esa-sceva/satcom-chunk-collection huggingface dataset:
# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
## 3 Research Methodology
### Experimental Setup  
#### 3.2.1 Datasets  
In order to examine the performances of both orders of combining feature selection and over-sampling, two related breast cancer datasets are considered. The first one is based on the KDD Cup 2008 breast cancer dataset ([[https://www.kdd.org/kdd-cup/view/kdd-cup-2008](https://www.kdd.org/kdd-cup/view/kdd-cup-2008)]([https://www.kdd.org/kdd-cup/view/kdd-cup-2008](https://www.kdd.org/kdd-cup/view/kdd-cup-2008)) (accessed on 15 February 2021)), which contains 102294 data samples, and each data sample is represented by 117 different image features, which are extracted from 4 X-ray images per patient. Particularly, the class imbalance ratio is 163.2.  
The second dataset is based on the Breast Cancer Wisconsin Dataset downloaded from the UCI Machine Learning Repository ([[https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28) original%29]([https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28) original%29) (accessed on 15 February 2021)). It is composed of 699 data samples, in which each data sample is represented by 10 features including clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion, single epithelial cell size, bare nuclei, bland chromatin, normal nucleoli, and mitoses. In addition, the class imbalance ratio is 1.86.  
To train and test the classifier, the 5-fold cross validation method is used to divide each dataset into 80% and 20% training and testing sets. This means that every subset will be trained and tested five times, and the average prediction accuracy can obtained consequently be. In other words, each patient data will be used as the training and testing data example. In addition, the class imbalance ratio of the training set in each fold is controlled to be the same as the original dataset.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 3 Heuristic Methods on a Simple Thermal Model Reduction and Correlation
### Simple Model--Thermal Mathematical Models  
The thermal model represents an optical instrument mounted on an external panel of a satellite operating in low Earth orbit (LEO). As shown in Figure 4a, the satellite is depicted using a simple schematic for an external instrument configuration, orbiting the Earth with a 90-min period. During its orbit, the satellite is exposed to solar radiation when it is outside Earth's shadow, while the external panel radiates heat into deep space and exchanges thermal energy with the satellite's internal components.  
Figure 4b illustrates the instrument concept design. In this design, the Spacecraft Platform Mechanical Interface is attached to Support Structure 1, which is connected to Support Structure 2, which supports the Optical Instrument at its top. This generic configuration was selected because it typifies the design commonly used for optical instruments on satellites.  
Figure 5a,b present the Thermal Resistance Networks for the detailed thermal mathematical model (DTMM) and the reduced thermal mathematical model (RTMM), respectively. Although the DTMM shown in Figure 5a is already a simplified thermal model, it is still referred to as the \"detailed\" model in this study because it serves as the reference for evaluating the reduced model's accuracy. As discussed in the Introduction (Section 1) and similar to the approach by [PERSON] [17], it is necessary to define both a reference model (DTMM) and a reduced model (RTMM), where the RTMM has fewer nodes but still captures the key thermal behaviour of the DTMM. Despite the simplicity of the DTMM, the process of reducing and correlating the RTMM is difficult.  
Figure 4: Aerospace instrument concept design illustration: (**a**) orbit of a satellite with an external instrument. (**b**) External instrument concept design where the Spacecraft Platform Mechanical Interface is attached to Support Structure 1, which is connected to Support Structure 2, which supports the Optical Instrument at its top.  
Figure 5: Thermal Resistance Networks for (**a**) detailed TMM; (**b**) reduced TMM.
