esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Load Bearing and Deformation Capacity  
Test results of the load bearing and deformation capacity of key points are listed in Table 1. According to the seismic test regularity [21], the ultimate load Pu born by the specimen was the load corresponding to the condition when the specimen bore the maximum load. The ultimate displacement \(\Delta\)u of the specimen was the deformation under the effect of the ultimate load. The crack load \(P_{\sigma}\) and crack deformation \(\Delta_{\sigma}\) were the load and crack of the initial specimen cracking; the yield load \(P_{y}\) and yield deformation \(\Delta\)y were the load and deformation during specimen yielding. The crack load, yield load, and ultimate load of the gypsum-concrete dense-column composite board were 86.31 kN, 150.52 kN and 168.32 kN. The corresponding deformation was 0.44 mm, 2.38 mm, and 7.34 mm, respectively. These values are the average of the key points on the three sample skeleton curves. The purpose is to easily evaluate the mechanical properties of similar samples. It is implied that both the load and deformation increased nonlinearly. The ultimate load bearing capacity of this composite board was 8.59% higher than that of the composite board (155 kN) described in Ref. [22], which only had vertical gypsum channels, while no thermal insulation system was adopted. Accordingly, the ductility coefficient was improved by 76%, comparing to the 1.75 in Ref. [22]. Hence, it is implied that by adding external thermal insulation system to the gypsum-concrete dense-column composite board, the entire load bearing and deformation performance of the composite board was improved. The ductility coefficient of 3.08, which is greater than those of similar boards, indicates that this board type has better seismic performances.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 5 Evaluation
### Results and Discussions
#### 5.3.2 Packet Loss Rate  
Figure 5a shows a comparison of the packet loss rates and mobile data flow rates of different mobility support methods in Scenario 1. We find that our proposal performs better in terms of packet loss rate than MAP-ME and the OPT with the same mobile data flow rate. We found that the average packet loss rate was not much different when the mobile data flow rate was lower than 4 Mbit/s, which is attributed to the bandwidth of the switching paths formed by the three mobility support methods meeting the requirements for data transmission. Packet loss is mainly caused by the inability of the consumer to  
Figure 4: Comparison of average packet delays in different scenarios. (**a**) Average packet delay vs. mobile data flow rate; (**b**) average packet delay vs. number of mobile data flows.  
receive packets during the handover process. The reason why the packet loss rate of the DAFM is lower than that of other approaches is that the DAFM can quickly obtain the latest network address of the consumer through the NRS, thus quickly redirecting data flows to the consumer's new location. This process can be completed in a certain time, subsequently reducing the packet loss rate. The OPT and MAP-ME need to transmit special data packets to realize data redirection. The time required to complete this process is related to the real network topology and link delay. When the mobile data flow rate is higher than 4 Mbit/s, we find that the packet loss rate of the OPT increases significantly because the bandwidth of its switching path cannot meet the data transmission requirements, resulting in link congestion and packet loss. Similarly, if the switching path formed by MAP-ME cannot meet the requirements of data transmission, MAP-ME will also cause a large number of packets to be lost. Figure 5b shows a comparison between the packet loss rate and the number of mobile data flows for different mobility support methods in Scenario 2. We can observe that the DAFM performs better in terms of packet loss rate than MAP-ME and the OPT with the same number of mobile data flows. The DAFM can allocate multiple data flows to different switching paths, which can effectively prevent the occurrence of a situation in which a single switching path cannot meet the transmission requirements of all data flows, which commonly results in the loss of a large number of data packets.
## 3 Massive-MIMO Sparse Uplink Channel Estimation Using a First-Order Statistics Based Approach
### SiT Based Least Squares (SiT-LS) Channel Estimation Approach  
The least squares estimate of CIR between \(n_{t}\)-th transmitter and \(n_{t}\)-th receiver can be obtained from the linear model in Equation (14), as proposed in [50], and is given below  
\[\hat{\mathbf{h}}_{n_{t},n_{t}}^{\text{SiT-LS}}=\arg\min_{\tilde{\mathbf{h}}_{ n_{t}n_{t}}}\|\hat{\mathbf{d}}_{n_{t},n_{t}}-\mathbf{C}_{n_{t}}\tilde{ \mathbf{h}}_{n_{t}n_{t}}\|_{2}^{2}. \tag{17}\]  
The above estimate can also be obtained as  
\[\hat{\mathbf{h}}_{n_{t},n_{t}}^{\text{SiT-LS}}=(\mathbf{C}_{n_{t}}^{H}\mathbf{ C}_{n_{t}})^{-1}\mathbf{C}_{n_{t}}^{H}\hat{\mathbf{d}}_{n_{t}n_{t}}. \tag{18}\]  
To obtain the channel estimate for non-zero mean noise, set \(\tilde{P}\geq L+1\), omit the first row from \(\mathbf{C}_{n_{t}}\) and \(\hat{d}_{n_{t}n_{t},0}\) from \(\hat{\mathbf{d}}_{n_{t}n_{t}}\).
