esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
#### 3.2.1 Fine Tree  
Fine Tree [44], one of the Decision Tree classification models, optimizes the structure of the Decision Tree by employing preprocessing methods, such as pruning and removing unnecessary branches and nodes, thereby reducing overfitting and balancing model complexity through parameter adjustments [45]. It aims to improve accuracy by combining the predictions generated by multiple trees using Ensemble methods [46]. Additionally, by applying regularization [47] to control overfitting and enhance the model's generalization ability, Fine Tree helps Decision Tree classifiers achieve better results in data prediction tasks.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results
### Input Impedance  
The antenna's design assumes that the impedance of the power line should be 50 \(\Omega\). In the case of large discrepancies, it is possible to use a matching system. However, adding another system introduces additional losses and generates additional costs [35; 36; 37]. The designed width and length of the feed line in the antenna has a complex input impedance of Z = 56.1 + j5.08 \(\Omega\) at the resonance frequency of 28.00 GHz, while at 27.50 GHz the complex input impedance is Z = 51.57 - j10.73 \(\Omega\), and at 28.35 GHz the complex input impedance is Z = 59.6 - j1.39 \(\Omega\). Detailed input impedance values for the proposed antenna as a function of frequency are shown in Figure 7.  
Figure 6: The chart of the voltage standing wave ratio as a function of frequency for the proposed antenna working in the 5G system.  
Figure 7: The chart of the complex input impedance as a function of frequency for the proposed antenna working in the 5G system (real part, blue line and imaginary part, green line).
## 4 Methodology
### Turning Simulation--2024-T351
Cutting force evolution and its comparison with the experimental results is shown in Figure 3. A summary of 64 cutting simulations is given in Table 3, where \(V\) is the cutting force (m/min); \(C\) is the feed (0.4 mm); \(F\) is the contact friction; \(RF\) (N) is the cutting reaction force; \(R\) (degrees) is the tool rake angle; and \(T\) is the temperature of the tool-chip interface. For further details, interested readers may consult the published work in [PERSON] et al. [42]. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{4}{c}{**Physical Parameters**} \\ \hline
**Property** & **Work Piece (A2024-T351)** & **Tool (Tungsten Carbide)** \\ \hline Density, \(\rho\) (Kg/m\({}^{3}\)) & 2700 & 11,900 \\ Elastic modulus, \(E\) (Gpa) & 73 & 534 \\ Poisson ratio, \(\
u\) & 0.33 & 0.22 \\ Specific heat, \(C_{p}\) (J\(\cdot\)Kg\({}^{-1}\cdot\)\({}^{\circ}\)C\({}^{-1}\)) & \(C_{p}\) = 0.557\(T\) + 877.6 & 400 \\ Thermal conductivity, \(\lambda\) (W\(\cdot\)m\({}^{-1}\cdot\)C\({}^{-1}\)) & \(25\leq T\leq 300\): \(\lambda=0.247T+114.4\) & 50 \\ Thermal conductivity, \(\lambda\) (W\(\cdot\)m\({}^{-1}\cdot\)C\({}^{-1}\)) & \(300\leq T\leq T_{mml}\): \(\lambda=-0.125T+226.0\) & - \\ Expansion, \(\alpha\) (\(\upmu\)m\({}^{-1}\cdot\)\({}^{\circ}\)C\({}^{-1}\)) & \(\alpha+8.9\times 10^{-3}T+22.2\) & - \\ \(T_{melt}\) (\({}^{\circ}\)C) & 520 & - \\ \(T_{mom}\) (\({}^{\circ}\)C) & 25 & 25 \\ \hline \hline \multicolumn{4}{c}{**Johnson-Cook Material Parameters (Identified for A2024-T351)**} \\ \hline \(A\) (MPa) & \(B\) (Mpa) & \(n\) & \(C\) & \(m\) & \(D_{1}\) & \(D_{2}\) & \(D_{3}\) & \(D_{4}\) & \(D_{5}\) \\
352 & 440 & 0.42 & 0.0083 & 1 & 0.13 & \(-1.5\) & 0.011 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Work piece and cutting tool properties. Figure 2: Turning simulations model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feed, \(f\) (mm)} & \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Cutting Speed, \(V\) (m/min)**} \\ \cline{3-5}  & & **200** & **400** & **800** \\ \hline \multirow{2}{*}{0.3} & Force Frequency & 778 N & 769 N & 769 N \\  & Frequency & 128 Hz & 290 Hz/37.8 kHz & 500 Hz/90.7 kHz \\ \hline \multirow{2}{*}{0.4} & Force Frequency & 988 N & 978 N & 976 N \\  & Frequency & 120 Hz/10.3 kHz & 351 Hz/32.4 kHz & 889 Hz/64.8 kHz \\ \hline \multirow{2}{*}{0.5} & Force Frequency & 1216 N & 1196 N & 1192 N \\  & Frequency & 256 Hz/16.2 kHz & 476 Hz/22.7 kHz & 1026 Hz/45.3 kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results—Turning AA2024 [42]. Figure 3: Parametric sensitivity analysis (cutting reaction force (N), chip stress (MPa), and tool-chip interface temperate (\({}^{\circ}\)C)) [53].
# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 4 Results
### Seasonal XCO\({}_{2}\) Anomalies
During DJF and MAM, we see the signature of strong emission from fires in the anomalies and flux maps (Figure 6, right column). We would expect however to see negative anomalies during SON due to the strong biospheric sink, but we find only slightly smaller positive anomalies. In the Indian peninsula we find positive XCO\({}_{2}\) anomalies as expected from anthropogenic emissions when the biospheric uptake is low. Seasonal patterns are also evident in the largest anthropogenic emission areas, i.e., China, central Europe and eastern USA. For example, China and central Europe show positive anomalies during all seasons, while in eastern USA these are offset by a very strong biospheric sink in JJA. Also, the positive XCO\({}_{2}\) anomalies in China and central Europe are lower during JJA and MAM, respectively. Arid and semi-arid regions, including the Sahara, Tibetan Plateau and Australia, are out of sync with the rest of their hemispheres, with negative anomalies in the winter and small positive anomalies in the summer. Figure 5: Seasonal OCO-2 solar-induced chlorophyll fluorescence (SIF) at 757 nm for different seasons (from September 2014 to August 2015). Finally, we note signatures of outflow over the Atlantic ocean with positive XCO\({}_{2}\) anomalies from the biomass burning emissions in Africa. Furthermore, the spring time outflow along the dominant westerlies from China to North America is also visible from Figure 4, as also observed, e.g., for tropospheric ozone in several studies (e.g., [33; 34]).
# Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation
## 2 Materials and Methods
### Cluster Head Selection  
Every hub evaluates the sink distance toward the beginning of the arrangement stage; afterward, they share the leftover energy subtleties and distance data with their neighbors. Every hub's likelihood is determined utilizing an irregular number somewhere in the range of 0 and 1.  
Here Equation (3) will determine the utilization of Euclidean distance equation for Sink distance.  
\[\mathbf{d_{sink-node}}\ =\ \sqrt[2]{(\mathbf{xpos}_{sink}-\mathbf{xpos_{node}})}.^{2}+(\mathbf{ypos _{sink}}\ -\mathbf{ypos_{node}}).^{2}. \tag{3}\]  
where \(\mathbf{xpos_{sink}}\) is the sink node's \(x\)-status, \(\mathbf{xpos_{node}}\) is the node's \(x\)-position, \(\mathbf{ypos_{node}}\) is the node's \(y\)-position, and \(\mathbf{ypos_{node}}\) is the node's \(y\)-position.  
The node's remaining energy is resolved utilizing the Equation (4)  
\[\mathbf{RE}=\mathbf{E_{initial}}-\mathbf{E_{consumed}} \tag{4}\]  
Here, we denote \(\mathbf{E_{initial}}\)--sensor node; \(\mathbf{E_{consumed}}\)--energy utilized in 1 round.  
