esa-sceva/satcom-chunk-collection huggingface dataset:
# The Evolution and Development Trends of LNG Loading and Unloading Arms
## 3 Development of Land-Used LNG Loading and Unloading Arms  
Land transportation of liquefied natural gas mainly relies on tank trucks [47; 48]. In this process, the skid-mounted system plays a crucial role. It has functions such as temporary storage of LNG, safety monitoring, refueling metering, and loading and unloading. The loading and unloading process is completed through land-used LNG loading and unloading arms. A land-used LNG loading and unloading arm usually consists of parts such as arm pipes, connection devices, control systems, and safety systems. After docking with the loading and unloading port of the receiving end, LNG is transported to the tank through the arm pipes. Fluid loading onto vehicles usually adopts two methods: top-loading and bottom-loading to adapt to different working environments and operational requirements. The top-loading method is to load through the top inlet of the tank truck, and operators need to work at a relatively high position. The bottom-loading method is to load through the bottom inlet of the tank truck, which requires sufficient operating space at the bottom of the tank truck. These two loading and unloading methods have significant differences in aspects such as the structural form of the loading and unloading arm, automatic control, interface form, and sealing requirements [49]. Bottom-loading is mostly used for the loading and unloading of liquefied natural gas [50].
# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
## 3 Methodology
### Step1: The Framework of CCL: C-YOLOv4
#### 3.2.2 Improvement of Backbone Network  
C -YOLOv4 takes CSPDarknet53 as the backbone network, which has a strong feature-extraction capability. But it contains five residual blocks and 104 layers of convolutional network. The parameter number and the computational effort of this network are very large. In addition, the container code area is a small or medium single-detection target. CSPDarknet53 has excessive performance in CCL, which leads to poor detection effect.  
MobileNetV3 is a lightweight network. It applies depthwise separable convolution, mobile inverted bottleneck convolution, and the SE (Squeezed and Excitation) attention mechanism. Depthwise separable convolution is a special type of convolution, compared with conventional convolution, which can obtain the same output results while significantly reducing the computation and parameter number.  
Therefore, MobileNetV3 is adopted as the backbone network of C-YOLOv4, which can reduce the hardware requirements of the algorithm and improve the recognition speed under the condition of ensuring the recognition accuracy.  
Figure 2: The structure of improved CCL framework.  
Input denotes the shape of the feature map. Operator denotes the type of the feature-extraction structure. Exp size denotes the number of channels after feature map ascending dimension. Out denotes the final number of output channels of this layer. SE denotes whether there is a Squeeze-And-Excite in that block. NL denotes the type of nonlinearity used. HS denotes h-swish activation function, and RE denotes ReLU activation function. S denotes stride; Bneck is a bottleneck convolution module.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 2 Related Work
### Tensor-Based Anomaly Detection Approaches  
Tensor-based approaches can be divided into three classes: (i) Supervised, (ii) semi-supervised, and (iii) unsupervised approaches. Supervised approaches are based on prediction [10], classification [11], and the dimensionality reduction [12]. Semi-supervised approaches use normal data for a tensor construction, and for the baseline, decomposition results are used. The anomaly is estimated by observing the examples that do not pass the null hypothesis test [13], or fails to align with the baseline by comparing the eigenvectors and eigenvalues of the factor matrices [14]. Most of the unsupervised approaches rely on the manual anomaly detection performed by the field expert after the decomposition [15].  
Tensor decomposition methods fins their usages in traffic-related research especially in modeling of time-evolving traffic networks modeling [16; 17], traffic data anomaly detection [18], road segments travel time estimation [19], correlation analysis of spatiotemporal traffic data [20], traffic parameters prediction [21; 22], and missing data imputation [23].  
This paper presents the tensor-based method for extracting the road traffic patterns on a city-wide scale represented by the graph-based map segmentation. Using the method for detecting the anomalous road segments expands the efforts to use tensor-based methods in road traffic-related studies. The unsupervised method is proposed, which is validated using the expert's knowledge extracted from the HCM.
