esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology: Lean Tools and KPIs  
There is a great variety of tools used in the Lean approach, as well as for Lean Healthcare. They can have different characteristics and aims, making it necessary to select the one that best suits the defined objectives. Some of them, such as Value-Stream Analysis (VSA), are designed for the evaluation and optimisation of processes, minimising waste. This technique was implemented in the laboratory serology area to map the workflow followed by HIV and COVID-19 samples from the reception in the pre-analytical area, sample preparation, testing and technical review, until the final clinical review and validation. The whole value of the process chain was analysed and quantified in terms of process time, tasks, resources, materials, information and wastes. After the Value-Stream Mapping (VSM) analysis, a list of actions to improve the process was generated and Value-Stream Design (VSD) was performed, defining the optimised future process. Before the introduction of the TLA, the VSA played a crucial role as a reference point in the serology workflow, serving as a primary source for improvement ideas.  
In the study, process activities were classified as value-added (VA), non-value-added (NVA) and semi-value-added (SVA), and different types of wastes were identified such as waiting times, over-processing, unnecessary staff movements, samples' transports, and defects. Based on these, we also assessed Key Performance Indicators (KPIs) in the pre- and post-TLA scenarios to measure the process efficiency in terms of time and cost, which are the following:  
* Turnaround Time (TAT): process lead-time or time elapsed from sample arrival in the laboratory to clinical validation--Equation (1);
* Total Equipment Time (TET): time the sample is processed in the laboratory equipment;
* Total Direct Labour Time (TDLT): time required by the laboratory staff for sample processing, checking or validation;
* Total Waiting Time (TWT): time the serology sample is waiting for the next workflow operation, classified according to its origin, personnel or equipment;
* Total Process Time (TPT): the Turnaround Time without considering waiting times, which represents the effective diagnosis time, as in Equation (1): \[\text{TAT}=\text{TPT}+\text{TWT}=\text{TET}+\text{TDLT}+\text{TWT}\] (1)
* Average cost per test (ACT): total cost per test, considering equipment, material and direct labour costs;
* Total Labour Cost per Day (LCD): total daily direct labour cost, considering the overall number of tests processed daily;* Productivity: number of tests processed/number FTEs required on an average day to complete the work. In this case, fulltime equivalent (FTE) number is equivalent to the laboratory staff number, in view of their category of fulltime employees;
* Average improvement ratio: value calculated as the ratio between the pre-TLA and post-TLA scenarios to quantify the benefits of Lean implementation and automation.
## 2 Materials and Methods  
### Raw Materials  
PU prepolymers are usually synthesized through the reaction between isocyanates and polyols. In this paper, diphenyl methane diisocyanate (MDI-50) was chosen for the isocyanate, polytetrahydrofuran diol (PTMEG2000) for the polyol, and demethyl thiolotuene diamine (DMTDA) for the chain extender. The properties of MDI, PTMEG, and DMTDA are shown in Table 1.  
To investigate the effect of aggregate type on adhesion properties, two different aggregates were selected in this paper. The properties of basalt and limestone are shown in Table 2. In addition, two sizes were chosen for both limestone and basalt aggregates: 2.36-4.75 mm and 4.75-9.5 mm. The aggregate gradation is 80% for 2.36-4.75 mm and 20% for 4.75-9.5 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Molecular Formula** & **Molecular Weight** & **Appearance** \\ \hline MDI-50 & C\({}_{15}\)H\({}_{10}\)N\({}_{2}\)O\({}_{2}\) & 250.25 & White or light-yellow liquid \\ PTMEG2000 & HO[CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)CH\({}_{2}\)O\({}_{6}\)H & 2000 & White wavy liquid \\ DMTDA & C\({}_{6}\)H\({}_{14}\)N\({}_{2}\)S\({}_{2}\) & 214 & Clear yellow to amber liquid \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of MDI, PTMEG, and DMTDA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Basalt** & **Limestone** \\ \hline Apparent density & 2.826 & 2.724 \\ Water absorption (\%) & 0.40 & 0.82 \\ Crushed stone value (\%) & 11.3 & 15.6 \\ Wear value (\%) & 15.9 & 18.7 \\ Polished stone value & 46 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of aggregates.  