As the nodes become CH, when the sensor node distance and sink are the nearest, and the remaining energy is higher than the left-over nodes in network.  
Below is Algorithm 1 which states about the choosing of Distance of Sink and left over node energy in regards to cluster head list and no of sinks nodes.  
Here  
Figure 3: Block diagram representation with MGSA, node selection, and data transmission process for proposed method.  
During the consistent state time frame, information is communicated. When multi-bounce pathways are utilized, the organization incorporates both intra- and between bunch correspondence. The ideal transfer hub is picked in view of factors such as the leftover energy of hubs, start to finish delay, and closeness among CH and part hubs. Transfer hubs are just sensor hubs that send information, starting with one sensor and then moving on to the next.  
The ideal transfer hubs in this work are chosen from among the sensor hubs of a WSN utilizing a multi-boundary FF. The MGSA presents a wellness capability that boosts various qualities, including hub energy, closeness to CHs, and delay. Every hub could fulfill the wellness capability by offering the most noteworthy conceivable benefit to pick the sensor hub as a forwarder hub. Multi-metric Fitness function is shown in the Equation (5) as per the following:  
\[\text{Fitness}(\text{n})=\left\{\text{Dist}_{\text{n-CH}}+\text{ E}_{\text{res}}\left(\text{n}\right)+\text{ D}(\text{n})\right\} \tag{5}\]  
where  
\(\text{Dist}_{\text{n-CH}}\) tells approximation of 'nth 'node and respective CH,  
\(\text{E}_{\text{res}}\left(\text{n}\right)\) represents 'nth' node balanced power, and  
D (n) shows 'nth' node delay.
# BODIPY-Based Nanomaterials--Sensing and Biomedical Applications
## 1 Introduction
### BODIPY Dyes  
Dipyrromethene boron difluorides (BODIPYs) constitute an essential part of modern diagnostic and healing approaches. While enclosing rich physicochemical features, these fluorescent dyes found their applications in multiple fields, including medicine, pharmacy, and environmental sciences. In various in vitro studies, BODIPY derivatives have been successfully used for labeling in evaluating drugs' efficacy, localization, distribution, detection, and mechanism of action [51; 52; 53]. They were also used as contrast agents in photoacoustic imaging (PAI) to improve in vivo imaging techniques, playing a huge role in providing cancer cells' location and metabolic activity [54]. The conjugation of BODIPYs with metallic nanoparticles has proved to be an effective practice that increases the dyes' properties.  
The derivatives of BODIPY have been studied for many reasons (Figure 5), of which the improvement of the permeability of cell membranes for nanomodications and their use in PDT are particularly important [27]. BODIPY-functionalized gold nanoparticles can be considered as PSs in antimicrobial PDT [55]. In addition, they may also act as optical sensors and play an essential role in cell imaging by enhancing the fluorescent signal from confocal microscopy and flow cytometry [56]. Peptide-functionalized gold nanoparticles with BODIPY have revealed a significant role in the controlled drug distribution in the future.  
There is a vast interest in the development of fluorescent chemosensors for the recognition of environmentally important analytes. It seems that environmental protection can be improved with BODIPY derivatives, as they can be applied to detect toxic metal ions, such as Cr\({}^{6+}\), Hg\({}^{2+}\), Ag\({}^{+}\), Cu\({}^{2+}\), and Cd\({}^{2+}\) in aqueous solutions. So far, BODIPY-based dual-channel chemodosimeters have been developed for physiologically harmful ions, such as mercury and silver [57]. BODIPY-functionalized magnetite fluorescent nanocomposites are of great interest, as they may be a new class of non-toxic sensors for environmental applications [58]. These improved analytical methods for monitoring environmentally-unfriendly metals could be beneficial due to growing water pollution.  
Figure 4: Advantages of photosensitizer–nanoparticle conjugation [39].  
In this paper, we have focused on BODIPY derivatives in conjugation with gold and silver nanoparticles, magnetic, ZnO, TiO\({}_{2}\) nanoparticles, MOFs, and UCNs. The review presents examples of such species that have been discussed in the scientific literature in the last decade, emphasizing their potential applications in nanomedicine, PDT, PACT, PTT, sensing of biologically relevant molecules, and tumor tissue imaging.  
