esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Architecture Design of the Cloud-Edge Hybrid Intelligent System (CEHIS)
#### 2.1.4 Service Delivery Paradigm  
The CEHIS service delivery paradigm is shown in Figure 4. \(Nodes=[Cloud\ 1,Cloud\ 2,\ Cloud\ 3,\ldots,Cloud\ h,Edge\ 1,Edge\ 2,Edge\ 3,\ldots,Edge\ k]\) represents the overall computational resources of the CEHIS, which consist of the cloud-based high-performance computing clusters and edge computing systems. During user experiments, the data acquisition system or users manually submit computational jobs \(Jobs=[J\_1,J\_2,J\_3,\ldots,J\_m]\) to the intelligent scheduling system. After these job tasks enter a queue pool, the scheduler assigns a computational node to each task, based on a predetermined strategy. The assigned computational node could be an edge node \"\(Edge\_i\)\" or a node within the cloud-based high-performance computing cluster \"\(Cloud\_j\)\". Theoretically, edge nodes are appropriate  
Figure 2: Data processing workflow of the SSRF MX beamline.  
for real-time computational tasks, whereas cloud nodes are optimal for workloads with large computational demands and no real-time requirements.
# Enabling Role-Based Orchestration for Cloud Applications
## 2 Background Technology for Cloud Orchestration
### Standard Models for Cloud Application Orchestration
Figure 1: TOSCA definition for a sample application. Figure 2: OAM application configuration sample. Kubernetes [11] is an open-source container orchestration framework that automates the management of container lifecycle as well as execution of containerized applications. Kubernetes cluster architecture consists of a master node and a set of worker nodes. The master node is responsible for the maintenance and management of the cluster, while the worker node runs a set of Docker containers for applications. This master/slave architecture is illustrated in Figure 3. The minimal deployment unit in Kubernetes is a pod that contains one or more Docker containers and provides shared storage and networking capability for the containers. Several management components in charge of the maintenance of the cluster state are located in the master node, which include API server, Controller Manager, Scheduler, and etcd component. The API server provides an entry point to control the entire Kubernetes cluster, and etcd is a highly available distributed key-value store that keeps data for the cluster state. The Scheduler determines which worker to assign which pod. To bring the system to a desired functioning state, Kubernetes relies on the Controller Manager that consists of a few controller processes such as node controller, job controller, etc. Each worker node is equipped with Kubelet, Kube-Proxy, and Docker container runtime. More specifically, the Kubelet process handles the pod state based on the commands from the master, and the Kube-Proxy process is a network proxy that resides on each node. Recently, container technology has been a popular choice as the base on which to build cloud orchestrator runtime for multi-component applications. Being represented by containers, the components of the applications can run fast and efficiently. In addition, Kubernetes as container orchestration technology provides a means to collectively manage related containers, which is found useful in deploying and managing multi-component cloud application. Figure 3: Kubernetes cluster architecture.
# Biotin Auxotrophy and Biotin Enhanced Germ Tube Formation in _Candida albicans_
###### Abstract  
Due to the increased number of immunocompromised patients, infections with the pathogen _Candida albicans_ have significantly increased in recent years. _C. albicans_ transition from yeast to germ tubes is one of the essential factors for virulence. In this study we noted that [PERSON]'s medium, commonly used to induce filamentation, contained 500-fold more biotin than needed for growth and 40-fold more biotin than is typically added to growth media. Thus, we investigated the effects of excess biotin on growth rate and filamentation by _C. albicans_ in different media. At 37 \({}^{\circ}\)C, excess biotin (4 \(\upmu\)M) enhanced germ tube formation (GTF) ca. 10-fold in both [PERSON]'s medium and a defined glucose-proline medium, and ca. 4-fold in 1% serum. Two biotin precursors, desthiobiotin and 7-keto-8-aminoelargonic acid (KAPA), also stimulated GTF. During these studies we also noted an inverse correlation between the number of times the inoculum had been washed and the concentration of serum needed to stimulate GTF. _C. albicans_ cells that had been washed eight times achieved 80% GTF with only 0.1% sheep serum. The mechanism by which 1-4 \(\upmu\)M biotin enhances GTF is still unknown except to note that equivalent levels of biotin are needed to create an internal supply of stored biotin and biotinylated histones. Biotin did not restore filamentation for any of the four known filamentation defective mutants tested. _C. albicans_ is auxotrophic for biotin and this biotin auxotrophy was fulfilled by biotin, desthiobiotin, or KAPA. However, biotin auxotrophy is not temperature dependent or influenced by the presence of 5% CO\({}_{2}\). Biotin starvation upregulated the biotin biosynthetic genes _BIO2_, _BIO3_, and _BIO4_ by 11-, 1500-, and 150-fold, respectively, and _BIO2_p is predicted to be mitochondrion-localized. Based on our findings, we suggest that biotin has two roles in the physiology of _C. albicans_, one as an enzymatic cofactor and another as a morphological regulator. Finally, we found no evidence supporting prior claims that _C. albicans_ only forms hyphae at very low biotin (0.1 nM) growth conditions.  
