esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Cell Phone Usage on Fall Risk and Postural Control During Free-Standing
###### Abstract  
Smartphone use raises concerns about forward head posture, and the high cognitive load and reduced situational awareness involved increase fall risk. This study aimed to assess the impact of smartphone usage (both holding and texting) on fall risk and postural control in healthy young adults. Thirty-three young adults completed three identical 20-s trials in static (AMTI platform) and dynamic (Balance Biodex platform) conditions, including (1) the baseline (eyes open and standing still), (2) passive phone use (holding and looking at the phone with the screen off), and (3) a dual task (texting while standing). Postural control was assessed using linear measures (CoP path length and the Fall Risk Test index) and nonlinear metrics (sample entropy, fractal dimension, and the Lyapunov exponent). The results indicated that smartphone use, especially texting, significantly increased CoP variability in the anteroposterior direction and Fall Risk Test values, highlighting reduced stability. A nonlinear analysis revealed decreased adaptability and the complexity of postural control during phone use. These findings suggest that mobile phone interactions impair balance and increase fall risk due to cognitive and physical distractions.  
blance assessment; mobile phone distraction; nonlinear measures; linear measures Article
# Design of Zoom Optical System from Visible to NIR-II for Vivo Fluorescence Imaging Device
## 2 Design Strategy  
### Zoom System Principle  
At present, zoom systems can be broadly classified into the following four types: the optical zoom system, the mechanical compensation zoom optical system, the dual-group linkage zoom optical system, and the full-motion zoom optical system. Generally speaking, there are difficulties in designing a zoom system, such as the complex calculation of the initial layout, large volume, many inflection points of the zoom curve, and many zoom curves. Generally, to achieve continuous zoom, it has to use the type of mechanical compensation zoom optical system, in which each component, in accordance with different laws and complex movements, can achieve the zoom function and reduce the effect of image surface position movement.  
The key to designing a zoom system is how to compensate for the image surface position, which needs to use the principle of image exchange. A component in each position has a corresponding image exchange position; the conjugate of these two states is the same, and the conjugate \(L\), can be expressed by the magnification of the component m:  
\[\begin{split} L=l^{\prime}-l&=f^{\prime}(1-m)-f^{ \prime}\Big{(}\frac{1}{m}-1\Big{)}\\ L&=f^{\prime}\Big{(}2-\frac{1}{m}-m\Big{)}\end{split} \tag{1}\]  
From this principle, a motion curve can be obtained; each component's m changes according to the state of the constant conjugate distance so that a zoom system with a constant image surface position can be theoretically obtained. However, there is an aberration in spherical lenses, so the image surface position can be allowed to move to a certain extent to obtain a better image quality.  
Based on this conclusion, the conjugated distance of each moving component remains the same. The design principal formula of the initial layout can be obtained as follows:  
\[\begin{split} L_{2s}+L_{3s}&=L_{2l}+L_{3l}\\ f^{\prime}_{2}\bigg{(}\frac{1}{m_{2s}}+m_{2s}\bigg{)}+f^{\prime}_ {3}\bigg{(}\frac{1}{m_{3s}}+m_{3s}\bigg{)}&=f^{\prime}_{2} \bigg{(}\frac{1}{m_{2l}}+m_{2l}\bigg{)}+f^{\prime}_{3}\bigg{(}\frac{1}{m_{3l}} +m_{3l}\bigg{)}\end{split} \tag{2}\]  
where L\({}_{2s}\) and L\({}_{3s}\), respectively, represent the conjugate distance of the second and third components of the optical zoom system in the short focal state. m\({}_{2s}\) and m\({}_{3s}\), respectively, represent the magnification of the second and third components of the optical zoom system in the short focal state. Those with the l corner scale have similar meanings as those with the s corner scale, representing the physical quantity in the long focal state. Satisfying this formula can make the image surface position shift as small as possible.
