esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cooling System Design and Cooling Simulation Based on UG and Moldflow
### Selecting the Proper Type of Conformal Cooling Channels for Thin-Wall Products  
Owing to the geometric liberty afforded by rapid metal processing techniques, such as selective laser sintering and selective laser melting, the fabrication of conformal cooling channels with versatile cross-sectional profiles and geometries is feasible. Among the prevalent options, spiral-shaped conformal cooling channels are commonly deployed due to their ability to maintain near-constant coolant velocity, despite their potential for elevated pressure drop. Conversely, scaffolded conformal cooling channels and Voronoi diagram channels exhibit lower pressure drops but pose challenges in achieving uniform coolant velocities, which may result in stagnation in certain channel segments.  
The impact of a channel cross-section on cooling performance was investigated by [PERSON] et al., who found that rectangular channels demonstrate superior cooling performance. However, this type of cross-section may not fully align with the manufacturabilityrequirements of additive manufacturing methods. On the other hand, elliptical, circular, and teardrop cross-sections are characterized by easier printability, without the need for support structures and stress concentrations.  
Considering the cylindrical shape of the target product, and taking into account the aspects of product shape, manufacturing cost, and feasibility of production processes, we chose spiral-shaped cooling channels with circular cross-sections. Figure 7 displays the model of simple spiral-shaped cooling channels.
## 3 A Feasible Solution and the Mathematical Formulations
### Mathematical Formulations
#### 3.2.2 Variables  
* \(x_{ij}\): This is a binary variable that is equal to one if node \(i\in V\) is assigned to controller \(j\in V\); otherwise, \(x_{ij}\) equals zero.
* \(y_{i}\): This is a binary variable that equals one if node \(i\in V\) acts as a controller node in the backbone SDN, and it equals zero otherwise.  
Figure 1: Representation of the Internet2 OS3E instance, which is divided into the input graph and separation between the switch and controller nodes on the left side of the figure. The graph of network Internet2 OS3E shows purple dots for the network switches, blue squares for the network controllers, and green lines for the installed network links. The right figure represents the connections between switches and controllers in the network solution. In particular, purple dots denote the network switches, the blue squares denote the network controllers, and the red lines, the connections between the switches and controllers, which are indirect and follow the shortest path according to the installed network.
# Image-to-Image Translation Using a Cross-Domain Auto-Encoder and Decoder
## 4 Results
### Datasets and Evaluation Metrics  
First, we used the cityscapes dataset [32] presented in a previous work for a quantitative comparison. The cityscapes dataset contains various distributions of pairs for semantic segmented images and photorealistic images of city pictures taken on the road, and it is a common and challenging dataset used to test image-to-image translation performance [3; 8]. We used 2975 image pairs for training and 500 pairs for testing. We also tested our approach on the horse2 zebra dataset to see how it affects the qualitative performance of object transfiguration. We used 1067 horse images and 1334 zebra images for training, and 120 horse images and 140 zebra images for testing. Cat2 dog, maps (satellite map), summer2 winter, and night2 day datasets were also used for qualitative comparisons.  
For measuring metrics to evaluate our models, we referred to the metrics used in existing methods: mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), inception score (IS), and Frechet inception distance (FID score). MAE and MSE are the most common ways of measuring the accuracy of the result images in computer vision tasks [15; 33]. In some cases, however, these are not appropriate for evaluating the results of the GAN approach, and thus, various methods use PSNR or SSIM to assess the quality of the GAN results quantitatively [17; 34; 35; 36; 37]. Recent methods also used the inception score [16; 38], or FID score [16; 39] to measure the visual quality and diversity of generated images. In this work, we used all six metrics for evaluation.  
Figure 5: CycleGAN-based generator architecture of our model. Cross-connected structure of the pre-trained encoder and decoder with feature mapping layer and residual connection of residual block. (**a**) Generator in our model for translating images from domain X to Y. (**b**) Generator in our model for translating images from domain Y to X.