To sum up:  
Photosensitizers based on BODIPY dyes can be considered for photodynamic therapy and antimicrobial treatment as well as for the treatment of skin surface lesions.  
BODIPY dyes reveal sensing properties towards the determination of heavy metal ions.
## 5 Negation and Speculation Detection
### Scope Recognition  
The scope recognition task extracts the tokens affected by a cue. When negation or speculation is detected using a Prefix-cue, the scope is to the right of that cue (forward in the sentence). On the other hand, if it was detected with a Postfix-cue, the scope is to the left of the cue (backward in the sentence).  
This step receives three elements for recognizing the scope: the sentence, the cue, and the regular expression used in the cue detection task. The sentence has to be previously tokenized, and POS (_Part of speech_) tagged. The scope is extracted using five rules: the first four rules were adapted from [57] proposal in order to recognize the scope for both speculation and negation cues. Additionally, a fifth rule that uses syntactic parse trees for scope recognition was added. These rules are detailed as follows:  
* \"_Probable carcinoma de pulmon_, _pero espero resultados de biopsia.\" (\"Probable lung carcinoma, but I will wait for biopsy results.\")
* _Rule 2:_ if a cue \(C_{1}\) is detected in a sentence containing contiguous cues \(C_{1},C_{2},C_{3},\dots,C_{n}\), the scope for each \(C_{i}\) will be given by the position of \(C_{i+1}\). For example in the sentence:
- \"_No dolor, no inflamacion, no dolores articulares, ni ficbre_. (No pain, no inflammation, no joint pain, no fever.), for each cue, the scope is given by the position of the next cue.
* _Rule 3:_ if the sentence length corresponds to \"_a short sentence heuristic_\", then the scope is given by the end of the sentence. The following example shows a short sentence with their cue and the scope (underlined):- \"**Posible cancer pulmonar.\" (\"Possible lung cancer.\")**
* \"Con la **sospecha** de cancer de pulmon, proponemos reunion del comite de tumores.\" (Given the possibility of lung cancer, we propose a meeting with the tumor committee.)
* \"_Signos **suggstivos** de nodulos pulmonares, con ficbre alta desde aver.\"_ (_Signs that suggest pulmonary nodules, with a high fever present since yesterday._)  
Figure 6 shows the parse tree generated for the above sentence. In this case, the scope is extracted from the sub-tree where the cue _\"suggtivos\"_ is located. We use UDPipe [59], an open-source NLP tool that performs a dependency parsing tree. The fifth rule is useful in sentences that do not contain any token that indicates the end of the scope.
# Trajectory Tracking with Adaptive Robust Control for Quadrotor
## Appendix A  
Assume the following variables  
\[x_{1}\triangleq\theta,\phi,\psi\quad x_{2}\triangleq\dot{\theta},\dot{\phi},\dot{\psi}, \tag{11}\]  
The position error is  
\[e\triangleq x_{d}-x_{1} \tag{12}\]  
where \(x_{d}\) is the reference. The closed-loop system is re-written like  
\[\left[\begin{array}{c}\dot{e}\\ \dot{x_{2}}\end{array}\right]=\left[\begin{array}{c}-x_{2}\\ c_{1}x_{2}+c_{2}\hat{k}_{p}e-c_{2}\hat{k}_{d}x_{2}\end{array}\right] \tag{13}\]  
The control aim is the asymptotic stability, i.e.,  
Equation (A7) is locally semidefinite-negative in the following region  
