esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Host Directed Therapy
### Inhibition of M. tuberculosis Induced Inflammation and Host Cell Death  
#### 2.4.1 The Role of Corticosteroids in TB Treatment  
It sounds counterintuitive to address the problem of active TB with anti-inflammatory drugs. However, for some clinical manifestations of the diseases, reduction of inflammation by using adjunctive corticosteroids has already become a well-established and lifesaving treatment approach. Addition of dexamethasone or prednisolone, two potent corticosteroids, to the antibiotic regimen for treatment of TB meningitisti improves survival and is considered as a valid therapeutic approach for TB affecting the central nervous system (CNS) [69], although care should be taken since individual responses to steroid treatment might differ. Several studies have tried to improve the outcome of pulmonary TB by lowering the inflammatory response using high doses of corticosteroids in combination with antibiotics. While it was found that this therapy leads to faster resolution of symptoms and lesions in radiographic examinations and a more rapid discharge from hospitals, a statistically significant survival benefit could not be shown (Table 1) [70; 71; 72]. In addition, high dose corticosteroids may result in serious side effects such as diabetes and psychiatric symptoms. Today, corticosteroids remain the treatment of choice in specific clinical situations such as CNS TB or hyperinflammatory syndromes e.g., the immune reconstitution inflammatory syndrome (IRIS) in HIV/TB co-infected patients. Investigations at the molecular level proved that dysregulation of inflammasome signaling and of secretion of various cytokines, including IL-1\(\gamma\), was associated with TB-IRIS in patients infected by HIV [73; 74], thus supporting the inclusion of corticosteroids in the treatment of TB patients at risk of developing IRIS [75]. Despite these evidences, a broader application of the drugs in TB treatment is currently not justified. However, clinical studies as well as ex vivo and in vivo experiments performed with these substances indicate that a more specific or tailored modification of the TB inflammatory response may provide a suitable approach to improve patient outcomes. Understanding the exact mechanism of action of corticosteroids in TB may help overcome this hurdle. Corticosteroids are broadly immunosuppressive drugs with multiple modulatory effects on leukocytes once bound to the main target, the corticosteroid receptor. Downstream effects include repression of pro-inflammatory transcriptional regulators like NF-kB as well as impaired release of cytokines such as TNF\(\alpha\) and IL-1 [76]. In addition, corticosteroidssuch as dexamethasone seem to have an _M. tuberculosis_ specific inhibitory effect on necrotic host cell death in vitro [77]. This effect seems to depend on inhibition of p38 MAP kinase which impairs mitochondrial membrane stability upon infection with _M. tuberculosis_. p38 MAP kinase is activated during _M. tuberculosis_ infection in vitro and in vivo and represents a possible host directed target with several clinically tested small molecule inhibitors available for repurposing.
# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 3 Adopted Techniques  
### K-Means Algorithm  
In our proposed system, the segmentation of dermoscopic images is performed using the k-means algorithm, which enables the clustering of the input data into several groups so that elements in the same group have high intra-class similarity while elements belonging to different clusters have low inter-class similarity. In particular, in the unsupervised learning k-means algorithm, each object belongs to only one cluster. The aforementioned method is hence a hard clustering procedure that is part of the region-based segmentation class [41]. This is an iterative procedure that splits the unlabeled data into k diverse clusters, where k is a predetermined value. Each cluster is identified by a centroid whose position needs to be conveniently selected considering that the center place affects the achieved result values. Each object of the dataset being tested is associated with the closer centroid. Therefore, the centroid for each cluster is the point that minimizes the sum of distances from all the objects belonging to the cluster.  
After the analysis of all the objects composing the dataset, the k-means algorithm computes \(k\) centers, which correspond to the centroids of the k clusters previously identified. The procedure continues iteratively until the position of the centers does not change.  
Denoting with X and M the sets of data points and centers, respectively, the k-means aims to minimize the objective function, which is the squared error function defined as:  
\[V=\sum_{i=1}^{k}\ \sum_{j=1}^{c_{i}}\|x_{j}-\mu_{i}\|^{2} \tag{1}\]  
where \(c_{i}\) is the number of data points composing the \(i^{\text{th}}\) cluster, \(k\) is the number of cluster centers, and \(\|x_{j}-\mu_{i}\|\) represents the Euclidean distance between the \(i^{\text{th}}\) centroid and the generic data point of the \(i^{\text{th}}\) cluster.
