esa-sceva/satcom-chunk-collection huggingface dataset:
# X-ray Photoelectron Spectroscopy in Mineral Processing Studies
## 2 Speciation of Surface Products  
### Oxidized Surfaces of Metal Sulfides  
#### 2.1.1 Detection of Elemental Sulfur, Sulfate, and Oxysulfur Species  
Some important possibilities and complications of the XPS method have been highlighted in early studies performed at single minerals and synthetic metal sulfides. [PERSON] and [PERSON] [40] have undertaken one of the first XPS (ESCA) research on sulfide minerals to resolve contradictions about the products of PbS oxidation under various conditions found with common surface-insensitive techniques. It has been stated that lead sulfate is the major product of oxidation of PbS in air and aqueous environments, while elemental sulfur formed in the early reaction stages; another product is PbO followed by slow adsorption of hydroxide ions. [PERSON] [41] has compared oxidation of main sulfide minerals in air and water and found sulfate and, in the case of Fe-bearing minerals, Fe(III) (hydr)oxides as the main surface products, with the rate of sulfation decreasing in the order FeS\({}_{2}>\) CuFeS\({}_{2}\geq\) PbS\(>\) ZnS, which looks debatable at present [10; 35; 36; 37; 38; 39]. [PERSON] and [PERSON] [42] have studied the initial surface oxidation of natural PbS crystals using angle-resolved photoelectron spectroscopy and pointed out that elemental sulfur tends to be lost in vacuum but some S remained trapped under an oxidized layer. [PERSON] and [PERSON] [43] have characterized chalcopyrite surfaces with a focus on the formation of elemental sulfur as a hydrophobizing agent under collectorless flotation conditions and suggested that the presence of polysulfide species could explain some contradictions between XPS and other methods.  
[PERSON] and [PERSON] [44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55] have performed a series of studies on the sulfide minerals reacted in air, flotation- and leaching-related aqueous media, and highlighted the existence of metal-deficient surfaces involving S-S bonding and the necessity to cool the samples down to liquid nitrogen (LN) temperatures in order to preserve volatile surface species. In particular, galena crystals oxidized in air or reacted with aqueous solutions and then dried with Ar flushing were cooled to about 180 K during evacuation and measured at LN temperatures [44]. Elemental sulfur formed due to oxidation of galena was demonstrated to remain at the frozen samples and to volatilize after warming up to room temperature in UHV. Furthermore, [PERSON] et al. [56] have shown that the sulfur can be kept on such cooled samples under intense synchrotron radiation.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 2 Methodology
### Simulated Leak Test  
Leak detectability can be best evaluated with a fluid withdrawal test, in which fluid is removed from a real pipeline, metered, and transferred to storage. However, these are rarely conducted due to high cost and complexity, especially when the removed fluid is of large quantity [18]. By contrast, a simulated leak test utilizes a computer model to mimic a leak and thus is less expensive, easier to conduct, and more flexible for testing various normal or abnormal operating conditions. A leak is simulated in a hydraulic model by opening an imaginary valve on the modelled pipeline (Figure 1). This method was adopted in this study because it is hydraulically correct in its representation of the real pipeline and has been accepted as a satisfactory testing method for evaluating the performance of leak detection systems (e.g., [19; 20; 21]). The Simulator module of the SPS software suite was used to generate simulated leaks. Model results, primarily pressure and flow rate, but can also include viscosity, density, and temperature, at every sensor location were output to a data file. Gaussian distributed noise was added to the simulated data to closely mimic real-world sensor noise. This data file was in exactly the same format as the data file written by the SCADA system, and it was then used to drive the leak detection system to observe its response to the simulated leak.
