esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design Principle
### Electrical Systems  
As presented in Figure 10, the electrical systems are divided into two parts: the side of the robot and the control station. The signal is transmitted between the side of the robot and the control station through an RS485 interface with Modbus RTU protocol using two control cables. A PLC (programmable logic controller) in the control station is configured as the master and the side of the robot as the slave. For electrical power, two sets of 48V DC power supplies are used to supply the robot and the control station. In addition, 8-core AWG24 cables are connected to the front and rear camera for data transmission.  
Figure 9: A kinematic model of the in-pipe robot with (**a**) perspective view and (**b**) side view.  
The side of the robot uses a microcontroller to receive control commands and to send the measurement data from the sensors, e.g., temperature and humidity, to the control station. The main tasks of the control station are to control the robot's movement, to display the measurement data and real-time feedback from the camera, and to supply the electrical power to the robot. As presented in Figures 11 and 12, the robot can be controlled via visual control panels that are divided into two parts: a camera display panel (Figure 11) and the robot's control panel (Figure 12). The camera display panel is equipped with two screens to display the real-time video and images from the robot's front and rear cameras. The images and videos can be recorded and saved to external hard drives via USB ports. The robot control panel is equipped with power switches, status indicator lights, a joystic to control the robot's motion, and a 7-inch Siemens SIMATIC HMI touch screen panel. The touch screen panel displays the internal temperature and humidity of each robot module and the robot's speed.  
Figure 10: The schematics of the robot’s electrical diagram.
# Towards Adaptive Multipath Managing:
## 3 System Detail Design
Moreover, in order to maximize the network resource utilization possibly, in MPTCP-LM\({}^{3}\), a path \(p_{j}\) in \(p_{\text{R\_list}}\) can be put into \(p_{\text{A\_list}}\) for multipath transmission, if its current transmission quality, \(\mathbb{R}_{p_{j}}\), is not lower than the average transmission quality of other high performing paths (in order to make sure \(p_{j}\) has little transmission quality difference compared to other high performing paths), namely,  
\[\mathbb{R}_{p_{j}}\geq\frac{1}{\text{count}(p_{\text{A\_list}})}\times\sum_{k =1}^{\text{count}(p_{\text{A\_list}})}\mathbb{R}_{p_{k}}, \tag{9}\]  
where \(\text{count}(p_{\text{A\_list}})\) is the number of paths in \(p_{\text{A\_list}}\). \(p_{k}\) is the \(k^{\text{th}}\) path in \(p_{\text{A\_list}}\). The pseudocode of multipath management mechanism used in MPTCP-LM\({}^{3}\) is presented in Algorithm 1. For convenience, we adopted the following notations used in Algorithm 1 (as shown in Table 2). For Algorithm 1, the time complexity is primarily influenced by the first \"for\" statement (from Line 2 to Line 4), the second \"for\" statement (from Line 16 to Line 21), and the sort algorithm in Line 5. For the two \"for\" statements, the time complexity of each \"for\" statement is \(\text{O}\left(n\right)\) (we assumed the second \"for\" statement was executed). As for the sort algorithm, the time complexity was \(\text{O}\left(\text{nlog}_{2}\text{n}\right)\) because the QuickSort algorithm was used for sorting the paths. Since there was \(f\left(\text{n}\right)\,=\,2\text{n}\,\,+\,\text{nlog}_{2}\text{n}\), therefore the time complexity of this algorithm was \(\text{O}\left(\text{nlog}_{2}\text{n}\right)\). More simply, the total number of paths collected by Algorithm 1 can be expressed by using the following mathematical model:  
\[path\_num=\left\{ \begin{array}{ll}path\_num,&\text{if }buf\_block\_count=0;\\ \left\lceil\frac{1}{2}\times path\_num\right\rceil\,,&\text{if }buf\_block\_count<3;\\ 1\,,&\text{if }buf\_block\_count{=3}.\end{array} \right. \tag{10}\]
# Longer Ubiquinone Side Chains Contribute to Enhanced Farnesol Resistance in Yeasts
###### Abstract  
Ubiquinones (UQ) are intrinsic lipid components of many membranes. Besides their role in electron-transfer reactions there is evidence for them acting as free radical scavengers, yet their other roles in biological systems have received little study. The dimorphic fungal pathogen _Candida albicans_ secretes farnesol as both a virulence factor and a quorum-sensing molecule. Thus, we were intrigued by the presence of UQ9 isoprenologue in farnesol-producing _Candida_ species while other members of this genera harbor UQ7 as their major electron carrier. We examined the effect of UQ side chain length in _Saccharomyces cerevisiae_ and _C. albicans_ with a view towards identifying the mechanisms by which _C. albicans_ protects itself from the high levels of farnesol it secretes, levels that are toxic to many other fungi including _S. cerevisiae_. In this study, we identify UQ9 as the major UQ isoprenoid in _C. albicans_, regardless of growth conditions or cell morphology. A _S. cerevisiae_ model yeast engineered to make UQ9 instead of UQ6 was 4-5 times more resistant to exogenous farnesol than the parent yeast and this resistance was accompanied by greatly reduced reactive oxygen species (ROS) production. The resistance provided by UQ9 is specific for farnesol in that it does not increase resistance to high salt (1M NaCl) or other oxidants (5 mM H\({}_{2}\)O\({}_{2}\) or 1 mM menadione). Additionally, the protection provided by UQ9 appears to be structural rather than transcriptional; UQ9 does not alter key transcriptional responses to farnesol stress. Here, we propose a model in which the longer UQ side chains are more firmly embedded in the mitochondrial membrane making them harder to pry out, so that in the presence of farnesol they remain functional without producing excess ROS. _C. albicans_ and _Candida dubliniensis_ evolved to use UQ9 rather than UQ7 as in other _Candida_ species or UQ6 as in _S. cerevisiae_. This adaptive mechanism highlights the significance of UQ side chains in farnesol production and resistance quite apart from being an electron carrier in the respiratory chain.  
f arnesol resistance; ubiquinone side chain; yeasts; oxidative stress +
Footnote †: journal: Microorganisms
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 2 Related Work  
### Ground Vibrations from Blasting  
In opencast mining, predicting blast-induced ground vibrations and their frequencies is essential for the safety of nearby structures and inhabitants. Researchers have employed various techniques to predict and control these vibrations, including neural network approaches which have shown promising results [1; 2; 3; 4; 24]. Various factors influence the intensity and frequency of ground vibrations, including the explosive type, blasting method, blast-to-structure distance, and the rock and soil's geological properties. Studies over the years have provided insights into the impact of blasting on surrounding structures [6; 13; 14; 15; 25] and informed guidelines for explosive use and human vibration exposure in buildings [11; 12]. Understanding explosion-generated strain pulses in rock is also crucial [16; 17]. Previous work has emphasized the importance of controlling vibration in mines using advanced predictors [18]. Multivariate analysis for predicting blast-induced ground vibrations has also been explored [10]. In the domain of recommendation systems, matrix factorization techniques have been foundational. [PERSON] et al. provided an overview of these techniques for recommender systems [21]. GroupLens introduced an architecture for collaborative netnews filtering [22], and [PERSON] et al. investigated rank selection in non-negative matrix factorization [23]. For urban planning, visualizing predicted ground vibration is essential, and Web-GIS systems have been developed to aid this [20].
## 4 Discussion
### The WUE Characteristics  
Our study determined that the average annual WUE in the FPENC was 1.32 \(\pm\) 0.11 gC kg\({}^{-1}\)H\({}_{2}\)O\({}^{-1}\). Using a similar approach, [PERSON] et al. (2015) and [PERSON] et al. (2017) reported the ecosystem WUE from a global scale, with [PERSON] et al. finding an annual WUE of 1.7 gC kg\({}^{-1}\)H\({}_{2}\)O\({}^{-1}\), which also exceeds our findings [11; 57]. The value in our study is lower than the values reported in a previous study of Indian forest (1.72-2.02 gC kg\({}^{-1}\)H\({}_{2}\)O\({}^{-1}\)) [6; 58], and also falls short of 1.55 gC kg\({}^{-1}\)H\({}_{2}\)O\({}^{-1}\) reported for China [9]. And it was higher than the annual average WUE (1.24 gC kg\({}^{-1}\)H\({}_{2}\)O\({}^{-1}\)) in Loess Plateau [8]. Another research paper in an aridity area of China (the area contains the FPENC) showed that the average WUE during growing season was 1.67 \(\pm\) 0.98 gC kg\({}^{-1}\)H\({}_{2}\)O\({}^{-1}\), which was slightly higher than our results. The discrepancies between our findings and previous studies may stem from two reasons, one is the differences in environmental conditions and vegetation types, different research areas may exhibit differences in environment features and vegetation types, leading the difference in WUE [9; 21; 23]. Another reason is the data source. We focused on the pixel analysis using the MODIS product in the FPENC, and other studies studied the WUE in aridity of China based on the meta-analysis at 31 EC sites [23]. Previous research has reported the underestimation of MODIS GPP and ET in arid area [52].  
