esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Whole-Body Scanning of Patients  
Each patient was given an 1110 MBq I-131 solution one hour before the in vivo gamma camera scanning, and the schedule of 1, 4, 24, 48, 72, and 168 elapsed hours were preset to obtain six data groups for each patient. The preset protocol of the gamma camera for data collection was \(\pm\)10% peak width of the I-131 main 364 keV peak, high-energy general-purpose (HEGP) collimator, FOV of 40 \(\times\) 54 cm\({}^{2}\), and 256 \(\times\) 1024 matrix size. Each patient underwent a 20-22 min scan at 10 cm/min scan speed. Obtaining high-quality images is of top priority for nuclear medicine [13], being instrumental for diagnosing the injured regions. This study was approved by the IRB Committee of the Far Eastern Memorial Hospital, New Taipei City, Taiwan, with the credential No. FEMH 106132-F. Figure 2 illustrates the original plots from the gamma camera of patients No. 2 and 8 for comparison. The six plots were consecutively downloaded from the facility after the gamma scanning. The marked ROIs were assigned as oral, thyroid, liver, stomach, kidney-R, kidney-L (right and left), and bladder, whole body (BF + WB), and BKG (background, i.e., part of thigh) to derive the count \(\times\) pixel\({}^{-1}\)\(\times\) s\({}^{-1}\) for further analysis. In addition, the BKG values were subtracted from all net count rates at various ROIs to eliminate overcounting in the analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Patient** & & & & & \\
**No.** & **Gender** & **Age (Years)** & **Weight, W** & **Height, H** & **Days after I-131 Administration** \\  & & & & & **upon Surgical Operation** \\ \hline
1 & M & 42 & 74 & 164 & 38 \\
2 & F & 49 & 60 & 158 & 36 \\
3 & F & 57 & 61 & 149 & 25 \\
4 & F & 57 & 54 & 145 & 35 \\
5 & F & 56 & 55 & 165 & 33 \\
6 & F & 67 & 65 & 156 & 32 \\
7 & F & 48 & 50 & 155 & 29 \\
8\({}^{\ast}\) & F & 40 & 47 & 150 & \textgreater{}4400 \\
9 & F & 61 & 58 & 151 & 35 \\
10 & M & 51 & 101 & 168 & 36 \\
11 & M & 42 & 103 & 178 & 28 \\
12 & F & 61 & 57 & 164 & 33 \\ Average & & 52.6 \(\pm\) 8.6 & 65.4 \(\pm\) 18.4 & 158.6 \(\pm\) 9.5 & 32.7 \(\pm\) 4.0 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of twelve patients with a papillary thyroid cancer diagnosis who underwent the whole-body scanning for further analysis of the iodine biokinetic model. \({}^{\ast}\) Case No. 8 was diagnosed as having a relapse of hyperhyroidism after partial ablation of the thyroid gland 12 years before.
## 3 Proposed Scheme to Secure Device Layer Network
### Proposed Secure Communication Protocol  
Blockchain technology as a distributed system allows avoiding Single Point of Failure (SPF) of centralized schemes. Blockchain permits us to tackle security concerns such as identity management, availability, and integrity and offers a solution for traceability to follow the history of each device activity while recording all its transactions.  
Since constrained devices are unable to support the heavy processing that blockchain technology introduces for mining and cryptographic operations, a private blockchain is adopted and sited on a set of base stations closer to the sensed field (i.e., M2M area), which leads to more advantages, since it permits short delays and early control of devices. The blockchain is designed to contain all information related to each device. Figure 4 depicts the content and the block structure.  
Figure 4: Blockchain block structure.  
Figure 3: M2M base station.  
On the other hand, we choose Practical Byzantine Fault Tolerance (PBFT) to be implemented as lightweight consensus algorithm suitable for private blockchain. PBFT consensus guarantees data integrity and reliability even if \(\mathrm{N}/3-1\) of the N network nodes are compromised. Thus, a new block is validated and added to the blockchain if \((2*\mathrm{N}/3)+1\) nodes, as a minimum, attain the consensus.  
Before the communication takes place, the identification process is lunched with three phases: pre-registration, registration and authentication.
## 5 Experimental Results
### Performance
#### 5.2.3 Performance: Main Findings  
In general, the results presented in this subsection demonstrate the following:  
* The performance of _PO-count_ is very close to that of _NSGA-II_, both in terms of hypervolume and the fraction of dominated solutions.
