esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, despite these advances, batch addition of effluents derived from anaerobically digesting animal waste manure to open-ponds also has limitations: first, manure effluents and even their dilutions tend to be turbid and can block transmission of sunlight to microalgae cultivated in second-stage outdoor open-ponds [18], which has been shown to significantly influence productivity [22]. Second, economic losses and pollution due to effluent ammonia volatilization [23] during hot summers would likely be higher in the large-scale second-stage open-ponds proposed here with high surface areas, temperatures, pH, and wind-speeds. Third, the combination of the UV wavelength of polychromatic light (i.e., outdoor sunlight) and high concentrations of protonated ammonia in batch-added AD effluent are also reportedly more toxic to the PSII photosynthetic apparatus of phototrophs at the proposed elevated pH, where uncontrollable, diffusive, non-active transport across the cell boundary occurs [24]. Rather than incorporating the aforementioned batch addition of AD manure effluents to supply nitrogen and phosphorous for the second of a two-stage microalgal cultivation process, a novel method involving fed-batch addition of AD effluent derived from food waste was developed. Food waste from municipalities and food processing facilities represents another alternative source of AD effluents. Food waste diversion from landfills is considered as a way to increase landfill capacity and reduce methane emissions, with a large potential energy content amounting to 2% of total U.S. energy consumption [25]. Compared to the turbid dairy manure effluents, light yellow-brown effluent from starchy food-waste additionally represents greater microalgal open-pond light transmission. An effluent fed-batch strategy at high pH in the proposed second stage culture should employ an exponentially increasing mass flow [26]. This would need to balance (1) the rate of effluent supplementation required for both sufficient growth and terminal nitrogen starvation-induced lipid accumulation, with (2) the rate of microalgal ammonia consumption and (3) the rate of high pH-induced volatilization. In this study, proof of concept was established by cultivating for the first time an oleaginous, multi-trophic, haloalkaline-tolerant microalgae in an improved two-stage process using integrated, sustainable supplies of organic carbon from wheat-straw for mixotrophic first-stage and nitrogen and phosphorous from food waste for the phototrophic second stage. Carbon and nutrient sources were additionally analyzed in various conditions.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 4 Analysis of Normal Section Capacity
### Existing Calculation Methods
#### 4.1.2 Model Validation and Analysis  
To verify the calculation model, the finite element analysis (FEA) model established in [24] was used to increase the sample size. The ultimate bearing capacities of models with various reinforcement areas, concrete strengths, eccentricities, and some other parameters are listed in Table 3. Based on the calculation methods and Equation (1), the calculated ultimate capacities of all the tested SSRC columns and FEA models are also summarized in Table 3. The corresponding average ratios of \(N_{\text{u}}/N_{\text{u}}^{\text{t}}\) were 1.058 and 1.117, the standard deviations were 0.193 and 0.158, and the variation coefficients were 0.037 and 0.025. It can be noted that the deviation of the calculated results using the current equation is large for both the test columns and the FEA models. When the relative eccentricity, \(e_{0}/h_{0}\), is small,  
Figure 9: Calculation diagram for stainless-steel-reinforced concrete (SSRC) columns subjected to eccentric load: (**a**) small eccentricity, (**b**) limiting eccentricity, and (**c**) large eccentricity.  
the calculated result is smaller than the obtained result. When \(e_{0}/h_{0}\) is significant, the calculation result is greater than the test result. This demonstrates Equation (1) cannot accurately calculate the ultimate load of SSRC columns under eccentric loading.
## 3 Methodology
### Geostatistical Methods  
Although the IDW is largely applied, it lacks any direction-specific (anisotropic) information, thus ignoring the spatial correlations which are not incorporated into the estimation. In some scientific contributions, the IDW was used as a basic approach (i.e., a reference) to assess the spatial prediction [53]. This weakness does not occur in geostatistical approach. For interested readers a detailed presentation of geostatistical theories can be found in [26; 54; 55; 56]. Usually the spatial interpolation is performed by using a weight of observed regionalised values to evaluate a regionalised value in unmeasured points. Kriging is a widely used interpolation technique that takes into account the spatial dependent correlation of environmental variables assigning more weight to neighbouring points [56]. Kriging also provides an uncertainty estimate. Semi-variogram modelling [57] is the first and the key step between spatial description and spatial prediction in kriging computation. After computing the empirical semi-variogram, for all pairs of locations in a given distance, this is fitted with a theoretical model, which is a function of the lag of data pair values, that is used to evaluate the weight by means of the different equation systems of the several krigings: ordinary kriging (OK), kriging with an external drift (KED), and cokriging (COK). In this study, we did not provide local variograms, considering that, as demonstrated by [PERSON] [53], their use leads to a small reduction in prediction errors.
