esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning and Compressing: Low-Rank Matrix Factorization for Deep Neural Network Compression
## 3 Algorithm
### Complexity Analysis  
Our algorithm consists of two seperate steps: the learning step and compression step, which both coincide in the limit \(\mu\rightarrow\infty\) through multiple iterations. The former step is the weights training process, which can be solved multiple times by the SGD method. Its time is usually several times that of the original DNN model training time. The latter step is a low-rank approximation of the weight matrices, which can be solved by low-rank MF methods (CUR decomposition and TSVD in this manuscript).  
Assume w.l.o.g. \(\mathbf{W}\in\mathbb{R}^{m\times n}\). The complexity of the learning step is \(I_{l}\times I_{s}\times N\times\mathcal{O}(m\times n)\), where \(I_{l}\) denotes the number of iterations for learning step, \(I_{s}\) denotes the number of times the SGD method is used in each iteration, \(N\) denotes the size of the public dataset. The compression step is divided into solving the optimal rank by enumeration and matrix low rank approximation. For Equation (10), it finds the optimal rank by enumeration, and the complexity of computing optimal rank \(r_{k}\) is \(\mathcal{O}(R^{2})\), \(R\) denotes the size of the singular value matrix. For TSVD, it is obvious that its complexity is \(\mathcal{O}(m^{2}\times n+n^{2}\times m)\). For the CUR decomposition (Algorithm 1), its process mainly consists of SVD, normalized statistical leverage scores calculation, column selection, and matrix operation. The complexity of the first operation is \(\mathcal{O}(m^{2}\times n+n^{2}\times m)\), of the second operation is \(\mathcal{O}(r\times n)\), of the third operation is \(\mathcal{O}(c\times(m+n))\), and of the fourth operation is \(\mathcal{O}(m\times c^{2}+2\times m\times c+n\times c^{2})\). Therefore, the total complexity of the CUR decomposition is \(\mathcal{O}(m^{2}\times n+n^{2}\times m+r\times n+c\times(m+n)+m\times c^{2}+2 \times m\times c+n\times c^{2})\). Consequently, the complexity of compression step is \(I_{l}\times\big{(}\mathcal{O}(R^{2})+\mathcal{O}(m^{2}\times n+n^{2}\times m +r\times n+c\times(m+n)+m\times c^{2}+2\times m\times c+n\times c^{2})\big{)}\).  
In a word, the computing complexity of this algorithm (the joint optimization of CUR decomposition) is \(I_{l}\times I_{s}\times N\times\mathcal{O}(r\times n)+I_{l}\times\big{(} \mathcal{O}(R^{2})+\mathcal{O}(m^{2}\times n+n^{2}\times m+r\times n+c\times( m+n)+m\times c^{2}+2\times m\times c+n\times c^{2})\big{)}\).
## 3 Results
### Post-Test Characterization  
In order to check the mechanical and textural stability of the catalysts, TEM studies were also carried out on spent catalysts (Figure 4A-F).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Entry** & **T [\({}^{\circ}\)C]** & **Time [min]** & **X\({}_{\text{FUR}}\) [\%]** & **S\({}_{\text{FA}}\) [\%]** & **CB [\%]** \\ \hline
1 & 110 & 30 & 45 & 82 & 93 \\
2 & 110 & 70 & 61 & 80 & 93 \\
3 & 110 & 120 & 86 & 72 & 92 \\
4 & 110 & 240 & 100 & 82 & 93 \\
5 & 80 & 120 & 31 & 92 & 92 \\
6 & 80 & 240 & 65 & 89 & 96 \\
7 & 150 & 120 & 100 & 67 & 75 \\ \hline \end{tabular}
\end{table}
Table 2: Screening of the catalytic conditions using Au/MnO\({}_{2}\)-NF catalyst. P = 12 bar (air), furfural:Au molar ratio = 100:1.  
Figure 3: The furfural oxidation reaction on different MnO\({}_{2}\) oxides and Au modified samples: P = 12 bar (air), T = 110 \({}^{\circ}\)C, reaction time 2 h, furfural:Au molar ratio = 100:1. C stands for the furfural conversion, S for the selectivity in furoic acid and Y for the yield of furoic acid, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Entry** & **Catalyst** & **X\({}_{\text{FUR}}\) [\%]** & **S\({}_{\text{FA}}\) [\%]** & **CB [\%]** \\ \hline
1 & Au/MnO\({}_{2}\) -NF & 82 & 74 & 92 \\
2 & Au/MnO\({}_{2}\)-NW & 75 & 64 & 93 \\
3 & Au/MnO\({}_{2}\)-Comm & 53 & 37 & 91 \\ \hline \end{tabular}
\end{table}
Table 3: Recycling tests. P = 12 bar (air), T = 110 \({}^{\circ}\)C, reaction time 2 h, furfural:Au molar ratio = 100:1.  