Figure 1: Flowchart of schematic structure.  
Three reagents with known surface free energy parameters were used to test the contact angle of aggregates and PU. The surface free energy parameters of distilled water, ethylene glycol, and formamide are shown in Table 3.
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 2 Related Work
### Tumor Detection  
[PERSON] et al. [40] introduced a hybrid bio-inspired algorithm named WOA_APSO, which merges the whale optimization algorithm (WOA) with adaptive particle swarm optimization (APSO) to refine feature selection in a CNN for lung CT image classification, thereby improving tumor detection accuracy. [PERSON] et al. [41] developed DLCTLungDetectNet, a deep learning framework that employs a CNN with FusionNet, integrating features from ResNet50 and InceptionV3, to enhance early lung cancer detection in CT scans. Their model outperforms traditional architectures like VGG16 and Inception v3, setting a new standard in automated lung tumor detection. [PERSON] et al. [42] proposed a novel method for early lung cancer detection in CT images using patch processing and deep learning to classify tumors efficiently, aiming for accurate detection with less computational time and improved performance metrics. [PERSON] et al. [43] introduced an advanced lung tumor detection approach leveraging cloud-based IoT for data collection and employing an optimized fuzzy C-means neural network (OFCMNN) for segmentation, followed by classification with a kernel multilayer deep transfer convolutional learning (KM-DTCL) ensemble to provide superior diagnostic performance. [PERSON] et al. [44] developed a deep learning model, ECNN-ERNN with autoencoders, for early detection of lung malignancies in CT images, utilizing a modified framework to achieve high accuracy and insightful feature extraction. [PERSON] et al. [45] proposed CNN-5 CL. This novel deep learning methodology uses an 11-layer CNN, including five convolutional layers, for precise pulmonary nodule classification in CT images, benchmarked with LIDC/IDRI datasets. [PERSON] et al. [46] introduced a method that combines a CNN with the AlexNet Network Model for early lung tumor classification, distinguishing malignant from benign tumors with greater accuracy and addressing the challenge of premature lung cancer diagnosis. [PERSON] and [PERSON] [47] unveiled a 3D CNN-based pulmonary nodule detection model featuring an encoder-decoder architecture, a dynamically scaled cross-entropy method for reducing false positives, and a squeeze-and-excitation structure to capitalize on channel dependencies.
## 5 Experimental results and discussion
### Performance evaluation of EE-TGA and \(E^{3}\)-FA on standard unconstrained benchmarks
#### 5.1.1 Performance evaluation of EE-TGA
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Modality** & **Dimension** & **Input domain** & **Global minimum** \\ \hline F1 & Multimodal & 2 & [0,\(\pi\)] & \(f(x^{*})=-0.8013\) at \(x^{*}=(2.20,1.57)\) \\ F2 & Unimodal & 16 & [\(-\)30,30] & \(f(x^{*})=0\) at \(x^{*}=(1,...,1)\) \\ F3 & Unimodal & 256 & [\(-\)100,100] & \(f(x^{*})=0\) at \(x^{*}=(0,...,0)\) \\ F4 & Multimodal & 128 & [\(-\)30,30] & \(f(x^{*})=0\) at \(x^{*}=(0,...,0)\) \\ F5 & Multimodal & 2 & [\(-\)5.12,5.12] & \(f(x^{*})=0\) at \(x^{*}=(0,...,0)\) \\ F6 & Unimodal & 2 & \(-10\leq x_{i}\leq 10\), \(i=1,2\) & \(f(x^{*})=-1\) at \(x^{*}=\pi\) \\ F7 & Multimodal & 2 & [\(-\)600,600] & \(f(x^{*})=0\) at \(x^{*}=(0,...,0)\) \\ F8 & Multimodal & 2 & [\(-\)10,10] & \(f(x^{*})=-186.7309\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Benchmark function details (\(E^{3}\)-FA simulations). Figure 3: Benchmark function plots. Similar to the case of EE-TGA simulations, based on the results presented in Table 8, the \(E^{3}\)-FA approach managed to establish better exploitation and exploration than the original FA and, in most benchmark functions, obtained better results. The comparative analysis of convergence speed between the FA and \(E^{3}FA\) for \(F5\) benchmark is given in Figure 4. From the presented figure, it can be seen that \(E^{3}FA\) managed to obtain the optimum solution somewhere between the 80 th and 85 th iteration.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 2 Methodology
### Different Data Structures to Verify Model Stability  
Hydrological series are classified into two categories based on the ordering principle: sequential structure and monthly structure [33]. The sequential structure is based on chronological order and is used in the study of many articles to discuss the model performance. The monthly structure is divided based on months, and the runoff series is reordered based on January through December. In this paper, the monthly series are substituted into the model for forecasting in the hope of further validating the stability of the developed model. The schematic diagram of these two data structures is illustrated in Figure 2.
