learn a useful model, shortening the time of training largely for ENRNM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Process** & **Computational Complexity/Sample** & **Time Needed (s)** \\ \hline \hline
**WLNM** & edge (link) pattern encoding & \(\mathcal{O}(k^{3})\) & 44.912 \\ \hline
**Node2 vec** & node pattern encoding & \(\mathcal{O}\left(l/k\left(l-k\right)\right)\) & 10.326 \\  & link pattern encoding & \(\mathcal{O}\left(n\right)\) & 0.500 \\ \hline
**ENRNM** & node pattern encoding & \(\mathcal{O}\left(l/k\left(l-k\right)\right)\) & 10.628 \\  & edge pattern encoding & \(\mathcal{O}(k^{3})\) & 46.646 \\  & link pattern encoding & \(\mathcal{O}\left(n\right)\) & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The processes and computational complexity per sample. And we calculate the time needed in the process of link representaion for dataset USAir. \(K\) indicates the nodes’ amount of subgraph. \(l\) denotes the walk length, \(k\) indicates the context size. \(n\) denotes the amount of nodes of the network.  
Figure 6: Weight graph from the input layer to the first hidden layer.  
Figure 7: Batch accuracy (**a**) and batch loss (**b**) varying along with the epoch of dataset USAir.
## 3 System Descriptions and Assumptions
### Cache Chord Mechanism
#### 3.3.2 IP Indexing  
In the ring topology, each server node \(\mathrm{Nod}X\) identifies the first server node encountered in the clockwise direction as its successor node, and the first server node encountered in the counterclockwise direction as its predecessor node. \(\forall\mathrm{Nod}X\) maintains the IP addresses of its successor and predecessor nodes, facilitating rapid access to the resources and cache IP lists of adjacent nodes.  
Based on this structure, any server node \(d\) can determine the application's relative position based on the hash value of the corresponding identifier \(kid_{k}\). By sequentially querying other server nodes in the order of the ring, the corresponding resource can be found in the respective successor node. However, the complexity of this query algorithm is not optimal. Assuming there are \(D\) servers in the ring, the query complexity is \(O(D)\), and in the worst case, all nodes need to be traversed to find the target element.  
To accelerate search processes, Cache Chord introduces the finger table mechanism [27]. Specifically, each server node \(\mathrm{Nod}X\) maintains a table consisting of successor nodes, each with a length of \(\mathrm{m}\). This list includes the IP addresses corresponding to these nodes. The sequence of nodes in this table is subject to \(\mathrm{Nod}\left\{(X+2^{i-1})\bmod 2^{m}\right\}\), where \(i\in\{1,2,...,m\}\). However, if the \(i\)-th server node does not exist in the ring, the node closest to the \(i\)-th position is saved as the \(i\)-th entry in the finger table, denoted as \(\mathrm{FingerTable}(i)\). When \(\mathrm{Nod}X\) seeks to retrieve a resource node \(\mathrm{Key}Y\), it initially searches for the IP address of the resource among its stored successor and predecessor nodes. If the corresponding identifier \(Y\) for the task is not found, the server searches the finger table to locate the server node with a hash value greater than \(Y\) and that is closest to it. If the node with the highest number in the finger table is still less than \(Y\), the process jumps to \(\mathrm{Nod}\left(X+2^{m-1}\right)\), which is \(\mathrm{FingerTable}_{\mathrm{max}}(i)\), and repeats the aforementioned steps until \(Y\) is found, as detailed in Algorithm 1.  
The integration of the finger table and cache IP list mechanisms enables a binary search-like method in Cache Chords, effectively converging the time complexity of resource searches to \(O(\log D)\), where \(D\) is the total number of server nodes. Comparing the complexity of the finger table-based Algorithm 1 with the previously mentioned exhaustive search,according to L'Hopital's rule, we can obtain \(\lim\limits_{D\rightarrow\infty}D^{-1}\log D=0\). This implies that such a nonlinear search method can locate the corresponding resources with fewer execution cycles. Figure 3 shows a simulation of a Cache Chord of size \(2^{7}\), demonstrating the process of server node \(nid_{d}\) mapping to the Cache Chord and searching for application Key78.