# Experimental Study on Mechanical Properties of Graded EPS-Steel Fiber-Reinforced Concrete
###### Abstract  
This study designs the aggregate gradation and calculates the benchmark mix proportion of concrete based on two sets of regression coefficients. Copper-plated micro-steel fibers and graded EPS (expanded polystyrene) particles, with varying volume ratios of cementing materials, were incorporated into concrete specimens of C30, C40, and C50 grades made from fine aggregates and gravel. The specimens underwent compressive strength, flexural strength, and slump tests to investigate and analyze the effects of steel fibers and graded EPS on the mechanical properties of concrete. A modified equation for the similar bending-to-compression ratio coefficient was established, and a method for calculating the extreme value of the similar bending-to-compression ratio under the combined action of graded EPS and steel fibers was proposed, in order to predict the optimal dosage of steel fibers and graded EPS. The results show that for concrete of all grades (fine aggregate and gravel groups), the compressive and bending failure modes exhibited high consistency. After incorporating EPS, the specimens displayed some ductility during compressive failure, and compressive strength was negatively correlated with the volume fraction of EPS. As the volume fraction of EPS increased, the workability of the concrete decreased. At 7 days of curing, the uniaxial compressive strength of the concrete specimens was 60-75% of the 28-day strength. The tensile failure of graded EPS-steel fiber concrete was divided into four stages. For every 0.25% increase in steel fiber volume fraction, the increase in flexural strength was most likely between 14.35% and 17.61%, with the maximum growth rate being approximately 27%, and the distribution of growth rates was relatively dispersed. When applying the current bending-to-compression ratio model to calculate the flexural strength of concrete under the combined action of graded EPS and steel fibers, a significant deviation from the experimental values was observed. By introducing the modified similar bending-to-compression ratio coefficient equation, a conversion relationship between compressive strength and flexural strength was established. The adjusted R-value was 0.989, and a comparison of the predicted values with the experimental results showed high accuracy, offering considerable reference value.  
m +
Footnote †: journal: Applied Sciences
# Application of Compact Folded-Arms Square Open-Loop Resonator to Bandpass Filter Design
## 2 Circuit Modelling of Bandpass Filter  
A fifth-order BPF circuit has been chosen as a case study in this paper. The circuit model is formed by transforming a model normalized fifth-order Chebyshev lowpass filter prototype into series and parallel LC resonators, as demonstrated in Figure 2. The conversion relies on Equations (1) and (2) [1]; \(g_{1},g_{2},g_{3},g_{4}\) and \(g_{5}\) are the filter parameters; \(Z_{0}\) and \(Z_{6}\) are the characteristics impedances at the input/output terminals; C\({}_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\) and \(C_{5}\) are the calculated capacitance values; L\({}_{1}\), \(L_{2}\), \(L_{3}\), \(L_{4}\) and \(L_{5}\) are the calculated inductance values. The intended filter is composed with a center frequency, \(f_{0}=2.2\) GHz, a fractional bandwidth, \(FBW=10\%\), a 0.04321 dB passband ripple, and a 20 dB passband return loss.  
\[C_{p}=g_{p}/\omega_{0}Z_{\circ}FBW\,;\,L_{p}=Z_{\circ}FBW/g_{p \omega_{0}}\,;\,p\,\rightarrow\,odd\,\,numbers \tag{1}\] \[C_{s}=FBW/g_{\circ}\omega_{0}Z_{\circ}\,;\,L_{s}=g_{s}Z_{\circ} /\omega_{s}FBW\,;\,s\,\rightarrow\,even\,\,numbers \tag{2}\]Admittance inverters (also known as J-inverters) are used to further transform the filter circuit model by converting all the series LC components (that is, L\({}_{2}\)C\({}_{2}\) and L\({}_{4}\)C\({}_{4}\) in this case) to parallel LC elements. This conversion gives rise to a BPF circuit model with equal shunt-only LC resonators and admittance inverters (that is, J-inverters), as demonstrated in Figure 3. The J-inverter values are determined using Equation (3) [1]. The J-inverters in Figure 3 are then replaced with inductor-only networks, as demonstrated in Figure 4. This relies on the transformation procedure reported in [1]. The J-inverter transformation makes it possible for the circuit model to be simulated in the circuit simulator of advanced design software (ADS), and the simulation results are described in Figure 5. The inductance values of the inductor-only networks are determined using Equation (4).  
\[J_{01}=g_{0}/_{Z_{0}}\,;\,J_{d,d+1}=\sqrt{\
icefrac{{g_{i}}}{{g_{d}}}}/_{Z_{0} }\,;\,d=1,2,3,\dots \tag{3}\]  
\[L_{i,i+1}=\
icefrac{{1}}{{J_{i,i+1}}}=\
icefrac{{1}}{{J_{i,i+1}}}=0,1,2,\dots \tag{4}\]  
Figure 3: Fifth-order BPF circuit model with equal LC resonators and admittance inverters (\(J_{01}=0.02\), \(J_{12}=0.0168\), \(J_{23}=0.0124\), \(C=14.0548\) pF, \(L=0.3724\) nH).