# Management of Scalp Biofilm in Local Dermatosis: From In Vivo Visualization to Optimal Treatments
## 3 Results
### Effect of Antimicrobial Agents on the sRA  
Based on the results, we decided to try using an antibacterial agent to reduce sRA. Climbazole (CBZ) is widely known for its powerful antifungal and antibacterial activity against _Malassezia_, _C. acnes_, and _S. aureus_; therefore, it is commonly used in antidandruff hair care products [19]. In addition, hexamidine disethionate (HD) has also demonstrated in vivo antifungal and antibacterial effects [20]. To decrease sRA without causing chemical or physical damage to the scalp, we administered a wash-off scalp care product containing 0.3% CBZ (Figure 5A).  
After two weeks of using the scalp care product, it was observed that 87% of subjects (n = 27) experienced a significant reduction in the sRA. However, 13% of subjects (n = 4) still continued to exhibit an sRA that was not easily removed, which suggests that not only a chemical agent but also a physical agent should be used. Therefore, to overcome this hurdle, we applied a scalp care product with a scrub to physically reduce the biofilm formed on the outer stratum corneum layer of the scalp (Figure 5B). The average sRA in the non-scrub group (n = 31) was 44.9%; however, the salt scrub group (n = 31) had an average sRA of 24.6%, indicating that physically removing the stratum corneum layer is very effective in decreasing the sRA.  
Although scalp care products applied with a scrub have shown a dramatic effect in reducing he sRA, they should be used with caution, especially for individuals with severe symptoms like itchiness, psoriasis, dandruff, or seborrheic dermatitis. This is because the salt scrub could cause minor physical damage to the scalp with continuous administration with shampooing. In addition, among the severe sRA group, with higher levels of dandruff and itchiness, scalp symptoms were not alleviated with treatment using wash-off products.  
Figure 4: Representative images of SC obtained from the (**A**) staining-positive and (**B**) staining-negative groups (red: _C. acnes_; green: _S. aureus_).  
Figure 5: The sRA decreased after the administration of scalp care products: (**A**) shampoo with CBZ; (**B**) shampoo with salt scrub; (**C**) tonic with HD (* \(p\) < 0.05, ****_p_ < 0.0001).  
We further investigated the efficacy of the scalp leave-on product with 0.1% HD with respect to subjects with severe seborrheic dermatitis and itchiness (n = 6). After a 2-week administration period, the subjects' sRA values decreased from 70.6% to 54.6%, which means that HD exhibits effective antimicrobial activities with respect to reducing the sRA in the scalp (Figure 5C). Therefore, the administration of antibacterial and antifungal agents such as CBZ and HD, along with physical assessment, could alleviate scalp symptoms and biofilm accumulation.
## 1 Introduction  
Agriculture is undeniably one of the major sectors of primary industry which, according to the United Nations Food and Agriculture Organization (FAO), has to address the challenge of raising its productivity by up to 60% during the 21 st century to provide sufficient food supply for the increasing world population [1]. This aim must be achieved taking into account the imperative necessity of using natural resources according to sustainable strategies due to the escalating pressures on ecosystems, biodiversity, lands, and water. Precision Farming (PF), also reported in the literature as precision agriculture (PA) or precision ag, refers, as in [2], to a production system which promotes innovative farming management practices for monitoring, controlling, and optimizing agricultural production processes. PF, as in [3], is generally defined as the undertaking of proper actions at the right intensity at the right place and time, while as in [4], PF can be applied for managing the inputs and outputs based on quality and quantity coefficients in order to control production, as well as environmental and economic risks, by accomplishing more suitable and targeted usage of finite resources. As deriving from the aforementioned definitions, the adoption of PF practices which provide credible, cost-effective, and user-friendly solutions is a significant factor for the benefit of sustainable growth. Concisely, the aims of PF in terms of sustainability are as in [5]:  
* Optimizing the usage of available resources to enhance the sustainability and profitability of agricultural production;
* Reducing negative environmental impacts;
* Improving the quality of the social aspects of relevant stakeholders and their working environment. These aims can be utterly achieved by integrating ubiquitous computing advancements as in [6] and conceptual innovations of \"smart\" agricultural production in the context of Industry 4.0. The Industry 4.0 concept, which is based on a strategic framework initially introduced by the German National Academy of Science and Engineering (acatech) as in [7] and was the theme of the World Economic Forum (WEF) annual meeting in 2016 [8], is a cutting-edge trend which is considered to affect the production potentials of all industrial sectors as in [9; 10; 11], through the establishment of a digitalized environment where physical and virtual objects can interconnect and interact autonomously [10; 11; 12]. Since the implementation of digitalization technologies within the agricultural sector, as part of the Industry 4.0 approach, has latterly set forth to be attributed with the term \"Agriculture 4.0\" as indicatively in [13; 14; 15], this terminology is going to be adopted for the purposes of this paper.
