esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature-Level Camera Style Transfer for Person Re-Identification
## 5 Experiments
### Ablation Study
#### 5.6.4. Comparisons on RAP Dataset  
The RAP dataset [46] is a newly published large-scale dataset which contains 84,928 images of 2589 identities. It is designed for both pedestrian attribute recognition and person re-ID tasks. The proposed CST approach is further evaluated on the RAP dataset. Following the dataset split protocol suggested by the authors of the RAP dataset [46], and the training settings suggested in [PERSON] et al. [47], the experiments are carried out using the MGN baseline [31]. The comparison results are listed in Table 7. It is shown that the CST approach improves the performance of the MGN baseline and outperforms the SOTA methods. The significant improvement in mAP demonstrates that the proposed CST approach can alleviate camera style variance in general.  
\begin{table}
\begin{tabular}{c c c} \hline
**Market-1501** & **mAP** & **Rank-1** \\ \hline MGN * [31] & 86.3 & 93.9 \\
**MGN + CST** & **87.9** & **95.0** \\ MGN-global & 83.5 & 92.7 \\
**MGN-global + CST** & **84.3** & **93.2** \\ MGN-part2 & 75.2 & 90.3 \\
**MGN-part2 + CST** & **76.3** & **91.0** \\ MGN-part3 & 77.2 & 91.2 \\
**MGN-part3 + CST** & **77.3** & **91.8** \\ \hline \end{tabular}
\end{table}
Table 6: The performance comparison when we applied the proposed CST on different branches of MGN. Results of the proposed methods are marked as +CST, and better results are marked in bold.  
* The results of MGN are reported by our reproducing.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**RAP** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline [PERSON] et al. (Upper Body) [47] & 42.4 & 64.8 & 81.4 & 85.9 \\ [PERSON] et al. (Full Body) [47] & 46.3 & 69.0 & 83.6 & 88.1 \\ MGN [31] & 48.1 & 71.2 & 85.5 & 90.5 \\
**MGN + CST** & **56.2** & **75.3** & **88.1** & **92.2** \\ \hline \end{tabular}
\end{table}
Table 7: The performance comparison between the proposed CST with the MGN baseline and state-of-the-art approaches on the RAP dataset. Results of the proposed method are marked as +CST, and better results are marked in bold.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Method** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline \multirow{4}{*}{Market-1501} & none & 75.0 & 89.1 & 96.4 & 97.9 \\ \cline{2-5}  & one-hot & 76.1 & 90.6 & 96.5 & 98.0 \\ \cline{2-5}  & concat & 76.0 & 90.6 & 96.4 & 97.7 \\ \cline{2-5}  & **ABN** & **78.9** & **91.3** & **97.4** & **98.5** \\ \hline \end{tabular}
\end{table}
Table 5: The performance comparison between different camera style transfer strategies. Better results are marked in bold.
# On Fuzzy and Case-Based Dynamic Software Development Process Modeling and Simulation Approach
## 6 Discussion
#### Limitations of the Current Approach and Future Works  
This research has some limitations. The main drawback is that the simulation results are based on randomly generated HF data. It would be more precise if data were to be gathered from a real SDP. Moreover, this could ensure reliable verification of the proposed fuzzy and case-based dynamic SDP modeling and simulation approach. Consequently, the following future works are planned:  
* Adapt other SDP methodologies with the proposed fuzzy and case-based dynamic SDP modeling and simulation approach.
* Gather data from real SDPs and prepare them for model verification.
* Extend experiments with different SDP data, such as by including new HF input parameters, changing or combining agent roles, and so on.
* Investigate the overfitting behavior of the model.