## 3 Experimental Section
### Mass Spectrometry Analysis  
Protein spots were excised and prepared for mass spectrometry analysis (MS) as previously described [19]. Tryptic digests were separated on an UltiMate 3000 HPLC system (Thermo Fisher Scientific, Dionex, CA, USA). Peptides were loaded on a trapping column (PepMap C18, 5 um particle size, 300 um i.d. \(\times\) 5 mm) equilibrated with 0.1% TFA (trifluoracetc acid) and separated on an analytical column (PepMap C18, 3 um, 75 um i.d. \(\times\) 150 mm) applying a 60 min linear gradient from 2.5% up to 40% ACN with 0.1% formic acid. The HPLC was directly coupled to an LTQ XL linear ion trap mass spectrometer (Thermo Fisher Scientific, Dionex, CA, USA) equipped with a nanoelectrospray ionization source (Proxeon). The electrospray voltage was set to 1,500 V. The mass spectrometer was operated in the data-dependent mode: 1 full scan (_m_/_z_: 450-1,600) was followed by maximal 10 MS/MS scans with CID. When a neutral loss of 32.6 or 49 Da was detected, a MS3 scan was triggered. The collision energy was set at 35%, Q-value at 0.25 and the activation time at 30 ms. Fragmented ions were set onto an exclusion list for 20 s.  
Raw spectra were interpreted by Mascot 2.2.04 (Matrix Science) using Mascot Daemon 2.2.2. The peptide tolerance was set to 2 Da, MS/MS tolerance was set to 0.8 Da. Spectra were searched against the Arabidopsis database from 09.09.2009 (TAIR), with tryptic specificity and 2 missed cleavages allowed. Carbamidomethyl was set as a fixed modification of cysteine, oxidation of methionine and phosphorylation of serine and threonine were set as variable modifications.  
MASCOT results were loaded into Scaffold (Version 2.04, Proteome Software). Peptide identifications were accepted, if they could be established at greater than 95% probability as specified by the Peptide Prophet algorithm. To be considered as relevant, protein identifications required a minimum of two unique peptides.
# Controlling Traffic Congestion in a Residential Area via GLOSA Development
## 2 Materials and Methods
### Software VISSIM  
In this context, VISSIM version (PTV 2022 Student) has emerged as a user-friendly platform that addresses these limitations. It models traffic actors like drivers, traffic lights, and autonomous vehicles as autonomous agents and uses AI techniques to enable short-term traffic light control, route replanning, and demand-supply coeffects studies [12]. With the increased dissemination and computing power of mobile devices, distributed AI applications like intelligent routing algorithms and distributed optimization of traffic lights have become feasible. The goal is to use a simulation to improve transportation in a target area and keep vehicles away from residential areas during rush hours to reduce sources of pollution [23]. The target group of this research is drivers.  
PTV Group created VISSIM, which is a straightforward traffic simulation program. VISSIM stands for simulation model, which translates to Traffic in Cities Simulation Model. It is widely used by transportation planners, engineers, and researchers to model and analyze various traffic scenarios. It simulates individual vehicles and their interactions with each other and the road network, providing detailed information about traffic flow, travel times, delays, and safety. This software uses advanced algorithms to model complex traffic situations, including signalized and unsignalized intersections, roundabouts, and highway networks. It also supports the integration of public transport, pedestrians, and bicycles into the simulation.  
In addition, VISSIM is a powerful tool for transportation planning and analysis, allowing users to evaluate and optimize traffic operations, safety, and environmental impact [24]. It is widely used in research, urban planning, and traffic engineering in smart city projects worldwide.