germ-tube formations; desthiobiotin; [PERSON]'s medium; 7-keto-8-aminoelargonic acid (KAPA); upregulation of _BIO2_; _BIO3_; _BIO4_; mitochondrial _BIO2_; biotin starvation +
Footnote †: journal: Microorganisms 2016, 4, 37; doi:10.3390/microorganisms403037
## 3 Results  
### Spatial Distribution of WUE  
The annual average WUE was 0.60 \(\pm\) 0.36 gC kg\({}^{-1}\) H\({}_{2}\)O during 2002-2021 in the study area (pixels with a NULL value in the desert and Gobi were not included in the statistics). The WUE showed obvious spatial heterogeneity (Figure 5). The annual average WUE tended to increase from west to east, ranging from 0.01 to 2.63 gC kg\({}^{-1}\) H\({}_{2}\)O. Higher values of WUE occurred in relatively humid areas, such as the eastern QM and the oases of HC. In arid and sparsely vegetated areas, such as the western QM, WUE was low. The spatial pattern of WUE in spring and fall is similar to the spatial pattern of the annual average WUE.  
It was further investigated by two major geographical units, the QM and HC regions. The WUE in the QM showed obvious longitude zonality with an eastward-increasing tendency. In the western, central, and eastern QM, the multi-year average WUE was 0.28, 0.47, and 0.70 gC kg\({}^{-1}\) H\({}_{2}\)O, respectively. While the distribution of WUE in the oases within the HC was exactly the opposite, showing an eastward-decreasing trend. The average WUE in the HC was 1.09 \(\pm\) 0.34 gC kg\({}^{-1}\) H\({}_{2}\)O, with significant spatial variations. The annual average WUE value was the lowest in Dajing Oasis at the eastern end of HC, while it was the highest in Dunhuang Oasis at the western end of the HC. The WUE of Dunhuang Oasis was approximately 79% greater than that of Dajing Oasis.  
The oases of HC were ranked by their annual average WUE in the following order:  
Dunhuang Oasis (1.90) > Guazhou Oasis (1.80) > Huahai Oasis (1.79) > Yumen Oasis (1.76) > Jinta Oasis (1.66) > Jiuquan Oasis (1.54) > Minqin Oasis (1.39) > Jinchang Oasis (1.37) > Zhangye Oasis (1.34) > Wuwei Oasis (1.21) > Dajing Oasis (1.06). The unit for the numbers in parentheses is gC kg\({}^{-1}\) H\({}_{2}\)O.  
The average annual WUE was overlaid with a vegetation-type map to obtain the WUE characteristics for each vegetation type within the study area. During 2002-2021, the average WUE of the seven vegetation types ranged from 0.2 to 1.15 gC kg\({}^{-1}\) H\({}_{2}\)O (Figure 6), with the highest WUE (1.15 gC kg\({}^{-1}\) H\({}_{2}\)O) for cropland, followed by broadleaved and coniferous forests with 0.90 and 0.75 gC kg\({}^{-1}\) H\({}_{2}\)O, respectively. Grassland, shrubs, meadows, and alpine vegetation followed in that order. Overall, the WUE of woodlands was significantly higher than that of shrubs and herbaceous plants, except for cropland affected by human activities.
## 3 Results and Discussion
### Thermogravimetric Analysis (TGA)  
The thermogravimetric analysis (TGA) serves as an important tool for identifying the temperature at which organic substances are removed from the specimens. As can be observed from Figure 9, there was an 8% of weight loss of the HAp ceramics within the temperature range of 30 \({}^{\circ}\)C to 282 \({}^{\circ}\)C. The weight loss was attributed to the removal of incorporated water and moisture in the HAp ceramics. No significant weight loss was observed during the temperature rise to above 300 \({}^{\circ}\)C since no other substance was added to the specimens.  