WUE is greatly influenced by vegetation types, prior research has established that forests typically demonstrate a higher WUE compared to croplands, while grasslands exhibit the lowest levels [21; 52]. In our research, the hierarchy of WUE among various vegetation types was as follows: ENF > MF > CSH > WSA > DNF > DBF > GRA > SAV > CRO > OSH. This ranking slightly deviates from previous study that reported WUE in the order of DBF > DNF > EBF > CRO > OSH > ENF > MF > GRA [9]. The discrepancies between our findings and previous studies may stem from differences in the research area, which lead to the discrepancy in environmental conditions, plant species, and cultivation practices. Our research showed that WUE in ENF and MF remained at a relatively high value compared to other vegetation types, corroborating findingsfrom earlier studies [21; 23; 50]. In our study, the WUE for cropland was measured at 2.0 gC kg\({}^{-1}\)H\({}_{2}\)O, lower than values observed in a sub-humid area [52], highlighting that temperature and precipitation significantly influence cropland WUE. The WUE observed in FPENC grasslands fell within the ranges established in previous research [23; 59].
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 2 Materials and Methods
### In Vivo Analysis
#### 2.3.1 Wall Shear Stress  
The evaluation of the wall shear stress was performed by means of computational fluid dynamic (CFD) simulations in order to characterize the local fluid dynamic patterns and quantify local hemodynamic disturbances. The reconstructed geometry of the aortic lumen for individual aortas was imported in Icem (version 2019.2; Ansys, Canonsburg, PA, USA) and discretized into tetrahedral elements with a final mesh density of approximately 2 to 3 million elements selected following a mesh sensitivity analysis. The use of a boundary layer consisting of prismatic elements allowed for an improved result accuracy at the geometry boundary where the variable of interest, the wall shear stress, was computed. The CFD commercial software Fluent (version 2019.2; Ansys, Canonsburg, PA, USA) was used to run transient-time simulations and reproduce the aortic fluid dynamics during a cardiac cycle. A semi-implicit method for pressure-linked equations (SIMPLE algorithm) was employed to couple pressure and velocity and solve the Navier-Stokes equations for the blood modelled as an incompressible, Newtonian fluid undergoing laminar flow [4; 5]. A second order upwind formulation and a second order implicit transient formulation were used for spatial and temporal discretization respectively. Boundary conditions were imposed in order to enable a solution of the fluid flow equations. Specifically, a velocity boundary condition was defined at the computational domain inlet [30], an outflow boundary condition was prescribed at the outlets with 50% flow division into each of the iliac arteries and a no-slip condition was imposed at the wall of the fluid domain (fluid interface) assumed as rigid. The results of CFD simulations were post-processed to derive the time-averaged wall shear stress (TAWSS) representing the shear-stress loading on the aortic wall averaged over the cardiac cycle as expressed by the equation  
\[\text{TAWSS}=\frac{1}{\text{T}}\int_{0}^{\text{T}}\lvert\text{WSS}(\text{s}, \text{t})\rvert\text{dt}, \tag{1}\]  
where T is the duration of the cardiac cycle and WSS(s,t) is the magnitude of the wall shear stress vector at a specific location (s) and time (t).
