esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Anionic Polysaccharides in the Development of 3D Bioprinting Technology
## 1 Introduction
Generally, in the last decades, the most described polysaccharides used in bioink to obtain 3D bioprintable scaffolds and hydrogels are hyaluronic, acid, alginate, chondro sulfate, chitosan, agarose, gellan, pullulan or cellulose [11; 12]. Their main advantages are their appropriate mechanical/rheological properties, printability, biocompatibility, biodegradability and non-toxicity. Among them, polyuronides and, above all, alginates--which are linear anionic polysaccharides that have been shown to be capable of self-assembly to form acidic or ionic crosslinked hydrogels with interesting mechanical properties--are considered ideal candidates with or without chemical functionalization [13; 14; 15]. Anionic polysaccharides such as alginate are composed of carboxylated sugars (COO-) which are specific reactive groups largely described in literature to be easily functionalized by a chemical or enzymatic process in order to generate interesting hydrogel structures with biomedical applications in tissue engineering. Consequently, this review aims to discuss state of the art 3D printing technologies and its application to design biomaterials using polysaccharides and predominantly, anionic polysaccharides such as alignates and hyaluronic acid.
# Agricultural Evolution: Process, Pattern and Water Resource Effect
## 3 Results  
### Spatiotemporal Changes of Cultivated Land  
#### 3.1.1 Cultivated Land Evolution Process  
The evolution of the structure of the cultivated land from 1990 to 2015 in the West Songnen Plain is shown in Figure 2. Over the last 25 years, cultivated land increased from 3,175,947 ha in 1990 to 3,807,719 ha in 2015, increasing by 19.89% (about 631,772 ha). Among which, paddy field and dry farmland expanded by 56.99% and 10.92%, respectively, compared with their own area in 1990. The area of paddy field showed a continuously rising trend since 1990 while dry farmland showed an increasing trend during 1990-2010 and a decreasing trend from 2010 to 2015. The proportion of paddy to cultivated land increased from 6.35% to 12.32% while that of dry farmland decreased from 93.65% to 87.68%.  
Figure 3 describes the spatial distribution of land-use pattern in four time-intervals (1990, 2000, 2010, 2015) in the study area, indicating an expansion trend of cultivated land, especially the northward movement of paddy field in the past 25 years. The paddy rice planting has moved to the northernmost part of the Songnen Plain in the study period. It can also be intuitively seen that the proportion of cultivated land is quite large.  
Figure 3: _Cont._  
Figure 2: The evolution of the structure of the cultivated land from 1990 to 2015.
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## 4 Channel Capacity Performance Analysis
### Statistics of the End-to-End Instantaneous Capacity  
The expression of the end-to-end instantaneous capacity \(C(\gamma_{e})\) of the multi-hop relay network is given in (23). To obtain the statistics of \(C(\gamma_{e})\), we define an auxiliary variable \(C_{n}^{*}\) called partial channel capacity as follows:  
\[C_{n}^{*}(\gamma_{e}) =\frac{\alpha_{n}}{\ln(2)}\cdot\ln\Bigl{(}1+\frac{\gamma_{e}}{ \beta_{n}}\Bigr{)} \tag{30}\] \[C(\gamma_{e}) =\sum_{n=0}^{\infty}C_{n}^{*}(\gamma_{e})\approx\sum_{n=0}^{N}C_ {n}^{*}(\gamma_{e}), \tag{31}\]  
where \(N\) is calculated from (13). With the assumptions in Section 2, it is straightforward to show that the auxiliary variables \(C_{n}^{*}\) (\(n=0,1,\ldots,N\)) are mutually independent.  
Next, by applying the concept of transformation of random variables on (30) ([45], pp. 100-108), the PDF \(f_{C_{n}^{*}}(c)\) of the variable \(C_{n}^{*}\) (\(n=0,1,\ldots,N\)) can be obtained from  
\[f_{C_{n}^{*}}(c)=f_{\gamma_{e}}(\gamma)\frac{d\gamma_{e}}{dC_{n}^{*}}\Bigr{|} _{\gamma_{e}=C_{n}^{*-1}(c)}=\frac{\ln(2)\cdot\beta_{n}\cdot 2^{\frac{c}{ \alpha_{n}}}}{\alpha_{n}}\cdot f_{\gamma_{e}}(\beta_{n}\cdot 2^{\frac{c}{ \alpha_{n}}}-\beta_{n}), \tag{32}\]  
where the function \(f_{\gamma_{e}}(\cdot)\) is expressed in (24), \(\alpha_{n}\) and \(\beta_{n}\) are given in (8) and (10), respectively.  
