esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Experimental Results for Wuhan  
Compared to the CIA and LGC datasets, the Wuhan dataset exhibits three distinct characteristics: first, it covers an urban area with more complex land cover textures; second, it comprises Landsat-GF images rather than the traditional MODIS-Landsat pairs; and third, the dataset is relatively small in scale. In this study, 30 October 2017 is selected as the reference time and 18 October 2015 as the prediction time. Figure 9 shows the global fusion results of different STF models on the Wuhan dataset. Compared to Figure 9a, the results of CTSTFM, which focuses on spatial detail reconstruction, exhibit noticeable color errors relative to the true image. According to Table 3, this issue likely stems from poor prediction in the near-infrared band, indicating insufficient learning of near-infrared features by the model. FSDAF shows noticeable artifacts in the densely built-up areas near the center of the image. For the water region at the top of the result image, MLFF-GAN, STF-Trans, and CTSTFM all display visible stitching marks. Special attention is given to the water area marked by the black box in Figure 9a, where significant changes occurred during the study period. Compared to 18 October 2015, the water area decreased by 30 October 2017. The zoomed-in view and corresponding error map are shown in Figure 10. The AAD map reveals that the three traditional methods and the temporal change-focused MLFF-GAN failed to capture this change effectively. In contrast, the proposed method
## 3 Results and Analysis
### Surface Resistivity  
Figure 14 presents the results of a four-electrode resistivity analysis on the cement mortar samples produced with varying levels of the TPSW material as a partial sand replacement. This study was conducted at a fixed water-to-cement ratio of 0.5: At 3 days, the resistivity values for the replacement levels of 0%, 5%, 10%, and 15% were 8.1 k\(\Omega\)-cm, 8.5 k\(\Omega\)-cm, 8.9 k\(\Omega\)-cm, and 9.2 k\(\Omega\)-cm, respectively, with differences from the control group within 13.5%. At 28 days, the resistivity values increased to 11.2 k\(\Omega\)-cm, 12.3 k\(\Omega\)-cm, 13.5 k\(\Omega\)-cm, and 14.2 k\(\Omega\)-cm for the same replacement levels, where the differences expanded to within 26.8%. At 91 days, the resistivity values for the respective replacement levels reached 20.2 k\(\Omega\)-cm, 22.3 k\(\Omega\)-cm, 24.2 k\(\Omega\)-cm, and 26.1 k\(\Omega\)-cm.  
The electrical resistivity of cement mortar increased with higher replacement levels of waste PS materials. At 91 days, all the samples exceeded 20 k\(\Omega\)-cm, which met the durability benchmarks for resistance to ion penetration and environmental degradation. While the increased resistivity was desirable for durability, the trend was inversely correlated with the compressive strength and ultrasonic pulse velocity. This suggests that higher replacement levels, although beneficial for resistivity, could adversely affect the structural performance by increasing the porosity and reducing the material density. The enhanced resistivity indicates improved resistance to ionic migration, which is beneficial for long-term durability. However, the potential trade-offs in mechanical and structural performance must be carefully considered when optimizing the replacement levels for specific applications. Balancing durability with strength and integrity remains critical to achieving a sustainable and functional material design.  
Figure 14: Resistivities of the TPSW cement mortars with different W/C ratios at different ages.