## 4 Case Study
### Simulation Result
#### 4.2.2 Scenario 2  
Table 4 illustrates the ship's initial information about the multi-ship scenario case 2. For the large ship of TS\({}_{1}\), TS\({}_{2}\), and TS\({}_{4}\), OS is the give-way vessel taking manoeuvres to avoid the collision. However, OS is the stand-on vessel in the encountering situation  
Figure 12: Dynamic feasible manoeuvring interval of scenario 1.  
Figure 13: Simulation results of scenario 1: (**a**) Trajectory of OS and TSs; (**b**) Distance between OS and different TSs; (**c**) DCPA between OS and different TSs; (**d**) Speed of OS.  
with TS\({}_{3}\). The dynamic feasible manoeuvring decision interval calculated by the IVO algorithm is illustrated in Figure 14, where the \"D\" telegraph, \"S\" telegraph, \"H\" telegraph, and \"F\" telegraph are represented by the black colour, red colour, green colour, and blue colour, respectively.  
Figure 15a shows the ship's motion trajectory when the OS avoids collision by altering course 35\({}^{\circ}\) to the starboard side. The ship's relative distance curves between TS\({}_{1}\), TS\({}_{2}\), TS\({}_{3}\), and TS\({}_{4}\) are shown in Figure 15b, which reach the lowest point in P\({}_{1}\), P\({}_{2}\), P\({}_{3}\), and P\({}_{4}\). The minimum relative distance is greater than the safety value, which means the TS is not invading the ship domain. Figure 15c shows the differences in DCPA between this ship and the TS, which shrinks at first and then increases when the course is altered. As shown in Figure 15d, the tendency of OS speed is variable in the whole procedure of altering course and then tends to be stable when finishing the altering action.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ship list** & **Position (mile)** & **Course (\({}^{\circ}\))** & **Speed (knot)** & **LOA (m)** & **TCS (s)** & **CRI** \\ \hline OS & (0, 0) & 000 & 12.5 & 225 & - & - \\ TS1 & (1, 5,0) & 200 & 14.5 & 225 & 451 & 0.009 \\ TS2 & (0, 5.5) & 175 & 14.5 & 299 & 569 & 0.001 \\ TS3 & (\(-\)1.0, 3.5) & 145 & 9.0 & 225 & 410 & 0.124 \\ TS4 & (0.3, 1.5) & 000 & 8.0 & 245 & 852 & 0.525 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial information of the ships for simulation in scenario 2.  
Figure 14: Dynamic feasible manoeuvring interval of scenario 2.  
Figure 15: _Cont._
## 3 Results and Discussion
### Evolution of Side Velocities  
Figure 8 shows the velocity evolution measured by the hot-wire sensor. One can clearly see the outward and return phase. However, one can also describe the cycle as a combination of compression and expansion phases. Those phases occur between the two successive minimum velocity values. The compression phases match the hot to cold flow displacement where velocity should be counted positively, whereas the expansion phase is associated with the cold to hot displacement where velocity should be counted negatively. Typical velocity evolutions on each regenerator's sides are shown on Figure 8a-c for porosities of 30%, 35%, and 40%, respectively. Experiments were conducted for the same frequency (close to \(Fr\approx\) 0.7 Hz) that corresponds to the lowest tested frequency. The relative uncertainties depend on the frequency \(Fr\) and vary from \(\pm\)1.16% to \(\pm\)2.33% for the Str_30% regenerator, whereas it is almost constant at \(\pm\)2.30% for the Str_35% and Str_40% regenerators.  