## 2 AMTB Filter without Need for Clutter Density  
The object dynamic and observation models are defined as:  
\[\mathbf{x}_{k}=\mathbf{\Phi}_{k-1}\mathbf{x}_{k-1}+\mathbf{w}_{k-1} \tag{1}\]  
\[\mathbf{z}_{k}=\mathbf{H}_{k}\mathbf{x}_{k}+\mathbf{v}_{k} \tag{2}\]  
where \(\mathbf{x}_{k}\) and \(\mathbf{z}_{k}\) are the state and observation vectors; \(\mathbf{\Phi}_{k-1}\) and \(\mathbf{H}_{k}\) are the state transition and observation matrices; and \(\mathbf{w}_{k-1}\) and \(\mathbf{v}_{k}\) are the zero-mean Gaussian process and observation noises where \(\mathbf{Q}_{k-1}\) and \(\mathbf{R}_{k}\) are their covariance matrices.  
The AMTB filter without the need for clutter density propagates the track labels, existence probabilities of objects and their PDFs. We assume that the set of existing objects and the set of potential birth objects at time step \(k-1\) are  
\[\left\{r^{e}_{i,k-1},N(\mathbf{x}_{i,k-1};\mathbf{m}^{e}_{i,k-1},\mathbf{P}^{e}_{i,k-1}), \ell^{e}_{i,k-1}\right\}_{i=1}^{N^{e}_{k-1}} \tag{3}\]  
\[\left\{\varepsilon_{i,k-1},\mathbf{m}^{b}_{i,k-2},N(\mathbf{x}_{i,k-1};\mathbf{m}^{b}_{i,k -1},\mathbf{P}^{b}_{i,k-1})\right\}_{i=1}^{N^{b}_{k-1}} \tag{4}\]  
where \(\mathbf{x}_{i,k-1}\) denotes the state vector of object \(i\) at time step \(k-1\); \(N^{e}_{k-1}\), \(\ell^{e}_{i,k-1}\) and \(r^{e}_{i,k-1}\) denote the number of existing objects, track label and existence probability of existing object \(i\), respectively; \(\varepsilon_{i,k-1}\), \(\ell^{b}_{i,k-1}\) and \(N^{b}_{k-1}\) are the index of the relative measurement with potential birth object \(i\), track label of potential birth object \(i\) and number of potential birth objects at time step \(k-1\), respectively; and \(\mathbf{m}^{b}_{i,k-2}\) denotes the mean vector of potential birth object \(i\) at time step \(k-2\). The PDFs of the existing object and potential birth object are assumed to be Gaussian and they are given by \(N(\mathbf{x}_{i,k-1};\mathbf{m}^{e}_{i,k-1},\mathbf{P}^{e}_{i,k-1})\) and \(N(\mathbf{x}_{i,k-1};\mathbf{m}^{b}_{i,k-1},\mathbf{P}^{b}_{i,k-1})\), respectively, where \(\mathbf{m}^{e}_{i,k-1}\) and \(\mathbf{m}^{b}_{i,k-1}\) are the mean vector, and \(\mathbf{P}^{e}_{i,k-1}\) and \(\mathbf{P}^{b}_{i,k-1}\) are error covariance matrices. The recursion of the ATMB filter without the need for clutter density is as follows:
## 3 Results and Discussion
### Simulation Results
Figure 5: Trend of simulated temperature vs. residence time. (**a**) Mean temperature, (**b**) standard deviation and (**c**) COV. However, to check the thermal uniformity along the longitudinal axis, a second data processing step was carried out by dividing the whole length of the treatment chamber into three equal parts of 1 m each and grouping the 3D samples into three different subsets. The corresponding data are reported in Table 3.  
A1 showed a significant increase in the temperature in the central section of the treatment chamber, while between the initial and final section there were no significant differences. Also, in A2, there was a significant increase in temperature in the final section near the plant outlet compared to the previous two. On the other hand, A3 showed a greater heating uniformity along the treatment chamber; in fact, no significant difference in temperature was found along the whole tube length. Figure 6 shows the different thermal behaviors for the three arrangements considered for the residence time of 140 s.  
By an inspection of Figure 6a, it is clear a hot spot in the central zone of the treatment chamber, thus confirming that a single MW source is not able to ensure uniform temperature heating over the entire length. Moreover, by inspecting Figure 6b,c, an improvement in the heating uniformity, starting from A2 to A3, can be observed. Therefore, numerical simulations identified the A3 as the optimal one since it ensures the best uniformity of microwave heating of the almonds. This result was used to develop and construct the MW-assisted prototype plant. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Residence** & **1 m** & **2 m** & **3 m** \\ \hline
**Time [s]** & \(\mu\pm\Sigma\) & \(\mu\pm\sigma\) & \(\mu\pm\sigma\) \\ \hline \multicolumn{4}{c}{1 MW source 7.5 kW} \\ \hline  
\hline \end{tabular} Different letters in rows denotes significant statistical differences according to Tukey test (\(p<0.05\)). \(\mu-\)average temperature, \(\sigma-\)standard deviation.  
