esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Physical Model  
The mobile robot used for validating the dynamic model is a mechatronic platform, designed with relatively high-performance components that work together to ensure its efficient and precise operation.  
The robot's control system was built around two microcontroller development boards with an AT Mega 2560 controller (Microchip, Chandler, AZ, USA). These boards were chosen for their high processing power and extensive array of input/output pins, making them ideal for complex robotic applications. They offer a significant improvement over development boards in terms of I/O capabilities and processing power, essential for managing multiple sensors and motor controls.  
Figure 5: Simulink-Simscape block diagram for actuating the mobile robot including the friction forces.  
Figure 6: Block diagram for measuring the resistant torque at the wheels and consumed current.  
For motion control, the robot uses two DualVNH5019 (Pololu Robotics and Electronics, Las Vegas, NV, USA) motor driver shields. These high-power motor drivers can operate from 5.5 to 24 V and deliver a continuous 12 A (30 A peak) per motor, or 24 A (60 A peak) to a single motor when combined. They offer current-sense feedback, accept high PWM frequencies for quieter operation, and are equipped with a variety of protection features such as reverse-voltage protection, thermal shutdown, and short-circuit protection.  
The robot is actuated by four 12 V DC motors, each capable of reaching a maximum speed of 200 RPM. These motors are controlled by the DualVNH5019 motor driver shields, allowing for precise and responsive movement.  
Powering the robot is a D24V150 Fx (Pololu Robotics and Electronics, Las Vegas, NV, USA) family step-down voltage regulator, which is a high-current synchronous switching regulator that efficiently generates lower output voltages from input voltages as high as 40 V. It has an efficiency of 80% to 95%, an instantaneous current limit of approximately 32 A, and can typically support continuous output currents between 5 A and 20 A.  
The robot's power system includes a 14.8 V Li-Fo battery, coupled with a 12 V, 15 A voltage regulator, ensuring stable and adequate power for the motors and electronic components.  
To monitor the robot's power consumption, an ACHS-7123 (Pololu Robotics and Electronics, Las Vegas, NV, USA) current sensor was integrated into its circuit. This sensor can handle a bidirectional current input up to 30 A and outputs a proportional analog voltage. It features low internal resistance (-0.7 m\(\Omega\)), electrical isolation up to 3 kV RMS, a bandwidth of 80 kHz, high accuracy, and a wide operating temperature range (-40 \({}^{\circ}\)C to 110 \({}^{\circ}\)C).  
The mobile robot used for validating the dynamic model in the differential drive configuration is presented in Figure 7a, and that used to validate the omni drive configuration is presented in Figure 7b.  
The components used for developing the mobile platform are presented in the electronic diagram below; see Figure 8.  
Figure 7: Developed four-wheeled mobile robot: (**a**) conventional wheel design; (**b**) Mecanum wheel design.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 1 Introduction
### Medical Imaging Modalities  
Radiologists specialize in the diagnosis and treatment of diseases by utilizing a range of imaging modalities, including x-rays, angiography, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), nuclear medicine, and sonography. Each modality differs fundamentally in the technique used to acquire medical images.  
Technologies, utilization rates for MRI, CT, and other modalities have continued to climb in both Ontario, Canada, and the United States, based on a study of over 135 million imaging examinations conducted by Kaiser Permanente, UC San Francisco, and UC Davis. This trend has raised concerns about potential overuse of medical imaging.  
A study published on 3 September 2019, in the American Medical Association Journal, provided the first comprehensive analysis of imaging trends across diverse populations. Although the early 2000s saw a modest decline in imaging utilization, a subsequent rise in MRI and CT usage has been observed, particularly among adult outpatient groups, with pediatric CT utilization showing a decline in recent years. Figure 1 illustrates the trends in the use of medical imaging technologies within the U.S. healthcare system.