## 5 Results
### Ranking Frequency Error of Stand-Alone MCDMs and AHP-MCDMs  
The obtained ranking results were showing frequency errors when comparing each applied method, which could mislead the decision. This problem was solved by evaluating the frequency of the same rankings. The pairwise frequency error was calculated by Equations (1) and (2). The sum of the standard deviation defines the frequency error and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Alternatives/Interventions Priority Results**} \\ \cline{2-5}  & **A1** & **A2** & **A3** & **A4** & **A5** \\ \hline \(1\)—ELE-NS & 3 & 5 & 1 & 2 & 4 \\ \hline \(2\)—ELE-NI & 4 & 5 & 1 & 3 & 2 \\ \hline \(3\)—MOORA & 2 & 3 & 4 & 1 & 5 \\ \hline \(4\)—PROMETHEE & 4 & 5 & 1 & 2 & 3 \\ \hline \(5\)—TOPSIS & 3 & 2 & 4 & 1 & 5 \\ \hline \(6\)—VIKOR & 2 & 3 & 5 & 1 & 4 \\ \hline \(7\)—WPM & 4 & 5 & 1 & 2 & 3 \\ \hline \(8\)—WSM & 4 & 5 & 1 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Rank calculated using MCDMs with L\({}_{\text{nN}}\) method for normalization.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Name**} & **Results Consistency** \\ \hline L\({}_{\text{N-i}}\) & ELE-NS, ELE-NI, PROMETHEE, WSM \\ \hline L\({}_{\text{N-ii}}\) & ELE-NS, ELE-NI, PROMETHEE, WSM \\ \hline L\({}_{\text{N-max-min}}\) & ELE-NS, PROMETHEE, WSM \\ \hline E\({}_{\text{AN}}\) & ELE-NS, PROM, WSM, WPM \\ \hline L\({}_{\text{nN}}\) & WSM, WPM, TOPSIS, PROMETHEE, MOORA \\ \hline V\({}_{\text{N}}\) & WSM, PROMETHEE, ELE-NS \\ \hline L\({}_{\text{N-Sum}}\) & WSM, PROMETHEE, ELE-NS, ELE-NI \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of normalization methods.  
assist to observe the variation in decision by applying different MCDMs using the same judgments. The method aims to check the consistency of the results and to evaluate the reliability of outcomes. The ends results are shown in the pairwise matrices in Table 8.  
\[E_{ij}=\sqrt{\sum_{k=1}^{5}\left(A_{kM_{i}}-A_{kM_{j}}\right)^{2}} \tag{1}\]  
\[E_{j}=\sum_{i=1}^{8}\ E_{ij} \tag{2}\]  
where: \(i=\) no of rows, \(j=\) no of columns, \(k=\) no of alternatives, \(M=\) no of methods, \(A\) = ranking result of alternative, and \(E_{j}\) = sum of the variation in ranking results.
# Health Care Accessibility Analysis Considering Behavioral Preferences for Hospital Choice
## 3 Case Study and Analysis
### Data Collecting and Pre-Processing
#### 3.2.2 Questionnaire Data  
To quantify residents' healthcare choice habits, a questionnaire form was designed for this study, including residents' residential addresses, frequently visited hospital and tolerable travel time. A total of 98 valid online questionnaires were received, and whether the frequently visited hospital was the distance closest or the least time-consuming was analyzed. If one healthcare choice was the closest distance but not the least time-consuming, then the residents' healthcare choice habit was considered to be distance-priority. Otherwise, the residents' healthcare choice habit was considered to be time-priority. Finally, based on the ratio of the two types, a distance weight of 0.315 and a time weight of 0.685 were calculated.
## 3 Results and Discussion
### Compressive Strength
#### 3.2.1 Dry Compressive Strength  
The evolution of dry compressive strength (DCS) as a function of fiber content for different cement content is illustrated in Figure 15. From the obtained results, it can be  
Figure 14: Effect of varying Juncus fiber contents on the capillarity coefficient of CEBs stabilized with different percentages of cement.  