As can be seen from Figure 4C-F, the structure of the Au/MnO\({}_{2}\)-NW and Au/MnO\({}_{2}\)-Comm samples are preserved. However, an increase in the Au particle size was observed in both cases. Specifically, the Au NPs sizes corresponded to 3.3 \(\pm\) 0.9 nm and 16 \(\pm\) 4 nm in diameter in the catalyst containing MnO\({}_{2}\) nanowires and commercial MnO\({}_{2}\), respectively. In the case of the Au/MnO\({}_{2}\)-NF sample (Figure 4A,B), the structure of the support and average Au particle size were conserved. This indicates a better stability of this catalyst when compared to commercial and NW-based samples.
# Control Chart Concurrent Pattern Classification Using Multi-Label Convolutional Neural Networks
## 2 Methods
### Multi-Label Convolutional Neural Network  
Multi-label classification is a classification problem in which multiple target labels can be assigned to each sample (instance). In multi-class classification, each sample can only be assigned to a single class. Researchers have proposed various methods to deal with multi-label classification problems, including the problem transformation method and algorithm adaptation [46]. The present study considers the latter method.  
The multi-label classification problem is equivalent to building a model to map the input vector \(x\) to a binary value vector \(y\). Each element of the vector \(y\) is assigned a value of 0 or 1, which represents whether the label appears. In multi-label classification, a data sample can belong to multiple classes. With slight modification, a multi-class CNN can be applied to a multi-label classification problem.  
One element that must be modified is the activation function of the last layer of the network. In multi-class classification, the Softmax function is generally used. However, the Softmax function considers the scores of the last layer of neurons, and then converts the scores into probabilities that sum to one. In multi-label classification, to make the output of each class independent, the sigmoid function must be used as the activation function for the last layer of neurons. The sigmoid function converts the score of the last layer of neurons to a value between 0 and 1 independently, without being affected by the scores of other neurons. If the output of a certain class is greater than the preset threshold (usually 0.5), the sample data are classified into that class. Because several classes may have outputs greater than the threshold, the sample data can be classified into more than one class.  
Another element that must be changed is the type of loss function. In multi-class classification, the categorical cross-entropy is typically chosen as the loss function. In the multi-label classification, the use of binary cross-entropy allows the network to independently optimize the loss of each class.
# Treatment Strategies in Neutrophil Dermatoses: A Comprehensive Review
## 4 Hidradenitis Suppurativa
### Treatment
#### 4.4.3 Systemic Treatment  
Antibiotics  
Regarding systemic treatment, we must emphasize the role of antibiotics. Among them, in the first line, we find oral tetracyclines, which are very useful because of their anti-inflammatory effects. Subsequently, in the second and third lines, we find dindamycin-rifampicin and metronidazolezole/moxifloxacin/rifampicin, respectively. There is also a report of the use of etapenem in cases refractory to other lines of antibiotic therapy, with an adequate response.  
The use of dapsoe should be relegated to third-line treatments, especially in those patients with moderate involvement (Hurley I-II) if the first- and second-line treatments have failed. The dose used varies between 25 and 200 mg daily, and it is recommended for at least 3 months [66].  
There is little evidence about the use of zinc in patients with Hurley stage I and II; despite this, it seems that its key role in innate immunity could be favorable in certain cases [67].  
Retinoids  
Retinoids are also a fundamental part of the therapeutic arsenal for this pathology. They are usually relegated to the second or third line, when antibiotics have failed. The best known is acitretin, at doses of 0.2 to 0.88 mg/kg per day. It seems that there are many factors that predispose one to a better response with this drug, among which are a family history of HS, elevated levels of activity, and a history of acne conglobata [68; 69; 70]. In the case of isotretinoin, evidence about its use is contradictory; usually, the dose used is 0.5 to 1.2 mg/kg [69; 70].
## 3 Mechanical Vibration Tests
### Comparison of Transmittance Functions of Tested Fastening Methods  
The conducted experimental studies on the transmittance of various mounting techniques have shown that bolted connections are characterized by the most favorable transmittance function profile--throughout the entire examined range of 0-200 Hz, an almost constant value was observed. Slightly worse results, with deviations of about 2-3% from the constant value, were obtained for the vacuum chuck, which demonstrates its significant potential for use as a mounting technique with the possibility of rapid disassembly in vibration studies. A comparison of the transfer functions for the presented mounting techniques is illustrated in Figure 24.  
