esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Metal rotor structures and composite rotor structures are completely different in terms of design, analysis and production. The rotor is formed by bonding composite materials in layers or winding them in the form of yarn [12; 13; 14; 15; 16]. The design phase should take into account the effect of the rotor material on the manufacturing process. Composite materials have uncertainties at the micro level. Therefore, reasonable safety factors should be selected, and reliable detection methods should be used. Generally, composite materials have higher specific strength than metal materials, but metal materials are isotropic, so theoretically they can be made into a constant stress disc. Composite materials are orthotropic, so they never have the shape of a constant stress disc. However, composite materials are more expensive than metal alloys. The decreasing price of lithium batteries over the years has put pressure on the unit cost of FESS [17]. Studies report that the production cost per unit power is between USD 600 and USD 2400/kW, and the operation and maintenance costs are around USD 5.5/kW year [17]. It is reported that a FESS with a nominal power of 250 kW costs between USD 250 and USD 350/kW, and the corresponding unit energy cost is between USD 1000 and USD 5000/kWh [18]. The International Renewable Energy Agency estimates that the unit energy cost of FESS will decrease by 35% by 2030 [17]. In this study, the effects of different materials on the energy storage capacity of the flywheel and the maximum speed that the rotor can reach are simulated with the finite element analysis program (ANSYS 2019 R3 v19.5). In the literature, multi-rim flywheel structures have been realized for steel, aluminum and carbon fiber [19]. In some studies, structures up to seven layers have been obtained by wrapping carbon fiber layers with metal strips. However, here, the metal layer is used only as a binding intermediate material [14]. In the study, 11 variations with the same rotor dimensions but different layer thicknesses are compared with each other. While steel is preferred for bearing housings and inner rotor material in the literature, titanium material is also analyzed in this study in addition to the steel shaft. The thickness of the outer-layer composite material is kept variable, and the effect of the thickness change on the maximum speed is observed. Layered structures are also compared with monolithic (steel, titanium and carbon composite) rotors, and the advantages and disadvantages of the multi-layer structure compared to the monolithic structure are determined. In the structures examined within the scope of this study, both the metal inner core and the composite outer layer store energy.
## 2 Materials and Methods
### Piezolectric Material  
The second part of the composite was made of PZT, lead zirconate titanate, a ceramic perovskite material with the chemical formula Pb[Zr\({}_{x}\)Ti\({}_{1-x}\)]O\({}_{3}\) (0 \(\leq x\leq\) 1). At present, there are a large number of materials that exhibit the piezoelectric effect; however, most of them are brittle and expensive. In the present paper, PZT oxide ceramic, which exhibits one of the highest piezoelectric coefficients [17], was chosen for the investigations. The material is the commercially available P1-type Micro Fiber Composite (MFC) patch supplied by Smart Material Corporation. Based on the producer information, this type of material is made up of PZT-5 microfibers (130 \(\times\) 150 \(\upmu\)m\({}^{2}\)), encased between two Kevlar/Kapton sheets and polarized along their length. The P1 type of MFC utilizes the d\({}_{33}\) effect for actuation and elongates up to 1800 ppm if operated at the maximum voltage rate of \(-\)500 V to +1500 V [18]. A piece of P1 MFC patch serves as a reference sample R in further measurements.
