esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods
## 2 Literature Background
### Optimisation Methods
#### 2.2.7 Differential Evolution  
Methods such as differential evolution, self-organising migrating algorithm (SOMA), evolutionary strategies, and genetic algorithm are evolutionary computation methods. These methods use the term 'individual' instead of'solution candidate', but the meaning is the same.  
The differential evolution technique belongs to the class of evolutionary algorithms. In fact, it is founded on the principles of mutation, crossover, and selection. However, it was originally conceived [42] for continuous problems and it uses a weighted difference between two randomly selected individuals as the source of random variations. The method is very effective and has recently become increasingly popular. Thus, the core of the method is based on a particular manner of creating new individuals. A new individual is created by adding the weighted difference between the two individuals with a third; if the resulting vector is better than a predetermined individual, the new vector replaces it. Thus, the algorithm extracts information about direction and distance to produce its random component [39; 43].  
The algorithm of differential evolution uses selection which is carried out between the parent (solution candidate) and its offspring in the population. The better individual of the two compared individuals is then assigned to a population that completely replaces the parent population. The question is how to create an individual that will subsequently be crossed with the parent. There are several ways of creating such an individual. The first method generates an individual (mutation) using three different individuals randomly selected from the parent population. These individuals must further satisfy the condition that they must be different from the individual just selected from the parent population.  
The second basic method of generating a new individual by mutation is using four randomly different individuals in a population and the best individual in the population that is different from these individuals. Further, the best individual and the four selected individuals must be different from the individual just selected from the parent population.  
A detailed description of our implemented method of differential evolution is described in [37].
## 4 Modeling and Results
### Datasets Used for Modeling
#### 4.1.2 Kuzushiji Kanji  
The Kuzushiji-Kanji dataset comprises 140,426 grayscale images of handwritten Japanese kanji in the cursive kuzushiji style. Each image is 64 \(\times\) 64 pixels, maintaining a consistent size for training. This dataset is publicly available in Ref. [30]. The dataset is notably unbalanced, with class distributions ranging from 1766 samples to only a single example per class. In this research, three subsets were created, containing the one hundred fifty, two hundred, and three hundred most-sampled classes. This dataset does not initially include a train/test split. Therefore, consistent with previous research [12] using this dataset, a 70/30 train/test split was applied.  
Prior to training, the images underwent the following preprocessing similar to the images in the CASIA-HWDB dataset: conversion to tensor format and normalization to scale pixel values between \(-1\) and 1. This ensured compatibility with the training pipeline and helped to maintain consistency across the datasets.
## 6 Simulation Setup and Results
### Results  
All the KPIs are analyzed and presented for satellite and terrestrial links separately, in order to pinpoint the effects of DSS techniques on different networks in the system. In the end, three simulation sets were executed, two for uplink and one for a downlink scenario, each evaluating the two proposed DSS techniques and comparing them to baseline scenario.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 4 Multi-Sensor Data Fusion Framework
### Manifold-Based Unscented Kalman Filter
#### 4.3.1 System Prediction  
The a priori state of the robot's overall system can be defined as follows.  
\[\hat{X}_{n}=f\big{(}\hat{X}_{n-1},\omega_{n}\big{)} \tag{16}\]  
The former \(\hat{X}_{n-1}\) represents the state estimate from the previous time step. According to [29], predictions for the previous time step's state can be made using the three-axis acceleration and three-axis angular velocity data from the IMU, eliminating the need to perform Unscented Transformation (UT), which approximate the Gaussian distribution using a certain number of parameters.  
\[\lambda=(\alpha^{2}-1)2q \tag{17}\] \[W_{c}^{j}=\frac{1}{2(\lambda+2q)},\,j=1,\ldots\ldots,4q\] \[\tilde{\varsigma}_{j}=\pm\text{col}\sqrt{(\lambda+d)p_{n-1}}),\,j= 1,\ldots\ldots,4q\]  
When applying Unscented Transformation (UT) in robotics, it is common to select \(\lambda=1\) to reduce the algorithm's uncertainty. Here, \(W\) represents the weight term in the subsequent pose estimation and \(q\) represents the system's state dimension. We select the parameter changes \(\tilde{\varsigma}_{i}\) for the sigma points. Due to the state points transitioning into themanifold space, the selection of sigma points cannot follow the conventional approach. The sigma points can be updated as follows.  
