esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Application of Reinforcement Learning Algorithm in the Development of Mobile Robot
#### 4.1.3 Motion Control and Adaptation with RL  
Reinforcement learning has also been used to solve the motion control problem in mobile robot applications. Often, point stabilization control is a major shortcoming of traditional algorithms. This is because it usually encounters several constraints, including non-linear factors, non-holonomic conditions, and under-actuated constraints, among other constraints of mobile robots. However, the identification of the control action through end-to-end training can be achieved using DRL to solve these constraints of mobile robots. Two reviewed studies confirmed RL's importance in mobile robot motion control. For example, [PERSON] et al. [44] utilized deep reinforcement learning to solve the motion control problem encountered by non-holonomic constrained mobile robots. To build the DRL, a point stabilization kinematic control law was used. The experiment's result confirmed the success of RL in motion control as it effectively identified the stabilization control point of the non-holonomic mobile robot. Furthermore, [PERSON] and [PERSON], [45] experimented with neural network-based reinforcement learning to overcome collision encounters by mobile robots in both dynamic and static environments. Network planning and Q-learning were interestingly used to solve the path-planning problem of the mobile robot. Safe and swift navigation was achieved in a scenario with the availability of global information. The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. [PERSON] et al. [46] adopted trajectories from a practical life motion planner and previously executed trajectories as feedback observations. This reinforcement algorithm model helped the mobile robot with motion adaptation behavior concerning environmental changes.
## 2 Methodology
### A Protocol for the Assessment of Gait of Parkinson's Disease Patients Using Wearable Sensors
#### 2.2.1 The Walk Straight and Turn Test  
The first test of the protocol is the Walk Straight and Turn (WST) test in which participants, starting from a standing position, walk in a straight route for 10 m, then turn back and return to their starting position (Figure 2a). The WST Test is organized in line with the description of the MDS-UPDRS item 3.10-Gait and contributes to the rating of items 3.11-Freezing of Gait and 3.13-Posture, whilst in parallel, it provides valuable information for the estimation of stride amplitude and speed, as well as heel strike and turning. The WST test is performed two times at slow, normal, and fast walking speeds, as perceived by each participant. The importance of exploring the gait characteristics at different walking speeds, especially for PD patients, has been revealed in several published studies [16; 46; 47]. Gait analysis on both slow and high speeds has shown that gait characteristics are significantly affected and can provide valuable information for differencing gait between different groups of participants and on PD patients with mild and moderate severity.
# Missing Data Imputation in the Internet of Things Sensor Networks
## 4 Experiments  
### Dataset Description  
For the purpose of our simulation, we made use of a dataset presented by [PERSON] et al. [5], which is publicly available on the University of California Irvine (UCI) repository [34]. The dataset contains the concentration measures of target pollutants collected from a measurement site. These concentration values were used as a benchmark to tune a regression system that was designed to calibrate the response of the multi-sensor device. This device was configured to accommodate five metal oxide sensors and two solid state sensors to capture data on the temperature and relative humidity in the environment. The specified station provided concentration estimation values for CO (mg/m\({}^{3}\)), C\({}_{6}\)H\({}_{6}\) (ug/m\({}^{3}\)), non-metalic hydrocarbons (NMHC) (ug/m\({}^{3}\)), NO\({}_{2}\) (ug/m\({}^{3}\)), NO\({}_{x}\) (ppb). The data was sampled, showing hourly averages of the concentration results. However, the NMHC analyser went offline after 8 days causing a series of missing data. Hourly average values of the multi-sensor device was sampled, showing concentration levels indicated by NO\({}_{x}\), CO, O\({}_{3}\), and NO\({}_{2}\) metal oxide (MOX) chemiresistors in addition to relative humidity and temperature sensors. More information on the MOX chemiresistor is presented in a research by [5].  
