esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Empirical Studies
### Comparison of Different Techniques  
#### 6.2.1 Initial Ordering  
We have four methods to obtain an initial ordering for the geographic graphs and two for non-geographic graphs. Here we compare these methods to each other based on the value of the cut obtained by chopping the resulting order into equal contiguous pieces. In order to make this meaningful, we report all the cut sizes as the fraction of cut edges to the total number of edges in the graph.  
Figure 4 compares the results of two methods: RandInit produces a random permutation of the vertices, hence, as we observed previously, gets a cut size of approximately \(1-\frac{1}{k}\); AffCommNeigh (using the AffinityOrdering with the similarity oracle based on the number of common neighbors) clearly produces better solutions with improvements ranging from 20% to 10% (more improvement for smaller number of parts). The AffCommNeigh tree in this case was pretty shallow (much lower than the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & \(|V|\) & \(|E|\) & **Max Degree** \\ \hline LiveJournal & 4,847,571 & 42,851,237 & 20,333 \\ Twitter & 41,652,230 & 1,202,513,046 & 2,997,487 \\ Friendster & 65,608,366 & 1,806,067,135 & 3615 \\ World-roads & \(>\)10\({}^{8}\) & \(>\)10\({}^{9}\) & \(<\)20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics about datasets used. (Numbers for World-roads are approximate).  
theoretical \(\log n\) upper bound) with only four levels. Whenever we report results for different number of partitions in one plot, \(k=2,4,8,16,32,64\) are used for experiments.  
Figure 5 compares the results of three methods, each producing a cut of size at most 3% and significantly improving upon the \(1-\frac{1}{k}\) result of RandInit: Compared to Hilbert, the two other methods AffInvDist (using the inverse of geographic distance as the similarity oracle) and AffCommNeigh, respectively, obtain 90% to 50% and 95% to 70% improvement; once again the easier instances are those with smaller \(k\). The corresponding trees, with 13 and 11 levels, respectively, are not as shallow as the one for Twitter. The quality of the cuts correlates with the runtime complexity of the algorithms: RandInit (fastest and worst), Hilbert, AffInvDist and AffCommNeigh (best and slowest). Note that although the implementation of Hilbert seems more complicated, it is significantly faster in practice.  
For the following experiments, we focus on AffCommNeigh since it consistently produces better results for different values of \(k\) as well as both for Twitter and World-roads. Specially in diagram legends, this initialization step may be abbreviated as Aff.
## 3 Results and Discussion in General
### Results
#### 3.1.3 The Experimental Family House EB2020
The ATP in the experimental house EB2020 consists of 20 circuits connected via two circulators. In the diagram in Figure 28, the branches are labeled as \"thermal barrier 1\" and \"thermal barrier 2\". According to the diagram in Figure 28, the heat produced by the solar energy-energy roof (ESR) is stored in a ground heat storage (GHS), while it is also capable of being placed in a combination storage vessel when the circumstances are right. A heat exchanger is attached between the primary side, where the glycol-based antifreeze is circulated, and the secondary side. A fireplace insert, which serves as the heat source for the storage vessel, can also provide heat to the ground heat storage tank. A low-temperature gas boiler serves as an additional heat source for the storage vessel. The storage tank also has a 6 kW electric heating insert. The storage tank provides low-temperature radiant floor heating. The ground source heat store or the storage tank may be used to provide ATP. The energy system's control, regulation, and monitoring were implemented via an Internet connection. This made it possible to control how the power system operated and to alter the input parameters as necessary. It was possible to start or stop the operation of ATP1, ATP2, or floor heating using an Internet connection to set the desired input temperature, or to start the system with the epithermal control by setting the required indoor temperature. Figure 29 shows that the measured data were saved on the server and accessible at all times. The program allowed for the display of the heat given in GJ to ATP1, ATP2, and underfloor heating at any time (last 24 h, week, month, year, or it was possible to enter a specific period). Additionally, the output and flow rate of ATP1, ATP2, and underfloor heating could all be seen. Figure 27: Placing pipes of active thermal protection on the facade. [Author: [PERSON]]. Figure 28: Wiring diagram of the technical solution of the energy systems of the experimental house. EB2020. [Author: [PERSON]].