## 1 Introduction
[PERSON] [15] further emphasized that support systems under dynamic loads must have sufficient yielding capacity to prevent instability caused by vibrations. In isolated working faces, selecting suitable support systems is especially critical. Coal burst occurs due to the accumulation of elastic energy in rock and coal seams during excavation or mining activities [16]. When this energy exceeds the bearing capacity of the surrounding rock, a sudden release occurs, leading to instability, often referred to as stress-type coal bursts [17]. Recent studies have significantly advanced our understanding of coal burst mechanisms, prediction methods, and prevention strategies, which are critical for improving mining safety. For instance, in terms of coal burst theory, [PERSON], et al. [18] analyzed factors influencing stress concentration in isolated working faces, such as burial depth, coal seam thickness, and goat dimensions. They utilized numerical simulations to examine how these factors affect stress distribution and plastic zone changes, deriving a stress equation for coal in isolated working faces. [PERSON], et al. [19] further explored the stress distribution of isolated coal pillars and determined that high static loads could cause critical instability, especially when disturbed by dynamic loads, thus triggering coal bursts. Regarding coal burst prediction, [PERSON], et al. [20] developed a coal burst prediction model using data mining techniques and a laboratory test database. Their analysis of coal burst cases helped identify relationships between contributing factors, enhancing predictive accuracy. [PERSON], et al. [21] proposed a new energy index for coal burst, combining elastic deformation energy and impact energy, to better predict coal burst risks in various strata [22]. When it comes to prevention and control, [PERSON], et al. [23] devised strategies combining regional and local approaches, focusing on controlling coal bursts in irregular coal pillar areas. Measures such as large-diameter drilling and roof hydraulic fracturing have proven effective in specific cases [24]. [PERSON] and [PERSON] [25] also demonstrated the importance of monitoring stress and seismicity, using pressure relief blasting to reduce stress in working faces. Despite these advancements, there remains a gap in understanding the overall-local interaction of coal bursts in isolated working faces. Existing methods do not fully account for the combined impact of the overlying strata's spatial structure and stress distribution in these complex environments. This study aims to address this gap by focusing on the prediction and prevention of coal bursts in the deep, isolated working faces of Wuyang Mine in Shanxi Province. A novel prediction model is proposed, integrating both overall and local coal burst risks before mining activities begin. Additionally, targeted prevention and control methods are developed for different coal burst types, with the reliability of these methods being validated through practical application.
## 2 Literature Review
### Existing MCDM Approaches Adopted for Similar Problems
The research contemplated 12 factors combined into two main criteria (technical and economic) to minimize construction costs while maximizing power output. The research concluded that Saudi Arabia exhibits substantial potential for solar PV projects, covering approximately 65.1% of the total studied area. [PERSON] et al. [PERSON] et al. (2018) addressed a solar site selection problem in Eastern Iran using MCDM. Specifically, they combined the BWM, the Grey Relational Analysis (GRA), and VIKOR to rank potentially suitable locations. Furthermore, an innovative Monte Carlo Simulation-Based (MCSB) approach was introduced to analyze the sensitivity of results produced from VIKOR and GRA, confirming the robustness of the first one over the second one. [PERSON] et al. [PERSON] et al. (2018) developed an innovative approach to select solar panels using newly developed MCDM methods, such as Characteristic Objects Method (COMET) combined with TOPSIS and Stable Preference Ordering Towards Ideal Solution (SPOTIS). The methods used in the study were rank-reversal-free and precise, integrating objective entropy weighting to determine the weights of the criteria. The final results demonstrated the effectiveness of the proposed approach in solving the problem of solar panel selection. Another widely used decision-making method is ELECTRE TRI, which is employed to sort alternatives into categories based on multiple criteria. In the solar energy field, focusing on PVs, [PERSON] et al. [29] applied the ELECTRE TRI methodology to assess urban roofs for their suitability to install PV systems. This evaluation incorporated multiple criteria, such as energy potential, economic viability, and structural conditions. The approach enabled them to identify the roofs most suitable for solar installations, helping in urban planning for solar energy deployment. [PERSON] and [PERSON] [30] implemented a decision-making framework to assess the performance and select the location of solar PV plants in India. They utilized Fuzzy ELECTRE TRI to compare seven PV plants by taking into account uncertainties in the data, such as environmental variability connectivity and accessibility. This approach enhanced the robustness and reliability of their evaluations.
# Portable L-Band Radiometer (PoLRa): Design and Characterization
## 3 Radiometer Characterization  
The following subsections describe the experimental characterization of the PoLRa radiometer. First, the active cold source (ACS) characterization procedure is described; second, stability and radiometric resolution are discussed; and third, the radiometer uncertainty quantification is presented.
# Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence
## 2 Related Work  
### Vision and Language Understanding  
There have been many efforts in understanding vision and language, which focus on making a connection between visual and linguistic information. Various applications need such a connection to realize tagging [7; 8; 9; 10], retrieval [11], captioning [12; 13; 14], and visual question answering [15; 16; 17; 18].  
