esa-sceva/satcom-chunk-collection huggingface dataset:
# Functional Yogurt: Types and Health Benefits
## 2 Functional Yogurt Types
### Others
#### 2.5.2 Lactose Free  
Lactose, which is composed of glucose and galactose, is found in high amounts in milk and dairy products. It has been identified as the main source of problems experienced after the consumption of milk and dairy products. The number of individuals experiencing indigestion, nausea, and discomfort after consuming milk and dairy products has rapidly increased in the last 10 years [123]. These problems prevent individuals from consuming milk and dairy products, depriving them of the health benefits these products bring. Currently, various lactose-free food products are being produced. There is also a lactose-free option available for yogurt products. This functional product not only provides the benefits but also minimizes the problems that can arise from lactose for lactose-intolerant individuals [122].  
\(\beta\)-galactosidase is an enzyme derived from fungi that is widely used in dairy products to hydrolyze lactose [121]. A recent study investigated the potential use of whey powder for producing lactose-free yogurt without added sugars [120]. Considering that the addition of whey powder increases the lactose content, lactose was hydrolyzed into glucose and galactose using the \(\beta\)-galactosidase enzyme. These monosaccharides, which are sweeter than lactose, make whey powder a suitable alternative to sugar. This study involved incorporating the \(\beta\)-galactosidase enzyme from _Aspergillus oryzae_ into yogurt containing different concentrations of whey powder, resulting in a considerable reduction in lactose content postfermentation compared with that of the control sample. A sensory evaluation indicated that samples with whey powder contents ranging from 0% to 10% presented an increased milky taste and creaminess. Additionally, an increase in whey powder content was associated with a more yellowish color of the samples. These products were found to have higher protein and fat contents than the control sample. The texture parameters revealed that the lactose-free samples maintained a firmer structure during storage. There was no significant difference in the water-holding capacities between the lactose-free samples and the control sample, but overall quality improvements were observed in the lactose-free samples.  
A study aimed at producing lactose-free probiotic yogurt utilizing _Bifidobacterium animalis_ ssp. _lactis_ (_B. animalis_ ssp. _lactis_) in both free and microencapsulated forms [37]. During storage, the survival rates of the probiotics in all of the samples were in accordance with the standards. The pH values of all samples decreased throughout storage, with the lowest pH observed in the sample containing free probiotics. The sample with INU presented an improved texture and reduced syneresis, whereas the sample with free probiotics showed increased syneresis. In conclusion, microencapsulation and appropriate storage conditions were found to influence the survival rate of probiotics and the physical and textural properties of yogurt.
# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
## 3 Roll Eccentricity Signal Detection Based on SFFT-IAA
### Frequency Band Partition Based on SFFT
#### 3.2.2 Critical Technical Problems of SFFT  
1. Frequency spectra rearrangement  
According to the displacement and scaling properties of Fourier transform, \(\sigma,\tau\in[0,N-1]\) are randomly selected, the parameter \(\sigma\) is odd and is relatively prime to \(N\), and the operation regarding mod \(N\) is reversible. Then, signal \(x(n)\) can be rearranged according to Equation (19) and the rearranged signal sequence \(p(n)\) is:  
\[\left(P_{\sigma,\tau}x\right)_{n}=x_{(\sigma n+\tau)mod\ N} \tag{19}\]  
2. Window function filter  
The bucketization operation is realized by filtering \(p(n)\). [PERSON] et al. [25; 26] proved that the flat filter is more efficient and convenient than other filters. Thus, the window function filter \(f(n)\) adopts a flat window function with a bandwidth of \(N/B\). In actual operations, the flat window function can be operated at the design stage of the algorithm by multiplying the sinc function with a Gaussian window function in the time domain [27]; this operation depends only on the signal length \(N\) and sparsity \(K\). The signal after rearranging and filtering can be represented as:  
\[y(n)=p(n)\cdot f(n) \tag{20}\]  
3. Frequency downsampling  
This process of frequency downsampling is implemented by time domain aliasing, as shown in Equation (21).  
\[q(i)=\sum_{j=0}^{N/B-1}y(i+Bj)\ \ \ i\in[0,B-1] \tag{21}\]  
Figure 4: The flowchart of the SFFT algorithm.  
The \(B\) points FFT operation is performed on the time domain aliased sequence \(q(i)\) to obtain \(Q(k)\). Since \(B\) is much smaller than \(N\), the time complexity of the SFFT algorithm is much lower than that of the FFT algorithm.  