## 5 BS Beamforming Matrix Design  
With the phase shift matrix of RIS \(\mathbf{\Phi}\) fixed, according to (37), the beamforming matrix of BS reaches the optimal solution when \(\text{tr}\left(\mathbf{F}_{all}\mathbf{F}_{all}^{H}\right)=P_{\text{max}}\) is satisfied, i.e., BS maintains the maximum transmit power. Based on the SVD, the cascaded channel \(\mathbf{H}_{all}\) can be expressed as  
\[\begin{array}{ll}\mathbf{H}_{all}&=\mathbf{\Lambda}\mathbf{\Sigma}\mathbf{V}^{H}\\ &=\mathbf{\Lambda}\begin{bmatrix}\Sigma_{1}\\ &\ddots\\ &&\Sigma_{n_{s}}\\ &&&\ddots\\ &&&\Sigma_{N_{s}}\\ \end{bmatrix}\mathbf{V}^{H}\\ \end{array} \tag{70}\]  
where \(\mathbf{\Lambda}\in\mathbb{C}^{N_{t}\times N_{t}}\) is the left singular matrix, \(\mathbf{V}\in\mathbb{C}^{N_{t}\times N_{t}}\) is the right singular matrix, and \(\Sigma_{1},\cdots,\Sigma_{N_{t}}\) is the singular value obtained from the decomposition of \(\mathbf{H}_{all}\). If \(\mathbf{H}_{all}\) is given, the optimal beamforming matrix of BS can be designed as  
\[\mathbf{F}_{all}=\mathbf{\Lambda}\mathbf{P}^{\frac{1}{2}} \tag{71}\]  
where \(\mathbf{P}\triangleq diag(\mathbf{p})\in\mathbb{C}^{N_{t}\times N_{t}}\) is the power allocation matrix obtained according to the water injection power allocation method [38], \(p_{n_{s}}\) is the best allocated power for the \(n_{s}\)-th transmit data stream, and \(\sum\limits_{i=1}^{N_{t}}p_{i}=P_{\text{max}}\).
## 3 CES-KOS Resolution Features
### Flow Simulations: Grid Effects
Notably, the finer CES-KOS grid result aligns more closely with DNS streamlines (see Figure 10 in Ref. [34]), although this is not displayed here. The figure relates to the detachment and reattachment points reported in Table 3 in comparison with measurements [33]. Despite small discrepancies in the exact locations of separation and reattachment, the \(G_{2}\) prediction shows better correspondence to the shock location obtained experimentally than the \(G_{1}\) result. The CES-KOS model characterizes the reattachment location slightly downstream due to relatively long separation bubble length. Figure 12 shows pressure (\(C_{p}\)) and skin friction (\(C_{f}\)) predictions. For a more detailed discussion of grid effects, we consider four sub-regions of the domain. The initial region extends from \(-1.0\leq x/c<0.2\), capturing a turbulent boundary layer with a pressure gradient transitioning from zero to adverse to favorable. The second region covers \(0.2\leq x/c<0.6\), incorporating the remaining favorable pressure gradient region and ending with the interaction between shock and boundary layer. The third region stretches from \(0.6\leq x/c<1.25\), which covers the separation bubble. Lastly, the fourth region covers the reattachment region, \(1.25\leq x/c\leq 2.5\). The \(C_{p}\) profiles show a clear grid dependence (with significant variations) in the first and second regions, whereas the \(C_{f}\) profiles are nearly grid-independent. Specifically, the \(G_{2}\) simulation accurately captures the elbow-like feature in the pressure distribution at \(x/c\approx 1.15\) caused by the bump's slope change; the \(G_{1}\) grid simulation fails to replicate this feature. There is a slight overestimation of the \(C_{f}\) peak in the third region, with refinement leading to a monotonic decrease closer to the measurements. Refinement also improves the predicted shock location upstream, aligning better with the experiment (see Table 3). In post-shock regions, while surface pressure agrees satisfactorily overall, separation and reattachment points differ slightly between simulations and the measurement. For instance, in the \(0.6\leq x/c<1.25\) region, separation occurs at \(x/c=0.8338\) (\(G_{1}\)) and \(x/c=0.7030\) (\(G_{2}\)), with predictive error margins of 4-22%. The \(G_{2}\) reattachment point (\(x/c=1.1188\)) matches the measured \(x/c=1.1000\)[33] better than the \(G_{1}\) estimation (\(x/c=1.1479\)). Figure 11: Time-averaged streamline across the flow domain colored by mean velocity magnitude: \(G_{1}\) case **left**, \(G_{2}\) case **right**. Figure 12: CES-KOS simulations: profiles of pressure and skin-friction coefficients for the two grids applied.
