esa-sceva/satcom-chunk-collection huggingface dataset:
# Stochastic-Based Spin-Programmable Gate Array with Emerging MTJ Device Technology
## 2 Architecture Overview  
To a large extent, the architecture of SPGA resembles that of the conventional well-structured island style FPGA as shown in Figure 2a, in which an array of logic blocks (called Configurable Logic Block, CLB, or Logic Array Block, LAB, depending on vendor) are surrounded by pre-fabricated programmable routing channels and I/O pads. However, the SPGA architecture depicted in Figure 2b differs from the existing norm in at least two aspects. First, in the traditional CMOS-based FPGA technology, all configurable bits are implemented with memory devices such as 6-T SRAM. In the SPGA, we do not use binary bits to \"define\" our target logic function. Instead, we use the spin-based magnetization to configure the logic blocks. Second, the functionality of the memory-based LUT hinges on the concept of K-map and is based on the Boolean algebra. In a sharp contrast, the stochastic-based LUT uses a new curve-based method to reinterpret and redefine Boolean logic functions, therefore can efficiently implement a logic block with a configurable number of logic inputs and outputs without negatively causing excessive hardware overheads. Finally, given the 3D \"nature\" of the MTJ-CMOS hybrid device technology, we can redesign the interconnect architecture of the SPGA. Specifically, the LB inputs and outputs connect first to local segments. These segments can then be programmable connected to segments in neighboring routing blocks and/or to interconnect segments in a routing channels via programmable buffers and muxes with buffered outputs. The interconnect segments can be directly connected to form longer segments using programmable buffers without going through routing blocks (we shall refer to such interconnects as bypass interconnects).
## 3 Results and Discussion
### Experiment 2: The Heat-Flow Process  
Figure 7 shows the PDSC results of the heat-flow experiment. As shown in Figure 7, before 200 \({}^{\circ}\)C, there was no significant exothermic change in either oil sample. With the increase in temperature, the crude oil sample underwent an evaporation phase change. When the reaction temperature was below 200 \({}^{\circ}\)C, the evaporation phase change exceeded low-temperature oxidation, resulting in a constant zero heat-flow curve without any changes. As the temperature increased between 200 \({}^{\circ}\)C and 300 \({}^{\circ}\)C, the samples began to show changes in heat flow, marked by the appearance of the first heat-flow peak. In this temperature range, the exothermic heat released during the low-temperature oxidation of crude oil exceeded the endothermic heat associated with the evaporation process. When the temperature reached between 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C, a second heat-flow peak appeared. It was lower compared to the first peak, with a more pronounced decrease, especially in the M oil sample.  
With increasing pressure, under the condition of sufficient contact between the oil sample and air, the entire heat-flow curve shifts to the left, indicating progressively lower reaction temperature thresholds for each stage. The heat release in the LTO stage increased significantly, while there was almost no change in the heat release in the HTO stage. From Figure 7b, it can be observed that, at 5.0 MPa pressure, oil sample M shows a sharp increase in heat release during the low-temperature oxidation stage, rising dramatically around 270 \({}^{\circ}\)C, where the heat flux becomes a straight line. Subsequently, the heat flow rapidly changes to zero. It is speculated that due to the higher proportion of light components in this oil sample, under high pressure, combustion may have occurred at a certain temperature, leading to the significant consumption of crude oil.
## 5 Conclusions  
This work has demonstrated the validity of crack detection with the depth of 0.1 mm at a deeply buried depth by combining vector coherence factor weighting with phased array ultrasonic S\(-\)scan imaging in the presence of crack detection of in\(-\)service bolt shanks. Moreover, the experimental result shows that the crack with 0.1 mm at the buried depth of 90 mm on the surface of the in\(-\)service bolt shank is detected efficiently by DAS algorithms. Furthermore, the image's background noise and the bolt shank's secondary echo are suppressed. Additionally, the SNR value of the crack in the DAS_VCF images is improved after being weighted with the vector coherence factor compared with the DAS images. What is more, compared with DAS images, the SNR value of crack at the buried depth of 70 mm in DAS_VCF images is improved by 11.67 dB with the focus depth at 60 mm.  