## 6 OCTA in Type 1 MNV Secondary to AMD
The OCTA categorization of type 1 MNV secondary to AMD has become essential both in research field and in the clinical setting to have a better knowledge about the prognosis and response to treatment. [PERSON] et al. described early MNV changes after intravitreal anti-VEGF using OCTA imaging, such as a decrease in MNV mean area after treatment clearly visible at en face OCTA images and reduction in pigment epithelial detachment (PED) height and central macular thickness (CMT) on an OCT scan [59]. Only after 48 h, in most cases, a decreased visibility of the littler choroidal branching vessels was detected, which was not always associated with a persistence of flow from larger vessel trunks. Similarly, [PERSON] et al. had already observed the behavior of new vessels during anti-VEGF treatment, providing qualitative and quantitative changes in the morphology and flow of the network [60]. A reduction of VEGF after injections seems to lead vessel vasoconstriction combined with the flow signal regression [61]. One of the main limitations of OCTA in investigating type 1 MNV remains the lack of subtle leakage identification, which is easily seen from the traditional FA. Nevertheless, the traditional FA combined with ICGA is really time consuming and requires the use of dye with possible systemic complications. [PERSON] et al. have reported that FA does not give more information for the management of exudative AMD if compared with OCT imaging [58]. Another important OCTA limitation that deserves mentioning is represented by a projection artifact that in AMD conditions is from inner retinal vessels to both above and under RPE. To solve projections-related problems, the masking technique was firstly proposed blocking the larger vessel projection of the en face superficial retinal slab to the en face outer retinal and choriocapillaris slabs [62]. Unfortunately, this method removed the flow signal from blood vessels in the outer retinal layer as well. Another projection removal system proposed was the subtraction of flow signal of superficial vessels from flow signal detected in the deeper retinal layers, thus allowing a better exploration of MNV extension. Nevertheless, it also caused the flow signal attenuation of the choroidal lesions [63; 64]. A new algorithm for projection-resolved OCTA (PR-OCTA) with a high within-visit repeatability has been developed, acting on the single voxels, thus providing more resolution of MNV on both en face and cross-sectional images [65; 66; 67]. The current standard of classifying MNV type seems to remain a multimodal imaging approach that utilizes both FA/ICGA and OCTA combined with the co-registered structural OCT. However, OCTA can give a new understanding of the vascular features and changes over time and/or after treatment of macular new vessels and has become a fast imaging tool in clinical real-life settings for the follow up and management of exudative macular lesions.
## 5 Proposed Optimization Problem
### Operational constraints of EV  
EVs serve a dual purpose in modern HEMSs, acting both as a mode of transportation and a flexible energy storage resource. The charging and discharging constraints for EVs are designed to optimize battery performance and longevity. These constraints ensure that the EVB is charged and discharged within specified limits to avoid overcharging or deep discharging, which can degrade the battery's health and efficiency. Maintaining the SOC within optimal levels enhances the battery's lifespan and ensures readiness for use when needed. Additionally, these constraints help seamlessly integrate the EV into the HEMS, allowing it to act as a backup power source during peak demand or as a storage solution for excess renewable energy. By managing the EV's charging and discharging cycles effectively, the system can leverage the EV's capacity to enhance overall energy efficiency and reliability.  
\[P_{rEV,max}\geq P_{rdis},P_{rch}\geq P_{rEV,min} \tag{32}\] \[70\%\leq \text{SOC}_{EV}\leq\ 80\% \tag{33}\]  
The SOC of the EVB should remain between 70% and 80% to ensure optimal operation. The effectiveness of the EVB's charging and discharging process can be determined using the following formula:  
\[\eta_{EV}=\frac{C_{EV,Pr}\times R_{EV}}{D_{EV}^{Home}\times CA_{EV}^{Platt} \times C_{pr}(t)} \tag{34}\]  
The availability status of the EV is given as follows:  
\[LOC_{EV}=\left\{\begin{array}{cc}1&\quad\quad\quad EV\ \text{available}\\ 0&\quad\quad\quad EV\ \text{not\ available}\end{array}\right. \tag{35}\]  
The provided constraints ensure the optimal operation of the EVB by regulating its charging and discharging power levels and maintaining the SOC between 70% and 80%, a range that balances the dual purposes of the EV for both transportation and energy storage. The charging and discharging power constraints ensure that the EVB operates safely within its power limits, while the efficiency of the EVB's charging and discharging process is evaluated using a specific formula that considers various operational factors. Additionally, the availability status of the EV is monitored, determining whether the EVB can participate in charging or discharging at any given time. These constraints work together to effectively integrate the EVB into the energy management system, optimizing its use for both transportation and energy support while preserving battery health and ensuring reliability.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 4 Evaluation
### Testing Scenarios
#### 4.1.2 Scenario S2  
Scenario S2 starts in a room that is self similar to multiple other rooms in the building. After recording the room, the device is moved outside. When only the room and the adjacent hallway are observed, localization is inconclusive. The guessed position jumps all over as similar-sized rooms can be found through the building. After walking about 14 m, the device observes the corner of the hallway. At this frame, the point cloud is sufficiently distinct and the localization can determine the position with an accuracy of 12 cm and 1\({}^{\circ}\). This scenario shows that self similarities can be mitigated by an increased observed area. The surrounding geometry must be distinguishable from similar environments to give a reliable localization result.