## 2 Materials and Methods
### The Total Time of Hydrographic Survey Conducted with MBES  
The calculated time of survey execution is a variable that depends on many factors that cannot be fully predicted. Therefore, time estimation is not an easy task. External factors may extend the total survey time by 5-20% [28]. If the survey work is carried out for the first time in a given basin and there is little information on the bottom shape and depth changes, it may turn out that due to the occurrence of shallows, it is justified to increase the number of survey lines. Such a decision will certainly extend the duration of the survey. Other factors influencing the extension of the time of stay at sea include deviation from the survey line as a result of the excessive approach of another vessel or the setting out of fishing nets by fishermen. The resulting gaps in the data must be covered by re-survey operation. Lack of access to RTK corrections and significant deterioration of position accuracy will make it necessary to repeat measurements on a given survey line. The survey duration may also be extended if it is necessary to perform additional tests, such as quality assurance performance test on multi-beam data. The purpose of this test is to obtain an estimate of the accuracy and, most significantly, of the repeatability of a multi-beam echosounder dataset. The algorithm for determining the hydrographic survey time seems to be one of the essential procedures used by the project manager (the hydrographic division commander onboard navy ship) responsible for a given project.  
Given all the dependencies (1)-(19), the total time necessary to perform the hydrographic survey using a multi-beam echosounder \(T_{son}\) is calculated as follows:  
\[\begin{split} T_{son}=\frac{\left(\frac{W}{2\cdot 0\cdot\text{tg} \left(\frac{W}{2}\right)\left(1-\frac{W}{2\cdot 0\cdot\text{tg}}\right)}+1\right)\cdot L _{pp}}{V_{pp}}+T_{21}\cdot\left[\left(\frac{W}{2\cdot 0\cdot\text{tg}\left(\frac{W}{2} \right)\cdot\left(1-\frac{W}{2\cdot 0\cdot\text{tg}}\right)}+1\right)-1\right]+n_{sep} \cdot\\ T_{sep1}+\frac{N_{pc}\cdot W}{V_{pc}}+\frac{L_{pp}\cdot N_{pc}}{(N_{ pc}+1)\cdot V_{2p}}+T_{kal}+n_{cc}\cdot T_{cc1}+2\cdot\frac{S_{proj}}{V_{rpi}}+T_{up}+T_{ us}+\\ n_{ob}\cdot(T_{bat}+T_{ss})\end{split} \tag{20}\]  
The time calculations proposed in this chapter can be an invaluable help in estimating the costs of measurement works, are indispensable in planning ships and hydrographic cutters out to sea, and can be successfully applied to the development of Designs and Technical Tasks by the Polish Naval Hydrographic Office and the Naval Hydrographic Security Division.
## 2 Fundamentals of the New Algorithms
### Analytical Expression of the CAF
So, if we can guarantee that the adjacent coefficients (\(a_{-2}\)\(a_{-1}\)\(a_{1}\)\(a_{2}\)...) are far smaller than \(a_{0}\), the mathematical expression of the CAF in the main lobe (subscript \"ml\") can be written as  
\[S_{i}(\overline{\tau},\overline{f}_{d})_{\mathrm{ml}} =R_{i,i}(\overline{\tau},\overline{f}_{d})_{\mathrm{ml}}+\sum_{m \
eq i}R_{m,i}(\overline{\tau},\overline{f}_{d})_{\mathrm{ml}}\] \[\approx\frac{A_{i}}{2}e^{-j\varphi_{i}}a_{0}\mathrm{Sinc}(\Delta f \pi T_{d})e^{-j\Delta f\pi T_{d}} \tag{18}\]  
where \(a_{0}=a_{0(i,i)}=R(\Delta\tau)\), and \(\Delta\tau=\tau_{m}-\overline{\tau}\). As a conclusion the approximate expression of the CAF in its main lobe is  
\[S_{i}(\overline{\tau},\overline{f}_{d})_{\mathrm{ml}}\approx\frac{A_{i}}{2}e^ {-j\varphi_{i}}R(\Delta\tau)\mathrm{Sinc}(\Delta f\pi T_{d})e^{-j\Delta f\pi T _{d}} \tag{19}\]  
Figure 3: The combination of sinc functions. Figure 2: The value of \(a_{k}\) when \(\tau_{m}-\overline{\tau}=\Delta\tau=0.2\)\(T_{ch}\) in the GPS case. The validity of this approximation can be improved in two ways:  
1. By enlarging the integration time \(T_{d}\). In fact as \(T_{d}\) increases, the adjacent components will \"move\" away relatively. In other words, as \(T_{d}\) increases the width of the lobe decreases, while the distance between two sinc functions stays constant, as it depends on the code period. 2. By decreasing the values of the adjacent coefficients (\(a_{-2}\;a_{-1}\;a_{1}\;a_{2}\;...\)). This can be obtained by improving the accuracy of code delay estimate, so as to work close to the maximum of \(R(\Delta\tau)\). Based on the CAF expression in Equation (19), new algorithms for a better estimation of the Doppler frequency are discussed hereafter, both in ideal (_i.e._, noiseless, Section 3) and realistic (Section 4) scenarios.