###### Abstract  
The acoustic voice quality index (AVQI) is a multiparametric tool based on six acoustic measurements to quantify overall voice quality in an objective manner, with the smoothed version of the cepstral peak prominence (CPPS) as its main contributor. In the last decade, many studies demonstrated its robust diagnostic accuracy and high sensitivity to voice changes across voice therapy in different languages. The aim of the present study was to provide information regarding AVQI's and CPPS's performance in normophonic non-treatment-seeking subjects, since these data are still scarce; concatenated voice samples, consisting of sustained vowel phonation and continuous speech, from 123 subjects (72 females, 51 males; between 20 and 60 years old) without vocally relevant complaints were evaluated by three raters and run in AVQI v.02.06. According to this auditory-perceptual evaluation, two cohorts were set up (normophonia versus slight perceived dysphonia). First, gender effects were investigated. Secondly, between-cohort differences in AVQI and CPPS were investigated. Thirdly, with the number of judges giving G = 1 to partition three sub-levels of slight hoarseness as an independent factor, differences in AVQI and CPPS across these sub-levels were investigated; for AVQI, no significant gender effect was found, whereas, for CPPS, significant trends were observed. For both AVQI and CPPS, no significant differences were found between normophonic and slightly dysphonic subjects. For AVQI, however, this difference did approach significance; these findings emphasize the need for a normative study with a greater sample size and subsequently greater statistical power to detect possible significant effects and differences.  
acoustic voice quality index; smoothed cepstral peak prominence; normative data +
Footnote †: journal: Accepted: 14 June 2019; Published: 21 June 2019
## 3 Results
For each error, magnitude is tested in the range 0.25 to 1 cm, and distinction is made between systematic (dark grey) and random (light grey) errors. Black dots represent the outliers. The asterisks define the significance level of the two-tailed Wilcoxon signed-rank paired test between error types, for each error magnitude: * for \(p<0.05\), ** for \(p<0.01\), **** for \(p<0.0001\). When comparing the two factors contributing to head model inaccuracy, i.e., the electrode positioning technique and the head tissue segmentation method, we observed that the former one had significantly less impact on the estimation of the connectivity matrices (Figure 7). Indeed, the z-values for 3D scanning and digitizer techniques were always significantly higher (\(p<0.001\)) than those obtained for the different head tissue segmentation methods (Table S4). Lastly, we contrasted intra- and inter-network connectivity values for each resting-state network and frequency band, and examined the impact of head model inaccuracy (Figure S5). A correlation analysis conducted using the standard analysis workflow as a reference confirmed that the head tissue segmentation had a stronger impact on connectivity estimation than electrode positioning (Figure 8 and Figure S6). In line with other analyses conducted in this study (Figures S3 and S4), VAN and LN were found to be the most sensitive networks to variations in the head modeling strategy, whereas DAN was the most robust one. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Head Tissue Segmentation Methods** & **df** & **Sum Squares** & **Mean Square** & **F** & \(p\)**-Value** \\ \hline Network & 5 & 1.18 & 0.24 & 19.52 & \textless{}0.001 \\ Band & 4 & 5.83 & 1.46 & 120.42 & \textless{}0.001 \\ Segmentation method & 2 & 0.29 & 0.15 & 12.17 & \textless{}0.001 \\ Residuals & 78 & 0.94 & 0.01 & & \\ Total & 89 & 8.24 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ANOVA testing for head tissue segmentation methods. Three factors were assessed: resting-state network (DMN; DAN; VAN; LN; SMN; VN), frequency band (delta; theta; alpha; beta; gamma) and segmentation method (12-layer WTS; 3-layer WTS; 3-layer template). Figure 7: Correlation between the reference connectivity matrices and those obtained when introducing errors in the head model. These errors were related to the electrode localization technique (dark grey) or to the head tissue segmentation method (light grey). Black dots represent the outliers.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### Peptidyl-Prolyl Isomerase-B (PpiB)  
Peptidyl-prolyl isomerase (Ppiase) is a kind of cyclophilin that can help intercellular proteins fold, and it is widely expressed in human and bacteria cells. Two kinds of Ppiase exist in Mtb, PpiA and PpiB, which are encoded by _Rv0009_ and _Rv2582_, respectively [40]. PpiB is relevant to mycobacterial biofilm formation [28]. [PERSON] et al. constructed _M. smegmatis_ recombinant strains overexpressing Mtb PpiA (Ms_PpiA) and PpiB (Ms_PpiB) and found increased biofilm formation in Ms_PpiB, whereas Ms_PpiA and the control strain generated only basal level of biofilm [28]. Surprisingly, this feature of PpiB provides the possibility to interfere with Mtb infections. When two FDA-approved drugs, cyclosporine-A and acarbose, were added to an Mtb culture medium separately, biofilm formation significantly decreased. The blocking effects of cyclosporine-A and acarbose were accomplished by mitigating PpiB protein activity, and their binding sites in PpiB are conserved across infectious pathogens [28]. Furthermore, the presence of these two drugs increased the sensitivity of Mtb to the current anti-TB drugs INH and ethambutol, thus reducing their dosage in anti-TB therapy [28].
