esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Acquisition and Research Method
### GIS Spatial Analysis: Location, FAR, Accessibility, and Housing Conditions
\begin{table}
\begin{tabular}{c c c} \hline
**Measurement Item** & **Equation** & **Meaning** \\ \hline FAR & \(F_{x}=F\times L\) & \(F\) is the single-story building area, \(L\) is the number of floors of the building \(F_{x}\) is the total building area, \(A_{x}\) is the plot area \\ \hline Community accessibility & CA = NQPDAn in sDNA + \(N_{\text{C1}}\) & NQPDAn is an indicator in sDNA that calculates global accessibility, reflecting the accessibility level of roads based on its numerical value. \(N_{\text{C1}}\), Number of transportation stations within 300 m around the community. CA means community accessibility. \\ \hline Housing conditions & HC = \(T_{H1}+N_{H2}+\frac{S_{x}}{S}\) & \(T_{H1}\), the community house price; \(N_{H2}\), the number of households in the community; \(S_{y}\), greening area of the community; \(S\), area of the community; and HC means housing conditions. \\ \hline \end{tabular}
\end{table} 
Table 4: Measurement method and analysis equation. \begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Source** & **Time** \\ \hline Basic information & Data acquisition source is Gaode, Suzhou Map, Accessed from: [https://1 bs.amap.com/](https://1 bs.amap.com/) (accessed on 1 September 2020). & 2020 \\ \hline POI, AOI data & Suzhou Anjuke Second-hand Housing Trading Website. Accessed from: [[https://su.fang.anjuke.com/](https://su.fang.anjuke.com/)]([https://su.fang.anjuke.com/](https://su.fang.anjuke.com/)) (accessed on 1 September 2020). & 2020 \\ \hline \end{tabular}
\end{table} 
Table 3: A detailed description of the data source used in the research. presented in five categories using the natural discontinuity method throughout the entire research scope. In addition, we also calculated the combination of three indicators: FAR, housing price, and accessibility, and the results were visually overlaid by three indicators, presenting different results based on different colors.
## 6 Simulation Result
### Evaluation of Driving Performance  
Table 8 presents a comparative analysis of each algorithm in terms of three key navigation metrics: Travel Distance, Route Completion, and Success Rate.  
TQC achieved the best performance across all three navigation metrics. Notably, its Success Rate reached 80%, indicating that the learned policy was capable of consistently completing routes under diverse road conditions.  
SAC exhibited a comparable performance to TQC in terms of Travel Distance and Route Completion, but its Success Rate was slightly lower, at approximately 65%. This suggests that while SAC displayed robust route-following behavior, it may have encountered penalties that led to task failures. Similarly, TD3 showed a reasonably high Route  
Figure 8: Comparison of driving performance at T-, Cross-, and Round-Intersections for each algorithm. For each RL algorithm, its driving trajectories at different training stages are visualized (green for 200 k steps, red for 600 k steps, and blue for 1 M steps). The legend indicates the success or failure of each trajectory, and in cases of failure, the cause of failure is also specified.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Travel Distance (m)** & **Route Completion** & **Success Rate** \\ \hline DDPG & 1888 & 0.23 & 0.00 \\ TD3 & 6435 & 0.77 & 0.60 \\ SAC & 6960 & 0.83 & 0.65 \\ TQC & **7614** & **0.91** & **0.80** \\ CrossQ & 1843 & 0.22 & 0.00 \\ PPO & 5800 & 0.69 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Metrics for evaluating driving performance after 1 M steps of learning (Bold text indicates the method that achieves the best performance for each metric).  
Completion of 77%, yet its Success Rate dropped to 60%, highlighting limitations in its generalization or rule adherence.  
In contrast, both CrossQ and DDPG demonstrated significantly lower values in Route Completion and Success Rate. In particular, both algorithms recorded a Success Rate of 0%, indicating a complete failure in mission completion.  