## 2 Materials and Methods
### Defining Value Ranges and Quantization  
The next step defines the range (\(M\)) of possible synthetic values assigned to each feature. The range is obtained by analyzing the reference dataset, obtaining the minimum and maximum values and the step-size between different values. For categorical, logical, or ordinal variables, this is straightforward. However, continuous features require a quantization step to convert continuous values into discrete intervals suitable for controlled synthetic data generation. Quantization is a key element for continuous features to allow discrete, controlled synthetic values for comparison and validation.
# Gut Microbiota as an Endocrine Organ: Unveiling Its Role in Human Physiology and Health
## 4 Gut Microbiota Metabolites and Hormonal Alterations in Human Physiology
Notably, this topic is a new field of investigation in this area of research. A total of 114 researchers identified a bacterial protein mimicking the host's hypothalamic peptide using a proteomic approach [135]. Specifically, bacterial caseinolytic protease B (ClpB) showed sequence homology with host peptide a-melanocyte-stimulating hormone. Mice immunised with ClpB exhibit increased levels of the a-melanocyte-stimulating hormone autoantibody and increased food intake compared with control mice [136]. Consistent with these findings, mice force-fed with the wild-type strain of _Escherichia coli_ exhibit altered food behaviours compared with mice administereda strain of _E. coli_ lacking ClpB, thus confirming the involvement of ClpB in the modulation of the host's food pattern [118]. In humans, eating disorders are associated with autoantibodies against neuropeptides, consistent with the higher plasma ClpB levels detected in patients with eating disorders compared to healthy subjects [137]. The same team of researchers also suggested an autoantibody-independent role for ClpB in modulating food intake. Indeed, a colonic infusion of proteins from _E. coli_ (containing ClpB) stimulates the release of intestinal anorexia hormones, whereas intraperitoneal administration of the proteins activates anorexia neurons in the hypothalamus. Evidence of the role of bacterial proteins in modulating host metabolism was also provided by the study by [PERSON] et al. [138]. Indeed, in their study, they found that daily oral administration of the bacterial protein Amuc_1100, displayed on the outer membrane of _A. muciniphila_, improves the gut barrier function and partially recapitulates the beneficial effects of the live bacterium [139]. The proposed mechanism activates a receptor of the innate immune system, toll-like receptor 2. Similarly, a recent study has reported the production of some proteins that exhibit high homology with human peptide hormones, such as insulin and IGF-1, by viruses of the human microbiome. In vitro and in vivo studies have shown that those viral insulin/IGF-1-like peptides modulate the host's physiology [140]. In conclusion, gut microbial metabolites play a significant role in appetite regulation, with SCFAs, succinate, Trp, BAs, BCAAs, and gut bacterial proteins influencing satiety signalling pathways. Understanding the complex interactions between these metabolites and host physiology is essential for developing targeted interventions for eating disorders and metabolic diseases.
## 3 Results and Discussion
### Regulation of ABC-Transporters  
The response to HXT showed a reduced transcription of two ABC-transporters (\(lp\_2739\)-\(lp\_2740\) and \(lp\_2743\)- \(lp\_2744\)) that were downregulated in response to other phenolics such as GA [11] and p-coumaric acid [7], resveratrol [12; 16] or oleuropein [13; 17]. The gene pair \(lp\_2739\)-\(lp\_2740\) codes for an ABC transporter homologous to the BceAB transporter of _Strepcoccus pneumoniae_[41] or its ortholog YsaBC of _Lactococcus lactis_. This BceAB-like module counteract the activity of bactircan in \(B\). _subtilis_, an antibiotic that blocks the lipid II cycle and is associated to cell envelope stress response in LAB [28]. This BceAB system has not been studied in \(L\). _plantarum_ and thus substrates, mode and direction of substrates transport are unknown [42]. The genes \(lp\_2743\)-\(lp\_2744\) constitute an ABC-transport system homologous to that encompassed by the YtrA operon from _Bacillus subtilis_[43] which often responds to cell wall antibiotics. To judge by these expression profiles, \(L\). _plantarum_ WCFS1 could downregulate these transport systems to control the traffic of HXT and other phenolic compounds across the membrane. In addition, \(lp\_3040\), a gene putatively coding for a multidrug efflux pump, was upregulated. Like other multidrug resistance systems [44], this transporter is probably dedicated to remove various substances from the cytoplasm, among which could be HXT.