Conceptualization, J.H., M.C. and Y.C.; methodology, Q.K. and L.X.; software, Q.K. and Y.C.; validation, M.C., Q.K. and L.X.; formal analysis, C.L. and Y.C.; investigation, M.C. and L.X.; resources, J.H. and C.L.; data curation, Q.K. and Y.C.; writing\(-\)original manuscript preparation, J.H., M.C. and Q.K.; writing\(-\)review and editing, J.H., C.L. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant number 62161028, 51705232) and supported in part by the Natural Science Foundation of Jiangxi (Grant No. 20192 BAB216026).  
Not applicable.  
The authors would like to thank Key Laboratory of Non-destructive Testing Technology, Ministry of Education, Nanchang Hangkong University List for their help in the experimental setup.  
The authors declare no conflict of interest.
## 3 Results
### Separability Analysis  
The separability between the land use and cover classes mapped by kNN was evaluated using the JMD. Tables 2 and 3 show the JMD values for the years 2018 and 2023.  
In 2018, the separability between water and vegetation, with NDVI (1.827), NDWI (1.953), and the NIR band (1.980), was high, indicating a good distinction between these classes. However, the separability between urban area and vegetation showed values below 1 for NDWI, suggesting less consistency in distinguishing these classes. Following the same evaluation pattern used for 2018, the separability between the classes for 2023 was assessed (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Class 1** & **Class 2** & **Blue** & **Green** & **Red** & **NIR** & **NDVI** & **NDWI** \\ \hline Water & Urban area & 0.819 & 0.719 & 1.187 & 1.993 & 1.144 & 1.435 \\ Water & Bare soil & 0.983 & 0.819 & 1.022 & 1.992 & 1.452 & 1.770 \\ Urban area & Bare soil & 0.165 & 0.108 & 0.116 & 0.024 & 0.273 & 0.330 \\ Water & Vegetation & 0.408 & 0.570 & 0.528 & 1.980 & 1.827 & 1.953 \\ Urban area & Vegetation & 1.317 & 1.410 & 1.690 & 1.628 & 1.710 & 0.904 \\ Bare soil & Vegetation & 1.521 & 1.612 & 1.649 & 1.551 & 1.529 & 1.398 \\ \hline \end{tabular}
\end{table}
Table 2: JMD for the year 2018.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Class 1** & **Class 2** & **Blue** & **Green** & **Red** & **NIR** & **NDVI** & **NDWI** \\ \hline Water & Urban area & 1.478 & 1.545 & 1.987 & 2.000 & 1.276 & 1.909 \\ Water & Bare soil & 1.911 & 1.880 & 1.991 & 1.999 & 1.591 & 1.957 \\ Urban area & Bare soil & 0.154 & 0.129 & 0.053 & 0.262 & 0.213 & 0.245 \\ Water & Vegetation & 0.644 & 0.060 & 1.408 & 1.980 & 1.948 & 1.996 \\ Urban area & Vegetation & 1.324 & 1.622 & 1.900 & 1.815 & 1.816 & 1.178 \\ Bare soil & Vegetation & 1.824 & 1.910 & 1.936 & 1.839 & 1.867 & 1.840 \\ \hline \end{tabular}
\end{table}
Table 3: JMD for the year 2023.  
In 2023, the JMD values also indicated good separability for the water vs. vegetation classes, with an emphasis on NDWI (1.996). For urban area vs. vegetation, the separability was effective, with high values in the red band (1.900), NIR (1.815), NDVI (1.816), and NDWI (1.178). The separability between bare soil and vegetation remained high for all variables, with values above 1.823.