* Our results support the finding from [39]. We show that, contrary to expectations, _NSGA-III_ results in lower values of hypervolume than _NSGA-II_ for large numbers of objectives. However, we also show that for large values of \(n_{k}\), the solutions produced by _NSGA-III_ are rarely dominated by those produced by _NSGA-II_. At the same time, _NSGA-III_ does dominate some fraction of solutions produced by _NSGA-II_. This shows that _NSGA-III_ can be beneficial for many-objective optimization problems. This observation was not reported in [39].
* Finally, our experiments clearly demonstrate the advantages of probability-based algorithms for many-objective optimization problems. Both _PO-prob_ and _PO-prob\({}^{*}\)_ result in solutions that cover larger hypervolumes and are less often dominated by the solutions of other algorithms.
# Detecting Green Mold Pathogens on Lemons Using Hyperspectral Images
## 3 Results and Discussion
Figure 13 shows that the normalized spectral intensity distribution defined by Equation (2) for the spectral range of 500 nm to 560 nm is derived as shown in Figure 14. In Figure 14, 19 curves are presented because the curve for the fresh lemon is used as the reference one. The numbers 1 to 19 correspond to the samples numbered 2 to 20, respectively. Figure 14 shows that (1) the difference between samples is more distinctive than that in Figure 13; (2) \(\alpha\) values are increasing with increasing sample numbers for the specified spectral range and the normalized spectral intensity has the highest value at 550 nm for the samples numbered 9-20; (3) the \(\alpha\) values of sample 2 and 3 at 550 nm are 1.15 and 1.3, respectively; and (4) among samples 2-14, sample 2 to 5 and 6 to 14 shows the highest \(\alpha\) value differences for the spectral range 540 nm to 560 nm. The difference is more than 0.15. The coefficient \(\alpha\) characterizes the degree of spectral characteristic changes in the lemon skin, i.e., the degree of pathogen infections on the lemon samples. The increasing \(\alpha\) values with increasing lemon sample numbers indicate that the pathogens are growing more as the sample number increases. In Figure 14, the \(\alpha\) value of the sample 20 at 550 nm is close to 4.2. This value represents the completely pathogen infected lemon. For the samples 17 to 20, the \(\alpha\) values are in the range of 3 to 4.2. Therefore, the degree of pathogen infections on lemons can be quantified by calculating \(\alpha\) values only at 550 nm. According to Figure 13, the \(\alpha\) value that can be considered as a fresh lemon will be up to 1.15 to \(<\)1.3. The skins of lemons viewed with a microscope with the magnification of 200 are shown in Figure 15. Figure 15 compares the surface states of samples 1 and 6. The fresh lemon surface specified by number 1 have no stains, i.e., it is clean but the sample 6 specified by number 2 is mottled by the pathogens. The thinner color areas represent the pathogen infected areas. When the infection goes deeper, all surfaces are densely covered by pathogens, as shown by sample 20 in \[\alpha_{\lambda_{k}}^{j}=\frac{I_{\lambda_{k}}^{j}}{I_{\lambda_{k}}^{0}} \tag{2}\]0 The \(\alpha\) value of the sample 6 is 1.51. However, it is noticed that the surface is greatly damaged, though it is hard to find the pathogen presence in sample 6 in Figure 1 with naked eye. Thus it can be said that it is not safe to use the lemon with \(\alpha\) value of 1.51. It is considered that the \(\alpha\) is useful to quantify the degree of pathogen infection on lemon skin.
## 3 Positioning System Experimental Platform
### Positioning System of the WinBus  
The positioning system of the WinBus fuses different information from different sensor systems. Figure 2 shows the positioning system function block. GPS, SLAM, and reference chassis data are all combined into one positioning system. The information for the applications is the coordinates, both absolute and relative. The two elements that make up the absolute positioning system are GPS and SLAM. Autonomous cars may locate their position using these two technologies' absolute latitude and longitude information. The relative coordinates are from the wheel speedometer, steering angle, and IMU. The positioning system predicts coordinates and guarantees stability through dynamic models.
## 3 Objectives and Hypothesis  
The underlying design of a user interface for any piece of information technology is a determining factor in making any system efficient. [PERSON] [17] explains that significant costs are incurred as a result of future revisions and redesign when the initial design of an interface proves inadequate. He also argues that it proves costly in terms of technological use. Urban interfaces are no exception.  
