esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Capacity Requirement for Multi-Terminal Cooperation in Container Ports
## 4 Managerial Insights
### Analysis on Workload Significance
While the shortened expected duration-of-stays associated with the high rate of trans-shipment TEUs (i.e., at 90%) leads to the reduced storage space capacity requirement, the higher rate of transferring TEUs increases the requirement for storage space capacity to put up incoming containers (thereby producing an offsetting effect for storage space capacity savings). The offsetting effect revisits when the trans-shipment rate is lowered to 75% leading to the storage space requirement increases to nearly 2.6%. Figure (b)b shows that the requirement uncertainty on storage space is somewhat unaffected when the trans-shipment rate decreases from 90% to 80%. The requirement uncertainty on storage space saw a sharp drop of 68.1% with further reductions in the trans-shipment rate from 80% to 75% under the higher transferring rate of 75%. As the  
Figure 11: Changes of QC intensity requirement for trans-shipment and transferring levels. Figure 12: Changes of storage space requirement for trans-shipment and transferring levels. trans-shipment rate falls, the expected duration-of-stays for TEUs at the yard lengthens and reduces the requirement uncertainty though the higher transferring rate (i.e., 75%) works in the opposite direction. Further reduction of trans-shipment rate (i.e., 75%) magnifies the uncertainty reduction of storage space requirement with longer duration-of-stays for TEUs. The requirement uncertainty on storage space is much variable for the trans-shipment rates when the transferring rate is lower (i.e., 60%). Overall, there is a tendency towards the declination of the requirement uncertainty when the trans-shipment rate decreases for the same reason as that of the higher transferring rate. The truck queue length is generally shorter when the rate of trans-shipment TEUs and interterminal rates of transferring TEUs are low (Figure 12(a)). On the contrary, under the higher transferring rate, increasing the rate of trans-shipment TEUs results in increased traffic congestion on the path transporting containers between terminals. This leads to a reduction in arrival rate of trucks which, in turn, results in an average 2.4% reduction in queue length at the gate. When the trans-shipment rate is decreased to 75%, the traffic congestion is less effective in alleviating gate congestion. Instead, a reduced amount of trans-shipment TEUs transferred is shown to be more effective in reducing the truck arrival rate with the truck queue at the gate being shortened by 4.1%. The plot of gate congestion range in Figure 12(b) behaves very similarly to that of queue length in Figure 12(a) since the uncertainty on truck queue is attributed to the traffic congestion for trucks transporting containers between terminals, as well as the arrival rate of trucks at the gate. Variations of truck queues at the gates decrease with a shorter average queue length and increase with a longer queue of trucks.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 4 Multi-Object Detection for Inland Ships
### Shuffle Attention Mechanism Network  
In convolutional neural networks (CNNs), the attention mechanism operates on the feature map to capture the relevant attention within the feature map. In recent years, the attention mechanism has made significant breakthroughs in image and natural language processing and has been proven to be beneficial in improving model performance. There are mainly two categories of attention mechanisms in current research: namely, spatial attention mechanisms and channel attention mechanisms. The former focuses on capturing pixel-level relationships at spatial locations, locating the target, and performing spatial transformations or obtaining spatial weights, while the latter captures inter-channel dependencies. Shuffle Attention is a model that efficiently combines these two types of attention mechanisms. It first groups channel features to obtain sub-features from multiple groups and then applies the spatial and inter-channel attention mechanisms to each sub-feature using Shuffle Attention units. Finally, the features from different groups are fused using the Channel Shuffle operation. Adding an attention mechanism to the detection network can improve detection accuracy. The network structure of Shuffle Attention is illustrated in Figure 2.  
The Shuffle Attention structure is in front of the SPPF module in the last layer of the BackBone, as well as the corresponding detection layers in the Head part of YOLOv5s. The Shuffle Attention structure helps the model better resist confusing information and focus on relevant target objects. Shuffle Attention structure is depicted in Figure 3.  
