esa-sceva/satcom-chunk-collection huggingface dataset:
# A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer
## 4 Experiments
### Comparison with State-of-the-Art Methods  
**Results on Market-1501 dataset.** DukeMTMC-reID is used as the source domain to evaluate the performance of the proposed method on Market-1501. The results are compared with the representative works in different directions, including methods based on style transfer [16; 34; 52], methods based on pseudo-label estimation [42; 51], and methods in mining domains [9; 23; 24; 25]. As shown in Table 3, the proposed method is superior to the current leading methods in terms of Rank-1 accuracy and mAP.  
**Results of DukeMTMC-reID dataset.** Market-1501 is used as the source domain, and the performance of the proposed method is evaluated on DukeMTMC-reID. As shown on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Duke to Market**} \\ \cline{2-5}  & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline None & 77.8 & 88.1 & 91.7 & 48.4 \\ Channel & 78.8 & 88.3 & 91.7 & 48.6 \\ Position & 78.7 & 88.9 & 92 & 48.5 \\ Position-Channel & 77.9 & 88 & 91.5 & 50.5 \\ Channel-Position & 79.1 & 88.7 & 92.5 & 53.5 \\ Channel+Position & **80.5** & **89.5** & **93.2** & **60.1** \\ \hline \hline \end{tabular}
\end{table}
Table 2: An ablation study on DukeMTMC-reID to Market1501. The best accuracy is reached when channel and location attention are added.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Market1501**} & \multicolumn{4}{c}{**DukeMTMC-reID**} \\ \cline{2-9}  & **R-1** & **R-5** & **R-10** & **mAP** & **R-1** & **R-5** & **R-10** & **mAP** \\ \hline PTGAN [16] & 38.6 & - & 66.1 & - & 27.4 & - & 50.7 & - \\ SPGAN [52] & 51.5 & 70.1 & 76.8 & 22.8 & 41.1 & 56.6 & 63 & 22.3 \\ CamStyle [53] & 58.8 & 78.2 & 84.3 & 27.4 & 48.4 & 62.5 & 68.9 & 25.1 \\ HHL [50] & 62.2 & 78.8 & 84 & 31.4 & 46.9 & 61 & 66.7 & 27.2 \\ MAR [9] & 67.7 & 81.9 & - & 40 & 67.1 & 79.8 & - & 48 \\ PAUL [24] & 68.5 & 82.4 & 87.4 & 40.1 & 72 & 82.7 & 86 & 53.2 \\ ARN [54] & 70.3 & 80.4 & 86.3 & 39.4 & 60.2 & 73.9 & 79.5 & 33.4 \\ ECN [23] & 75.1 & 87.6 & 91.6 & 43 & 63.3 & 75.8 & 80.4 & 40.4 \\ UDA [55] & 75.8 & 89.5 & 93.2 & 53.7 & 68.4 & 80.1 & 83.5 & 49 \\ PAST [41] & 78.4 & - & - & 54.6 & 72.4 & - & - & 54.3 \\ SSG [42] & 80 & 90 & 92.4 & 58.3 & 73 & 80.6 & 83.2 & 53.4 \\ CV-DA [51] & 79.7 & 89 & 91.4 & 59.8 & 71.1 & 81.2 & 84.2 & 52.6 \\ \hline Ours & 80.5 & 89.9 & 93.2 & 60.1 & 71.2 & 81.7 & 84.3 & 53.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with state-of-the-art cross-domain methods on Market-1501 and DukeMTMC-reID. The proposed method shows better accuracy on both datasets.  
the right side of Table 3, the performance of the proposed method is competitive with other state-of-the-art methods.