In many instances, when urban interfaces are developed only budget constraints, investment requirements, and functional and technical challenges are taken into consideration. In other words, efforts are limited to ensuring they automate the entire process or part of the process whilst overlooking other essential elements that end up affecting the user experience. Questions that could and should be asked include: Where should a device with a digital interface be physically placed? Does the timetable in which it will be used affect the user experience? What should the surrounding setting/context be like? Is the interface only a screen, or are there other physical elements that influence the experience, i.e., a keyboard, a cash dispenser? Is the interactive menu intuitive? Does it work in the same manner for the local residents as it does for visitors? How long should it take to perform an action on an urban machine? Is it accessible for people living with disabilities? Are there any cultural factors, such as language, that might affect the user experience? What role does age play when people use technology? What should happen when someone uses an interface for the first time? Are other digital channels available that could complement the experience, e.g., a website or mobile app?  
Clearly, many questions exist concerning urban interfaces. Within in the context of User Experience (UX), if such questions are not properly addressed or evaluated during the design phase then poor user experience will result. Consequently, this will have to be compensated for later through extra efforts [14]. Furthermore, a poorly designed interface will generate negative perceptions, frustrations and complaints against the organization responsible for its creation, as explained by [PERSON] [18].  
Different heuristics and guidelines already exist to evaluate the usability of an interactive system, however these proposals do not specialize in urban interfaces. Although it is possible to adapt them to evaluate interfaces of this type, authors such as [PERSON] [19] or [PERSON] [20] espouse tailor-made heuristics for testing specialized interfaces and explain why it is important to avoid using 'generic' heuristics. The purpose of this, as explained by [PERSON] [21], is to ensure a more effective process is available. The aforementioned process should make the gathering of more valuable information possible, in other words, information pertinent to the context of the interface being studied.  
Thus, the guiding purpose of this work is to analyze the importance of having a clearly defined methodology and tool for evaluating user experience as it relates to the domain of urban interfaces.
## 3 Methods
### Segmentation of the Lung Anatomical Structures
#### 3.1.5 Trachea and Bronchial Tree Segmentation  
Following the segmentation of the lung regions, the next step was to segment the trachea and bronchial tree. These two organs carry out the distribution of oxygen in the lungs and show air density on the CT scans, as depicted in Figure 5a. According to the lung anatomy, the trachea is a tube-like air-filled structure which is located between the two lungs, and it enters each lung by branching two main bronchi. Based on this fact, the lesions with air density were roughly segmented using the threshold density of \(-900\) HU [20], as illustrated in Figure 5b. From these air-filled segments, a single lesion which locates the middle of the two lungs was segregated as the trachea, as shown in Figure 5c. Similar to the previous step, trachea segmentation was also executed slice-by-slice, and then the isotropic image was reconstructed. After the isotropic reconstruction, the tube-like structure of the segmented trachea was visualized, as in Figure 5c.  
After tracheal segmentation, segmentation of the bronchial airways was attempted using 3D region growing. Using the center voxel of the segmented trachea as a seed point, region growing was iteratively performed based on different threshold values. Similar to [20], we set the initial threshold value as \(-900\) HU and increased this by 64 HU in every iteration to find the airway regions. However, region growing may suffer from leakage of segmentation in some cases as the bronchial airways are spread throughout the lungs. For instance, Figure 5e depicts the leakage of bronchial airway segmentation into the parenchyma (lung regions). For such cases, the threshold value needed to be adjusted in each iteration to prevent leakage. If the total volume of the segmented airway tree in the current iteration was at least twice that of the previous iteration, region growth leakage was observed. Thus, the threshold value was reduced by half to stop the leakage. In this way, the trachea and the bronchial tree were successfully segmented, as shown in Figure 5f.  
Figure 4: Segmentation of the lung regions: (**a**) input 2D computed tomography (CT) slice, (**b**) segments of air-filled regions, (**c**) suppressed air-filled regions outside of the body, (**d**) connection between the two lungs, (**e**) separation of the left and right lungs using morphological erosion, (**f**) isotropic structure of the segmented lung regions.
## 3 Results  
### Antiviral Activity of Saucerneol Isolated from S. chinensis against EV  
To evaluate the antiviral activity of saucerneol, an active compound isolated from _S. chinensis_ against EV71, CVA16, and CVB3 cells, a cell viability assay was conducted using virus-infected Vero cells. Saucerneol demonstrated approximately 90% inhibition of CVB3-induced cytotoxicity at a concentration of 2 \(\upmu\)g/mL, which was comparable to the positive control, rupintrivir (Figure S4). Saucerneol also showed antiviral activity against EV71 and CVA16 in Vero cells (Figures 1A and S5).  