Finally, it is known that the PDF of the sum of multiple independent random variables, each of which has a PDF, is the convolution of their separate density functions ([45], pp. 182-186). Therefore, from (31), the PDF \(f_{C}(c)\) of the end-to-end instantaneous channel capacity \(C\) is expressed as  
\[f_{C}(c) =(f_{C_{0}^{*}}*\cdots*f_{C_{N}^{*}})(c)\] \[=\underbrace{\int\cdots\int}_{N-\text{fold}}f_{C_{N}^{*}}(c_{N}) \cdot f_{C_{N-1}^{*}}(c_{N-1})\cdots f_{C_{1}^{*}}(c_{1})\cdot f_{C_{0}^{*}}( c-c_{N}-c_{N-1}\cdots c_{1})\,dc_{1}dc_{2}\ldots dc_{N}, \tag{33}\]  
where \(*\) denotes the convolution operator, and the expression of the PDFs \(f_{C_{n}^{*}}(c)\) (\(n=0,1,\ldots,N\)) is given in (32) and (13). There exists no closed-form solution to the convolution in (33), but it can be numerically evaluated using mathematical softwares such as Mathematica and Matlab.  
The variance of the channel capacity is a measurement of the spread of the instantaneous capacity around the average capacity. The variance of the end-to-end instantaneous capacity, denoted as \(\sigma_{C}\), is defined as  
\[\sigma_{C}=\int_{0}^{\infty}(c-C_{avg})^{2}\cdot f_{C}(c)\,dc, \tag{34}\]  
where \(C_{avg}\) is the end-to-end average capacity given in (A9), and \(f_{C}(c)\) is the PDF of the end-to-end instantaneous capacity expressed in (33). Closed-form analytical expression for the variance of the channel capacity given in (34) is very difficult to obtain. Nevertheless, the result can be obtained numerically, as will be presented in Section 5.2.
###### Abstract
In addition, the role of CBCT in oral, dental, and maxillofacial surgery, orthodontics, airway assessment, temporomandibular joint disorders, endodontics, and periodontology appears to be important [10; 11]. Measurements and evaluations performed with CBCT enable the determination of the length of the edentulous area, alveolar bone thickness, and crest height [12; 13]. One of the advantages of CBCT for orthodontic purposes relates to its ability to provide images of the alveolar bone surrounding the teeth buccally and lingually. The only imaging diagnostic modalities available to evaluate and quantify buccal and lingual bone plaques are multislice CT and CBCT. In orthodontics, CBCT should be indicated to assess buccolingual thickness deficiencies in the alveolar ridge of adult patients subjected to critical tooth movement where buccolingual bone absence would affect orthodontic treatment [14]. In patients with edentulous crests in whom orthodontic treatment is planned, there have been no studies evaluating the status of the available residual alveolar bones in these edentulous regions compared to symmetrical toothed regions. Therefore, it is obvious that there are no data about the process and success of this treatment in patients who are planning to close edentulous spaces with orthodontic tooth movement. In order to determine whether the alveolar bone width in the edentulous region is sufficient for orthodontic tooth movement, the residual alveolar bone width in the edentulous region should be investigated. In addition, it is important to identify the possible insufficiency of the bone width according to the regions and to identify it as a risk factor. We developed this study to find answers that can fill in the information that is missing in the literature. The aim of this study was to evaluate the change in the residual alveolar bone width in the edentulous region compared to the alveolar bone width on the symmetrical toothed side using CBCT in individuals with a single missing tooth in whom orthodontic tooth movement was planned. The null hypothesis of this study was that there is no difference in bone width between the edentulous region and the symmetrical toothed region in patientsplanning orthodontic treatment, and, therefore, bone width is not a risk factor for tooth movement in the edentulous region.
# A Modified Quad Q Network Algorithm for Predicting Resource Management
## 4 Experiment and Results
### DQN, Dueling DQN, Double Dueling DQN, and QQN Learning Results on Apple's Total Resource  
Since the model was customized for specific datasets, the datasets type was changed and Total Resource on Apple was used. The learning process was achieved by measuring the reward based on this datasets.  
Figure 11 presents learning results by substituting the DQN algorithm based on the proposed Total Resource on Apple. After the learning process, the minimum reward was \(-57\), the maximum reward was \(53\), the maximum loss value was approximately \(4257\), and the minimum loss value was \(9.6\).  