Overall, the results suggest that a higher Travel Distance and Route Completion generally correlate to a higher Success Rate. Since the Success Rate reflects full Route Completion, while Travel Distance and Route Completion represent partial progress, the latter two can be seen as necessary, but not sufficient, conditions. Completing a full route successfully requires further policies that incorporate the robust handling of traffic regulations and lane-keeping across the entire route.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 3 Crucial Techniques
### High-Speed Techniques
#### 3.1.1 Speculative DFE with Customized MUX  
Figure 2 shows the simplified diagram of the 64-way parallel 1-tap speculative DFE. Compared with the expanded DFE design [3], it removes the multiplication, summation, and decision unit of the first tap and its coefficients from the feedback loop. These three units are then copied into four copies (there are only four decisions) and placed at the data entry. When each data period comes, it first calculates the four decision results separately to get the new four decision results. These results are then selected to obtain the final output, which is more suitable for high-speed circuits and parallel data links. Figure 3 visually shows the timing propagation diagram of the speculative DFE. The output of each MUX depends on the selection result of its previous data [11], so the selection process of this parallel data should be completed within one clock cycle. This means the selection for each MUX should be finished in one symbol width [8]. For the 100 Gb/s PAM4 (i.e., the symbol width equals 20 ps), the selection-to-data (S-to-D) delay of each MUX (i.e., shown in Figure 2) should be less than 20 ps. However, the standard MUX for 28-nm CMOS is around 40 ps, which cannot satisfy the timing requirements. To solve this problem, a customized MUX shown in Figure 4 is adopted. The S-to-D delay consists of the delay of two transmission gates and two inverters. The simulation results show that  
Figure 1: DSP main channel.  
the maximum S-to-D delay of the customized MUX is around 16 ps, which can satisfy the timing requirement.
###### Abstract  
The usable satellite spectrum is becoming scarce due to static spectrum allocation policies. Cognitive radio approaches have already demonstrated their potential towards spectral efficiency for providing more spectrum access opportunities to secondary user (SU) with sufficient protection to licensed primary user (PU). Hence, recent scientific literature has been focused on the tradeoff between spectrum reuse and PU protection within narrowband spectrum sensing (SS) in terrestrial wireless sensing networks. However, those narrowband SS techniques investigated in the context of terrestrial CR may not be applicable for detecting wideband satellite signals. In this paper, we mainly investigate the problem of joint designing sensing time and hard fusion scheme to maximize SU spectral efficiency in the scenario of low earth orbit (LEO) mobile satellite services based on wideband spectrum sensing. Compressed detection model is established to prove that there indeed exists one optimal sensing time achieving maximal spectral efficiency. Moreover, we propose novel wideband cooperative spectrum sensing (CSS) framework where each SU reporting duration can be utilized for its following SU sensing. The sensing performance benefits from the novel CSS framework because the equivalent sensing time is extended by making full use of reporting slot. Furthermore, in respect of time-varying channel, the spatiotemporal CSS (ST-CSS) is presented to attain space and time diversity gain simultaneously under hard decision fusion rule. Computer simulations show that the optimal sensing settings algorithm of joint optimization of sensing time, hard fusion rule and scheduling strategy achieves significant improvement in spectral efficiency. Additionally, the novel ST-CSS scheme performs much higher spectral efficiency than that of general CSS framework.  
collaborative sensing; LEO mobile satellite systems; compressed detection; spectral efficiency; hard fusion scheme; scheduling strategy +
Footnote †: journal: Sensors
## 2 Definition of System Requirements and Assessment of Fitting Communication Technologies
### Communication Technology  
Based on the abovementioned requirements, the VANET technology was selected as an appropriate communication technology. A communication route to each relevant vehicle is found by an ad hoc routing protocol. For this sake, we evaluated the performance of three commonly used reactive and hybrid ad hoc routing protocols, i.e., AODV, GPSR, and DYMO. The proactive protocols (including the Zone Routing Protocol which operates in a proactive mode within a single network zone) were not considered in this work for their relatively high signalization overhead, which makes them inapplicable in the context of the limited available bandwidth of the VANET. The values of parameters and the selected routing protocols are summarized in Table 1.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 6 Evaluation
### Compared with Predictive Models  
Figure 9 shows the performance comparison between our model and three existing analytical models (K_average, K_histogram and K_opt_storage [19]) on the three target architectures. The y-axis represents the achieved performance of the four K-selection methods in terms of accuracy calculated by Equation (1). For each matrix, the SpMV performance with the K obtained from one of the three methods is \(Gflops_{k\_test}\), and the SpMV performance with the best K is \(Gflops_{k\_best}\).  
