esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Free-Motion Task Implementation on a Serial Metamorphic Manipulator
## 3 Main Theoretical Results  
### Task Paths  
For theoretical and experimental validation, circular and rectangle paths (CP/RP) were selected in different areas of the manipulator workspace, as shown in Figure 4a. Rectangle paths were selected in order to study the effect of rapid direction changes and the effect of a variable TCP velocity when direction was constant.  
Figure 4: SMM prototype and task paths visualization. (**a**) Task paths executed in this work. RP had a side length of 20 cm, and the direction of motion is indicated by the arrows in the above image. CP had a radius of 15 cm and rotation was along the +Z axis. IP for each path was extracted from the optimization problem solved as discussed in [28]. (**b**) The SMM prototype at the reference anatomy. 1. Stepper motor (active joint 1). 2. Dynamixel PH-54-S200 actuator (active joints 2 and 3). 3. Custom-built passive modules to achieve anatomy metamorphosis. 4. Main controller board. 5. Manually triggered safety button. 6. Three-dimensional force sensor.  
Figure 3: Illustration of the chromosome variables and the task points for a discrete-value objective function. MATLAB GA optimization settings: crossover function = ‘_crossoverscattered_’, mutation function = ‘_mutationgaussian_’, selection function = ‘_selectionroulette_’, \(EliteCount=~{}5\), \(PopulationSize=~{}100\), \(MaxGenerations=~{}50\), \(FunctionTolerance=~{}0.01\). The MATLAB solver ‘_ga_’ was used, and the remaining parameters were set to default values.  
Circular paths were selected to study the effects of circular motion, which inherently involves variable acceleration and velocity profiles. These varying loads required the control system to adeptly manage the torque and velocity outputs to maintain precise trajectory tracking. For both tasks selected, the necessity to handle the imposing dynamic variations without compromising accuracy and stability was investigated in order to obtain a deeper understanding of the effect of SMM mechanical design and consider the areas to improve by implementing a more sophisticated control architecture.
# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 2 Image Sampling Methods  
Imaging sampling is essential for a successful intracellular micromanipulation through which the recognition, localization, tracking, reconstruction, motion, and orientation control of target specimens and manipulation tools heavily rely on the quality of sampled images. In other words, image sampling methods must be selected on the basis of several aspects, such as the size, morphology, distribution of specimens, imaging resolution, sampling speed and accessibility of the adopted instruments as well as their integration capability with other manipulation tools. For example, computer tomography (CT) and magnetic resonance imaging (MRI) are widely used in the visualization of bones, tissues, and organs, but they are unsuitable for imaging small-scaled specimens, such as cells and organelles [40; 41].  
This section mainly focuses on advanced biological imaging technologies at a cellular or subcellular level, such as commercially available state-of-the-art microscopes, including confocal fluorescence microscope (CFM), two-photon fluorescence microscope (TPFM), light sheet fluorescence microscope (LSFM), lab-built photoacoustic microscope (PAM), traditional wide-field fluorescence microscope (WFFM), advanced structured illumination microscope (SIM), and stimulated emission depletion microscope (STEDM) with a super-resolution imaging ability.
# A Systematic Review of Computational Fluid Dynamics Models in the Stomach and Small Intestine
## 4 Discussion and Future Directions
Three sets of 500 particles were initially randomly distributed inside spheres (red, glue and blue) located respectively in the distal antrum, proximal antrum and corpus. due to the fluid-structure interactions required to compute the disintegration of particulate matter. The progress made in utilizing GPU acceleration of particle-based methods [74] also appears to be a viable route for developing future models of gastric motility and digestion. Future CFD models of the small intestine should incorporate the complex physical properties of chyme, which can be achieved through multiphase modeling approaches. The rheology of chyme also undergoes changes as it traverses the length of the small intestine, owing to both mechanical and chemical processes. More precise experimental measurements of digesta rheology, especially in human subjects, are necessary to improve the accuracy of in silico models in capturing the fluid properties of chyme. Small intestine models incorporating nutrient absorption should model the enzymatic digestion occurring within the bulk digesta and the kinetics of transport proteins in the intestinal epithelium. Validation of CFD models is crucial for their successful application in predicting the gastric flow and mixing patterns. While significant progress has been made in developing and refining these models, validation remains a challenge, with only 6/30 studies presenting evidence of validation. Experimental validation is necessary to establish the accuracy and reliability of the numerical results, especially in complex physiological systems like the GI tract. However, obtaining experimental data for validation purposes can be difficult, as it requires invasive techniques or non-invasive imaging techniques that are limited by resolution and accessibility. Flow visualization experiments conducted within a phantom model [75; 76] could provide the necessary data for numerical validation.