\[X_{n}^{j}=f(\varphi(\hat{X}_{n-1},\xi_{j}),\;\omega_{n}) \tag{18}\]  
The covariance is computed in the tangent space. First, it is necessary to define the equivalent functions complementarily using the left-invariant and right-invariant properties, as defined in Equation (3). Additionally, we define the inverse transformation mapping from the Lie group to the Lie algebra \(\varphi^{-1}:\log M\times M\in{}^{d}\).  
\[\begin{split}\varphi_{\hat{x}}^{-1}(x)=\log(\hat{x}x^{-1})( \text{Right})\\ \varphi_{\hat{x}}^{-1}(x)=\log(x^{-1}\hat{x})(\text{left})\end{split} \tag{19}\]  
The covariance in the manifold-based form can be determined as follows.  
\[\sum_{n}=\sum_{j=1}^{2d+1}W_{c}^{j}\varphi_{\hat{x}_{n}}^{-1}(x_{n}^{j})( \varphi_{\hat{x}_{n}}^{-1}(x_{n}^{j}))^{\text{T}} \tag{20}\]
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 4 Experimental Results and Performance Analysis
### Performance Analysis
#### 4.3.3 Optimization Effect in Computing Gram-Schmidt Coefficient  
Figure 9 presents the performance comparison of the standard and optimized Gram-Schmidt coefficient calculations at different scales. The maximum speedup ratio is 1.50, the minimum speedup ratio is 1.38, and the average speedup ratio is 1.42.
## 3 Analysis of Effects of Co-channel Interference on EBPSK Waveform
### Effects of Interferences with Different Initial Phase on Time-Domain Waveform  
Considering co-channel interference, if its initial phase is 0, the added interference can be thought of as the amplitude increasing of all-zero sequence in the decomposed sequences, _i.e.,_ in-phase stacking,  
Figure 1: The decompose of the EBPSK signal. (a) EBPSK signal. (b) Pure sine wave. (c) Jumping sequence.  
Figure 2: The response of the decomposed sequence to the filter. (a) The response of the EBPSK signal to the filter. (b) The response of the pure sine sequence to the filter. (c) The response of the jumping sequence to the filter.  
which will cause both the amplitudes of jumping part and non-jumping part to increase, but the amplitude difference remains unchanged. On the other hand, with the increasing of the initial phase, the phase difference of the interference response and all-zero response become larger, and the amplitude enhancement role becomes poor, _i.e.,_ the part of waveform will be cancelled because of the existing of the phase difference, thus, the impulse amplitude decreases too. Figure 3 gives the time-domain response waveforms in no interference, and when initial phase is 0, \(\pi/2\) and \(\pi\), respectively, where \(SIR=0 dB\), which verifies the correctness of the above intuition explanation. All in all, the produced impulse in the case of the zero initial phase is much higher than no interfered and other initial phase cases.
## Materials and Methods
### Drying Experiments  
To parametrize the model, sun drying experiments were performed on the premises of IRRI. Three batches of experiments were performed during the rainy season (October 2011 and November 2012) and dry season (May 2012 and April 2013). The paddy rice was spread out on a black PVC tarpaulin with a bulk height (\(z_{pa}\)) of 40 mm. The grains were mixed manually by using a wooden rake at 1-h intervals during daylight.  
Figure 4: Top view of shadows created by the circular shield, the pyranometers and the support rod with (**a**) unshaded paddy rice, (**b**) unshaded circular shield, (**c**) pyranometer shadow, (**d**) support rod shadow, (**e**) crescent shade of the circular shield, and (**f**) shaded part of the circular shield.  
To investigate the drying performance, omega sensors (OM-EL-USB-2, Omega, Stamford, CT, United States of America) were used to monitor temperature and relative humidity over the paddy rice surface, and a separate omega sensor was used to measure ambient conditions. The precision of the temperature and relative humidity measurements of the omega logger was \(\pm\)2 and \(\pm\)5%, respectively. These sensors were placed at different positions, as shown in Figure 5. Total solar radiation was measured by a pyranometer (CMP11, Kipp & Zonen, Delftechpark, Delft, Netherlands). Pyranometers were connected to a data logger (34970A, Agilent Technologies, Inc., Loveland, CO, United States of America). Data were logged at 5-min intervals and merged with the data from the temperature/humidity loggers after each drying experiment.  