4. Hash mapping reconstruction method Define a hash function for the mapping interval \([N]\rightarrow[B]\) as: \[h_{\sigma}(k)=round[(\sigma\cdot k)_{N}\cdot B/N]\] (22) where \(round\) means rounding. The offset function is defined as: \[O_{\sigma}(k)=(\sigma\cdot k)_{N}-h_{\sigma}(k)(N/B)\] (23)  
The coordinate \(k\) of \(d\cdot K\) large values in \(Q(k)\) is put into the set \(J\). Through reverse hash mapping, the coordinates of \(d\cdot K\cdot(N/B)\) original images in \(J\) can be obtained as:  
\[I=\{k\in[0,N-1]|h_{\sigma}(k)\in J\} \tag{24}\]  
For each \(k\in I\), the frequency can be calculated as:  
\[X^{\prime}(k)=Q(h_{\sigma}(k))W_{N}^{\pm k}/F(O_{\sigma}(k)) \tag{25}\]
## 2 Description of the Systems Under Investigation and Experimental Setups
### Free Vibration Testing  
The free vibration test utilized a modal impact hammer with a soft blue tip. The blue soft tip was chosen since it captures the frequencies necessary for good, high-quality data [30]. The experimental testing was conducted in the same manner for each BARC system under consideration. Measurement points are crucial in impact testing, and in order to keep as many variables as consistent as possible, the same individual would impact the BARC at the same location on each system with the same amount of force as best as they could manage [34]. As mentioned previously, three accelerometers were placed on each BARC system: the RC accelerometer, the side accelerometer, and the top accelerometer, all pictured in Figure 2.
## 3 Pilot Experiment and Results  
### Gait Analysis for Healthy Persons  
Gait is an important indicator for studying human health. The commonly used gait conversion process is mainly divided into seven stages [21], as shown in Figure 8.  
As to a single foot, the gait can be divided into five stages according to Figure 8 (shown in Figure 9):  
1. The rear foot touches the ground;
2. The entire foot touches the ground;
3. The rear foot leaves the ground and only forefoot touches the ground;
4. The entire foot leaves the ground;
5. The foot strike.  
Figure 8: The seven stages of gait conversion process: (**A**) Foot strike, (**B**) opposite toe-off, (**C**) reversal of fore-aft shear, (**D**) opposite foot strike, (**E**) toe-off, (**F**) foot clearance, (**G**) tibia vertical.  
Figure 7: Illustrative images of testing process.  
Ten healthy persons were invited to participate in this testing, each of them exercised 10 times for 2 min per time, then the typical gaits of the entire foot pressure, forefoot pressure, and rear foot pressure were acquired to analyze the walking characteristics, as shown in Figure 10. The red solid line represents the pressure of whole foot, the blue dot dash line represent s the fore foot pressure, and the green dashed line represents the rear foot pressure. The five stages (T1-T5) of a single gait can be obtained by analysis the pressures of the whole foot, fore foot and rear foot easily. We can find that the gait conversion process of T1-T5 was consistent with the standard gait conversion process (A)-(E) described above. It shows that the app is able to obtain the plantar pressure of the normal gait for effectively expressing the gait conversion process.
## 3 Results and Discussion
### _Maltolytic and Sacarboxylic Activity_  
For industrial yeast cultures, two parameters are important: viability and vitality. Viable cultures with low vitality will be characterized by a slow fermentation process, and may also have a respiratory defect and produce undesirable metabolites. The method of vitality determining is based on the assessment of maltolytic and saccharolytic activity, i.e., the ability to use both sugars anaerobically by fermentation. The available literature provides relatively little data regarding the influence of drying and rehydration on maltolytic and saccharolytic activities. The analysis of dry yeast activity enables evaluation of the quality of the yeast after the drying process. Maltose is the main sugar fermented by yeast in the word, which is why maltolytic activity is very important for brewer yeast [38]. Differences in maltolytic and saccharolytic activity between the analyzed strains were found. The lager yeast strain _Sp_ W30/70 was characterized by the highest maltolytic activity; the highest saccharolytic activity was observed for the ale _Sc_ T58 yeast strain. Rehydration with PTWAir did not influence the maltolytic activity of yeast, while PTWN decreased this parameter. The lowest value was observed for the ale strains _Sc_ T58 and S33. This may be due to the fact that the plasma-treated water under nitrogen (PTWN) decreased the yeast viability and, as a result, contributed to a lower consumption of sugars by the analyzed yeast strains.