## 3 Results and Discussions
### Ground-Based Validation
#### 3.2.3 RMZ and Evora Stations  
The radiative transfer-based single-channel approach (PMW) met LSA SAF target accuracy at the Evora and RMZ sites (RMSD around 2.0 K, bias \(<\) 1.0 K; Table 3 and Figure 3b,d).  
For Evora, all models, including GSW, had a significant positive bias, while we observed a negative bias for all models at the RMZ station (0.7-1.2 K and -0.5--0.9 K, respectively; Table 3 and Figure 3b,d). The biases are known from previous validation studies of the operational LSA SAF LST dataset and are given [20] as 0.8 K and -0.4 K for Evora and RMZ, respectively. These biases partially reflect the achievable accuracy with _in situ_ LST. These have to represent large-scale satellite footprints covering several square kilometers: although the land cover at the Evora and RMZ validation sites is spatially quite homogeneous [35], they represent a mixture of grass, background soil and trees, which cause shadows and complicate the ground-based LST determination [40]. The negative biases observed for RMZ are thought to be related to the site's high elevation (1360 m a.s.l.), which may not be correctly accounted for by the LST retrieval algorithms. The PMW and SMW do not perform an orographic correction, _i.e._, we use atmospheric profiles as the model input, which corresponds to the ECMWF grid cell height and not to the elevation at the station.
## 3 Results and Discussion  
### Foamed Concrete Density  
In this investigation, foamed concrete mixes were calculated as having a dry density ranging from 450 to 500 kg/m\({}^{3}\). The mixes were designed based on the concept that chemically bound water is about 20 wt% of cement content. The slump flow of all mixes was measured and adopted by adjusting the superplasticizer dosages to achieve the targeted consistency class of F3/F4 (EN 206-1). The mixes developed had high stability without segregation or bleeding. Figure 2 shows the hardened densities of the foamed concrete. It is clear from the results that the concrete density obtained was close to the designed values (450-500 kg/m\({}^{3}\)). Small variations in the dry densities of the foamed concrete mixes can be observed due to the incorporation of fine lightweight aggregates and fly ash. The incorporation of fly ash slightly increased the dry density of the foamed concrete. This improves the microstructure of the foamed concrete, because of its high filling ability [36]. However, the addition of fine Liaver reduced the dry density of the foamed concrete significantly, because of its low specific gravity. Compared to the fine Liaver mixes, the foamed concrete mixes incorporated with fine perlite exhibited higher dry density, because of the high specific gravity of the crushed perlite.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 3 Request Expectation Index Based Cache Replacement Algorithm
### Request Expectation Index \(Rxi(i,j)\)
#### 3.1.3. Chunk-Level Internal Request Expectation Index \(IRXI_{f_{i}}(j)\)
When an ICN router receives a request for chunk \(C_{l,g}\), the cache space is traversed and the value of \(IRXI_{f_{i}}(j)\) within file \(f_{i}\) is checked for an updating or recovery. According to Algorithm 1, the time complexity of \(IRXI_{f_{i}}(j)\) update/recover process is \(O(n)\), where \(n\) denotes the total number of chunks cached in the router. ```
0:\(P_{f_{i}}(j)\), \(\Delta\mu\), \(C_{l,g}\)
1:while All cached chunk \(C_{i,j}\) in the cache space do
2:if\(i=I\)then
3:if\(j>g\)then
4:\(IRXI_{f_{i}}(j)\gets P_{f_{i}}(j)+\Delta\mu\)
5:elseif\(j=g\)then
6:\(IRXI_{f_{i}}(j)\gets P_{f_{i}}(j)\)
7:endif
8:endif
9:endwhile
```  
**Algorithm 1**\(IRXI_{f_{i}}(j)\) Update/Recover  
In summary, according to Equations (1)-(4) and (7), the \(RXI(i,j)\) of chunk \(C_{i,j}\) is given, which is the g  
Figure 4: \(IRXI_{f_{i}}(j)\) calculation.
