esa-sceva/satcom-chunk-collection huggingface dataset:
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 3 Tensors and Multilinear Forms
\begin{table}
\begin{tabular}{c c c} \hline
**Multilin. Forms** & **Transformations** & **Tensors** \\ \hline
**real-valued in \(P\) vectors** & \(\underset{p=1}{\overset{p}{\times}}\mathbb{R}^{I_{p}}\
i(\mathbf{x}^{(1)}, \cdots\mathbf{x}^{(P)})\ \longmapsto\ f\big{(}\mathbf{x}^{(1)}, \cdots\mathbf{x}^{(P)}\big{)}\in\mathbb{R}\) & \(\mathcal{A}\in\mathbb{R}^{L_{p}}\) \\ \hline
**real-valued in one vector** & \(\mathbb{R}^{I}\
i\mathbf{x}\ \longmapsto\ f\big{(}\underbrace{\mathbf{x}, \cdots,\mathbf{x}}_{P\text{ terms}}\big{)}\in\mathbb{R}\) & \(\mathcal{A}\in\mathbb{R}^{[P,I]}\) \\ \hline \end{tabular}
\end{table} 
Table 6: Multilinear forms and associated tensors. \begin{table}
\begin{tabular}{c c c} \hline
**Forms** & **Matters/Tensors** & **Homogeneous Polynomials** \\ \hline
**Bilinear** & \(\mathbf{A}\in\mathbb{R}^{I\times I};\mathbf{y}\in\mathbb{R}^{I},\mathbf{x}\in \mathbb{R}^{I}\) & \(f(\mathbf{x},\mathbf{y})=\mathbf{y}^{T}\mathbf{A}\mathbf{x}=a_{ij}y_{i}x_{j}\), \(i\in\langle I\rangle\), \(j\in\langle J\rangle\) \\ \hline
**Quadratic** & \(\mathbf{A}\in\mathbb{R}^{I\times I};\mathbf{x}\in\mathbb{R}^{I}\) & \(f(\mathbf{x})=\mathbf{x}^{T}\mathbf{A}\mathbf{x}=a_{ij}x_{i}x_{j}\), \(i,j\in\langle I\rangle\) \\ \hline
**Real multilinear in \(P\) vector** & \(\mathcal{A}\in\mathbb{R}^{L_{p}};\mathbf{x}^{(p)}\in\mathbb{R}^{I_{p}}\) & \(f\big{(}\mathbf{x}^{(1)},\cdots\mathbf{x}^{(P)}\big{)}=a_{\mathbb{I}_{p}}\prod_{p= 1}^{P}x_{i_{p}}^{(p)}\), \(i_{p}\in\langle I_{p}\rangle\), \(p\in\langle P\rangle\) \\ \hline
**Real multilinear in one vector** & \(\mathcal{A}\in\mathbb{R}^{[P,I]};\mathbf{x}\in\mathbb{R}^{I}\) & \(f\underbrace{(\underbrace{\mathbf{x},\cdots,\mathbf{x}}_{P\text{ terms}})}_{p\text{ terms}}=a_{\mathbb{I}_{p}}\prod_{p=1}^{P}x_{i_{p}}\), \(i_{p}\in\langle I\rangle\), \(p\in\langle P\rangle\) \\ \hline \end{tabular}
\end{table} 
Table 7: Multilinear forms and associated homogeneous polynomials. * Analogously to quadratic forms obtained from bilinear forms by replacing the pair \((\mathbf{x},\mathbf{y})\) with the vector \(\mathbf{x}\), real multilinear forms can be expressed using just one vector \(\mathbf{x}\in\mathbb{K}^{I}\). In the same way symmetric quadratic forms lead to the notion of symmetric matrices, the symmetry of multilinear forms is directly linked to the symmetry of their associated tensors.