\end{table} 
Table 3: Simulated temperature at 1, 2 and 3 m lengths of treatment tube, versus the residence time. Figure 6: Trend of the simulated temperatures after 140 s of treatment for A1 (**a**), A2 (**b**) and A3 (**c**).
## 1 Introduction  
### Earthquakes: A Need for Rapid Assessment of Structural Damage  
Earthquakes destroy buildings and cause the loss of human lives in various regions of the world. The crisis situations that follow have many complex aspects [1]. Among them, a quick assessment of damage to buildings and the scope of destruction in the affected region is crucial to making the right decisions. Thus, it is better to have rapidly available, \"good enough\" analysis, rather than \"perfect\" information and analysis that comes too late. Late analysis, no matter how good, is of little use in designing immediate life-saving interventions [2].  
Modern buildings are usually quite resistant to damaging factors, but some still suffer from strong earthquakes, the degradation of materials, land sinking, etc. [3, 4]. Moreover, many old buildings are exposed to great risks from earthquakes and other damaging factors [5, 6]. The rapid detection of partial, frequently non-visible damage to buildings is particularly important since the degree of risk that it poses is high. However, such a risk is not always visible to the occupants of the buildings or to rescue teams. Moreover, timely information allows decision-makers to take appropriate measures, and thus avoid more serious consequences. Permanent seismic structural health monitoring systems can significantly reduce the human and economic losses from earthquakes. Given the millions of public and residential buildings, and the common dwellings in seismic zones, installing permanent structural health-monitoring systems on such buildings is feasible, provided that such systems are simple and cost-effective [7].  
This paper presents studies on the suitability and accuracy of detecting structural cracks in brick masonry by exploiting the breakage of ordinary silica optical fibers bonded to its surface. Brick masonry is common in buildings in many locations where baked clay brick is the main building material [8; 9]. Furthermore, virtually the same technique can be applied to other masonry structures found worldwide [10; 11].
# Interferometric Study of the Heat Transfer Phenomena Induced by Rapid Heating of Nickel Sheet
## 2 Experiments
### Experimental Methodologies  
The experimental setup for this study had various devices to check the interference. Thus, controlling the sensitivity of these devices is very important to secure the reliability of the results. The measuring process of the thermo-acoustic wave is established to the preliminary experimental study, the main aim of which is to evaluate the data reproduction and processing.  
Before the main experiment, charging and discharging devices, trigger systems, the measuring device, and the high-speed camera needed to be calibrated to ensure their reliability. Then, the test specimen of this experiment, the nickel sheet, was produced in various sizes. A thermocouple (0.127 mm, K-type, Omega) was fixed on the surface to measure the temperature of the specimen via the electric arc welding method. The left and right sides of the specimen were compressed by bus bars, which were connected to the circuit for rapid heating.  
The capacitor was charged to a set voltage, and the various values of the charging voltage, such as 16 V, 19 V, and 21 V, etc., were measured using the multi-meter. The reliability of the capacitor under continuous charging and discharging was estimated in a sample test according to the various temperature values from 300 to 500 \({}^{\circ}\)C [31].  
A Mach-Zehnder interferometer and high-speed camera were prepared for measurements. The pulse generator acts as a trigger which allows each device to operate simultaneously. The temperature distribution and visualization image data were automatically measured for about 0.3 s. After one experiment, performed in an open or closed space with the specimen, the conditions of the experiment were reset, and the experiment was repeatedly carried out in order to check the reproducibility of these experiments. Actually, there were some uncertainties about these experiments. The uncertainty in this study could be divided into operational aspects of the experimental equipment and errors in data processing. In terms of the operation of the experimental equipment, there were the dimensions of production of the specimens, the precision of the charging voltage, etc., the misalignment of the experimental equipment, and the inherent error of the measurement equipment. In addition, the errors in data processing occur mainly in the process of measuring the deformation distance of the finite pattern line to calculate the Nusselt number.