Meanwhile, a higher percentage of weight loss was observed for the HM30-1.25 specimen compared to the HAp. It was observed that the weight loss during thermal calcination in the temperature range of 30 to 630 \({}^{\circ}\)C occurred due to the removal of water (8%) and the binder substance (7%). Subsequently, a marginal weight loss curve was observed within the temperature range of 850 \({}^{\circ}\)C to 1050 \({}^{\circ}\)C. In this temperature range, metakaolin which is the source of aluminosilicate dissolved and resulted in weight loss. Thus, it can be concluded from the findings that the initial geopolymerization contributed to lowering the sintering temperature due to the absence of sodium from the alkali activator and metkaolin which acted as a self-fluxing agent. This was also reported in the previous work related to kaolin-GGBS geopolymer ceramics [31].
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 4 Experiments
### Ablation Study
#### 4.3.2 Ablation of UFI Module  
The UFI module, proposed in Section 3.2.1, is an initialization module that employs the multi-disparity window scan-and-select method to provide more appropriate initialized hidden state and more accurate initial disparity for the following refinement process. The UFI module is evaluated in rows 5 and 10 of Table 1. Compared to the method without UFI (rows 4 and 9), the disparity EPE is reduced by 3.9%. This is attributed to the fact that the UFI module decouples the initialization from the iterative refinement process, thus easing the parameter optimization burden on the iterator. The disparity accuracy of models at each iteration are shown in Table 2. The UFI module is evaluated in rows 3 and 6. Compared to the baseline (rows 1 and 5), the initial disparity EPE and Bad1.0 are reduced by about 27.5% and 31.8%, respectively, showing that the main contribution of UFI is in improving the initial disparity accuracy.  
Figure 7: Illustration of the output of the MUIR and the alternative output filtering. (**a**) The input left image; (**b**) the estimated disparity map; (**c**) the disparity error map; (**d**) the error map converted from the estimated uncertainty; (**e**) the estimated matchability mask; (**f**) the error-filtering ratio curve (the original EPE is 0.223 px); (**g,h**) the point cloud converted from the estimated disparity map w/o filtering (**g**) and with filtering threshold set to 1 px (**h**). The results are best viewed when zoomed in.
# Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
## 7 Results and Evaluations
### Results of Real-Time Filtering Bid Requests Process  
After classifying audienceld and (IP, UA), the proposed RFBR system is also implemented. In particular, the first day of our RFBR process starts from the second day of the real sample bid request dataset and it continues until the last day (2-14 July 2018). The first day (1 July 2018) is used to generate and append the abnormal audiencelds and (IP, UA) to Audienceld_BlackList and IPUA_BlackList.  
Figure 11: Real-Time Filtering Bid Requests.  
The results obtained from the real-time bid request labeling process are shown in first two graphs of Figure 13. The average number of bid requests labeled non-intentional in real time is 3539 bid requests, representing 0.4% of the total number of bid requests per day. This number is not high but is also considered as a good result since the data set is the sample and some rules could not extract from this kind of dataset. Moreover, the percentage of bid requests labeled non-intentional on the first three days is not high; however, as more and more abnormal audiencelds and (IP, UA) are appended to the Audiencel_BlackList and IPUA_BlackList, the percentage of bid requests labeled non-intentional also increases in the following days. In particular, the results of number of bid requests labeled non-intentional in first three days are 3012 (0.339%), 2305 (0.258%), and 2208 (0.245%) respectively, of the total number of bid requests on that day. For the next three days, the number of labeled bid requests increased more and more. However, the number of labeled bid request on the ninth day was reduced again compared to the previous days; this result can also be explained when the abnormal audiencelds and (IP, UA) observed on this day are also much lower in number than on other days.  
Together with the bid requests labeled non-intentional in the real-time process, we also extract the URLs and domains from these non-intentional bid requests to observe and evaluate. The average number of distinct extracted URLs per day is 1411, and after thirteen days of the process, we obtained 17,134 distinct URLs and 180 distinct domains. These results of extracting URLs and domains from non-intentional bid requests show that we have obtained a large number of distinct URLs but a small number of domains.  
Figure 12: Results of labeling abnormal (IP, UA) and audiencelds by four rules; Audienceld_BlackList and IPUA_BlackList in each day.
# Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea
## 2 Study Area and Data
### Economic Assessment and GHG Reduction  
In this study, an economic assessment is performed in terms of the cost of the PV system and the profit from the power production. The cost of a PV system can be separated into the initial installation costs and operating costs. According to the Korea Energy Economics Institute (KEEI, Ulsan, Korea) [29], the installation cost is about 1.43 USD/W, and the annual operating cost is about 10.38 USD/kW. The RPS system was used to estimate the revenue generated by electricity production and sales. The RPS system calculates the electricity sales revenue by summing the system marginal price (SMP) and renewable energy certificates (REC). Here, SMP refers to the revenue from the sale of electricity through the Korea Power Exchange (KPX, Naju, Korea). REC is a tradable, non-tangible energy commodity that represents 1 MWh of electricity generated from an eligible renewable energy resource. Since the prices of SMP and REC are constantly changing, this study used the average value of the amount traded in Korea since 2018. SMP and REC prices are 82.93 USD/MWh and 89.06 USD/MWh, respectively. In addition, in Korea, the power generated by a floating PV is multiplied by a REC weight of 1.5 308.  