The first observation is that the amplitude of the velocity of the fluid in the hot side is less when the piston pushes the fluid from the hot side to the cold side of the regenerator during first half cycle. This is because the volume of fluid during this phase is subjected to compression generated by the movement of the piston. As a result, the density increases under the effect of the increase in pressure. In addition, the regenerator plays the role of an obstacle, slowing the flow of the fluid as a result of head losses. At the same time, the fluid in the cold side of the regenerator is decompressed. Its velocity increases when fluid exits the regenerator. We can also observe that exiting velocities are more disturbed, implying turbulence generation in the exiting flow. Measured axial velocities are similar to those  
Figure 8: Evolution of velocities at hot and cold inlet sides of regenerator ((**a**): Str_30%, (**b**): Str_35%, (**c**): Str_40%. \(L_{1}\) = 60 mm, \(C_{1}\) = 64.4 mm) versus phase for temperature gradient \(\Delta T_{1}\) = 70 \({}^{\circ}\)C/10 \({}^{\circ}\)C.  
obtained by [PERSON] et al. [16], [PERSON] and [PERSON] [11], [PERSON] and [PERSON] [15], and [PERSON] et al. [9]. During the second half cycle, fluid passes from the cold to the hot side of the regenerator, and the reverse situation occurs.  
The second observation is that the minimum velocity, obtained with the structures Str\(\_\)35% and Str\(\_\)40%, do not correspond to the moment when the piston is in the top dead center (TDC) extreme position. The absolute speed reaches its minimum value at a rotation angle of about \(\theta=15^{\circ}\) and \(190^{\circ}\) (instead of \(0^{\circ}\) and \(180^{\circ}\)). This divergence also exists with the Str\(\_\)30% structure and is more significant. The explanation may lie in the geometry of the Str\(\_\)30% structure, which generates a large variation in density (the effect of gas compression) as a result of its more compact and disturbing structure.
###### Abstract  
The source of oil and gas and the stages of oil and gas accumulation in the \"double-Paleo\" field of the HZ-A structure in the Pearl River Mouth Basin are analyzed, and the spatiotemporal coupling relationship of the key conditions of oil and gas accumulation are discussed to reconstruct the process of oil and gas accumulation. Based on previous research results, which are based the characteristics of biomarker compounds, the oil and gas in the HZ-A structure double Paleogene field came from the Paleogene Wenching Formation hydrocarbon source rocks in the HZ26 sub-sag. By means of the casting thin section identification and inclusion homogenization temperature measurement, this paper reveals the three major hydrocarbon accumulation periods and corresponding fluid charging types in the \"double-Paleo\" field of the HZ-A structure in the Pearl River Mouth Basin. The results show that 13.8-10 Ma is the charging period of low mature crude oil, 10-5.3 Ma is the charging period of mature crude oil, and from 5.3 Ma is the natural gas charging period. Based on actual geological, drilling, logging, and seismic data, the key conditions for hydrocarbon accumulation in the HZ-A structure \"double-Paleo\" field are sorted out; that is, the source conditions are characterized by high-quality lacustrine source rocks generating early oil and late gas and a near-source continuous hydrocarbon supply. The reservoir conditions are characterized by weathering and superposition of a fracture zone that transforms into a reservoir, and a large-scale sandstone rock mass that transforms into a reservoir. The caprock conditions are characterized by the stacking of several thin mudstones that form a seal and the combination of multiple lithologies that block hydrocarbon migration. The trap conditions are characterized by multistage uplift structure traps and fracture-lithology combination control traps. The transport conditions are characterized by multi-stage cross-bed transport of source-connected faults and lateral differential transport of shallow sand in deep fractures. Finally, oil and gas accumulation models of the HZ-A structure double Paleogene field were established, and the accumulation process was reconstructed. The overall process involved three stages, with the first stage being the localized oil-displacing-water mode, the second being the large-scale oil-displacing-water mode, and the third being the late progressive gas-displacing-oil mode.  
double Paleogene field; oil and gas source; accumulation periods; accumulation conditions; accumulation process; accumulation model +
Footnote †: journal: Applied Sciences
# PCB Network Analysis for Circuit Partitioning
## 5 Experimental Results
### Evaluation Indices  
Since the netlist partition is treated as a segmentation-like problem, the evaluation indices are borrowed from the image processing field. We compute partition accuracy (ACC), Dice similarity coefficient (Dice), intersection over union (IOU), sensitivity (SE), and precision (PR). These evaluation indices are computed from true positive (TP), true negative (TN), false positive (FP), and false negative (FN) [27]. Figure 8 shows how to compute TP, TN, FP, and FN in our case, in which  
\[\text{TP} = A\cap B\] \[\text{FN} = A-A\cap B\] \[\text{FP} = B-A\cap B\] \[\text{TN} = C-A\cup B\]  
The ground truth set is in the green block, the partition set from the proposed method is in the red block, and components set in the gray block are the whole possible set.  