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 3 Implementation of Modeling Methods
### Self-Organizing Mechanism
#### 3.1.1 Neuronal Growth  
Unlike the Euclidean distance, the Mahalanobis distance can fully take into account the width of neurons when calculating multidimensional data points. The Mahalanobis distance index first rotates the variables according to the principal components to make the dimensions independent of each other and then normalizes, so that the dimensions are jointly distributed. Using the covariance matrix can make the calculation results not be affected by the correlation between dimensions and variables, which can provide a more reasonable metric for calculating the distance between the sample and the central neuron. The Euclidean distance can be:  
\[dis(x_{p},c_{j})=\sqrt{\sum_{t=1}^{k}{(x_{pt}-c_{jt})^{2}}}. \tag{9}\]  
[PERSON] et al. [27] developed a generalized intelligent evolutionary fuzzy system that uses the non-axial parallel rule of the multivariate Gaussian kernel to better cover clustered samples, calculates the Mahalanobis distance between the input sample and the Gaussian center in the regular evolution stage, and adds new Gaussian neurons according to the rules if the threshold is exceeded. Similar to [27], if the sample has too large a Mahalanobis distance for the current neuron, it means that the influence range of the current center is too small, so this sample needs to be subjected to feature extraction. The distance factor between the input vector \(x_{p}=[x_{p}^{1},x_{p}^{2},\ldots,x_{p}^{k}]^{T}\) of the pth observation and the center \(c_{j}=[c_{1j},c_{2j},\ldots,c_{kj}]^{T}\) of the \(j\)th affiliation function can be defined as [28]:  
\[md_{j}(x_{p})=\sqrt{(x_{p}-c_{j})^{T}S_{j}^{-1}(x_{p}-c_{j})},j=1,2,\ldots,r, \tag{10}\]  
\[S_{j}(X_{p})=diag(\sigma_{1j}^{2}(x_{p}^{1}),\ldots,\sigma_{rij}^{2}(x_{p}^{r })), \tag{11}\]  
where \(\sigma_{j}(X^{k})\) and \(\sigma_{ij}(X_{ij}^{k})\) are the dynamic width vector of the \(i\)th dimension in the \(j\)th neuron of the \(k\)th observation and its dynamic width, respectively.  
Calculate the marker of the smallest Mahalanobis distance from each center and the root-mean-square error (RMSE):  
\[J=\arg\min_{1\leq j\leq r}(md_{j}(X^{p})), \tag{12}\]  
\[e^{p}=\hat{y}^{p}-y^{p}, \tag{13}\]  
\[\text{RMSE}^{i}=\sqrt{\frac{1}{p}\sum_{i=1}^{p}e^{i}}. \tag{14}\]  
Unlike the evolutionary method in [27], SOFNN-CA uses error criteria to evaluate the approximation of the entire system, and, if the error decreases or does not change, the sample can be well-estimated, so neurons are added only when neither the error criterion RMSE nor the Mahalanobis distance criterion are met:  
\[\text{RMSE}^{i}>\text{RMSE}^{i-1}\text{ and }md_{j}(X^{p})>md_{th}. \tag{15}\]  
This indicates that the sample has new features to learn and then adds the \(k\)th sample to the network as a new MF neuron.  
\[c_{j+1}=X^{p},\sigma_{j+1}=rand(0,1),w_{j+1}=\frac{e_{j}(t)}{h_{j+1}(t)}, \tag{16}\]  
where \(\sigma_{j+1}\) is a random number between 0 and 1.
## 3 EDAS Prototype Specifications
### Event Sources  
An event source, a thing in the EDAS context, can be visualized in functional layers, as shown in Figure 4. The application layer contains the actual application(s), temperature sensing in our case, along with the necessary security components, tools and protocols. The processing layer must have at least  
Figure 3: Prototype environment.  
two components for EDAS to work efficiently, an event framework and a local adaptor. The _Event Framework_ typically comes as an out-of-the-box service with most applications and includes an event handler and a logging utility.  