# Research on the Positioning Performance of GNSS with a Low-Cost Choke Ring Antenna
## 3 Results and Discussion  
As a result of the research, the time courses of geographic coordinates were recorded, i.e., latitude, longitude, and altitude for the dedicated GNSS antenna and the GNSS choke ring antenna. Then, the above-mentioned coordinates were converted to the ECEF system: \(\text{ECEF}_{\text{x}}\), \(\text{ECEF}_{\text{y}}\), and \(\text{ECEF}_{\text{z}}\). It enabled the determination of positioning errors: \(\text{e}_{\text{ECEF}_{\text{x}}}\) (Figure 7), \(\text{e}_{\text{ECEF}_{\text{y}}}\) (Figure 8), \(\text{e}_{\text{ECEF}_{\text{z}}}\) (Figure  
Tables 2 and 3 show the basic quantitative statistics of the following parameters: HDOP, VDOP, PDOP, number of satellites in use, ECEF\({}_{\chi,\chi,\chi,\chi}\) coordinate errors, and ECEF total errors for the dedicated antenna and choke ring antenna.  
accuracy is visible in the analysis of the average values of the total positioning error in the ECEF system, which recorded a decrease in value of approximately 7 times. The analysis of the values of the above-mentioned parameters and errors shows that the dedicated antenna is characterized by a higher quality of positioning, i.e., lower errors. These values, however, do not apply to the positioning accuracy, and, therefore, selected measures of positioning accuracy in the 2D (Figure 11) and 3D (Figure 12) systems were determined for both variants of GNSS antennas considered in the article (described in Section 2.4).  
All of the specified measures of accuracy both for the 2D (Figure 11) and 3D (Figure 12) systems indicate that the dedicated antenna is more accurate than the antenna with a choke ring cover. The use of a choke ring antenna in a 2D system is associated with a deterioration of accuracy by 51% according to the DRMS and 2 DRMS measures and by 36% according to the CEP measure. Similarly, in the case of the 3D system, it is associated with a deterioration of accuracy by 41% according to the SEP measure, 48% according to the MRSE measure, and 41% according to the 90% SAS and 99% SAS measures. It should be noted that the mentioned measures differ in probability (Table 1), so the most reliable results are the 2 DRMS measures for the 2D system and the 99% SAS for the 3D. In both cases, the result does not exceed 0.02 [m], which means that the choke ring antenna still maintains a high positioning accuracy, despite the observed drops in accuracy measures.
## 2 Materials and Methods
### Eye-Tracking Measurements  
The eye-tracking device utilized in this study was the REDn Eye Scientific device, developed by SMI in Germany, with a sampling rate of 60 Hz, an accuracy of 0.4\({}^{\circ}\), and a spatial resolution of 0.03\({}^{\circ}\). The eye-tracking data of the participants during the reading tasks were recorded on a laptop equipped with SMI BeGaze 3.7, the data analysis software. To enhance the accuracy of data collection, all participants underwent calibration for five points on the monitor prior to beginning the task, ensuring that calibration values were within 0.6\({}^{\circ}\) before proceeding with the tasks.  
Eye-tracking data analysis was conducted using SMI BeGaze 3.7 software. In the reading tasks, the sentences within the presented slide were designated as AOIs. Employing temporal and spatial eye-tracking variables, the gaze exploration processes and the cognitive processing of children during reading were examined [30]. Time-related variables included the total fixation duration (TFD) and average fixation duration (AFD) within the AOI. For a spatial-related variable, the scanpath length was utilized. Fixation refers to sustained gaze on an AOI for about 180-330 ms [44]. The TFD represents the total time (ms) that the AOI was fixated during the reading task, and the AFD is calculated by dividing the TFD within the AOI by the number of fixations. Scanpath length represents the sum of the lengths (in pixels) of gaze movement paths during the tasks, which encompass both progressive and regressive saccades. These fixation durations and scanpath lengths have been shown to be linked to the type of stimuli presented and an individual's reading proficiency [32; 45; 46].
## 2 Materials and Methods
### Growth-Related Gene Expression Profiles in Shrimp Using Quantitative Realtime PCR
#### 2.4.3 Determination of Fatty Acid Analysis in Feeds and Shrimp Using GC-MS  
Powder shrimp samples (50 mg) were mixed with an ice-cold Chloroform:MeOH (2 mL; 1:1 \(v/v\)) for 10 min using multi-tube vortexer (Allsheng, Hangzhou, China), sonicated for 15 min at 4 \({}^{\circ}\)C and then centrifuged at 3500\(\times\)\(g\) for 20 min at 4 \({}^{\circ}\)C. The supernatant was collected in a sterile glass tube and dried under a constant stream of nitrogen (TurboVap, Caliper life science, Hopkinton, MA, USA) at 35 \({}^{\circ}\)C for 30 min. The dried samples were transesterified into FAME by adding 1% H\({}_{2}\)SO\({}_{4}\)-MeOH (1 mL) and heating at 50 \({}^{\circ}\)C for 2 h. The samples were left at room temperature to cool before being vigorously mixed with 15% NaCl (1 mL) and hexane (1 mL) and centrifuged at 3500\(\times\)\(g\) for 5 min. The upper phase was collected into a 1 mL crimp top vial (SU860064, Supelco, Bellefonte, PA, USA) and dried under a constant stream of nitrogen for 10 min at 45 \({}^{\circ}\)C. The dried samples dissolved with hexane containing 5 ppm of internal standard, methyl nonanoate (9:0) and injected onto a GC (7890A, Agilent Technologies, Santa Clara, CA, USA).  
