esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Big Data Frameworks:
## 5 Comparative Analysis of Big Data Frameworks
### Feature Vector Related to Architectural Components  
Since, big data frameworks distribute workloads across multiple processors, that requires splitting and distributing data files, managing the storage of data in a distributed  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Feature Vectors** & **Components of Feature Vector** \\ \hline General features & Main backers, maturity, implementation language, support for programming languages. \\ Architectural features & Architecture model, data storage, resource management, scheduling, security. \\ Data processing & Data processing model, execution model, processing mode, support for in-memory processing. \\ Performance & Latency, throughput, scalability. \\ Fault tolerance & Failure identification, failure handling, high availability. \\ Machine learning & Native support, compatibility, supported ML algorithms \\ Stream processing & Processing guarantees, state management, data source, processing format, stream primitives \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of feature vectors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Hadoop** & **Spark** & **Storm** & **Samza** & **Flink** \\ \hline Main backers & Google, Yahoo! & AMP Lab & Backtype, Twitter & LinkedIn & dataArtisans \\ Implementation & Java & scala & clojure & scala, java & java, Scala \\ language support & most of the high & java, Scala, python, R & any programming language & JVM languages & java, Scala, python, R \\ Maturity & very high & high & high & medium & low \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of big data frameworks with respect to general features.  
file system, scheduling tasks across the available computing and ensuring security. In this feature set we grouped together architecture related features such as architecture model, resource manager, storage, scheduling and security. Table 4 summarizes the comparison of the frameworks against architecture related features. It can be seen that four of most popular frameworks are based on master/slave architecture, whereas, Samza has no system wide master, instead Samza's architecture is based on publish/subscribe model. Most of the frameworks under discussion support resource management through Hadoop YARN and Apache Mesos. All the frameworks provide reliable and fault tolerant computing through different abstractions. Many big data applications are migrating from in-house storage and preferred to be deployed in cloud environment where different users can easily access or maintain privacy-sensitive data that leads to privacy and security risks. To provide authorization and authentication for computing nodes, Hadoop and Storm use the Kerberos authentication protocol [61]. Spark uses a password-based configuration as well as Access Control Lists (ACLs) to ensure security. Flink uses Kerberos and TLS/SSL authentication and Samza has no built-in support for security.
## 2 Materials and Methods
### Case of Study  
In complex metallic structures, it is often required to weld some parts or to print stiffeners by using additive manufacturing to enhance mechanical performances. These kinds of operations induce structural distortions, due to the installed residual stresses.  
Predicting those induced distortions allows for finding the optimal operations sequencing to minimize them, or to design a modified structure such that the induced distortions will produce the wished final structure geometry fulfilling the requested tolerances.  
In the case study addressed here, we consider the metallic shell structure whose geometry and dimensions are shown in Figure 3, constituted of steel (\(E=210\) GPa, assumed described from an adequate elastoplastic behavior). The structure is being rigidified by printing two stiffeners on it. Each stiffener consists of four printed layers, and, consequently, a sequence of eight unitary operations will define the global process. As it can be noticed, many possibilities exist of sequencing these eight unitary operations, and the final accumulated structure distortion depends on the sequencing choice made. The final state for a particular sequencing choice is shown in Figure 4.  
The structure model consists of a quadrilateral elements mesh of 1.76 mm thickness. The mesh contains about 42,000 nodes. The printed stiffeners are modeled by using solid elements, and are applied at two different locations (left and right stiffeners), four layers in each stiffener (eight unitary operations), using a predefined sequencing. The high-fidelity simulations are carried out by using the commercial software VPS that considers the right material behavior (needing a fine-tuning material calibration) and its integration by using an experienced finite element discretization taking into account the irreversible inelastic (plastic) behavior. For additional details on the considered model and its finite element discretization, the interested reader can refer to the VPS user manual [26].  
Figure 3: Metallic structure considered in the present case study.  