ACC \[= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}\] (4) Dice \[= \frac{2\text{TP}}{\text{FP}+2\text{TP}+\text{FN}}\] (5) IOU \[= \frac{\text{TP}}{\text{FP}+\text{TP}+\text{FN}}\] (6) SE \[= \frac{\text{TP}}{\text{TP}+\text{FN}}\] (7) PR \[= \frac{\text{TP}}{\text{TP}+\text{FP}}\] (8)  
\(A\) is the ground truth set, \(B\) is the partition set, \(C\) is the whole candidate set, and \(A\), \(B\), and \(C\) are shown in Figure 8. Since we have multiple partition subsets in each netlist, the final evaluation is a weighted index through all subsets. The equation expression is as follows:  
\[\mathbf{I}=\sum_{i=1}^{N}\frac{|\mathbf{S}_{i}|}{|\mathbf{S}|}\cdot\mathbf{I} _{i} \tag{9}\]  
in which \(N\) is the number of partition sets obtained from the method, \(\mathbf{S}_{i}\) is the \(i\)th partition subset, \(\mathbf{S}\) is the whole set, and \(\mathbf{I}_{i}=\) [ACC, Dice, IOU, SE, PR] is the score vector for the \(i\)th partition subset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Netlist ID**} & \multirow{2}{*}{**No. of Subsets**} & \multicolumn{8}{c}{**No. of Same Modules**} \\ \cline{3-11}  & & **2** & **3** & **4** & **5** & **6** & **7** & **10** & **12** & **13** & **20** \\ \hline ANDZ & 57 & 7 & 1 & 1 & 0 & 0 & 1 & 2 & 2 & 0 & 0 \\ N1 & 36 & 3 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ N2 & 147 & 2 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ N3 & 120 & 5 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ N4 & 152 & 7 & 3 & 2 & 0 & 3 & 0 & 0 & 0 & 0 & 0 \\ N5 & 123 & 9 & 0 & 1 & 1 & 2 & 0 & 0 & 0 & 0 & 0 \\ N6 & 144 & 10 & 3 & 0 & 2 & 0 & 1 & 0 & 0 & 0 & 0 \\ N7 & 53 & 1 & 0 & 8 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ N8 & 94 & 6 & 1 & 3 & 1 & 0 & 1 & 0 & 0 & 0 & 0 \\ N9 & 46 & 5 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ N10 & 90 & 14 & 2 & 9 & 0 & 1 & 0 & 2 & 1 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of the number of subsets and repetitive modules in each netlist dataset.
## 3 Results
### Inter-Annual Variability of NDVI and GPP  
The calculated CV values illustrated the IAV of GPP and NDVI among the aridity gradients (Figure 5a) and the ecosystem types (Figure 5b). As shown in Figure 5a, the IAV of GPP steadily decreased with decreasing climate aridity. The IAV values for GPP were 0.81, 0.80, 0.80, 0.75, 0.64, and 0.39 in hyper-arid, arid, semi-arid, dry sub-humid, humid, and hyper-humid gradients, respectively. The IAVs of NDVI were 0.10, 0.18, 0.26, 0.26, 0.22, and 0.14 across the same respective gradients. In contrast, the IAV of GPP showed a steady decline, while the IAV of NDVI exhibited fluctuating trends, showing an increasing trend followed by a subsequent decrease. The IAV of GPP in both arid and semi-arid regions remained constant at 0.80, while NDVI increased from 0.18 to 0.26. Conversely, in semi-arid and dry sub-humid regions, the IAV of NDVI was consistent at 0.26, while GPP decreased from 0.80 to 0.75. These phenomena suggested that NDVI exhibited greater variability compared to GPP, particularly in arid and semi-arid regions. Conversely, GPP showed more variability than NDVI in semi-arid and dry sub-humid regions. Combined with Figures 4a and 5a, the R\({}^{2}\) of GPP-NDVI and IAV of NDVI displayed a similar trend, peaking at AI values between 0.2 and 0.5 (semi-arid areas) before decreasing. This result indicated that changes in NDVI govern the coupling between NDVI and GPP.  
Figure 5b illustrated the similar trend in the IAV of both GPP and NDVI among different PFTs. The IAV of GPP was lowest in EBF at 0.20 and largest in GRA at 0.77. Other values were as follows: ENF (0.43), DNF (0.70), DBF (0.51), MF (  
Figure 4: The boxplot for NDVI (**a**) and GPP (**b**) values across the arid gradient and PFTs. The colored boxes show the range from the 25 th to 75 th percentiles, while the whiskers indicate the standard deviation. The black line represents the median (M) trends.  
Figure 5: Comparison of the strength of the inter-annual variability (coefficient of variance: the ratio of standard deviation to mean) of NDVI and GPP for AI gradients (**a**) and different PFTs (**b**).  
