esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### ibrip  
Experiments were performed as previously described [45]. Briefly, cells were crosslinked with 0.4 \(\mathrm{Jcm^{-2}}\) of UV\({}_{254\mathrm{nm}}\) in cold PBS, harvested by scraping and pelleted. Crosslinked cells were resuspended in DNase I digestion buffer containing 50 mM Tris pH 7.5, 0.5% NP-40, 0.1% sodium lauroyl sarcosine, protease inhibitors, RNasin, 10 mM vanadil ribonucleoside and 600 U Turbo DNAse and incubated at 37 \({}^{\circ}\)C for 15 min with rotation. Next, lysates were further supplied to the final concentrations of 1% sodium lauroyl sarcosine, 0.1% sodium deoxycholate, 0.5 M lithium chloride, 25 mM EDTA and 25 mM EGTA and incubated at 37 \({}^{\circ}\)C for 5 min. The lysates were spun at 2500 rpm 10 min and the supernatants were collected. The pellets were resuspended in lysis buffer containing 50 mM Tris pH 7.5,0.5 M lithium chloride, 1% NP-40, 1% sodium lauroyl sarcosine, 0.1% sodium deoxycholate 20 mM EDTA and 20 mM EGTA, incubated on ice for 10 min, heated to 65 \({}^{\circ}\)C for 5 min, and spun at room temperature; and the supernatant combined with the previous supernatants. The combined supernatants were pre-cleared by incubation with the washed MyOne streptavidin C1 magnetic beads (ThermoFisher) at room temperature for 30 min with rotation. The Streptavidin beads were then magnetically separated from the lysate samples using a Dynamag magnet (Life Technologies). Capture 3'biotin-TEG oligonucleotides and beads were mixed and incubated in 2\(\times\) binding buffer containing 10 mM Tris-HCl pH 7.5, 1 mM EDTA and 2 M NaCl at room temperature for 20 min, washed with 1\(\times\) binding buffer and then resuspended in lysis buffer. Pre-cleared lysates and oligonucleotides-conjugated beads were preheated to 65 \({}^{\circ}\)C for 5 min, mixed and incubated at 65 \({}^{\circ}\)C for another 15 min in the hybridization oven, followed by slowly reducing the temperature at 37 \({}^{\circ}\)C and 1 h incubation. Beads with captured hybrids were washed three times at 37 \({}^{\circ}\)C in wash buffer1 containing 50 mM Tris, pH 7.5, 0.3 M LiCl, 1% SDS, 0.5% NP-40, 1 mM DTT and protease inhibitors, followed by treatment at 37 \({}^{\circ}\)C for 10 min with 20 U of Turbo DNase I in DNase digestion buffer supplemented in 0.3 M LiCl, protease inhibitors and RNasein. After washing two more times at 37 \({}^{\circ}\)C with wash buffer1, the lysates were further washed at 37 \({}^{\circ}\)C for 5 min in wash buffer2 containing 1% SDS, 1 mM DTT, 5 mM EDTA, 150 mM NaCl and 1 mM PMSF. The proteins were eluted at 70 \({}^{\circ}\)C for 5 min in elution buffer containing 10 mM Tris, pH 7.5 and 1 mM EDTA. The eluted proteins eluted were lyophilized and stored at \(-20\)\({}^{\circ}\)C until processing for mass spectrometry. As the negative control, _LacZ_ (non-mammalian) capture probes were used. The Sequences of oligonucleotides are listed on Table S7.
## 4 Asphalt Binder Test Results Analysis
### Binder Fracture Energy  
The binder fracture energy test results under TFOT aging are shown in Figure 4a. It is seen that the trend between binder MSCR \(R_{3,2}\) and fracture energy matched well. Specifically, the SBS modified binders showed the highest MSCR \(R_{3,2}\) and fracture energy values, whereas the Evotherm M1 and 3G binders displayed the lowest magnitudes.  
