esa-sceva/satcom-chunk-collection huggingface dataset:
# Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres
## 3 Results and Discussion
### Fourier-Transform Infrared (FTIR) Analyses  
Figure 3 shows the FTIR spectra of untreated pine sawdust (UPSD) and cellulose fibres (CF) before they were used for adsorption. The application of NaOH to pine sawdust resulted in the dissolution of lignin and soluble extracts, improving the chemical reactivity of the surface functional groups [31]. Acid hydrolysis modified pine sawdust's physical and structural properties [22]. In Figure 3, a shift from 3336.87 cm\({}^{-1}\) (UPSD) to 3322.50 cm\({}^{-1}\) for cellulose fibres (CF) indicated a change in the hydroxyl group (-OH). Additionally, the  
Figure 2: Mechanism of acid hydrolysis of cellulose [26].  
peak at 2886.80 cm\({}^{-1}\) suggested the presence of aliphatic C-H group stretching vibrations of the -CH\({}_{3}\) and -CH\({}_{2}\) groups, implying the existence of aliphatic hydrocarbon chains. No shift was detected, indicating that the functional groups directly attached to the aliphatic chains remained intact. However, their intensity decreased, suggesting a reduction in the concentration of aliphatic C-H groups following acid treatment. The band at 1726.52 cm\({}^{-1}\) (UPSD) C=O signifies that the stretching vibration of the C=O of carboxyl groups shifted to 1721.73 cm\({}^{-1}\) in the cellulose fibres (CF). The peaks observed at 1587.67 and 1506.27 cm\({}^{-1}\) (UPSD) denote N-H and N-O; in the cellulose fibres (CF), the two bands appeared to be merged at 1597.24 cm\({}^{-1}\). UPSD shows peaks at 1425.14 cm\({}^{-1}\) and 1257.30 cm\({}^{-1}\) associated with the stretching of the carboxylic/aromatic hydroxyl group (-OH) of the phenol group, and the band at 1025.94 cm\({}^{-1}\) shows the stretching vibration of the C-O of the primary alcohol group. However, the carboxyl/aromatic hydroxyl (-OH) stretching band for the cellulose fibres (CF) shifts to 1434.45 cm\({}^{-1}\), and the C-O of the primary alcohol shifts to 1021.89 cm\({}^{-1}\).  
Figure 4 compares the major functional groups for UPSD and cellulose fibres (CF) after adsorption. A shift and a decrease in intensity were observed, indicating that a metal binding process occurred at the surface of the adsorbents. Following the metal biosorption process, distinct alterations were noted in the wavenumbers (3338.18 cm\({}^{-1}\), 2977.77 cm\({}^{-1}\), 2901.16 cm\({}^{-1}\), 1166.32 cm\({}^{-1}\), and 1054.06 cm\({}^{-1}\)) [32]. The observed shifts, accompanied by a slight decrease in peak intensity, indicated the participation of hydroxyl and carboxyl groups in the adsorption of chromium ions (VI). Additionally, the bands within the range of 1625 to 900 cm\({}^{-1}\), primarily originating from C-OH groups, exhibited a slight decrease in intensity after the reaction. This reduction signified the removal of these groups from cellulose due to oxidation by Cr (VI). Consequently, cellulose's C-OH groups served as the solution's reductant for Cr (VI) [33].  
Figure 3: Infrared spectra of untreated pine sawdust (UPSD) and cellulose fibres (CF) before adsorption.