## 1 Introduction  
In many fields, changes influencing polar and cryospheric environments have proven to have global impacts. Earth observation and remote sensing tools are well-suited to applications in polar and cryospheric research, allowing observations at lower initial cost to the researcher, higher frequency and at a synoptic scale complementary to _in situ_ measurements. Whether studying glacier extent and behavior, ice sheet activity, sea ice extent, lake and river ice, snow cover extent and water equivalent, ocean circulation, tree line migration, permafrost lake dynamics, circumpolar environmental impact assessment or penguin populations, recent research has clearly demonstrated the increasing importance of Earth observation. Nevertheless, many products and sources of data are restricted or highly priced.  
Encouragingly, as highlighted in the Antarctic Treaty, a SCAR (Scientific Committee on Antarctic Research) standing resolution and the 2007-2009 International Polar Year [1], polar research has made clear the importance of open data access, archiving and sharing between research programs. Indeed, many remote sensing data providers also espouse the ethos of free and open access to quality data sets in order to enable researchers to pursue experimental, interdisciplinary, international and innovative Earth observation research economically. Remote sensing data are often provided by (inter)governmental agencies, which can streamline the ability to provide data in a coordinated fashion. As a prominent example, the Landsat program has revolutionized the use of its data through a new data policy [2].  
Recently, rapid developments in web tools, storage space, Internet bandwidth and the availability of computing resources have changed the remote sensing landscape. The challenge has moved from obtaining any data at all to identifying high-quality data fit for addressing a particular research question.  
Therefore, the aims of this paper are to provide a review of primary open access remote sensing data sources, products and tools and to discuss the opening of data access in polar and cryospheric remote sensing. This can serve as a primer for students, for polar scientists new to remote sensing or remote sensing specialists new to polar science. In a time of tightening budgets and opening research, this article will, to the best of the authors' capabilities, describe the developments of different types of data (multispectral imagery, synthetic aperture radar, passive microwave data, active microwave scatterometry and others), provide some references for follow-up and point towards raw data, products, and tools to manipulate data, which are all openly available.
from the LED light source. A spatial filter serves to limit the coherence of the light source. However, although using a partially coherent LED light source can reduce the speckle noise, it also causes a blurring of holographic contents because the LED light source has a lower coherence than a coherent light source, such as light amplification by the stimulated emission of radiation (LASER).  
[PERSON] et al. analyzed a reconstructed holographic image according to the coherence properties by using different light sources, such as diode-pumped solid-state lasers, LEDs, laser diodes, and super luminescent LEDs [19]. They analyzed the temporal and the spatial coherences according to light sources in relation to the speckle contrast and image sharpness of the holographic image. They proposed that a spatial filter size smaller than 300 \(\upmu\)m is sufficient to obtain a sharp holographic image close to the hologram plane using the LED. However, they did not suggest an optimal size for the spatial filter. In addition, although expressible depth is an important characteristic in holographic displays, they did not specify the distances where holographic images can be clearly expressed relative to the spatial filter size.  