## Methods  
10 tissue samples made of _lamina propria_ and _vocalis_ sublayers were excised from vocal folds of the same human larynx (male donor, 76 yo), to avoid inter-subject variability, and enable imaging at micro-scale resolution. The dissection yielded to 5 samples from each left/right vocal fold [4], of typical dimensions \(15\times 5\times 3\) mm\({}^{3}\). Several conditions of tissue conservation were used before testing (ethanol, gel, cryopreservation). First, a multimodal imaging study was conducted to investigate the tissue fibrous architecture at rest, thanks to three complementary techniques: (1) Synchrotron 3D X-ray imaging at high-resolution (\(0.65^{3}\) um\({}^{3}\)), using the microtomographs of the ESRF's ID19 beamline; (2) Biphotonic 3D microscopy imaging (\(0.35\times 0.35\times 0.70\) um\({}^{3}\)) using the confocal microscope of Leon Berard's Cancer Center (LSM 780, Laser 900 nm); (3) Histological stainings (Hematoxylin-Eosin-Saffron), differentiating vocal-fold constituents on 2D micrographs. Then, several samples were subjected to uniaxial tensile tests interrupted by relaxation steps, and combined to 3D X-ray imaging using a mechanical device positioned onto the ESRF microtomographs. Holding conditions were optimized to avoid stick-slip effects and tissue damage.
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
###### Abstract  
Low-spatial-resolution measurements from contact sensors and excessive measurement noise have impeded the implementation of vibration-based damage detection. To tackle these challenges, we propose a novel vision-based damage detection method combining multi-scale signal analysis theory and data fusion algorithm. For high-spatial-resolution vibration measurements, phase-based optical flow estimation algorithm is adopted to deploy virtual sensors on the structure, yielding reliable mode shapes. We then introduce the concept of entropy into damage detection. A novel damage index, defined in Gaussian multi-scale space and named multi-scale local information entropy (MS-LIE), is proposed. The MS-LIE integrates the multi-scale analysis component and the entropy analysis component, addressing both the issue of detection sensitivity and noise immunity, thereby showcasing enhanced performance. Moreover, a data fusion technique for multi-scale damage information is developed to further mitigate the noise-induced uncertainty and pinpoint damage locations. A series of numerical and experimental scenarios are designed to validate the method, and the results indicate that the proposed method accurately detects single and multiple damages in noisy environments, obviating the need for baseline data as a reference.  
Keywords:damage detection; optical flow; local information entropy; multi-scale analysis; data fusion; noisy environments +
Footnote †: journal: Applied sciences
## 3 Results
#### 3.1.2 Antioxidant Capacity and Compounds
For instance, in the present study, a time range was established from 0.5 min up to 30 min, while in the study conducted by [PERSON] et al. [15], the time range was from 4 h to 24 h.  