\[Accuracy=Gflops_{k\_test}/Gflops_{k\_best} \tag{1}\]  
We see that, for the three tested platforms, in general, the accuracy of our model is optimal, which is significantly better than that of \(K_{histogram}\), \(K_{opt\_storage}\) and \(K_{average}\). In terms of average accuracy, our model outperforms \(K_{histogram}\), \(K_{opt\_storage}\) and \(K_{average}\) by 26.1%, 5.4%, and 7.7%, respectively, on KNL; 61.1%, 47.3% and 59.4%, respectively, on AMD EPYC; and 72.6%, 109.2% and 77.7%, respectively, on Phytium 2000+. Figure 7c shows that, although the native formats of HYB5 have a poor performance, our model on Phytium 2000+ can provide superior performance and still outperform the state-of-the-art formats. Note that \(K_{average}\) yields better performance than \(K_{histogram}\) on KNL (Figure 9a).
## 3 Related Work
Similarly, the Integrated Public Alert and Warning System (IPAWS-Open) [28] provides services to receive, authenticate, and route standards-based messages from alerting authorities to all types of alert systems, via several communication pathways, including public broadcast, cellular networks and the Internet. In [29] the authors introduce an emergency fire management system in the Web of Object infrastructure. They integrate the formation and management of Virtual Objects (ViOs), which are derived from real world physical objects and virtually connected to each other in the semantic ontology model. This allows information reusability, extensibility and interoperability, which enable ViOs to uphold resource orchestration, federation and collaboration. All these aspects are necessary for the coordination of actions in the case of fires. Although the previous proposals are interesting and contribute to improving the effectiveness of the first response process, they are focused on addressing a particular aspect of the problem. Moreover, none of them allow designers to represent the type of components that participate in an IoT-based solution, the role played by each of component, and the way in which the shared information is captured and disseminated through these networked solutions. In the next section we present the basic concepts behind a Human-centric Wireless Sensor Network (HWSN) [4], which allows modeling the nodes interaction and the information flow in IoT-based systems. Moreover, we indicate how these concepts can be used to determine the components roles, their capabilities for information processing and communication. As mentioned before, there are various transversal design issues, such as information trustworthiness, security and privacy that do not have been formally considered in the model of a HWSN. However, these design aspects can be included in the network model through the use of complimentary elements, such as roles [7], location [8,9] and reputation of the nodes [10].
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 5 Conclusions  
Aiming at the problem of insufficient wave velocity calculation accuracy of the commonly used inversion method for complex geological models, this paper introduces the frequency domain FWI method into seismic ahead prospecting in tunnels and carries out the corresponding method. The adjustment and optimization make it suitable for the complex environment in a tunnel and can calculate more accurate wave velocity results, which lays the foundation for the subsequent acquisition of imaging results and compressive strength distribution results.  
Due to the complexity of the underground geological conditions, various undesirable geological conditions will be encountered during tunnel construction, which will do harm to the construction. This paper chooses several types of typical undesirable geological bodies commonly encountered in tunnel construction and establishes models of lithological interfaces, faults, and karst caves. In terms of the imaging effect of the frequency domain FWI, the analysis of its advantages and shortcomings is given. The forward simulation andFWI study of tunnel seismic wave ahead prospecting were carried out, which verified the validity and reliability of the method in this paper. It provided a basis for the detection and geological interpretation of typical undesirable geological bodies in actual engineering.  