In this paper, we analyzed the effect of the spatial coherence of the LED light source in a digital holographic display. To find the optimal spatial coherence of the LED light source, we proposed an optical structure to control the spatial coherence in a holographic display system using a digital micro-mirror device (DMD), which functions as a spatial filter. By analyzing the change in the holographic image according to the spatial filter size, we obtained the optimal size of the spatial filter. In addition, we reveal the relationship between the coherence properties of the LED and expressible object distances in holographic displays.
## 2 Materials and Methods
### Design of Experiments--Response Surface Methodology (RSM)  
The evaluation of the impact that PEF pretreatment and microwave-convective drying parameters have on selected dependent variables was performed utilizing the central composite design (face-centered, CCF). The design was also used to assess the possible optimization of both processes. Two variable factors were chosen for the study, each differentiated by three levels. Microwave power (factor 1) was differentiated with the following levels: 100, 200, and 300 W. Specific energy intake (factor 2) was set at 1, 3.5, and 6 kJ/kg. The parameters of PEF treatment and drying conditions were selected based on the results of preliminary experiments. In essence, the study was arranged into performing 11 experiments corresponding to a specific combination of each factor at each level, with triple repetition conducted at the center point (0, 0). The actual values of parameters are presented in Table 1. To regularize current values of factors, they were subjected to normalization by attributing coded values to them. These coded values ranged from 1 to \(-\)1 and were assigned in the following manner: the highest level of the factor was appointed with 1, the medium level of the factor was appointed with 0, and finally the lowest level of the factor with \(-\)1. The following parameters were used as responses: drying time to MR = 0.02, water activity, hygroscopicity after 72 h, rehydration ratio, relative dry matter content, total phenolic content, ability to scavenge ABTS\({}^{\bullet+}\) radical cations and DPPH\({}^{\bullet}\) radicals based on the \(EC_{50}\) values. Moreover, reference processes, carried out without any pretreatment at three different levels of microwave power, were performed for comparison purposes.
## 4 Evolution Characteristics of Plastic Zone in Jointed Rock Mass
### Impact of Joint Internal Friction Angle on Plastic Zone Range  
The influence of the joint internal friction angle on the plastic zone range of high-temperature rock mass was analyzed. Based on Equation (21) and other parameters which remained unchanged, the plastic zone radii of the high-temperature tunnel at joint internal friction angles \(\varphi\) of 15\({}^{\circ}\), 20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\) were calculated using MATLAB software. In addition, when the joint spacing of the three joint groups was set to 1.0 m, the joint internal friction angles were set to 15\({}^{\circ}\), 20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\), respectively, other parameters remained unchanged, and the variation in the plastic zone range of rock mass in the high-temperature tunnel was studied. The relationship between the joint internal friction angle and the plastic zone radius is shown in Figure 12.  
As shown in Figure 12, as the joint internal friction angle continues to increase, the plastic zone range of the high-temperature rock mass decreases gradually. The plastic zone range of the vertical joint group in the rock mass is the largest, followed by the horizontal joint group, and finally the inclined joint group. For instance, when the internal friction angle \(\varphi\) was 15\({}^{\circ}\), the plastic zone range of the vertical joint group, the horizontal joint group, and the inclined joint group in the rock mass was 6.85 m, 5.21 m, and 4.49 m, respectively. The radius value of the plastic zone of the inclined joint group is close to the theoretical derivation, indicating that this derivation formula is more applicable for calculating the plastic zone range of inclined joint groups. Meanwhile, as the internal friction angle increases, the sensitivity of the plastic zone radius to the internal friction angle gradually decreases. Referring to the literature [1,35], highly similar conclusions were also obtained in this study. However, references [1,35] were focused on the influence of the internal friction angle on the evolution of the plastic zone in homogeneous rock mass. Through comparison, it was found that the presence of joint groups mainly changed the morphological characteristics of the plastic zone.