## 2 Materials and Methods  
### Ensiling of Material  
Maize (_Zea mays_) has been harvested at Frankenforst, the education and research centre for animal production (longitude: 7\({}^{\circ}\)12\({}^{\prime}\)22\({}^{\prime\prime}\) E, latitude: 50\({}^{\circ}\)42\({}^{\prime}\)49\({}^{\prime\prime}\) N) at the University of Bonn, Germany. Maize of the variety \"Canon\" was used. It was harvested in September 2014 by a single-row Pottinger MEX GT chopper (POTTINGER Landtechnik GmbH, Grieskirchen, Austria) and chopped to a theoretical chopping length of 5 mm. The chopper included a grinding component which hit the kernels of the maize plants and led to good chopping quality. The maize contained dry matter with a mean of 355 g/kg (standard deviation = 10 g), as found in the samples taken at the day of ensiling.  
Four different silage treatments were produced for the trial: An untreated control (treatment CON) was ensiled. Two other treatments were ensiled with biological inoculants, one containing _Lactobacillus buchneri_, _Lactobacillus plantarum_, and _Pediococcus acidiliacti_ (treatment B1; Bonsilage Twin MS, H. Wilhelm Schaumann GmbH, Pinneberg, Germany; 1 g per ton silage; min. 2.5 \(\times\) 10\({}^{11}\) lactic acid-producing bacteria per g Bonsilage Twin MS), and the other one containing _Lactobacillus buchneri_, _Lactobacillus plantarum_, and _Lactobacillus thammosus_ (treatment B2; Bonsilage Twin MF, H. Wilhelm Schaumann GmbH, Pinneberg, Germany; 1 g per ton silage; min. 2.5 \(\times\) 10\({}^{11}\) lactic acid-producing bacteria per g Bonsilage Twin MF). Treatment CHEM was produced with a chemical silage additive (Silostar Liquid HD, H. Wilhelm Schaumann GmbH, Pinneberg, Germany) containing sodium benzoate, potassium sorbate, and sodium acetate, which has been dispensed with 2 L per ton of silage. All additives are used in practice. The additives were applied manually with a pressurized air duster connected to a compressor.  
Twenty-four polyethylene buckets with a volume of 65 L were used for the experiment as they are recommended by [PERSON] et al. [14]. Twelve of these buckets (three of each treatment) were filled with 36 kg fresh matter (FM) (196 kg dry matter/m\({}^{3}\)) as a low-density variation (LD), and another twelve (three of each treatment) with 48 kg FM (261 kg dry matter/m\({}^{3}\)) as a high-density variation (HD).  
After filling with a hydraulic press, as described in [PERSON] et al. [15], the buckets were sealed for six months using an airtight cover with a rubber seal and a clamping ring and were laid on their sides to avoid an enrichment of CO\({}_{2}\) inside the bucket and to simulate a silo on a farm.
## 1 Introduction
### Motivation and Research Gap  
Although DT technology holds immense promise, its implementation to date is in its relative infancy regarding transportation infrastructure. Large measures of most of the ongoing studies address specific areas of interest, such as either traffic management systems or monitoring infrastructure health, with studies being confined for the most part to individual components of the transportation network [28; 29]. For example, some DT models simulate road conditions to predict bottlenecks or hotspots of congestion, while applications continuously monitor the structural health of bridges and tunnels. In addition, most current implementations are narrow and focus more on specific use cases than on  
Figure 1: Schematic diagram of urban transportation infrastructure digital twin platform architecture (inspired by and summarized from the literature [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]).  
providing an overall integrated solution for managing transportation infrastructure [30]. DTs will need to be developed further through collaboration among researchers, policymakers, and stakeholders on strategic issues like data integration, real-time processing, and standardization [31]. This collaboration among several stakeholders is very important in the context of this research in overcoming specific technical and logistical problems associated with the implementation of DT systems. The approach would be much more comprehensive if it considered all aspects of the lifecycle of transportation systems--from urban planning and construction to ongoing operation and maintenance. However, to realize the full potential of DT technology, more research is needed on the integration of such systems across all phases of infrastructure development and operation. In such a holistic approach, there will be the opportunity to better coordinate various stages in managing the transportation system, thereby developing more effective strategies for planning, construction, and maintenance. DT technology can facilitate optimal resource allocation, reduce downtime, and thus drastically cut lifecycle costs by incorporating insights from various stages of transportation infrastructure management.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 4 Evaluation
### Simulation Setup  
For our simulations, we use PeerfactSim.KOM [36; 37; 38], an event-based simulator for peer-to-peer protocols, in order to obtain realistic results and insights on our research. Each simulation uses Chord and has been run with 10 different random seeds, so that all values in the graphs represent the average over 10 runs. Each algorithm has been tested separately and independently from other merging algorithms. In addition, each simulation uses GNP coordinates [39] to estimate delays in the fundamental network realistically. The network layer was extended to strictly isolated dedicated regions, any connection in or out of selected regions, which are dropped in selected times. Reasonable approximations for jitter and message delay are integrated into the simulator by using measurements from the PingEr project [40]. Except in Scenario E, we do not consider churn and packet loss, as both attributes might obscure the characteristic behavior of a specific merging algorithm. We present our simulation setup in Table 1.  
