esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of a Tracked In-Pipe Robot's Obstacle-Crossing Performance
## 3 Results
### Analysis of the Robot's Ability to Pass Obstacles  
During the prolonged service of oil and gas pipelines, various degrees and forms of deformation may arise due to factors such as temperature fluctuations and pressure changes. These deformations can impact the internal space of a pipeline, thereby imposing higher demands on robots operating within it. Additionally, weld seams are an inevitable part of pipeline construction, and the uneven surface at these seams can impede the movement of robots within a pipeline. Therefore, designing a pipeline robot with robust obstacle-crossing capabilities is of paramount importance. For the purpose of analysis, we assumed that the obstacle was square, with a height \(h\) and a width of 20 mm. The forces acting on the robot are illustrated in Figure 4.  
Applying the static equilibrium equations yields,  
\[\left\{\begin{array}{l}N_{1}(\cos\alpha-\mu_{1}\sin\alpha)=2\mu N_{2}+2\mu N _{3}\\ N_{1}(\sin\alpha+\mu_{1}\cos\alpha)=2N_{2}\cos 60^{\circ}+2N_{3}\cos 60^{ \circ}\end{array}\right. \tag{6}\]  
where \(\alpha\) is the obstacle-crossing angle; \(\mu_{1}\) is the friction coefficient between the driving wheel and the obstacle; \(\mu\) is the friction coefficient between the driving wheel and the pipe wall; and \(N_{1}\) is the reaction force of the obstacle on the driving wheel.  
Simplifying \(N_{1}\) yields:  
\[\frac{\cos\alpha-\mu_{1}\sin\alpha}{\sin\alpha+\mu_{1}\cos\alpha}=2\mu \tag{7}\]  
\[\left\{\begin{array}{c}\sin\alpha=\frac{r-h}{r}\\ \\ \cos\alpha=\frac{\sqrt{r^{2}-(r-h)^{2}}}{r}=\frac{h\sqrt{\frac{2h}{r}-1}}{r} \end{array}\right. \tag{8}\]  
Figure 4: Force diagram of pipe robot for obstacle crossing.  
Simplification yields:  
\[h_{\text{max}}=r(1-\frac{1-2\mu\mu_{1}}{\sqrt{1+\left(2\mu+\mu_{1}\right)^{2}}}) \tag{9}\]  
Equation (9) presents the mathematical model for the maximum obstacle-crossing height of the oil and gas pipeline robot. Given parameters include \(r=25\) mm, \(\mu=0.4\), and \(\mu_{1}=0.25\). When one of the three sets of traveling mechanisms encounters an obstacle while the other two operate normally, the robot's obstacle-crossing height is 14 mm. In a scenario where all three traveling mechanisms encounter a circular obstacle, with a friction coefficient of 0.25 between the driving wheels and the obstacle, the robot's obstacle-crossing height is 7.5 mm.
###### Abstract  
The mathematical modeling of fermentation processes allows for the formulation of predictions about the kinetics of biomass growth and metabolite production as well as setting or verifying the best operative conditions in view of the economical convenience of the process. For this purpose, we performed a kinetic study of a rice flour fermentation process using _Lactobacillus paracasei_ CBA L74 with and without pH control; the pH value was set to 5.8 under pH control. Monod, Logistic, and Contois models were proposed to describe the bacterial growth rate in both conditions. The best mathematical model, which was able to describe the experimental data obtained without pH control, was the Contois model, as the specific growth rate was influenced by both the glucose reduction (from 14.31 g/L to 10.22 g/L) and the biomass production (2 log growth) that occurred during fermentation. Conversely, when pH control was implemented, both Monod and Contois models satisfactorily described the specific growth rate trend. The estimated kinetic parameters confirmed that biomass production (2 log growth) and glucose consumption (from 14.31 g/L to 6.06 g/L) did not affect the microorganism's growth capacity when the fermenting medium was maintained at an optimal pH. The lactic acid production rate described by the Luedeking-Piret model did not appear to be linked to growth in the absence of pH control while, on the other hand, this model was unsuitable for describing the experimental lactic acid concentration when pH control was applied. The kinetic modeling of lactic acid production and the percentage of added glucose in the protocol with controlled pH will be optimized in the future.  