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 1 Introduction
To cope with the known challenges and difficulties, a novel framework and network based on the Internet of spacecraft is presented and analyzed. The novelties and contributions of this paper can be unfolded in several aspects as follows:  
1. A comprehensive review of spacecraft projects, astronomical research, space exploration missions of critical milestones in human history is introduced and reviewed. 2. A thorough review, investigation, and analysis of the outer-space threats, planetary defense, and space colonization are presented. The theories and scientific findings of the origins of asteroids and comets plus the technologies of detection and mitigation of potentially hazardous objects are investigated and analyzed. 3. A novel framework named the Solar Communication and Defense Networks (SCADN) is proposed. It consists of a large number of distributed spacecraft/spaceships which are capable of enabling the Internet of distributed deep-space sensing, communications, and defense. An in-depth analysis is conducted to analyze how SCADN can increase the success rate of early detection of asteroids/comets/objects jeopardizing the safety of Earth and other colonized planets/moons. Furthermore, the SCADN-based mitigation strategies of preventing outer-space objects triggered extinction events are also presented and discussed. 4. Last but not least, to better serve the common interest of all mankind, several legal matters and legislative concerns about founding the SCADN framework have been presented and discussed. The remainder of this paper is organized as follows. Section 2 reviews and analyzes current multi-planetary colonization projects and plans, with life's transition from sea to land and terraforming perspectives discussed. Section 3 presents a detailed review and in-depth analysis of the outer space threats and events, followed by a brief historical review of planetary defense. Section 4 further shows the state-of-the-art technologies of planetary defense. Section 5 proposes the concept of the SCADN framework to cope with potential space threats and discusses how the SCADN framework can mitigate them and facilitate extraterrestrial colonization. Eventually, Section 6 concludes this paper, and Section 7 gives the future research directions.
## 4 Role of Ap4A: Functions in Normal and Cancer Cells
### Interaction with Purinergic Receptors
#### 4.5.4 P2X7 Receptors  
Recent evidence suggests that diadenosine tetraphosphate contributes to tumor development and immune modulation through its interaction with purinergic receptors, particularly P2X7. While Ap4A is not a primary agonist for P2X7 receptors, it can modulate their activity indirectly, which plays a significant role in the TME. Activation of P2X7 in tumor cells has been shown to trigger a range of downstream signaling pathways, including PI3K/AKT, JNK, and Wnt/\(\beta\)-catenin cascades, which collectively support tumor cell survival, proliferation, invasion, and angiogenesis. Specifically, this receptor modulates inflammatory cytokine production, such as IL-1\(\beta\) and TNF-\(\alpha\), often through calcium-dependent signaling, and can promote the expression of matrix metalloproteinases like MMP2, facilitating tumor metastasis [44]. Furthermore, P2X7 activation can lead to the formation of membrane pores, promoting cell death in immune cells and contributing to immune evasion by tumors. Thus, Ap4A's indirect modulation of P2X7 receptors may alter cytokine release and inflammatory mediator production, potentially affecting the immune system's capacity to recognize and eliminate tumor cells. Given P2X7's crucial role in immune regulation and its involvement in tumor cell apoptosis, targeting this receptor presents a promising strategy for cancer therapy aimed at restoring immune activity within the tumor microenvironment. Additionally, P2X7 receptor is well-known for its ability to form membrane pores and mediate calcium influx upon ATP binding. The interaction of Ap4A with this receptor activates several pathways, such as the AMP-activated protein kinase (AMPK) signaling cascade. AMPK, a key regulator of energy homeostasis, becomes activated in a calcium- and ROS-dependent manner via P2X7 in microglia and macrophages. This mechanism underlies metabolic reprogramming that supports cellular adaptation under stress, highlighting Ap4A's role as a metabolic \"alarmone.\" For instance, tumor cells--often residing in a hypoxic or nutrient-depleted microenvironment--leverage this adaptability through P2X7-mediated upregulation of glycolytic enzymes and more efficient utilization of intracellular glycogen stores [45]. For instance, inhibiting P2X1R and P2X7R has been observed to reduce mitochondrial activity, intracellular calcium levels, and proliferation in various cancer cell lines, such as leukemia cell lines [46].
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 4 Proposed Protocol  
This section describes in detail the hierarchical k-dimensional tree algorithm (H-kdtree). Unlike conventional WSN routing protocols like LEACH, HEED, TEEN, etc. [47; 48], which used a few variations on Equation (1) to form clusters and select CH nodes according to nodes' residual energy and their distance to the Sink/BS node, H-kdtree uses the one-dimensional clustering principle taken from the k-d tree algorithm. This algorithm generates a hierarchical two-hop network topology similar to LEACH's.  
Next, we will explain the clustering mechanism of the k-d tree algorithm intuitively, using the data from Table 1.  
The data in Table 1 are divided into two clusters, starting data partition with data from the \(x\) dimension, implementing the median value (\(v_{m}\)), where \(v_{m}=(v_{max}+v_{min})/2\). For the data in Table 1, we have a median value of 53.5 for the \(x\) dimension, as shown in Figure 5.  