For the electromagnetic chuck, the obtained results disqualified the tested prototype from applications in vibration studies. The transmittance function exceeded the threshold level (1.25) already at around 170 Hz, and as the frequency increased, it grew at a very rapid pace, multiplying the amplitudes of the induced vibrations. The cause of this situation could presumably be due to the following two factors:  
1. Resonance of internal structures in the electromagnetic chuck. There is a coil embedded in a soft material inside, which may induce such adverse effects;
2. Insufficient thickness of a ferromagnetic material plate (3 mm steel sheet) that was used trying to avoid an excessive weight increase in the prototype electromagnetic chuck, which, however, prevented the proper development of electromagnetic forces.
## 2 Mission and System  
### 2.1. Ale-1  
Figure 1 shows the appearance of the flight model of the micro-satellite ALE-1 together with the positions of the related components, and Table 1 provides its specifications. The main mission of ALE-1 is to demonstrate the technology of an \"artificial shooting star\" generation by ejecting small metal pellets from orbit to Earth, where interaction with the atmosphere will cause a luminous phenomenon. This technological demonstration has two aspects: entertainment and scientific observation. The entertainment aspect involves the development of a service that allows people to enjoy shooting stars from any ground position at any given time. The scientific observation aspect aims to gather data on luminous phenomena in the upper atmosphere, which can be used to investigate the characteristics of the upper atmosphere and the re-entry behavior of small objects [6; 7; 8; 9].  
In order to prevent collisions with other spacecraft in orbit, it is crucial to ensure that the pellets are carefully ejected. This is especially important for the International Space Station (ISS), which operates at an altitude of 400 km and houses critical experimental facilities and astronauts. To minimize the risk of any pellet impact with the ISS, ALE-1 needed to transfer from its initial orbit at an altitude of 500 km to a lower altitude below 400 km prior to commencing its main mission [10; 11; 12].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Values** \\ \hline Satellite & Mass [kg] & 68.16 \\  & Dimensions [mm] & 440 \(\times\) 500 \(\times\) 539 \\ \hline \multirow{4}{*}{SDOM} & Mass [kg] & 3.88 \\  & Dimensions [mm] (Stored configuration) & 277 \(\times\) 211 \(\times\) 222 \\  & Film size [mm] & 2500 \(\times\) 2500 \\ \cline{1-1}  & Thickness of the film [\(\mu\)m] & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of ALE-1.  
Figure 1: Micro-satellite ALE-1 and its component configuration.  
As the orbit transfer device, we installed an SDOM, which will be described in detail in the next section. The SDOM utilized a sail to increase the atmospheric drag to lower the orbit altitude and separated the sail section after ALE-1 had descended about 100 km, allowing it to remain in the lowered orbit for an extended period.
# Research on Thermal and Heat Insulation Properties of Aerogel Heat-Insulating Reflective Coatings
## 3 Data Analysis
### Characteristic Data Analysis
The closed hot box test conditions increased the hot end temperature by 60 \({}^{\circ}\)C compared to the open test conditions. The XPS board showed excellent thermal insulation and heat preservation performance in working Condition 1, making the air temperature difference between the cold and hot ends 55.2 \({}^{\circ}\)C, much higher than the 38.2 \({}^{\circ}\)C of the aerogel board. The air temperature difference between the hot and cold ends of the XPS board was 4.7 \({}^{\circ}\)C, which was lower than that of the blank board at 5 \({}^{\circ}\)C. This indicates that the reflectivity of the aerogel coatings allowed the heat transferred by the infrared lamp to be reflected into the test environment, reducing the amount of heat passing through the specimen. The open hot end environment was conducive to the reflective properties of the aerogel coatings, effectively reducing the amount of heat passing through the specimen. It is worth noting that the XPS board had the most significant temperature difference between the sample's hot and cold ends in Condition 2, as the XPS board surface \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Test Conditions** & **Feature Data** & **Blank Board** & **Aerogel Board** & **XPS Board** \\ \hline \multirow{2}{*}{Test Condition 1} & Damping factor & 0.560999743 & 0.469470229 & 0.323897396 \\  & Time lag/min & 20 & 25 & 140 \\ \multirow{2}{*}{Test Condition 2} & Damping factor & 0.553571429 & 0.368421053 & 0.459770115 \\  & Time lag/min & 5 & 40 & 360 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculation of damping factor and time lag of specimens. absorbs more heat, resulting in a higher surface temperature. The temperature difference between the hot and cold ends of the specimens under both conditions was the smallest for the blank plate, followed by the aerogel board, and the largest was for the XPS board. It can be seen that the efficiency of heat transfer in the XPS board is the lowest; aerogel coating does not take into account reflectivity, and the aerogel coating itself also has a specific function of blocking heat transfer, but the amount is lower than that of the XPS board.