Additionally, the fracture energy value of the SBS modified binder was obviously altered per temperature change. The plot between shear strain and shear stress that was used to calculate fracture energy was checked, as shown in Figure 4b. It is seen that the shear strains corresponding to the maximum shear stresses are close to each other, whereas the maximum shear stress values decreased with increased temperature. Thus, the reduced fracture energy for SBS modified binders at an elevated temperature was majorly caused by declined maximum shear stress.  
Figure 4: Binder Fracture Energy Test Results for (**a**) TFOT Aging; and (**b**) TFOT Aged SBS Binders at Varying Temperatures.  
The above analysis shows that the MSCR testing at intermediate temperatures is sensitive enough to differentiate various binder types and additive contents with good repetitions. Next, asphalt mixture SCB-IFIT and IDT testing were conducted to check if the asphalt MSCR test has a similar ranking with a mixture fatigue test. The SBS modified asphalt binder showed the best fracture resistance in the asphalt MSCR and fracture test, and was accordingly selected as the binder used in the mixture tests. Base binder was also selected as the control. Different aging durations of loose mix or compacted specimens were considered to evaluate if the relationship between asphalt MSCR and mixture fracture tests was stable under different oxidative conditions.
## 2 Related Work
### Mobile Anchor--Static Node
In this study, the authors used the MA for disaster management. Therefore, the necessity of the MA to avoid obstacles on the path was established. The MA moves along a predefined trajectory and broadcasts beacon messages at beacon points. When an obstacle is detected, the MA will move around the obstacle and broadcast location messages on roundabout flags. As mentioned, the collinearity problem, which is one of the most fundamental problems in designing static path planning models, stands out as the first condition to be addressed while developing a model. SCAN, DOUBLE- SCAN models were limited by this problem. The authors overcame this with the HILBERT model. However, this model also faces a coverage problem as it cannot reach the nodes at the corners of the ROI. Therefore, coverage is another of the conditions that should be considered in the design. Reliable channels should be used to achieve realistic results. If a static path planning model achieves successful results with different localization methods, it is adaptable to different environments. However, this was not demonstrated in the M-curves study. Although path length does not directly affect the localization error rate or the number of localized nodes, it helps determine the time required to complete the localization process. It can also affect other critical metrics such as energy consumption. Therefore, the path length of the designed path planning model should be as short as possible. The LMAT model is a path planning model with good localization performance in general. However, the path length is quite long. Additionally, note that the studies' performance evaluations in Figure 4 in the presence of obstacles have not been studied, except for Z-curve. This study's main contribution is to measure the performances of the proposed scheme and the path planning schemes in Figure 4 in the presence of obstacles and compare them using different evaluation criteria. All path planning models have been tested in the same environment, and this environment is the obstacle-presence environment where a third-order Z-curve is applied [9]. The obstacle-handling trajectories of the path planning models in this environment are given in Figure 5. Detailed information on the design stages of the path planning model proposed in this study and the comparison of the model's error performance in the obstacle-presence environment with the known and strong static path planning models in the literature will be given in the following sections. In addition, the localization performance of the model proposed in this study at low resolutions is superior to other models. The model, which gives very good results in high resolution, can give very bad results in low resolution. The opposite can also be seen. However, there are no major differences between high-resolution localization performance and low-resolution localization performance in the proposed model. Figure 5: The obstacle-handling trajectories of the path planning models.
# Residual Stress in Laser Welding of TC4 Titanium Alloy Based on Ultrasonic laser Technology
In this paper, ultrasonic laser technology, a noncontact method using laser for the generation and detection of ultrasonic waves, is used to measure residual stress in laser welding of TC4 titanium alloy. The technology is based on determining the small ultrasonic wave velocity change caused by stress. The residual stress of a TC4 titanium alloy plate is simplified to plane stress state. A method of pre-stress loading is proposed and the acoustoelastic coefficients are obtained. The residual stress distribution measured is compared with the results of strain gauge measurements. Then, the influence of heat input, welding speed, and heat treatment on residual stress is discussed. The results show that the effect of heat input on residual stress is significant. With increasing heat input, the peak value of residual stress increases. When the welding power is constant, faster welding speeds are preferred. Heat treatment can reduce the residual stress to a certain extent, but it is still necessary to control the residual stress by optimizing the welding process parameters. The contribution of this paper is important for the optimization of laser welding parameters and the evaluation of residual stress.