In this quite simplistic case study, there are many possible sequences. This number grows exponentially when considering more stiffeners, and/or much more layers in each. The simulation of all the possibilities is out of reach in most of industrial applications because each calculation can be computationally expensive (several hours calculation). Each printed layer in each stiffener will modify the structure state (deformation), and the final deformation will strongly depend (due to the nonlinear behavior) on the chosen sequence of unitary operations. Figure 5 shows the states after each of the eight unitary operations when considering the sequence \(\{1,2,1,2,1,1,2,2\}\), where 1 refers to a layer applied in the left stiffener and 2 when it is applied on the right stiffener. Because of the software employed, seams are all present in the model, even if they have still not been mechanically activated.
## 3 Optimization of the Field-Null Configuration
### The Iterative Optimizing Scheme
Under this circumstance, the CS current \(I_{CS}\) and part of the PF currents \(I_{PF,known}\) can be treated as known, while the remaining part of PF currents \(I_{PF,unknown}\) is to be solved. The eddy currents in passive conductors could pose a problem in solving this problem, as these currents are determined by Equation (5) in the form of time derivatives that interfere with the linear nature of Equation (11). Here, a simple scheme is introduced to also treat the eddy currents as \"known\" and to obtain them by iteratively solving Equations (11) and (5), as shown by the flow chart in Figure 3:  
The scheme is proven to be efficient and usually converges in roughly 30 iterations. Figure 4 shows an example of minimizing the \(|B_{\perp}|\) in a circular region with \(R=1.78\) m and \(r=0.65\) m, where Figure 4a-c shows the contour plot of optimized \(|B_{\perp}|\) in iteration cycles 1, 29 and 30, respectively. Figure 4d shows the sum of eddy currents in iteration cycles 1, 29 and 30. It can be seen that the difference between \(I_{Eddy}\) in iteration cycle 1 and 29 is large, but the difference between cycles 29 and 30 is negligible, and the \(|B_{\perp}|\) distribution is almost the same between cycles 29 and 30, indicating that this scheme converges fairly well. Thus, the calculation of eddy currents could be regarded as self-consistent. Figure 3: A flow chart showing the iterative scheme to optimize the field-null configuration and simultaneously obtain the eddy currents in a self-consistent way.
## 3 Results
### Tolerance to Oxgall/Bile and Low pH of the Strain MK1/3, Postbiotic Activity  
_L. lactis_ MK1/3 has grown sufficiently in BHI broth supplemented with 5% oxgall/bile. Its count reached 5.00 \(\pm\) 0.00 CFU/mL (log10) at 24 h compared with its counts at time 0 (1.76 \(\pm\) 0.02 CFU/mL). Using 3% oxgall/bile concentration, the growth was higher at time 0 than in broth with 5% oxgall. It increased at 24 h (7.09 \(\pm\) 0.09 CFU/mL, log 10). This means that the strain MK1/3 counts were increased up to 24 h in both oxgall % concentrations. Similarly, the strain MK1/3 tolerates pH 3; only a 21% decrease in cell count was noted compared with counts at time 0 (5.54 \(\pm\) 0.06 CFU/mL), and after 180 min (1.15 \(\pm\) 0.15 CFU/mL, log 10).  
Using the qualitative method, we measured the postbiotic activity of the strain MK1/3 against the principal indicator _Enterococcus avium_ EA5 released in the 9 mm-diameter inhibitory zone. Evaluated inhibitory (postbiotic) activity of concentrated substance (CS) reached 1600 AU/mL against the principal indicator strain EA5. Moreover, the other indicator bacteria were tested, such as 22 different enterococcal species strains isolated from ewe-milk lump cheeses, including 6 strains _Enterococcus facecium_, 11 E. _f faecalis_ and 5 _Enterococcus_ spp. The growth of all strains was inhibited with inhibitory activity in the range from 100 up to 3200 AU/mL. The species strains _E. faecalis_ were more susceptible to CS from the strain MK1/3 than _E. facecium_. In the case of six _E. facecium_ strains, inhibitory activity was measured from 100 up to 200 AU/mL. The strains _E. facecium_ EF5/2, EF1/1, EF8B/1, and EF13A were inhibited with activity of 100 AU/mL. The strain EF 4/2 was inhibited with inhibitory activity of 200 AU/mL. Inhibitory activity against _E. faecalis_ strains reached 100, up to 3200 AU/mL. The indicator strain EE21E1 was inhibited with activity of 3200 AU/mL. Seven strains (EE31E2, EE33E4, E23E9, EE36E4, EE10E4, EE25E1, EE22E5) were inhibited with activity of 1600 AU/mL. The inhibitory activity 800 AU/mL was measured against the strains EE5E4 and EE11E4. Only indicator strain EE2/3 was inhibited with activity of 100 AU/mL. The strains _Enterococcus_ spp. 21B/2, 22B/2, 23B/1, and 9A/1 were also inhibited with activity of 100 AU/mL.