Image tagging is a task to learn a connection between image and tag, a short description of the image in a few words. Generally, tags are assigned by multi-label classification using a binary classifier, e.g., nearest neighbor [7]. There are, in the literature, works on more complex cases. [PERSON] et al. tagged images using practical vocabularies for mobile users on social media [8]. They learned missing and defective tags of the images from the training dataset. [PERSON] et al. recovered missing tags from partially tagged images [9]. They used low-rank approximation to partiallyoptimize completed tags from the given tags. [PERSON] et al. addressed zero-shot image tagging, which assigns tags according to unseen objects [10]. They located objects and perform binary classification using the multiple Instance Learning (MIL) framework. Image captioning is a task for generating description of images [19]. This task needs a connection between sentences and visual information, such as objects, their attributes, and their relationships. A common approach is based on CNNs and RNN [13]. Specifically, CNN extracts a set of feature vectors from an input image. Then, RNN iteratively transforms each feature vector into words, resulting in a caption. [PERSON] et al. use a graph structure to describe object, attribute, and relationship in images [14]. The graph structure facilitates making connections to texts. Visual question answering is a task to generate answer description for a question about an image [15; 16; 17]. This task requires further understandings of visual and textual information. A model needs to extract a target from the question, find the target in the image, and generate an answer. Thus, two connections are necessary, question-to-image and image-to-answer.
## 3 Results and Discussion
### Liquefaction and Saccharification
By using the results of Box-Behnken design, Box-Behnken optimizer was used to determine optimum conditions of saccharification, an optimum parameter combination suggested as 60 \({}^{\circ}\)C, 0.8 mL of amyloglucosidase, and 72 h. Validation for this optimum combination was performed with three replications and 34.9 g/L glucose was obtained, which is very close the estimated value by the optimizer. It is reported that the highest dextrose equivalent of 94% was achieved after acid hydrolization of fresh potato tubers [11]. Another study showed that 825.1 mg/g glucose content can be obtained when 100 units of \(\alpha\)-amylase and 80 units of amyloglucosidase to hydrolyze potato pulp with enzymes from Sigma [17]. [PERSON] _et al._[18] reported that 19.37 g/L of total amount of sugars was achieved after acidic hydrolysis of potato peel waste. Studies show that glucose conversions of starchy materials are subject to characteristic of raw materials, type of enzyme, dosage of enzyme, temperature, and pH. Waste potato mash is a potential fermentation medium for industrial ethanol fermentation. Although a promising glucose concentration (34.9 g/L) was generated by enzyme treatment, glucose concentration was still far away from desired glucose level for typical ethanol fermentation. The amount of waste potato mash and enzyme were increased simultaneously while ratio obtained from Box-Behken RSM optimization was kept constant. To enhance the conversion yield, amount of enzyme and waste potato mash were increased with linear approach and evaluated. The reason for increasing only the amount of enzymes and waste potato mash and holding the time and temperature is that it was seen that enzyme and amount of waste potato mash have a significant effect on the glucose conversion yield from the results of Box-Behnken RSM. There was a linear regression between glucose concentration and amounts of enzyme and dry weight of waste potato mash with a 0.9985 R2 (data not shown). Based on the average of three replications, 16.16 g dry weight/100 mL DEIONIZED WATER was the best choice with 93.5 g/L glucose yield at the end of saccharification as well as viscosity of the mixture, which allows a reasonable agitation. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Run** & \multicolumn{3}{c}{**Factors**} & \multicolumn{2}{c}{**Glucose (g/L)**} \\ \cline{2-5}
**Order** & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & **Experimental** & **Predicted** \\ \hline
1 & 45 & 1 & 5.572 & 20.7 & 22.1 \\
2 & 45 & 0.6 & 48 & 24.5 & 22.9 \\