kinetic modeling; pH control; rice flour fermentation; specific growth rate; lactic acid production rate; biokinetic parameters +
Footnote †: journal: Applied Sciences
# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 2 Architectures for Coordinated Control of DDEV Chassis
### Decentralized Architecture  
A decentralized control architecture can be regarded as a natural evolution of early automotive electronic control systems' development, and it remains the most widely used architectural form in traditional fuel vehicles to this day, with its typical structure shown in Figure 1. In a decentralized control architecture, each major functional subsystem of the vehicle chassis, such as the drive/braking system [24, 25, 26, 27], steering system [28, 29, 30, 31], and suspension system [32, 33, 34, 35], is equipped with an independent Electronic Control Unit (ECU). Each ECU is independently responsible for sensor information processing, control logic computation, and actuator drive control for its corresponding subsystem.  
Figure 1: Decentralized architecture.  
The core characteristic of this architecture lies in its high degree of modularity and the independence among its subsystems. Each subsystem and its controller can be considered a relatively independent unit, typically developed and supplied by different vendors, while the vehicle manufacturer is primarily responsible for defining interface specifications and completing the system's integration. This model can, to a certain extent, shorten development cycles and optimize economic benefits [36]. For conventional vehicles, the decentralized architecture can satisfy their basic functional requirements under most operating conditions. However, complex nonlinear coupling relationships exist among the vehicle's longitudinal, lateral, and vertical dynamics [37; 38]. The limitations of the decentralized architecture become particularly evident near or at the handling limits. Inherent issues--such as insufficient information sharing, a lack of a unified decision-making mechanism, and potential response delays--make it difficult to achieve effective decoupling and deep coordinated control of the vehicle's three-dimensional dynamic behaviors.
## 7 Guidelines on Developing Biopolymeric Microparticles
### Characterization of Microparticles Loaded with Chemical and Biological Agents
#### 7.3.3 Encapsulation Efficiency, Loading Capacity, and Swelling Degree
The swelling degree of the chitosan layer is lower in low pH media because of the strong interactions between protonated amino groups of chitosan and carboxylate anions of alginate. An increase in pH results in more ionization of the functional groups, and close to pH 6.5, chitosan becomes deprotonated, reducing the complexation disrupting the integrity of the structure, allowing a higher swelling degree [154]. _In vitro_ investigation of microcapsules in deionized water (pH \(\sim\)6) reveals swelling of the particle and the decrease in pH, due to the ionization of carboxylic groups [145]. Respectively, data from papers of [PERSON] et al. [130] and [PERSON] et al. [131,132] reveal that microparticles prepared at lower cation concentration result in a higher swelling degree, while with higher cation concentration swelling degree decreases. This is also reported in other literature data where higher Ca\({}^{2+}\) concentration reduces the swelling degree of Ca-alginate microparticles [154,130,155]. The swelling degree significantly depends on the cation concentration, as well as the presence of the chitosan layer. Higher swelling of chitosan-coated microparticles can be ascribed to its capabilities to uptake more water [156]. More available cations affect the structure and properties of the alginate gel matrix, and more specifically, the size of cavities that can accommodate water [141], causing the formation of a denser network that swells less [157]. Formation of Cu-alginate or Ca-alginate microparticles with the use of high starting concentration of cations form much faster, and thus, are smaller and stiffer mainly because of higher reticulation degree and lower retention of water in the matrix. Lower concentrations mean relatively slower gelation and matrix with compromised integrity. When preparing microparticles, the swelling degree is an important measure for the prediction of rate-controlling release mechanisms of encapsulated agents and should always be part of microparticles properties characterization. The difference in microparticles size also may significantly influence the swelling degree. With the increase in microparticles size, the chitosan layer gets thinner [158], and larger surface area makes bigger microparticles have a higher swelling degree [130]. _T. viride_ spores in microparticles somewhat decrease crosslinking degree, which can be ascribed to the mechanical interactions, as well as to the change in matrix structure because of the electrostatic repulsions between negatively charged spores and negative alginate groups. According to [PERSON] et al. [159], the zeta potential of Ca-alginate gel matrix is -10 V. Limitation of hydrogels swelling degree may be ascribed to the crosslinks and may be used as a measure of crosslinking extent [160].