\[\hat{V}(e,x_{2})\leq 0\quad\forall\quad x_{2}\in\mathds{R},e\in[\sigma_{2}- \sigma 1,\sigma 2+\sigma 1]\] (A10)  
with  
\[\sigma_{1} = \sqrt{\frac{x_{2}^{2}\Big{(}2-c_{1}+c_{2}\hat{k}_{d}\Big{)}+\frac {\Big{(}x_{2}-2x_{2}c_{1}+2x_{2}c_{2}\hat{k}_{d}-x_{2}c_{2}\hat{k}_{p}\Big{)}^ {2}}{2c_{2}\hat{k}_{p}}}}\] (A11) \[\sigma_{2} = \frac{\frac{x_{2}}{2}-x_{2}c_{1}+x_{2}c_{2}\hat{k}_{d}-\frac{x_{2 }c_{2}\hat{k}_{p}}{2}}{2c_{2}\hat{k}_{p}}\] (A12)  
This guarantees a local asymptotic stability around the origin. Increasing the value of \(k_{p}\) increases the range of values for \(\hat{k}_{d}\), including negative values. The parameter \(c_{1}\) is a coefficient with uncertainty because is measured by experimental tests. So, assume a upper bound \(c_{1m}\) (i.e., \(c_{1m}\geq c_{1}\)) the inequality (A9) is rewritten like  
\[2\Big{[}2\hat{k}_{p}(9+4k_{d})+5\hat{k}_{d}\Big{]}\leq\frac{(c_{1m}-4)^{2}}{c_ {2}}\] (A13)  
The same tuning guaranteed stability for higher values of \(c_{1}\); therefore, robustness to uncertainties in \(c_{1}\) is demonstrated.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 3 Kinetics Models and Their Development  
The study of kinetics is of great significance to reveal the law of migration and transformation of pollutants and meet the needs of reaction process development and reactor design. With the development of research, the reaction kinetics of SCWO has developed from simple models to complex ones, from macroscopic study to microscopic research at molecular and atomic levels. [PERSON] et al. [81] have collected the information on main model compounds treated with SCWO.  
According to the different modeling methods, the kinetic models of model compounds in the SCWO process can be divided into three categories: empirical model, semi-empirical model and DCKM. The DCKM mainly takes a single substance as the treatment object since it covers a large number of intermediate elementary reaction paths. The data of elementary reaction dynamics are generally derived from quantum chemistry and transition state theory and verified by standardized experiments. When the predicted value is consistent with the experimental result, the reaction mechanism can be explained theoretically, thus guiding the regulation and control of the reaction process. For the simultaneous existence of multiple reactants, especially for real pollutants, the studies of reaction kinetics mainly use empirical models. Since it is difficult to derive accurate reaction rate expressions from elementary reactions and empirical models cannot explain the main reaction processes, the semi-empirical model is derived from a simplified reaction network with measurable changes in intermediate concentration.
# Design of an Active and Passive Control System of Hand Exoskeleton for Rehabilitation
## 4 Active and Passive Control
### Modeling  
The coordinates of the exoskeleton fingers are shown in Figure 5. Every finger has four joints, which are joint MCP with two DOFs, i.e., yaw (MCP1) and pitch (MCP2), joint PIP and joint DIP. The joint angles of MCP are \(\theta_{0}\), \(\theta_{1}\), the angles of joint PIP and joint DIP are \(\theta_{2}\), \(\theta_{3}\) respectively. The lengths of links are \(a_{0}\), \(a_{1}\), \(a_{2}\), \(a_{3}\), where \(a_{0}=0\).  