## 3 Improved LEACH Protocol
### Clustering Based on Underwater Energy Propagation Model
#### 3.2.1 Optimization of the Cluster Number
Similarly, (6) can be approximated as follows:  
\[\overline{r^{BS}}\approx E\Big{(}r_{i}^{BS}\Big{)}=\int_{0}^{2\pi}\int_{0}^{ \sqrt{\frac{2}{\pi}}}r\rho_{2}(r,\theta)\cdot rdrd\theta=\frac{2\pi}{3}\cdot \frac{l^{3}}{\pi^{\frac{3}{2}}}\cdot\frac{K}{l^{2}}=\frac{2l}{3\sqrt{\pi}}\cdot K \tag{8}\]  
where \(\rho_{2}(r,\theta)\) denotes the probability density function of the cluster head node positions, and \(\rho_{2}(r,\theta)=\frac{K}{l^{2}}\) when this distribution is uniform. Substituting (7) and (8) into (4), \(R_{S}\) is given by:  
\[R_{S}\approx N\cdot\left(\frac{2 NI}{3\sqrt{\pi}}\cdot\frac{1}{K^{\frac{3}{2}}} \right)+K\cdot\left(\frac{2l}{3\sqrt{\pi}}\cdot K\right)=\frac{2N^{2}l}{3 \sqrt{\pi}}\cdot\frac{1}{K^{\frac{3}{2}}}+\frac{2l}{3\sqrt{\pi}}\cdot K^{2} \tag{9}\]  
To obtained \(K_{opt}\), let \(\frac{dR_{s}}{dR}=0\), i.e.,:  
\[\frac{dR_{s}}{dR}\Big{|}_{K=K_{opt}}=-\frac{N^{2}l}{\sqrt{\pi}}\cdot\frac{1}{ K^{\frac{3}{2}}}+\frac{4l}{3\sqrt{\pi}}\cdot K=0 \tag{10}\]  
\(K_{opt}\) is given by:  
\[K_{opt}=\sqrt[7]{\frac{9N^{4}}{16}} \tag{11}\]
## 4 Discussion
### VQ Is Correlated with VOT  
Two conditions of VH were analyzed in this study: one group with PHV associated with five different laryngeal pathologies and one group with NPHV. All VH conditions have examples of three-grade deviant VQ (mild, moderate, and severe), despite not being equally distributed by gender.  
In our data, all VQ characteristics correlate with VOT, which is in consonance with similar studies [30; 31]. VQ can range from not deviant to severely deviant in patients with VH, and is not a key determinant to distinguish patients in the NPVH group between those from the PVH group. In other words, the presence or absence of a phonortaumatic structural lesion is insufficient to indicate the degree of vocal deviation. Furthermore, it is essential to consider that vocal tract adjustments are critical to vocal output and the phenomenon of dysphonia.  
Based on our findings, we conclude that voicing onset, even as measured objectively, seems to play an important role in the perception of VQ deviation. From a broad perspective, our findings show that the more deviant the VQ is, the longer the VOT duration. Furthermore, the VOT Confidence Interval, i.e., the data spread, increased as the OS became more severe. All vocal parameters composing VQ, namely, roughness, breathiness, and strain, were significantly correlated with VOT, while vocal strain and roughness had a highly significant association. This endorses the assumption that increased tension in the vocal mechanism, as represented by audible strain and roughness (increased adduction), impacts the voicing onset and perceptual VQ.
## 3 Theory of Reduced-Scale Model
### Equivalent Relationship of Reduced-Scale Model to UHV Transformer  
By utilizing the field-circuit coupling model mentioned above, the electromagnetic quantities of the UHV transformer and RSM can be calculated, as shown in Figures 5-8.  
Figure 5 show the excitation current waveform of the UHV and RSM under various DC bias current, respectively. Discrete Fourier transform (DFT) is applied to the waveform of the excitation current of the UHV transformer under DC bias. The harmonic magnitudes of excitation current under various DC bias currents are shown in Figure 6. Due to the distortion mainly concentrated below the 6 th harmonic and less sensitive of high order harmonics to DC bias current, the figure only shows the harmonics under 6 th order. As can be seen from Figure 6, the fundamental and second harmonic rose almost linearly with the DC bias current, whereas, the growth of higher harmonics slowed down gradually. Figure 7 shows the ratio of the second harmonic to the fundamental of excitation current with various DC bias current of the UHV transformer and RSM. By means of the same ratio, the equivalent relationship of DC bias current of the UHV transformer and RSM was obtained, as can be seen in Figure 8. Table 3 presents the results of the equivalent relationship of DC bias current under the same saturation degree.  