Immediately after the hourly mixing operation, a sample of about 10 g of paddy rice was taken from each of the positions shown in Figure 5. Due to the preceding mixing, a sufficient mixture of the rice layers was assumed. The samples were weighed by an electronic balance with a precision of \(\pm\)0.001 (PS-20, Voltcraft, Hirschau, Germany). Dry matter mass \(m_{DM}\) (g) was determined by drying the sample in a ventilated oven (ESP-400 Series, Blue M, White Deer, PA, United States of America) at 130 \({}^{\circ}\)C for 16 h [59]. The moisture content \(MC\) was calculated on dry basis.  
\[MC=\frac{m_{ini}}{m_{DM}} \tag{18}\]  
where \(m_{ini}\) (g) is the initial mass of the sample.  
From the initial mass of paddy rice \(m_{0}\) (kg) used per experimental batch (about 500 kg), the instantaneous mass \(m_{t}\) was calculated for one-hour intervals:  
\[m_{t}=\frac{m_{0}}{(1+MC_{0})}\cdot(1+MC_{t}) \tag{19}\]  
where \(MC_{0}\) is the initial moisture content and \(MC_{t}\) is the instantaneous moisture content at time \(t\).
## 5 Results and Discussion
### Performance Evaluation of Multi-Class Predictions  
This section focuses on a comprehensive comparison of the multi-class prediction performances of the algorithms evaluated in the study. The performance metrics of kNN, RF, XGBoost, DNN, and the proposed hybrid DNN+RF model were analyzed to assess their ability to predict driver injury severity across multiple classes (Figures 4-8). In the results, 1 stands for Class 1 (i.e., uninjured), 2 stands for Class 2 (i.e., injured), and 3 stands for Class 3 (i.e., killed).  
The hybrid DNN+RF model achieved the highest F1-Scores for Classes 1 (uninjured) and 3 (killed), with values of 0.88 and 0.99, respectively, and tied with the standalone DNN in Class 3 performance. Its macro-average F1-Score of 0.86 and weighted-average F1-Score of 0.92 were also among the highest, outperforming the standalone DNN by a small margin and significantly surpassing other models like XGBoost and RF. The hybrid model's performance in Class 3, which has the largest support, likely contributed to its strong  
Figure 8: Multi-class performance results and confusion matrix of DNN+RF.  
Figure 6: Multi-class performance results and confusion matrix of XGBoost.  
Figure 7: Multi-class performance results and confusion matrix of DNN.  
weighted averages, underscoring its capability to handle the dominant class effectively without compromising the overall balance.  
The challenges with Class 2 (injured) remain evident across all models, as this class consistently exhibits the lowest F1-Scores, reflecting difficulties in distinguishing between this intermediate class and the others. Despite this, the hybrid DNN+RF's higher recall for Class 2 (0.64) compared to models like XGBoost and RF indicates its improved ability to identify cases of injury, a critical factor in injury severity prediction.  
The hybrid DNN+RF model consistently demonstrates a superior performance across most metrics, especially in terms of macro and weighted averages, suggesting its overall robustness and adaptability to the multi-class classification problem. The model's weighted average performance emphasizes its potential for real-world applications where class distribution often varies. This analysis also highlights the persistent challenge of accurately predicting Class 2, which may warrant further investigation or tailored modeling approaches.
# Smart Warehouses: Rationale, Challenges and Solution Directions
## 5 Related Work  
SLR studies are widely performed by researchers who focus on the application of information and communication technology (ICT) in the industry [33; 34; 35; 36]. In addition to the SLR studies, researchers publish traditional literature review papers on the application of ICT in the industry [31; 37; 38]. There are a few traditional literature review articles on smart warehousing. The first survey discusses the underlying technologies for CPS-based smart warehouses. [PERSON] et al. [9] explain how the existing techniques in CPS can help to build smart warehouses and focused on four issues during the application of CPS in smart warehouses. They presented the following research directions: Blockchain-based bookkeeping subsystem, shelf-life prediction with multi-source data fusion, multi-robot collaboration via reinforcement learning, and high-level activity recognition with deep learning. [PERSON] and [PERSON] [39] synthesized the knowledge gathered from several reports, such as Gartner reports and interviews performed with managers having several warehouses. They discuss how the Internet of Things can contribute to smart warehouses. [PERSON] [40] reviewed and compared smart warehouses and traditional warehouses. As for the disadvantages of smart warehouses, he explained that smart warehouses require high investment costs, new machines, and different skills. Moreover, he states that a software fault can cause the entire warehouse system to stop. Despite these challenges in smart warehouses, traditional warehouses have more disadvantages such as over-handling items, inefficient operations, damaged materials due to the processes, inefficient space management, and inefficient materials handling the equipment. As in the case of the company Amazon, a smart warehouse might cost around $775 million, but its return on investment (ROI) is far beyond this investment cost.