## 2 Materials and Methods
### Reagent Preparation  
Carboxylic acid-functionalized silica microparticles (Bangs Labs, Fishers, IN, USA), 1 \(\upmu\)m in diameter, were activated with an excess of \(N\)-ethyl-\(N^{\prime}\)-(3-dimethylaminopropyl)carbodiimide and n-hydroxysuccinimide (0.5 mmoles of each) at pH 6.4 in 1 mL of 100 mM 3-(\(N\)-morpholino) propanesulfonic acid to form the succinimidyl ester. Then particles were washed once with 100 mM 3-(\(N\)-morpholino)propanesulfonic acid and subsequently twice with phosphate buffered saline (PBS; 138 mM NaCl, 2.7 mM KCl, 10 mM Na\({}_{2}\)HPO\({}_{4}\), pH 7.4). The capture antibody was added to a final concentration of 0.6 g/L and the solution was raised to pH 8.15 with 1 M NaHCO\({}_{3}\) and reacted at 4 \({}^{\circ}\)C for four hours. Any remaining activated ester was quenched with 200 mM glycine in PBS and washed in PBS three times. The particles were then twice blocked with 1% (w/v) bovine serum albumin for 30 min at 4 \({}^{\circ}\)C. Next, the particles were washed in wash buffer (0.08% (w/v) Tween-20, 0.02% (w/v) Pluronic F127, 0.09% (w/v) n-dodecyl \(\beta\)-d-maltoside, 0.8 mM NaN\({}_{3}\), 0.1% (w/v) BSA, in PBS) and resuspended in wash buffer to a concentration of 12% solids. The procedure was the same for each capture antibody for each pathogen. Antibodies used were: anti-_Shiegella_ species (rabbit polyclonal, Kirkegaard & Perry Laboratories, cat# 01-90-01, immunogen: heat-killed whole cells), anti-_E. coli_ O157:H7 (goat polyclonal, Kirkegaard & Perry Laboratories, cat# 01-95-90, immunogen: heat-killed whole cells), anti-_Listeria_ species (goat polyclonal, Kirkegaard & Perry Laboratories, cat# 01-90-95, immunogen: antigens from various strains of _Listeria_), anti-_Salmonella_ (goat polyclonal, Kirkegaard & Perry Laboratories, cat# 01-91-99, immunogen: heat-killed whole cells).  
Detection antibodies were labeled with AlexaFluor 647 (Life Technologies, Carlsbad, CA, USA). For each pathogen, the same polyclonal antibody was used for detection. The same procedure was used to label each detection antibody. Antibodies were first buffer exchanged into PBS using desalting spin columns (7 kDa molecular weight cut-off, Pierce). Then 100 \(\upmu\)g of activated dye was dissolved into 4 \(\upmu\)L of dimethylsulfoxide and added to 100 \(\upmu\)g of antibody in 176 \(\upmu\)L of PBS. Then 20 \(\upmu\)L of 1 M NaHCO\({}_{3}\) was added to raise the pH to 8.15. The reaction proceeded at room temperature for 15 min in the dark with end-to-end rotation. After the reaction was complete, unreacted dye was separated from the antibody by another desalting column. Dye to antibody ratios were determined spectrophotometrically by UV absorbance.
## 2 Applications of Nanotechnology in the Petroleum Industry
### Exploration and Reservoir Characterization  
Recently, hydrocarbon exploration has begun to be carried out in more challenging areas involving high risk and investment. It is therefore necessary to develop methods that are technically and economically feasible for exploration. \"Nano-sensors\" have been proposed for seismic characterization, interpretation, and formation evaluation in geochemical exploration [45]. When NPs are injected into the reservoir, some will pass through pores while others will be adsorbed onto the grain surface. Information on the chemical and physical properties of the rock and 3D distribution of the formation can be obtained from analysis of the adsorbed NPs [46]. Moreover, core and surface characteristics of NPs can be engineered that will induce significant changes in optical, magnetic, and electrical properties, making them ideal candidates for sensors or imaging agents [47].  