## 3 Formal Definition of the Solving Problem  
**Closed-set text classification problem**. Let \(X\) represent the input space where \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) and \(C\) represent the set of predefined classes \(C=\{c_{1},c_{2},\ldots,c_{m}\}\), where \(m\) is the number of predefined classes. A classifier is a function \(f:X\to C\) that maps an input \(x_{i}\in X\) to one of the known classes \(c_{j}\in C\). The goal in a closed-set classification problem is to efficiently and accurately train the classifier \(\Gamma\) with the input-output pairs \(\{(x_{1},c_{1}),(x_{2},c_{2}),\ldots,(x_{n},c_{n})\}\), which learns to approximate \(f\). This involves minimizing the error between the predicted and actual classes, using a loss function specifically designed to enhance accuracy, thus ensuring robust performance even under varying input conditions.  
**Open-set text classification problem.**In addition to the standard closed-set classification task, this approach involves a binary classification problem to determine the presence of potentially new classes not covered by the predefined class set \(C\). Here, all classes in \(C\) are labeled as _known_, and any other classes are labeled as _new_, effectively transforming \(C\) into \(\{c_{1},c_{2}\}\) where \(c_{1}\) represents _known_ and \(c_{2}\) represents _new_ classes. The input space \(X\) not only encompasses texts \(\{x_{1},x_{2},\ldots,x_{n}\}\) of _known_ classes but also includes additional texts \(\{x_{n+1},x_{n+2},\ldots,x_{n+N}\}\) potentially belonging to _new_ classes. A classifier \(f_{binary}:X\rightarrow\{c_{1},c_{2}\}\) maps an input \(x_{i}\in X\) to either \(c_{1}\) or \(c_{2}\). The objective is to train the classifier \(\Gamma_{binary}\) with input-output pairs \(\{(x_{1},c_{1}),(x_{2},c_{1}),\ldots,(x_{n},c_{1}),(x_{n+1},c_{2}),(x_{n+2},c_ {2}),\ldots,(x_{n+N},c_{2})\}\), aiming to approximate \(f_{binary}\).  
This dual framework facilitates a comprehensive system that handles the well-defined task of closed-set classification and adapts to emerging text categories in an open-set environment, thereby enhancing the system's applicability and robustness in real-world scenarios.
# Functionality Investigation of the UAV Arranged FMCW Solid-State Marine Radar
## 7 Conclusions  
The solid-state Simrad 4G(tm) FMCW marine radar was successfully installed under a low-cost custom-built octocopter UAV and elevated to an altitude of 120 m as a proof of concept. Increasing the altitude of the radar allows for a considerably wider radar range, both due to the altitude and the possibility of rapid change of the position. No interferences  
Figure 22: Radar response plotter view at the new measurement site.  
from the radar to the UAV and vice versa were observed, showing that such integration is suitable for daily applications. Two types of external interferences were observed:  
* Ray-type interferences caused by external radio signals, which were much stronger at higher altitudes.
* Circle-type interferences caused by low frequency external signal penetration due to insufficient radio screening of the receiver.  
The mentioned interferences may be corrected or avoided by applying detailed radio spectral analysis and adaptive filtration, though surveillance of the sea using UAVs with radars onboard is still possible despite mentioned interferences. The sources of both kinds of interferences affect the radar at ranges up to 6 km. Thus, during the missions over the sea the probability of interferences will be marginal.  
The radar on the UAV can be used for the identification of vessels as a back-up option in case the AIS system fails. It can also be used for border control, anti-piracy measures, anti-drone systems, ice monitoring and in other applications.  