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 6 _Toxoplasma gondii_  
Toxoplasmosis is a zoonotic opportunistic parasitic infection that affects humans and animals worldwide [156; 157] and is caused by the protozoan _Toxoplasma gondii_, clade Sar Alvelata, phylum Apicomplexa, class Conoidasida, subclass Coccidia, family Sarcocytidae [2]. Genotyping of this parasite is carried out by multiplex PCR tests targeted on microsatellite markers located in different genes. These markers are distinguished in typing markers (_TUB2_, _W35_, _TgM-A_, _B18_, _B17_, _M33_, _IV.1_, and _X1.1_) that differentiate _T. gondii_ genotypes I, II, and III and fingerprinting markers (_M48_, _M102_, _N60_, _N82_, _AA_, _N61_, and _N83_) that differentiate between strains [156].  
It was estimated that about one-third of the global population is infected with _T. gondii_, which is ranked fourth globally and second in Europe among foodborne parasites [157]. Felids are the definitive hosts, in which sexual reproduction occurs, and shed the infectiveocysts, while other animals and humans are intermediate hosts. Up to 70% of the cat population is infected with _T. gondii_, and cats can shed millions ofocysts in their feces. Oocysts may survive for several years in the environment and infect intermediate hosts through water and food [11; 157; 158; 159; 160; 161; 162]. Upon infection, the parasites differentiate into tachyzoites that rapidly divide asexually and spread in the body, causing toxoplasmosis [160; 161].  
Humans and animals can become infected with _T. gondii_ also through the ingestion of the parasite in the form of tissue cysts containing slowly replicating bradyzoites [161; 162] that originate from tachyzoites and can differentiate back into tachyzoites [160].
# Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
## 2 Methodology
### Violent Pose Classification Model  
In the previous section (i.e., Section 2.4), we explained how violent poses or actions are detected; the next step is the classification of these actions, which is achieved using the LSTM classifier. LSTM network is a kind of recurrence neural network (RNN) architecture also known as RNN [41; 42; 43]. This is the architecture used to solve sequential data tasks and mainly focuses on the problem of vanishing gradient. Here, architecture has three dense layers and three LSTM layers or hidden layers; each layer is shown below in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Learning rate & \(1\times 10^{-5}\) \\ Momentum & 0.98 \\ Weight decay & 0.001 \\ Batch size & 16 \\ Optimizer & Adam \\ Dimensions & \(416\times 416\) \\ Epochs & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental model specification.  
The input shape is taken (30,132), in which 30 is the sequence length or time steps and 132 denotes the number of dimensions or features. Subsequently, the first hidden layer is the lstm_input layer, which has an input of 1024 and 512 units as output, and the second hidden layer is lstm_1, which has an input of 512 and 256 units as output; the return sequence is true and the activation function is relu. The third layer is lstm_2, which has an input of 256 and 128 units as output; the return sequence is false and the activation function is relu. Now, the dense layers are fully connected, and the first layer and second dense layer have 64 and 32 units with relu activation function, respectively. The final dense layer has an equal number of units and actions provided in the dataset. This dense layer activation function is softmax, which can also be used for multiclass classification problems. The learning rate is 0.000005 and the optimizer is Adam. The loss function was categorical cross entropy at the time of the training process of the model. These are the suitable parameters that provide the best performance to classify the violent action pose.
# Central Non-Linear Model-Based Predictive Vehicle Dynamics Control
## 4 Results
### Sinusoidal Steering
#### 4.3.3 Pitch Behavior  
The qualitative evaluation for the control objective of reducing pitching movements is likewise realized by examining the pitch angle curves during the sinusoidal steering. The pitch angle curves are shown in Figure 11.  
The positive influence of central predictive control is likewise evident during the sinusoidal steering. Compared to the passive vehicle and the conventional control approach, the pitching movements are reduced.  
Figure 11: Control Quality Sinusoidal Steering–Pitch Behavior–Pitch Angle Curves.  
Figure 10: Control Quality Sinusoidal Steering–Self-Steering Behavior–Pseudo Quantity Curves.  