Figure 11: Learning results by substituting the DQN algorithm based on the proposed Total Resource on Apple.  
Figure 10: Comparative analysis of the proposed Q Network reward for the value of Total Resource on Google.  
Figure 12 presents learning results by substituting the Dueling DQN algorithm based on the proposed Total Resource on Apple. After the learning process, the minimum reward was \(-228\), the maximum reward was \(7\), the maximum loss value was approximately \(176\) K, and the minimum loss value was \(43\).  
Figure 13 presents learning results by substituting the Double Dueling DQN algorithm based on the proposed Total Resource on Apple. After the learning process, the minimum reward was \(-39\), the maximum reward was \(58\), the maximum loss value was approximately \(2814\), and the minimum loss value was \(7\).  
Figure 14 presents learning results by substituting the Quad Q Network algorithm based on the proposed Total Resource on Apple. After the learning process, the minimum reward was \(0\), the maximum reward was \(42\), the maximum loss value was approximately \(118\), and the minimum loss value was \(0.6\).  
Figure 12: Learning results by substituting the Dueling DQN algorithm based on the proposed Total Resource on Apple.  
Figure 13: Learning results by substituting the Double Dueling DQN algorithm based on the proposed Total Resource on Apple.  
Figure 15 shows comparative analysis of the proposed Q Network reward of Total Resource on Apple. We observe that reward values stabilize quickly in the order of DQN, Dueling DQN, Double Dueling DQN, and Quad Q Network. Depending on Datasets. It is absolutely difficult to judge the performance of DQN and Dueling DQN. However, in the case of the proposed Quad Q Network, experiments using previous datasets also confirmed that fast performance derivation could result from loss value reduction, further resulting in fast stabilized reward values.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### Pks1  
Using the transposon technique, [PERSON]'s team screened out five Mtb mutants that had an impaired ability to produce biofilm. The worst mutant had defects in the _Rv2946c_ (_pks1_) gene, and the other mutants were interrupted in _Rv0021c_, _Rv0199_, _Rv0252_ (_nirB_), and _Rv3883c_ (_mycP1_) [27]. Researchers later found that the _pks1_ mutant can only bear immature and flimsy biofilms. However, when _pks1_ is expressed at different genome sites in this mutant, more mature biofilm can be observed even compared with wild-type Mtb [27]. Furthermore, according to [PERSON] and colleagues, several genes participate in regulating the expression of _pks1_, of which _Rv0042c_, _sigK_, _Rv2258c_, and _Rv3557c_ show a positive impact and _sigB_, _Rv2745c_, and _Rv3583c_ regulate the expression negatively [39].
## 3 System Identification
### GP-NARX Model  
In order to perform multiple steps ahead of prediction, it is necessary to make iterative one-step-ahead predictions while feeding back the predicted output and the measured input at previous steps. The fed-back data are organized in a vector \(\mathbf{z}_{k}\), defined as:  
\[\mathbf{z}_{k}=(y_{k-1},\ldots,y_{k-n_{a}},u_{k-1},\ldots,u_{k-n_{b}})^{T} \tag{12}\]  
A Gaussian process is introduced to model the relationship between such a vector, also called model regressor, and the actual output. By rearranging Equation (11) and imposing Equation (12), a GP-NARX model in the form of Equation (1) can be obtained, with a schematic representation, as in Figure 1.
## 4 Experimental Results  
To validate the effectiveness of the proposed vehicular VLC system, empirical experiments were conducted through implementing the proposed vehicular VLC system on the RB073 Arduino Car Learning Kit, as depicted in Figure 7. A VLC-based chain network was established using three vehicles (i.e., leading, middle, and trailing vehicles) driving within a tunnel scenario, as illustrated in Figure 8. The use of three vehicles is sufficient to demonstrate the core functionality and practicality of the proposed VLC-based chain communication network, particularly in sequential vehicle scenarios where multi-hop communication, dynamic beam alignment, and data handoff are critical. The middle vehicle, in particular, plays a pivotal role in receiving, comparing, and relaying the driving dynamics data between the leading and trailing vehicles, thereby simulating a realistic multi-node communication flow. In the current design, communication is limited to vehicles in the same lane, forming a linear VLC chain. While vehicles in adjacent lanes may introduce potential optical interference, this effect is minimized in our setup through the use of adaptive optical beam alignment, which continuously adjusts the LED orientation to maximize the signal-to-interference-plus-noise ratio (SINR) between consecutively aligned vehicles.  