3 & 45 & 0.6 & 48 & 22.4 & 22.9 \\
4 & 30 & 0.6 & 24 & 22.7 & 22.4 \\
5 & 60 & 0.6 & 24 & 21.1
## 6 Worst-Case Latency Analysis of the FWOS Model
### Service Curve Determination  
As discussed before, the targeted traffic obtains the required service according to its contention-free intervals in each node. The length of the contention-free interval varies from an open window to another, depending on their related overlapping situations, as shown in Figure 10. However, their configurations are repeated after the hyper-period (\(T_{GCL}^{h}\)), which represents the least common multiple (LCM) of cycles for all TT queues, as follows [13]:  
\[T_{GCL}^{h}=\underset{1\leq i\leq N_{q}}{\text{LCM}}\left(T_{i}^{h}\right)\]  
Therefore, the number of \(Q_{k}^{h}\) contention-free intervals in the hyper-period are given by \(M=T_{GCL}^{h}/T_{k}^{h}\). By taking the \(i\)-th cycle as a reference, the upper and lower bounds of the service curve for the \(Q_{k}\) traffic can be given by [12]  
\[\beta_{k}^{h,u,i}(t)=\sum_{j=i}^{i+M-1}\beta_{k}^{h,u,j,i}(t) \tag{29}\]  
\[\beta_{k}^{h,l,i}(t)=\sum_{j=i}^{i+M-1}\beta_{k}^{h,l,j,i}(t) \tag{30}\]  
where  
\[\beta_{k}^{h,u,j,i}=\beta_{T_{GCL}^{h}\overline{W}_{k}^{h,j}}\left(t+T_{GCL}^ {h}-\overline{W}_{k}^{h,j}-R_{k}^{h,j,i}\right) \tag{31}\]  
\[\beta_{k}^{h,l,j,i}=\beta_{T_{GCL}^{h}\overline{W}_{k}^{h,j}}\left(t+T_{GCL}^ {h}-\overline{W}_{k}^{h,j}-WT_{k}^{h,j}-R_{k}^{h,j,i}\right) \tag{32}\]  
The terms \(\overline{W}_{k}^{h,i}\) and \(WT_{k}^{h,i}\) are determined using Equations (22), (27), and (28). Note that \(\beta_{T,L}^{h}(t)\) is formulated for the TDMA protocol [12] as  
\[\beta_{T,L}^{h}(t)=C_{h}\,.max\left\{\,\left\lfloor\,\frac{t}{T}\,\right]L\,, \,t-\left\lfloor\,\frac{t}{T}\,\right\rfloor(T-L)\right\} \tag{33}\]  
where \(C_{h}\) represents the data rate of the corresponding physical link. Depending on the arrival instance of the earliest frame, the service curve can be determined. As depicted in Figure 12, the best servicing (upper service curve) is obtained if the first frame arrives at the node's ingress port when the associated contention-free interval has instantaneously started. On the other hand, the frame faces the worst resource service (lower service curve) if it arrives at the end of the contention-free interval, i.e., there is no guaranteed service for the frame at that open window.  
Figure 12: Upper and lower bounds of the service curve based on the waiting time limits.  
However, the aggregated bounds of the service curve obtained in Equations (29) and (30) can be slightly different if we change the reference window in the calculation. Therefore, the upper and lower bounds of the service curve for \(Q_{k}^{h}\) traffic are the largest and lowest instantaneous values, respectively, from all possible service curves [12], i.e.,  
\[\beta_{k}^{h,u}(t)=\underset{1\leq i\leq M}{max}\left\{\beta_{k}^{h,u,i}(t)\right\} \tag{34}\]  
\[\beta_{k}^{h,l}(t)=\underset{1\leq i\leq M}{min}\left\{\beta_{k}^{h,l,j}(t)\right\} \tag{35}\]  
An example of the upper and lower service curves is depicted in Figure 12.
## 3 Results and Discussion
### Cross-Validation (CV) and Partial Least Squares Regression
#### 3.4.2 P LSR Calibration
The outlier ID column contains the index of the samples in the data set that were considered as outliers. In summary we found that firstly, the average of reflectance in the VIS-NIR recorded from soil samples was useful as a descriptive type of information about the carbon content, showing an appreciable relationship with soil TC, especially in the andisol samples. However, it was not appropriate for building a robust model for predicting soil TC. Secondly, the Savitzky-Golay filter was effective in eliminating the most visible noise in the spectral data. Thirdly, using the random_state parameter in the iterative process, we rapidly found the best calibration/validation subsets for model calibration. We attempt with this study to offer additional support for an effective application of soil spectroscopy. In contrast to most published works, we report this negative case of the soil spectroscopy technique to show the potential model overfitting and misinterpretation of the results by soil scientists with little experience in data analysis. The results presented here need to be interpreted with caution because of the unusual behavior observed in reflectance for predicting soil carbon content.