## 5 Dust Suppression Experiment  
By varying the surface tension and viscosity of the spray solution, the atomization effect of the solid cone nozzle can be observed under different conditions. However, we need to explore how to reduce the dust by optimizing the spray parameters. Therefore, measuring the dust removal rate in the experiment can provide a practical reference for application, further demonstrating the significant impact of surface tension and viscosity changes on spray dust removal. This can provide practical guidance for industrial and mining industries. The corresponding dust-fall rates are displayed in Table 1.  
Figure 10: Joint distribution of droplet size and velocity: (**a**) Legend, (**b**) Water, (**c**) 2% dodecyl alcohol, (**d**) 5%dodecyl alcohol.  
The above table shows that the dust reduction rate of dodecyl alcohol solution is higher than that of pure water. As the surface tension decreases from 71.97 mN\(\cdot\)m\({}^{-1}\) to 33.60 mN\(\cdot\)m\({}^{-1}\) and the viscosity increases from 0.89 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s to 1.12 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s, the rate of dust suppression increases. The distribution characteristics of the fog field indicate that the reduction of surface tension causes the expansion region of the droplet to be closer to the nozzle, while the increase of viscosity makes the stable region of the droplet more continuous. This leads to more uniform droplet size and velocity distribution in the fog field, resulting in an improvement in the dust suppression rate. In comparison with the dust fall rate of full dust and respirable dust, the dust reduction rate of total dust using 2% dodecyl alcohol solution is 13.58% higher than that of pure water, while the dust reduction rate of respirable dust is 17.67% higher than that of pure water. Under the same working conditions, changing the surface tension and viscosity can more significantly affect the dust fall rate of respirable dust.
## 3 Results and Discussion
### Limitations  
We want to call attention to the fact that the PSD method using the fast Fourier transformation (FFT) process results in a PSD plot with averaged intensity over the observed frequency range of interest. So, the presented PD levels do not expose some severe or extreme vibration levels that can happen during real delivery. Furthermore, each product has a different sensitivity to vibration, so this study can provide a simulation environment that can be used in practice to check the adequacy of packaging.  
The CEP delivery services usually use other vehicles like minivans, mid-size trucks, or heavy trucks to deliver shipments; therefore, data from this study have limited use for the entire parcel delivery chain.  
Last, but not least, it has to be recognized by the readers of this paper that the theoretical dynamics of stacked packages can be described as nonlinear free vibration of a coupled mass-spring system. Hence, various geometrical sizes and different weights of packages will change the vibration intensity in a nonlinear way.  
Of course, there are also obstacles from an economic point of view that cannot be avoided. A higher intensity vibration may require stronger packaging, which, in turn, can lead to significant cost overruns and the risk of overpacking. This issue can only be resolved after careful economic consideration, i.e., with the combined examination of damage risks or the possible damage boundary of the product and additional packaging costs.
## 4 Conclusions  
The study presented the use of multiple HSI modalities and multivariate analysis methods for the detection and classification of grain flows. Three types of optical spectra (i.e.; reflectances of VNIR, SWIR and fluorescence) were obtained from the acquired HSI 3D data cube of the eight grain flours. PCA was applied to reduce the data dimension and select the optimal wavelength for ROI extraction. Four chemometric methods, LDA, PLSDA, SVM and CART, were demonstrated and compared with accuracy (A) and kappa coefficient (K). The reflectance of the VNIR, SWIR and fluorescence HSI demonstrated the model's potential for classification of multiple grain flours. Among them, LDA showed the best classification accuracy (A = 97%, K = 0.97) with normalization preprocessing in SWIR modality. VNIR showed the best accuracy (A = 92.37%, K = 0.91) with SVM and fluorescence showed the best accuracy (A = 92.28%, K = 0.91) with LDA. Furthermore, the LDA model with SWIR can distinguish grains of the same type (MF1 and MF2) but different particle sizes through spectral analysis (A = 91%, K = 0.88). This aspect can be studied in a future work to improve the accuracy of quality control in the agro-food industry. Moreover, alternative machine-learning methods and feature-selection methods will be studied in the future by using a big dataset.  
Conceptualization, Y.S.; methodology, Y.S., J.L.; software, A.L., B.K.; investigation, J.L., B.K.; data curation, B.K., A.L.; writing--review and editing, Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the agenda research project (No. PJ01350301), National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.  
Figure 7: LDA results of eight grain flours according to VNIR (**a**), fluorescence (**b**) and SWIR (**c**). LDA in 3D space is fluorescence range (**d**).  