In Figure 5, the algorithm shows the formation of two clusters. Each of the clusters found contains three variables:  
* Dimension used for the division (\(x\) or \(y\)),
* Median value (\(v_{m}\)),
* Limits of the nodes in each cluster.  
The limits on the \(y\) dimension for \(cluster_{1}\) and \(cluster_{2}\) are, respectively, \(29\leq y_{(cluster_{1})}\leq 75\) y \(29\leq y_{(cluster_{2})}\leq 93\). The structure in Figure 5 is divided in the same way but alternating the dimension, which in this case would be y, obtaining a new structure with four clusters, as shown in Figure 6.  
\begin{table}
\begin{tabular}{l c c} \hline  & **Dimension \(x\)** & **Dimension \(y\)** \\ \hline node 1 & 54 & 93 \\ node 2 & 80 & 55 \\ node 3 & 96 & 86 \\ node 4 & 74 & 77 \\ node 5 & 42 & 68 \\ node 6 & 22 & 45 \\ node 7 & 11 & 53 \\ node 8 & 27 & 75 \\ node 9 & 64 & 29 \\ node 10 & 81 & 63 \\ \hline \end{tabular}
\end{table}
Table 1: Test data.  
Figure 5: k-d tree algorithm with \(x\)-dimension.  
Figure 6: k-d tree algorithm with dimension on \(y\).  
Based on the structure obtained so far, as shown in Figure 6, we change dimension and begin to create new partitions, as shown in Figure 7. The process shown so far is repeated iteratively until the stop condition is met, being the number of clusters or the minimum group condition.  
Algorithm 2 has the \(k\) variable as the input parameter. The \(k\) variable represents the number of clusters desired (this is a parameter similar to the Algorithm 2 has the \(k\) variable as the input parameter. The \(k\) variable represents the number of clusters desired (this is a parameter similar to the \(k\)_-means_ algorithm). If \(k=3\), the first iteration obtains the data in Figure 5. Up to this point, we have two clusters. In the next iteration, we would obtain four clusters, but since the goal is to obtain three clusters, we take the clusters obtained so far and we select the cluster with the highest node count and partition only this cluster. In this way, we obtain the desired three clusters. In case the two clusters obtained in the first iteration have an equal number of nodes, one of them is chosen randomly.
## 7 Discussion
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Type** & **Definition** & **Usage** & **Example** \\ \hline Area Chart & Similar to a line chart but with the area beneath the line filled, suitable for showing cumulative data or stacked values. & To illustrate the internal workload of an athlete during period of training (e.g., a month) & \\ \hline Box Plot (Box-and-Whisker Plot) & A graphical representation of the data’s summary statistics, including the median, quartiles, and outliers, providing insights into the data’s spread. & To summarize the volleyball player’s serve accuracy. Boxplot makes it easier to find outliers. \\ \hline Heatmap & A matrix-like representation of data using colors to indicate values, helpful for visualizing relationships in a table. & To highlight areas where a player spent the most time, indicating their preferred positions on the field. \\ \hline \multirow{4}{*}{Radar Chart (Spider Chart)} & A chart with multiple axes radiating from a central point, useful for comparing multiple variables on different scales. & To compare the agility, speed, endurance, and lower limb strength of different track and field athletes. The chart can showcase each athlete’s strengths and weaknesses in these areas. & \\ \hline Bubble Chart & Like a scatter plot, but with additional size dimensions represented by the size of the data points. & Plot the age, height, and weight of basketball players in a bubble chart to visualize the distribution of these attributes within the team. The size of each bubble can represent a player’s minutes played in recent games. & \\ \hline Gantt Chart & A type of bar chart used in project management to show the timing and duration of tasks or events. & To outline the training schedule of a triathlete preparing for a competition. The chart can depict the phases of swimming, cycling, and running training leading up to the event. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Based on the observations of this study, we propose a performance analysis model that encompasses all the technologies discussed in this paper. This theoretical model can help visualize the intricate path of data across various sections and how they transform into valuable information, guiding the definition of goals for performance improvement. We can observe how the use of new technologies can enhance connections and the speed of communication with all the entities involved in the process (e.g., athletes, coaches, analysts). This highlights the future potential for designing new intelligent systems for different modalities.