The original dataset contains real missing values on all columns ranging from as low as 3.91% to a high of 91%, with the highest rate recorded from the GT sensor which we disregarded in our analysis. Assessing the accuracy of imputation methods would be impossible if the dataset is analysed with real missing values present. Therefore, we employed CCA to generate another dataset with fully observed values and created artificial missing values to assess the strength of the imputation techniques on different rates of missing data. An outline of the methodology is clearly described in the following section.
## 4 Deep Learning Methods for HDR Reconstruction
### GAN for HDR Reconstruction
#### 4.2.1 Single LDR to HDR Reconstruction with GAN  
Reconstruction of HDR images from a single LDR input has been tackled by various methodologies, and in this scenario, GANs stand out for their ability to produce high-quality results. Hence, in the approach developed by HDR-cGAN [138], reconstruction is treated as an image-to-image translation problem using a conditional GAN (cGAN) [139], where the LDR image is concatenated with both the generator output and the ground truth (GT) HDR image in the discriminator, ensuring that the HDR output is guided by the LDR input. The generator first passes the LDR input through a Linearization module that converts the nonlinear LDR image into linear irradiance. Next, the Overexpression Region Correction (OERC) module estimates pixel values in saturated regions from the overexposure mask based on the method proposed in [140]. After that, the OERC output is fed into a Refinement module, which is responsible for correcting any remaining irregularities. The discriminator follows the PatchGAN [141] framework, evaluating the authenticity of the generated HDR image at local patches instead of assessing the entire image, which allows for finer corrections in specific regions.  
Another notable approach is GlowGAN [10], which consists of three models: a generator, a discriminator, and a camera model. The generator and discriminator are based on StyleGAN-XL [142], and the camera model is the main difference among other architectures, multiplying the generated HDR image by an exposure value to fix the dynamic range and forming an LDR image similarly to how a camera functions. The camera model acts as an intermediary between generator and discriminator, allowing training on datasets containing only LDR images.  
Figure 8: GAN architecture.  
These models differ mainly in their objectives. Both HDR-cGAN and GlowGAN can generate HDR images with few artifacts. However, GlowGAN stands out for its ability to train without the need for paired datasets, i.e., without the original HDR images.
## 2 Materials and Methods
### Energy Efficiency (\(E_{\text{eff}}\))  
As suggested by Table 2, the influence of the drying parameters (\(MP\) and \(ST\)) and the interaction effect of \(MP\)-\(ST\) were significant (\(p<0.00001\)) on the E\({}_{\text{eff}}\) of the samples pretreated by blanching and ultrasound. Figure 5 also represents the interaction effect of \(MP\)-\(ST\) on the E\({}_{\text{eff}}\) of (a) controls, (b) ultrasonicated, and (c) blanched carrots. As observed, the E\({}_{\text{eff}}\) rose linearly or quadratically by increasing the \(MP\) and \(ST\). The highest E\({}_{\text{eff}}\) (22.39%) was achieved at the power of 300 W in the ultrasonicated samples with 2 mm thickness;  
Figure 4: Response surface for the effects of variables (microwave power and thickness) on the SEC value of carrot slices: (**a**) control, (**b**) ultrasonicated, and (**c**) blanched samples.  
the lowest E\({}_{\rm ef}\) (7.78%) was also related to the power of 100 W and 6 mm thickness in the control samples. The simultaneous use of higher _MP_ and thinner samples led to a faster removal of the surface moisture. A rise in the _MC_ gradient also increased the microcracks within the samples. These cracks caused a volume change, further accelerating the removal of moisture and declining the _D\({}_{t}\)_, which in turn enhanced the E\({}_{\rm ef}\)[6]. The increment in the E\({}_{\rm ef}\) by raising the _MP_ and reducing the _ST_ was reported before [8; 55]. On the other hand, a comparison of the various pretreatments showed that the highest E\({}_{\rm ef}\) was achieved in the blanching pretreatments while the untreated samples exhibited the lowest E\({}_{\rm ef}\). Pretreatments improved the E\({}_{\rm ef}\) as they increased the destruction and accelerated the removal of moisture. These results coincided with the previous reports [18; 28].