## 4 Optical Measurement Process
### Loss Analysis of the Waveguides Fabricated Using the Mosquito Method
From the above investigation, it is revealed that the insertion loss of the GI core polymer waveguides fabricated using the Mosquito method is sensitive to the index profile, namely the diffusion time for core and cladding monomers. The loss result in Figure 12 shows the similar tendency: Ch. 1 and 2, and Ch. 11 and 12 show higher insertion loss, corresponding to long and short interim times, respectively. (The high loss in Ch. 6 could be accidental.) Therefore, in order to maintain the low insertion loss for all the parallel cores, control of the monomer diffusion is a key issue. The group of Keio University is currently investigating how to manage the diffusion from both material and process points of view. The insertion loss variations measured on the photo-addressed waveguides however are, on average, lower than those of Mosquito waveguides with the exception that photo-addressed waveguide 4 was damaged so no result was recorded. On the glass waveguides under test, the waveguides fabricated according to parameter set 1 exhibited lower loss at 1310 nm than those fabricated by parameter set 7. However, the set 7 waveguides exhibited lower loss at 850 nm than the set 1 waveguides. This implies that the glass waveguide fabrication parameters can be tuned to a given operational wavelength. The planar polymer Mosquito waveguides under test consistently showed lower insertion loss at 850 nm than at 1310 nm, while the planar glass waveguides under test consistently showed a lower insertion loss at 1310 nm than at 850 nm. In glass waveguides, the insertion losses at 850 nm are higher than at 1310 nm, due to the formation of silver ion clusters in the glass matrix, which induce stronger intrinsic scattering at 850 nm than at 1310 nm. This effect can be mitigated by changing the glass composition and improving the waveguide process. In polymer waveguides, the insertion losses at 1310 nm are higher than at 850 nm due to the intrinsic absorption profile of the polymer material, which is particularly affected by the vibration frequency of the C-H bonds in the polymer material [13].
# Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis
## 2 Time-Dependent FMEA Model (TD-FMEA Model)
### Risk Evaluation Modeling
#### 2.2.3 Loss of Failure  
If an inherent failure-cause exists within a system, there must be a cost associated with its elimination or prevention of the failure-cause. If a failure-cause is not detected until system failure occurs, it will add not only the failure-cause removal cost but also the system failure cost. The additional costs incurred as a result of system failure are the fixed cost and variable cost, i.e., a threshold cost and opportunity cost for system non-operation due to the system failure. The opportunity cost depends on the difference between failure-cause detection time \(s\) and failure occurrence time \(t\). Let \(L(s,\,t)\) be a loss function for a system with several failure-cause. As we mentioned above, the loss is only the failure-cause removal cost for \(s<t\). However, for \(s\geq t\), the loss is the sum of a failure-cause removal cost, threshold cost of the system failure, and an opportunity cost of the system non-operation proportional to \(s-t\). That is,  
\[L(s,t)=\left\{\begin{array}{ll}\alpha&,s<t\\ \alpha+\beta+\theta(s-t)&,s\geq t\end{array}\right.. \tag{8}\]  
Here, \(\alpha\), \(\beta\), and \(\theta\) denotes a failure-cause removal cost, threshold cost of the system failure, and proportional factor of opportunity cost because of the system non-operation.