# Utilization of By-Products from the Fruit and Vegetable Processing Industry in Pasta Production
## 3 Incorporation of By-Products
### Culinary Quality
#### 3.2.3 Texture  
As with water absorption, there are also significant differences in the texture of pasta with added by-products. Some authors attribute a reduction in pasta firmness to interferences in, and the weakening of, the gluten network [58]. However, other authors who observe the same weakening of the gluten network report opposite results, with an increase in firmness [63; 71]. Unfortunately, as with water absorption, the explanations for these phenomena are often limited to comparisons with previous studies that align with their findings. Since there are prior studies with differing results, it is always possible to compare with one that matches the observations of each study.  
To identify potential explanations for the reported results, we can focus on the method used to measure pasta texture. In some studies, a compression test is conducted by placing the pasta perpendicular (vertically) to the instrument [63; 71], while in others, it is placed horizontally [93]. In some cases, the orientation or compression distance is not specified [79], and in others, a cutting test is performed [46,49,52,95]. However, even though these differences make it difficult to compare studies, there is also no consistency when the same measurement method is used or even with the same type of pasta.  
Another possible explanation for these differences may lie in the type of by-product incorporated and its composition, particularly regarding the type of fiber it contains. As mentioned earlier, [PERSON] et al. [104] observed different trends when incorporating different types of fiber. Unfortunately, most studies do not characterize the fibers in the by-products beyond categorizing them as soluble or insoluble in some cases, nor do they describe their functional properties. More in-depth studies on the influence of different types of fibers present in by-products, such as cellulose, pectins, and others, would be valuable for gaining a better understanding of their effects.
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 3 Our Method
### Adaptive Frame Sampling
#### 3.1.1 Mutual Information  
Mutual information is a quantitative indicator that measures the correlation between two random variables. It relies on entropy and joint entropy. Entropy is a measure of the uncertainty of a random variable, and joint entropy examines the overall complexity of all possible outcomes given two random variables. Specifically, given two discrete random variables \(X\) and \(Y\), the mutual information between them \(MI(X,Y)\) can be calculated as follows:  
\[MI(X,Y)=H(X)+H(Y)-H(X,Y) \tag{1}\]  
where \(H(X)\) and \(H(Y)\) respectively are the marginal entropies of random variables \(X\) and \(Y\), and \(H(X,Y)\) is the joint entropy of \(X\) and \(Y\). Marginal entropy and joint entropy can be calculated as follows:  
\[H(X)=-\sum_{x\in X}p(x)\log p(x) \tag{2}\]  
\[H(X,Y)=-\sum_{x\in X,y\in Y}p(x,y)\log p(x,y) \tag{3}\]  
where \(p(x)\) represents the probability density function of \(X\), and \(p(x,y)\) represents the joint probability density function of \(X\) and \(Y\). If you put Equations (2) and (3) into Equation (1), you can further obtain the following equation:  
\[MI(X,Y)=\sum_{x\in X,y\in Y}p(x,y)\log\frac{p(x,y)}{p(x)p(y)} \tag{4}\]  
To calculate the mutual information between two consecutive frames of infrared images, the marginal probability distribution can be obtained through from the respective histograms, and the joint probability distribution can be obtained using the joint histograms. Mutual information can measure the similarity between two frames of images [34]. The stronger the correlation between the two frames of images, the greater the mutual information. The weaker the correlation, the smaller the mutual information; that is, the more significant the motion between the current and subsequent frames. The smaller the mutual information, the more mutual information occurs when the two images are exactly the same.  