## 3 Results
### OF Measurements
Figure 3: Crossline beam profiles, measured by the different solid-state detectors, and % respective difference over the entire off-axis range between microDiamond and microSilicon measurements and diode E data. For the 20, 30, and 40 mm applicator diameters, OFs agreement between the microSilicon data and the reference values was always within 1.2%. All OF values measured by microDiamond agreed with the reference data: differences were typically within 1.4%, with a maximum value of 2.5% for 4 MeV and the smallest aperture. Figure 4: Output factor values, measured by all dosimeters, for all aperture diameters and beam energies. Measurement uncertainties were within the sizes of point indicators. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**4 MeV**} & \multicolumn{2}{c}{**6 MeV**} & \multicolumn{2}{c}{**9 MeV**} & \multicolumn{2}{c}{**12 MeV**} \\ \hline \multicolumn{2}{c}{**Beam**} & & & & & & & & \\ \multicolumn{2}{c}{**Diameter**} & \multicolumn{1}{c}{**Dosimeter**} & \multicolumn{1}{c}{**OF**} & \multicolumn{1}{c}{**Deviation**} & \multicolumn{1}{c}{**OF**} & \multicolumn{1}{c}{**Deviation**} & \multicolumn{1}{c}{**OF**} & \multicolumn{1}{c}{**Deviation**} \\ \multicolumn{2}{c}{**(mm)**} & & & & & & & & \\ \hline \multirow{3}{*}{20} & d-E & **0.367** & - & **0.506** & - & **0.648** & - & **0.687** & - \\  & m-D & 0.376 & 2.5 & 0.513 & 1.3 & 0.650 & 0.3 & 0.686 & 0.1 \\  & m-Si & 0.364 & 0.8 & 0.500 & 1.2 & 0.645 & 0.5 & 0.680 & 1.0 \\ \hline \multirow{3}{*}{30} & d-E & **0.560** & - & **0.751** & - & **0.957** & - & **1.005** & - \\  & m-D & 0.560 & 0.0 & 0.751 & 0.0 & 0.959 & 0.2 & 1.010 & 0.4 \\  & m-Si & 0.557 & 0.6 & 0.744 & 0.9 & 0.960 & 0.3 & 0.999 & 0.6 \\ \hline \multirow{3}{*}{40} & d-E & **0.656** & - & **0.874** & - & **1.121** & - & **1.180** & - \\  & m-D & 0.661 & 0.7 & 0.873 & 0.2 & 1.117 & 0.4 & 1.176 & 0.3 \\  & m-Si & 0.657 & 0.2 & 0.869 & 0.6 & 1.111 & 0.9 & 1.167 & 1.1 \\ \hline \multirow{3}{*}{50} & M & **0.672** & - & **0.889** & - & **1.140** & - & **1.205** & - \\  & d-E & 0.684 & 1.7 & 0.897 & 0.8 & 1.155 & 1.3 & 1.218 & 1.1 \\ \cline{1-1}  & m-D & 0.675 & 0.4 & 0.897 & 0.9 & 1.156 & 1.4 & 1.215 & 0.8 \\ \cline{1-1}  & m-Si & 0.672 & 0.1 & 0.890 & 0.1 & 1.149 & 0.8 & 1.213 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: OF values measured for all beam energies with all the dosimeters for the 50 mm diameter tubular applicator (ionization chamber Markus, reported as M, diode E as D-E, microDiamond as m-D, and microSilicon as m-Si) and only with solid-state dosimeters for the remaining applicators.
# Anomalous Humidity Dependence in Photoacoustic Spectroscopy of CO Explained by Kinetic Cooling
## Materials and Methods
### Measurement of Humidity Dependent 2f-WM-QEPAS Signals of CO
The humidity of the mixed gas was determined in a separate transmission FTIR measurement (Vertex 80v, Bruker Optics, Ettlingen, Germany). To this end, absorption measurements of the gas at varying humidities were taken in a 10 cm gas cell at ambient pressure and the volumetric concentrations of H\({}_{2}\)O were obtained from the absorption spectra based on a reference spectrum of H\({}_{2}\)O from the IR spectral database of the Pacific Northwest National Laboratory (PNNL) [16]. Experimentally, it was found that the concentration of CO varied between the measurements of different humidity even though the nominal concentration as set by the gas mixing unit should be constant. The actual concentration of CO was therefore measured using wavelength modulation spectroscopy with second harmonic detection (2_f_-WMS). To this end, the QEPAS-module and the reference gas cell were removed from the setup and a 10 cm gas cell was inserted in the beam path. The measured 2_f_-WMS signal was used to normalize the QEPAS signals recorded at varying humidity to CO concentration.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 5 Performance Evaluation
### Performance Evaluation of Algorithm MCOTO
#### 5.2.1 The Execution Time  
The workstation used in the experiments is an Intel(R) Core(TM) (i5-3230 at 2.6 GHz) machine. We fixed the number of nodes at 200 and varied the number of topics in the range of [40,80,120,160,200], while the number of relevant nodes (including publishers and subscribers) is 30, in which 20% are publishers. These relevant nodes are generated randomly. In MCOTO, the number of topics at the first level is half of the total number. The results are shown in Figure 5. Note that the time magnitude is \(10^{4}\) ms. As we can observe, the shape of the topic tree greatly affects the efficiency of the algorithms. The execution time under three cases increases along with the number of topics (Figure 6). When the topic tree is a single-branch tree, the execution time is the smallest. When all of the topics have no inclusion relationship, the execution time is maximum.