## 3 System Modeling with Petri Nets  
With an \(OD\) being viewed as a control command, this work develops a method to schedule the system from a control perspective as done in [21; 22]. Although the PN models in [31; 32; 33] are hybrid ones with both discrete and continuous places, they are not developed to describe the continuous material flows as required by the \(OD\)s. Thus, we modify the hybrid Petri net model presented in [21; 22], due to the fact that they can describe the continuous material flows well. We first present models for devices including tanks and pipeline, then integrate them to develop the model for the whole system. A reader is referred to [34; 35] for the basic knowledge of PN.  
The above scheduling problem involves discrete-event variables, such as the source and destination of an \(OD\), and continuous variables, such as volumes of crude oil and flow rates. Hence, both discrete and continuous places, and discrete and continuous transitions are needed to model the system. In the proposed PN model, a discrete place with a discrete token in it is used to implement the control logic of the system. A token in a continuous place can be treated as both discrete and continuous ones. When treated as a discrete one, it is used to describe some attributes such as if there is oil in a tank, and if the oil in a tank is ready to be discharged. When it is treated as a continuous one, it is used to describe the volume of oil in a tank. Note that a token in a continuous place indicates that there is oil in the tank modeled by the place. Also, a tank can hold one type of oil at a time. Thus, in the model, a continuous place can hold at most one token. If a token is sent to a continuous place that holds one token in it, the two tokens are merged into one with the volume being the sum. The flow rate of oil is described by the firing rate of a continuous transition. Moreover, colors are introduced to identify different types of crude oil, and time is also needed to describe the scheduling problem.  
The proposed PN model is a kind of continuous-colored-timed PN (CCTPN) defined as _CCTPN_ = (\(P_{D}\cup P_{C}\cup P_{E}\), \(T_{D}\cup T_{T}\cup T_{C}\), \(I\), \(O\), \(\Phi\), \(M_{0}\)), where \(P_{D}\), \(P_{C}\), and \(P_{E}\) are sets of discrete, continuous, and enforcing places, respectively; \(T_{D}\), \(T_{T}\), and \(T_{C}\) are sets of discrete, timed, and continuous transitions, respectively; \(I\) and \(O\) are input and output functions, respectively; \(\Phi\) represents the color sets defined on tokens in places; and \(M_{0}\) is the initial marking. The icons for the model are shown in Figure 1.
# Improving the Dyeability and Anti-Wrinkle Properties of Cotton Fabric via Oxidized Raffinose
## 3 Results and Discussion
### Anti-Wrinkle Finishing and Dyeing of the Fabrics
In Figure 4a, the reactive groups (halogen atom-chlorine) of the reactive dye undergoes the covalent bonding reaction with the hydroxyl groups of the cellulose, which reduces the hydroxyl content of the fiber. In the reaction of oxyRa with the cellulose hydroxyl group (Figure 4b), the hydroxyl group itself was introduced into the fiber chain. Thus, the number of hydroxyl groups in the treated fabric did not \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Samples**} & **Curing** & **Curing** & **WRA (\({}^{\circ}\))** & **TS (\%)** & **WI** & **k/s** & \(\Delta\)E \\  & & **Temperature (\({}^{\circ}\)C)** & **Time (s)** & **WRA (\({}^{\circ}\))** & **TS (\%)** & **WI** & **k/s** & \(\Delta\)E \\ \hline
1 & Reference & 150 & 120 & 118.54 & 100.00 & 83.15 & / & / \\
2 & Reference-1 & 150 & 120 & 129.35 & 98.50 & / & 19.63 & 0 \\
3 & OxyRa \({}^{\mathrm{a}}\) & 150 & 120 & 245.23 & 64.90 & 62.35 & / & / \\
4 & OxyRa-1 \({}^{\mathrm{a}}\) & 150 & 120 & 207.15 & 60.01 & / & 24.02 & 2.43 \\
5 & OxyRa-2 \({}^{\mathrm{a}}\) & 150 & 120 & 238.43 & 58.07 & / & 13.88 & 3.49 \\
6 & DMDHEU \({}^{\mathrm{a}}\) & 160 & 120 & 247.25 & 65.82 & 68.42 & / & / \\