## 4 Experiments
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Existing Model** & **Proposed Model** \\ \hline Grid & \(7\times 7\) grid & \(19\times 19\) grid \\ \hline Layers & 24 convolutional layers & 23 convolutional layers \\ \hline Anchor Boxes & Fixed-size anchor boxes & Multi-scale anchor box \\ \hline Bounding box/cell & 2 bounding box/cell & 9 bounding boxes/cells, with each bounding box having 9 multi-scale anchors. \\ \hline Dataset & \multicolumn{2}{c}{PASCAL VOC 2007 dataset with 20 classes} \\  & and 9963 images containing 24,640 annotated objects. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the existing model with the proposed model on various parameters. class present in the dataset is calculated and compared for the existing model with that of the proposed model. Lastly, the mAP is calculated wherein it can be seen that a fair amount of increase was observed in the proposed model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Grid Size** & **mAP (\%)** \\ \hline \multirow{3}{*}{YOLO} & \(7\times 7\) & 63.4 \\  & \(9\times 9\) & 68.93 \\  & \(11\times 11\) & 77.39 \\ \hline \multirow{2}{*}{Proposed Model} & \(15\times 15\) & 80.29 \\  & \(19\times 19\) & 84.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of mAP on various grid sizes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer No.** & **Layer Type** & **Filters** & **Kernel Size** & **Output** & **\#Parameters** & **\#FLOPS** \\ \hline
1 & Convolutional & 32 & \(3\times 3\) & \(608\times 608\times 32\) & 864 & 319,389,696 \\ \hline
2 & Max & - & \(2\times 2\) & \(304\times 304\times 32\) & 0 & 11,829,248 \\ \hline
3 & Convolutional & 64 & \(3\times 3\) & \(304\times 304\times 64\) & 18,432 & 1,703,411,712 \\ \hline
4 & Max & - & \(2\times 2\) & \(152\times 152\times 64\) & 0 & 5,914,624 \\ \hline  
& 249,523,200 \\ \hline  & & & & & & \\ \end{tabular}
\end{table} 
Table 5: Calculation of learnable parameters and FLOPS for the proposed model. In our work, the value of dropout in the Figure 11 was considered wherein the loss value was minimal after successful completion of 150 epochs. In our work, the dropout value of 0.5 was considered when the loss obtained was 0.25, which is comparatively lower than the loss values of 2.4 and 1.5, respectively. Figure 12 compares the epoch versus IoU accuracy. The X-axis is the number of epochs, and Y-axis indicates IoU accuracy. As the number of epochs starts increasing, it indicates
## 3 Results and Discussions
### Prevention and Control Strategies for Flood Disasters in Study Area  
Urban waterlogging disasters mainly occur in highly developed urban areas. Although Zhengzhou spans the two river basins of the Yellow River and the Huaihe River, the waterlogging disasters mainly occur in the tributaries of the Jialu River in the Huaihe River Basin. These disasters include the \"720\" torrential rainstorm. Therefore, the study area of this paper is mainly 2750 km\({}^{2}\) in Zhengzhou, of which the key area is the urban planning and construction area of 1945 km\({}^{2}\).  
With the purpose of flood risk prevention and control, and based on a comprehensive understanding of the actual geological, hydrological, and climatic conditions and the current urban development situation, a general idea for flood risk prevention and control in the study area is proposed as follows: taking the Jialu River basin where the city is located as a complete system, following the idea of \"combining ecological geological methods with engineering construction to control flood risk; coordinating upstream, middle and downstream, upstream conservation and storage, and developing both midstream (urban) storage and drainage, with downstream emphasis on drainage and coordination above ground and underground\", so the risk of flood disasters occurring in the study area can be minimized.  
It is evident that to fully implement a composite plan for flood control; it is necessary to combine the geological and non-geological methods. Non-geological methods include planning of the flood discharge, design and use of underground drainage pipe networks, and storage of reservoirs, lakes, and wetlands. The geological method is based on the geological conditions of the study area, fully following and applying hydrogeological laws in sponge city engineering and water ecological engineering construction, fully utilizing the ecological geological theory in upstream or source ecological restoration engineering, and utilizing engineering geological conditions to build underground water storage engineering, in order to improve the capacity of rainwater and flood absorption, and achieve the goal of reducing the risk of urban flood disasters.