## 2 Study Area and Data
### Hj-1A/B Overview  
The HJ-1 constellation contains two optical satellites (HJ-1A/B, launched in September 2008). The primary goal of the optical satellites HJ-1A/B is to revisit any place in the world within two days for environmental and disaster monitoring [26]. To achieve the designed revisit frequency, HJ-1A/B were distributed in the same altitude and orbit plane, with a phase delay of 180 degrees [25]. The orbit of HJ-1A/B is sun-synchronous circular orbit at 649.093 km altitude with a 10:30 a.m. \(\pm\) 30 min descending node [32]. This satellite passing time was selected as the best compromise between minimizing cloud cover and ensuring suitable sun illumination. It is also close to the Landsat local overpass time and matches that of Terra MODIS. The HJ-1A/B has a global positioning system onboard to measure the exterior orientation, which can achieve systematic geometric location accuracy of 10-20 pixels.
# Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
## 5 Conclusions  
Crowd counting is becoming increasingly popular in computer vision, as it is relevant to an extensive range of applications. In supervised learning, particularly, its performance  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Frames per Second** \\ \hline CSRnet & 8\(\sim\)10 \\ SFCC & 3\(\sim\)5 \\ Ours & 1\(\sim\)2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Real-time analysis in the inference phase.  
Figure 9: Comparison of Computational Cost Analysis with other methods.  
has dramatically improved. However, in many real-world scenarios, the different angles, exposures, location heights, and complex backgrounds of photos, along with limited annotation data, lead to supervised learning methods not working satisfactorily, and many suffer from overfitting problems. In this research, we focused on training synthetic crowd data and examined how to transfer knowledge to real-world datasets in two key phases: feature extraction and density estimation. The adaptive domain-invariant feature-extracting module aims to align the feature level with the source and target domains. In addition, the density-map-estimator module, based on dynamic-\(\beta\) MAML, trains the model in few-shot scenarios to improve generalization. Furthermore, we used a counting-map refiner to optimize the coarse density map into a fine density map and then regressed the crowd size. Finally, we compared our proposed method to the benchmark and achieved superior performance in cross-domain scenarios. The proposed method also has some limitations, such as more time to train domain-invariant features in the domain-adaptation phase and the need for more synthetic data to cover different scenarios. However, the advantage is that synthetic data is easier to generate and label annotations for in batches than is real-world data, which is equivalent to replacing manual annotation time with computational time.  
Conceptualization, [PERSON] and H.X.; methodology, X.H.; software, [PERSON] and J.X.; validation, [PERSON], J.X. and X.H.; formal analysis, [PERSON] and J.W.; investigation, X.H. and J.X.; resources, X.H.; data curation, X.H., J.X. and J.W.; writing original draft preparation, X.H. and J.X.; writing review and editing, H.X. and J.X.; visualization, X.H.; supervision, J.X.; project administration, X.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request due to restrictions e.g., privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 3 The Proposed Model
### Model Description  
Inspired by FCN, a classical semantics segmentation algorithm, we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of encoder \(E\) and decoder \(D\). As shown in Figure 2, the encoder consists of residual network and dilated convolutions, while the decoder consists of deconvolutions. The encoder is used to extract feature maps from input images, which is a down-sampling process. After that, the feature extracted by encoder is enlarged by the decoder, and the final output is the probability maps with the same size as the input images. The whole model can be expressed as:  
\[M=D(E(X;\theta_{E});\theta_{D}) \tag{1}\]  
In Equation (1), \(\theta_{E}\) represents the parameters of the encoding module \(E\), \(\theta_{D}\) represents the parameters of the decoding module \(D\), \(M\) is a probability map with 2 channels, in which the value of each position represents the probability of crack and non-crack.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 5 Experimental Results and Evaluation  
In this section, we present the simulations to verify the routing algorithm with minimum global energy consumption under the constraints and evaluate the performance of the proposed algorithm.  