# Potential of Liquid-Crystal Materials for Millimeter-Wave Application
## 4 Microstrip-Line-Type Liquid Crystal Phase Shifter  
There are generally two approaches to develop LC MMW devices. One uses a quasi-optical device, which was described in Section 3. In this section, we describe another approach, which can be attained by integrating LC materials into high-frequency electric circuits. In particular, the integration of LCs with planar-type circuits is popular because it can have a thin planar cell structure, which allows a similar fabrication technology to a flat-panel display. We investigated the LC phase shifter by adopting an excellent planar-type waveguide (microstrip line), which is a very common and reliable waveguide. However, it is not useful to introduce LC materials onto the substrate to control the MMW propagation because the electromagnetic wave propagates inside the dielectric substrate. Thus, we developed a novel conversion circuit to allow integration of the LC material onto the microstrip line [49; 50; 51]. There is another advantage of the planar-type LC phase shifter, whereby it is easy to expand its functionality to add other necessary parts onto the same substrate as the LC material. In this study, we integrated a planar-type antenna with the LC phase shifter to investigate the minimal twin antenna array.
## 3 Results  
In order to estimate the accuracy and precision of the models, we performed in Cloud-Compare software the Cloud to Mesh (C2M) distance between the original point cloud and the models created by the data-driven and model-driven methods. Table 1 summarises the means and standard deviations of the Cloud to Mesh distances for all models.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Modelling Type** & **Model** & **Formal Result** & **Mean C2M [m]** & **Standard Deviation C2M [m]** \\ \hline Tri-mesh & & & 0.066 & 0.612 \\ Data-driven free-form & & & & \\ \hline Quad-mesh & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean and standard deviation values of the C2M distance of data-driven and model-driven models.  
Figure 15: Corrected building footprint and extracted model 7: correct segmentation according to DSM (**a**), model 7 (**b**), model 7 textured in two views (**c**,**d**).  
In Appendix A, the results of tri-mesh, quad-mesh, and model 7 were reported for full reading. These latter 3D models showed the best values of the mean and standard deviation.  
In addition, the simplification rate obtained by transforming a tri-mesh model into a quad-mesh model was assessed according to three reduction indicators: number of models vertices, number of model faces, and size of the files with the * PLY extension (expressed in kiloBytes). Table 2 shows the results achieved in relation to triangular and quadrangular  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modelling Type** & **Model** & **Formal Result** & **Mean** & **Standard Deviation** \\  & & & **C2M [m]** & **C2M [m]** \\ \hline Model 1 & & & & \\ (Segmented footprint & & & & \\ based on DSM) & & & & \\ Model 2 & & & & \\ (Segmented footprint & & & & \\ based on the point cloud) & & & & \\ Model 3 & & & & \\ (footprint automatically & & & & \\ segmented according to & & & & \\ nadir image) & & & & \\ Model-driven & Model 4 & & & \\ (OSM footprint) & & & & \\ Model 5 & & & & \\ (OSM segmented footprint) & & & & \\ Model 6 & & & & \\ (footprint segmented & & & & \\ interactively based on & & & & \\ nadir image) & & & & \\ Model 7 & & & & \\ (interactive editing of the & & & & \\ footprint and roof) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._mesh; from the observation of this table, it is possible to note that for all three indicators taken into consideration, there is a simplification rate higher than 92%.
# Semantic 3D Mapping from Deep Image Segmentation
## 4 Results
### Application: Mapping a Domestic Environment  
In the second experiment, we semantically mapped a real home environment. Items of interest are shown in Table 2. This experiment illustrates how the mapping process can be integrated along with other robot capabilities in a real scenario.  
Figure 11 shows the mapping process of a segmented image during the second experiment. We have used a wheeled robot (Figure 10(a)) equipped with a 360 deg laser RPLidar A-2 and an Orbbec Astra RGB-D camera. The computer is an Intel Core i7 2.2 GHz with a GPU NVidia GeForce GTX 1070.  
Detections are transformed to the frame map and inserted in the semantic map as octomap cells, each with a color representing its class and a probability, as shown in Figure 10(b),c. The final map is shown in Figure 10(d).  