## 3 Results
### Volatile Metabolites
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & **Washed** & & \\ \cline{2-6}  & \multicolumn{4}{c}{_Canephora_} & & & _Arabica_ & \\ \cline{2-6}  & **Light** & **Medium** & **Dark** & **Light** & **Medium** & **Dark** \\ \hline
**Pyroels** & 1-methyl-pyrrole & 1251:09 \(\div\) 78.49 & 1609.56 \(\div\) 51.72 & 1067.36 \(\div\) 50.44 & 1394.81 \(\div\) 198.7 & 1460.39 \(\div\) 133.85 & 1963.01 \(\div\) 49.34 \\
1-ethyl-pyrrole & 694.41 \(\pm\) 4.92 & 474.82 \(\pm\) 8.53 & 356.15 \(\pm\) 46.49 & 246.55 \(\pm\) 46.62 & 292.73 \(\div\) 22.86 & 259.27 \(\div\) 109.96 \\
1-pently-pyrrole & 981.21 \(\div\) 41.82 & 636.35 \(\div\) 55.84 & 562.95 \(\pm\) 65.38 & 160.55 \(\pm\) 24.53 & 237.19 \(\div\) 44.92 & 214.84 \(\div\) 3.57 \\ pyrrole & 533.38 \(\pm\) 5.32 & 504.53 \(\pm\) 28.89 & 326.06 \(\div\) 39.78 & 284.17 \(\div\) 62.43 & 316.42 \(\div\) 46.40 & 509.91 \(\div\) 25.64 \\
1-methyl-pyrrole- & 77.64 \(\pm\) 5.93 & 58.14 \(\pm\) 7.19 & 61.60 \(\pm\) 8.07 & 87.50 \(\pm\) 25.05 & 107.72 \(\div\) 14.97 & 115.49 \(\div\) 9.23 \\
2-acetyl-1-methyl- & 16.56 \(\pm\) 0.80 & 10.61 \(\pm\) 1.62 & 12.34 \(\pm\) 2.10 & 13.80 \(\pm\) 2.67 & 16.15 \(\pm\) 2.25 & 16.94 \(\pm\) 0.90 \\
2-ethyl-2-methyl- & 47.05 \(\pm\) 2.05 & 47.31 \(\pm\) 2.77 & 43.12 \(\pm\) 3.93 & 11.28 \(\pm\) 3.06 & 11.40 \(\pm\) 1.30 & 16.08 \(\pm\) 0.62 \\
1-furfurfuryl-pyrrole & 214.71 \(\pm\) 2.96 & 124.27 \(\pm\) 9.91 & 121.91 \(\pm\) 10.93 & 100.20 \(\pm\) 11.79 & 115.61 \(\pm\) 11.36 & 132.21 \(\pm\) 2.79 \\
2-acetyl-pyrrole & 59.22 \(\pm\) 14.65 & 22.24 \(\pm\) 5.27 & 34.25 \(\pm\) 7.63 & 22.78 \(\pm\) 2.52 & 45.84 \(\pm\) 11.90 & 28.99 \(\pm\) 2.25 \\
1-HYP-role-2 & 170.20 \(\pm\) 54.82 & 64.28 \(\pm\) 15.64 & 73.27 \(\pm\) 3.71 & 104.26 \(\pm\) 16.33 & 194.13 \(\div\) 54.17 & 118.18 \(\pm\) 12.37 \\
**Pyrozes** & & & & & & \\
2-methyl-pyrazine & 217.09 \(\pm\) 2.03 & 244.91 \(\pm\) 50.53 & 251.04 \(\pm\) 49.33 & 163.91 \(\pm\) 60.36 & 136.38 \(\pm\) 37.05 & 170.44 \(\pm\) 15.83 \\
2-6-dimethyl-pyrazine & 58.03 \(\pm\) 0.71 & 75.32 \(\pm\) 19.81 & 71.78 \(\pm\) 10.42 & 36.24 \(\pm\) 15.92 & 27.88 \(\pm\) 10.10 & 29.34 \(\pm\) 3.77 \\
2-dimethyl-pyrazine & 54.93 \(\pm\) 1.72 & 65.42 \(\pm\) 9.32 & 59.49 \(\pm\) 10.60 & 36.23 \(\pm\) 15.36 & 26.57 \(\pm\) 9.32 & 32.08 \(\pm\) 4.02 \\
2-ethyl-pyrazine & 116.96 \(\pm\) 0.64 & 123.86 \(\pm\) 18.15 & 112.57 \(\pm\) 15.10 & 76.22 \(\pm\) 24.95 & 77.45 \(\pm\) 19.21 & 92.60 \(\pm\) 4.30 \\
**Furan derivatives** & & & & & & \\
2-pently-furan & 4625.53 \(\pm\) 227.61 & 6613.43 \(\pm\) 222.83 & 6125.76 \(\div\) 77.02 & 4307.49 \(\pm\) 784.14 & 3226.46 \(\pm\) 1475.85 & 5674.83 \(\pm\) 151.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Content (%/kg; mean \(\pm\) standard deviation) of volatile compounds identified in CS samples.