## 10 Discussion  
Table 1 compares the number of multiplications (\(\times\)) and additions (\(+\)) of real numbers necessary to determine the DFTs for real data vectors according to the proposed algorithms and [PERSON]'s algorithms designed specifically for small data lengths \(N\). We compare our solutions with the corresponding [PERSON]'s algorithms because although these solutions are quite old they are the best in terms of multiplicative complexity. It should be remembered that the hardware multiplier is a very resource-intensive unit. The multiplier is the most resource-intensive and energy-consuming arithmetic unit, occupying a large area of the chip and dissipating a lot of power. Therefore, the use of complex and resource-intensive FPGAs containing a large number of multipliers without a special need is impractical. They use more power, take up more PCB space, and generate more heat than simpler chips. Thus, using less sophisticated chips and lower thermal management overheads translates into reduced processor size, weight, power consumption, and cost, as well as increased reliability as an added benefit.  
The advantage of the presented Winograd-type algorithms in comparison with the Cooley-Tukey algorithms is that the critical path in the graph of any of the obtained algorithms contains only one multiplication. If there is more than one multiplication in the critical path of the algorithm, then this will create additional problems for the implementation of computations. As a result of multiplying two \(n\)-bit operands, a \(2n\)-bit product is obtained. The need for repeated multiplication requires an additional amount of manipulations with the operands and therefore requires more time and effort than when we are dealing with only a single multiplication.  
It is easy to observe that the numbers of necessary multiplications are the same for the proposed algorithms as in [PERSON]'s algorithms, but the number of needed additions is smaller in the case of the solutions presented in the article. It must be said that the difference in the number of addition operations for the compared solutions is minimal and is not the main trump card of our paper. Our main goal was to reveal those aspects and features of the organization of calculations of small-size real-valued DFTs that were not disclosed in the available literature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(N\)} & \multicolumn{3}{c}{**Proposed Solution**} & \multicolumn{3}{c}{**[PERSON]’s Small-Lengths DFTs**} \\  & \(\times\) & + & \(\times\) & + \\ \hline
3 & 2 & 4 & 2 & 6 \\
4 & 0 & 6 & 0 & 8 \\
5 & 5 & 13 & 5 & 17 \\
6 & 4 & 14 & - & - \\
7 & 8 & 30 & 8 & 36 \\
8 & 2 & 20 & 2 & 26 \\
9 & 10 & 36 & 10 & 44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the number of arithmetic operations for the proposed algorithms and [PERSON]’s algorithms described in [1].
## 3 Materials and Methods
### Experimental Test-Bed  
In this study, the automated clutch system was developed for application in a utility tractor (U62, LS Mtron, Korea) which is mostly used for paddy farming in Korea. This tractor model was equipped with a mechanical engine with 45 kW power at 2500 RPM, which is the rated engine rotational speed. The specifications of the tractor were as follows: MT, four-wheel drive, dimensions of 3778 (L) \(\times\) 1775 (W) \(\times\) 2531 (H) mm, and total weight of 2296 kg. The synchro-mesh-type manual transmission installed in the tractor comprised two direction gears, four driving shift gears, and four range shift gears. The experimental test-bed was constructed using the tractor's powertrain, clutch housing, AC motor (HS-45/2, Hyundai, Korea) instead of engine, three-phase inverter to control the motor speed, two actuators, and links, as shown in Figure 9. The motor was selected as identical to the tractor engine specification, and the rated torque and rotational speed were 175 Nm and 2500 RPM, respectively. A magnetic proximity sensor (MP-981, ONO SOKKI, Japan) was installed to measure the rotational speed of the engine shaft and drive shaft. In addition, the pulse signals from the sensors were measured by a digital input module (NI 9401, National Instruments, USA), which is capable of bidirectional communication with a maximum clock rate of 10 MHz. An analog input module (NI 9205, National Instruments, USA) with 100 kS-s\({}^{-1}\) sampling rate and 16-bit resolution was used to measure the signals from the potentiometer in the actuators. A motor driver (KDD850 UH, Kaesung Control LAB, Korea), which is capable of bidirectional control with a range of voltage outputs between 0-12 V, was used to control the positions of the actuators. To control the motor driver, an analog output module (NI 9263, National Instruments, USA) capable of voltage output in the range between \(-\)10 and 10 V was used. Data were acquired by a data acquisition module (NI cDNA9172, National Instruments, USA) with a 5 MS-s\({}^{-1}\) sample rate. To control the actuators installed in the experimental test-bed, an automatic control program capable of controlling I/O modules was developed using LabVIEW (Version 2009, National Instruments, USA), which is capable of various signal processing functionalities, where the control period was determined at 0.200 s.  