7 & DMDHEU-1 \({}^{\mathrm{a}}\) & 160 & 120 & 219.74 & 60.07 & / & 24.39 & 1.96 \\
8 & DMDHEU-2 \({}^{\mathrm{a}}\) & 160 & 120 & 230.32 & 61.73 & / & 6.91 & 8.46 \\
9 & GA \({}^{\mathrm{a}}\) & 150 & 120 & 251.21 & 64.54 & 71.84 & / & / \\
10 & GA-1 \({}^{\mathrm{a}}\) & 150 & 120 & 212.53 & 66.40 & / & 20.17 & 0.43 \\
11 & GA-2 \({}^{\mathrm{a}}\) & 150 & 120 & 229.42 & 65.93 & / & 8.02 & 7.29 \\
12 & BTCA \({}^{\mathrm{b}}\) & 180 & 120 & 262.64 & 58.88 & 68.13 & / & / \\
13 & BTCA-1 \({}^{\mathrm{b}}\) & 180 & 120 & 211.85 & 54.73 & / & 23.52 & 1.99 \\
14 & BTCA-2 \({}^{\mathrm{b}}\) & 180 & 120 & 229.16 & 53.89 & / & 1.78 & 16.40 \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\) used 20.12 g/1 MgCl\({}_{2}\)-6H\({}_{2}\)O as the catalyst, and \({}^{\mathrm{b}}\) used 20.12 g/L NaH\({}_{2}\)PO\({}_{2}\)-H\({}_{2}\)O as the catalyst. The pH of all finishing agents was 4.32.  
\end{table} 
Table 4: Properties of fabrics treated with oxyRa, DMDHEU, GA and BTCA. Figure 5: K/S and apparent color of treated fabrics. decrease significantly, the key to be able to apply the anti-wrinkle finishing to the fabric before the dyeing process. After DMDHEU, GA and BTA were combined with cellulose, the number of hydroxyl groups consumed was more than that of the hydrophilic groups introduced with the molecule, resulting in serious decrease in hydrophilicity and poor color staining of the fabric. Therefore, their dyeability was in order of high to low: oxyRa > BTA > DMDHEU > GA.  
**Scheme 2. Reaction mechanism of the reactive dye and four crosslinking agents with cellulose. ((a) was the reaction mechanism of the reactive dye EC-3 GL, (b-e) was the reaction mechanism of OxyRa, Ga, DMDHEU and BTA). **
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery
### Thermal Hazards of a Single Battery and Battery Pack  
Due to the high energy density of the LIB and the inherent hazards of battery components described above, it is common for the LIB to experience thermal hazards especially under abusive conditions. For a single battery, the thermal hazards are generally exhibited as high-temperature, ejection, combustion, explosion and toxic gases during thermal runaway. As for a battery pack, thermal failure propagation within the pack can also be observed.
# Experimental and Numerical Studies on the Impact Energy Absorption of Cutting Shear Rings
## 5 Discussion
### Influence of Shear Ring Thickness  
When the impactor impinges on the energy absorption device with the same shear ring spacing and different shear ring thickness at a speed of 10 m/s, the changes in key parameters are shown in Figure 9. Figure 9a-c shows the velocity curve of the impactor with time. Combined with Figure 9h, it can be seen that the thickness of the shear rings is positively correlated with energy absorption. When the thickness increases from 2.0 mm to 2.5 mm, the energy absorption of the device rises slowly. When the thickness increases from 2.5 mm to 3.0 mm, the energy absorption of the device obviously rises. Figure 9d-f shows the change curve of the impact force in the energy absorption process over time.  
Figure 8: Collision energy absorption test results and simulation comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Velocity** & **Ring Thick** & **Spacing (mm)/Number** & **Energy** & **Impact Force** \\  & **(m/s)** & **(mm)** & **of Shear Rings** & **Absorption (f)** & **(kN)** \\ \hline
1 & 10.0 & 2.0 & 51.67/4 & 1685.11 & 84.12 \\
2 & 10.0 & 2.0 & 31.00/6 & 2499.02 & 83.50 \\
3 & 10.0 & 2.0 & 22.14/8 & 3307.99 & 8  
Combined with Figure 9g, it can be seen that the thickness of the shear rings and the peak force show a linear trend.  
Figure 9: Impact process key parameter curve. (**a**) Velocity—51.67 mm/4; (**b**) velocity—31.00 mm/6; (**c**) velocity—22.14 mm/8; (**d**) impact force—51.67 mm/4; (**e**) impact force—31.00 mm/6; (**f**) impact force—22.14 mm/8; (**g**) summary—peak force; (**h**) summary—energy absorption.