Considering the potential risk of over-complexity in the network that may hinder the model's ability to fit the training data, a Yolov5s architecture with the Shuffle Attention only to the backbone part (ShuffleBackBone) is also designed, as depicted in Figure 4.  
Figure 2: The Shuffle Attention Network.  
Figure 4: YOLOv5s Shuffle BackBone.  
Figure 3: YOLOv5s Shuffle Attention.
## 2 Materials and Methods  
Unless otherwise indicated, all chemicals and solvents were obtained from commercial suppliers and used as supplied without further purification. Cetyltrimethyl ammonium bromide (CTAB), sodium fluoride (NaF), tetraethyl orthosilicate (TEOS), N-(trimethoxy-silylpropyl)-ethylenediaminetriacetic acid carboxy-triethoxysilane (TEDTA-COOH), 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC), N-hydroxysulfosuccinimide (NHS-sulfo), and paraformaldehyde (PFA) were all purchased from UFC Biotechnology (Riyadh, Saudi Arabia) and Sigma-Aldrich (MO, USA.). Superparamagnetic iron oxide MNPs were purchased from BOC Sciences (NY, USA), CMB-500N, size -500 nm. The following antibodies and recombinant proteins were purchased from Sino Biological (Beijing, China): anti-MERS-CoV spike protein S1 mouse monoclonal antibody, FITC-labeled (cat # 40069-MM23); anti-MERS-CoV spike protein S1, Rabbit polyclonal antibody (cat # 40069-T52); and Recombinant MERS-CoV spike protein (cat # 40069-V08H). Donkey anti-Rabbit APC-labeled secondary antibody was purchased from Thermo-Fisher, USA (Cat # A-31573). MERS-CoV pseudoviral particles (MERSpps) were generated and quantified as relative light units per ml (RLU/mL) following established protocols [42]. These pseudovirus particles are equivalent to the HIV lentiviral core, displaying the MERS-CoV spike protein on their surface. The MERSpp genome carries a luciferase gene that will be utilized as an infection reporter. Vesicular Stomatitis Virus-Glycoprotein lentiviral particles (VSV-Gpp) were produced in the lab in HEK293T cells using the Lenti-vpack Lentiviral Packaging kit (OriGene). VSV-G lentivirus particles display the envelope glycoprotein (VSV-G) and contain a heterologous lentiviral (HIV) core. VSV-Gpp expresses the GFP optical reporter gene upon infection. These pseudoviral particles (MERSpp and VSV-Gpp) are replication-incompetent and capable of a single round of infection.
# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 4 Vessel Warning for Bridge Area Based on Vessel Posture
### Real-Time Early Warning Method for the Safety Potential Field of a Bridge Area
Target ships that are too far away from the bridge area can be temporarily disregarded, and ship state prediction is performed only when the target distance is less than the threshold value \(D_{d}\). \(D_{d}\) can be determined by the navigational conditions of the specific waters, with 2 nm taken as the interim value. In the above steps, by continuously updating the information and predicting the status of the target ship, and by interacting with the bridge area hazardous obstacle model in real time, the safety decision making of ship collision prevention in bridge-area waters can be achieved. These time-series rolling calculations can adapt to the manoeuvring and unpredictable behaviour of the target ship and improve the safety of the bridge-area waters. The specific flow is shown in Figure 12.  