Microsoft defines an event as a message generated by an object to signal the occurrence of an action, which qualifies a user interaction or any system change, _etc._[13]. Events are thing-specific (applications or devices) information with unique attributes describing a particular change. They can be status information notifying about the battery, CPU or memory levels; a thing internal change, such as a computation error caused within a body sensor; a particular type of user interaction, such as updating a bank account number, a password change or inserting incorrect information; or an external stimuli, e.g., a location change update notified by a GPS sensor. Events are received by a handler, for instance the Java Event Handler [14], which listens to a particular event raised and invokes further methods necessary for handling. One typical method is to log that event into a local or remote file or DB present on the data and communication (DC) layer.  
The _Local Adaptor_ parses the adaptation request and calls the particular application and security API to adopt the new settings locally. The adaptation request received is a string detailing the new security parameters to be adapted as a result of deemed risk and an _AppID_ identifying the application that raised the event, which will adopt the new settings. The local adaptation process is shown in Figure 5a, and an example adaptation request is shown in Figure 5b. Most of the low-end devices, such as body sensors, are not equipped with the local event logging facility because of the limited storage capacity and sometimes do not have remote logging capabilities. The EDAS _Communication Agent_ hooks onto the output stream of the event framework via the _EventCol_ interface, collects the events as they are raised locally and sends them to the EDAS platform via an event DB using HTTP request. Besides event communication, the agent also serves incoming connections. Thus, complying with the memory constraints, this design does not require any local storage for the events. Events from sources like a smartphone or those having a local logging facility, such as a file or DB, can be collected through the _LogCol_ interface from the facility. Event source components and the interfaces between them can be seen in Figure 6.  
Figure 4: Event source abstraction.  
Figure 5: Local adaptation at the thing level. (**a**) Local adaptation process; (**b**) example adaptation request.  
Figure 6: Event source component diagram.
## 2 Methods and Data
### Fitting Method  
The attenuation relationship was acquired using a nonlinear fitting process. The goodness of fit of the model is evaluated based on the adjusted R-squared, which is the sample R-squared adjusted for the number of observations and degrees of freedom. The adjusted R-squared, which is equivalent to the ratio of the sum of squares of regression to the total sum of squares, represents the proportion of the total variation in the dependent variable (y) that can be explained by the independent variables in the regression model. It is one of the indicators used to assess the effectiveness of the established model. An adjusted R-squared value close to 1 indicates a better fit of the regression model to the observed values, while a value close to 0 demonstrates a poor fit of the regression model to the observed values. The fitting function expression is chosen based on the attenuation model, and initial values, parameter bounds, and fitting convergence constraints for each fitting parameter are set. We used two iterative algorithms, namely the Levenberg-Marquardt optimization algorithm and the orthogonal distance regression method, for fitting the data. Each set of data was fitted using both methods, and the result with higher goodness of fit was selected as the final attenuation relationship. The optimization algorithm is an optimization technique rooted in the least squares problem. Its primary goal is to minimize the sum of squared residuals, which represents the squared differences between observed values and model predictions. On the other hand, the orthogonal distance fitting method aims to determine the curve or model that best fits a given dataset. Its fundamental principle involves minimizing the sum of squared orthogonal distances between observed data points and the fitted curve. This approach not only considers residuals that are perpendicular to the fitted curve but also residuals orthogonal to the tangent direction of the curve.
## 5 Conclusions  
The current MEC system does not consider the mobility of user devices. In practical application scenarios, the positions of user devices are mostly unfixed. Therefore, in the future, we will design joint optimization algorithms for task offloading considering user device mobility and UAV trajectory.  
Furthermore, when the MEC system assisted by UAVs contains a large number of UAVs, existing algorithms struggle to obtain good path planning and task offloading strategies. This is because these algorithms must deal with a huge amount of input, making it difficult for the algorithms to converge. Next, we will further explore path planning and task offloading solutions in UAV-assisted large-scale MEC system environments.  