Firstly, we give the universal parameter settings for the simulations. For the energy model, we set noise power \(\sigma^{2}=10e-9\) and system bandwidth \(B=1\) MHZ. The distance between mobile devices follows the uniform distribution with \(d_{i,j}\in[10,20]\) m. The channel power gain between mobile devices is modeled as \(h_{i}^{m}=10^{-3}d_{i}^{-\alpha}\phi_{i}\), where \(\phi_{i}\) represents the short-term fading, which is assumed to be an exponentially-distributed random variable, and \(\alpha\) denotes the path-loss exponent and here is set \(\alpha=2\).  
The settings are used in the simulations in the following subsections unless specified otherwise. To avoid occasionality, the simulations are repeated 100 times for each variable value and we average the result, but the other unfixed parameters are randomly selected according to the above settings every time to simulate the real environment.
## 4 Temperature Finite Element Model and Test Comparison
### Temperature Finite Element Model
#### 4.1.2 Boundary and Initial Conditions for the Temperature Field  
Boundary condition settings are crucial in the numerical simulation of the temperature field for U-shaped girder bridges, which specifically include the following four aspects [45]: Shadow area boundary conditions: In the 3D finite element model of U-shaped girder bridges, surface elements in shadowed areas do not receive direct solar radiation, whereas elements within the sunlight areas are exposed to direct solar radiation. Solar scattered radiation: All surface elements of the structure can receive scattered solar radiation. Ground-reflected radiation: Only surface elements that ground-reflected sunlight can reach receive solar-reflected radiation. Radiation and convection between the structure and the atmosphere: For all surface elements of U-shaped girder bridges, the radiation between the structure and the atmosphere is combined with the convective effects, completing the interaction of radiation and convection between the structure and the atmosphere.  
The initial conditions need to be set accurately before determining the transient numerical solution of the temperature field of the U-shaped girder bridges [46]; that is, the temperature distribution of the U-shaped girder bridges at the initial moment must be defined. This study adopted a free vibration damping decay mechanism, assuming that the initial temperature field is uniformly distributed. The difference between the initial temperature field set by the model and the actual temperature field will gradually decrease according to an exponential decay pattern. As the computation time increases, this difference will continually decrease, eventually leading the temperature field of the U-shaped girder bridge to converge with the ambient temperature, ensuring the accuracy of the numerical simulation.
## 3 Model Results for the Tested Area
### Interpretation of Subsidence Causality Factors
#### 3.3.1 Rock Type  
The type of rocks is found to have an influence on Alexandria's surface deformation due to the city location on the North Western margin of the Nile Delta. Table 2 presents the highest, lowest, mean, and standard deviation values of deformation based on each lithological type. Artificial reclaimed surfaces on the Mediterranean Cost, carbonate ridges, and gravel in the west show stable surfaces compared to other city parts. Hence, those types of rock was assigned the lowest weight among other rock types combined with reclaimed sand with an average annual vertical velocity of \(-0.29\) mm. Stabilized sand dunes (\(-0.46\) mm/yr), and oolitic beach and beach ridge (\(-0.69\) mm/yr), were given a slightly higher weights of 7 and 8 respectively, given their relative low annual subsidence average.  
Sabkha deposits in the South Western part of Alexandria had the highest mean subsidence among all lithological types with a mean subsidence of \(-3.51\) mm/yr. This high subsidence average value resulted in a high weight for Sabkha deposits of 35. The Nile silt in the eastern and central parts of the city has the second subsidence average with value of \(-3.09\) mm/yr. Therefore, a relatively lower weight of 30 was given to this lithological type in contrast to the sabkha deposit.