Figure 11: Hierarchical warning based on safety potential field. Figure 12: Flowchart of a bridge-area safety potential field and ship collision warning.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 3 Results and Discussion
### Mechanism of the Methylene Blue Removal  
Based on the structure characterization, the kinetic analysis, the adsorption isotherm studies, and the intraparticle diffusion model studies, a possible mechanism for removing methylene blue is as follows: In a methylene blue solution at pH 7, methylene blue migrates from the aqueous solution to the surface of the composite materials first. Secondly, at the solid-liquid interface, methylene blue bonds with the active sites of composite materials by liquid film diffusion and intraparticle diffusion, then being removed by chemical adsorption, predominantly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} **Isotherm** \\ **Model** \\ \end{tabular} & \multicolumn{3}{c}{**Langmuir Model**} & \multicolumn{3}{c}{**Freundlich Model**} \\ \hline Composite material & AC-CuO & AC-ZnO & AC-TiO\({}_{2}\) & AC-CuO & AC-ZnO & AC-TiO\({}_{2}\) \\ \hline Nonlinear expression & & \(Q_{e}=\frac{K_{L}Q_{max}C_{e}}{1+C_{e}}\) & & \(Q_{e}=K_{F}C_{e}{}^{-n}\) & \\ \hline \multirow{2}{*}{Fitting parameters} & \(K_{L}=0.1035\) & \(K_{L}=0.0341\) & \(K_{L}=0.0339\) & \(K_{F}=1.2609\) & \(K_{F}=1.1485\) & \(K_{F}=1.1677\) \\  & \(Q_{max}=12.08\) & \(Q_{max}=32.71\) & \(Q_{max}=33.36\) & \(n=0.7094\) & \(n=0.8711\) & \(n=0.8704\) \\ \hline R\({}^{2}\) & 0.95 & 0.99 & 0.99 & 0.92 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of correlation coefficients for kinetic model fitting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} **Number of** \\ **Regenerations** \\ \end{tabular} & \multicolumn{3}{c}{**Regeneration Efficiency**} \\ \cline{2-4}  & **AC-CuO** & **AC-ZnO** & **AC-TiO\({}_{2}\)** \\ \hline
1 & 100 & 97.97 & 99.30 \\ \hline
2 & 88.78 & 91.91 & 96.70 \\ \hline
3 & 79.50 & 66.60 & 66.85 \\ \hline
4 & 62.27 & 45.73 & 24.03 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Influence of regeneration numbers on the removal effect of methylene blue.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} **Number of** \\ **Regenerations** \\ \end{tabular} & \multicolumn{3}{c}{**Loss Rate**} \\ \cline{2-4}  & **AC-CuO** & **AC-ZnO** & **AC-TiO\({}_{2}\)** \\ \hline
1 & 4.24 & 6.12 & 4.83 \\ \hline
2 & 10.82 & 9.49 & 9.03 \\ \hline
3 & 17.74 & 17.83 & 16.89 \\ \hline
4 & 25.33 & 30.17 & 34.24 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Regeneration loss rate for the removal of methylene blue.
# Study on the Influence of Moisture Content and Void Ratio on the Disintegration of Red Clay
## 4 The Influence of Moisture Content and Void Ratio on Disintegration  
Combining the results of NMR and disintegration tests, it is found that when the moisture content of red clay is constant, the disintegration rate increases with the increase of pore volume. Yet, when the difference in pore volume is not significant, the disintegration rate of red clay with different moisture contents is significantly different. The T\({}_{2}\) spectrum area of 26%\(-\)1.1 is 142,880.66, with a disintegration rate of 0.055 g/s, while the T\({}_{2}\) spectrum area of 28%\(-\)1.15 is 161,720.88, with a disintegration rate of 0.035 g/s. The disintegration rate decreases as the increase of pore volume. Thus, the secondary peak area of the T\({}_{2}\) spectrum at 26%\(-\)1.1 is 141,704.67, and the disintegration rate is 0.055 g/s. Similarly, the secondary peak area of the T\({}_{2}\) spectrum at 30%\(-\)1.15 is 179,888.55, and its disintegration rate is also 0.055 g/s. Although the volume of large pores increases, the disintegration rate does not increase significantly. It is evident the disintegration rate of red clay is not significantly correlated with pore volume or large pore volume.  