The net present value (NPV) of the PV system was also calculated by Equation (5).  
\[\text{NPV}=\sum_{t=1}^{N}\frac{E_{t}-C_{t}}{(1+r)^{t}}-C_{0} \tag{5}\]  
where \(N\) is the system operating period (20 y in this study), \(E_{t}\) is annual electricity sales revenue (USD), \(C_{t}\) the annual operating cost (USD), \(r\) is the discount rate, and \(C_{0}\) is initial cost (USD). A discount rate  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Parameter** & **Value** \\ \hline \multirow{8}{*}{**Solar cell module**} & Model & SPR-210-BLK \\  & Length (m) & 1.56 \\  & Width (m) & 0.80 \\  & Power capacity (kW/unit) & 0.21 \\  & Efficiency (\%) & 16.9 \\  & Nominal Operating cell temperature (\({}^{\circ}\)C) & 46 \\  & Temperature coefficient (\(\%\)/\({}^{\circ}\)C) & \(-\)0.4 \\  & Losses (\%) & 1 \\ \hline \multirow{8}{*}{**Inverter**} & Model & SPR-12000f \\  & Efficiency (\%) & 95.5 \\ \cline{1-1}  & Capacity (kW/unit) & 12.5 \\ \cline{1-1}  & Losses (\%) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters for the photovoltaic systems considered in this study.  
of 5.5% was applied in this study, according to KEEI [30]. The payback year can be determined by calculating \(N\), satisfying the condition that NPV becomes zero in Equation (5).  
GHG reduction was also calculated in this study by multiplying the GHG emission reference value (0.4415 tCO\({}_{2}\)/MWh) and total amount of power (MWh/year). GHG reduction refers to the amount of GHG generated when the same amount of power produced through renewable energy is generated through the fossil energy system.
# Determination of the Waterway Parameters as a Component of Safety Management System
## 4 Materials and Methods
### Vessel and Area Parameters  
The following research conditions were adopted:  
1. Maximum vessel: Bulk carrier: \(L_{c}\) = 230 m, \(B\) = 36.0 m, \(T\) = 11.0 m
2. Under keel clearance: \(\Delta\) = 1.5 m
3. Maximum vessel speed limit: \(V\leq 8\) knots
4. Tug assistance: none
5. Permissible hydrometeorological conditions: Time of day: no limits Wind: speed up to 10 m/s, direction--no limits Current: speed up to 1 knot, direction--along the fairway Wave: none Visibility: above 2 NM
1. Navigation system used: Good visibility: 1. day \begin{tabular}{l l}  & (b) & PNS \\  & (c) & Terrestrial \\ (2) & Good visibility: \\  & (a) & night \\  & (b) & PNS \\  & (c) & Terrestrial \\ (3) & Restricted visibility: \\  & (a) & \(Z<2\) Mm \\  & (b) & PNS \\  & (c) & Radar \\ \end{tabular}   
Taking all the above data into consideration, the sample calculations checking the correct operation of the algorithm are presented further. Firstly, the positioning method, time of day, and the visibility are set:  
1. positioning method: terrestrial based on lateral navigation marks;
2. day;
3. good visibility \(Z>2\) NM. Next, the other factors according to Equation (8):  
1. \(d_{1}\)--vessel speed = 8 kn;
2. \(d_{2}\)--prevailing transverse wind = 0 kn;
3. \(d_{3}\)--prevailing transverse current = 0 kn;
4. \(d_{4}\)--prevailing longitudinal current = 1 kn;
5. \(d_{5}\)--height and length of the significant wave, h\({}_{\rm W}\) = 1.0 m, \(\lambda_{\rm W}\) = 50.0 m;
6. \(d_{7}\)--type of bottom = soft;
7. \(d_{8}\)--the ratio of the depth to the draft of the vessel h/T = 1.136; Finally, the data regarding the water area:  
1. r (m)--distance between beacons;
2. x (m)--distance to the closest beacon;
3. h (m)--depth of the water area;
4. type of the coast: dredged. According to Equation (18), the width of the fairway at the bottom for a safe depth is as follows (for the straight parts of the fairway):  
\[D=d_{m}+2d_{n}^{k}(1-\alpha)+2d_{r} \tag{25}\]  
\[d_{m}=d_{mp}+d_{1}+d_{2}+d_{3}+d_{4}+d_{5}+d_{7}+d_{8} \tag{26}\]  
\[d(1-\alpha)=\sqrt{\left[1.16\times 10^{-3}\Big{(}1+\frac{x}{r}\Big{)}\right]^{ 2}+\left(\frac{1.5L_{c}}{57.3}\right)^{2}}[{\rm m}] \tag{27}\]  
\(d_{mp}=1.5\)\(B\) (good steerability) = 54.00 m;  
\(d_{m}\), according to Table 2, is even \(d_{m}\) = 54.00 + 0 + 0 + 0 + 0 + 0 + 18.00 = 72.00 m:  
\(d_{r}\), according to Table 3, is as \(d_{r}\) = 0.5 \(\times\) B = 0.5 \(\times\) 36.00 = 18.00 m;  
\[d(1-\alpha)=\sqrt{\left[1.16\times 10^{-3}\Big{(}1+\frac{1600}{3200}\Big{)} \right]^{2}+\left(\frac{1.5\times 230}{57.3}\right)^{2}}=6.63{\rm m} \tag{28}\]  