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 5 Experimental Results
### Comparison of CPU Utilization between QMTSF, Q-Learning, PSO, RR, and Random  
After validating the performance advantages of QMTSF in terms of the makespan and average task processing time, we further compared the CPU utilization for the different scheduling methods.  
From Figure 7, it can be observed that as the task number increases, the CPU utilization of the various scheduling algorithms increases as well. QMTSF exhibits the highest CPU utilization, which, combined with its excellent performance in task execution time, indicates that this scheduling method can quickly complete tasks within a short time and occupy the CPU for a relatively shorter duration. This approach can simultaneously reduce the overall runtime of the entire cloud data center, allowing for more idle time compared to the other scheduling algorithms. Because the power consumption during CPU operation is much higher than during idle time, this can help to achieve the goal of reducing the overall power consumption of the cloud data center. The CPU utilization of the Q-Learning algorithm is second only to QMTSF. Additionally, the figure shows that the PSO scheduling algorithm outperforms RR scheduling, while random scheduling performs the worst. This indirectly confirms the results obtained in the previous experiments regarding task execution time.  
Based on the above experimental results, it can be observed that when compared to RR, Random, PSO, and Q-Learning, the QMTSF framework has significant advantages in terms of both makespan and average task processing time. Furthermore, it leads to an improvement in CPU utilization and a reduction in the overall energy consumption of the cloud data center. In particularly, as the number of tasks increases, the performance improvements realized by QMTSF compared to traditional scheduling algorithms such as RR and random assignment become very evident.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 5 Data Security and Privacy Considerations
### Location Privacy Analysis
This comprehensive approach provides a more accurate representation of the privacy implications in our distributed MANET-based LBS system. The relative magnitude of privacy loss between LBS-based and cell-based scenarios will depend on the specific values of these parameters in a given implementation. For example, if cells typically contain many nodes (_N_j is large) and a significant number of queries are issued within cells (_V_j is large), the loss of privacy based on cells could be substantial. Conversely, if the LBS is frequently queried (\(U_{\text{LBS}}\) is large) or if the _k-anonymity_ sets are small, the loss of privacy based on LBSs could dominate. Therefore, we may say that the total privacy loss of the system is  
\[APL(System)=APL(LBS)+APL(cell) \tag{5}\]  
Thus, the complete equation for the system's APL is  
\[ \begin{split}& APL(System)=U_{\text{LBS}}\times\log_{2}(m/k) \\ &\qquad\qquad+\frac{1}{m}\sum_{j=1}^{m}V_{j}\times N_{j}\times \log_{2}m\end{split}  \tag{6}\]  
This formulation provides a comprehensive view of the privacy implications in the entire system, clearly showing how the total system privacy loss is composed of two components: the privacy loss from LBS queries and the privacy loss from cell-based queries. Importantly, it highlights that a user who needs to access the LBS experiences a cumulative privacy loss, encompassing both the cell-based loss (from interactions within their local MANET cell) and the LBS-based loss (from querying the centralized LBS). This loss of privacy in the dual layer reflects the reality of our hybrid system, where users first attempt to resolve queries within their local cell before resorting to using LBSs.