## 2 OBS Networks
### Functional Mechanisms of OBS Nodes
#### 2.2.2 Signaling  
For transporting an assembled burst over the optical core a temporary connection must be established between a given pair of edge nodes. This procedure, by which the allocation and configuration of network resources required for burst transmission is accomplished, starts with signaling and proceeds with routing and wavelength assignment. Signaling is, therefore, an important aspect of any network architecture. It specifies the protocol by which connection requests are handled, and its operation determines whether or not the network resources are efficiently utilized. For OBS networks it is even more critical due to the bufferless nature of the core network where contention for resources can lead to data loss. In OBS networks signaling is implemented out-of-band, either by means of a dedicated wavelength channel or by means of a separate control network, and can rely on a distributed approach using one-way reservation or on a centralized approach using end-to-end reservation. Several variations of signaling protocols exist for OBS depending on how and when the resources along a route are reserved for a burst. Their underlying principles can be characterized by the following general attributes [23,24]: direction of the connection setup, which can be one-way or two-way; the starting point where the resource reservation  
Figure 3: Functional architecture of OBS nodes (adapted from [18]).  
process initiates, where source initiation or destination initiation can be considered; reservation/release of wavelength channels, where explicit or implicit schemes can be adopted.
# Force Analysis of Masonry Cave-Dwelling Structure Based on Elastic Center Method
## 2 Research Object and Methodology
### Derivation of Internal Force Calculation Formula Based on Unhinged Arch Model
#### 2.3.5 Calculation of Principal Coefficients and Free Items
\begin{array}{rl}EI\Delta_{2P}&=\int\overline{M}_{2}M_{P}(x)ds=\int\big{(}y -y_{s}\big{)}M_{P}(x)ds=\int R\Big{(}\frac{\sin\varphi_{B}}{\sigma_{B}}-\cos \varphi\Big{)}M_{P}(\varphi)ds\\ &=-2\gamma_{2}R^{4}\int_{0}^{\sigma_{B}}\Big{(}\frac{\sin\varphi_{B}}{\sigma_ {B}}-\cos\varphi\Big{)}\times\Big{(}\frac{\sin^{2}\varphi}{2}-\frac{\sin \varphi\sin\varphi}{2}-\frac{\sin\varphi\sin 2\varphi}{4}-\frac{\cos^{3}\varphi}{3}+ \frac{1}{3}\Big{)}Rd\varphi\\ &+2R\int_{0}^{\sigma_{B}}\Big{(}\frac{\sin\varphi_{B}}{\sigma_{B}}-\cos\varphi \Big{)}\times\Big{(}-\frac{P_{t}}{2}R^{2}\sin^{2}\varphi\Big{)}Rd\varphi\\ &=-\gamma_{2}R^{5}\frac{\sin\varphi_{B}}{\sigma_{B}}\int_{0}^{\sigma_{B}}\Big{(} \sin^{2}\varphi-\varphi\sin\varphi-\frac{\sin\varphi\sin 2\varphi}{2}-\frac{2\cos^{3} \varphi}{3}+\frac{2}{3}\Big{)}d\varphi\\ &+\gamma_{2}R^{5}\int_{0}^{\sigma_{B}}\Big{(}\sin^{2}\varphi\cos\varphi-\varphi \sin\varphi\cos\varphi-\frac{\sin^{2}2\varphi}{4}-\frac{2\cos^{4}\varphi}{3}+ \frac{2\cos\varphi}{3}\Big{)}d\varphi\\ &-P_{t}R^{4}\int_{0}^{\sigma_{B}}\Big{(}\frac{\sin\varphi_{B}}{\sigma_{B}}\sin ^{2}\varphi-\sin^{2}\varphi\cos\varphi\Big{)}d\varphi\\ &=-\gamma_{2}R^{5}\frac{\sin\varphi_{B}}{\sigma_{B}}\Big{(}\frac{7\varphi_{B}}{ 6}-\frac{\sin 2\varphi_{B}}{4}+\varphi_{B}\cos\varphi_{B}-\frac{5\sin\varphi_{B}}{3}- \frac{\sin^{3}\varphi_{B}}{9}\Big{)}\\ &+\gamma_{2}R^{5}\Big{(}\frac{\sin^{3}\varphi_{B}}{\sigma_{B}}-\frac{7\sin 2 \varphi_{B}}{24}+\frac{\varphi_{B}\cos 2\varphi_{B}}{4}-\frac{3\varphi_{B}}{8}+ \frac{\sin 4\varphi_{B}}{96}+\frac{2\sin\varphi_{B}}{3}\Big{)}\\ &-P_{t}R^{4}\Big{(}\frac{\sin\varphi_{B}}{2}-\frac{\sin 2\varphi_{B}}{4\sigma_{B}}- \frac{\sin^{3}\varphi_{B}}{3}\Big{)}\\ &=-(b_{4}-b_{5})R^{5}-b_{3}P_{t}R^{4}\\ &\text{where:}\end{array}  \tag{19}\]  