[PERSON] and [PERSON] wrote the paper and conducted the research; [PERSON] and [PERSON] were responsible for collecting and analyzing data; [PERSON] was responsible for experiments; [PERSON] provided method guidance; [PERSON] provided project support and method guidance and reviewed the paper; [PERSON] put forward research ideas, provided research guidance and reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science & Technology Program of Department of Transport of Shandong Province (2019B47_2).  
Not applicable.  
No data available.  
The authors thank the anonymous reviewers for carefully reading the manuscript and providing many helpful comments to improve the quality of this work.  
The authors declare no conflict of interest.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Triple Colocation Validation  
Triple colocation is a powerful means of assessing the performance of remotely sensed environmental parameters when they are compared with other near-coincident estimates of the same parameter. It attempts to decompose the differences between the two into individual components of error due to each of them. In order to apply the method, it is necessary to assemble near-coincident estimates of the environmental parameter from three different sources. These matchup \"triplets\" provide an overconstrained system of equations for the parameters of their joint probability distribution, from which the individual distributions can be factored [40]. Decomposition of the joint distribution is only possible if the errors in each of the three estimates are mutually uncorrelated. If this is not the case, the triple colocation results may be inaccurate. This possibility is addressed by assembling more than three near-coincident matchups and then performing the triple colocation analysis on all possible triplets. If the sources are sufficiently different (e.g., predictions by different models or measurements by different types of sensors), then the degree and type of mutual correlation between their errors can be expected to differ, and the validity of the resulting triple colocation analysis likewise to differ. However, if the results are found to be similar, this is an indication that the effects of mutually correlated error may not be significant. In the following applications of the triple colocation method, we assemble four or more matchup sources and compare the results derived from all possible triplets taken from among them.  
Triple colocation performance assessment is performed separately for storm and non-storm conditions. Away from major organized storms, coincident matchups are assembled between the FDS and NOAA CYGNSS winds, the winds measured by ASCAT-B and GMI, and the ERA5 reanalysis winds. For locations in and near major storms, coincident matchups are assembled between the MRG and NOAA CYGNSS winds, the winds measured by SFMR, and the HWRF model winds. Note that with both types of matchups, there are more than three available wind speed products to be intercompared.
## 1 Introduction
The E-Nose is a chemical measurement system that detects volatiles through an array of gas sensors, generating a fingerprint response to the sample gases [21]. Recent advancements have promoted the E-Nose as a bionic device that simulates the mammalian olfactory system [22]. This has spurred interest in using the E-Nose as a cost-effective alternative to human'sniffers'. However, in 2014, [PERSON] proposed that the E-Nose is not fully effective for measuring odor qualities [23]. Although it mimics the biological structure of olfaction, the gas sensors of a classical E-Nose are weakly selective and lack a specific filter function for odorants similar to odor receptor cells. Moreover, unspecific gas sensors are not odor sensors but react on volatiles regardless of whether they are aromaticity compounds. Therefore, the currently designed E-Nose is limited in its effectiveness for precise odor measurement. This study aims to identify the aroma-active compounds in five different dry-cured hams using a combination of GC-MS-O and E-Nose technologies. The GC-MS-O experiments evaluated the odor description, intensity, and hedonic assessment of the aroma-active compounds. The principal component analysis (PCA) method was used in conjunction with GC-MS-O to differentiate odor attributes among the hams. Additionally, correlations between the GC-MS-O results and E-Nose responses were analyzed to enhance understanding of how these technologies relate to the human perception of odor. This work offers new insights into designing E-Nose systems that better replicate the biological structure of the sense of smell.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 7 Evaluations
### Improving Discovery for Two Neighbors  
Spear enables the evaluation of neighbor discovery protocols supporting symmetric and asymmetric duty cycles, which facilitates the generation of the optimal schedule.  