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **This** & [35] & [36] & [37] & [38] & [39] & [40] & [41] \\  & **Work** & **MEJ24** & **CICC21** & **TMTT20** & **Sensors17** & **JSSC18** & **TMTT17** & **TVLS17** \\ \hline Technology (nm) & 28 & 180 & 180 & 28 & 110 & 160 & 180 & 65 \\ System & BLE & IoT & IoT & BLE & BLE & IoT & GNSS & BLE \\  & Sensor & Sensor & & & & Sensor & & \\ RX Architecture & Low-IF & \(\mathrm{N}/\mathrm{A}\) & \(\mathrm{N}/\mathrm{A}\) & Low-IF & Low-IF & \(\mathrm{N}/\mathrm{A}\) & Low-IF & Low-IF \\ ADC Type & CT- & \(\mathrm{Q}\Sigma\mathrm{AM}\) & DT-\(\mathrm{\Sigma AM}\) & DT-\(\mathrm{\Sigma AM}\) & CT-\(\mathrm{\Sigma AM}\) &
\begin{tabular}{c} SAR + \\ DT-\(\mathrm{\Sigma AM}\) \\ \end{tabular} & CT-\(\mathrm{Q}\Sigma\mathrm{AM}\) & CT-\(\mathrm{Q}\Sigma\mathrm{AM}\) \\ ADC ENOB (bit) & 10.9 & 16.1 & 14.5 & 10.5 & N/A & 19.3 & 10.0 & 10.2 \\ Sampling & 32 & 2.5 & 0.2 & 32 & 128 & 2 & 460 & 200 \\ Frequency (MHz) & 2 & 0.01 & 0.0008 & 2 & 1 & 0.001 & 33 & 5 \\ ISR (dBc) & 54.2 & \(\mathrm{N}/\mathrm{A}\) & \(\mathrm{N}/\mathrm{A}\) & 32 & N/A & N/A & 59.3 & 59.6 \\ DR (dB) & 70.3 & 102.6 & 94.1 & 66 & N/A & 120.3 & 64 & 74.3 \\ SNDR (dB) & 67.5 & 98.6 & 89.3 & 65.2 & N/A & 118.1 & 62.1 & 62.9 \\ SNR (dB) & 68.9 & 101.5 & 91.9 & N/A & N/A & 119.1 & 64.5 & 64.4 \\ Supply Voltage (V) & 1.0 & 1.8 & 1.5 & 1.04 & N/A & 1.8 & 1.8 & 1.2 \\ ADC Power (mW) & 0.81 & 1.3 & 0.004 & \({}^{\star}\) 1.2 & 2.4 & 0.28 & \({}^{\star}\) 25.4 & 4.2 \\ ADC Area (mm\({}^{2}\)) & 0.145 & 0.648 & 0.75 & 0.18 & 0.24 & 0.25 & 0.73 & 0.39 \\ \({}^{**}\) FOM\({}_{\mathrm{S}}\) (dB) & 164.3 & 171.5 & 177.1 & 158.2 & N/A & 185.8 & 155.1 & 165.1 \\ \({}^{**}\) FOM\({}_{\mathrm{W}}\) (fJ/conv.) & 103.2 & 934.2 & 104.8 & 201.7 & N/A & 213.1 & 369.8 & 368.1 \\ \hline \hline \end{tabular}  
* Estimation by subtracting other circuit power consumption from the total receiver power. \({}^{**}\) FOM\({}_{\mathrm{S}}\)\(=\) SNDR(dB)\(+10\)-\(\log_{10}\left[\frac{\mathrm{BW}(\mathrm{Hz})}{\mathrm{F}(\mathrm{W})}\right]\), \(\mathrm{FOM}_{\mathrm{W}}=\frac{\mathrm{P}(\mathrm{W})}{\mathrm{2.BW}(\mathrm{Hz}) \mathrm{2^{N}}\mathrm{new}}\) where ENOB \(=\)\(\frac{\mathrm{SNDR(dB)}-176)}{6.02}.  