On the other hand, the extraction time also significantly influenced (\(p\) = 0.0364) the the antioxidant activity, measured by the ORAC method. Moreover, the temperature also affected the ORAC value even stronger (\(p\) = 0.0167) than the extraction time (Figure 7). Concerning the extraction time, it can be seen that at 20 \({}^{\circ}\)C and pH = 8.5, the antioxidant activity was 1766.48 \(\upmu\)M TE at 0.5 min. However, when the time increased up to 30 min, the antioxidant activity was augmented (1842.10 \(\upmu\)M TE). Furthermore, increasing the temperature also resulted in an improvement of the antioxidant activity. In our study, at pH = 8.5 and after 30 min of extraction (optimal conditions), the antioxidant activity measured by the ORAC assay was enhanced from 1842.10 \(\upmu\)M TE at 20 \({}^{\circ}\)C up to 2340.01 \(\upmu\)M  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Run \#**} & \multicolumn{2}{c}{**Time of**} & \multicolumn{2}{c}{**Temperature**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**ABTS (\(\upmu\)M TE)**} & \multirow{2}{*}{**ORAC (\(\upmu\)M TE)**} \\  & **Extraction (min)** & & & & \\ \hline
1 & 15 & 20 & 7 & 658.89 & 1416.81 \\
2 & 30 & 20 & 8.5 & 701.41 & 1842.10 \\
3 & 30 & 20 & 5.5 & 760.11 & 1681.80 \\
4 & 0.5 & 20 & 8.5 & 696.02 & 1766.48 \\
5 & 0. TE at 50 \({}^{\circ}\)C. As can be seen, the ORAC values increased with the increasing temperature and extraction time, despite the fact that all the possible antioxidant compounds studied in the present work decreased under these conditions. This may be due to the presence of other compounds not identified in this study that may have had an impact on the antioxidant capacity measured by the ORAC method. It has been seen that the ORAC assay had a higher affinity for lipophilic compounds. Therefore, it could be that with these conditions, the amount of antioxidant lipid compounds extracted increased, contributing to the enhanced antioxidant capacity values measured by the ORAC assay [33]. [PERSON] et al. [35] found lower values for the ABTS and ORAC assays when measuring the antioxidant capacity of _P. tricornutum_ residual biomass (67.93 and 106.22 uM TE/g dry weight, respectively). However, this difference can be explained by their use of a microalgae by-product from biofuel production instead of full microalgae. Then, the presence of the antioxidant compounds would be lower than in the original microalgae.
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 2 Basic Knowledge and Literature Review
### Basic Communication Structures for Multi-Population Optimization  
The classical island model proposed in the early days is shown in Figure 6. In this model, each sub-population and its corresponding optimization algorithm are integrated together as a node. In other words, every node includes not only the state of the sub-population but also the algorithmic process, and it sends and receives the state of specific individuals during the communication with other nodes by point-to-point communication. This undoubtedly increases the difficulty of communication and is not conducive to the large-scale migration of individuals. Besides, the lack of flexibility makes it more difficult to add or remove nodes at runtime.  
To improve the means of communication, the Pool model [59], shown in Figure 7, uses a central Pool to store the individuals and the Pool is accessible by all nodes. Each node interchanges individuals with the Pool, so the communication between nodes will not be affected when adding or removing nodes. Even so, the coupling between the population state and the algorithm still exists; as a result, the framework of the population and algorithm need to be rebuilt when adding a new node and both the size of the sub-population and the parameters of the algorithm cannot be changed during the running time.  
An improved Pool model [60] extends the capabilities of the Pool and decouples the state of the sub-population and the algorithm as shown in Figure 8. In this model, the state of all sub-populations is stored in the Pool and a sub-population, which is sent to the corresponding algorithm as a parameter, which is generated by taking random samples of the population. The unified storage of population states can support more methods of population migration.  
Figure 5: Migration modes: (**a**) circular migration; (**b**) interaction migration.
# Towards Rack Utilization in Internet Datacenters: An Approach Based on Dynamic Programming
## 5 Experiment  
### Experimental Settings  
In this section, an experimental platform equipped with an 8-core Intel Xeon Processor E5606 (2.13 GHz) (Santa Clara, CA, USA) is adopted to compare the server placement algorithms.  
All of the dataset comes from Tencent IDC (Chengdu, China), which is the largest Internet Service Provider (ISP) in Asia. Using the Tencent Configuration Management Database (CMDB), five racks (No. 0301-B01 \(\sim\) No. 0301-B05) and their 52 servers at Guanghua server room of China Telecom (Chengdu) are selected as the sample. The operating system of all servers is the Suse Linux Enterprise System 10 SP2 (64 bit) (Seattle, WA, USA). The number of servers with different models and the server arrangement on each rack are listed in Table 3. These five racks have the same hardware parameters and the same maximum power of 4048 W. The model \"4 Nodes\" takes four nodes as a rack unit, which cannot be split in the actual server placement process. In addition, we round up the power threshold to the next integer so as to simplify the computation.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variables and Functions** & **Description** \\ \hline dp(0) & The core function based on dynamic programming \\ _remainPower_ & The remaining power of a rack \\ _power_ & The power of a server \\ \(i\) & The \(i^{th}\) server \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variables and functions in Algorithm 2.  