Our method matches other state-of-the-art 3D segmentation approaches while it does require complex operations. For example, [PERSON] et al. [24] achieved accuracies around 70% using depth-aware convolutional operations, thus intensive training. [PERSON] et al. [25] also obtain precisions around 75% training models with occupancy and multi-class labeling, but it requires training from scratch with a large-scale dataset of synthetic 3D scenes with dense volumetric annotations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stat.** & **Erosion** & **Proposed Method** \\ \hline Maximum & 0.88 s & 0.42 s \\ Average & 0.37 s & 0.27 s \\ Minimum & 0.15 s & 0.04 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of processing times for erosion and the proposed methods in seconds.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Color** \\ \hline Cup & FF000 \\ Bottle & 00 FF00 \\ Chair & 000 OFF \\ Oven & FF0 OFF \\ Tv & 00 FFFF \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classes of objects used in the mapping process, with their associate colors.  
Figure 11: Classes of objects used in the mapping process, with their associate colors.
## 4 Experiment  
The estimation of measurement accuracy is performed by comparison with the measured values from a commercial instrument. SENSOFAR-S-neox is a high-precision 3D profiler based on white light interference, with 5 nm vertical resolution with 5 mm vertical measuring ranges. The heights of 16 micro bumps obtained using the proposed methods and Nikon instrument are shown in Table 1. In this experiment, the heights of five bumps (nominal height 50 \(\upmu\)m) located on a column were measured point by point using the Sensofa instrument the proposed method with the system setup mentioned above, respectively. The corresponding measurement results were recorded in Table 1. The experimental results  
Figure 8: Image data processing flow.  
Figure 9: Photograph of the experimental setup.  
showed that the measurement accuracy of this method is within 1.5 \(\upmu\)m compared with commercial instruments.  
Repeated measurement experiments were carried out for a specific bump to verify the stability of the measurement device proposed in this paper. The bump was measured ten times, and the measurement results are shown in Table 2. The average value of ten measurements was 49.68 microns and the standard deviation is 0.82 microns.
# Changes in the Cell Division of Chang Liver Cells Induced by Simulated Microgravity
## 1 Introduction  
Throughout the evolution of life on Earth, both physical and biological processes have been influenced by gravity [1]. Over time, gravity affects natural selection by limiting the size of an organism to an acceptable range. In order to cope with gravity, living organisms must develop support systems such as properly supported motor structures, rigid cell membranes, and the regulation of fluid flow in the body [2]. Many devices have been developed and used on Earth to simulate microgravity conditions, such as rotating wall vessels, clinostats, and random positioning machines [3].  
Microgravity also acts on many different types of cells. It can alter proliferation [4] and differentiation [5], or change the composition of the extracellular matrix [6], adhesions, and the migration of cells [7]. SMG has been reported to reduce the movement and invasionof several cancer cell lines, especially inhibiting the proliferation of lymphocytes [8]. The proliferation of L-540 and HDLM-2 cell lines was inhibited by SMG conditions [9]. Proliferative inhibition was also observed in HepG2 exposed to SMG [10]. The alteration of cell division is one of the significant reasons for proliferative changes in cells. Earlier research has shown that the reduced rotation in microgravity results in a reduction in apoptosis, with only minor alterations in the growth factors regulating cell division [11]. Cancer cell lines exposed to microgravity display severe disruption of the cell cycle and the microRNA gene networks, according to expression profiling research [12]. The implications of SMG for cell division, however, have not been thoroughly studied. The effects of SMG on the formation of the contractile ring, the polar spindle microtubules, and spindle formation have not been well described during cytokinesis. Cytokines is the process by which the cytoplasm is divided once the genetic material is equally divided among the daughter cells [13]. Malfunctions of cytokinesis can lead to abnormalities in cell division, such as the positioning of the division plane, cleavage furrow ingression, midbody formation, and abscission [14]. Cells are very sensitive to changes in simulated microgravity [15; 16], especially in the cytoskeletal structure and the orientation of cell division [17; 18; 19]. This research was conducted to determine how CCL-13 cell division changes under SMG, demonstrated by the alteration in the cell cycle, changes in the cell area and nuclear area, changes in the nuclear/cytoplasmic ratio, and changes in the transcript expression of genes associated with the cell cycle. Moreover, this study also assessed the morphology of divided cells, such as cell shape and microvilli appearance, and assessed cleavage furrow formation and the polar spindle microtubules that contribute to cell division during cytokinesis.