Numerous researchers have investigated the novel application of NPs as nano-sensors. In 2007, [PERSON] et al. [20] introduced the idea of using hyperpolarized silicon NPs as nanosensors in oil and gas exploration. Hyperpolarized NPs were first tested with promising results in bio-medical engineering as alternative tracker particles for magnetic resonance imaging (MRI) [48; 49]. [PERSON] [21] proposed the detection of oil-microbes in the reservoir using nano-optical fibers based on the principle of Resonance Raman Spectroscopy (RRS). As microbes only survive in specific conditions, this method can be used to indirectly measure reservoir properties such as pressure, temperature, and salinity. [PERSON] et al. [23] were granted a patent for real-time two dimensional analysis for oil reservoir evaluation, using an engineered carbon nanostructure and surface sensors. Magnetic NPs for detecting flood front and oil-water interfaces have been studied, and the result showed the capability of travel-time tomography to detect magnetic NPs in different media, which was a big step in the utilization of magnetic nanosensors [24]. Similarly, [PERSON] et al. [25] successfully employed superparamagnetic NP for tracking floodfront using a crosswell magnetic sensing method. The most recent advance in the use of nanotechnology in oil and gas exploration is the utilization of reservoir nano-robots by [PERSON] et al. [50], who successfully tested a nano detection device which integrates a reservoir sensor, a micro-dynamic system, and micro-signal transmission. A patent was granted to [PERSON] and [PERSON] [22] for a nano-robot system for well logging and measurement.  
These studies show that NPs have the potential to be utilized for reservoir characterization and hydrocarbon exploration. However, the problem that needs to be solved to make nanosensors feasible lies in protecting the sensor from degradation by fouling while at the same time getting the sensors to reach reservoir fluids.
# Task-Oriented Adversarial Attacks for Aspect-Based Sentiment Analysis Models
## 3 Related Work
#### Aspect-Based Adversarial Example Model  
In our previous work [18], we introduced a model to specifically design adversarial examples for aspect-based sentiment analysis. This model takes into account the characteristics of aspect-based analysis to drive input modifications while preserving input semantics and imperceptibility to modifications.  
To design adversarial examples for aspect-based analysis, two primary challenges must be faced: (i) identifying the terms that express the aspect-opinion, and (ii) determining the possible modifications for these terms. First, for a given opinion, it is necessary to accurately identify the term that uniquely represents the sentiment (positive, negative, or neutral) for each mentioned aspect, thus establishing an aspect term relationship. Second, for each identified term, the set of potential modifications must be defined, ensuring that each possible modification maintains the input semantics while effectively misleading the deep learning model. Based on these considerations, we introduced the aspect-based adversarial examples model (referred to as the ABAA model), which is defined as follows:  
* Given an opinion \(x\), which consists of a set of terms T (where each \(t\in T\) can be uni-gram or n-gram) and a set \(A\) representing the different evaluated aspects, for each aspect \(a_{i}\in A\), there exists a term \(t\in T\) specifically related to \(a_{i}\). This term allows the model \(M\) to classify the expressed user's opinion \(y_{a_{i}}\): \(M(x,a_{i},t_{a_{i}})=y_{a_{i}}\).
* To set the aspect term relation \(t_{a_{i}}\), it is necessary to identify the term \(t\in T\) that is specifically related to \(a_{i}\). For this, the semantic proximity between the aspect \(a_{i}\) and the terms within \(x\) is calculated. The semantic proximity can be expressed as \[SP(a_{i},t_{i})=[0,1]\] (2) An \(SP(a_{i},t_{i})\approx 0\) will mean that \(t_{i}\) is not related to \(a_{i}\), while \(SP(a_{i},t_{i})\approx 1\) indicates a relation between \(t_{i}\) and \(asp_{i}\).
* The objective of aspect-based adversarial examples is to create an adversarial example \(x^{\prime}\) by modifying \(t_{a_{i}}\) to generate \(t^{\prime}_{a_{i}}\), causing the model \(M\) to misclassify the opinion: \(M(x^{\prime},asp_{i},t^{\prime}_{asp_{i}})\
eq y_{asp_{i}}\)
* At the same time, the aspect-based adversarial examples \(x^{\prime}\) must satisfy the following conditions:
* Any possible modification of \(t_{a_{i}}\) must maintain the semantic proximity SP to the original term, i.e., \(SP(t_{i},t^{\prime}_{i})\approx 1\).
* The modified opinion \(x^{\prime}\) should be semantically similar to the original opinion \(x\). For this, the semantic similarity between \(x\) and \(x^{\prime}\) must be controlled.