where \(r\) is taken from Table 4, and \(\times\) is taken as \(r/2\) as the worst case. The width of the fairway at the bottom for a safe depth is  
\[{\rm D}=72.00+2\ \times 6.63+2\ \times 18.00=121.26\ {\rm m}\]
## 4 Case Studies  
As mentioned earlier, one of the main pillars of Education 4.0 is not only to teach students the theory behind disruptive technologies, such as ML, but also to provide the learning material so that they can have the opportunity of applying the acquired knowledge to develop problem-solving skills, as well competencies such as design mindset, transdisciplinary approach and computational skills [3]. Thus, the previous ML algorithms were applied over three different datasets while showing and analyzing the results so that they can be employed by students to learn more about their application.  
Moreover, providing this test bench is not only useful for students but also for teachers as support material on Machine Learning lessons. For example, it can be used as a project in which students must replicate the results presented here so that they can develop computational skills over ML problems. Furthermore, teachers can entrust students to design their own experiment so that they apply the methods on a different dataset of their choice to see how it works, compare the obtained performance, and analyze why they were good or bad classifiers for the proposed new dataset.  
For this work, the proposed test bench consists of the following three datasets: the Cleveland Clinic Foundation Heart Disease dataset, the Banknote Authentication dataset, and the Breast Cancer Wisconsin dataset, which are available at the UCI Machine Learning Repository. These datasets were chosen to be about medicine and economics for students to notice that the applications of ML are multidisciplinary and not limited to engineering problems.  
The former dataset contains the data of 296 patients, which is composed of 14 attributes per patient, including the classification of each of them. There are two categories in which the patients are classified: healthy patients and heart disease patients. The second dataset contains 1372 items with four attributes, where each item is labeled as genuine or fake depending on its attributes. Finally, the Wisconsin dataset has 30 features for each of the 569 instances, where each instance is classified as malignant or benign. Prior to the implementation of the classification algorithms, the datasets were standardized.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 4 Performance Analysis
### Multipath Error Envelope
#### 4.1.1 BPSK(1)  
Figure 32 shows the multipath error envelope for the BPSK(1) signal with infinite bandwidth. Comparisons are made between the coherent discriminator of the LS method, the non-coherent discriminator of the LS method, the discriminator of W2 and the discriminator of the double-delta correlator. It can been seen that the two discriminators of the LS method have almost the same area. Furthermore, the multipath error envelope of CCRW W2 is about the same as the envelope of the double-delta correlator, while it does not have an embossment in the 0.9 chips. The area of each multipath error envelope is shown in Table 6, the coherent LS discriminator has a minimum envelope area, which is 95.6% of the non-coherent LS discriminator envelope, 47.3% of the W2 envelope and 44.9% of the double-delta correlator envelope.  
The multipath error envelops of the BPSK(1) signal with the 4.092-MHz bandwidth are shown in Figure 33. Each discriminator has a zero-crossing point offset at different levels. The LS coherent discriminator has a zero-crossing point shift of 0.01 chips, and the CCRW W2 discriminator has a shift of about 0.075 chips. However, the zero-crossing points of the other two discriminators are not affected by the limited bandwidth. In all, the coherent LS discriminator has a minimum envelope area, which is 88.2% of the non-coherent LS discriminator envelope, 92.7% of the W2 envelope and 83.8% of the double-delta correlator envelope.  
Figure 32: Multipath error envelopes for S-curves of the BPSK(1) signal (infinite bandwidth).