\[ \begin{array}{rl}&b_{3}=\ \left(\frac{\sin\varphi_{B}}{2}-\frac{\sin 2\varphi_{B} \sin\varphi_{B}}{4\sigma_{B}}-\frac{\sin^{3}\varphi_{B}}{3}\right)\\ &\end{array}  \tag{20}\]\[b_{4}=\gamma_{2}\frac{\sin\varphi_{B}}{\varphi_{B}}\left(\frac{7\varphi_{B}}{6}- \frac{\sin 2\varphi_{B}}{4}+\varphi_{B}\cos\varphi_{B}-\frac{5\sin\varphi_{B}}{3}- \frac{\sin^{3}\varphi_{B}}{9}\right) \tag{20}\]  
\[b_{5}=\gamma_{2}\left(\frac{\sin^{3}\varphi_{B}}{3}-\frac{7\sin 2\varphi_{B}}{24}+ \frac{\varphi_{B}\cos 2\varphi_{B}}{4}-\frac{3\varphi_{B}}{8}+\frac{\sin 4 \varphi_{B}}{96}+\frac{2\sin\varphi_{B}}{3}\right) \tag{21}\]
## 5 Discussion
### Unconformity and Oil and Gas Reservoir Formation  
Combined with the above research results, the typical unconformity hydrocarbon accumulation models of the Baikouquan Formation in the Mahu Depression are summarized as \"sourcing-cross layer accumulation, fault communication source and reservoir, uncontormity lateral transport and distribution, mudstone lateral docking\". The cross-layer accumulation model on the source is an important feature of the formation of the Mahu large oil region. According to the contact relationship between faults and source rocks, it is divided into source-through faults and non-source-through faults. The source-through faults refer to the faults that connect the source rocks and reservoirs and open during the accumulation period, which are the main channels for the vertical migration of oil and gas. The main source rocks in the Mahu Sag are distributed in the Permian Fengcheng Formation. At the end of the Permian Hercynian-Indosinian period, the strata were greatly subsided by tectonic movement, forming a number of east-west compression-torsion faults, disconnecting the Baikouquan Formation reservoir and directly communicating with the lower source rocks. After reaching the hydrocarbon generation threshold, the oil and gas moved upwards and transported laterally along the unconformity surface. When the fault was encountered, it migrated upwards along the fault to the reservoir (Figure 13). The development of weathered clay layer provides good sealing conditions for oil and gas reservoirs, so the weathered clay layer unconformity can usually effectively store oil and gas. Because the oil and gas in the Mahu Sag are mainly in the oblique section zone and the sag area, the study area is controlled by the shallow water, large coarse-grained fan delta deposition. From the edge to the center of the sag, alluvial fan, fan delta plain subfacies and fan delta front subfacies are developed in turn. The high-yield oil and gas layers are mainly  
Figure 12: Reservoir profile of the Ma 131 well area.  
concentrated in the fan delta front subfacies, and the oil and gas layers in the plain subfacies have low production and are mostly composed of oil and water layers [33]. During the deposition period of the first member of the Baikouquan Formation, the multi-stage fan delta front subfacies were widely superimposed and distributed in the center of the sag and the slope area. After deposition, most of them were tight glutenite, which makes it difficult to store oil and gas, so the oil and gas were mainly distributed in the second member of the Baikouquan Formation. The glutenite at the bottom of the first member of the Baikouquan Formation and the P/T unconformity surface jointly facilitated the migration of oil and gas. At the same time, due to the fracture effect, the mudstone layer was disconnected, placing the reservoir in lateral contact with the mudstone of larger thickness [34; 35; 36; 37; 38]. As a result, the conglomerate reservoir of the Baikouquan Formation exhibits good lateral sealing and preservation conditions.