## 3 Results
### Simulation Analysis
#### 3.2.3 The Impact of Meteorological and Hydrological Factors  
The relative contributions of climatic and hydrological elements to variations in lake ecological variables were extracted, as shown in Figure 5. Lake water temperature exhibited a significant effect on WGPP, WNPP, and Chl-a, with the relative contributions of WT for WGPP and WNPP surpassing 0.4. Increases in WT have the potential to enhance the water environment and stimulate the growth of aquatic vegetation growth. Furthermore, specific humidity and water volume also contributed to these three lake ecological indicators. The combined contribution from the local climate and hydrology on these indicators exceeded 0.5, indicating the substantial influence of local climate and lake hydrology on lake ecological changes. In contrast, WTN and WTP exhibited relatively small contributions from climatic and hydrological elements, with all relative contributions being less than 0.1. This suggests that WTP and WTN may be more sensitive to lake ecological interaction processes. Additionally, concentrations of CO\({}_{2}\) and O\({}_{2}\) within the lake were found to correlate with WTN and WTP. Biological processes (e.g., nutrient uptake by phytoplankton) and sediment nutrient release play crucial roles in regulating water nutrient concentrations within the lake. Changes in these processes would influence WTP and WTN dynamics over time. In summary, WGPP, WNPP, and Chl-a appear to be more susceptible to changes in the local climate and hydrology compared to water nutrients.
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 4 WEC Control Using Estimated Instantaneous Frequency
### Capture Width Ratio (CWR)  
To quantify WEC performance, the CWR is utilized as a key metric. CWR represents the ratio between the wave energy flux per unit width and the normalized power harvested by the WEC over time. The Capture Width Ratio can be thought of as somewhat analogous to the coefficient of power for a wind turbine. It is the fraction of environmental power available to the device that is converted to mechanical power.  
The wave energy flux per unit width is a fundamental metric in wave energy studies, representing the transport of wave energy across a unit crest length. In deep water, where wave dispersion follows \(\sigma^{2}=gk\), the wave energy flux is given by the following:  
\[\overline{P}_{\mathrm{wave}}=\frac{\rho g^{2}}{64\pi}H_{s}^{2}T_{e}, \tag{19}\]  
where \(\rho=1000\) kg/m\({}^{3}\) is the water density, \(g=9.81\) m/s\({}^{2}\) is the gravitational acceleration, \(H_{s}\) is the significant wave height, and \(T_{e}\) is the energy period of the wave.  
This expression is derived from the general wave energy transport equation:  
\[\overline{P}_{\mathrm{wave}}=Ec_{g}, \tag{20}\]  
where \(E=\frac{1}{16}\rho gH_{s}^{2}\) is the wave energy density, and \(c_{g}=\frac{gT_{e}}{4\pi}\) is the deep-water group velocity.  
By substituting these expressions and simplifying, we obtain Equation (19), which provides an analytical formulation for wave energy flux per unit width. This equation is widely used in ocean wave energy research [18].  
For a JONSWAP spectrum characterized by a peak period of \(T_{p}=9\) s, a significant wave height of \(H_{s}=2.4\) m, a peak modulation factor of \(\gamma=4\), and the energy period of \(T_{e}\approx\frac{T_{p}}{\Gamma\Gamma}=8.18\) s, the wave energy flux is computed as follows:  
\[\overline{P}_{\mathrm{wave}}\approx 22.56\ \mathrm{kW/m}. \tag{21}\]  
This value serves as the reference for evaluating the efficiency of WEC power absorption through the CWR.  
The normalized average power captured by the PTO is expressed as follows:  
\[\overline{P}_{\mathrm{P}\mathrm{P}\mathrm{t}}=\frac{E_{\mathrm{P}\mathrm{t} }}{D_{\mathrm{WEC}}T_{\mathrm{sim}}}\quad(\mathrm{W/m}), \tag{22}\]  
where \(E_{\mathrm{P}\mathrm{t}\mathrm{o}}\) is the total energy harvested by the PTO at the end of the simulation, \(D_{\mathrm{WEC}}\) is the WEC body's diameter interacting with the incoming wave, and \(T_{\mathrm{sim}}\) is the total simulation time.  
The CWR is then computed as follows:  
\[CWR=\frac{\overline{P}_{\text{pto}}}{\overline{P}_{\text{wave}}}. \tag{23}\]  
In the context of WEC performance, the CWR provides a normalized metric that quantifies how effectively a device converts available wave energy into usable mechanical power.