Lastly, the latest research on UAV flights in MEC environments reveals the critical impact of factors such as wind speed, precipitation, visibility, terrain, and signal interference on UAV flights. These influencing factors are of great significance in practical scenarios. In our future work, we will delve deeper into more realistic simulation scenarios, including UAV flight models, task offloading modeling, and MDs' motion trajectories.  
Conceptualization, Y.D.; methodology, Y.D.; software, J.F.; validation, J.F. and Z.G.; formal analysis, Z.G.; investigation, J.F. and Z.G.; resources, Y.D.; data curation, J.F.; writing--original draft preparation, J.F.; writing--review and editing, Y.D. and L.Y.; visualization, J.F.; supervision, L.Y.; project administration, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 12: Average latency of algorithms with different data sizes.
## 3 Results and Discussion
### Analysis of Tar Absorption Process
#### 3.2.3 Optimum Operation Conditions  
Selecting the optimum (most economical) operation conditions should consider the requirements of the process as well as the operation cost and annualized charges on equipment. The requirement for tar concentration depends on the intended end use of the produced gas (gas application) [3]. Several researchers reported that the tar concentration should be up to 50-100 mg/Nm\({}^{3}\) for ICE and less than 5 mg/Nm\({}^{3}\) for gas turbines [2]. According to [PERSON] et al. [43], the tar concentration should be less than 0.1 mg/Nm\({}^{3}\) for Fischer-Tropsch synthesis.  
As previously mentioned, the removal efficiency is affected by three parameters: the temperature, the solvent volumetric flow rate, and the height of the bed. It is clear that increasing the bed height enhances the removal efficiency of tar components, but on the other hand, this leads to an increase in the amount of packing material needed to fill in a packed column which means more cost will be added to the total cost of the plant. Therefore, the bed height depends mainly on the requirement for tar concentration. It should meet these requirements considering the solvent volumetric flow rate to be set at a minimum value. Increasing the volumetric flow rate of the soybean oil solvent enhances the removal efficiency of tar components, but on the other hand, this leads to an increase in the  
Figure 10: Effect of bed height on the removal efficiency of tar components at solvent volumetric flow rates of 53.63 and 73 mL/min, the temperatures are 30 \({}^{\circ}\)C (**above**), 40 \({}^{\circ}\)C (**middle**), and 50 \({}^{\circ}\)C (**below**).  
operations cost. This operation cost results from the electrical energy consumed by the recycling pump. The optimum volumetric flow rate is linked to annualized charges on equipment. The total annualized cost should be calculated, which is equal to the sum of operation cost and annualized charges on equipment. The optimum flow rate of the solvent meets the minimum of the total annualized costs.  
The temperature of the solvent has to be selected carefully. It has been observed that a decrease in the solvent temperature will increase the removal efficiency. However, the heat duty that should be added to the process to cool the solvent to the setup temperature (the inlet solvent temperature to absorber) should be considered. Therefore it is necessary to estimate the heat duty used to cool down the solvent temperature. The heat duty was calculated by ASPEN PLUS software. The results are illustrated in Figure 11; it clears that the heat duty to cool-down the solvent decreases by increasing the temperature of the solvent. From this curve, one can conclude that the solvent with high temperature is preferred for the deliberate process, but in a condition that this temperature achieves the requirement of tar concentration.
# Angle-Regulating Rule of Guide Vanes of Variable Geometry Turbine Adjusting Mechanism
## 3 Results and Discussion
### Evaluation and Comparison of Two Adjusting Mechanisms  
Figure 15 shows the variable geometry turbine of the variable cycle engine. Figure 16 shows the comparison of two adjusting mechanisms at 673.15K. Figure 16a shows the comparison of the driving forces of two adjusting mechanisms. The maximum driving force of the paddle rod adjusting mechanism is 284.88N. The maximum driving force of the push-pull rod adjusting mechanism is 1072.27N. In addition, in the whole adjustment process, the driving force of the paddle rod adjusting mechanism is smaller than that of the push-pull rod adjusting mechanism. Because the driving force is small, the deformation  
of the component is small in the process of movement. It is easier to realize the accurate adjustment of the guide vane angle.  