The main physical change during the disintegration of red clay is the expansion of the diffusion layer thickness. In order to comprehensively consider the influence of moisture content and void ratio on the disintegration characteristics, both factors are transformed into 'combined water saturation' from the water distribution in soil. The combined water saturation refers to the ratio of the mass of combined water of red clay under the natural state to the mass of combined water under fully saturated state. Its formula is as follows:  
\[S_{r}^{\prime}=\frac{m_{1}}{m}\times 100\% \tag{5}\]  
\(S_{r}^{\prime}\) represents the combined water saturation (%);  
\(m_{1}\) is the mass (g) of combined water in its natural state;  
\(m\) is the mass (g) of combined water in its saturated state.
# May the Extensive Farming System of Small Ruminants Be Smart?
## 3 Results
### Wearable Sensors
#### 3.2.1 Electronic Animal Identification  
Electronic identification (EID) systems are a mandatory technological component under EU laws (Official Journal of the European Union, 9.1. 2004) [73]. The registration of goats and sheep born in 2010 or later with electronic identification has received considerable attention. Depending on the application, the sensors for small ruminants' identification can be found in ear tags, ruminal boluses, or injected chips under the skin [19]. Electronic identification systems allow each animal to be identified and data to be stored and updated for various decision-making processes [25]. Passive EID tags are small and rely on storing an information code and a copper coil that briefly charges the transmitter through energy transmitted by an active reader [4; 24]. According to EU legislation, using EIDs is mandatory for sheep and goat species. It represents an opportunity to introduce the PLF system in extensive management systems. Radio frequency identification (RFID) systems operate on different radio frequency levels, which determine their transition distance and ability to pass materials (noise to signal ratio): low frequency (LF, 125-134.2 kHz), high frequency (HF, 13.56 MHz), and ultra-high frequency (UHF, 860 MHz in the EU or 915 MHz in the US). According to [PERSON] et al. [45], advanced technologies for animal identification and production monitoring based on RFID may also be a valid tool for individual ewes' health and welfare problem-solving. In the case of extensive livestock farming, real-time identification and monitoring are essential to control herds. [PERSON] et al. [19] have reported an accuracy of around 96% using GPS sensors and drones.
## 4 Results of Experiments
### Results of the Experiments
Logistic Regression and Multinomial Naive Bayes achieved notable accuracy rates of 88.45% and 86.58%, respectively, with Logistic Regression having the highest F1-score among traditional models at 88.38%. The Arb-MCNN-Bi hybrid model, particularly when using AraBERT for feature extraction, demonstrated the best performance with a precision of 96.57%, recall of 96.56%, F1-score of 96.56%, and accuracy of 96.68%. This highlights the superiority of advanced DL architectures and transformer-based feature extraction techniques like AraBERT in ASA on the HARD. The performance comparison of various models for ASA on the LABR dataset demonstrates the effectiveness of DL models over traditional ML algorithms. Traditional models like Logistic Regression and Multinomial Naive Bayes performed relatively well, achieving F1 scores of 80.11% and 80.12%, respectively. However, DL models, especially those utilizing transformer-based feature extraction techniques like AraBERT, significantly outperformed them. The Arb-MCNN-Bi hybrid model feature extraction stood out with the highest performance, attaining a precision of 92.37%, recall of 92.26%, F1-score of 92.31%, and accuracy of 92.93%. This highlights the superior capability of transformer-based models in capturing and analyzing the complexities of sentiment in Arabic text, making them the most effective choice for this task. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature Extraction**} & \multicolumn{4}{c}{**Performance**} \\  & & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} LSVM \\ PAC \\ MNB \\ LR \\ DT \\ KNN \\ RF \\ \end{tabular} } & N-Gram, TF-IDF & 81.44 & 87.74 & 84.43 & 84.92 \\  & N-Gram, TF-IDF & 82.30 & 86.88 & 84.53 & 84.59 \\  & N-Gram, TF-IDF & 88.25 & 84.22 & 86.19 & 86.58 \\  & N-Gram, TF-IDF & 86.91 & 89.91 & 88.38 & 88.45 \\  & N-Gram, TF-IDF & 75.85 & 87.19 & 81.13 & 79.83 \\  & N-Gram, TF-IDF & 76.75 & 76.50 & 76.64 & 76.88 \\  & N-Gram, TF-IDF & 85.40 & 85.95 & 85.68 & 85.70 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} MCNN \\ AraBERT \\ \end{tabular} } & Word2 Vec(SG) & 90.12 & 89.87 & 89.99 & 90.02 \\  & FastText(SG) & 90.14 & 90.04 & 90.09 & 90.24 \\  & AraBERT & 92.54 & 91.15 & 91.84 & 92.11 \\ \hline \multirow{2}{*}{BiLSTM} & Word2 Vec(SG) & 91.81 & 92.14 & 91.97 & 92.04 \\  & FastText(SG) & 92.14 & 92.45 & 92.29 & 92.54 \\  & AraBERT & 94.15 & 94.54 & 94.34 & 94.75 \\ \hline \multirow{2}{*}{BiGRU} & Word2 Vec(SG) & 92.22 & 90.03 & 91.11 & 91.4 \\  & FastText(SG) & 93.53 & 93.22 & 93.37 & 93.44 \\  & AraBERT & 94.05 & 94.11 & 94.08 & 94.13 \\ \hline Arb-MCNN-Bi & AraBERT & 96.57 & 96.56 & 96.56 & 96.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance Comparison of Various Models for Arabic sentiment analysis Using Different Feature Extraction Techniques on HARD.
## 2 Methodology and Validation  
### Analytical Modeling of the Temperature Calculation  
The schematic flow of the variables in temperature evolution is shown in Figure 2. The induction heating process is governed by the Maxwell equations, as shown in Equations (1)-(4) [2]. \(I\) and \(f\) are the current intensity and frequency of the induction coil. The induced power density of eddy current in the surface q\({}_{V}\) is calculated by the Maxwell equations and then the power P\({}_{L}\) applied as input parameters in the temperature calculation can be obtained. The calculation model of the temperature is used as a uniform moving heating source, which will be illustrated as follow.  
\[\
abla\times H(I,f,\mu_{\text{MFC}})=J+\frac{\partial D}{\partial t}, \tag{1}\]  
\[\
abla\times E=-\frac{\partial B}{\partial t}, \tag{2}\]  
\[\
abla\cdot D=\rho, \tag{3}\]  
\[\
abla\cdot B=0, \tag{4}\]  
where, \(J\) is the source current density generated by the current intensity \(I\) of the induction coil. \(H\) is the magnetic field strength, \(E\) is the electric field strength, \(D\) and \(B\) are the electric flux density and the  
Figure 2: Schematic flow of the variables in temperature evolution.  
where, \(Fo_{s}\) is the Fourier number which is related with the length \(a_{h}\) and the time \(t\)-\(t^{\prime}\).  
By substituting the Equations (10)-(12) in Equation (9), the final temperature formula can be derived as follow:  
\[\begin{array}{c}dT_{t^{\prime}}=T-T_{0}\\ =-\frac{P_{t}}{2^{2}\rho ca_{h}b_{h}c_{h}}\int_{0}^{t}Erfh(x+v(t-t^{\prime}),a _{h},t^{\prime})\\ \times Erfh(y,b_{h},t^{\prime})Erfh(z,c_{h},t^{\prime})dt^{\prime}\\ =-\frac{1^{\prime}}{2^{3}\eta}\int_{0}^{t}Erfh\Big{(}\frac{x+v(t-t^{\prime})}{ a_{h}},Fo_{a_{h}}\Big{)}Erfh\Big{(}\frac{y}{b_{h}},Fo_{b_{h}}\Big{)}Erfh\Big{(} \frac{z}{c_{h}},Fo_{c_{h}}\Big{)}\frac{dt^{\prime}}{t},\end{array} \tag{13}\]  
where, \(v\) is the moving velocity of the heated material, \(\eta\) is the heating efficiency and is defined as [20]:  
\[\eta=\frac{\rho ca_{h}b_{h}c_{h}(T_{m}-T_{0})}{P_{L}t}, \tag{14}\]  
where, \(T_{0}\) is the ambient temperature, \(T_{m}\) is the melt temperature of the heated material Inconel 718.