## 6 Discussion  
To ensure a comprehensive understanding of the performance degradation of the CDC damping force and the adequacy of the durability spec, we conducted a meticulous three-stage RLDA-based damper performance degradation verification:  
1. We applied a 3000 km real vehicle road test to analyze the pseudo damage to the damper from a reliability engineering perspective, ensuring the practical relevance of our methods. Based on the real vehicle road test, the characteristics of each road surface were determined. An additional RLDA based on 500 km was performed for a simulation test to develop a simulation test mode, and 60,000 km of a driving simulation test was performed. The test results confirmed that the tensile damping force caused substantial performance degradation.
2. The currently set target durability was assessed to confirm a similar performance degradation pattern.  
It was confirmed that the damping force was reduced by more than 10% after one million Soft/Soft tests and 500,000 Hard/Soft and Soft/Hard tests each. The main expected cause of the damping force reduction is that, unlike the compression stroke when generating tensile damping force, the piston rod inside the damper operates between the external environment and the oil chamber, so friction wear and oil exposure due to the damper stroke can cause oil leaks. It can be determined that this is due to changes in internal oil conditions such as cavitation. In addition, the performance deterioration evaluation for oil leaks over 3000 km of driving was calculated based on the damper stroke distance. Based on the rig damper stroke mileage, the front damper corresponds to 6.2 km, and the rear wheel corresponds to 8.1 km. When the damper durability specification is defined as two million operations under the conditions of 25 mm stroke and 4 Hz, it corresponds to 200 km based on the damper stroke mileage. When compared with the actual stroke, it can be converted to a target driving mileage of 160,500 km. If the same calculation is made for the rear damper, it can be calculated as 81,400 km.
## 4 Compensation of Measured Weight by Load Cell  
In an automatic pouring machine, as presented in Figure 1, the load cell for measuring the weight of the liquid in the ladle was equipped at the bottom of the automatic pouring machine. Consequently, the weight measured by the load cell was influenced by the movement of the ladle on the \(z\)-axis (see Equation (9)) due to the synchronous control noted in the previous section.  
To obtain only the weight of liquid outflow from the ladle, the influence of the movement on the \(z\)-axis was subtracted from the measured weight as follows:  
\[W_{Lc}(t)=W_{L}(t)-W_{Lz}(t), \tag{15}\]  
where \(W_{Lc}\) kg is the compensated weight, and \(W_{Lz}(t)\) is the weight transformed from acceleration \(a_{z}\) m/s\({}^{2}\) of the ladle's movement on the \(z\)-axis and can be estimated as follows:  
\[\frac{dW_{Lz}(t)}{dt}=-\frac{1}{T_{L}}W_{Lz}(t)+\frac{1}{T_{L}}\frac{M}{g}a_{ z}(t). \tag{16}\]  
The acceleration can be estimated by the SSKF, which is derived in the following section.  
Figure 6a presents the results of the measured weight by the load cell. The sampling interval of the load cell data are 0.02 s. The black and magenta solid lines indicate the weights before and after compensation, respectively. The dashed line indicates the weight of the outflow liquid in the simulation using the pouring flow-rate model with weight compensation. The vibration of 4.5 Hz has appeared in the measured weight. It is caused by the resonant vibration of the automatic pouring machine excited by the pouring motion. In the design of flow-rate estimation, the vibration can be regarded as the process noise of the load cell. Figure 6b presents the angular velocity of the motor for tilting the ladle. Figure 6c,d present the acceleration of movement of the ladle on the \(z\)-axis and the transformed weight, respectively. As presented in Figure 6, the influence of movement on the \(z\)-axis was reduced to within the compensated weight.  
Figure 6: Experimental results of the weight compensation system. (**a**) Weight data measured by load cell; (**b**) Angular velocity of ladle tilting; (**c**) Acceleration on \(z\)-axis estimated by SSKF; (**d**) Weight data transformed from estimated acceleration on \(z\)-axis using Equation (16).