Figure 8: (**a**) Procedure for PID coefficient determination; and (**b**) position control of clutch and shuttle shifting actuator.
## 4 Discussion
### Federated Learning Overview
Second, the aggregate server coordinates the participant nodes in the FL training scheme without exposing the participant's local data to the network; thus, data privacy and security can be guaranteed. FL is divided into two categories based on the aggregation schema: (a) centralized FL and (b) decentralized FL. As shown in Figure 5a, for centralized FL, the central server selects a subset of nodes at the beginning of training and aggregates the model updates received from client nodes. As nodes, the medical institutions periodically communicate the local updates \(w_{t-1}^{k}\) with a central server to learn a global model \(w_{t}^{g}\). The central server aggregates the updates and sends back the parameters of the updated global model. However, if the centralized server fails, the whole FL environment will collapse. This failure is one of the reasons that the decentralized FL was proposed. Specifically, all nodes coordinate themselves and work together from node to node to develop a global model in decentralized FL, as shown in Figure 5b. **RQ2**: What are the FL methods proposed by scholars to solve challenging medical applications from a data properties perspective?
## 3 Deviation Analysis of New Integrated TBM Disc Cutter
### Calculation of Assembly Deviation between Disc Cutter and Cutter Holders  
There is a clearance between the disc cutter mounting hole on the cutter holder and the disc cutter shaft, and the two are fastened by bolts. The fit deviation will affect the distance and posture between the two fastening bolts. The deviation is shown in Figure 5. Due to the symmetry of the new integrated disc cutter system, only the right-side matching pair is calculated. The local coordinates are shown in Figure 6.  
Figure 5: Tolerance of disc cutter and cutter holder. 1—Disc cutter, 2—Cutter holder.  
There are two parallel sub-connection pairs between the mounting hole on the cutter holder and the disc cutter. The cylindrical surface-cylindrical surface connection pair is formed by the inner wall of the mounting hole and the shaft surface of the disc cutter, which is set to \(T_{3}\). The face-face connection pair is formed by the end face of the mounting hole and the end face of the disc cutter shaft, which is set to \(T_{4}\).  
According to the accuracy requirements, the tolerance field spinor of \(T_{3}\) is as follows.  
\[\left\{\begin{array}{l}u_{C6}=w_{C6}=\frac{0.033+0.021+0.1+0.2}{0.033+0.02 1+0.1+0.2}{13.94}=0.0254\\ v_{C6}=\beta_{C6}=0\end{array}\right. \tag{24}\]  
So,  
\[T_{3}=\left[0.177\quad 0\quad 0.177\quad 0.0254\quad 0\quad 0.0254\right]^{T} \tag{25}\]  
According to the accuracy requirements, the tolerance field spinor of \(T_{4}\) is as follows.  
\[\left\{\begin{array}{l}u_{P5}=w_{P5}=0\\ v_{P5}=0.02+0.02=0.0400\\ \alpha_{P5}=\delta_{P5}=\frac{0.02+0.02}{28.75}=0.00139\\ \beta_{P5}=0\end{array}\right. \tag{26}\]  
So,  
\[T_{4}=\left[0\quad 0.0400\quad 0\quad 0.00139\quad 0\quad 0.00139\right]^{T} \tag{27}\]  
Same with CFR1.  