* Kinematics:  
Due to the coupling effect between joint PIP and joint DIP, the ratio is \(\theta_{2}:\theta_{3}=1:u\), and MCP1 is an adaptive DOF without actuating. The kinematic relationship between the tip position and the joint angles is:  
\[\mathbf{P}_{t}=\left[\begin{array}{c}x_{t}\\ y_{t}\\ z_{t}\end{array}\right]=\left[\begin{array}{c}c\theta_{0}(a_{2}c\theta_{12} +a_{1}c\theta_{1}+a_{3}c\theta_{123})\\ s\theta_{0}(a_{2}c\theta_{12}+a_{1}c\theta_{1}+a_{3}c\theta_{123})\\ a_{1}s\theta_{1}+a_{2}s\theta_{12}+a_{3}s\theta_{123}\end{array}\right], \tag{1}\]  
* Dynamics:  
The dynamic model of the exoskeleton finger is the foundation of controller design and simulation study. In this paper, the Lagrange method is adopted to build the dynamic model in joint space in order to facilitate the study of the control method. For the exoskeleton model moving in the \(n\)-dimensional space, the Lagrange dynamic equation in joint space is:  
\[\mathbf{D}(\mathbf{\theta})\ddot{\mathbf{\theta}}+\mathbf{h}(\mathbf{\theta},\dot{\mathbf{\theta}})+ \mathbf{G}(\mathbf{\theta})+\mathbf{J}^{\mathsf{T}}\mathbf{F}_{ext}=\mathbf{\tau}, \tag{2}\]  
where \(\mathbf{D}(\mathbf{\theta})\) is the inertia matrix, \(\mathbf{D}(\mathbf{\theta})\in\mathbf{R}^{3\times 3}\), \(\mathbf{h}(\mathbf{\theta},\dot{\mathbf{\theta}})\) is the centripetal and Coriolis force, \(\mathbf{h}(\mathbf{\theta},\dot{\mathbf{\theta}})\in\mathbf{R}^{3\times 1}\), \(\mathbf{G}(\mathbf{\theta})\) is gravity item, \(\mathbf{\tau}\) is the joint actuating torque vector, \(\mathbf{\tau}=\left[\begin{array}{cc}\tau_{1}&\tau_{2}&\tau_{3}\end{array}\right] ^{\mathsf{T}}\), \(\mathbf{F}_{ext}\) is the force on the exoskeleton fingertip, \(\mathbf{F}_{ext}=\left[\begin{array}{cc}F_{x}&F_{y}&F_{z}\end{array}\right]^{ \mathsf{T}}\). Considering the coupling effect between joint PIP and joint DIP, i.e., the two SPRM with parallel mechanism are joined together and actuated by the same motor, the motor torque is the sum of the torques of the two coupling joints.  
Figure 5: Structure diagram of the single exoskeleton finger.
# Characteristic of _Pseudomonas syringae_ pv. _atrofaciens_ Isolated from Weeds of Wheat Field
###### Abstract  
The aim of this study was the identification of the causative agent of the basal glume rot of wheat _Pseudomonas syringae_ pv. _atrofaciens_ from the affected weeds in wheat crops, and determination of its virulent properties. Isolation of _P. syringae_ pv. _atrofaciens_ from weeds of wheat crops was carried out by classical microbiological methods. To identify isolated bacteria, their morphological, cultural, biochemical, and serological properties as well as fatty acids and Random Amplification of Polymorphic DNA (RAPD)-PCR (**Polymerase chain reaction**) profiles with the OPA-13 primer were studied. Pathogenic properties were investigated by artificial inoculation of wheat plants and weed plants, from which bacteria were isolated. For the first time, bacteria that are virulent both for weeds and wheat were isolated from weeds growing in wheat crops. It was shown that the fatty acids profiles of the bacteria isolated from the weeds contained typical for _P. syringae_ pv. _atrofaciens_ fatty acids, in particular, hydroxy acids: 3-hydroxydecanoic, 2-hydroxydodecanoic, and 3-hydroxydodecanoic. RAPD-PCR profiles of the newly isolated strains were identical to those of the collection strains _P. syringae_ pv. _atrofaciens_ UCM B-1011 and _P. syringae_ pv. _atrofaciens_ UCM B-1014 and contained a dominant fragment of 700 bp. The isolated strains, according to their phenotypic and genotypic properties, were identified as _P. syringae_ pv. _atrofaciens_. It was established that the causative agent of basal glume rot of wheat _P. syringae_ pv. _atrofaciens_ is polyphagous and capable of infecting a wide range of plants. The main control measure for cereals diseases caused by _P. syringae_ pv. _Atrofaciens_--crop rotations with nonhost species, should be revised, and alternative control methods must be proposed.  
_Pseudomonas syringae_ pv. _atrofaciens_; basal glume rot; wheat; weeds; RAPD; fatty acids; phenotypic and genotypic properties