## 2 Materials and Methods
### Standards and Chemicals  
Individual standards for 2-methyl-4-chlorophenoxyoxyacetic acid (MCPA), buprofezin, 2,4-dichlorophenoxyoxyacetic acid (2,4-D), aminomethylphosphonic acid (AMPA), boscalid, chlorpyrifos, difufufenican, epoxiconazole, glyphosate, malathion, metalaxyl, metazachlor, metolachlor, penconazole, prousilfocarb tebuconazole, fefthurtin, tetraconazole, and terbuthylazine were of analytical grade, obtained from Sigma-Aldrich (Steinheim, Germany). A multiresidue standard for organochlorine pesticides (at 100 ug/mL each) was obtained from Restek (Bellefonte, PA, USA). Penconazole-d7 was obtained from Toronto Research Chemicals (Toronto, ON, Canada). Glyphosate 1,2-13C2-15N was obtained from [PERSON] (Augsburg, Germany). Water used in the extraction and dilutions was ultrapure, produced in a Milli-Q plus system from Millipore (Bedford, MA, USA). Absolute ethanol (EtOH) and methanol were of HPLC grade, obtained from Honeywell (Charlotte, NC, USA). Dichloremethane was GC-MS grade, purchased from Carlo-Erba (Emmentingen, Germany). The remaining materials were all purchased from Sigma-Aldrich, namely, C\({}_{18}\) SPME LC-Tips, trifluoroacetic anhydride and trifluoroethanol (\(\geq\)99% purity), 25% aqueous ammonia solution (p. a. grade), 3-ethoxy-1,2-propanediol, guolonolactone, and D-sorbitol (analyte protectants) of purities 98%, 95%, and 99%, respectively. Stock solutions for the pesticides were prepared in methanol at 250 ug/mL, and kept refrigerated at \(-\)20 \({}^{\circ}\)C, for at most one month. Dilutions for injection and soil spiking were performed in methanol. A stock solution of the three analyte protectants was prepared in a 50/50 (_v_/_v_) solution of ethanol-methanol at 1000 ug/mL each [7]. The internal standard solution (penconazole-d7 and glyphosate 1,2-13C2-15N) was prepared with 80/20 (_v_/_v_) methanol-water at 800 ng/mL each.
## 5 Experimental Methods for Measuring CTE of Concrete  
Performing CTE testing will aid in improving the prediction of how mixture-specific thermal response will impact pavement performance [45]. Concrete's CTE can be ascertained using a variety of test techniques. AASHTO T 336, \"Standard Test Method for the Coefficient of Thermal Expansion of Hydraulic Cement Concrete\", is the most utilized one. T 336 was accepted as a standard test method in 2009 and is based on AASHTO TP 60-00, \"Provisional Test Method for the Coefficient of Thermal Expansion of Hydraulic Cement Concrete\". All states' Departments of Transportation use T 336 as their standard test technique, except for the Texas Department of Transportation (TxDoT), which employs a modified version of it [46]. In contrast to the Texas method, which determines the concrete CTE as the slope of the deformation versus temperature curve, the AASHTO TP60 method determines the CTE as the length change over a temperature change following the specimen's attainment of thermal equilibrium [47]. The TxDoT method for calculating the CTE of concrete entails measuring the length change of concrete specimens exposed to different temperatures. Concrete specimens are made by cutting cores or cylinders to a predetermined length, soaking them in saturated lime water, and then measuring the length changes in a temperature-controlled water bath to calculate the concrete's CTE. Temperature, displacement, and time are recorded at regular intervals when the temperature is cycled between 10 \(\pm\) 1 \({}^{\circ}\)C (50 \(\pm\) 2 \({}^{\circ}\)F) and 50 \(\pm\) 1 \({}^{\circ}\)C (122 \(\pm\) 2 \({}^{\circ}\)F). Plotting temperature against displacement and running regression analysis on the rising and falling temperature phases yields the CTE [48]. The different techniques used for measuring the CTE values of concrete are discussed in detail in Table 1.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Ref. ** & **Method** & **Mechanism** & **Measurement** & **Limitations** & **Precision** \\ \hline
[43] & \multirow{6}{*}{AASHTO TP} & \multirow{6}{*}{AASHTO TP} & Saturated concrete & The segments are repeated until the difference in the CTE between two successive segments is less than 0.9 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C & \\  & & & & \\  & & sample subjected to a temperature gradient & The segments are repeated until the difference in the CTE between two successive segments is less than 0.9 \(\times\) 10\({}^{-6}\)/\({}^
## 4 Proposed RCSA Method
### Detailed Procedure for Proposed RCSA Method
#### 4.2.1 Construction of Candidate Routing Paths  
We first construct a set \(\mathcal{P}_{i,j}=\{p_{1}^{[i,j]},p_{2}^{[i,j]},\ldots,p_{K}^{[i,j]}\}\) of candidate routing paths for each pair of sender node \(i\in\mathcal{V}\) and receiver node \(j\in\mathcal{V}\) on the target graph \(\mathcal{G}\) in advance. Our proposed RCSA method chooses a routing path from the constructed candidate paths when a new lightpath-setup request arrives. In this paper, we construct the candidate paths based on the \(K\)-shortest-path algorithm [28]. The modulation level \(M_{p}\) for each candidate path \(p\in\mathcal{P}_{i,j}\) is pre-determined according to the path length. The procedure of the \(K\)-shortest-path algorithm is described below.  