\[CFR_{7}=\left[0.177\quad 0.0400\quad 0.177\quad 0.00139\quad 0\quad 0.00139 \right]^{T} \tag{28}\]  
The Jacobian-Torsor model is as follows.  
\[FR_{t}=J_{CFE7}[CFE_{7}]=\left[\begin{array}{l}0.117\\ 0.0332\\ 0.237\\ 0.00139\\ 0\\ 0.00139\end{array}\right] \tag{29}\]  
Figure 6: Local coordinates of cutter holder and disc cutter.  
where  
\[I_{CFZ}=\begin{bmatrix}1&0&0&0&4.875&-43.275\\ 0&1&0&-4.875&0&0\\ 0&0&1&43.275&0&0\\ 0&0&0&1&0&0\\ 0&0&0&0&1&0\\ 0&0&0&0&0&1\end{bmatrix}_{CFZ7} \tag{30}\]  
Then, the assembly deviation between disc cutter and cutter holder is calculated by the extreme value method, and the tolerance is shown in Figure 6.  
According to the loop method, all dimensions are added rings. So,  
\[A_{0}=\begin{bmatrix}0&0&\pm 1.2&0&0&0\end{bmatrix}^{T} \tag{31}\]
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 5 Discussion  
### Potential Uncertainties of SAR and Optical Sensors for Snow Monitoring  
Remote sensing data possess some uncertainties that should be considered for a proper interpretation of the presented results. MODIS products have limitations in snow and ice cloud classification [70]. Additionally, the presence of forest canopy in montane regions is  
Figure 9: Examples of the combination of SAR (HRS&I SWS) and optical (MODIS) remote sensing products for the following dates: 04-February-2017 (winter) and 18-April-2018 (spring).  
another source of uncertainty for SCA detection with optical and SAR remote sensing [71]. Furthermore, the PSCPI used in this research presents some limitations for a proper comparison among the Iberian mountains and the analysis of the temporal changes on SCA due to the differences in cloudiness distribution [72].  
SCA monitoring through optical satellite sensors is primarily constrained by adverse weather conditions. Therefore, we conducted an assessment of the monthly cloud cover throughout an average year (see Figure 11). In this study, we found that the mountain ranges located at the north-central and northwestern quadrant of the Iberian Peninsula (Cantabrian System and Iberian Range) experienced the highest cloud cover during much of the snow season (PCPI ranging from 53% to 60%), surpassing the PCPI values of the Central System and the Pyrenees by around 4-5% and 10%, respectively. However, cloudiness was markedly reduced during the snowmelt phase, so that even in some months of the snowmelt season (May in the Cantabrian System and May and June in the case of the Central System), the cloud coverage in the Pyrenees was slightly higher. As expected, the Sierra Nevada consistently exhibited the lowest cloud cover during snow season (PCPI around 35% in winter and between 38% and 44% in spring). These findings are consistent with other studies that have identified maximum cloudiness in the Iberian Peninsula as being concentrated along the Atlantic-Cantabrian coastal area and the Pyrenees, mainly associated with large-scale northwesterly winds and the influence of cold fronts [73; 74].  
Both SAR and optical satellite sensors encounter limitations due to dense forest cover as it reduces the radar backscattering signal and obscures the underlying snowpack [75; 76]. Dense forest areas are predominantly concentrated at lower elevations in the Iberian mountains and decrease progressively with elevation. In general, dense forest is sparse or non-existent at elevations where a seasonal snowpack exists, except in the case of the Pyrenees and the Iberian Range (Figure 12). These montane regions exhibited the greatest average TCD values (exceeding 50% at elevations below 1500 m.a.s.l. and 1730 m.a.s.l., respectively), whereas the Central System consistently displayed the least forest density (average TCD under 27%). The Sierra Nevada and the Cantabrian System also featured limited forest cover (average TCD around 30% below 2000 m.a.s.l. in the Sierra Nevada and about 35% below 1400 m.a.s.l. in the Cantabrian System).