esa-sceva/satcom-chunk-collection huggingface dataset:
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### Low-Noise Voltage Amplifier  
In sensor readout systems, a crucial component is the low-noise amplifier (LNA), which significantly enhances signal quality by amplifying incoming signals while adding minimal noise. This system employs a state-of-the-art commercial discrete operational  
Figure 3: Architecture of the SAM V71 family.  
amplifier with a unity-gain bandwidth of 500 MHz. It has high precision and exhibits low voltage noise, with a voltage offset of less than one millivolt to ensure minimal interference. Unusually, we opted for a cathodic readout of the SiPM, allowing the use of a standard coaxial cable with a single inner conductor for signal transmission. This atypical method is selected for its specific advantage of enabling the SiPM to be positioned at a distance from the board. The primary function of the LNA is to linearly adjust the voltage level of incoming signals, ensuring they meet the input requirements of both the discriminator and the ADC.  
Given that the incoming signals can be as short as tens of nanoseconds, it is advisable to employ an amplifier with a swift response to minimize any potential signal degradation. The LNA incorporates a high-performance operational amplifier boasting high input impedance, thus ensuring minimal noise interference within the relevant frequency band of our photodetector. Configured in an inverting setup, this operational amplifier provides the necessary operational gain for optimal system performance (Figure 4). The signal output from the LNA is replicated onto an MMCX (Micro-Miniature Coaxial) connector, allowing precision studies.
# Review of Coal-Fired Electrification and Magnetic Separation Desulfurization Technology
## 2 The Form of Sulfur in Coal  
The composition and structure of coal are very complicated. The form of sulfur in coal can be divided into two categories: inorganic sulfur and organic sulfur, as shown in Figure 1[5].  
The presence of inorganic sulfur is mainly disulfide and sulfate. The majority of disulfide sulfur exists in the form of pyrite, and only a small part exists in the form of marcasite, both of them are homogeneous polycrystals, sulfate sulfur exists mainly in the form of gypsum, and relevant data show that inorganic sulfur accounts for 60% to 70% of total sulfur. Organic sulfur exists through forms that combine with the organic structure of coal, for example, mercaptan (-SH), sulfide, disulfide (-S-S-), aromatic cyclic sulfur represented by thiophene system, directly bonded with coal matrix, accountingfor about 30-40% of the total sulfur content. In addition, there will be a small amount of elemental sulfur in the coal, PbS, CuFeS2, FeAsS, and ZnS [6; 7; 8]. Among them, the inorganic sulfur in coal is easier to remove than organic sulfur, and the high sulfur coal in China is generally pyrite, which has a good correlation with ash. Therefore, in the short term, the main direction of our country is still to remove inorganic sulfur from coal.
# Influence of Xanthan Gum-Based Soil Conditioners on the Geotechnical Properties of Soils
## 4 Results and Discussion
### Water Absorption of Untreated Soils and Mixtures  
The water absorption of the untreated soils and mixtures is presented in Figure 5. Due to the comparable results of the tests performed in two or more replicates, the water absorption is presented as their average value.  
In general, the addition of the SCs increases the w\({}_{\text{A,24h}}\) for all types of soils. The mixtures of S1 or S3 with a high dosage of SC_R exhibited approximately 80% higher water absorption compared to untreated soils, while for S2, the increase was 53%. The overall highest increase in w\({}_{\text{A,24h}}\) was observed for the mixtures of soil S3, specifically SC_CG H and SC_ZZC H, which were over 100% in both cases. Soil S2 had a similar increase in the w\({}_{\text{A,24h}}\) for all three types of SCs at a high dosage, which is approx. 53%. These results highlight the influence of both the soil type and the dosage of the SCs on water absorption. However, the type of the SC is not an influential parameter, which was also confirmed by the ANOVA test (\(\alpha\) = 0.05).
## 3 Study Area and Data
### Data Collection
#### 3.1.2 POI Data  
Applying internet data capture technology, open data were obtained through the Amap API. The considered objects are the points of interest in Beijing in May 2017, amounting to 723,154 records, each including six numeric fields: name, longitude, latitude, address, type 1, type 2, and type 3.
# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 3 Proposed Approach/ Methodology
### Embedding  
In ABSA tasks, pre-trained language model BERT offers rich feature information. Therefore, we input the sentence-aspect pair (\(S\), \(A\)) into BERT in the form of \"[\(CLS\)] \(S\) [\(SEP\)]\" to initialize the aspect-aware word vectors, where \"[\(CLS\)]\" represents the symbolic marker encoding the overall sentence-level representation, while \"[\(SEP\)]\" serves as the separator between the context and the aspect. The calculation in BERT is as follows:  
\[\{H^{c},H^{a}\}=\text{BERT}(\{[\text{CLS}],\text{S},[\text{SEP}],\text{A},[ \text{SEP}]\}) \tag{1}\]  
In the equation, \(H^{c}=\{h_{1},h_{2},\ldots,h_{n}\}\in R^{n\times h_{dim}}\) is the word embedding of contextual word and \(H^{a}=\{h_{a+1},h_{a+2},\ldots,h_{a+m}\}\in R^{m\times h_{dim}}\) is the word embedding of aspect word, where \(h_{dim}\) is the dimensionality of each word vector, \(n\) is the length the sentence and \(m\) is the length of the aspect term.  
Figure 2: Framework of the proposed LDEGCN model.
# A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra
## 3 Results and Discussion
### Pesticide Mixture Signal Analysis
60 Fuji apples were used for detecting Raman spectral signals. 600 sample signals of deltamethrin, acetamiprid and the mixture of them were acquired. The Raman spectral information of deltamethrin, acetamiprid and the mixture of them with same pesticide concentration were detected from same apple sample to reduce the effect of sample background. Through analyzing massive full-spectrum Raman signal of deltamethrin and acetamiprid it was found that signal of characteristic peaks at 574, 1380 and 2167 cm\({}^{-1}\) was not strong enough for quantitative analysis. Thus characteristic peaks at 634, 735 and 1114 cm\({}^{-1}\) were used for data analysis of pesticide mixture. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pesticle Concentration** & 100 & 10 & 1 & 10\({}^{-1}\) & 10\({}^{-2}\) & 10\({}^{-3}\) \\
**(mg\(\cdot\)kg\({}^{-1}\))** & 100 & 10 & 1 & 10\({}^{-1}\) & 10\({}^{-2}\) & 10\({}^{-3}\) \\ \hline
**T Value** & 0.016 & 0.031 & 0.039 & 0.046 & 0.087 & 0.365 \\
**RSD (\%)** & 17.31 & 13.26 & 10.66 & 8.87 & 6.35 & 3.79 \\
**Standard Deviation** & 17.33 & 1.69 & 0.145 & 0.0113 & \(7.67\times 10^{-4}\) & \(2.82\times 10^{-5}\) \\
**Average Value (mg\(\cdot\)kg\({}^{-1}\))** & 83.29 & 8.37 & 0.868 & 0.0899 & \(9.38\times 10^{-3}\) & \(9.79\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection results comparison of mixed pesticides concentration and real concentration. Figure 3: Spectrum of pesticide mixture and single pesticide with concentration of 100 mg\(\cdot\)kg\({}^{-1}\). a: spectrum of deltamethrin sample; b: spectrum of deltamethrin and acetamiprid mixture. c: spectrum of acetamiprid sample.
## 4 Cases Study
### Portugal Arch Bridge Case Study
#### 4.2.1 Scenario 1: \"Do Nothing and Rebuild.\" (Reference Scenario)  
In the first case, the reliability indicator over time, \(\beta_{(t)}\), was estimated using a bi-linear degradation model: \(\beta_{(t)}=\left\{\begin{array}{c}\beta_{(0)},\ 0\leq t\leq t_{i}\\ \beta_{(0)}-(t-t_{i})\alpha,\ t\geq t_{i}\end{array}\right.\)  
Where, \(t_{i}\) is the time of initiation of a deterioration; \(\alpha=0.07/year\) is the deterioration rate of a reliability; and \(t\) is the age of the bridge. This layout is being implemented on the existing structure; thus, it is presumed that the degradation processes started previously. As such, we anticipate no delay in the onset of degradation. In this approach, \(\beta_{0}\) represents the reliability index at that point of the most recent inspection or the initial index that was reduced because of the qualitative assessment.  