Figure 4: Field-circuit coupling model. (**a**) Eighth magnetic field model. (**b**) Circuit model.  
Figure 5: Excitation current waveform under DC bias current. (**a**) UHV transformer. (**b**) reduced-scale model.  
Figure 6: Harmonic magnitude of UHV transformer under DC bias current.  
Figure 7: Ratio of the second harmonic to the fundamental under DC bias of the UHV and RSM.
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 2 Problem Definition and the Basic Model
### Optimization of GPU Cluster Utilization  
Our primary goal of scheduling is to optimize GPU utilization by efficiently assigning \(n\) task instances \([t_{1},\dots,t_{n}]\) to the appropriate machines \([m_{1},\dots,m_{N}]\), considering the resource requirements of each task instance and the available resources of each machine. The hierarchical structure of jobs, tasks, and task instances is depicted in Figure 2. Our scheduling algorithm first merges the list of task instances with available machines in the cluster, to create a candidate-scheduling list \(V\) containing all feasible machine and task instance pairs \((m_{j},t_{i})\). Only pairs where a machine \(m_{j}\) can accommodate a task instance \(t_{i}\) based on resource availability, are included in the candidate list. For a machine \(m_{j}\) to host a task instance \(t_{i}\), the available CPU, memory, and GPU resources \([P_{j},Q_{j},\ R_{j}]\) of the machine \(m_{j}\) must meet the CPU, memory, and GPU resource demands \([p_{i},q_{i},\ r_{i}]\) of the task instance \(t_{i}\). The key objective is to find the pair of task instance and machine with the highest GPU utilization, denoted as \([m^{*},t^{*}]\). By assigning task instance \(t^{*}\) to machine \(m^{*}\), the scheduling of a single task instance is completed. This process is repeated for all task instances submitted to the cluster. The details of this algorithm are outlined in Algorithm 1, which provides a step-by-step description of the process.  
Figure 1: The basic architecture of the proposed scheduling system.
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
## 4 The Relationship between the Beacon Collision Probability and Beacon Error Rate
In contrast, the probability of more than 10 devices colliding simultaneously is 0, as determined by the PMF described in Equation (6). Packet collisions on a beacon happen with the probabilities outlined in Equations (6)-(9). However, not all collisions result in a beacon loss. This is because, when \(\frac{C}{T}>\gamma_{th}\), the beacon is successfully received by the EN despite a collision (ref. Figure 5, Section 2-B and Section 3-C). Hence, the overall performance depends not only on the distance to a GW, but also on the interference observed by the ENs. Closer ENs to a GW experience a better \(RSS\), increasing their chance of having \(\frac{C}{T}>\gamma_{th}\). The expected values of \(\frac{C}{T}\) versus the network size is presented in --Section 6). For a beacon to be lost, the following conditions must be met:  
1. The signal must be below the sensitivity level; otherwise,
2. A collision must occur,
3. The C/I on the packet, calculated with the overlap ratio, needs to be worse than a threshold. (The thresholds and the co-channel interference model is presented in Section 3-C) With this introduction, the beacon loss rate can be approximated as follows: \[ \begin{array}{rl}P_{b,\;err}(D)&\approx\frac{1}{N_{cn}}\sum_{k=1}^{N_{cn}} \int_{y=-\frac{D}{2}}^{y=\frac{D}{2}}\int_{x=-\frac{D}{2}}^{x=\frac{D}{2}}p_{ r}(RSS(x,y)_{k}<\beta)dxdy+P_{c}(N)\\ &\times\frac{1}{N_{cn}}\sum_{k=1}^{N_{cn}}\int_{y=-\frac{D}{2}}^{y=\frac{D}{2} }\int_{x=-\frac{D}{2}}^{x=\frac{D}{2}}p_{r}(RSS(x,y)_{k}\geq\beta)p_{r}\Big{(} \frac{C}{T}(x,y)_{k}<\gamma_{th}\Big{)}dxdy.\end{array} \] (10)  
The terms \(x\) and \(y\) represent the location of each EN relative to the GW to which it is connected. The term \(D\) represents the size of the area border in meters where the beacon error rate calculation is made. The term \(p_{r}(RSS(x,y)_{k}>\beta)\) represents the probability at which the \(RSS\) (in dBm) is greater than the sensitivity, represented by the term \(\beta\) (in dBm). The probabilistic sum of all possible collisions for a total of \(N\) gateways is represented by the term \(P_{c}(N)=F(x\mid N,p_{c})\), given in Equation (7). The term \(p_{r}\) represents the probability of its argument. For example, \(p_{r}\Big{(}\frac{C}{T}(x,y)_{k}<\gamma_{th}\Big{)}\) represents the probability at which the carrier to interference ratio (\(\frac{C}{T}\)) at location \((x,y)_{k}\) observed at the EN \(k\) (\(k\in\{1,2,\ldots,N_{en}\}\)), is less than a threshold \(\gamma_{th}\) dB. Our PHY layer measurements show this threshold to be \(\gamma_{th}\approx 7\) dB (ref. Section 3). The closed-form expression for calculating C/I while considering the overlap ratio is given in Equation (1). Figure 15: The probability of x devices colliding out of a total \(N=5000\) devices.