\end{table} 
Table 1: Performance summary of the proposed RX and comparisons to the state of the art with \(\mathrm{\Sigma AM}\). number RS-2023-00229689, and in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and Information and Communications Technology (MSIT) of Korean Government under grant number RS-2024-00445552. The EDA tool was supported by the Integrated Circuit Design Education Center (IDEC), Korea. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within the article. **Conflicts of Interest:** The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Methodology and System Modeling
### Energy Storage System Model  
Whenever the combined output power of WT and PV generation surpasses the capacity of load demand, the ESS transitions into the charging state. The amount of energy stored at any given time \(t\) is primarily determined by the difference between the sum of the total PV and WT generation, and the load demand.
## 5 Performance Evaluation
This is the case, because it is not possible to predict when UEs will need resources. To illustrate this effect, let us consider a scenario with \(N\) = 300 users that generate aperiodic traffic (\(\lambda\) = 0.1 packets). We shall then suppose that users request a maximum latency of 1 ms and a reliability level equal to 1-10\({}^{-5}\). Satisfying this demand requires reserving 300 RBs (one per UE) in a 1 ms time windowA lower number of resources would be necessary if traffic was periodic and we could estimate when each UE would require resources for their transmission. In this case, several UEs could share the same RB if they generate their packets at different time instants. This would reduce the total number of RBs necessary to serve all users. This is not possible in the case of aperiodic traffic, since  
Figure 8: Latency introduced in grant-based scheduling. we cannot predict when a UE would need radio resources. Figure 7 shows that our implementation of 5G NR grant-free scheduling with 4-repetitions and shared resources can support 300 UEs (with their latency and reliability demands) with only 48 RBs in a time window of 1 ms. This is 84% less radio resources than if we reserve dedicated resources per UE (with aperiodic traffic) for their first transmission using semi-persistent scheduling. These results clearly show that the implemented 5G NR grant-free scheduling with shared resources can better support URLLC applications with aperiodic traffic and stringent communication requirements than other existing proposals. However, the conducted analysis (e.g., Figure 7) has also shown that new solutions will be needed to guarantee very demanding reliability and latency levels such as those foreseen for some URLLC services in 3 GPP Release 16.
## 5 Conclusions  
This paper proposed a RSWW-OSSS for supporting overlay D2D communication and cooperative uplink transmission in a cellular network hosting multiple EUs and D2D transmission pairs. Based on the proposed RSWW-OSSS, the EUs intend to improve the QoS with the aid of cooperative assistance provided by the D2D transmission pairs. Moreover, the D2D transmission pairs are encouraged to forward data for the EUs, in exchange for accessing the cellular spectrum to convey their own data. In order to further improve the spectrum efficiency, NOMA technology is used by the D2D transmission pairs to simultaneously transmit the EU's and its own data within the same spectrum and at different transmission power. In this paper, the behaviours of EUs and D2D pairs are modelled as a two-stage Stackelberg game. Furthermore, the backward induction is exploited to derive the optimal strategies for the sake of maximizing the utilities of both the EUs and the D2D transmission pairs. Moreover, a one-to-one stable matching algorithm is developed for the sake of generating the stable cooperative matchings between the EUs and the D2D transmission pairs. Finally, we analyse the stability of the matchings generated by the proposed matching algorithm. The simulation results show that the proposed RSWW-OSSS is capable of significantly providing \(EUL_{m}\) the transmission rate 41% higher than the non-cooperative direct transmission rate \(R_{m}^{dir}\). Moreover, the D2D transmissions can achieve 105 b/s/Hz by exclusively occupying EUs' licensed spectrum resource. According to the simulation results, the proposed RSWW-OSSS performs greater than exiting methods as well.  