**Acknowledgments:** We would like to thanks to [PERSON], [PERSON], and [PERSON] in Department of Agricultural Engineering who prepared the samples and supported experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Thermodynamic Analysis and Effect of Temperature on Lactic acid Adsorption  
The effect of temperature (303.15 to 353.15 K) on lactic acid adsorption onto the adsorbents is shown in Figure 2a. The temperature was raised to 353.15 K to test the effectiveness of the adsorbents to adsorb lactic acid from a model fermentation broth representing _T. neapolitana_ fermentation, which operates at 353.15 K (or 80 \({}^{\circ}\)C). Both GAC and RLX425 exhibited a decrease in \(q_{e}\) and recovery efficiency values for temperatures higher than 313.15 K. Increasing the temperature from 303.15 to 353.15 K decreased the adsorption capacity of GAC and RLX425 by \(\sim\)13% and 50%, respectively. When the temperature exceeded 313.15 K, desorption of lactic acid was observed for both GAC and RLX425. In contrast, the \(q_{e}\) (24 mg lactic acid/g adsorbent) and recovery efficiency (\(\sim\)51%) of AMB400 remained nearly constant throughout the tested temperature range (303.15-353.15 K), thus making the resin more suitable for in situ applications than GAC and RLX425.  
Figure 1: Effect of lactic acid adsorption onto GAC, RLX425 and AMB400 at 303.15 K. (**a**) Contact time (0–24 h) with 4.5 g/L of lactic acid in the bulk solution without initial pH correction and an adsorbent dose of 10% (\(w/v\)); (**b**) different initial pH (2.0–6.5) with 4.5 g/L of lactic acid in the bulk solution and an adsorbent dose of 10% (\(w/v\)); (**c**) various adsorbent doses (5–30%) with 4.5 g/L of lactic acid in the bulk solution accompanied by initial pH corrections and (**d**) different initial lactic acid concentrations (1.5–36.3 g/L) accompanied by initial pH correction with an adsorbent dose of 10% (\(w/v\)).  
The thermodynamic parameters for lactic acid adsorption were calculated from Figure 2b by using the Van't Hoff equation. The thermodynamic model assumed (i) fluid density and viscosity are constant and (ii) isothermal operation during the adsorption of lactic acid. The corresponding \(\Delta G^{0}\) values are shown in Table 3. The results clearly show that the adsorption process by both GAC and RLX425 is exothermic (i.e., \(\Delta H^{0}\) < 0) and adsorption by AMB400 is endothermic (i.e., \(\Delta H^{0}\) > 0). The \(\Delta G^{0}\) < 0 for all the tested adsorbents indicate that the adsorption process is favorable (GAC > AMB400 > RLX425) and spontaneous.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 3 Techniques for Explainable AI
### Representation-Based Interpretation Techniques  
#### 3.3.1 Sparse Linear and Shallow Decision Tree ML Models  
These are known for their ease of interpretation, requiring only minimal human effort. However, the pursuit of greater predictive accuracy in ML has led to the adoption of deeper and more complex models, albeit at a cost. These sophisticated models often involve millions of interconnected deep neural networks, making them exceptionally challenging to interpret. They appear as black boxes to human observers, eroding trust in their predictions. While there are techniques available for visually inspecting the first layer of a neural network, understanding the intricate workings of deep learning models remains a significant challenge. In recent years, there has been a growing research focus on developing methods to gain deeper insights into these models. Among these techniques, saliency maps, as introduced by [PERSON] et al. [45], and activation maximization [46] have emerged as common tools for interpreting image classification models. Saliency maps have also found utility in text classification tasks. The interpretability of attention mechanisms also sparks considerable debate and ongoing research efforts.
## 3 Methods
### Consistency Loss  
We propose regularizing patch-level features explicitly with consistency representation learning, as shown in Figure 2. First, we create augmented views of an input image \(x\) that includes local and global views. Each augmented view is an input to the teacher and student networks, which have the same architecture but different parameters. The final output of the [CLS] token contains the overall semantic information of the image, and the patch embedding vectors focus on a more local region (i.e., each patch). The outputs from each [CLS] token should be similar in the embedding space since the augmented views generated from a single image share the same semantic information. To this end, we consider the pretraining objective in DINO, a noncontrastive SSL framework:  
\[\mathcal{L}_{[CLS]}=-P\Big{(}g_{[CLS]}\Big{)}^{\top}\log P\Big{(}l_{[CLS]} \Big{)}, \tag{1}\]  
where \(g_{[CLS]}\) and \(l_{[CLS]}\) are image-level representations, the outputs of the projection head \(h\), and \(P\) denotes a softmax transformation. By minimizing the cross-entropy loss, the model learns to match the image-level representations of the augmented views.  