# Investigation of Properties Alternation during Super-Critical CO\({}_{2}\) Injection in Shale
## 4 Conclusions  
In this paper, the mechanical, pore structure and mineralogical alternation that occur in the Middle Bakken sample injected with Sc-CO\({}_{2}\) were investigated and analyzed. A series of tests including the nano-indentation test, low-temperature N\({}_{2}\) adsorption test, and whole-rock XRD analysis were conducted to evaluate the properties change of the samples with varied Sc-CO\({}_{2}\) injection duration. Some conclusions were drawn as follows:  
The nano-indentation test illustrated that the Young's modulus of the samples tends to decrease gradually with the injection time incrementally which agrees with the results by [PERSON] et al. [16]. and [PERSON] et al. [18]. The Sc-CO\({}_{2}\) adsorption behavior was noted as the main reason for the pore swelling, which contributes to the rock strength decrease. Also, a universal relation between the Young's modulus and the Sc-CO\({}_{2}\) injection time was proposed.  
Low-temperature N\({}_{2}\) adsorption results showed that Sc-CO\({}_{2}\) treatment affects the meso-pore volume efficiently rather than the micro and macro pore, and the PSD results also give evidence of the dominating meso-pore alteration. The surface area of the samples with the varied Sc-CO\({}_{2}\) treatment was observed as tending to deduct with the Sc-CO\({}_{2}\) treatment time increasing.  
The whole-rock XRD analysis revealed that the mineral components of the sample except quartz and muscovite were decreasing corresponding to the Sc-CO\({}_{2}\) flooding duration increasing. The chemical reaction between the minerals and carbonic acid explained the mineralogical alteration of the samples which has also been proved by [PERSON] et al. [14].  
This fundamental research investigated the effect of the Sc-CO\({}_{2}\) injection process to the shale formation from the aspect of geomechanical variation, geophysical alteration and geochemical vibration, and can provide a guide for CO\({}_{2}\) injection to tight shale formation and the CO\({}_{2}\) storage process.  
Conceptualization, methodology and experiments, S.W. and K.L. ([PERSON]); data analysis, J.H.; Supervision, K.L. ([PERSON]); software, H.W.; writing--original draft preparation, S.W.; writing--review and editing, B.J.  
This research received no external funding.  
The authors would like thank the Advanced Analytical Characterization Laboratory at the University of North Dakota (UND), for the permission to use their equipment and especially the assistance from [PERSON].  
The authors declare no conflict of interest.  
Figure 9: Mineral content alteration with varied Sc-CO\({}_{2}\) injection time. (**a**) sample at depth of 9848 ft; (**b**) sample at depth of 9850 ft.
# A Four-Port Dual-Band Dual-Polarized Antenna for Ku-Band Satellite Communications
## 2 Antenna Design and Working Mechanism
### Working Mechanism
According to Figure 6b, employing the shorting pin in the HB antenna improves the isolation between its two orthogonal polarization ports (\(|S_{43}|\)). The current distribution of the HB antenna without and with the shorting pin at 14 GHz is illustrated in Figure 6c. Without the shorting pin, an induced current will occur when the HB antenna is excited through **P3** and flow to **P4** (see Figure 6c), thus resulting in poor isolation between two ports. The function of the shorting pin is to enhance the isolation by shorting this induced current. As shown in Figure 6c, a strong current is generated in the shorting pin, while the induced current on the feed via **P4** becomes weaker. Figure 4: Step-by-step antenna design for the HB antenna (other components are hidden for clear illustration). (**a**) HB-Ant.1. (**b**) HB-Ant.2. (**c**) HB-Ant.3. (**d**) HB-Ant.4. (**e**) Proposed HB antenna.