There are two time slots while NOMA is used during the second time slot for the sake of improving the spectrum efficiency. In future work, NOMA will be exploited in both the first time slot and the second time slot. EUs decide their transmission power during the first time slot while D2D pairs decide their transmission power during the second time slot versus two equal time slots, namely \(t_{1}=t_{2}=0.5\) T.  
**Author Contributions:** [PERSON] supervised the paperwork and comments. [PERSON] was responsible for designing and simulating the optimal resources allocation algorithm and writing the paper; [PERSON] designed the network model, edited the paper and analysed simulation results.  
**Funding:** This work has been supported by National Natural Science Foundation of China [grant number 61501244], [grant number 61501245]; Natural Science Foundation of Jiangsu Province [grant number BK20150932]. Priority Academic Program Development of Jiangsu Higher Education Institutions, Innovation and Entrepreneurship Doctoral Program of Jiangsu, Startup Foundation for Introducing Talent of Nanjing University of Information Science & Technology.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### WSN Simulation  
The CupCarbon Network Simulator was used to evaluate the topology, routing, and energy efficiency of the wireless sensor network (WSN) across different forest environments. The simulations covered both dense and sparse forest scenarios, with performance compared to the conventional 2D Mesh topology. Three routing techniques were implemented: adaptive inter-tier routing (for dense forests), hybrid Adaptive-Semi-Adaptive (for sparse forests), and adaptive routing for 2D Mesh. Figure 8 shows a WSN designed using CupCarbon, representing a portion of a 2D mesh topology.
## 3 Results and Discussion
### Comparison of Leaf Segmentation Performance
#### 3.2.2 Comparison of Camera Types
Table 10 shows that the visible camera achieved the best performance for average and maximum F1 score on daytime images with values of 0.584 and 0.794, respectively. The NoIR camera achieved the best performance for average and maximum F1 score on daytime (whole day) images, with values of 0.333 (0.396) and 0.636 (0.599), respectively. From Tables 8-10, we can conclude that the visible camera is the best choice for daytime images. When considering Recall only, the thermal camera is the best choice for nighttime and whole day images. When considering F1 score, the NoIR camera is the best choice for nighttime and whole day images.
## 5 Simulation Results and Discussion
### Case 1: Total Cost, Reserve Cost, Direct Cost and Penalty Cost vs. PDF Parameters  
In this case, total cost, penalty cost, direct cost and reserve cost of two WTs are plotted against Weibull PDF parameters, keeping the shape parameter (\(k=2\)) constant while the scale parameter (\(c\)) is changed from 2 to 16 and respective changes in costs are plotted at a fixed scheduled WT power. A suitable schedule WT power of 25 MW and 20 MW are selected for WT 1 and WT 2, respectively. The schedule power selected is about one-third of the total installed capability, which is reasonable because practical WTs have a capacity between 30% and 45% [53]. The curves for WT 1 and WT 2 are plotted in Figures 7 and 8, respectively. The minimum value of total cost occurs around a value of 8 of the scale parameter (\(c\)); as the value of \(c\) increases, penalty cost increases while the reserve cost becomes constant after a certain value of \(c\).  
Figure 7: Cost vs. Weibull scale parameter (\(c\)) at bus #5.  
In a similar way, the SPV penalty cost, reserve cost and direct cost are plotted against lognormal PDF parameters from 2 to 7 at a fixed value of standard deviation (\(\sigma=0.6\)). A suitable value of scheduled SPV power is chosen at 20 MW. The minimum value of total cost occurs at a lognormal mean value of 5.5; the penalty and reserve cost have the same values at \(\mu=5.7\). After this, the point penalty cost increases, due to which a sharp increase occurs in total cost. SPV output power is highly dependent on lognormal mean \(\mu\) i.e., at a low value of \(\mu\), a high reserve is necessary and vice versa. The variation in SPV cost vs. lognormal mean (\(\mu\)) is plotted in Figure 9.