```
1:Services in \(serverList\) and racks in \(rackList\);
2:\(decisionList\)\(/*\) The placement decision from servers to racks \(*\)/
3:procedureDP(\(i\), \(remainPower\))
4:if\(i\) equals the length of \(serverList\) or \(remainPower\) is not sufficient for \(i^{th}\) server then
5: return 0, 0;
6:endif
7:if all servers starting from the \(i^{th}\) server have the maximum power \(p\) and mapping \(m\) from servers to racks recorded in \(d\) under the constraint of \(remainPower\)then
8: return \(p\), \(m\);
9:endif
10: (v1, \(m\)1) = DP(\(i\)+1, \(remainPower\));
11: (v2, \(m\)2) = DP(\(i\)+1, \(remainPower\)-\(serverList.i.power\))+\(serverList.i.power\);
12:if\(v1\)>v2 then
13: do not choose the \(i^{th}\) server, refresh \(value\) and \(map\) with \(v1\), \(m1\);
14:elseos the \(i^{th}\) server, refresh \(value\) and \(map\) with \(v2\), \(m2\);
15:endif
16:\(d\).add(\(value\) and \(map\));
17: return \(value\) and \(map\);
18:endprocedure
19:for each rack \(r\) in \(rackList\)do
20: (tempv, tempm) = DP(0, \(r.remainPower\));
21:\(decisionList\).add(tempm);
22:endfor
23:return \(decisionList\);
```  
**Algorithm 2** The Dynamic Programming Based Server Placement
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 2 GBDT Algorithm
### Gradient Boosting
#### 2.1.1 Problem Restatement  
For the short-term photovoltaic power prediction, the PV historical data is the input data set of the GBDT algorithm. For the given data set \(\{\mathbf{x}_{i},y_{i}\}_{1}^{N}\) selected randomly from data samples, the training target is to find the best function \(F^{*}(\mathbf{x})\), which forms a generalization of the relationship between input data \(\mathbf{x}\) and output data \(y\) and can then be used to generate outputs for inputs which are not trained. In other words, for the joint probability distribution of all \((y,\mathbf{x})\), the expected value of the loss function is minimized, as shown in (1).  
\[F^{*}(\mathbf{x})=\underset{F(\mathbf{x})}{\text{argmin}}\Phi(F(\mathbf{x}))= \underset{F(\mathbf{x})}{\text{argmin}}E_{y,\mathbf{x}}\Psi(y,F(\mathbf{x})) \tag{1}\]  
where \(E_{y,\mathbf{x}}\Psi(y,F(\mathbf{x}))=E_{\mathbf{x}}\big{(}E_{y}\Psi(y,F( \mathbf{x})\mid\mathbf{x})\big{)}\)  
Loss function \(\Psi(y,F)\) represents the deviation between the output value and the actual value. In general, for a regression problem, the least square function is chosen as the loss function, i.e., \(\Psi(y,F(\mathbf{x}))=(y-F(\mathbf{x}))^{2}\). The optimal solution of \(F(\mathbf{x})\) requires several iterations to approximate.  
Suppose \(F(\mathbf{x})\) is the sum of a series of \(F(\mathbf{x};\mathbf{P})\), where \(\mathbf{P}=\{\mathbf{P}_{1},\mathbf{P}_{2},\cdots\}\) is a series of parameters. Hence \(F(x;P)\) can be expanded as the form of (2).  
\[F(\mathbf{x};\mathbf{P})=\sum_{m=0}^{M}\beta_{m}h(\mathbf{x};\mathbf{a}_{m}) \tag{2}\]  
where \(\mathbf{P}=\{\beta_{m},\mathbf{a}_{m}\}_{0}^{M}\), \(h(\mathbf{x};\mathbf{a}_{m})\) are the basis functions, \(M\) is the number of iteration steps, and \(\beta_{m}\) is the iteration weights.