Figure 16b shows the comparison of the adjustment travels of two adjusting mechanisms. When the deflection angle of the guide vane is \(-10^{\circ}\), the driver displacement of the paddle rod adjusting mechanism is \(-41\) mm. Furthermore, the driver displacement of the push-pull rod adjusting mechanism is \(-8\) mm. When the deflection angle of the guide vane is \(+10^{\circ}\), the driver displacement of the paddle rod adjusting mechanism is \(+38\) mm. Furthermore, the driver displacement of the push-pull rod adjusting mechanism is \(+9\) mm. During the adjustment process from \(-10^{\circ}\) to \(+10^{\circ}\), the driving travel of the paddle rod adjusting mechanism is \(79\) mm. The driving travel of the push-pull rod adjusting mechanism is \(17\) mm. The larger the driving travel is, the more favorable it is for the guide vane angle to be accurately adjusted and maintained. At the same time, the institute provides necessary experimental data. The results of the joint computing system are in good agreement with the experimental data. The angle of the guide vane can be accurately adjusted by the paddle rod adjusting mechanism. It is superior to the push-pull rod adjusting mechanism.
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
###### Abstract  
Phonocardiography (PCG) signals that can be recorded using the electronic stethoscopes play an essential role in detecting the heart valve abnormalities and assisting in the diagnosis of heart disease. However, it consumes more bandwidth when transmitting these PCG signals to remote sites for telecare applications. This paper presents a deep convolutional autoencoder to compress the PCG signals. At the encoder side, seven convolutional layers were used to compress the PCG signals, which are collected on the patients in the rural areas, into the feature maps. At the decoder side, the doctors at the remote hospital use the other seven convolutional layers to decompress the feature maps and reconstruct the original PCG signals. To confirm the effectiveness of our method, we used an open accessed dataset on PHYSIONET. The achievable compress ratio (CR) is 32 when the percent root-mean-square difference (PRD) is less than 5%.  
autoencoder; deep learning; one-dimensional convolutional neural network (1D CNN); phonocardiogram (PCG); signal compression; telecare Article
# SambotII: A New Self-Assembly Modular Robot Platform Based on Sambot
## 5 Self-Assembly of SambottII
### Outlines of the Docking Strategy
#### 5.4.2 Position and Angle Adjustment Phase  
Because the active robot just move forward directly to complete the docking without extra measurement in the third phase, it should make position and angle adjustment in this step so as to assure that the distance and angle are within specific ranges.  
During this phase, a series of adjusting movements need to be performed. In each adjusting movement, the active robot rotates at first to adjust its orientation and then moves forward to adjust its position, because SambottI is a differential wheeled robot. After each adjusting movement, it has to rotate again to face the passive docking surface to check if the specific tolerance condition has been met. Each move-and-check operation generally costs much time. In order to enhance the efficiency, this procedure is further divided into two steps: the rough aiming step and the accurate alignment step.  
In the rough aiming step (Locomotion & Aim), the active robot moves a relatively larger distance in each adjusting movement under a loose tolerance condition: L (distance) < 40 cm and \(\theta\) (deviation degree) < 45\({}^{\circ}\). When this condition is met, the active robot will stop in front of the passive docking surface and face it.  
The laser-camera unit has its best performance when L < 40 cm and LED-camera unit has better accuracy when \(\theta\) < 45\({}^{\circ}\). That is why L < 40 cm and \(\theta\) < 45\({}^{\circ}\) is chosen as the finish condition of this phase.  
In the accurate alignment step (Adjust & Align), robot moves a smaller distance in each adjusting movement under a strict tolerance condition: L < 20 and \(\theta\) < 5\({}^{\circ}\). This condition ensures that the robot  
Figure 15: The docking procedure.  
can just move forward for docking without check L and \(\theta\) for a second time, due to the permissible errors of docking mechanism.  
The laser-camera is used to get distance during the whole phase, because it is more accurate than the LED-camera. Moreover, when the four LEDs are too far or the angle \(\theta\) is too large, LEDs may not be clearly recognized for measuring.