In Setup A.1, we investigate the performances of various merging algorithms with 1024 nodes in total, while merging three overlay networks without any network partitioning events. This scenario should reveal whether each algorithm is able to unify multiple separated Chord rings. We want to filter bogus mechanisms out and obtain a first feeling of how each algorithm performs under simple conditions. Thus, at the beginning of this scenario, three different Chord rings are created with 341, 341, and 342 nodes, respectively. Within the 10 th minute, two nodes, each selected from one of the two rings with 341 nodes, start to merge one and the same contact node from the 342-node ring. After 180 min, the simulation is finished. In Setup B.1, we compare the performance of the most promising approaches from A.1 in a large-scale network with 10,242 nodes. Two Chord rings are formed at the beginning of the scenario. In the 10 th minute, a contact node from one ring is given to the initiator node in the other ring, so that a merging procedure is started. The simulation is finished after 180 min. The Gossip-based Ring-Unification Algorithm is tested with a fanout parameter of 4, since this value is suggested by the algorithms authors in [7] This setup finally shows that our Ring Reunion Algorithm and its parallelized versions Reunion2, Reunion3, and Reunion4 (4, 8, and 16 instances) are superior in terms of merging reliability and merging speed. Within this setup, we also observe the effect of different fanout settings for the Ring Reunion Algorithm. In Setups C.1-5, we evaluate approaches for identifying new nodes for merging. The simulations of Setups C.1-2 begin with a join phase. After 150 min 1024 nodes have built a
## 2 Conventional SB_DSICED3_W/OF Scheme
### System Model
After channel estimation and the equaliser processes, these pilot symbols are eliminated and the binary output \(\hat{x}_{Sol}[k]\) of the signal of interest can be obtained from the equalised signal \(\tilde{y}^{\prime}[n]\) via demodulation, de-interleaving, and decoding in the decoder process. This transmission model is called the conventional algorithm, and we name it as the _SB_DSICED3_W/OF_ scheme, for the Semi-Blind Digital Self-Interference Cancellation, Equalisation, Demodulation, De-interleaving, and Decoding Without Feedback scheme. Next, we briefly introduce the channel coding scheme named the 5G QC-LDPC coding, encoder, and decoder processes, which is used to form the transmitted signal \(x_{SI}^{\prime}(t)\) and \(x_{B}^{\prime}(t)\) from Users A and B, respectively.
# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 2 Related Work
The modified AlexNet architecture was established by replacing the last layers with the intended layers. The authors used 85 X-ray images and 203 CT scan images of COVID-19 and 85 X-ray and 153 CT scan images of normal cases. The accuracies obtained for the proposed model on X-ray images and CT scan images were 94% and 94.1%, respectively. Similarly, the accuracies obtained for the modified AlexNet Architecture were 98% and 82%. A study [37] was made where a pre-trained ResNet50 architecture and an ensemble of pre-trained VGG16, ResNet50, and a custom CNN were deployed for differentiating between COVID-19 and pneumonia using chest X-ray images. The former achieved an accuracy of 89.2%, while the latter succeeded in achieving an accuracy of 91.24%. ResNet-18, ResNet50, SqueezeNet, and DenseNet-121 were used as pre-trained models in research using transfer learning where the last layer of the architectures was trained to distinguish between COVID-19 and non-COVID-19 chest X-ray images. The authors employed 184 COVID-19 and 5000 non-COVID-19 chest X-ray images for training and testing purposes. SqueezeNet and ResNet18 achieved a slightly better performance than the other models. CoroNet, a deep learning architecture, was proposed to detect the subject virus. The architecture was based on Xception and deployed a dropout layer and two fully connected layers at the end of the model, with a total number trainable parameters of 33,969,964. The model achieved accuracy of 99%, 95% and 89.6% on 2-class, 3-class, and 4-class, respectively [38]. The pre-trained VGG16 architecture was deployed, with new layers added at the top, to detect and differentiate between COVID-19, bacterial pneumonia, and normal cases [39]. The dataset contained 224 chest X-ray images of COVID-19, 700 images of bacterial pneumonia, and 504 healthy cases. The authors achieved accuracies of 96% and 92.5% in two-class and three-class, respectively. The highest accuracy achieved so far on COVID-19 detection using lung X-ray images is 99.658% for 3-class classification, as per the covered literature [40]. The previous studies had limitations in that a high-class imbalance was observed and, also, they included very few COVID-19 chest X-ray images. As already mentioned, the highest number of images for COVID-19 used was 850, whereas the number of COVID-19 samples in the subject research work is about 4.25 times this number (3616 images). Therefore, the previous work had limitations regarding the low number of COVID-19 datasets and class imbalance. The current work has more than four times the highest dataset of COVID-19 images ever utilized, and the authors were interested to see the performance results for such a contrast between the current dataset and the previous datasets.