## 5 Application of de Finetti Style Theorems in Random Network Analysis
Observe that the above property \(\mathcal{P}\) satisfies the condition that it holds regardless of the value of \(X\) (with probability 1), where \(X\) represents the random points on which the geometric random graph model \(\mathbf{G}(X)\) is built. Therefore, by Theorem 12, the property remains valid for \(\mathbf{G}(X\!\mid\!C)\), as well, no matter how tricky and complicated condition is introduced, as long as the condition holds with positive probability (even when \(n\to\infty\)). Note that this cuts through a lot of complexity that may otherwise arise if we want to prove the same claim directly from the specifics of the model. **Example 5**. : _Consider the variant of Erdos-Renyi random graphs, where each edge is added independently with some probability \(p\). These random graphs are often denoted by \(G_{n,p}\), where \(n\) is the number of vertices. For constant \(p\), they fit in our general random graph model concept, choosing \(X\) now as an i.i.d. sequence of Bernoulli random variables, representing the edge indicators. Let \(x(G_{n,p}),A(G_{n,p})\) and \(\delta(G_{n,p})\) denote the vertex connectivity, edge connectivity and minimum degree of \(G_{n,p}\), respectively. All these graph parameters become random variables in a random graph. A nice (and quite non-trivial) result from the theory of random graphs (see [PERSON] [23]) is that for any \(p\), the following holds:_  
\[\lim_{n\to\infty}\Pr\big{(}\,\kappa(G_{n,p})=\lambda(G_{n,p})=\delta(G_{n,p})\, \big{)}=1. \tag{15}\]  
_The intuitive meaning of (15) is that asymptotically both types of connectivity parameters are determined solely by the minimum degree. The minimum degree always provides a trivial lower bound both for \(\kappa(G_{n,p})\) and \(\lambda(G_{n,p})\), and in a random graph asymptotically they both indeed hit this lower bound, with probability 1._  
Now we may ask: what happens if we introduce some condition? Let \(\mathcal{G}\) be a subset of graphs that represents a condition that \(G_{n,p}\) satisfies with some constant probability \(q,\ 0<q<1\), for every \(n\). That is \(\Pr(G_{n,p}\in\mathcal{G})=q\), for every \(n\). Observe that (15) holds regardless of the value of \(X\), because (15) is valid for every \(p\). Therefore, it can be used as property \(\mathcal{P}\) in Theorem 12. Thus, if we condition on \(G_{n,p}\) falling in \(\mathcal{G}\), the relationship (15) still remains true, by Theorem 12. Note that if \(\mathcal{G}\) is complicated, it may be very hard to prove directly from the model that (15) remains true under the condition \(G_{n,p}\in\mathcal{G}\). Fortunately, our result cuts through this complexity. It is also interesting to note that in this case \(X\) is a Bernoulli sequence, so for this case, it would be enough to use the original de Finetti Theorem in the proof, rather than the more powerful Hewitt-Savage Theorem.
# Progressive Teaching Improvement For Small Scale Learning: A Case Study in China
## 3 Method
### Data Visualization  
In this small scale dataset, a total of 1089 course feedback records have been collected from 16 lessons with 113 students enrolled. Since the first goal of this study is to give teachers a first impression after each class, feedback data are visualized immediately to teachers. Three data analysis and visualization techniques are adopted to help teachers understand students' feedback.  
**Multiple Choice Selection.** Multiple choices are dominating questions in the feedback form. A bar chart is used to display the numbers of students' selections. Thus, teachers can see the distribution of students' choices on every question and fine-tune their teaching plans in the next lesson. As shown in Figure 6a, 48 students said they needed to review knowledge points after class, 6 stated that they did not understand these points, and 22 believed they understood what they learned. From this bar chart, teachers could learn to ask students questions about these points to check their understandings.  