## 3 Results  
Profiling of Bioactive Compounds in Rhinanthus Angustifolius Extracts through Untargeted UHPLC-QTOF-MS  
The untargeted phytochemical profiling of _R. angustifolius_ extracts was carried out through an ultra-high-pressure liquid chromatography quadrupole-time-of-flight mass spectrometry (UHPLC-QTOF-MS), recognized as a powerful analytical tool because of its high sensitivity. Overall, this approach allowed the putative detection of 570 bioactive compounds (BCs), characterized by 309 terpenoids, 248 polyphenols, and 13 alkaloids. The list of BCs, divided among classes and sub-classes, and reported together with their raw abundance values, identification score (>70%), and mass spectra, are provided in Supplementary Materials (Table S1). Regarding terpenoids, 245 sesquiterpene lactones, 44 triterpenes, and 20 limonoids were detected. The most representative terpenoids were iserisoside-K and -D, taraxacin, and erioflorin acetate belonging to the sesquiterpene lactones class. Additionally, soyasapogenol-A, -E -O-beta-glucuronate, lupeol dihydrocinamate, bevirimat, and brusatol were included under the triterpenes group. Finally, we detected amotsangin-A and -B, ohchnolide B, and 11-oxocnorin G, as the most representative limonoids (Table S1). In this work, different phenolic compounds (PCs) were putatively annotated in _R. angustifolius_ extracts--i.e., 127 flavonoids (including 38 flavonoids, 32 anthocyanins, 32 flavones, and 25 other flavonoids), 50 low-molecular-weight phenolics (LMWPs), 44 phenolic acids, 21 lignans, and 6 stilbenes. The major flavonoid compounds were driven by pelargonidin, cyanidin, peonidin, delphinidin, dihydroquercetin and its 3-O-glycos conjugation form, followed by eriodictyol, genistein, hesperidin, apigenin, luteolin, kaempfer, and isorhamnetin. Meanwhile, for phenolic acids, we found an abundance of hydroxycinnamic acids (i.e., caffe acid, cinnamic acid, p-coumaroyl malic acid, and p-coumaric acid 4-O-glucose), followed by hydroxybenzoic acids (i.e., benzoic acid, gallic acid, and syringic acid). Regarding lignans, anhydro-seciosolariciresinol, arcigenin, 7-hydroxymatairesinol, dimethylmatairesinol, and pinoresinol were the most representative compounds. Finally, alkyl(methyl)phenols, hydroxybenzaldehyde, hydroxycumarins, phenolic terpen, tyrosols derivatives, and stilbenes were also found (Table S1). Finally, other BCs found in R. _angustifolius_ extracts were represented by alkaloids, such as schelhammeridine, colchiethine, trigamine, and speciosamine (Table S1).
## 1 Introduction
### Related Works
An image-based 3D model can be reconstructed by unprojecting such RGB-D image frames to represent the target scene as a colored 3D point cloud [30]. One of the advantages of representing a 3D scene as a point cloud is that there is a one-one correspondence between points in the 3D space and pixels in the image space. Template-based 3D scene reconstruction uses a set of RGB-D image templates to represent individual viewpoints of a scene, where the RGB information in each template describes the scene's visual appearance, and the depth image along with the camera parameters offers the geometric structure [13; 14; 31]. Given a prelearned template-based 3D scene model, in the inspecting phase, we can perform a prelearned viewpoint classifier to locate the viewpoint in which the 3D points are unprojected from a single test RGB image frame, which is captured by an RGB camera in a navigated drone. However, for an uncalibrated camera with a known focal length, at least two images are required to recover their poses [32]. The minimal number of images required in recovering the pose of a calibrated camera is known as the perspective-3-point-problem (P3P), which consists of three 3D-2D point correspondences. Many solutions are available for the general case when more information is available [24; 25; 26; 27; 28; 29; 30]. When the environment in the scene can be controlled, [PERSON] et al. present an interesting approach that uses curvilinear features in focus for registering a single image to a 3D Object [33]. However, the usage of hand-crafted features for recovering the pose of a single image is obviously not a trivial problem. In this work, our contribution is to push beyond this limit by dealing with the case of an uncalibrated camera seeing one viewpoint using deep learning. Once the depth image and the camera parameters of a single RGB image are obtained, 3D point cloud registration algorithms [34; 35; 36] can be applied for viewpoint inspection in a 3D space.
