esa-sceva/satcom-chunk-collection huggingface dataset:
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
### Contribution of This Paper  
There is--to the best of our knowledge--no completely published method available for the optimal placement of infill balise groups for ETCS level 1 applications that satisfies the requirements for determining the number and exact positions of infill balise groups in real-world applications. Placing infill balise groups at non-optimal positions potentially has significant negative effects during operations, as it could increase the headway times unnecessarily when a train needs to slow down because the track section ahead is not yet clear. The results from the literature review yield some interesting starting points; however, most of them do not take all the important aspects, like system latency and sometimes the acceleration phase, into account.  
We therefore propose a method to optimally place infill balise groups and present it in the following sections. Our method combines different theoretical approaches from existing research such as segmenting the approach zone based on the balise groups and using the basic laws of motion as [PERSON] [37] did, and approximating the acceleration and deceleration curves akin to [PERSON] et al. [38] but on a much more granular scale. Our work contributes to existing research by providing a comprehensive algorithm and program enabling researchers and engineers to model a wide variety of scenarios with ample customizability and minimal limitations. It provides the capabilities of realistically modelling the train characteristics as well as the infrastructure in an easy-to-use format not available to date. With our work, it is now not only possible to determine the optimal locations of infill balise groups but also to precisely quantify the effect of non-optimal locations on the operations in applications of ETCS level 1. Moreover, it enables benchmarking other strategies and existing rules of thumb for placing infill balise groups against the optimum locations. Finally, our work can be used in the context of capacity assessments of ETCS based on the latest specifications by providing researchers a tool to automate placing infill balise groups on a microscopic level, so far often performed with approximate or global settings.
## 2 Materials and Methods
### Out-of-Bag Predictor Importance  
OOB predictor importance in the context of RFs is a crucial aspect for understanding the contribution of each predictor variable to the model's predictive performance. This approach provides a robust and internal measure of feature importance [39,40]. OOB predictor importance measures the contribution of each predictor or feature in the RF model [41]. It is often computed by permuting the values of a predictor in the OOB samples and observing the resulting increase in prediction error. The higher the increase, the more important the predictor is considered [42].  
Although PCA, FA, and OOB predictor importance serve different purposes, integrating them can form a comprehensive approach for managing high-dimensional data. To elaborate, PCA and FA are utilized for feature extraction by means of dimensionality reduction. Drawing an analogy between the contribution of each original feature to the captured variance in a principal component and predictor importance in an RF, we incorporated OOB predictor importance to pinpoint influential predictors within the specific model being utilized.  
Figure 3 depicts the schematic flowchart diagram illustrating the process of this study. This study began with data preparation, followed by GIS spatial analysis, specifically utilizing the spatial join tool to merge the point data with village boundary data, facilitating the examination of relationships between point features and their spatial context within villages. Subsequently, eleven factors were established, and dimensionality reduction techniques, including PCA, FA, and OOB predictor importance, were applied. In this study, the PCA, FA, and OOB predictor importance were implemented using MATLAB R2023a by the MathWorks [51]. In particular, PCA and FA were employed to achieve dimensionality reduction and identify latent variables. These methods, while effective, rely on linear combinations of variables. Consequently, they may exhibit limitations when dealing with nonlinear or complex relationships between variables. In response to such scenarios, the study introduced the OOB predictor importance in the context of RFs. This nonlinear approach provides valuable insights into the relationships between variables, particularly in situations where linearity is not sufficient. Finally, the identification of  
factors contributing to human-caused fires was conducted. The ensuing sections will delve into a comparison and discussion of the obtained results.
### Contribution  
This work contributes a novel design methodology that reconfigures a SCRaM instead of merging the responsibilities of its actuator. This work presents a novel approach to the goal of actuator reduction through the substitution of one linear actuator with a rotary alternative and removing other redundant linear actuators from the SCRaM. The process allows a soft continuum manipulator to possess three DOF using only three actuators, thus achieving \(n\) DOF through \(n\) actuators, resulting in a minimum reduction of 25% in the number of required actuators by SCRaMs while maintaining their workspace. The presented method allows a SCRaM to employ three actuators in comparison to typical multi-segment SCRaMs, thus achieving a 50%, 66%, and 75% reduction in comparison to other SCRaMs, such as [7; 8; 9], respectively. Furthermore, the reduction ratio reaches 86% in comparison to SCRaMs employing 21 actuators to drive their seven body segments, such as [10].  