## 2 CAD System Overview
### Liquid detection and merging  
In CTC, fecal materials are often tagged by orally administered contrast agents that are opaque to X-rays, appearing as the bright regions in the colon in Figure 5 (a). These materials can assist in differentiating residual solid stool versus tissues like polyps, since the resulting intensity in the CT image for the agent is high, usually in the range \([500,1300]\). In this step of the algorithm, the liquid contrast agent is detected and merged with the segmentation mask. Liquid is detected primarily using three features:  
1. The liquid has a high intensity.
2. The liquid is below an air-filled region.
3. The interface between the liquid and air is planar.  
This function performs a simple thresholding in the original image using a high threshold \(T_{h}=350\). The voxels above the threshold are labeled into distinct regions. For each region, an analysis is done on each axial slice to determine its flatness. A bright region is considered _flat_ in a 2D slice if \(T_{f}\) of its pixels have the same \(y\) (corresponding to the axis of gravity) value. \(T_{f}\) is determined as \(max(15,0.2\cdot R_{w})\), where \(R_{w}\) is the width of the region's bounding box. To fill the partial volume between the air and the liquid region, rays are cast from the liquid region upwards along the \(y\) axis towards the air, and the voxels in between are added to the segmentation mask, as demonstrated in Figure 5 (b).  
After this step, the colon segmentation is complete and the foreground voxels in the mask represent the colonic region of interest used in subsequent processing. A sample colon segmentation appears in Figure 5 (c).  
**Figure 5.** Original slice (a) and colon segmentation after liquid detection and merging (b).  
Final colon segmentation, with overlaid CAD marks (c).
# Big Data Perspective and Challenges in Next Generation Networks
## 4 Current State-of-the-Art and Open Issues  
In this section, we briefly discuss some open issues. This also gives a direction on further advancements of studies at the crossroad of big data and 5G, as addressing these issues can give major breakthroughs.  
**Proactive caching and computing:** Computing and caching cost for next generation networks can be minimized through modern big data analytics approaches. In this way, resources are to be efficiently distributed and utilized resulting in balancing the caching and computing overhead. For instance, the intermediate and final results should only be stored if meaningful, as storing all the information is costly.  
**Security and Privacy:** Big data analytics uncovers the hidden information from within the huge data. Consequently, massive data analysis can cause security and privacy issues. During the storage, management and processing stages, data should be encrypted well, to assure that it cannot be manipulated or altered. Moreover, access to the data should be allowed for authorize entities only via secured channels. Thus, security and privacy issues are key concerns for such a massive data analysis and should be addressed intelligently.  
**Big Heterogeneous data:** Big data sources are of different types with different data rate, mobility, and packet loss. The analysis of heterogeneous data in wireless networks is challenging. Heterogeneous data bring spatio-temporal dynamics. Thus, unconventional approaches are required for big spatio-temporal data analysis in mobile networks.
# Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
## 3 Results
### Macelignan Reduced Glutamate-Induced Cytotoxicity in HT22 Cells  
A cell viability experiment was performed to determine the neuroprotective effect of macelignan (1, 5, 10, 20 \(\upmu\)M) pretreatment in glutamate-exposed HT22 cells. According to the previous studies, excellent cell viability was determined to be above 90%, and 60-90% cell viability led to slight cytotoxicity [38; 39]. In this study, the data showed that HT22 cells exposed only to macelignan at concentrations ranging from 1 to 10 \(\upmu\)M were more than 90% viable, which demonstrated an excellent cell viability of macelignan at 1-10 \(\upmu\)M. However, when cells were exposed only to 5 mM glutamate, the cell viability decreased to 34.21 \(\pm\) 2.95% in comparison to the non-treated control cells (100 \(\pm\) 1.59%). Conversely, after the treatment of glutamate after pretreatment with macelignan at 1, 5, 10, and 20 \(\upmu\)M for 2 h, the cell viability was 50.26 \(\pm\) 1.72%, 90.50 \(\pm\) 1.25%, 90.92 \(\pm\) 1.03%, and 90.92 \(\pm\) 1.69%, respectively (Figure 2a). Moreover, the decrease in cell viability was reversed by NAC, the positive control, at concentrations of 0.5, 1, and 2 mM (Figure 2b). The HT22 cell morphology was observed using a microscope (Figure 2c). Glutamate exposure caused cell damage by inducing cell shrinkage and nuclear condensation, whereas macelignan exhibited the original cell morphology. These results showed that in HT22 cells, macelignan significantly increased the cell viability of glutamate exposure.