Conceptualization, S.R. and A.L.; methodology, D.B. and R.P.; formal analysis, S.R. and R.P.; investigation, A.L., R.P.; writing--original draft preparation, R.P. and D.U.; writing--review and editing, R.P. and D.U.; visualization, A.L., D.B. and S.R.; supervision, R.P. and D.U.; and funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the European Regional Development Fund according to the supported activity \"Targeted Research in Smart Specialization Areas\" under Measure No. 01.22-LMT-K-718.  
Data sharing not applicable.  
The authors would like to thank the editors and reviewers for many constructive suggestions and comments that helped improve the quality of the paper.  
The authors declare no conflict of interest.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods
#### 5.2.7 Ant Colony Optimization-Based Methods  
The ant colony algorithm was proposed by [PERSON] and [PERSON] in the 1990s [19]. This algorithm is inspired by the behavior of ant colonies. The researchers found that ants communicate with each other through a kind of chemical called a pheromone. The ants can leave pheromones in the path that they pass by and perceive the pheromone during their movements. The pheromone can guide the movement of ants, so the collective behaviors of ant colony algorithms consist of a large amount of ants. The phenomenon can provide positive feedback to the algorithm to enhance the routing selection process. Therefore, once there is a route which many ants pass by, then the probability that this route will be chosen by other ants will be high. The ants can use this kind of information exchange to find food. Based on this method, the ant colony algorithm can achieve global optimization. The ant colony algorithm can be used to address combinatorial optimization problems such as the asymmetric traveling salesman problem, vehicle routing, WSN routing, _etc._  
There are many researches that use the ant colony algorithms to improve the routing performance of wireless sensor networks, such as in [102; 103; 104; 105; 106]. In [102], the ant colony algorithm works with LEACH (Low Energy Adaptive Clustering Hierarchy) to choose the best next hop relay node. In [103], the AFSA (Artificial fish swarm algorithm) algorithm finds a candidate optimal routing path roughly and the ACOA (ant colony optimization algorithm) algorithm helps the AFSA algorithm to calibrate this routing path. Similar algorithms can be found in [104; 105; 106]; in these algorithms the ant colony algorithms are used independently or combined with other intelligent algorithms to select the best routing or the best cluster head of the network.
# Chromatographic Separation of Rare Earth Elements as MGDA Complexes on Anion Exchange Resins
## 3 Results and Discussion
### Sorption Behavior of REE
#### 3.2.3 Effect of the Feed pH on REE Separation  
For a substantial number of the experiments, the initial pH of the feed pulse was greater than 12. Highly alkaline conditions were selected to ensure complexation of REEs with MGDA (pKa\({}_{1}\) = 9.9; pK\({}_{2}\) = 2.6; pK\({}_{3}\) = 1.5). A high pH also ensures negative charge of the complex, which is needed for the sorption to an SBA resin. The pH of the feed pulse decreases soon after injecting it into the column because the column was equilibrated with an acidic mobile phase. In the case of a water equilibrated column, the pH of the outcoming feed does not change drastically based on the pH values measured from the effluent fractions (Figure S1). A high feed pH is an important factor to ensure the sorption of the MGDA-REE complexes on the anion exchange resin [39].  
The use of feed with a pH between 9 and 10 (Feeds 3 and 4) resulted in different retention behavior for La and Nd complexes than with pH 12 (Figure 7). Due to the lower pH of the feed (pH = 9), the MGDA-La complex is almost immediately affected by the acid content of the column. This neutralized and partly protonated MGDA-La complex does not show any retention. La can be seen as a single sharp peak at the beginning of the chromatogram that does not tail significantly. The retention behavior of Nd has also changed. The position of the peak has shifted to the right in the chromatogram. Overall, the peak shape and its width is similar to feed with a pH of 12.20 (Figure 7). Since the MGDA-La complex does not interact with the resin's functional groups, the MGDA-Nd complex is the first to interact with the mobile phase after the feed has been injected to the column. In addition, the lower initial pH of the feed can contribute to altered retention. Retention of the MGDA-Eu complex did not change with the varied pH of the feed.  