The experiments evaluate the proposed vehicular VLC system under two distinct scenarios that represent common accident risks within tunnels. The first scenario (called Scenario 1), as illustrated in Figure 8a, involves vehicles driving at regular intervals when the leading vehicle suddenly halts. The second scenario (called Scenario 2), as illustrated in Figure 8b, considers a situation where a vehicle encounters unexpected traffic congestion while driving at a normal target speed. These scenarios were chosen to assess the system's ability to prevent chain collisions and hard braking incidents by enabling real-time data exchange between vehicles.  
To facilitate the monitoring and recording of real-time driving dynamics during empirical experiments, the proposed vehicular VLC system integrated Bluetooth communication through pairing the HC-06 Bluetooth modules installed at each vehicle with a laptop computer, which plays the role of data server. The data received from each vehicle were transmitted via a Bluetooth serial connection and logged using PuTTY software, which  
Figure 7: Implementation of the proposed vehicular VLC system on RB073 Arduino Car Learning Kit.  
enables the collection of structured log files for subsequent analysis. The recorded data were then processed and visualized in graphical form, providing deeper insights into system performance and the effectiveness of the proposed communication scheme under various experimental conditions.
## 3 Key Technologies and Methodologies  
### Holonic Control Architecture  
In the late of sixties, the term holon has been introduced for the first time by philosopher [PERSON] [10]. [PERSON] developed the term as a basic unit in his explanation of the evolution of the biological and social structures. Based on his observations that organisms (e.g., biological cells) are autonomous self-reliance units, which have a certain degree of independent control of their actions, yet they still subject to higher level of control instructions. His conclusion was that any organism is a whole \"halos\" and a part \"on\" in the same time, which derived the term holon [11]. In the early of nineties, the concept of holon has been redefined by the intelligent manufacturing systems consortium to fit the requirements of the factory of the future [12]. Hence the definition of the holon stands as an autonomous cooperative building block of the manufacturing system that can be used to transform, transport, store and/or validate the information and the physical signals [13].  
ADAptive holonic COntrol aRchitecture (ADACOR) model [14] defines four holon categories. The first holon category is the Resource Operational Holon (OH). An OH is usually a physical entity over the shop floor such as machine, conveyor belt, IR, worker, etc. The second holon category is the ProductHolon (PH). A PH stores the process and the product tasks needed to insure the correct manufacturing of a certain product. The third holon category is the Task Holon (TH). A TH is responsible for assigning the tasks and making sure they have been accomplished. The final holon category is the Supervisor Holon (SH). An SH is considered as an external expert that gives advices to the other holons. Yet the other holons are still free to decide their own actions. Furthermore, SH provides coordination services when it is needed to cooperate outside the boundaries of the workcell [15]. Figure 1a shows the functions of the ADACOR holons.  
The generic structure of the holon is shown in Figure 1b[16]. A holon in general contains two components [17]. The interface component physically connects the holon to the automation devices input/output (I/O). The holon physical component is responsible for controlling the data transfer from the automation devices to the holon and vice-versa. Furthermore it translates these data into useful information which can be processed by the holon. The communication component is the holon kernel. As it is responsible for communicating with other holons in the ADACOR, and interacting with the humans in the manufacturing system. All the information gathered from the other holons, the humans, and the automation devices are processed by the holon decision making algorithm, to produce a proper action based on the function of this holon in the ADACOR.
# Design of External Rotor Ferrite-Assisted Synchronous
## 3 Design of Fe-PMaSynRM for High Power Density
### Optimization Algorithm  
A multi-objective optimization algorithm has been adopted to extract an optimal external rotor PMaSynRM design with a high power density and low torque ripple. It has been shown that the differential evolution (DE) algorithm can be applied to solve the multi-objective motor design optimization with a finite number of constraints [27]. In this  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Explanation** \\ \hline \(R_{gi}\) & Air gap reluctance corresponding to the \(i\)-th iron segment \\ \(R_{sti}\) & Stator tooth reluctance corresponding to the \(i\)-th iron segment \\ \(R_{rgi}\) & Rotor yoke reluctance corresponding the \(i\)-th iron segment \\ \(R_{hi}\) & The reluctance of the \(i\)-th flux barrier \\ \(\Phi_{gi}\) & Air gap flux flowing from rotor to stator through \(R_{gi}\) \\ \(\Phi_{mi}\) & Flux through magnet in the \(i\)-th flux barrier \\ \(\Phi_{hi}\) & Flux through the flux barrier in the \(i\)-th flux barrier \\ \(f_{dsi}\) & Stator magnetic potentials corresponding the i-th iron-segment in d-axis MEC \\ \(f_{gsi}\) & Stator magnetic potentials difference across to the i-th flux barrier in q-axis EMC \\ \(f_{ri}\) & Rotor magnetic potentials on the i-th flux barrier in q-axis EMC \\ \(\Delta f_{r,i}\) & \(f_{ri}\)-\(f_{ri(i+1)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equivalent circuit parameters.  