To further enhance the quality of the representations, we propose a consistency loss \(\mathcal{L}_{con}\) for patch embedding vectors to learn internal fine-grained structures within an image:  
\[\mathcal{L}_{con}=-\frac{1}{N}\sum_{i=1}^{N=H\times W}P\Big{(}g_{patch}^{i} \Big{)}^{\top}\log P\Big{(}l_{patch}^{i}\Big{)}, \tag{2}\]  
where \(g_{patch}\) and \(l_{patch}\) are patch-level representations associated with overlapping regions of the two augmented views. As shown in Figure 2, the region of interest (RoI) align layer is applied to the corresponding overlapping features to match the spatial dimension of overlapping representations from two different global and local views. \(H\) and \(W\) are the height and width of the output feature map from the RoI align layer, respectively. The model is capable of learning local fine-grained features by encouraging the distribution of patch-level features corresponding to the same area (i.e., patches representing the same semantics) to be similar. As shown in Figure 1b, the proposed consistency loss significantly contributes to the generation of consistent representations for the overlapping patch tokens and the [CLS] token. The output feature of a specific patch depends on the surrounding patches because ViT is a self-attention-based architecture. However, overlapping patches still have the same semantic information because they originate from a single image. Therefore, it is reasonable to produce similar representations for these patches. Note thatthe consistency loss function is not calculated if there is no overlapping region between augmented views.  
The overall SSL objective is \(\mathcal{L}=\mathcal{L}_{[CLS]}+\lambda\mathcal{L}_{con}\), which learns both global- and local-level representations. We applied a stop-gradient operator to the teacher network, updated it with an exponential moving average (EMA) of the student parameters, and set the parameter \(\lambda=0.5\) for all experiments.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 6 Sleep Pattern Extraction and Sleep Quality Analysis
### Event-Based Sleep Analysis
#### 6.3.1 Feature Representation  
Time of sleep onset and sleep duration are key differentiators for personal sleep habits. Therefore, the sleep start time (\(T^{5}\_s\)), sleep end time (\(T^{5}\_e\)) and sleep duration (\(D^{5}\)) are included as features, to represent a sleep event. To encode the periodic nature of 'time' as a smooth function, we convert timestamps (\(T^{5}\_\{s,e\}\)) into a two-element trigonometric vector representation: \(([sin(2*pi*T^{5}\_\{s,e})/seconds\_in\_day),\ cos(2*pi*T^{5}\_\{s,e\}/seconds\_in\_day)])\).  
As stated in Section 6.1, sleep continuity variables (number of interruptions, length of interruptions, and sleep efficiency), are important measurements for quantifying sleep quality. We consider three different 'interruption' categories for representing a sleep event, i.e., toss and turns, awakenings, and breaks (according to the definitions in the previous Section 6.2). For each sleep event, we compute the number (\(\#_{Toss\&Turn}\), \(\#_{Anukening}\), \(\#_{Break}\)) and total duration (\(D_{Toss\&Turn}\), \(D_{Anukening}\), \(D_{Break}\)) of each interruption category, which resulted in 6 additional features. Sleep efficiency is computed as: \(1-(D_{Toss\&Turn}+D_{Awakening}+D_{Break})/D\). Furthermore, toilet breaks during a sleep event are considered to be of special interest in geriatrics, which we include as an additional descriptor.  
Figure 3: Sleep diary of a subject, indicating the ratio of time spent in bed within every hour.
# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
## 2 Related Work
### Supporting the Ad Creative Operation Processes  
Table 1 shows a classification of ML methods that support the process for ad creatives. The operation process for ad creatives generally includes the following: (1) created and submitted to the ad platform, (2) served to users on the platform, and (3) discontinued when they become less effective. There are several studies to support (1) and (2) based on ML methods, such as supporting the creation of high-performing ad creatives [1; 2; 10; 11], determining the best bid price [3; 12; 13], and deciding to whom to serve the ad creatives [5; 14; 15]. However, to the best of our knowledge, no studies have been conducted on supporting the discontinuation process. Thus, we focus on process (3) and develop a framework for predicting the appropriate timing for the discontinuation of a ad creative.  
The serving performance of ads can be inefficient for a variety of reasons, one of which is wear-out. Wear-out is an event caused by decreasing ad performance due to repeated serving to users. To control for wear-out, frequency capping, ([[https://support.google.com/google-ads/answer/117579](https://support.google.com/google-ads/answer/117579)]([https://support.google.com/google-ads/answer/117579](https://support.google.com/google-ads/answer/117579)); accessed on 28 March 2022) which restricts the number of times an ad creative is served to a user, is a traditional function, and the number of servings of an ad is useful in predicting the CTR and/or conversion rate (CVR) [6]. The event is commonly discussed in marketing science literature [16], and an ad-serving algorithm that incorporates wear-out as a feature has been previously proposed [17]. However, no studies have directly predicted the wear-out of ad creatives.  
In related studies about ad creative discontinuation, a bandit algorithm has been commonly used for selecting effective ad creatives [18]. Using the algorithm, the efficiency of ad creatives can be estimated correctly with fewer impressions. However, ad operators must decide which ads to discontinue based on their performance; determining the appropriate timing of discontinuation demands much experience.