(0.39), CRO (0.70), and CNVM (0.49). Similarly, the IAV of NDVI was lowest in EBF at 0.08 and highest in GRA at 0.25. The IAV values in other forest ecosystems were 0.11 in ENF, 0.12 in MF, 0.16 in DBF, and 0.21 in DNF. In CRO and CNWM, the values were 0.24 and 0.13, and in SAV and WSA, the value was 0.12. In forest ecosystems, the IAV for both GPP and NDVI exhibited synchronized changes, with the highest variability observed in DNF, followed by DBF, MF, ENF, and EBF. These differences likely reflect the varying adaptability and response characteristics of each forest type to environmental factors. Among these, EBF demonstrated a more stable response to environmental changes.
# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 3 Methods
### Statistical Analysis of Training Data  
Using the above-described process, 16,217 training datasets were generated for the proposed DNN model for prediction of morning ore production, and corresponding datasets concerning afternoon ore production equaled 16,005. Figure 7 depicts the distribution of morning and afternoon ore productions. As can be observed, the average morning ore production equaled 2987 tons, whereas the corresponding afternoon production equaled 2960 tons. Table 2 lists average values of the number of dispatched trucks, dumping zone utilizations, stay times, and travel times observed during morning and afternoon operations.  
Figure 6: Training-data generation for DNN model using packet data.  
Figure 7: Histogram depicting distribution of ore production during (**a**) morning and (**b**) afternoon operation times.  
The value of the Pearson correlation coefficient between input variables and ore production was calculated, and the correlation between them analyzed using training data (refer to Table 3). Equation (8) provides the relation for evaluating the Pearson correlation coefficient (\(r\)). In this equation, \(n\) denotes the number of samples; \(x_{i}\) and \(y_{i}\) denote values of the \(i\)th sample; \(\overline{x}\) denotes the mean of \(x\) samples; and \(\overline{y}\) denotes the mean of \(y\) samples. Values of the Pearson correlation coefficient between ore production and operation-time interval were observed to be 0.77 and 0.81 for the morning and afternoon operation times, respectively. The interval between operation times demonstrated the highest positive correlation with ore production, regardless of the operation time. Additionally, relative operation-end times and number of dispatched 60- and 80-ton trucks demonstrated a positive correlation with ore production, whereas relative operation-start time revealed a negative correlation with ore production.  
\[r=\ \frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{ i=1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}} \tag{8}\]
## 4 Personal Climatization Systems
### Foot Warmer and Heating/Cooling Systems for the Lower Body  
As shown by [PERSON] [64], heating of the feet improves the overall thermal comfort of individuals in cold environments, whereas cooling the feet causes an opposite effect. Findings of [PERSON] et al. [104] support that cooling the sole of the feet (Table A4-36) worsens a human's overall thermal comfort. Table A4 summarizes studies that deal with the climatization of the lower part of the human body. It becomes clear that only a few personal HVAC systems that condition the lower part of the body were investigated so far. Consequently, the number of studies that investigate the cooling performance of such systems is small. Conventional radiant heaters are not an adequate alternative to local footwarmers, because ascending warm air causes a cooling effect at the feet level, which is contrary to the desired heating effect. Furthermore, these systems have a higher energy consumption than locally applied foot-warmer systems [112].  
The \"Kotatsu\" is a local heater for the lower body, which is traditionally used in Japanese households. A climate chamber study conducted by [PERSON] et al. [25] showed that heating the lower body with temperatures between 32 \({}^{\circ}\)C and 38 \({}^{\circ}\)C ensures thermal comfort at an ambient temperature of 14 \({}^{\circ}\)C. According to [PERSON] et al. [112], a new developed box such as a radiant heater for the feet (energy demand: 30 W) causes a temperature effect that is equivalent to a 5 K increase in global ambient air temperature (Table A4-35). Furthermore, in this study ambient temperatures of 18.9 \({}^{\circ}\)C were evaluated as comfortable. They showed that the resulting HVAC energy savings ranges between 38 % and 75 % compared to mixing ventilation and for the location of San Francisco. According to [PERSON] and [PERSON] [113], local underfloor heating will save heating energy if the system is implemented in front and at the sides of a seat. In addition, the system must cover a total surface area of about 1 m\({}^{2}\) and operate at surface temperatures of 39 \({}^{\circ}\)C. In contrast, a local heat pad does not improve whole-body thermal sensation at ambient temperatures of 17.7 \({}^{\circ}\)C (Table A4-34) [95].