**Before-and-After Comparison.** The average of all per lesson multiple-choice answer indexes is calculated and shown in a line chart to evaluate the difficulties of different knowledge point sections. Thus teachers can pay more attention to the sections that students felt harder. Figure 6b indicates that 'Linked List' got the lowest difficulty score, which means that it was the easiest part for all students. While 'AVL Tree' had brought most troubles to students as it showed the highest score in difficulty. Other information like the spirit status of students in every lesson (spirit score) and the fun of the course (fun score) are calculated by the averaged students' options subhead per lesson on question 3 and 4 respectively. They are also displayed in Figure 6b, which can give teachers great understandings of the effect of course.  
**Comments Analysis.** Word cloud is a visual representation of keyword frequency and value [39]. A word more frequently appeared in a given article will be displayed in the word cloud image with bigger size. Such visualization strategy helps teachers get instant insight into the most important terms in the comments based on the size of words in word cloud image. Bigger words are more noticeable to teachers and may affect their teaching plans for solving students' trouble proposed with high frequency words in comments. A word cloud example is shown in Figure 6c. The Chinese character in the word cloud with the biggest size refers to 'coding', indicating that most students find reading code and coding a problem in this lesson.  
Figure 6: Data visualization of the processed data. (**a**) Multiple choice selection visualization; (**b**) Before-and-after comparison visualization; (**c**) Comments visualization via word cloud.
## 5 Performance Evaluation
### Experiment Settings  
We build a simulation environment for CNN task partitioning and offloading on the Python platform. All simulations are performed on a PC with an Intel (R) Core (TM) i7-11370H CPU @ 3.30 GHz, NVIDIA GeForce RTX 3050 Ti GPU, 8 GB RAM and Windows 10. The detailed experimental data settings are as follows:  
Edge computing network: Our edge network consists of 10 nodes, each with distinct regional characteristics, thus forming a collaborative network. Each node is assigned a specific service area. The data of our edge network nodes and structures are abstracted from the edge computing dataset [28] of Shanghai Telecom. The dataset shows the distribution of 3233 base stations in the Shanghai area, which constitute the infrastructure of mobile communication networks. The density and distribution of base stations reflect the coverage capability and service quality of mobile communication networks in Shanghai. The CPU cores of each edge node are evenly distributed within the range of [8; 16], the core computing frequency is 1.12 GHz and the data transmission rates between nodes are randomly distributed within [5; 20] Gbps. To account for the heterogeneity in propagation paths within the edge system, we utilized a \(10\times 10\) bandwidth matrix to represent the diverse bandwidth conditions among the edge nodes.  
Inference workload: Our partitioning model divides the linear chain structure into a nonlinear DAG structure, enabling parallel execution of the partitioned subtasks. This method can be applied to most large model inference tasks with convolutional layer structures, such as Transformer and RNN. Here, our simulation experiment takes the classic CNN model, VGG-16 [29], as an example to discuss the partitioning of inference tasks. VGG-16 consists of a total of 16 layers, but during the partitioning process, the workload of each subtask increases uniformly as the number of layers increases. Therefore, to demonstrate the performance of our partitioning model and task offloading algorithm, we can select one of the layers for simulation of partitioning and offloading. For VGG-16, the piece of input data is a \(224\times 224\) image with 3 channels.  
The banded distribution figures, constructed from gathered data across repeated experiments, provide a comprehensive visualization that underscores the robust consistency and validity of the derived conclusions. This approach ensures a heightened level of scientific rigor and credibility in the interpretation of the experimental outcomes.