\(K\)**-shortest-path algorithm:**  
1. \(\mathcal{P}_{i,j}\leftarrow\emptyset\) and \(k\gets 1\).
2. The shortest path \(p_{k}^{[i,j]}\) from node \(i\) to node \(j\) is computed using a standard shortest-path algorithm, e.g., [PERSON]'s algorithm, assuming that the cost of each link on \(\mathcal{G}\) is set to 1.
3. The path is selected as a candidate routing path, i.e., \(\mathcal{P}_{i,j}\leftarrow\mathcal{P}_{i,j}\cup\{p_{k}^{[i,j]}\}\); then, the cost of each link along this routing path is doubled.
4. If \(k=K\), the algorithm terminates; otherwise, \(k\gets k+1\), and the algorithm returns to Step 2.
# Providing Predictable Quality of Service in a Cloud-Based Web System
## 3 Web Cloud Earliest Deadline First Method and Web Switch
### Scheduling Section
The term \(d^{\prime}{}_{i}\) is a moment when the service should be finished and the response should be sent to the client. It is calculated for every request as follows:  
\[d^{\prime}{}_{i}~{}=~{}\tau_{i}^{(1)}+t_{max} \tag{2}\]  
where \(\tau_{i}^{(1)}\) is the moment of arrival of \(r_{i}\)th request. The deadline \(d_{i}\) is calculated only for requests being put into the queue module (not sent directly to the switching section)  
\[d_{i}~{}=~{}\tau_{i}^{(1)}+t_{max}-t^{\prime}{}_{ki} \tag{3}\]  
In order to calculate the deadlines, the service module stores information about service times in \(U^{\prime}{}_{i}~{}=~{}[t^{\prime}{}_{1i},\,\ldots,t^{\prime}{}_{ki},\, \ldots,t^{\prime}{}_{ki}]\), where \(t^{\prime}{}_{ki}\) is an estimated service time of the request belonging to the \(k\)th class and is the most actual information at the moment of arrival of \(r_{i}\)th request. The time \(t^{\prime}_{ki}\) is always updated when a service of request belonging to \(k\)th class is finished and the previous request is stored in the queue module. The new time is calculated as follows:  
\[t^{\prime}_{k(i+1)}\ =\ t^{\prime}_{ki}+\eta^{\prime}\big{(}\widetilde{t}_{i}-t^{ \prime}_{ki}\big{)} \tag{4}\]  
where \(\widetilde{t}_{i}\) is a service time of \(r_{i}\)th request and \(\eta^{\prime}\) is adaptation ratio determining the speed of changes, \(\eta^{\prime}e(0,\ 1)\). The time \(\widetilde{t}_{i}\) is measured by the switching section from the moment when the request is sent to the rest of the system to the moment when the response to the request arrives to the WCDF web switch. It should be noticed that time \(t^{\prime}_{ki}\) is calculated for an almost constant load of the system equal to \(n_{max}\). However, because \(n_{max}\) can change periodically, the adaptation ratio \(\eta^{\prime}\) should be big enough to let the vector \(U^{\prime}_{i}\) adapt quickly to the new conditions. Preliminary experiments showed that \(\eta^{\prime}\) should be equal to 0.6.