The quantitative evaluation confirms these results. Whereas the vehicle utilizing the conventional control approach deviates from the stationary pitch angle \(\theta_{\text{Ref}}\) by a root mean squared error of 0.0035 rad, which corresponds to 0.2005\({}^{\circ}\), the non-linear model-based predictive control results in a root mean squared error of 0.0025 rad, which corresponds to 0.1432\({}^{\circ}\).  
In comparison to the passive vehicle and the application of the conventional control approach, the utilization of the central predictive control system, thus, increases the ride comfort.
# Applications of Capacitive Imaging in Human Skin Texture and Hair Analysis
## 2 Materials and Methods
### Analysis of Capacitive Image  
In this section, image processing algorithms are applied on skin and hair capacitive images to demonstrate how texture and color information can be used in cosmetic and pharmaceutical industries. More specifically, algorithms are presented: (a) to detect the average surface area of skin polygons and associate the results with the chronological age of the subject, (b) to estimate the length of skin furrows and track progression over the course of time and (c) to calculate the water content and loss rate in human hair.
## 5 Conclusions  
TMDs are a multi-factorial and polysymptomatic pathology and their management must be customized for each patient. BoNT-A is one of the proposed treatments for the management of TMDs due to its analgesic and relaxing abilities. Thus, it has become an attractive choice as an adjuvant therapy in patients with myofascal TMDs who do not achieve a complete response with conservative management and pharmacotherapy. This systematic review of RCTs showed BoNT-A was safe and efficacious for the management of patients with muscular temporomandibular disorders and its effect extends beyond its muscle-relaxing effects. However, due to its dose-dependent adverse events, its efficacy to development of adverse events ratio should be assessed. Low doses are recommended when BoNT-A is considered for persistent orofacial pain related to temporomandibular disorders. Furthermore, BoNT-A has also been proposed for the treatment of bruxism associated with TMDs. Individual studies have shown promising results. However, the reported systematic reviews analyzing the effect of BoNT on bruxism reported different conclusions, suggesting that more clinical trials with more participants and comparisons of different dosages are needed. In conclusion, to further clarify the indications for BoNT-A, additional randomized controlled studies with larger sample sizes, lower levels of bias, and longer follow-up times must be conducted.  
Conceptualization, F.D.F. and A.L.; methodology, F.D.F.; software, M.C.; validation, M.C., G.C. and G.M.; formal analysis, F.D.F.; investigation, F.D.F.; resources, A.L.; data curation, M.D.B.; writing--original draft preparation, B.V.; writing--review and editing, E.A.C.; visualization, E.A.C.; supervision, E.A.C.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are available on request to the authors.  
[PERSON] was recipient of an Osteology Foundation Scholarship.  
The authors declare no conflict of interest.
## 3 Principle of the Prediction Model
### Feature Extraction  
The key features of the temperature field mainly include the temperature gradient, temperature distribution, melting pool information, and cooling rate. For the specific SLS process, the number of feature values depends on the pixel size of the analyzed temperature field images. Taking 40 \(\times\) 80 images as an example, the key extracted features of the temperature field include:  
1. Temperature gradient information: take the central point of the melting pool as a starting point and calculate the temperature gradient along eight directions--namely, upper, lower, left, right, upper left, upper right, lower left, and lower right--as well as five numerical values in each direction to obtain a total of 40 numerical values.
2. Temperature distribution information: maximum temperature, minimum temperature, average temperature, and the temperature standard deviation of each row, and column of the images result in 4 \(\times\) 80 + 4 \(\times\) 40 = 480 values.
3. Set three temperature thresholds to record the number of pixel points higher than the threshold.  
Figure 4: Data Structure of Key Features: (**a**) Extracted information from one temperature image; **(b)** The results of the maximum temperature distribution; **(c)** The result of minimum temperature distribution.  
Cooling rate: each pixel point has a cooling rate of 40 \(\times\) 80 = 3200 values.
* Record the time at which a pixel exceeds the temperature threshold: a total of 3 \(\times\) 40 \(\times\) 80 = 9600 values.
* Record the maximum temperature of the current layer, the length, width, area, and average temperature of the melting pool, the x and y coordinates of the maximum temperature pixel and the current layer number, with a total of eight values.  
Therefore, according to images with a size of 40 \(\times\) 80, the key features of the temperature field have a total of 13,331 values.