## 3 Discussion
### The Plant Basis Difference between B. marginatum var. stenophyllum and Other Four Species
marginatum_ var. _stenophyllum_, such as the members of _TPS_, _GPS_, and _DXS_ families (Figure 6). The biosynthesis pathway of terpenoids is usually divided into three stages: First, the C5 precursor (isopentenyl diphosphate, IPP) and its _cis-trans_ isomer (dimeryl diphosphate, DMAPP) are generated through the MVA/MEP pathway, where HMGR and DXS are the rate limiting enzymes of the MVA and MEP pathways, respectively. The second stage is the generation of the core carbon skeletons of terpene, namely, direct precursors (FPP, GPP, GGPP, etc.). Finally, further modification of the carbon skeleton (oxidation-reduction, acylation, glycosylation, etc.) is carried out to ultimately generate different terpenoids. The first two stages are shared by all terpenoids; the third stage determines the structural diversity of terpenoids [2]. The volatile terpenoids are mainly monoterpenes and sesquiterpenes. GPS is responsible for catalyzing the synthesis of the monoterenoid precursor GPP from IPP and DMAPP. TPS is located at the branching point of the isoprenoid biosynthesis pathway (IBP) and is a key enzyme in the synthesis of terpenoids. TPS can catalyze the formation of multiple products from a single substrate through subtle structural changes, which makes it a major contributor to the structural diversity of terpenoids in nature [43]. Based on transcriptome analysis and PCR analysis, it was found that the genes which are unique and widely present in _B. marginatum_ var. _stenophyllum_ (DXS, GPS, TPS) precisely cover the three stages of terpenoid synthesis. These results indicated that compared with other _Bupleurum_ species, the involvement of a large number of new gene members at multiple key nodes of the monoterpene synthesis pathway in _B. marginatum_ var. _stenophyllum_ might be an important reason for its high volatile oil content and more complex composition. Comparative genomic studies of _Wurfbainia longiligularis_ and _Wurfbainia villosa_ revealed that there are significantly more TPS members involved in the biosynthesis of volatile monoterpene in the former species, which has relative lower medicinal value, and the tissue-specific expression patterns between the two species were also different [44]. Therefore, it is indicated that the excessive involvement of gene members at key nodes might lead to a shift in synthesis direction from the main synthesis pathway to the branching pathway, ultimately resulting in a more complex component composition.
# Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System
## 3 Methodology
### Electrical Resistivity Measurement System  
Electrical resistivity measurement equipment generally consists of a power supply, ammeter, voltmeter, driving module, and electrodes (Figure 2a). The system uses four electrodes for current supply and potential measurements, with multiple buried electrodes collecting data via switching control. The lower part of Figure 2b illustrates the pseudo-depth level of the Wenner array [46]. The pseudo-depth level indicates the measurement position based on the electrode array configuration in the electrical resistivity apparatus. In an ideal semi-infinite ground, the pseudo-depth corresponds to an equal interval length of the AMNB. However, due to the variability in materials and structures under real ground conditions, the accuracy of the measured values may differ from that of theoretical calculations. Consequently, the resistivity measured by the equipment is expressed as the apparent resistivity.  
The configuration of the developed ERMS is shown in Figure 3, which outlines the connection flow of each component. The main measuring device integrates the current source, voltage source, controller, and switching module into a single unit and includes a battery for portable field operation. The current source utilizes a current sensor, Solid-State Relay (SSR), and Analog-to-Digital Converter (ADC), while the voltage source uses an Amplifier (AMP) and Pulse Voltage Regulator (PVR). The Main Control Unit (MCU) is based on the Arduino Due and MEGA, which are 32-bit microprocessor units, and includes a Wi-Fi module for wireless communication. The switching module uses a 16-channel Multiplexer (MUX) to facilitate the automatic control of multiple electrodes. The design  
Figure 2: General configuration of electrical resistivity exploration: (**a**) General components of electrical resistivity measurement equipment; (**b**) Pseudo-depth level of Wenner array.  
and development of the main measuring device were based on various studies related to electrical resistivity equipment development [47; 48; 49; 50]. The developed system includes additional components, such as a foldable electrode device, a water supply device, wireless operation capability, and a dedicated control program, which distinguish it from typical electrical resistivity exploration equipment.  
The data acquisition device was designed with the construction environment in mind, as shown in Figure 4, emphasizing resilience against external impacts, waterproofing, and dustproofing. The equipment case features a fan, battery status window, power switches, and emergency stop switches. Connectors for battery charging and electrode connection are mounted on the side, using a military-grade connector for secure fixation. The control and data calculation program is embedded in the MCU, and the measured data are transmitted wirelessly to a tablet PC. The main measuring device is operated via a graphical user interface (GUI).