# Seismic Response of a Large LNG Storage Tank Based on a Shaking Table Test
## 5 Conclusions  
Based on the research of existing scholars, this paper proposes to simplify the LNG storage tank into a three-degree-of-freedom mechanical model including dome, tank wall and bearing platform, establish the motion control equation of the LNG storage tank system, input the seismic excitation, and obtain it by the Newmark-\(\beta\) method. The main conclusions of the seismic response of the storage tank structure are as follows:  
(1) Through the shaking table test, the theoretical research on the earthquake resistance of the concrete outer tank under the action of one-direction, two-direction and three-direction ground motion is carried out. The acceleration response at the top is small and the acceleration response at the position of the dome is relatively large, which further concludes that in the process of seismic design of the storage tank structure, the strength of the dome part of the storage tank needs to be increased, and the vertical parts of the storage tank need to be strengthened.  
(2) For the non-isolated storage tank, the acceleration response generally shows a linear increasing trend, while for the isolated storage tank, the acceleration response first decreases with the increase in height, and when it reaches the middle of the tank wall, the acceleration response increases as the height increases.  
(3) By comparing the theoretical solution of the simplified mechanical model of various storage tanks with the test data of the shaking table, it is concluded that the three-degree-of-freedom model of the storage tank simplified as a dome, a cap and a pile foundation has  
Figure 16: Comparison of shaking table test data and theoretical solutions under the artificial wave: (**a**) dome acceleration time history and spectrum comparison; (**b**) cushion cap acceleration time history and spectrum comparison; (**c**) pile foundation acceleration time history and spectrum comparison.  
the best fitting effect. The real response under the action of ground motion provides new technical means.  
(4) By comparing the four kinds of seismic wave shaking table test data and theoretical solutions, the theoretical solutions of the tank dome, the cap and the pile foundation have a high degree of fitting with the actual shaking table test data, which fully reflects the effectiveness of this method. It has a wide range of performance and application and provides a theoretical basis for optimizing the seismic design of storage tanks.  
Writing--original draft, Z.C.; Methodology, L.Z. and J.F.; Writing--review and editing, Z.X.; Visualization, Y.L. and H.C.; Data curation, X.X. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
No data were reported in this study.  
The authors would like to thank the anonymous reviewers for their valuable comments on the manuscript.  
The authors declare no conflict of interest.
## Nomenclature  
\(A\): flow area  
\(b\): blade height  
\(B\): blockage factor  
\(c\): chord length  
\(C\): absolute velocity  
\(C_{f}\): friction coefficient  
\(C_{l}\): lift coefficient  
\(d_{s}\): specific diameter  
\(D\): diameter  
\(D_{eq}\): equivalent diffusion factor  
\(D_{F}\): diffusion factor  
\(h\): specific enthalpy  
\(H\): stagnation enthalpy  
\(i\): incidence angle  
\(l_{b}\): blade work input coefficient  
\(L\): length  
\(\dot{m}\): mass flow rate  
\(n_{s}\): specific speed  
\(P\): pressure  
\(r\): radius  
Re: Reynolds number  
\(s\): specific entropy; blade pitch  
\(t\): blade thickness  
\(T\): temperature  
\(U\): circumferential velocity  
\begin{tabular}{l l} \(w\) & width of flow channel \\ \(W\) & relative velocity \\ \(Y\) & total pressure loss coefficient \\ \(Z\) & blade number \\ \(\alpha\) & absolute flow angle \\ \(\beta\) & relative flow angle \\ \(\varphi\) & flow coefficient \\ \(\psi\) & work coefficient \\ \(\gamma\) & setting angle \\ \(\omega\) & angular velocity \\ \(\delta_{c}\) & clearance \\ \(\rho\) & density \\ \(\mu\) & slip coefficient; dynamic viscosity coefficient \\ \(\delta^{*}\) & derivation angle on design condition \\ \(\tau\) & torque \\ \(\Pi\) & pressure ratio \\ \(\sigma\) & solidity \\ \end{tabular}
## 5 Analytical Validation and Comparison with Existing Systems
### Rain Attenuation Validation  
Rain attenuation was modelled using the ITU-R P.838 empirical equation:  
\[A_{\text{rain}}(f,d)=kR^{\alpha}\cdot d, \tag{22}\]  
where \(k\) and \(\alpha\) are frequency-dependent constants, \(R\) is the rain rate, and \(d\) is the path length.  