# Benchmarking GPU Tensor Cores on General Matrix Multiplication Kernels through CUTLASS
## 2 Materials and Methods
### GPU Architecture and Programming Model
A synchronization barrier must be inserted right after the memory access instructions to ensure all data elements required for the current iteration are residing on shared memory already to be loaded into registers later. For each iteration tile, warps are responsible for temporarily loading data elements into registers and conducting WMMA calculations to save the intermediate results in registers, then storing them back to shared memory. After all iterations finish, the CTA should write the final results from shared memory back to global memory. Note that if multiple CTAs are responsible for the same target tile from matrix **C** (called the _split_k_ mode), some extra constraints must be added to avoid data race and ensure memory consistency. ### CUTLASS Design Principles

As discussed earlier, CUTLASS is a hierarchical and flexible library providing APIs abstracting at different level of granularities. It allows programmers to make use of these APIs to assemble the high-performance kernel and express their desires for various problem sizes. There are five levels of APIs that CUTLASS provides, as shown in Table 1. In CUTLASS, users are responsible for exploiting different levels of abstractions through the APIs to assemble the desired kernels meeting both performance and customization (Listing 2). The mainloop and epilogue, as the main body of GEMM kernels, are described via the collective level APIs, which orchestrate the copy/math micro-kernels with architecture-specific synchronizations. Meanwhile, the tile APIs are compiled into core GPU micro-kernels performing substantial math/copy operations spanning cooperative threads, and the _Atom_ APIs are architecture instructions associated with meta-information. We may conclude that the hierarchical CUTLASS decouples the API design: the _Collective_ and _Tile_ APIs aim at describing algorithm-specific CTA/warps orchestration, while the _Atom_ APIs target architecture-specific instruction generation. Note that in Table 1, the _Tile_ and _Atom_ APIs are decorated with the cute namespace prefix. CuTE is a layout library introduced in the latest CUTLASS 3.0 release [14]. The purpose of this library is to define and manipulate hierarchical, multidimensional layouts of threads and data. It is worth noting that almost all of these APIs or classes are templated, desiring some compile-time known information to specialize corresponding

Figure 5: Demonstration of tiled GEMM implementation on GPU, from [13]. The input matrices **A** and **B** are multiplied to form the output matrix **C**, where the calculation is conducted in the tile-wise way to produce the final results. Each CTA is responsible for one output tile in matrix **C**. implementations; then, they are compiled into dedicated instructions to copy or calculate the specific type. Like other numeric or operator libraries, CUTLASS allows users to call GEMM or any other kernels from the host.
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 2 Astronomical Observation Conditions in Stratosphere
### Seeing  
Seeing is the sum of three effects of the centroid motion, blur, and flicker of star images, which indicates the wavefront tilt, phase distortion, and amplitude fluctuation of light transmission, respectively, because the light refraction is affected by turbulence in the atmosphere [14]. This is an important parameter that must be measured by the astronomical observation station represents the atmospheric optical quality and directly determines the effect of astronomical observations. The atmospheric coherence diameter r\({}_{0}\) (Fried parameter) or the refractive index structure constant C\({}_{N}^{2}\) (where N is the refractive index) are commonly used for characterization [15]. By means of these instruments, such as the single star scidar (SSS), the differential Image Motion Monitor (DIMM), or the analysis of weather research and forecast (WRF) [16], the seeing of ground observation stations can be measured and calculated. Because the influence on light refraction in the stratosphere is much smaller than that in the troposphere, the measurement sensitivity of the existing instruments makes it difficult to meet the requirements. Meteorological research methods, such as meteor-logical balloons, can obtain the height profile of atmospheric pressure, temperature, wind speed, and other parameters, reverse and calculate the C\({}_{N}^{2}\), and then analyze the changing trend of atmospheric turbulence [14]. The basic rule is that above the altitude of 11 km, the C\({}_{N}^{2}\) value always decreases, which means that the intensity of turbulence decreases correspondingly, and the C\({}_{N}^{2}\) value above 20 km is very small [13]. This means that the effect of atmospheric turbulence on stratospheric astronomical optical observation can be ignored, and seeing does not need to be a measurement indicator.