observed that for each cement content, the incorporation of fibers leads to a decrease in DCS for all the specimens tested, compared to the composite without fibers, and the highest value corresponds to the sample with a cement content of 12% and without fibers, whereas the lowest value corresponds to the sample with a cement content of 4% and a fiber percentage of 0.2%. The graph shows clearly that an increase in the fiber ratio decreases the compressive strength. The decrease in strength between earth blocks with 0.2% fiber reinforcement and earth blocks without fibers is 10%, 15% and 17%, respectively, for 4%, 8% and 12% cement. Consequently, the incorporation of JF has a negative effect on DCS. Figure 15 shows also that the DCS increases with an increasing cement content, e.g., the average value of DCS for the unreinforced blocks is 7.16 MPa, 9.11 MPa, and 11.08 MPa for 4%, 8%, and 12% cement, respectively. The obtained results are in good agreement with previous work from the literature on the influence of cement content on the strength of CEBs filled with date palm fibers. This behavior was noticed by [PERSON] et al. [12], where similar correlations between cement content and DCS were highlighted. They reported in their study that the DCS reached average values of 7.5 MPa, 9.75 MPa, and 12.2 MPa for 5%, 6.5%, and 8% cement contents, respectively. This finding is also in agreement with the finding of [PERSON] et al. They concluded that the increase in cement from 4% to 12% led to an increase in DCS from 2.2 MPa to 5.00 MPa and that the better result of 6.20 MPa corresponded to 20% cement stabilization for the unreinforced CEBs [25]. On the other side, and based on our findings, the decrease in compressive strength for fiber addition could be explained by the low stiffness of JF, compared to earth-cement materials. This behavior has been highlighted by previous works [15, 52, 53]. It was noticed that the incorporation of fibers increases the void network in the blocks, which is the main factor leading to decreased DCS. Moreover, the weak interfacial bond between fibers and the matrix decreases the mechanical performance. Also, the non-homogeneous dispersion of fibers during the preparation of blocks related to their baling effects leads to lower compressive strength. It should be mentioned that the fractures are initiated around the surface of the fibers when the CEB is loaded, which highly accelerates the failure of the composite. Overall, it must be concluded that, in spite of this reduction in strength, the values found for DCS were very satisfactory, since it is recommended that the dry strength for CEBs be superior to 2.4 MPa at 28 days according to [PERSON] et al. [54].
## 2 Problem Description and Model Analysis  
### Problem Description  
The composition of the stepless flow control system for a reciprocating compressor is shown in Figure 1, which includes hydraulic, control, mechanical, and gas systems. The control system provides the control signal to the hydraulic system, and the hydraulic system provides the hydraulic pressure to the mechanical system. Since there is no feedback between the control and hydraulic systems, the two are coupled in one direction. The mechanical system provides motion-state parameters to the gas system, and the gas system feeds back gas forces to the mechanical system; therefore, the two subsystems are bi-directionally coupled. The stepless flow control system of the reciprocating compressor presents the characteristics of multi-system coupling, a complex coupling relationship, and the intersection of multiple parameters among subsystems.  
The valve is the coupling point of the mechanical and gas systems (Figure 2). At different stages, the movement of the valve plate directly affects the thermodynamic parameters, such as the pressure and flow rate of the gas in the cylinder. [PERSON] [5] and [PERSON] [8] observed that valve plate withdrawal is controlled by the unloader, and the decrease in the unloader withdrawal speed directly causes a change in the thermodynamic properties of the gas system, and the upper limit of the unloader is located on the valve plate. Therefore, the ejection and withdrawal speeds of the unloader can be optimized to reduce the impact of the unloader on the gas valve and valve plate on the sealing surface, while ensuring the thermomechanical properties of the gas system.  
The oil inlet pressure and reset spring force are the main power sources and resistance sources for the unloader ejection process, respectively, and the reset spring force and oil return pressure are the main power sources and resistance sources for the unloader withdrawal process, respectively. The matching values of the three parameters determine the unloader motion characteristics.  
Therefore, the oil inlet pressure (\(p_{in}\)), oil return pressure (\(p_{bf}\)), and reset spring stiffness (\(K_{mu}\)) were used as optimization variables in this study. The kinematic characteristic parameters of the unloader, i.e., ejection speed (\(v_{mu}^{ej}\)) and withdrawal speed (\(v_{mu}^{re}\)), and the thermodynamic property parameters of the system, i.e., gas work deviation (\(Error\_W\)) and flow displacement deviation (\(Error\_V\)), were the optimization objectives. Under different load conditions, the optimum adjustment area of the optimization variable is determined, to achieve the global minimum of the optimization objectives. On the premise of guaranteeing the regulating effect of the flow-regulating system of the compressor, the service life of valves and unloaders is extended, the maintenance cost of the compressor is reduced, and the economic output value of the compressor is increased.