## 3 Discussion
### Strain ZH27 Is a Promising Candidate for Pullulan Synthesis  
The target strain ZH27, while producing 40 g/L EPS, was obtained during the initial screening step. In comparison with EPS titers (<30 g/L) derived from the initial screening step in previous studies [2; 10], strain ZH27 is considered to produce a relatively high EPS titer. Moreover, it is even comparable to the EPS titer (45.7 g/L) produced by _A. melanogaster_ 13-2 at 96 h [8], suggesting that strain ZH27 may be a potential candidate for EPS production. It exhibited multiple cell types and variable colony colors (Figure 1A), which are quite similar to the features of _Aureobasidium_ strains [8; 9]. As well-known black yeasts, _Aureobasidium_ strains consist of five cell morphologies and synthesize several products (e.g., pullulan, polymalate, and biosurfactant) in response to environmental stress [26; 27]. These observations indicate that strain ZH27 may belong to one member of _A. melanogaster_ (Figure 1B). However, the colonies of strain ZH27 surrounded by radial mycelia displayed different characteristics compared with the colonies of other _A. melanogaster_ strains, which were covered by EPS but lacked radial mycelia [8; 13].  
Pullulan is composed of maltotriose units connected by \(\alpha\)-(1\(\rightarrow\)6) glycosidic bonds, which can be specifically hydrolyzed by pullulanase. Various methods, including TLC, FTIR, and nuclear magnetic resonance spectroscopy, have been employed to determine the molecular structure of pullulan [2; 19]. In this study, after optimization (discussed in the following part), the EPS produced by strain ZH27 was hydrolyzed by pullulanase, generating maltotriose and glucose (Figure 3a,b), which is consistent with previous results [10; 13]. Furthermore, FTIR analysis revealed that the EPS from strain ZH27 exhibited functional groups, such as O-C-O (1637.29 cm\({}^{-1}\)) and C-O-C (1159.03 cm\({}^{-1}\)), and characteristic \(\alpha\)-1,6-glucosidic and \(\alpha\)-1,4-glucoside linkages, which closely resembled those present in a standard pullulan (Table S2). These results are also in agreement with previous findings [2; 3; 28].
# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 2 Literature Review
### Relocation Model
The result showed that the proposed relocation model is more efficient in terms of serving a higher number of requests. Compared to operator based relocation algorithms, user based relocation, i.e., allowing the user to relocate the car, is relatively rare. [PERSON] et al. proposed a user based relocation scheme offering greater flexibility to the users [8]. The discrete event simulation (DES) model was applied to a case study to evaluate the proposed approach, and showed a significantly decreased rejection rate from the worst (no relocation) to the best (relocation of all vehicles by their users) scenario. [PERSON] et al. proposed simulation based user relocation methods for optimal vehicle assignment for station based one-way carsharing systems, including user based strategies of divided (trip splitting) and combined (trip joining) assignment [10]. Divided assignment can be implemented at a station with excess vehicles, reducing the number of vehicles at the initial station and increasing the vehicles at drop-off stations. Combined assignment can be implemented at a station that has less vehicles, increasing the available vehicles at the initial station and decreasing the parked vehicles at the drop-off station. They showed that this method was effective at keeping vehicle distribution balanced among stations for one-way carsharing systems. [PERSON] et al. proposed a forecasting model to predict the net flow of vehicles in a three-hour period using neural networks and support vector machines (SVMs) [12]. The net vehicle flow is supply (number of vehicles being returned to a station)--demand (number of vehicles being rented from a station). They showed that the multilayer perceptron neural network model had a slightly better forecast accuracy compared to SVM. [PERSON] et al. compared several time series forecasting methods, such as selective moving averages, [PERSON]'s model, [PERSON]'s model, and Tabu Search heuristics, and they were also compared to predict the net vehicle flow, with the predicted net flow being used by operators to decide the inventory and threshold of vehicles for each station [13]. They showed that implementing forecasting relocation models on real carsharing services improved the service level. [PERSON] et al. also studied the effects of the relocation time period, i.e., when to relocate vehicles; and showed that periodical relocation, i.e., relocation triggered every 6 h, had lower relocation costs compared to static relocation (immediate relocation once a station net flow exceeded or fell below a given threshold) [14]. Another critical factor influencing carsharing services is user behavior differences [15; 16; 17]. Many studies have shown that customers require a variety of transactions and distance configurations. Thus, this paper used a last-minute reservation, neural network based forecasting relocation models, and evaluated their performance with respect to various customer transactions by simulation.