GC-MS analysis was performed on an Agilent 7890A GC system coupled with Agilent 5975C mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). The FAME extracts (1 \(\mu\)L) were separated on a 30 m \(\times\) 0.250 mm \(\times\) 0.25 \(\mu\)m film thickness DB-FastFAME capillary column (G3903-63011, Agilent Technologies, Santa Clara, CA, USA) under thefollowing program: an initial temperature at 50 \({}^{\circ}\)C, ramp 35 \({}^{\circ}\)C min\({}^{-1}\) to 175 \({}^{\circ}\)C, ramp 3 \({}^{\circ}\)C min\({}^{-1}\) to 185 \({}^{\circ}\)C, ramp 1.5 \({}^{\circ}\)C min\({}^{-1}\) to 190 \({}^{\circ}\)C and ramp 3 \({}^{\circ}\)C min\({}^{-1}\) to 230 \({}^{\circ}\)C. Pulsed Split injection was conducted with a 15:1 split ratio, helium was used as the carrier gas and the injector temperature was 60 \({}^{\circ}\)C. The MS detection conditions were as follows: an interface temperature, 240 \({}^{\circ}\)C; ionization mode, EI+; electron energy, 70 eV; full/selected ion monitoring (SIM) scan acquisition mode; mass range, 55-382 amu. Samples were analyzed with the addition of the FAME standard injections to ensure the reproducibility of the chromatography. The processed chromatograms were identified by comparison of their retention time and mass spectrum using the FAME standard mixture and an online NIST mass spectral library. The peak areas of each FAME in the samples were normalized to the peak area of the internal standard (9:0) prior to analysis. The calibration curves were performed using the 37-component FAME mix with different concentrations ranging from 0.13 to 122.12 \(\upmu\)g/mL. Fatty acids in the samples were quantified based on the external calibration.
## 4 Optimization of Cluster Head Selection  
### Competition Probability of CHs  
The cluster head nodes need not only collect and process data detected by cluster member nodes, but also are responsible for forwarding data, thus the reasonable selection of cluster heads is very important. In addition, the influence of the number of cluster head nodes on the network lifetime is crucial [29]. If the cluster head node is too small, the covered cluster region will be greater, the longer the distance from member nodes to the cluster head will be, and the greater the energy consumption of data transmission. The amount of clusters is too large if the energy consumption of cluster head nodes is greater than that of the non-cluster head nodes, and the energy consumption of the whole network would be large. In this paper, taking the average power of \(\widetilde{E}\left(r\right)\) as a reflection of the residual energy and node energy, in addition to considering node coverage rate, one will obtain:  
\[p_{i}=p_{opt}[1-\frac{\widetilde{E}(r)-E_{res}(i)}{\widetilde{E}(r)}](1-\frac {1}{\phi(m)}) \tag{19}\]when the residual battery power of nodes is larger than the average energy, the average selection probability \(p_{i}\) of a node will increase the proportion of the corresponding value \(p_{opt}\). Otherwise, the average selection probability \(p_{i}\) of node will be smaller than \(p_{opt}\).