## 2 Architecture of CCSDS-like QC-LDPC Code for Overlap  
### QC-LDPC Code  
QC-LDPC codes are a type of LDPC codes whose PCM can be decomposed into cyclic submatrices of equal size [23]. For an \((n,k)\) LDPC code with a PCM of size \(m\times n\) and submatrix dimension \(l\), \(n\) and \(k\) represent code length before and after coding, respectively, and the PCM can be constructed using the base graph and shift factors \(S_{m,n}\), where \(0\leq S_{m,n}\leq l-1\). The 1s in the base graph are replaced by submatrices, and the 0s are replaced by zero matrices [24]. The origin submatrix can be either an identity matrix or a matrix derived from finite geometry, such as a double diagonal matrix. The shift factors specify the number of bits each submatrix should move to the right. For example, the cyclic submatrix \(I(1)\) is obtained by shifting the origin double diagonal submatrix one bit to the right.  
\[Q(1)=\left(\begin{array}{cccccc}0&1&1&0&\cdots&0\\ 0&0&1&1&\cdots&0\\ 0&0&0&1&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ 1&0&0&0&\cdots&1\\ 1&1&0&0&\cdots&0\end{array}\right)_{l\times l}\.\]  
Specifically \(Q(-1)\) denotes the zero matrix. And in this way, we can construct H, where \(m_{a},n_{b}\) indicate the number of submatrices in horizontal and vertical distribution:  
\[H=\left(\begin{array}{cccc}Q(S_{1,1})&Q(S_{1,2})&\cdots&Q(S_{1,n_{b}})\\ Q(S_{2,1})&Q(S_{2,2})&\cdots&Q(S_{2,n_{b}})\\ \vdots&\vdots&\ddots&\vdots\\ Q(S_{m_{a},1})&Q(S_{m_{a},2})&\cdots&Q(S_{m_{a},n_{b}})\end{array}\right)_{m \times n}\,\]  
The PCM of CCSDS (8176, 7154) LDPC is presented in Figure 1, whose block length is 8176 bits and the message length is 7154 bits. The PCM of CCSDS (8176, 7154) LDPC is composed of 32 submatrices in 2 rows and 16 columns. The size of submatrices is \(511\times 511\), and each submatrix satisfies the QC characteristic.
# A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading
## 4 Experiment and Results
### Experimental Results and Analysis
#### 4.2.5 Visualization of the Best Model Results  
The confusion matrices of DME and DR are shown in Figure 8. In general, both the matrices have a diagonal tendency, i.e., the predictions are close to the ground truth. DME1 and DR1 are the most misclassified grade.  
The receiver operating characteristic (ROC) curve and the area under the precision-recall curve (AUPR) of our method on the IDRiD dataset are displayed in Figure 9. These curves exhibit the stable training and classification performance of the proposed model.  
Visualization of some graded prediction scores is listed in Table 7. The table is divided into four columns: the input fundus image, the true label for DR and DME, the predicted label, and the graded score for the disease level. The grading score is defined as the probability that the model predicts for each disease. The bold data represent the grading scores of the correctly classified category.  
Figure 8: Confusion matrixes of MaMNet’s predictions. (**a**) DME’s Confusion Matrice; (**b**) DR’s Confusion Matrice.  
It can be seen from Table 7 that MaMNet can correctly distinguish the severity of DR and DME and obtain consistent results with the true labels of fundus images. When the grading level is correctly predicted, this level can achieve a higher grading score, while the other levels result in essentially 0.