## 2 Material and Methods
### Analysis from a Mechanical Engineering Standpoint  
The subsequent sections outline the comprehensive stages involved in performing a linear static analysis using FEM, alongside the associated working assumptions. These stages include the following:  
* Preprocessing: This stage entails the meticulous preparation of input data.
* Assignment of materials: In this step, the appropriate materials are assigned to each component of the engine. This includes specifying the mechanical properties such as Young's modulus, Poisson's ratio, and density, ensuring that the simulation accurately reflects the real-world behavior of the materials under various load conditions.
* Application of contacts: When interactions between different parts of the model are present, it is essential to define the type and nature of contacts. This includes specifying whether the contacts are frictional, bonded, or sliding, which significantly influences the accuracy of the stress and strain results.
* Boundary conditions: This stage involves the establishment of boundary conditions and applied loads. Constraints such as fixed supports, symmetry conditions, and applied forces or moments are defined to mimic the real operational environment of the model. Proper application of these conditions is crucial for realistic simulation results.
* Discretization (meshing): The model geometry is divided into finite elements, which facilitates numerical analysis. This process, known as meshing, involves creating a mesh of elements and nodes to approximate the geometry of the model. The quality of the mesh, including element size and type, affects the accuracy and convergence of the analysis.
* Identification of critical positions, determination of the deformation envelope and execution of the modal analysis and linear static analysis: The simulation of the  
Figure 4: Speed control system with all its elements.  
model's behavior under various loading conditions is performed in this stage. It includes identifying critical stress and strain locations, determining the envelope of deformations, and conducting modal analysis to evaluate the natural frequencies of vibration of the system. Linear static analysis is then executed to assess the response of the model under static loads, providing insights into potential areas of failure and overall structural integrity.  
By adhering to these stages, a thorough and precise linear static analysis using FEM can be achieved, leading to reliable predictions of structural performance and critical insights for design optimization.
## 4 Case Study
### Data Collection  
Our dataset is composed by conversations between users and human operators occurred from the start of 2018 to the end of 2019. We decided to use only data from these two years because, as the e-learning platform is constantly evolving, the requests from the users also keep evolving. For example, when a new feature is added to the platform, students might ask some questions about it and this generates a new class of requests. On the other hand, the resolution of bugs or the renewal of features can cause the disuse of one or more classes of requests.
# Flow Dynamics of Sulfate-Modified Water/Polymer Flooding in Micromodels with Modified Wettability
## 1 Introduction
### General Methodology and Approach  
The proposed workflow, as shown in Figure 2, helps to create an understanding of the role of the fluid-fluid interfacial interaction as a recovery mechanism other than a wettability alteration. Furthermore, an attempt is made to analyze the success of the mechanism of a hybrid EOR comprising modified water application, in combination with a polymer flood. The following steps were completed to achieve the objectives of this study:  
Brine Preparation and Optimization: Four different injection brings were prepared to correlate and cross-validate the fluid-fluid interaction and oil recovery results. The amount of sulfate in the brine and TDS were the key parameters for comparison.  
Figure 2: Adopted workflow in the research for the hybrid enhanced oil recovery (EOR) process evaluation.  
Polymer Diluted Solutions Preparation: Polymer solutions with an oil-to-polymer viscosity ratio of 1 and 2 at a shear rate 10 s\({}^{-1}\) were prepared to inject in the tertiary mode for viscosity control.  
Interfacial Tension and Interfacial Viscoelasticity Evaluations: Define the ionic activity and chemical interactions between oil polar compounds and four different brines at the liquid-liquid interface.  
Wettability and Geometry of the Porous Media: Micromodels with three wettability conditions were used to investigate the role of wettability on the fluid-fluid interfacial interaction. Moreover, two types of water-wet micromodels with different rock geometries/characteristics were further investigated to cross-analyze the results.  
Two-phase Modified Brine Injection combined with Polymer Flooding in Micromodels: Flooding experiments were performed with oil-saturated micromodels with an established initial water saturation. Brines were injected as the secondary mode, and polymer flooding was performed in the tertiary mode, with a mix-wet micromodel to evaluate and define the benefits and synergies of the combined EOR process.  
Secondary Mode Brine Flooding Comparison of Micromodels and Aged Core Plugs: A comparison was made between the recovery factors obtained from mixed-wet micromodel and aged core plugs, in order to understand and elucidate the fluid-fluid interaction as the recovery mechanism.