In addition to reducing the number of employed actuators, the presented manipulator maintains a volumetric workspace matching those reported by some multi-segment SCRaMs, such as [24], and exceeding others, such as [16; 25]. Hence, the significance of the presented method is the achievement of a relatively high actuator reduction ratio in SCRaMs without sacrificing their workspace. Accordingly, the presented actuator reduction method reduces the cost and complexity of a SCRaM and improves its efficiency and reliability by reducing the number of actuators and other moving parts while maintaining or improving the SCRaMs' required workspace.  
The reduction in the number of actuators reduces the cost, complexity, and weight of the SCRaM in addition to a large workspace, making it useful for a diverse set of applications that largely benefit from these traits. The reduced cost of the manipulator could be employed to fabricate a large horizontal version to be used for constraining floating sea junk or oil spills. The simplicity of the design could be realized in the teleoperation of the manipulator for the inspection of tall or submerged structures. Moreover, the lightweight feature is especially attractive for producing manipulators intended for space deployment.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 2 Delivery Systems for Hydrophilic Phytochemicals
### Nisosomes  
Nisosomes (Figure 2) are self-assembled vesicles, which are formed of non-ionic surfactants (e.g., alkyl ethers, sorbitan fatty acid esters) and lipids such as cholesterol. Due to the amphiphilic character, niosomes could deliver both hydrophilic and hydrophobic drugs. The first niosome-based formulation was patented by L'Oreal (France) in 1970s. Then, in 1986, Lancome (France) introduced to the market niosome formulation under the trade name \"Niosome\" [40; 41].  
Compared to liposomes, niosomes are more chemically stable. The active substance entrapment efficiency of niosomes is also better compared to liposomes due to the fact that the dominant compounds of liposomes such as phospholipids are unstable and tend to degrade. The presence of cholesterol in niosomes results in enhanced vesicle stability as well as entrapment efficiency. As non-ionic surfactants, from which niosomes are composed, are not carrying a charge, they also offer better stability of system. Furthermore, to increase the stability of niosomes, the charged molecules such as dicetyl phosphate or stearyl amine could be provided, which by prevent the vesicles aggregation [41; 42; 43; 44].
# A Mobility-Aware Adaptive Duty Cycling Mechanism for Tracking Objects during Tunnel Excavation
## 2 Background and Related Work  
### Duty Cycling Concept  
Human safety is the most important issue and the highest priority in many applications. WSNs can help people protect their life and avoid critical accident by continuous monitoring of moving objects and environments. In Intelligent Transport System (ITS), WSNs that use densely deployed sensors along the road and collect higher spatial resolution of traffic information improve the safety [3]. To use WSNs to reduce cable cost, deployment time, and uncontrolled processes in automation industry [4]. There can be losses and damages to workers and products if the automation equipment is not designed to reduce the risk of uncontrolled or dangerous situations. Struck-by-falling-object accident is the second leading cause of death in construction [5]. WSNs can reduce this accident by providing real-time location tracking and information sharing. In these kinds of applications, saving energy of self-powered sensor nodes is one of the critical issue because it is difficult and time-consuming process to detect the battery problem although they are located where the operator can access them.  
Battery-operated wireless devices must have strict power management strategies to prolong node lifetime [6]. The radio interface usually consumes more energy than other parts such as the CPU, memory, and sensors. This radio interface uses as much energy when it is waiting to receive data from other devices (i.e., idle listening) as when it is sending data to other devices [7]. Therefore, a sensor node saves its energy by using a duty cycling mechanism that periodically turns the radio interface on and off. The main goal of duty cycling is to reduce idle listening time [8]. However, it is difficult to turn on the radio interface only when it is necessary [9]. Duty cycling also has some side-effects such as increasing the collision rate, latency in message transmission, and the need for additional control messages [10]. Some duty cycling schemes reduce transmission and reception time windows. These smaller time windows increase the probability of collisions. In the case of multi-hopnetwork, message transmission is delayed because a sender occasionally has to wait for the next hop to wake up. Duty cycling also needs extra control traffic. The most common source of this overhead is synchronization. Achieving a practical balance between the network performance and energy saving is a complex problem, and many duty cycling mechanisms studies have been performed to solve this problem.
# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
###### Abstract  
Parkinson's disease (PD) classification through speech has been an advancing field of research because of its ease of acquisition and processing. The minimal infrastructure requirements of the system have also made it suitable for telemonitoring applications. Researchers have studied the effects of PD on speech from various perspectives using different speech tasks. Typical speech deficits due to PD include voice monotony (e.g., monopitch), breathy or rough quality, and articulatory errors. In connected speech, these symptoms are more emphatic, which is also the basis for speech assessment in popular rating scales used for PD, like the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn and Yahr (HY). The current study introduces an innovative framework that integrates pitch-synchronous segmentation and an optimized set of features to investigate and analyze continuous speech from both PD patients and healthy controls (HC). Comparison of the proposed framework against existing methods has shown its superiority in classification performance and mitigation of overfitting in machine learning models. A set of optimal classifiers with unbiased decision-making was identified after comparing several machine learning models. The outcomes yielded by the classifiers demonstrate that the framework effectively learns the intrinsic characteristics of PD from connected speech, which can potentially offer valuable assistance in clinical diagnosis.  
Parkinson's disease; speech processing; pitch synchronous segmentation; MFCC Article Parkinson's Disease Classification Framework Using Dynamics in Connected Speech.  
1
## 2 Digital Traffic Environment
### Digitization of TSS  
In order to provide proper collision avoidance decisions and suggestions to the navigator, the first step is to extract the static environment information from each part of the TSS. The dynamic information of the ships sailing in TSS, such as position, course, speed, etc., is extracted as well. These are then converted into digital information that can be recognized by computer programs.
## 3 System Architecture
### Deep Learning for Tracking and Detecting LEDs  
In the OCC system, the RoI algorithm is a widely used part. The majority of RoI methods involve real-time processing for object detection, utilizing both object-based and feature-based detection techniques [30]. In addition, deep-learning neural networks present a powerful technique in computer vision applications, encompassing tasks, such as object detection, image classification, object positioning, and image reproduction. You Only Look Once (YOLO) has emerged as a robust method in real-time object detection because YOLO algorithms process the entire image at once and predict bounding boxes, class probabilities, and confidence scores for those predictions simultaneously [31]. Because of this, YOLO algorithms are more suitable than RoI algorithms for real-time applications, particularly in IoT systems employing OCC techniques, where the transmitting devices could comprise LED arrays. As the transmitter side for data transmission in the proposed method, an \(8\times 8\) LED matrix, which consists of 64 individual LEDs that flash at high speed and can move, is utilized.  
Presently, the YOLO architecture comprises eight distinct versions, ranging from YOLOv1 to YOLOv8. The choice of a specific version is contingent upon the objectives of application tasks, enabling the avoidance of unnecessary resource and cost expenditures during real-world deployment. Fundamentally, YOLO can be categorized into two primary versions: tiny and base. With a smaller parameter set and a more straightforward network architecture than the base version, the tiny version is more suited for embedded devices and mobile development, particularly IoT systems. In this paper, we propose the YOLOv8 model for LED tracking and detection, which is highly applicable to real-world IoT systems and takes mobility effects into account. The model is designed to address the limitations of high hardware demands, which in turn is a determining factor for practical deployment in IoT applications. As a streamlined iteration of YOLOv8, the YOLOv8-tiny model boasts fewer layers, a higher detection speed, compatibility with portable devices, and reduced GPU resource requirements for training.