The proposed model closely matched the ITU-R tabulated results for rain intensities of 5 mm/h, 10 mm/h, 20 mm/h, and 50 mm/h, as shown in Figure 13.
## 3 Fuzzy Evaluation of Surrounding Rock Stability of Ioess Tunnel with Rich Water  
Fuzzy Analytic Hierarchy Process (FAHP) is an analytical method that combines Analytic Hierarchy Process (AHP) with fuzzy Set theory. The characteristic of FAHP is that, under the premise of studying the influencing factors, internal correlations, and essence of complex decision-making problems, it mathematically processes the decision-making process with fuzzy or limited information, ultimately yielding a decision-making plan for complex decision-making problems [42].  
[PERSON] et al. [24], based on a comprehensive consideration of multi-factor surrounding rock classification methods, introduced fuzzy theory and took into account various influencing factors such as rock mass strength and softening coefficient to establish a fuzzy classification model for surrounding rocks. They applied this fuzzy mathematical surrounding rock classification method to the construction of the Laoshan Tunnel in Nanjing. [PERSON] et al. [43] analyzed the mechanical properties, action mechanisms, and risks of water inrush and mud bursts associated with Tertiary semi-consolidated rocks based on laboratory mechanical tests and the fuzzy analytic hierarchy process (FAHP). [PERSON] et al. [44] proposed an optimized advanced prediction method for surrounding rock classification that integrates the fuzzy analytic hierarchy process and Tunnel Seismic Prediction (TSP) and applied this method in a practical case study of a highway tunnel project. Based on research findings from 122 seasonal frozen soil tunnels, [PERSON] et al. [45] identified four key influencing factors: hydrogeology, temperature, surrounding rocks, and engineering measures. He established an intelligent fuzzy comprehensive evaluation model, used the analytic hierarchy process to calculate the weights of each influencing factor, and identified the main risk sourcesof tunnel frost damage in cold regions, thereby avoiding the blindness in engineering construction. These studies suggest that the fuzzy analytic hierarchy process can enhance the scientificity and accuracy of decision-making in surrounding rock classification, disaster prevention, and disease remediation in tunnel engineering.  
The size of the plastic zone and the deformation of the surrounding rock formed after excavation disturbance are the main indicators reflecting the stability of the surrounding rock of a tunnel [7,15]. The stability of the surrounding rock of a loess tunnel with rich water not only depends on objective factors such as geological environment, tunnel section size, and tunnel depth, but also on various excavation and support production technical factors. Engineering disasters such as surrounding rock deformation, base softening, surface subsidence, sidewall sloughing, water and mud inrush, and lining cracking are the result of the combined influence of various factors. Therefore, using fuzzy Set theory to effectively measure various influencing factors and utilizing the principles of AHP to determine the weights between these influencing factors is conducive to decision makers formulating effective prevention and handling measures.
## 3 Results and Discussion
### Interfacial Shear Characteristics
#### 3.1.2 The Effect of Particle Size on Interfacial Shear Characteristics  
At the same surface hardness, the interfacial shear stress-shear displacement relationship curves for different particle size conditions are shown in Figure 6. Figure 6 shows the following: (1) For different particle sizes, the harder the surface is, the smaller the difference in the interfacial shear stress-shear displacement curves, which was mainly related to the plowing effect generated during the interfacial shear process. The plowing effect between soil particles and pile surface was mainly related to the angularity of the soil particles and surface hardness. The softer the surface hardness is, the more obvious the plowing effect. Additionally, the larger the particle size is, the sharper the angularity of the soil particles, and the more significant the corresponding plowing effect.  
Figure 5: Shear stress-shear displacement curves for different surface hardness. (**a**) Fine sand (0–90 mm). (**b**) Fine sand (0–10 mm). (**c**) Medium sand (0–90 mm). (**d**) Medium sand (0–10 mm). (**e**) Coarse sand (0–90 mm). (**f**) Coarse sand (0–10 mm).  
Figure 6: Shear stress-shear displacement curves of sand for different particle sizes. (**a**) #45 steel pile (Surface hardness HV = 660). (**b**) Aluminum alloy pile (Surface hardness HV = 350). (**c**) PMMA pile (Surface hardness HV = 95).  