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 2 Early Development and Evolution of Shaft Construction Technology
\begin{table}
\begin{tabular}{l l l} \hline
**Technology** & **Percentage Distribution** & **Geographical Application** \\ \hline
**Shaft boring system (SBS)** & \(\sim\)15\% of mechanized excavation methods & Commonly used in Europe, North America, and Australia for mining and infrastructure projects \\
**Shaft boring machine (SBM)** & \(\sim\)15\% of mechanized excavation methods & Primarily used in Europe, North America, and mining regions like Chile, South Africa, and China \\
**Blind shaft boring** & \(\sim\)10\% of mechanized excavation methods & Used in urban areas in Europe, Asia, and North America for metro and ventilation shafts \\
**Raise boring machine (RBM)** & \(\sim\)23\% of mechanized excavation methods & Widely used in mining regions (e.g., South Africa, Australia, Europe) and hydropower projects in Asia (e.g., China) \\
**Shaft boring road** & \(\sim\)10\% of mechanizedheader (SBR) & Commonly used in Europe for mining projects \\
**Shaft boring** & \(\sim\)5\% of mechanized extracellular (SBC) & Primarily used in mining regions (e.g., South Africa, Australia) and deep infrastructure projects \\
**Vertical shaft** & \(\sim\)20\% of mechanizedconstruction (VSM) & Widesh the Middle East, and Europe in urban infrastructure projects for ventilation, parking garages, and launch shafts \\
**Active controlled** & \(\sim\)2\% of mechanized prefabricated caisson (ACPP) & Primarily used in China for urban infrastructure projects \\ \hline \end{tabular}
\end{table} 
Table 1: Percentage distribution of mechanized systems and their geographical applications.
## 2 Materials and Methods
### Analysis of Fatty Acid Methyl Esters
Individual FAMEs were identified by comparing their retention times with those of authenticated standards (FAME Mix-37 components; trans-11 vaccine acid (11_t_-C18:1; TVA); cis-vaccenic acid (C18:1_n_-7, CVA, Supelco, Madrid, Spain); conjugated linoleic acid (9c,11_t_-C18:2, CLA, Matreya, State College, PA, USA) and docosquentaenoic acid (C22:5_n_-3; DPA)), and the results were expressed as g/100 g of fat. Nutritional implications were assessed by calculating the amount of saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), _n_-3, _n_-6 and long chain _n_-3 (LC _n_-3: eicosapentaenoic acid, C20:5_n_-3, EPA; docosquentaenoic acid, C22:5_n_-3, DPA) fatty acids, as well as the _n_-6/_n_-3 and the PUFA/SFA ratios. Furthermore, the following parameters were calculated as follows: atherogenic (AI) [C12:0 + (4 \(\times\) C14:0) + C16:0]/[(\(\Sigma\)MUFA) + (\(\Sigma\)PUFA)] and thrombogenic (TI) [C14:0 + C16:0 + C18:0]/[(0.5 \(\times\)\(\Sigma\)MUFA) + (0.5 \(\times\)\(n\)-6) + (3 \(\times\)\(n\)-3) + (_n_-3/_n_-6)] indices [41], and hypocholesterolemoemic ratio (h/H) [\(\Sigma\)(C18:1\(n\)-9, C18:1\(n\)-7, C18:2\(n\)-6, C18:3\(n\)-3, C20:3\(n\)-6, C20:4\(n\)-6)/\(\Sigma\)(C14:0 + C16:0)] [42]. It is recognized that some fatty acids can help to prevent or promote coronary thrombosis and atherosclerosis based on their effect on low-density lipoprotein (LDL) concentration and serum cholesterol. In particular, AI and TI indices indicate the effects of fatty acids on cardiovascular risk, while the h/H ratio reflects the functional effects of fatty acids on cholesterol metabolism [41; 42].