In this case, maintenance wasn't even thought about unless the reliability index exceeded 2, which is the maximum for state 5. It was anticipated that the highest reliability improvement \((\gamma=\beta_{0}-\beta_{state5}=4.26-2.00=2.26)\) would occur soon after entirely  
Figure 17: Portugal arch bridge’s vulnerable zones determination (_source: COST Action TUI406_).  
replacing the structure. The decrepit system has been swapped out; therefore reliability index has returned its \"virgin\" number of \(\beta_{0}\). They could postpone the degrading process by seven years by setting the \(t_{i}\) back to its original value. All bridges had to be replaced when their reliability index exceeded state 5 with no maintenance (0.07/year degradation rate).  
The relationship across qualitative and quantitative performance indicator scales is the key to qualitatively transforming reliability. For this scenario, only qualitative evaluations of availability, cost, and safety were conducted (Figure 18). During the transition from reliability level 4 to level 5, the availability value is at its peak during the bridge replacement. Since maintenance was not considered in this scenario, only bridge replacement costs were included. It was determined that a loss in user safety occurs faster than a decreased structural reliability. An original dependability index was estimated using a first-order second-moment method; it was revised downward after a recent visual check and a 5% drop in resistance. The result on KPIs development for this scenario is showed in Figure 19.  
Figure 19: Key performance indicator development in the first scenario (_source: COST Action TUI406_).  
Figure 18: Consistency of Semi-Quantitative Performance Indicators in the first scenario (_source: COST Action TUI1406_).
# Satellite Image Categorization Using Scalable Deep Learning
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.2 Data Augmentation  
Data scarcity is a major concern in deep learning solutions. Data augmentation is a powerful technique that can help to improve the accuracy and generalization of machine learning models, especially when the size of the training dataset is limited. Data augmentation can improve the performance of a machine learning model by providing it with a more diverse range of training data. It also helps to reduce overfitting by introducing variations in the training data, which makes the model more robust and generalizable. Data augmentation \(\exists\) is used to minimize the aforementioned issue, where \(\exists\in\mathrm{R_{0}},\mathrm{HS_{s}},\mathrm{VS_{t}},\mathrm{Sh_{u}}, \mathrm{Z_{v}},\mathrm{HF_{w}},\mathrm{VF_{x}},\mathrm{B_{y}}\) and  
\[\mathrm{R_{0}}(\mathrm{N(i,j)})=\mathrm{N(i*cos(\theta)-j*sin(\theta),i*sin( \theta)+j*cos(\theta))} \tag{2}\]  
\[\mathrm{HS_{s}}(\mathrm{N(i,j)})=\mathrm{N(i+s,j)} \tag{3}\]  
\[\mathrm{VS_{t}}(\mathrm{N(i,j)})=\mathrm{N(i,j+t)} \tag{4}\]  
\[\mathrm{Sh_{u}}(\mathrm{N(i,j)})=\mathrm{N(i+u*j,j)} \tag{5}\]  
\[\mathrm{Z_{v}}(\mathrm{N(i,j)})=\mathrm{N(i*v,j*v)} \tag{6}\]  
\[\mathrm{HF_{w}}(\mathrm{N(i,j)})=\mathrm{N(M-i-1,j)} \tag{7}\]  
\[\mathrm{VF_{x}}(\mathrm{N(i,j)})=\mathrm{N(i,N-j-1)} \tag{8}\]  
\[\mathrm{B_{y}}(\mathrm{N(i,j)})=\mathrm{y*N(i,j)} \tag{9}\]  
\(\mathrm{R_{0}}\) rotates an image by an angle \(\theta\), \(\mathrm{HS_{s}}\) shifts an image horizontally by s pixels, \(\mathrm{VS_{t}}\) shifts an image vertically by t pixels, \(\mathrm{Sh_{u}}\) shears an image horizontally by a factor of \(\mathrm{u}\), \(\mathrm{Z_{v}}\) zooms an image by a factor of \(\mathrm{v}\), \(\mathrm{HF_{w}}\) flips an image horizontally, \(\mathrm{VF_{x}}\) flips an image vertically, and \(\mathrm{B_{y}}\) changes the brightness of an image by multiplying each pixel by a factor of y. By using these data augmentation techniques, the machine learning model is exposed to more variations in the data, which can help it to generalize better to new and unseen data.