## 2 Materials and Methods
### Fabrication of Composite Samples  
Laboratory samples of polystyrene-based composites were fabricated by melt technology from granular polystyrene using a DSM Xplore 15 mL Microcompounder (Xplore, Sittard, The Netherlands). Compounding was carried out at 275 \({}^{\circ}\)C for 10 min at screw rotation speed \(\omega\) = 140 min\({}^{-1}\), mixing time comprised 3-4 min (until achieving constant viscosity). To reduce the oxidation of the polymer matrix, dry nitrogen was blown into the material supply zone at a rate of 500 mL/min. Upon completion of compounding, the composite melt was injected into the dies heated to 50 \({}^{\circ}\)C. The dies provided samples with the shapes of plates 50 \(\times\) 10 \(\times\) 1.5 mm\({}^{3}\) and blades with a working area of 20 \(\times\) 4 \(\times\) 1.5 mm\({}^{3}\). After being filled with the melt, the dies self-cooled down to room temperature in air. As we have shown earlier [26], even small changes in the technological process can lead to changes in the structure of the polymer material, which can affect the characteristics of the resulting material. In this regard, special attention was paid to maintaining identical conditions of the technological process. Samples made of pure PS were fabricated for control purposes.  
Distribution of filler particles in the polymer matrix was controlled by analysis of cryo-SEM images of the samples using a scanning electron microscope. Samples for SEM imaging were prepared following the standard procedure. The composite plate was fixed in a holder and immersed in liquid nitrogen for several seconds. After cooling, the free end of the sample was cleaved with the formation of a cleavage cross-section. The obtained sample was attached to a stub. To eliminate sample surface charge and to improve the image contrast, a thin layer of gold was deposited onto the cryo-cleaved surface. After that, the stub with the sample was loaded into the working chamber of the microscope, and the sample surface was scanned with an electron beam.  
Examples of cryo-SEM images of samples filled with 5 wt% of each filler are shown in Figure 1. As can be seen from the images, carbon particles were distributed uniformly in the polymer matrix. However, all three types of particles were observed to produce some small amounts of aggregates. As known [46; 47] fullerenes in a polymer matrix can form aggregates with sizes varying from a few nanometers to tens of micrometers. Such aggregates can be observed in Figure 1a,d as tiny white globules, while single C60 particles, 1.7 nm in diameter, are not resolvable in the microphotographs. PS-based composites with carbon nanotubes also contained some small amount of agglomerates up to several micrometers long, distributed uniformly in the bulk of the polymer matrix. Graphene particles in the composites were 3 to 8 \(\upmu\)m long, and 200 to 800 nm thick. The layered structure of graphene platelets is visible in Figure 1c.
# A Method of Ore Blending Based on the Quality of Benefication and Its Application in a Concentrator
## 2 Materials and Methods  
The raw ore used by a concentrator in this study was supplied by its own open-pit mining site, which limits the selectivity of beneficiation to a certain extent. The nature of this raw ore is very complicated. The composition was mainly low-grade ore, hematite, carbonate iron ore, and silicate iron ore. With the continuous mining of surface ore, the deep ore is continuously exposed. Due to this mine's complex material content, the nature of deep ore and surface ore is very different, resulting in significant changes in the original ore's nature during the mining process. Therefore, the existing methods could not be applied to this concentrator. Raw ore can be roughly divided into three categories. The red part of the ore body is rich in iron oxide (\(Fe_{2}O_{3}\)), which is called \"red ore\" by the workers and is a high-grade ore. The green part not far away is rich in ferrous ions, including ferrous carbonate (\(FeCO_{3}\)) and ferrous silicate (\(FeSiO_{3}\)). This part is challenging to beneficiate and is called \"green ore\" by the workers. Above them, the black part of the ore is mainly composed of iron tetroxide (\(Fe_{3}O_{4}\)). Workers call it magnetic iron, which is a very high-quality raw material. These ore are concentrated in one mining site, which shows that the nature of the ore on site is very complicated. This kind of complex raw ore leads to high real-time requirements for ore blending.  
The field operator's ore blending method is to put the property table of each ore blending block into Microsoft Excel and then mix the ore by a trial method based on considering product grade. Although this method can initially meet on-site production needs, its real-time performance is poor and wastes many human resources. Therefore, this research proposes an intelligent ore blending method, which can improve the real-time and robustness of ore blending. Before establishing the optimization model, it is necessary to analyze the constraint conditions of ore blending.