Figure 1: Antiviral activity of saucerneol against enterovirus A71 (EV71) in vitro. (**A**) Vero cells were infected with 5 \(\times\) 10\({}^{3}\) PFU EV71 and treated with saucerneol. Cell viability and cytotoxicity by saucerneol treatment were measured using the sulforhodamine B (SRB) assay. (**B**) Relative EV71 5\({}^{\prime}\) noncoding region (NCR) gene expression in EV71-infected Vero cells was determined using real-time PCR. (**C**) Western blot analysis of VP3 in EV71-infected cells after treatment with the vehicle or 0.4–50 \(\upmu\)g/mL of saucerneol for 24 h. The ratio of VP3 to \(\upbeta\)-actin based on the quantification of bands in the immunoblot. Rupintrivir was used as a positive control. For the validation assessment, (**A**) were obtained through three independent experiments and (**B,C**) were obtained through two independent experiments. The results are shown as the mean \(\pm\) SEM. *** \(p<\) 0.01 and *** \(p<\) 0.001 for comparison with the EV71-infected vehicle-treated group (Veh) based on one-way ANOVA with [PERSON]’s multiple comparison test.  
To confirm whether saucerneol inhibits the replication of EV71 in Vero cells, real-time PCR was used, and we found that saucerneol inhibited the viral genome by more than 50% at a concentration of 10 \(\upmu\)g/mL (Figure 1B). Additionally, saucerneol strongly suppressed the expression of VP3 protein of EV71 at a concentration of 2 \(\upmu\)g/mL (Figure 1C), suggesting that saucerneol strongly inhibits the viral replication and protein translation stages, which are the initial stages of the EV71 life cycle. Next, we confirmed the antiviral activity of saucerneol using confocal microscopy. As a result of analyzing the antiviral activity using immunofluorescence staining of the VP3 protein, the capsid protein of EV71, 24 h after viral infection, saucerneol significantly inhibited the viral VP3 protein to the same level as rupintrivir, the positive control (Figure 2).  
Overall, these results demonstrate the potential of saucerneol as an effective antiviral agent against EV71, CVA16, and CVB3.
# Focal Muscle Vibration for Stroke Rehabilitation: A Review of Vibration Parameters and Protocols
## 3 Results
### Vibration Parameters and Protocols
#### 3.4.3 Vibration Protocols  
In terms of the muscles where the vibration was applied, 20 different muscles were targeted, with five muscles for lower extremities and 15 muscles for upper extremities (Figure 3). Biceps brachii was targeted the most, followed by triceps brachii and flexor carpi radialis.  
Figure 3: Muscles targeted for the FMV and studies using each muscle.  
Figure 2: Vibration frequencies and amplitudes used in the included studies. The results were presented as (amplitude, frequency), [article number(s)]. NR means not reported in the article. Articles did not report amplitude were plotted as 0 mm.  
The frequency of treatments ranged from a single session [41; 44; 45; 46; 48; 49] to two [56], three [42; 42; 43; 47; 50; 54; 55; 58; 59; 60; 61; 62], or five times per week [51; 52; 53; 57]. The duration of the treatment protocol was one day [41; 44; 45; 46; 48; 49], three days [47; 50; 62], two weeks [51; 52; 53; 59], four weeks [54; 55; 56; 42; 54; 56; 60], six weeks [43; 56; 58], or eight weeks [57; 61]. The vibration durations were 14 to 20 s [41; 48], 5 min [45; 46; 49], 30 min [42; 43; 47; 50; 51; 52; 53; 54; 55; 56; 58; 59; 60; 62], or 60 min [57]. One study did not specify the duration of the vibration. Instead, the participant received repeated FMV for 35 s after a 15 s rest until they felt better [44].  
Thirty-seven different outcome measures were used across the 22 studies. The most common outcome measure was assessment of the spasticity via the modified Ashworth scale, which was used in 11 studies, followed by grip strength assessed in five studies, and Fugl-Meyer scale and box and block test evaluated in four studies each. Figure 4 shows all outcome measures and how commonly they were used across studies.