# Management of Plastic Wastes through Recent Advanced Pyrolysis Processes
###### Abstract  
Plastics are predominant in numerous sectors like packaging, agriculture, hardware, electronics, and many others. Annual plastic demand has been rapidly growing in the last few decades because of the increasing dependency on plastics. As a consequence, massive amounts of plastic waste are being generated every year. These plastic wastes are non-biodegradable, and hence their disposal poses a serious threat to the ecosystem and causes significant environmental problems such as endangering the safety of marine life, wildlife, air, water, and soil, etc. A large portion of plastic waste ends up in landfills, and only a small fraction is recycled. The continuous dependence on landfills as the main disposal method for plastic waste is costly and ineffective. Common solutions to plastic waste management are incineration and recycling; however, incineration emits harmful pollutants and greenhouse gases that contribute to ozone layer depletion and global warming; moreover, recycling is expensive and inefficient. As an alternative to recycling and incineration, the pyrolysis process can convert plastic wastes into more valuable fuel products. Pyrolysis is a thermal process that converts raw material into pyrolysis liquid, solid wax, and non-condensable gases in the absence of oxygen. This process is attractive because it is economical and energy-efficient, and it can be used to convert various types of plastic waste into valuable products. In recent years, there have been significant developments in pyrolysis applications in liquid fuel production from plastic wastes. This work reviews recent advances in and challenges for the pyrolysis process for converting plastic wastes into a valuable alternative fuel, focusing on studies of advanced pyrolysis processes published over the last five years. The paper also highlights the numerical modeling of pyrolysis of plastic wastes and the potential impact of pyrolysis on the future of sustainable waste-management practices of plastics.  
plastic; wastes; recycling; landfilling pyrolysis; fuels; numerical modeling; sustainability +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Evaluation of Method Performance in Artificially Contaminated Lettuce  
The practicability of the proposed fast-response magnetic enrichment strategy integrated with mPCR analysis was assessed in spiked lettuce with pathogenic _Listeria_ spp. at 10\({}^{1}\) CFU/mL to 10\({}^{6}\) CFU/mL. Fifteen bacterial strains were used to investigate the specificity of primers used in this study, which included 12 non-target and 3 target strains (Table 2). The results of mPCR analysis were shown in Table 2, where the selected specific primers only produced positive amplification results for target bacteria, which ensured the accuracy of mPCR analysis. Agarose gel electrophoresis was used to visualize the length of  
Figure 4: Optimization of experimental parameters. (**a**) Dosages of PEI-MNPs. (**b**) Incubation time of PEI-MNPs bound to _Listeria_. (**c**) Magnetic separation time of _Listeria_-PEI-MNPs complexes. (**d**) CE of pathogenic _Listeria_ spp. at the concentrations from 10\({}^{1}\) CFU/mL to 10\({}^{6}\) CFU/mL by PEI-MNPs in PBS and lettuce.  
the amplicon generated by mPCR amplification. The result of conventional mPCR detection without fast-response magnetic enrichment by PEI-MNPs was shown in Figure 5a, and the limits of detection (LOD) of _L. monocytogenes_ and _L. Ivanovii_ in artificially contaminated lettuce were both \(10^{3}\) CFU/mL. When mPCR was combined with the proposed fast-response magnetic enrichment strategy, the LOD of _L. monocytogenes_ and _L. Ivanovii_ were both reduced to \(10^{1}\) CFU/mL (Figure 5b), which significantly improved the detection sensitivity. The enrichment strategy based on PEI-MNPs eliminated the interference of lettuce substrate, and the concentration effect increased the concentration of target bacteria. Compared with conventional mPCR analysis without enrichment, the detection sensitivity of mPCR was significantly reduced by 2 orders of magnitude after a short time of magnetic enrichment. Due to the advantages of simple operation and low cost, magnetic enrichment combined with mPCR analysis proposed in this paper has great application potential in the analysis of pathogenic _Listeria_ spp. in food samples.