# A Multi-Degree of Freedom Tuned Mass Damper Design for Vibration Mitigation of a Suspension Bridge
## 3 Numerical Optimization and Simulation
### Numerical Optimization Results  
Two TMD devices are used to damp the mode pairs (1B,2T) and (2B,1T); one TMD is placed at the quarter length of the deck (TMD1), the second is located at the center of the deck (TMD2). A detailed description is shown in Section 4.2. A disturbance force \(f_{d}\) is applied at one fixed point of the deck edge and the displacement \(z\) is measured at another fixed point, as shown in a small graph of Figure 3. According to Equation (22), the frequency response functions of the primary system with two 2 DOFs TMDs are optimized, and the optimum frequency ratios and TMD damping ratios are obtained, as listed in Table 2.  
Figure 3: Numerical results: FRFs of the deck without and with the TMDs. The TMDs are targeted for the damping of the mode pair (2B,1T) and (1B,2T).  
Figure 3 shows the frequency responses of the deck _z/f\({}_{d}\)_, when: (i) without any TMD; (ii) the four classical TMDs are targeting to damp the two mode pairs (2B,1T) and (1B,2T), which is achieved by Den Hartog criterion; and (iii) the two 2 DOFs TMDs are targeting to damp the two mode pairs (2B,1T) and (1B,2T), which is optimized by that introduced in the present paper. The results indicate that the 2 DOFs TMD concept model is effective to suppress both the bending modes and torsional modes of the bridge system at the same time. But, if using the classical configuration of the TMD, we need four TMDs, each of them is tuned on a single mode at the same time: two translation TMDs, with a lumped mass identical to that used in the 2 DOFs TMD, and two other TMDs with moment of inertia identical to that of the 2 DOFs TMD too. Hence, comparing with the classical configuration of TMD, the two 2 DOFs TMDs can reduce the weight penalty.  
Figure 4 shows the FRF for different sensor locations, with the same TMD design as in Figure 3. This figure demonstrates the robustness with respect to the FRF used in the TMD design. Figure 5 plots the frequency response curve of the bridge equipped with TMD1, for different values of the TMD1 damping ratio \(\xi\). Here, the TMD1 damping ratio \(\xi\) is selected as 10% \(\xi_{opt}\), 25% \(\xi_{opt}\), 50% \(\xi_{opt}\) and \(\xi_{opt}\), respectively, and the \(\xi_{opt}\) is the optimum damping ratio of TMD1, which is listed in Table 2. From this figure, we see that the dynamic responses of the bridge deck always tend to decrease on increasing the damping ratio of TMD1. Furthermore, the frequency ratio is insensitive to the TMD damping ratio, but if the action is not perfectly resonant, the performance of TMD may decay seriously even though the value of the TMD damping ratio is very high [35]. For TMD structure design, unlike the damping ratio \(\xi_{j}\) which is difficult to quantify, the frequency ratio \(\
u_{j}\) is important parameter which can be used to guide the following TMD structural parameters design.
## 3 Retrieval
### Setup
#### 3.1.2 Measurement Error Covariance Matrix \(\mathbf{S}_{\epsilon}\)  
Strictly speaking, the measurement error covariance matrix does not only quantify the measurement errors and their correlations; it, additionally, accounts for the forward model error. However, for this study, we assume the measurement error to dominate and that no cross correlations  
Figure 3: SCIATRAN simulated OCO-2 measurement fitted with FOCAL. Geophysical _baseline_ scenario and _0-Scat_ retrieval setup, \(\theta_{0}=40^{\circ}\), parallel polarization. See Section 3.2 for definitions of geophysical scenarios and retrieval setups. **Top**: Simulated and fitted radiance measurement in gray and red, respectively. **Bottom**: Simulated measurement noise and fit residual \(\vec{\Delta y}=\vec{I}_{2}-\vec{I}_{1}\) (fit minus measurement) in gray and red, respectively. An estimate of the goodness of fit (relative to the noise) in fit window \(j\) is computed by \(\chi_{j}=(\frac{1}{m_{i}}\,\vec{\Delta y}_{j}^{T}\,\mathbf{S}_{\epsilon}^{-1} \,\vec{\Delta y}_{j})^{1/2}\).  
exist, i.e., \(\mathbf{S}_{c}\) becomes diagonal. We use the noise parameterization as provided by the same OCO-2 L1b example file mentioned above to compute the diagonal elements of \(\mathbf{S}_{c}\). The measurement error covariance matrix is of dimension \(m\times m\) and an example is illustrated in Figure 3 (bottom).