# A New In Vitro Model to Evaluate Anti-Adhesive Effect against Fungal Nail Infections
## 3 Results and Discussion
### In Vitro Evaluation of Fungal Adhesion to the Nail Plate  
#### 3.2.1 Sample Preparation  
Sample choice was a critical step in order to assess the validity of the procedure for evaluating the antiadhesive properties of different products and helping understand the mechanism behind the efficacy. The antifungal lacquer containing amorolfine (AP) was the best choice as a positive control, since the compound is well-known for its antifungal activity [29]. However, it was not so easy to find a suitable product which could act only as a physical barrier on the membrane (negative control). The negative control was necessary to understand the role of the physical barrier created by the products on the nails in avoiding fungal adhesion.  
For this purpose, a water-repellent base coat (BC) was chosen.  
Measurement of the contact angle was the technique applied in this work to verify the water-repellent properties of the BC on the membranes.  
Figure 5 shows the results of the contact angle measurements on the membrane alone and after treatment with a BC at three different time points (0 s, 30 s and 60 s) after water drop deposition. The results are expressed as the mean \(\pm\) SD (standard deviation) of the angle (\({}^{\circ}\)) between the surface and the water drop.  
Looking at the data reported in Figure 5, it is possible to appreciate the effect of the water-repellent barrier on the membranes. For the membranes alone, there was a reduction in the angle values over time, indicating an increase in the affinity of the membrane surface for the liquid over time. On the other hand, for the membranes treated with a BC, the angle value at the beginning (time 0) was \(66^{\circ}\), lower than that obtained from the untreated membranes (\(74^{\circ}\)). That result was coherent, with a smoother and more slippery surface after the treatment. After that, the contact angle remained quite constant, with very low variations from 0-60 s, showing real water-repellent activity.
# Technology-Economy-Policy: Biochar in the Low-Carbon Energy Transition--A Review
## 4 Technologies: Biochar Production
### Hydrothermal Technology  
The process involves the thermochemical conversion of wet biomass into hydrochar. Hydrochar is similar to the biochar obtained through use of the various processes discussed above. This process is similar to the natural process of coal formation [87]. Hydrothermal treatment is carried out at moderate temperatures (150-350 \({}^{\circ}\)C) under a pressure of 10-15 bars [75]. At higher temperatures and pressure, water undergoes a dramatic change in properties, acting more like an organic solvent and favoring reactions that are typically catalyzed by acids and alkali and that promote the decomposition of biomass [95]. Although the exact mechanism of hydrothermal treatment is not understood, it is expected to involve hydrolysis, dehydration, decarboxylation, aromatization, and recondensation. During the hydrolysis process, the biomass breaks down into saccharides and lignin, and in the dehydration process, water is removed from the biomass by eliminating the hydroxyl group. CO\({}_{2}\) is removed during the decarboxylation process, leading to aromatization. The various compounds that are formed during all these processes, i.e., furfural, are hydrolyzed and decompose into phenols, aldehydes, and acids. All of these compounds recondense with aromatic polymers, resulting in the formation of hydrochloric [10]. The main advantage of the hydrothermal process is its ability to convert wet biomass into carbonaceous solids with high efficiency without the need for energy-intensive drying. Other advantages include its conductive properties, controllable surface functions, the existence of a natural binder, and high calorific value, etc. [96]. At higher temperatures (\(\sim\)350 \({}^{\circ}\)C), the process shows a decrease in the yield of hydrochar (29%) and bio-oil (31%) and a significant increase in the gas fraction (67%) [97]. An increase in temperature also causes a decrease in the O/C and H/C ratio. [PERSON] et al. produced hydrochar from sunflower stalks using hydrothermal carbonization combined with the activation method, and the produced hydrochar had a high surface area of 1505 m\({}^{2}\) g\({}^{-1}\) and energy density of 35.7 Wh kg\({}^{-1}\)[98; 99].