## 5 Discussion  
### Effects of the Spatial Scale  
LULC changes can greatly change the Earth's energy balance and biogeochemical cycle, thereby affecting the surface characteristics and provision of ecosystem services. Therefore, LULC data are often used to calculate EVs. However, the calculation of EVs by LULC depends on the spatial scale of LULC.  
Studies have revealed differences between different LULC datasets (e.g., RES and ESA) that may be caused by different sensors or interpretation methods [37]. With changes in scale, the areas of different LULC types are always changing, and the overall area increases. Scale has substantial impacts on the areas of GL, CO and CL. Since the mode method was adopted in this paper to upscale the data, the LULC types in different spaces continue to converge to the mode values, which is similar to the results of existing spatial scale studies [38]. Overall, the EVs calculated by different LULC product data also differ. Compared with the EVs calculated for the ESA LULC data, those calculated for the RES data differ by approximately 28%, which is attributable to the difference in data quality between the LULC products. This difference remains at \(\sim\)28% potentially because EVs are inelastic with regard to the ecological value coefficients of various LULC types [39], meaning that the difference is relatively stable. With an increase in the LULC scale, the overall change trend of EVs also increases logarithmically, which indicates a certain quantitative relationship between EVs and the LULC scale. This relationship can be directly applied to the calculation of EVs for high-quality LULC data in future studies. From the perspective of the change trend of EVs, the change trend of EVs calculated by RES is more stable without trend fluctuation, so the calculation of EVs based on this data is more regular and more suitable for calculation of EVs. As the scale increases, LULC information loss will increase, so the smaller the scale, the higher the accuracy of EVs. To illustrate the reliability of our results, we obtain similar EVs through a comparison with previous studies conducted by scholars at the same spatial scale, such as previous studies that employed ESA [40] and RES [34] data.
# An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark
## 5 Implementation Details
### Histogram (HISTO)  
We provided two histogram implementations which use the OpenACC programming model. In both cases, the OpenACC data directive is used to reduce the number of data transfers between the CPU and GPU. There are a total of two memory transfers, one from CPU to GPU (image) and one from GPU to CPU (histogram). The first implementation (labeled as OPENACC_BASE) is based on the provided CPU implementation, whereas the second (labeled as OPENACC_BINS) implementation is based on the idea presented in [46]. In both implementations, the cudaMemset function is used to initialize the histogram to increase the performance. The implementation of OPENACC_BASE can be seen in Listing 2.  
```
1:inthisto_length=histo_width*histo_height;
2:intimg_length=img_width*img_height;
3
4#pragmaaccdatacopyin(img[0:img_length]){
5 copyout(histo[0:histo_length])
6
7 for(intiter=0;iter<numIterations;iter++){
8//lowlevelCUDAfunctionaddedtocincreaseperformance
9 cudMemset(
10 acc_deviceptr((void*)histo),
11
12100,
12 length*sizeof(unsignedint)
13);
14
15#pragmaaccparallelloop
16 for(unsignedinti=0;i<img_length;++i){
17 constunsignedintvalue=img[i];
18#pragmaaccatomicupdate
19++histo[value];
20}
21
22#pragmaaccparallelloop
23 for(unsignedinti=0;i<histo_length;++i){
24 if(histo[i]>UNTS_MAX){
25 histo[i]=UNTS_MAX;
26}
27}
28}
29}
```  
LBM is a memory-bound application. Therefore, the main objective in the case of LBM is to reduce the number of global memory accesses on the GPU and the number of data transfers between the CPU and the GPU. A reduced number of data transfers is achieved with the data directive. To reduce the number of global memory accesses on the GPU, we modified the original algorithm to use the same memory access pattern as the provided CUDA implementation. This pattern consists of reading all necessary values once per iteration and their storage in temporary variables. These variables are later used for all necessary calculations. However, this pattern required modification of the three main loops.