(2) The initial slope of the interfacial shear stress-shear displacement curve (interfacial initial shear stiffness) decreased with increasing particle size for different surface hardness conditions. For the #45 steel pile, the initial shear stiffness of the interface was 626.5 kPa/mm for fine sand, 460.9 kPa/mm for medium sand, and 231.4 kPa/mm for coarse sand.  
(3) The peak interfacial shear stress in the #45 steel pile and aluminum alloy pile decreased with increasing particle size. Taking #45 steel pile as an example, the peak interfacial shear stress was 16.3 kPa in fine sand, 17.1 kPa in medium sand, and 17.8 kPa in coarse sand. However, the peak interfacial shear stress in the PMMA pile increased with increasing particle size. For a soft surface, the larger the particle size was, the smaller the contact area between the soil particles and the model pile surface. Thus, the interfacial shear process more easily produced the plowing effect, and the peak interfacial shear stress decreased with increasing the particle size.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 5 Results
### Security Settings
#### 5.4.3. Watermarking  
Selected:  
The most common reason for selecting the watermarking feature was the extra layer of protection, as stated by three participants for the employee form, two participants for the W-9 form, and five participants for the medical form (a comment is shown below). Other reasons for selecting this option for an employee form were curiosity, document protectionfrom unauthorized access, and prevention of document copying. For the W-9 form, participants selected this option to protect personal information (3/19), ensure data integrity (2/19), and prevent copying (1/19). For the medical form, one participant stated distinguishing between the original document and its copy as the reason for using this feature.  
Participant P18 commented: _\"I think watermarking just adds another layer of protection in case someone wants to download the file and send it to a third party on a different email... So, for instance, if I get an email back saying, Hey, we need you to take the watermarking off, for whatever reason, then I would resend it and take it off.\"_  
Not Selected:  
The main reason for not selecting the watermarking option was that the participants were unsure of the usage, since three participants for the employee form, six participants for the W-9 form, and three participants for the medical form mentioned this. Many participants shared that they thought this option was not needed for these documents: the employee form (10/19), W-9 form (4/19), and medical form (9/19). Additionally, people did not select this to avoid a bad impression about access (1/19), because the document was already secure (1/19), and it being inappropriate to use this feature (1/19) for the employee form. For example, participant P5 commented, _\"Since this is going on the company file, I figured that was inappropriate the situation because they can just maybe do that themselves, or you know I trust them to not need to have this on.\"_
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 5 Estimation of Tilted Global Irradiation (GTI) from Horizontal Global Irradiation (GHI)
### Method  
As in Section 4, the data used here were measured in Bouzareah.  
Four data, among them GHI, were used as input:  
* The declination representing the position of the Earth from the Sun depending on the day number;the zenith angle characterizing the sun position, which influences the quantity and the quality of the sun radiation; when the sun is high in the sky (low zenith angle), the solar radiation is maximal (in clear skies). Moreover, as the optical path is minimal, the incident radiation is less absorbed;
* the extraterrestrial irradiation, EHI, used as a reference; depending on sky conditions, several values of GTI correspond to the same GHI. In diffuse radiation models, the clearness or diffuse index are often used to characterize the sky. When the clearness index is high, then the sky is clear and GHI is mainly composed of BNI.  
According to the rules described in Section 3.2, the number of hidden neurons in only one hidden layer will vary from one to eight. The ANN structure is shown in Figure 6.
## 2 Application of Membranes in Contamination Situations
In recent years, ceramic or zeolite composite membranes have emerged as high-performance RO and NF membranes that have been successfully commercialized for the separation of pollutants [10]. These membranes are generally made of composite materials and contain various fibers such as hollow or structural fibers, or sheet nanostructures such as graphene and layered silicates in the polymer matrix [11]. Several problems with the use of RO membranes, such as their high energy requirements and fouling of membrane surfaces, have resulted in the development of techniques [12] and FO membranes in which the osmotic gradient across the membrane plays an important role in mass transport and separation [13]. FO is more suitable and energy efficient for the treatment of membrane fouling wastewater (e.g., landfill leachate) which may not be economical for RO. Initially, FO was regarded as an effective pretreatment step for subsequent processes in which purified water could be recovered from the dilute solution [14]. However, FO as a single filter technique also finds some niche applications, such as diluting fertilizers and thickening fruit juices [15].