Let the mutual information between the \(F_{t-1}\) frame and the \(F_{t}\) frame be denoted as \(M_{t}\), which can be calculated as follows:  
\[M_{t}=MI(F_{t-1},F_{t}) \tag{5}\]  
where \(t\in 1,2,\ldots,T\), for frame \(F_{0}\), we simply define its mutual information \(M_{0}\) as 0. In order to facilitate the analysis of the motion information distribution, we inverted \(M_{t}\) and normalized it using \(l_{1}\) norm:  
\[M_{t}^{\prime}=\max_{i\in T}(M_{i})-M_{t} \tag{6}\]  
\[\hat{M_{t}}=\frac{M_{t}^{\prime}}{\sum_{T}M_{t}^{\prime}} \tag{7}\]  
the \(MI\) score \(\hat{M_{t}}\) can represent the motion information from the previous frame to the current frame. The larger the \(\hat{M_{t}}\), the more significant the motion between the two frames.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 3 Proposed Algorithms
### Computational Complexity  
The posterior probability-based detection, Viterbi detection, and simple phase detection all rely on the soft information output from FDE; therefore, this section only compares the complexities of these three symbol detection algorithms. Table 1 displays the complexities of the three symbol detection methods, with Viterbi detection exhibiting extremely high complexity.  
Compared to the simple phase detection algorithm proposed by [PERSON] et al. [29], posterior probability-based detection requires additional information merging and the calculation of log-likelihood values. However, the overall complexity is of the same order of magnitude as the simple phase detection algorithm, so the increase in complexity is limited. Moreover, due to the coding gain of the CPM signal, the posterior probability-based detection only necessitates a few instances of information merging. Simulation results indicate that, compared to simple phase detection, the posterior probability-based detection  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol Detection** & **Computational Complexity** \\ \hline simple phase detection & \(O(MN_{m})\) \\ posterior probability-based detection & \(O(MN_{m})+O(L_{m}MN_{m})+O(mL_{m}MN_{m})\) \\ Viterbi detection & \(O(PNM^{L})\) \\ \hline \hline \end{tabular} Note: \(N_{m}\) represents the length of the information sequence, while \(M\), \(P\), \(L_{m}\), and \(m\) denote the modulation order, the denominator of the modulation index, the simplified memory length of CPM, and the number of information merging, respectively.  
\end{table}
Table 1: Computational complexity of different symbol detection.  
significantly improves symbol detection performance for UWA communication with only a slight increase in complexity.
## 5 Conclusions  
To improve the stability of a power system with a large WF, a new control strategy was presented in this paper, in which an ASG and battery supply compensated power coordinately to a power system to improve its frequency stability and transient stability during the disconnection of a WF. In the control strategy, the ASG ensured the amount of compensatory power and the battery ensured the control speed of the compensatory power. The validity of the new control system was analyzed through simulation studies, and it was demonstrated that the battery first responded quickly to the system frequency deviation due to a grid fault followed by the disconnection of the WF and improved the frequency nadir, and then the ASG started to supply the compensatory power to recover the grid frequency to the rated frequency. As a result, it was confirmed that ASG and battery control based on the proposed new method could stabilize a power system effectively during the disconnection of a WF due to a grid fault. Consequently, it was concluded that the proposed new control system utilizing an ASG and battery has a significant value in improving the stability of power systems with a large WF.  