Further acidification of the feed would also change the retentions of the MGDA-Nd and MGDA-Eu complexes. A more acidic feed would render MGDA-REE complexes chargeless, or the complex would at least lose the negative charge that is needed for interaction with the resin phase.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 5 Chatbots as Statistical Consultants in Infectious Disease Settings: Potential and Reliability
### Potential of Chatbots as Statistical Advisors  
When faced with questions such as \"I have this data set, what statistical method should I use?\", chatbots such as ChatGPT can quickly generate plausible answers by leveraging their knowledge of general statistical principles. For example, if a user presents  
Figure 2: Example scenario of a novel hypothetic respiratory virus outbreak.  
time series data on infection rates, the chatbot might suggest using techniques such as ARIMA (AutoRegressive Integrated Moving Average) models or exponential smoothing to predict future trends [82]. For spatial data involving epidemic outbreaks, the chatbot might recommend geospatial analysis tools such as kriging or spatial autocorrelation, which are useful for identifying disease clusters [83]. These recommendations can be useful, especially in situations where human expertise is not readily available, making chatbot-guided suggestions a scalable solution for public health workers during emergencies.  
One of the strengths of the chatbot is its ability to handle different types of data. If asked a question about how to handle multivariable datasets, the chatbot might suggest principal component analysis (PCA) to reduce data dimensionality and identify key factors driving disease progression [84]. Where the user is dealing with survey data from affected populations, ChatGPT could recommend logistic regression or chi-square tests to explore relationships between variables, such as symptoms and risk factors. This wide range of functionality could support decision making at various stages of the epidemic response, from initial surveillance to resource allocation and intervention evaluation.
# Laboratory Evaluation of Wear Particle Emissions and
## 4 Conclusions  
This study aimed to evaluate the concentration of suspended dust and TRWP emissions of the PSMA mixture for each NMAS using the LTRWP tester, which was developed to analyze the wear particle emissions generated due to the friction between the pavement surface and tires. The main results of this paper are summarized as follows.  
1. The results of analyzing the tendency of wear particle generation using the LTRWP tester show that the cumulative number of wear particles over the test time has a directly proportional relationship with a coefficient of determination (R\({}^{2}\)) of 0.98. Therefore, the tendency for a certain level of wear to occur regularly over time was verified.
2. Suspended dust concentrations (for PM\({}_{2}\) and smaller) measured in real time by the sensor did not show any particular tendency with the NMAS of the PSMA mixture. However, for PM\({}_{5}\) and larger, the suspended dust concentration tended to increase as the NMAS increased.
3. In the correlation analysis between the suspended dust concentration and TRWP emissions, PM\({}_{2}\) did not satisfy the significance level of the estimation equation, and PM\({}_{5}\) was excluded due to multicollinearity issues. Hence, the TRWP emission estimation equation was developed using the suspended dust concentration for PM\({}_{10}\).
4. According to the results of the wear test for each NMAS of the PSMA mixture, RWP emissions increased by approximately 0.15 g as the NMAS increased by 1 mm. Thus, it was found that the TRWP emissions decreased by 20% when using the 6 mm SMA mixture compared to the 13 mm SMA mixture.  
The findings derived in this study are identical to past studies in terms of the NMAS SMA effects on TRWP emissions [6; 9; 10]. However, it should be noted that this study has several limitations. Future research should include additional experiments with various binder types, asphalt mixtures, gradations, aggregate types, test temperatures, speeds, and loads to better understand TRWP generation. Analyzing the environmental and economic impacts of the results is also crucial. Finally, researchers should propose management strategies for TRWPs to contribute to practical problem-solving.  
Conceptualization, J.L. and G.Y.; methodology, J.L. and G.Y.; investigation, Y.H. and O.K.; writing--original draft preparation, G.Y.; writing--review and edition, O.K. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Agency for Infrastructure Technology Advancement (KIA) grant, funded by the Ministry of Land, Infrastructure, and Transport (grant number RS-2019-KA152342).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.