## 3 Materials and Methods
### Data Processing
In order to find the superellipsoid that fits best regarding a given point cloud, we minimize the objective function  
\[\underset{\lambda}{min}\sum_{i=1}^{n}\big{(}\sqrt{\lambda_{1}\lambda_{2} \lambda_{3}}\big{(}F^{\lambda_{4}}(p_{i},\lambda\ )-1\big{)}\big{)}^{2} \tag{6}\]  
where the set \(\lambda\) defines the parameters \(A\), \(B\), \(C\), \(\varepsilon_{1}\), \(\varepsilon_{2}\), \(X_{0}\), \(Y_{0}\), \(Z_{0}\), \(a\), \(b\), \(c\), \(d\) of the 12-dimensional optimization problem and \(p_{i}\) with \(i=1\dots,n\) a particular point of the point cloud. The factor \(\sqrt{\lambda_{1}\lambda_{2}\lambda_{3}}\) accelerates the convergence of the minimization algorithm by increasing the gradients around deep minima of the fitting function parameter space [19]. To achieve better numerical value stability, the fitting function is raised to the power of \(\varepsilon_{1}\) as [PERSON] and [PERSON] propose in [24]. In contrast to most of the other researches regarding superellipsoid-, or the more general superquadric-model-fitting, where the Levenberg-Marquardt algorithm is used for solving the nonlinear optimization problem ([25; 26; 27]), we chose the interior point optimizer (IPOPT) software library. The underlying method applies a primal-dual interior-point algorithm with a filter line-search method and is specially designed for solving large-scale problems [28]. For a detailed explanation of the mathematical background, reference to the literature is provided. On the basis of the model parameters determined, the volume of the particular superellipsoid is calculated using the equation:  
\[V=2 ABC\varepsilon_{1}\varepsilon_{2}\beta\Big{(}\frac{\varepsilon_{1}}{2}+1, \varepsilon_{1}\Big{)}\beta\Big{(}\frac{\varepsilon_{2}}{2},\frac{\varepsilon _{2}}{2}\Big{)} \tag{7}\]  
where \(\beta(x,y)\) represents the beta-function defined by [29]:  
\[\beta(x,y)=2\int_{0}^{\frac{n}{2}}sin^{2x-1}\phi cos^{2y-1}\phi d\phi \tag{8}\]
# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 3 Minimally Invasive Methods
### Microneedle Array
The microneedle electrodes in the disposable sensor penetrate the skin and access the interstitial fluid to detect various biomarkers such as lactic acid and glucose electrochemically. The collected signals are then sent to the wearer's device wirelessly for data analysis (Figure 8c) [79]. To further improve the system, [PERSON] et al. combined this technique with controlled drug release, providing an advanced approach for diabetes management [80]. The microneedle patch developed by [PERSON] and his team enables visual glucose quantification and controlled insulin release. The microneedles are modified with 3-aminophenylboronic acid, crosslinked with chondro sulfate, and the tips are wrapped with mineralized insulin particles and glucose oxidase (GOx) for controlled insulin release. When glucose levels are high, GOx catalyzes the conversion of glucose into hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), triggering calcium layer dissolution and promoting sustained insulin release. The presence of H\({}_{2}\)O\({}_{2}\) also causes a color change in the horseradish peroxidase layer at the bottom of the patch, facilitating visual glucose quantification (Figure 8d) [80]. [PERSON] et al. introduced an agarose microneedle-coated silver design that can be embedded into the surface of a skin phantom. This approach offers high sensitivity comparable to stainless steel microneedles while also providing improved biocompatibility and cost-effectiveness [81]. Additionally, [PERSON] et al. developed an innovative biosensor featuring a polymer-based (methyl methacrylate) microneedle array designed for in situ in vivo measurements. These advances in microneedle technology not only promise more effective and comfortable CGM methods but also open new avenues for continuous, personalized treatment contributing significantly to the management of conditions such as diabetes.
## 2 Conceptual Model and Problem Procedure
### Wavelet Transform (Stationary State)--SWT
#### 3.2.2 Improved Wavelet Transform (Real-Time Boundary Stationary)  
As previously mentioned, border distortions may result from the RT-SWT's computation of scaling and wavelet coefficients for periodic and circular signals. In this work, border effects will be implemented to give more information in order to achieve fast fault detection [36].  