**Symmetric duty cycle**: suppose two neighbors select the same duty cycle \(\theta\) that increases from 0.1 to 0.3, we evaluate the average discovery latency in Figure 9a. As shown in the figure, the discovery latency (\(y\)-axis) of all algorithms decreases as \(\theta\) (\(x\)-axis) increases, and they have similar performances. This is because the discovery latency is proportional to \(\frac{1}{\theta^{2}}\) and the trends of these curves match the analysis. Finally, the optimal schedule can be generated automatically; for example, U-Connect is selected when \(\theta\in[0.1,0.13]\), while Hello is selected when \(\theta\in[0.13,0.16]\).  
**Asymmetric duty cycle**: suppose one node's duty cycle is fixed as \(\theta_{1}=0.2\) and the other node's duty cycle \(\theta_{2}\) increases from 0.1 to 0.3. Since Quorum is inapplicable for asymmetric duty cycles, we compare the other four algorithms. As shown in Figure 9b, the average discovery latency (\(y\)-axis) decreases as \(\theta_{2}\) (\(x\)-axis) increases, and the decreasing trends are much more gentle than in Figure 9a. This is because the discovery latency is proportional to \(\frac{1}{\theta^{2}}\) when \(\theta_{1}\) is a constant.  
Figure 8: Spear saves more energy by incorporating PWR and PDR compared to bare algorithms.  
(a) After \(2\hat{T}\); (b) After \(3\hat{T}\).  
Figure 7: Spear prolongs the lifetime with PWR and PDR.
## 3 Results and Discussion
### Inhibitory Activities against MAOs, AChE, BChE, and BACE-1 and Antioxidant Activity  
Among the 19 derivatives, four and three compounds showed effective inhibitory activities against MAO-A and MAO-B, respectively, with residual activities of \(<\)50% at 10 \(\upmu\)M (Table 1). Compound **30** was the most potent inhibitor of MAO-A, with an IC\({}_{50}\) value of 1.54 \(\upmu\)M, followed by **3a** and **3p** (IC\({}_{50}\) = 3.35 and 4.77 \(\upmu\)M, respectively). A structural comparison with **3a** indicated that the 3-F group in **30** increased its inhibitory activity against MAO-A. Compound **3s** was the most potent inhibitor of MAO-B, with an IC\({}_{50}\) value of 3.64 \(\upmu\)M, followed by **3t** and **3a** (IC\({}_{50}\) = 5.69 and 7.69 \(\upmu\)M, respectively). It was observed that MAO-B inhibitory activity was increased in the order of 3- 4- \(>\) 2-NO\({}_{2}\) groups in **3s**, **3t**, and **3r**, respectively. The selectivity index (SI) values of **3s** and **3a** for MAO-B over MAO-A were 4.31 and 0.44, respectively, indicating that **3s** and **3a** are moderately selective for MAO-B and MAO-A, respectively. Benzylidene-4-[(4-methylphenyl)sulfonyloxy] moiety in this study was less effective in potency and MAO-A selectivity than 3-benzylquinoxaline moiety previously reported, in which benzyl moiety strongly bound to MAO-A with aromatic interactions through Val210 and Phe208 [36]. On the other hand, all compounds weakly inhibited AChE and BChE, which retained \(>\)50% residual activities at 10 \(\upmu\)M, except for **3a**, which inhibited BChE with an IC\({}_{50}\) value of 16.1 \(\upmu\)M.  
Interestingly, **3e**, **3f**, and **3n** inhibited BACE-1 with IC\({}_{50}\) values of 8.63, 9.92, and 8.47 \(\upmu\)M, respectively, which were lower than the IC\({}_{50}\) of the quercetin reference. BACE-1 inhibitors have been studied as pharmaceuticals, and some of them have been submitted to phase I-III clinical trials [37]. The potency of **3n** against BACE-1 was slightly weaker than that of compound PC3, a piperazine-substituted chalcone, with an IC50 value of 6.72 \(\upmu\)M [38]. These results suggest that **3e**, **3f**, and **3n** can be considered potential agents for the treatment of AD. In an antioxidant assay using DPPH, lead compounds **3a**, **30**, **3p**, **3s**, and **3t** showed negligible inhibition (0.18-3.36%).