## 5 Predefined Path-Based Techniques
### Ring-Based Dual Paths (RDP)
#### 5.3.2 RDP Operations
When the trunk QuadBox receives a PREP message,it adds two route entries of the connection pair, one for each direction, to its forwarding table. Since RDP uses two paired QuadBox nodes to connect rings in order to avoid a single point of failure, there are two ring-based paths discovered and established for each connection pair of DANH rings. Figure 16 shows the process of forwarding unicast frames from source node 1 to destination node 10 under the RDP approach. In this case, RDP has discovered and established ring-based dual paths without any common nodes for the connection pair of DANH rings 1 and 3. The dual path consists of two ring-based paths, including the first ring-based path of QuadBox 2-QuadBox 3-QuadBox 4-QuadBox 13-QuadBox 5, and the second ring-based path of QuadBox 1-QuadBox 12-QuadBox 11-QuadBox 14-QuadBox 10-QuadBox 9-QuadBox 8-QuadBox 7-QuadBox 6. The source node 1 sends two copies of the frame simultaneously through each port in both directions in DANH ring 1. The first copy is forwarded to access QuadBox 1. The first and second copies then is forwarded through the ring-based dual paths and arrived to destination DANH ring 3 at access QuadBoxes 5 and 6, respectively. These access QuadBoxes lookup their MAC table and forward the copies to destination DANH ring 3. Finally, the destination node 10 receives two copies of the frame from both directions of DANH ring 3, passes the first frame to its upper layers, and discards the duplicate. Figure 16: Forwarding unicast traffic under the RDP approach.
# PetroBlock: A Blockchain-Based Payment Mechanism for Fueling Smart Vehicles
## 1 Introduction
Furthermore, it provides transparency like public-audibility, an append-only ledger of transactions involved in the chain. The recent emerging of blockchain technology supports the shifting of the traditional payment system at a gasoline pump into a secure automated system [7]. Smart contracts and Hyperledger Fabric can be employed for allowing a software platform to run without a trusted third party. This behavior leads to opening up opportunities for harnessing product-centric enterprise systems. In this study, a secure payment method is implemented to refuel smart vehicles at smart pumps. The designed model leverages the blockchain platform (i.e., Hyperledger Fabric [8; 9]) to secure the transfer of payment and share information among different gasoline pumps. Firstly, we propose a novel blockchain payment method to fuel smart vehicles in which a user can share sensitive information like credit card credentials or others in a secured, authentic chain of connected networks of the associated franchises of gasoline pumps. This blockchain model is extended towards a web-driven system by designing web front-end technologies like HTML5 and JavaScript for enhancing resource management in a network. Furthermore, we also configure composer-rest-server to expose REST API to visualize services related to the designed system. Afterward, smart contracts' functionality is embedded to enable data uniformity of card and bank information and other users' assets and other participants involved in the network. Afterward, an Access Control List (ACL) is defined, which provides access to only valid and authentic users. To enhance the model's efficiency and performance, we employ DBCouch [10] to manage a large number of records and evade form data redundancy problems within the blockchain File System. The performance of the proposed system is evaluated using Hyperledger Caliper [11; 12], a benchmarking tool for blockchain-based system [11; 13]. More precisely, the contributions of this paper are as following:  
* We present PetroBlock: a secure payment mechanism for refueling smart vehicles without human interaction, providing transparency, trust, and privacy. * The proposed model can share data among the system's users while securing sensitive information. * We evaluate the designed system using Hyperledger Caliper in terms of transaction latency (average, percentile, minimum and maximum), transaction throughput, resource utilization (e.g., CPU, memory, I.O. ). The rest of the paper is structured as follows: Section 2 explains the state-of-the-art related work on payment mechanisms for fueling smart vehicles. Section 3 presents the designed methodology, smart contract modeling, implementation, and storage design. Section 4 gives the details of the system results, and Section 5 concludes the paper.
# Parameter Optimization of Orbital TIG Welding on Stainless Steel Pipe
## 3 Results and Discussion  
### Visual Test Results  
Table 4 shows the geometry of welds, while the assessment according to the AWS D18.1 standard is presented in Table 5. The passing sample numbers were S3-S7 and S9. Sample numbers S1, S2, and S8 failed the AWS D18.1 standard because they failed to meet the weld height criterion. The height of these samples was already 0.3 mm higher than the criteria. In addition, sample S2 had a weld joint width greater than 4.0 mm, which did not meet the requirements. Table 4 also shows that sample S2 was dark gray with much porosity on the weld surface, indicating poor quality. The reasons for the failed S1 and S2 samples could be the small filler wire diameter of 0.8 mm and low HI value of 0.26 kJ/mm. A low HI can reduce the penetration depth, leading to low welding quality [25].  
The small filler especially influenced the concentration of the molten metal, leading to a poor weld bead contribution. Conversely, other samples, such as S4-S9, with larger filler wire diameters, had better weld beads as the heat contribution was better. Samples S4, S5, and S6 had a filler wire diameter of 1 mm for more even welding and finer metal wavelengths. Samples S7, S8, and S9, with a filler wire diameter of 1.2 mm, showed the smoothest and most uniform metal wavelength.  
Figure 3: Flowchart of microstructure sample preparation.  
Figure 2: Dimensions of the tensile testing sample (unit: mm).