# Sloping Farmlands Conversion to Mixed Forest Improves Soil Carbon Pool on the Loess Plateau
###### Abstract  
Vegetation restoration is considered a potentially useful strategy for controlling soil erosion and improving soil organic carbon (SOC) in arid and semiarid ecosystems. However, there is still debate regarding which vegetation restoration type is the best choice. In this study, four vegetation restoration types (i.e., grasslands, shrubs, forests and mixed forests) converted from sloping farmlands were selected to explore the SOC variation among the four types and to investigate which soil factors had the greatest effect on SOC. The results showed while the magnitude of effect differed between vegetation restoration type, all studied systems significantly increased SOC and labile organic carbon contents (\(p<0.01\)), soil nutrients such as total nitrogen (TN) (\(p<0.01\)), available nitrogen (AN) (\(p<0.01\)), total phosphorus (TP) (\(p<0.05\)) and available phosphorus (AP) (\(p<0.05\)), soil enzyme activities such as phosphatase (\(p<0.01\)), soil microbial biomass carbon (MBC) (\(p<0.05\)), and basal respiration (BR) (\(p<0.05\)), but had significant negative correlationswith polyphenol oxidase (\(p<0.05\)). However, the effects of vegetation restoration of farmland converted to natural grasslands, shrubs, forests and mixed forests varied. Among the types studied, the mixed forests had the largest overall positive effects on SOC overall, followed by the natural grasslands. Soil nutrients such as N and P and soil microbial activities were the main factors that affected SOC after vegetation restoration. Mixed forests such as _Robinia pseudoacia_ and _Cangana korshinskii_ are the best choice for farmland conversion on the central of the Loess Plateau.  
afforestation; carbon sequestration; forest soils; land management; land use; soil organic carbon +
Footnote †: journal: International Journal of Environmental Research and Public Health
## 5 Analysis of the Evaluation Results and Countermeasures
### Countermeasures and Suggestions  
According to the results of the analysis, to reduce the possibility of accidents in the road transport of dangerous chemicals, the first level of indicators in the \"high-risk\" factors can be targeted to take targeted prevention and control measures to improve road transport safety levels.  
(1) For transport personnel  
Focus on transport personnel, encourage transport personnel to actively participate in training, enhance safety awareness, learn the basic knowledge of road transport safety of hazardous chemicals and the handling methods of emergencies, and improve the level of relevant business knowledge. Relevant responsible persons should pay attention to the physical health and emotional state of drivers, escorts, and other transport personnel to reduce the possibility of road transport accidents involving hazardous chemical factors and to improve the level of transport safety.  
(2) Enterprise management  
Enterprises should strengthen the management of safety training education and training, safety management systems, safety supervision, and emergency rescue management, increase the content of driver safety training, improve the internal safety management system, strengthen the safety supervision of the road transportation of hazardous chemicals, take effective measures in time when problems are encountered, and pay attention to learning emergency rescue-related skills, with a view to fundamentally curbing the occurrence of transport accidents and ensuring safe transportation.  
(3) Chemicals  
The hazardous chemical factor exhibits the highest super entropy, indicating significant instability; that is, the risk is unstable. Therefore, enterprises should pay attention to the characteristics of dangerous chemicals, choose packaging materials that meet the standards, and not overload vehicles to transport dangerous chemicals. When carrying out loading, the packaging status of dangerous chemicals should be checked one by one to ensure that the packaging is intact, safe, and effective to reduce the possibility of accidents in the transport of dangerous chemicals and to improve transport safety.
## 3 Material and Methods
### Proposed Method
#### 3.2.1 Data Pre-Processing  
Collected MRI scans present differences among them mainly in \((i)\) voxel size and \((ii)\) intensity values. While the former differences come from the setting of diverse scanning parameters, the latter differences may come from the use of different magnetic field devices (which account with diverse signal to noise levels) as well as by the inherent biological and anatomical patients variability. For homogenizing the scans, all volumes were pre-processed by following three steps. Firstly, high-frequency noise was removed by using a spatial adaptive non-local means filter with automatic noise level estimation [46]. The chosen algorithm allows tackling not only the intra-patient noise level differences in the scan, but also the inter-patient one observed by the use of different MRI magnetic field devices. Secondly, volumes were resliced to reach an homogenous voxel size of 1.25 mm \(\times\) 1.25 mm \(\times\) 8 mm (minimum voxel size found among patients). Thirdly, after contrast enhancement with a gamma function (\(I_{out}=I_{in}^{\gamma},\gamma>0\)), intensity normalization within the epicardium inner region ([0-255]) was performed for reducing intra- and inter-patient image variability. Since the contrast agent tissue concentration changes within time and the intensities become brighter from the mitral valve to the apex causing inter-slice variability [12], slice normalization was performed by taking into account the left ventricular myocardium and blood-pool regions.