Figure 6: The q-axis equivalent magnetic circuit (EMC) mapped on the simplified rotor architecture for three flux barriers per pole.  
Figure 7: (**a**) d-axis EMC for single flux barrier per pole, and (**b**) q-axis EMC for single flux barrier per pole.  
study, Equation (6) is the multi-objective optimization function that has been utilized to obtain the optimal external rotor PMaSynRM design with a high power density and low torque ripple.  
\[OF=\sqrt{k_{1}\ \eta^{2}+k_{2}\ T_{em}{}^{2}+k_{3}\ \left(1/T_{r}\right)^{2}} \tag{6}\]  
\(k_{1}\), \(k_{2}\), and \(k_{3}\) are the weighting factors set to obtain optimal design with respect to high efficiency, average torque, and low torque ripple. \(k_{1}\) is set to 0.3, \(k_{2}\) is set to 0.4, and \(k_{3}\) set to 0.4. The objectives and constraints considered during design optimization are as follows:  
1. maximum torque > 18 Nm
2. minimum torque ripple < 5%
3. input current < 15 A rms
4. E\({}_{\text{loss}}\) < 5% of input power
5. rotor outer diameter = 190 mm
6. rotor speed = 1800 rpm
7. ratio of maximum reluctance torque to total torque > 0.3  
A set of 50 candidate solutions are populated in each iteration of the DE algorithm. Computation effort is one of the crucial factors in the DE optimization procedure. 20 iterations have been carried out in 136 min to generate the 50 final optimal solutions, which solve the multi-objective optimization function. Among the 50 optimal solutions, only one of the designs is selected based on the manufacturing feasibility. The flow diagram to obtain the final design proposed in this study is shown in Figure 8. The following subsections discuss the final optimal solution.
## 3 Methodology
### Random Forest(RF) Model  
The random forest model is an ensemble classification technique that was developed as an extension of classification and regression trees (CART) to improve the prediction performance of the model [58]. The RF model constructs numerous decision trees to estimate the spatial relationship between groundwater and various topographic factors that consist of either categorical or continuous response variables. The RF model functions in two steps. First, it constructs the plurality of decision trees; this is the learning step. Second, a test to classify a loaded input value or predict its loading is performed. The advantages of the RF model include extremely high accuracy, simple and fast learning and testing algorithms, ability to handle thousands of input variables without deleting variables, good generalization performance through randomization, and multi-class algorithm characteristics.  
Before running the RF model, we have defined two parameters. The first is the number of randomly sampled variables (\(m_{try}\)) to use in each tree building process and the number of trees (\(n_{tree}\)) to build in the forest to run. Both parameters should be optimized to minimize generalization errors.  
Figure 3: Flow chart of the study procedures.  
[PERSON] [59], [PERSON] and [PERSON] [60] stated that even a single variable (\(m_{try}=1\)) can performance to high accuracy, while [PERSON] [61] showed that more than two variables (i.e., \(m_{try}=2,3,4,\ldots,m\)) should be used to increase the accuracy of the model.  
The RF model consists of a combination of numerous trees generated by bootstrap samples using out-of-bag (OOB) errors. Two thirds of the samples are used for training, and the other 1/3 are used for verification. The OOB is an unbiased estimate of the generalization error. A detailed description of the mathematical formulation of RF model is found in [PERSON] [59].  
The goal of the RF model is to analyze the relationship between independent and dependent variables in the model building stage to determine the weights for each factor. In this study, in order to analyze correlations between groundwater and related factors, groundwater productivity data (T or SPC) was used as a dependent variable, and 18 groundwater-related factors were used as independent variables. The parameters used in the RF model are as follows: (1) the number of randomly sampled variables in each spilt (\(m_{try}\)), (2) the number of trees to be grown (\(n_{trec}\)), and (3) the minimum size of the observations at the end node of the tree (node size). These parameters were set to 500, 10, and 5, respectively using the STATISTICA 10.1 software [62].