## 2 Background  
### Epidemiological Modeling  
Epidemiological modeling plays an important role in shaping epidemic dynamics [13]. It can be used to model the transmission probability and, hence, predict the epidemic size, the chains of transmission throughout the population, and the likelihood of epidemic recurrence. Machine learning methods have been used to predict infection rates; however, these methods have not yet been successful in predicting infection hotspots and highlighting critical regions [15]. Statistical epidemic models, on the other hand, have been proposed and validated to model the spread of infectious diseases. Epidemic models, such as the SEIRD model [12], are well-established epidemiological modeling methods that can be used to understand the collected data and predict critical regions with different variables and dynamic environments.  
The SEIRD model implicitly assumes mass interaction for an infinitely sized population such that the resources hosting the disease are mixed in the population and the members of the population have identical contact rates. In the SEIRD model, the population is split into five groups: susceptible, exposed, infected, recovered, and dead. In the model, \(S(t)\) corresponds to the number of susceptible individuals, who are initialized at time t but can become infected if they come in contact with an infected individual. \(E(t)\) corresponds to the number of exposed individuals, which are those who are infected but are not infectious yet. \(I(t)\) corresponds to the number of infected individuals, who have the disease at time \(t\) and may infect susceptible individuals. \(R(t)\) corresponds to the number of recovered individuals. Finally, \(D(t)\) corresponds to the number of dead individuals. The sum of all the groups should be equal to the population size, i.e., \(S+E+I+R+D=P\), where \(P\) is the total number of individuals in the population.  
In this model, natural death cases and births are neglected. Additionally, once an individual has recovered, it is assumed that they will be quarantined and, hence, will not be susceptible again. To predict the spread rate in a population, a parameter called \(R0\) is used as an important indicator. \(R0\) corresponds to the average reproduction number, which is the number of secondary infected individuals, who were infected by primary infected individuals [16]. \(R0\) is a key quantity that predicts the evolution of an epidemic. Epidemiological, a lower \(R0\) is desirable because it means that the infection growth rate is smaller.
# Analysis and Test of the Tillage Layer Roll-Type Residual Film Recovery Mechanism
## 2 Materials and Methods
### Soil Trough Test  
A prototype of the complete machine was tested to verify the operational performance of the roll-type residual film recovery mechanism for the tillage layer. In March 2023, The residual film recovery performance tests were carried out in the soil tank test bench of Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Xinjiang Agri-cultural University [28]. This is shown in Figure 13. Tillage and watering were carried out on the soil tank before the experiment so that the experimental land in the soil tank was relatively flat, and the soil state was consistent with that of the actual field after tillage before spring sowing. The supporting engine of the test is a TCC electric four-drive soil trough test rig, operating at a speed of 4.5 km/h-5.0 km/h. The rotation speed of the film cutter shaft of the roll-type flow residual film recovery machine is 22.37 rad/s, the rotation speed of the pickup roller is 4.58 rad/s, the rotation speed of the unloading wheel is 4.5 rad/s, the pickup roller, and the unloading wheel are, respectively, driven by the hydraulic system of the soil tank test bench to rotate the hydraulic motor, and the speed control valve is used. The rotation of the roll to pick up the film is driven by the film-unloading wheel through the chain drive to ensure that the speed is 13.74 rad/s; The rotation of the reversing film-unloading roll is driven by the pickup drum through the chain drive. Its speed is 17.57 rad/s.  
Figure 11: Displacement curve of the spring-tooth tip. (**a**) Motion displacement curve in the X direction; (**b**) motion displacement curve in the Y direction.  
Figure 12: Speed change curve of the spring-tooth tip. (**a**) Motion speed curve in the X direction; (**b**) motion speed curve in the Y direction.  