## 1 Introduction  
Long-range transported (LRT) air pollution indicates the atmospheric movement of pollutants for more than 100 km, affecting neighboring countries' air quality across national boundaries. It is well known that Japan has suffered from LRT air pollution such as Asian dust (AD) and acid rain from the Asian continent [1; 2; 3; 4; 5]. However, different patterns of LRT may vary air pollutant concentrations in monitoring areas.  
Asian dust (AD), swirling sand from western China and Mongolia deserts to great heights by a turbulent wind, is long-range transported by westerlies towards Japan and the western United States [6; 7; 8]. During its transport, the air mass adopts large amounts of anthropogenic air pollutants, such as sulfur oxides (SOx), nitrogen oxides (NOx), polycyclic aromatic hydrocarbons (PAHs), and nitro-polycyclic aromatic hydrocarbons (NPAHs), emitted from industrial and metropolitan regions in China. Among these pollutants, SOx and NOx cause acid precipitate (rain and snow) on the west coasts of Japan and AD suppresses its acidification. Several NPAHs are secondarily formed from PAHs in the presence of OH and NOx radicals and a high concentration of AD [9]. It has been considered that these organic and inorganic pollutants were simultaneously long-range transported to Japan by westerly winds. They are thought to be common causes of hospital admission for respiratory, cerebrovascular, and cardiovascular diseases [10], cough prevalence [11; 12; 13], itchy eyes [14], asthma exacerbation [15], and seasonal allergic rhinitis [7; 16].  
On the other hand, long-range transportation of PAHs from China was sensitively detected at several remote sites or cities on the west coasts of Japanese islands: Noto peninsula [4; 17; 18; 19], Fukue [4; 19; 20], Okinawa [20], and Kanazawa [21; 22]. However, PAH concentrations did not always show the same variation as AD in the downstream areas [4; 13; 19; 22]. Our speculation is that particulate matters (PM) and PAHs in Japan are from different sources. Therefore, to clarify the different transportation mechanisms between AD and PAHs, the present study monitored PM and PAHs simultaneously in AD seasons in two Japanese cities, Fukuoka and Kanazawa, at a distance of 660 km. The results would benefit for investigating the cause of pollutant-related diseases (respiratory diseases, etc.) at different locations under weather phenomena similar to AD.
## 3 Experiment Setup and Evaluation
### Some Benefits and Limitations of Deep Learning vs. Machine Learning Algorithms  
Deep learning algorithms have come to revolutionize multiple areas, primarily due to the efficiency they provide. However, they are still represented as black boxes and in-depth studies are needed to explain their functioning thoroughly. Among the main advantages of deep learning algorithms is a considerable increase in efficiency across various evaluation metrics compared to classical algorithms. This has led to widespread use in crucial areas such as medicine, industry, agriculture, autonomous driving, and robotics.  
The utilization of CNNs eliminates the need for the feature engineering phase, which is customary and essential in classical algorithms. This phase involves selecting and normalizing features, among other procedures. Additionally, they can adapt to various conditions through parameter tuning, flexibility not achievable in classical algorithms. The primary disadvantage of these networks is the need for more understanding of their internal workings and the requirement for a large amount of labeled data for proper performance.  
On the other hand, classical algorithms provide a better understanding of their internal workings, and some require a smaller amount of labeled data. However, they generally exhibit lower overall performance.  
In the context of rock detection in visually contaminated mining environments, YOLO v4 has implemented substantial improvements in various areas, including:  
1. Efficient backbone with CSPDarknet53. Incorporates CSPDarknet53, an enhanced version of Darknet, using the CSP concept to improve feature learning efficiency. This results in a more adequate representation of visual features, which is particularly beneficial in visually complex mining environments.
2. PANet as Head. YOLO v4 uses this module to enhance information integration at different spatial scales. This is crucial in rock detection, as there can be significant variations in the size and shape of rocks in mining environments. PANet helps handle these variabilities, improving the model's ability to detect objects at different scales.
3. Hyperparameter optimization. Enables hyperparameter optimization, including grid size and anchor dimensions. Adjusting these parameters for rock features and mining environments improves the model's accuracy.
4. Improvements in post-processing. YOLO v4 has enhanced post-processing to reduce false positives and improve accuracy. For example, it uses an improved NMS to eliminate redundant detections. It has improved model stability, influencing the mitigation of issues such as oscillations in predictions or unstable behaviors in certain  
Figure 18: YOLO v4 algorithm failure.  
scenarios. It offers the ability to adjust specific post-processing parameters, such as thresholds and suppression criteria, providing flexibility to adapt the model to different conditions and application requirements.