## 2 Current Technology
### National Sports and Interests  
The information discussed so far applies to various levels of sporting performance, from amateur to professional. When considering stakeholders, how do states interpret \"performance\", and what are their objectives? Overseas, there are evident differences in sports performance evaluation models, particularly at high levels. The term \"sporting performance\" no longer refers to an individual athlete's results but rather to the ambition of obtaining international awards [38]. Programs such as Own the Podium (OTP) [39] demonstrate the state's interest in developing a method that involves both private and public schools and sports infrastructure to increase the number of medals won, especially in the Olympics and Paralympics. The model suggested by OTP has led to the creation of the \"Top Secret\" program, which concentrates efforts from a \"dream team of researchers\" to develop new training techniques, equipment, and technologies.  
These joint interventions have enabled incredible levels of technological implementation. The evolution of technology aimed at improving athlete performance has resulted in new instruments, such as wearables and tools, as well as innovations implemented in the stadiums and circuits [40] where the competition takes place. For instance, reducing the friction of skates in speed skating and reducing water resistance in swimsuits [41] has allowed athletes to break existing records remarkably. Reducing the weight of swimsuits, bicycles, and all wearable equipment worn by athletes has also enabled unprecedented performance.  
Although studies and research aim to enhance athletes' performance and competition, there are two distinct paths of technological development concerning supporting professionals: the first focuses on research aimed at promoting healthy training, energy balance, and expression of the athlete's complete physical potential, while the second aims to outperform athletes from other countries to ensure the prestige and reputation of the athlete's reference institution [42]. Technologies used to select the athlete with the highest probability of achieving a high performance, such as artificial intelligence applied to swimmer analysis, do not benefit the expression of the athlete's performance, but rather support economic or strategic decisions.
# The Cube Surface Light Field for Interactive Free-Viewpoint Rendering
## 4 Ray Casting Based Interactive Novel View Rendering
### Geometry Processing  
The purpose of geometry processing is to compute the parameters of the desired light rays and to query each ray's color from the cube surface light field textures. Since we use a unified cube geometry to parameterize the light rays by two intersections. This makes it possible to compute the desired light rays parameters only by computing the two intersection points between the rays emitted from the viewpoint and the cube surface. Since the cube surface light field uses the cube map to store the light rays, the color value of the corresponding light rays can be directly fetched by texture mapping, and the computational complexity of the light ray query is O(1).  
Figure 5 shows the relationship between an arbitrary pixel in the image plane of a novel viewpoint and the cube surface light field. Given the viewpoint parameters (camera parameters), all the light rays from this viewpoint to each pixel in the image plane can be defined, and the intersection points between each light ray and the cube surface can also be computed. Both generating light rays from the viewpoint to the image plane and testing intersections can be achieved by a single-layer ray casting. Different from traditional ray casting, single-layer ray casting only needs to query the pixel colors from the outermost image, instead of superimposing the pixel colors of all the intersected images.  
Due to the simplicity and regularity of the cube surface, computing the intersections is so simple that the cube surface light field rendering can achieve real-time performance on personal computers. After computing the light rays parameters, we need to query the color of these light rays. If the desired light ray is just located at a sampled point, the sampled color can be used for the color of this light ray. If the desired light ray is located in the under-sampled regions, different interpolation rendering can be used in texture mapping to blend the adjacent sampled light rays. In the cube surface light field rendering, the nearest neighbor interpolation (NNI), linear interpolation (LI), and bilinear interpolation (BLI) algorithms can be used to deal with those under-sampled light rays. In this paper, bicubic interpolation (BCI) and cubic B-spline interpolation (CBSI) algorithms are expanded to further reduce the aliasing artifacts. Algorithm 1 describes the pipeline of rendering novel views from arbitrary viewpoints.  
Figure 5: Relationship between a desired novel viewpoint, pixel in image plane and the cube surface light field in the rendering.