During the operation performance test of the roller plow residual film recovery machine, first, a square with an area of 1 \(\times\) 1 m was selected from cotton fields planted for more than 10 years in Shaya County, Xinjiang. The residual film was excavated at the soil depth of 0-50 mm, 50-100 mm, and 100-150 mm, with dust and moisture removal after--called its quality; Then, we selected a 1 \(\times\) 1 m area in the field and buried the residual film obtained from the field according to the depth of soil layer. Then, the operation test of the roller-type topsoil residual film recovery machine is carried out. Finally, the residual film of the soil taken through the test site is dug, and its quality is measured after removing dust and water. Equation (25) is utilized to compute the collection rate of the tilled layer, and the results of the field tests are shown in Table 1.  
\[J=\left(1-\frac{W}{W_{0}}\right)\times 100 \tag{25}\]
## 5 Single-Port vs. Multi-Port Approaches in Robot-Assisted  
Urologic Oncology  
Robotic-assisted surgery in urology relies primarily on two core systems: multi-port (MP) platforms, such as the da Vinci Xi, and single-port (SP) platforms, such as the da Vinci SP. These systems differ significantly in terms of design, ergonomics, and clinical application (Table 3). The MP systems use multiple trocar insertions for individual instruments, which provide excellent triangulation and a broad working space. By contrast, the SP platform uses a single 25 mm multichannel port to deploy a flexible camera and multiple wristed instruments, all through a single incision. This allows for minimized invasiveness, reduced postoperative pain, and improved cosmesis, but presents challenges, such as reduced traction and a steeper learning curve.  
The da Vinci Xi system offers high-definition 3D visualization, EndoWrist technology for enhanced dexterity, and easy docking for multi-quadrant access. It remains the most widely used platform for RARP, RARC, and RAPN. On the other hand, the SP system has enabled novel surgical approaches, such as transvesical RARP, retroperitoneal nephrectomy, and extraperitoneal procedures, which were previously difficult or impossible with MP systems. Studies have shown that SP surgery may offer comparable oncologic outcomes with advantages in pain scores and same-day discharge rates, although limitations remain, such as reduced lymph node yield and limited availability globally.  
Importantly, the SP system incorporates a relocation pedal allowing the surgeon to reposition the entire camera-instrument array within the body cavity without undocking, which is essential for surgeries in confined anatomical spaces. However, the reduced range of motion and diminished haptic feedback compared to MP systems can impact delicate maneuvers, such as nerve-sparing. Consequently, surgeon experience and careful patient selection remain critical to the success of SP robotic procedures.
###### Abstract  
The prevalence of obesity has increased significantly in developing nations over the past decade, particularly among adolescent girls. To assess the scale of this epidemic among female adolescents in South Africa, a systematic review was undertaken to investigate the connection between diet and obesity. Multiple databases (Google Scholar, Science Direct, Cochrane Library, PubMed, and Web of Science) were searched to identify studies investigating the associations between diet and various adiposity indices as outcomes. Of the 56 studies identified, 7 met the inclusion criteria. The age range of participants spanned from 11 to 21 years. Tabulation was used to report the data, study by study. The consumption of nutrients from animal sources exhibited a positive correlation with higher BMI-for-age Z scores (\(p\) = 0.02). Eating habits such as sporadic family meals (\(p\)\(\leq\) 0.02), irregular breakfast consumption (\(p\)\(\leq\) 0.05), and a high energy intake derived from fat were linked to an increased risk of adiposity. Additionally, factors such as socioeconomic status and residential location revealed associations with certain dietary intakes and adiposity. As more studies identify the causative role of diet in obesity, there is an urgent need for policy intervention and strategies to address the growing non-communicable disease burden in South Africa.  
adolescence; adiposity; diet; females; obesity; South Africa; review 202313108131  
(1.7%) [5]. Among females, South Africa had the highest prevalence (42%), while Ethiopia had the lowest (1.8%) [5]. An investigation comparing urban and rural areas in four African countries found that the prevalence was 85% among urban South Africans, 75% among urban Tanzians, 68% among urban Nigerians, and less than 50% among peri-urban Uganda [9].  
Considering the high prevalence of obesity and overweight resulting from rapid dietary changes during adolescence, a critical period of growth and development with significant implications for adulthood, this systematic review aims to assess the existing evidence linking diet and adiposity among adolescent females in South Africa. Additionally, the review explores other factors that influence this relationship.