## 2 Research Site and Experiments  
The research site lies in the Babao River sub-basin, which is part of the upper reaches of the HRB and has an area of approximately 2452 km\({}^{2}\) and an elevation ranging from 2640 to 5000 m. The annual average air temperature and precipitation are 1 \({}^{\circ}\)C and 270 to 600 mm, respectively. Grassland is the main form of vegetation cover and is mainly in the form of alpine meadow [17; 18]. The superstation is in A'rou Village of Qilian County, Qinghai Province (100.4643\({}^{\circ}\) E, 38.0473\({}^{\circ}\) N, 3033 m), as shown in Figure 1. The experimental site features comprehensive, precise, and multi-scale observations of land surface and hydrological processes in a cold region. Intensive and long-term profile meteorological observations of the wind, air temperature, and humidity at the study site were conducted at six layers: 1 m, 2 m, 5 m, 10 m, 15 m, and 25 m. The specific manufacturer and model of each observation instrument are shown in Table 1.  
Figure 1: Landscape of the study site and observation equipment studied at A’rou station.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 4 MAC Protocol Leveraging Wake-up Receivers
### Analytical Study of Scalability  
In this section, the scalability of SNW-MAC and traditional pseudo-asynchronous MAC protocols is evaluated in the context of star networks. The emphasis is put on the sink, which is in charge of gathering the packets from all the sensors. This section compares the achievable packet reception rates when SNW-MAC is used and when pseudo-asynchronous MAC protocols are used, in order to study how sustainable the proposed approach is with regard to the sink compared to these protocols. The number of nodes that compose the network is denoted by \(N\) (not including the sink), and the packet generation rate is modeled by a Poisson distribution of parameter \(\lambda\) packets per minute. Next, the expressions of the packet arrival rate at the sink are derived for SNW-MAC and pseudo-asynchronous MAC protocols when it is assumed that the only cause of packet loss is collisions and that all collisions are destructive, i.e., lead to corrupted packets.
## 2 Overview
### Layout of Monitoring Points  
The arrangement of monitoring points is to monitor some important buildings and structures within the mining area, such as main shafts, highways, etc.; these places have strict deformation and settlement restrictions. Due to the presence of roads, rivers, villages, factories, and other facilities above the ore body, as well as important mine infrastructures such as the main shaft, high pool, and filling station, some collapses have occurred during the excavation of the Yinluling mining area around the mine. In order to ensure the safety of the buildings and structures within the mining area during mining, it is necessary to monitor the surface subsidence, terrain curvature deformation, ground inclined deformation, and lateral deformation in these places. A total of 52 monitoring points have been established. In the Baojia mining area, 12 monitoring points have been set up due to the location of some buildings within the surface movement zone. Additionally, 3 monitoring points are located in the village, 4 in the river, 5 along the highway, 9 in the factory area, and 19 in the Yinhai mining area. Because there are villages, factories, and other places with frequent human activities around the mine, 30 important monitoring points are set up in 52 monitoring points. These points are designated as BJ1-BJ12, CZ1-CZ3, GC1, GC2, GC4, GC6-GC9, YH9-YH15, and YH17, with BJ representing the Baojia mining area, YH representing the Yinhai mining area, and the remaining points representing the Yinzhushan mining area (the following text uniformly uses YZS to represent this). The specific layout is shown in Figure 3.