As a future study, the authors are planning to incorporate reactive power control into the cooperative control system in order to stabilize a WF after a grid fault. Because reactive power control can contribute to maintaining system voltage, the condition of the FRT capability of a WF can be improved, and thus it can be expected that the reactive power control can stabilize the WF and then can prevent the WF from being disconnected from the grid. On the other hand, control during the operation mode change between the pumping and generating of an ASG is a serious problem that should be solved because an ASG cannot output any power during the operation mode change. The authors are going to solve the problem by utilizing an LFC hydropower plant in the cooperative control system because the response speed of LFC hydro generators is relatively fast.  
[PERSON] prepared the theoretical concept and designed the proposed control system. [PERSON] and [PERSON] designed the power system model and executed the simulation studies. [PERSON] wrote the manuscript. A.S., F.T., and R.N. made considerations on the simulation results. All authors have read and agreed to the published version of the manuscript.  
This research has not received any external funding.  
The authors wish to acknowledge [PERSON] of Meidensha Corporation for his valuable contributions to modeling of the control systems and simulation analyses.  
All authors declare there are no conflicts of interest. The Japan Electric Association.
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 2 The Instantaneous (Shannon) Spectral Entropy
### Instantaneous Spectral Entropy  
Similar to Equation (2), it is possible to define the probability distribution at time \(t\) as  
\[P(f,t)=\frac{\left|H(f,t)\right|^{2}}{\sum_{f}^{B}\left|H(f,t)\right|^{2}}, \tag{3}\]  
where \(H(f,t)\) can be any form of time-frequency (TF) representation of the signal (this aspect will be discussed in more detail in the next section). Then, the Instantaneous (Shannon) Spectral Entropy becomes  
\[\text{SSE}(t)=-\sum_{f}^{B}P(f,t)\log_{10}P(f,t). \tag{4}\]  
Generally speaking, several options are available for the TF analysis of a given signal. For this specific case, the Continuous Wavelet Transform was applied, using a Generalised Morse Wavelet as the mother wavelet.
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 3 Comparing EDZL Schedulability Tests
### Tightness  
We introduce a metric for evaluating the performance of the schedulability tests. We define _tightness_ as the ratio of the number of admitted test instances to the number of schedulable test instances. An ideal schedulability test for a scheduling algorithm should admit all task sets that are schedulable by the algorithm. The tightness of an ideal schedulability test would be 1.0. For a given set of test instances, a schedulability test that can admit more test instances will have a higher tightness. We can use tightness to compare the performance of schedulability tests.  
The tightness is a function of the set of test instances. If a different set of test instances is given, the set of schedulable test instances will be different, and thus the tightness of the same schedulability test will be different. Hence, we can employ tightness to compare the performance of schedulability tests for the same set of test instances. Furthermore, the tightness cannot be used to compare the schedulability test of different scheduling algorithms because the sets of schedulable test instances are different for the different scheduling algorithms. Note that a higher tightness does not imply dominance.  
To obtain the number of schedulable test instances, we implemented a custom simulator for EDZL scheduling and performed simulations with the dataset. The simulator was implemented using C language. Given a task set, the simulator computes a hyper-periodof the task set. At every time from 0 to the hyper-period (in the domain of integers), the simulator (1) checks the job release time of every task and generates jobs, (2) gives priority to jobs based on deadline and laxity, (3) selects jobs to execute, (4) decreases the remaining execution time of selected jobs, and (5) checks job completion or deadline miss. Then, it outputs the simulation result. For each test instance in the dataset, EDZL scheduling was simulated and we identified whether each instance was successfully scheduled without missing any deadline during a hyper-period. As a result, the total number of schedulable test instances is 990,451,970.  
Figure 6 shows the tightness of each EDZL schedulability test. The tightness of the demand-based test is much higher than other tests, as expected. The demand-based test can admit 45.15% more test instances than the slack-based test and 26.05% more test instances than the utilization-based test. And the union of the demand-based test and the utilization-based test (Demand + Util) admits 884,539,046 test instances, yielding a tightness of 0.8931.