In the following manner, the first level of the real-time boundary stationary wavelet transform (RTBSWT) scaling and wavelet coefficients related to the current sampling \(k\) are calculated by inner products between \(L\) samples of the time-domain signal \(x\) inside a circular sliding window \(\overset{\circ}{x}\) with \(\delta_{k}\) samples and \(L\) coefficients of the scaling \(H_{\varphi}\) and wavelet \(H_{\psi}\) filters [36]:  
\[S_{x}(l,k)=\frac{1}{\sqrt{2}}\sum_{l=0}^{L-1}\Big{(}h_{\varphi}(n)\overset{ \circ}{x}(k-L+n+1+l)\Big{)}, \tag{11}\]  
\[w_{x}(l,k)=\frac{1}{\sqrt{2}}\sum_{l=0}^{L-1}\Big{(}h_{\varphi}(n)\overset{ \circ}{x}(k-L+n+1+l)\Big{)}, \tag{12}\]  
where the current sampling time, \(k/f_{s}\), is associated with \(k>\Delta k-1\); the sliding window length is \(\Delta k>L\); the border distortion index is \(0<1<\)\(L\); and the periodized signal in \(\Delta k\) samples is \(\overset{\circ}{x}(k+m)=x(k-\Delta k+m)\) with \(m\in N^{*}\).  
\(x\) is broken down into \(L\) scaling and wavelet coefficients, as shown by Equations (11) and (12). These are composed of \(s(0,k)=s(k)\) and \(w(0,k)=w(k)\), which correspond to the RT-SWT scaling and wavelet coefficients, and \(s(l,k)\) and \(w(l,k)\) with \(l=0\), which are \(L-1\) coefficients known as boundary coefficients (coefficients with border distortions).  
The boundary coefficients in the RTBSWT algorithm are ascertained using a few last and first samples of the signal sliding window. Since the first transient-affected sample is grouped with samples from steady-state operations, there are notable variations in the magnitudes of SWT coefficients and those computed with border effects. Then, when transients affect a sample, using boundary coefficients rather than the traditional ones may present a highest magnitude, which facilitates the detection of disturbances.
## 4 Experimental Verification
As can be seen from Figure 7, we can see that F-CSGI and MIPO-CSGI will produce eight frames of the object as experimental results-1 or produce four frames and four intermediate states of the object as experimental results-2. S-CSGI still cannot obtain any information about the jumping \"square\". Figure 5: The experimental setup of the MIPO-CSGI. The experiments are then carried out on different cycle times \(T\) of the two middle frames \"\(T\)\" chosen from Figure 2 to investigate the effectiveness of MIPO-CSGI. DMD-2 displays two frames of the moving \"\(T\)\" at 1,024 \(\times\) 768-resolution under different cycle times \(T\) as 0.8 ms, 2.4 ms, and 6.4 ms, respectively. Here, each random speckle pattern is modulated by DMD-1 to 16 \(\times\) 16-resolution. To obtain two frames of the \"\(T\)\", we must modulate two 16 \(\times\) 16-resolution speckle patterns in each cycle time \(T\). With \(\beta\) set to 69.92%, we will obtain a 358 \(\times\) 512-resolution random matrix in MIPO-CSGI, and the bucket detector's sampling rate in F-CSGI is set to twice that in MIPO-CSGI and S-CSGI. Figure 8 depicts the experimental results of MIPO-CSGI, F-CSGI, and S-CSGI. Both MIPO-CSGI and S-CSGI are the same as in the previous experiments. Figure 6: Experimental results of reconstructing four frames of a jumping “square” using F-CSGI, S-CSGI, and MIPO-CSGI. Figure 7: Experimental results of reconstructing eight frames of a jumping “square”using F-CSGI, S-CSGI, and MIPO-CSGI. CSGI and F-CSGI can image the object \"T\" into two frames as experimental results-1 or two intermediate states as experimental results-2. The longer the cycle time \(T\) of the object, that is, the longer the DMD-2 displays each state of the moving object, as shown in Figure 8, the better the imaging quality of MIPO-CSGI. We cannot obtain the motion information of the moving \"_T_\" from the reconstruction results of S-CSGI. Furthermore, we compare the experimental results of MIPO-CSGI, F-CSGI, and S-CSGI for obtaining four frames of the \"_T_\" under different cycle times \(T\) as 0.8 ms, 2.4 ms, and 6.4 ms, respectively. Figure 9 shows that both MIPO-CSGI and F-CSGI can image the \"_T_\" into four frames, as in experimental result-1, or into two frames and two intermediate states, as in experimental result-2. Due to the use of a slow bucket detector, S-CSGI is consistently unable to image moving objects. Therefore, the effectiveness of the MIPO-CSGI is further validated.