## 2 Materials and Methods
### Data Preparation
The shear zone is defined as the occurrence of rapid and increasing displacement at a specific height over time. The shear zones were identified using a method based on the detection of anomalies using the DBScan algorithm, which is described in detail in [12]. The method uses a clustering algorithm to detect outliers that are not classified into any of the groups. Shears, defined as outliers, are best visible for the value of the derivative of the displacement. A sudden change in the slope of the displacement curve causes a sudden change in its derivative. The displacement value, its derivative, and height are the variables used in the model for shear detection. Based on these variables, the algorithm clusters the data into groups. All values not assigned to any of the groups, i.e., outliers, indicate shearing. To improve the results, the procedure was enhanced by smoothing the raw displacement signal with a median filter to remove noise that should not be considered shearing. However, the final results were validated by removing incorrectly detected shears. For this purpose, the angle of the shear was measured, and those that did not exceed the predetermined value were discarded. The shear zones determined in this way make it possible to determine the size of underground displacements resulting from shear. As in the case of benchmark displacements, it is possible to determine the velocity of these displacements as the value of the annual increment, and similarly acceleration--as the value of the velocity increment. Benchmarks and inclinometers may be located at various distances from each other, which are often relatively short. There may be a correlation between displacements recorded by devices located close to each other, and it can also be assumed that the displacement at a given point will be influenced by displacements in its vicinity. Therefore, a new variable was introduced to improve the analysis of spatial relationships, which provides information about changes in the neighborhood. This variable is the weighted average of benchmark displacements located within a certain distance from the selected benchmark. The weights were determined using the distance band method, considering the uneven distribution of measuring points. The procedure for calculating the value of the variable is as follows:  
* For each benchmark, a group of \(n\) neighbors is determined; these are benchmarks located within a radius smaller than \(d_{max}=700\)m (relative to the Euclidean distance) from the selected benchmark. * For each adjacent benchmark, a weight (\(w_{i},i\in(1,n)\)) is determined inversely proportional to the Euclidean distance (\(d_{i}\)) (the smaller the distance, the greater the weight, i.e., closer measurements have a greater impact on the result). \[w_{i}\sim\frac{1}{d_{i}},\] (7) where \(\sum_{i=1}^{n}w_{i}\)
* The variable displacement of adjacent benchmarks (\(x_{w}\)) is calculated as the sum of the products of displacements (\(x_{i}\)) and weights (\(w_{i}\)), i.e., the weighted average. \[x_{w}=\sum_{i=1}^{n}w_{i}\cdot x_{i}\] (8)
## 1 Introduction  
Wireless sensor networks are composed of a set of tiny autonomous nodes deployed with the purpose of collecting information from a supervised environment [1,2]. Data collected by sensors are usually transmitted via multihop communication to the sink. It is assumed that the sink node has unlimited computational resources. This operation is named convergecast in the literature [3,4]. There are many examples of convergecast applications: smart environments, habitat monitoring, monitoring of areas that present risks to humans (volcanoes, seismic instability regions, regions susceptible to the occurrence of hurricanes), precision agriculture, traffic control, _etc_.  
Convergecast uses a many-to-one communication model, while a typical _ad hoc_ network uses a many-to-many communication model. Therefore, routing protocols for _ad hoc_ networks are inefficient for WSNs. Trees, on the other hand, are traditional routing structures widely used in WSNs and present several advantages [5]. Sensor nodes need only to forward the collected data to their parent node in the routing structure and, thus, can easily select the appropriate transmission power to reduce power consumption. There is no need for a frequent route discovery mechanism, and sensor nodes do not have to maintain a table to store all routing paths. Moreover, trees are also the most appropriate structure for TDMA-based WSNs and for the use of a data aggregation strategy [3].
## 4 Numerical and Experimental Investigation of MR Semi-Active Control
### MR Damper Response  
The MR damper's force at each moment was obtained by the force sensor during the test. The hysteretic curves of the dampers under 0.72 g earthquakes of El Centro earthquake and Taft earthquake have been shown in Figure 14.  
As can be seen in the figures above, the damper hysteresis curve is plump. As a result, a large amount of seismic energy is dissipated by the MR damper which eventually caused the energy reduction in the upper part of the structure.