## 3 Methods and Results
### Sar  
#### 3.2.1 Single-Pass InSAR  
The advantages of using an interferometric SAR (InSAR) method for detecting and characterizing icebergs in sea ice have been demonstrated [30]. Single-pass TanDEM-X InSAR data enable analysis of iceberg topography and detect icebergs in sea ice with a high probability of detection. The same single-pass TDM InSAR technique was applied to extract the topography of forming hummocks (Figure 10(a)). The TDM pair of the StripMap mode, incidence angle 43\({}^{\circ}\), HH polarization and high resolution (slant range 1.2 m, azimuth 3.3 m), was acquired on December 12, 2014. The values of stereo DEM (29 April 2014), extracted from VHR WorldView-2 images, show that the height of the hummock peaks approached 25-29 m. However, the values of InSAR DEM over peaks of the same hummock is only about 18-21 m. The difference between VHR optical and InSAR DEMs of about 28% can be explained by the time difference between the SAR and optical images of more than seven months, including summer when the hummock peaks melted. Another reason for the lower values of InSAR DEM over peaks can be justified by a coarser resolution (3.3 m for TDM data vs. 0.5 m for WorldView-2 data), which makes the InSAR method less sensitive for sharp height variations.  
Figure 11: _Cont._
## 2 Materials and Methods
### Experimental Protocol
#### 2.2.1 Postural Balance Assessment  
Assessment of postural balance in the control condition included open-eye quiet stance trials while wearing shoes of different heel heights (Figure 1, upper left), namely (i) a low heel (1-2 cm), (ii) a medium heel (7 cm), and (iii) a high heel (10.1 cm) (Figure 2). In the dual-task conditions, postural balance assessment was carried out with the same quiet stance trials with the medium- and high-heel-height shoes (Figure 1, right). Participants stood on a force plate (Wii, A/D converter, 24-bit resolution, 1000 Hz, Biovision, Wehrheim, Germany) while maintaining a straight body posture with their arms hanging relaxed by their sides and their feet hip-width apart. They had their gaze fixed on an imaginary point on the wall 2-3 m in front of them, while their heads were kept parallel to ground level. The order of shoe wearing was randomized in both the control and the dual-task condition. In each shoe wearing condition, three trials lasting 30 s were recorded with a 1 min rest between shoes. For hygiene reasons, participants wore light stockings that were disposed afterwards. For the low-heel height, 3-4 different pairs of moccasin-style leather shoes with rubber soles and wooden heels were used (Figure 2A), whereas the medium- and high-heeled shoes were purchased by the same manufacturer in order to ensure the same type of shoe style (i.e., dress shoes with pump heels) and sole materials (Figure 2B,C). Heel base dimensions were 1 cm in width and 1 cm in length for both the medium- and high-heel heights, respectively. Prior to starting the experimental protocol, a brief shoe-fitting session was performed to ensure that all shoes were in appropriate ranges of size and fitted the participants comfortably.  
Valid trials were considered those in which participants had been effective in maintaining their balance throughout the trial's duration (i.e., 30 s). During the analysis, the recorded center of pressure (CoP) data from the force plate (Wii, A/D converter, 24-bit resolution, 1000 Hz, Biovision, Wehrheim, Germany) were filtered using a 2 nd bi-directional-order digital low-pass Butterworth filter with a 15 Hz cut-off frequency and analyzed with MATLAB custom-made scripts (R2012a, 64 Bit; Mathworks, Natick, MA, USA) from the 2 nd to the 27 th second (\(\Delta t=25\) s) of each 30 s trial time. The assessment of postural balance performance is typically based on the CoP displacement, whose derived values represent the geometrical location of the reaction force vector on the platform during quiet standing [46]. Postural balance performance was determined by the following parameters: (a) CoP path length, defined as the sum of Euclidean distances between adjacent measurement points, and (b) CoP sway range, defined as the range (i.e., from minimum to maximum) of the CoP values in the anteroposterior and mediolateral directions. To assess performance in the two-legged quiet stance trials, the average values of the three trials were used.