# New Method for Simultaneous Arsenic and Selenium Speciation Analysis in Seafood and Onion Samples
###### Abstract
The aim of this research was to (1) develop a method for the simultaneous speciation analysis of arsenic (As(III)-arsenite, As(V)-arsenate, DMA-dimethylarsinic acid, MMA-methylarsonic acid, and AsB-arsenobetaine), antimony (Sb(III), Sb(V)), chromium (Cr(III), Cr(VI)), and selenium (Se(IV)-selenite, Se(VI)-selenate, Se-Methionine, and Se-Cystine) during one analysis by hyphenated technique High-Performance Liquid Chromatography Inductively Coupled Plasma Mass Spectrometry (HPLC-ICP-MS); and (2) apply the new method to speciation analysis of seafood and onions matrices. The selected samples are widely consumed all over the world, and there is not currently a single and accurate method for simultaneous As, Sb, Cr, and Se speciation in all these matrices.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 3 Crucial Techniques
### High-Speed Techniques
#### 3.1.2 Fixed-Point Operation Strategy  
The implementation of traditional FFE involves multiplication, addition, and delay logic. This process requires consideration of speed and accuracy [6]. The more the number of taps, the more registers it needs, and the longer the calculation time will be. Moreover,  
Figure 4: Custom 4 to 1 MUX.  
Figure 3: The timing diagram of DFE MUX.  
Figure 2: Implementation of the 1-tap speculative DFE.  
the coefficients involved in the calculation process are floating decimal, and the balance between speed and accuracy needs to be considered in the design.  
This design adopts a fixed-point operation strategy to solve the above problems. The strategy adopts a parallel FFE structure, that is, each clock cycle reorganizes the input 64-way of parallel data with the parallel data of the previous cycle and the next cycle, and then splits it into a 64 \(\times\) 16 data matrix. Multiply this matrix with the coefficients corresponding to the 16 taps, and then add them to get the 64-way output of the FFE. Figure 5 shows the block diagram of the developed parallel FFE, where the FFE contains 8 pre taps and 7 post taps. Here, the bit numbers of the tap coefficients are chosen to be 9 bits to balance the speed and accuracy. To implement the multiplication and summation of the FFE, the 8-bit input data is extended to 21 bits by amplifying 9 bits and adding a 4-bit sign bit in the high order. After the multiplication and summation of the FFE, the lower 7 bits are first truncated to obtain the FFE output, leaving a 2-bit margin to maintain acceptable precision for DFE processing. After DFE processing, the lowest two bits will be further truncated to obtain the final equalized data with the same scale as the input data.  
Figure 6 shows the simulated eye map of FFE with different coefficient accuracies at 64 Gb/s. The channel loss at the Nyquist frequency is around 23 dB. Figure 6a,b compare the eye diagrams of FFE based on the fixed-point operation (9-bit coefficients) in hardware implementation and FFE based on floating-point operation in Matlab. It can be seen that the FFE based on fixed-point operation shows acceptable performance degradation. It can be seen that the 9-bit-coefficient FFE shows acceptable performance degradation. Figure 6c,d further show the simulated eye diagrams when applying 8-bit and 10-bit-coefficient FFE. As can be observed, the 10-bit-coefficient FFE shows ignorable improvement in contrast to the 9-bit one while the 8-bit coefficient FFE/DFE exhibits a visible deterioration.  
Moreover, Table 1 shows the vertical eye closure (VEC) and vertical eye-opening ratio (VEOR) [12] corresponding to Figure 6a,c,d. The VEC is defined as 20 log\({}_{10}\) (AV\(upp\)/\(Vupp\))\({}_{max}\), where the AV\(upp\) is the amplitude of the eye and the V\(upp\) is the eye height. And the VEOR is defined as \(-20\)log\({}_{10}\) ( (v - 1)/v), where the v = 10\({}^{VEC/20}\). Based on these simulation results, the 9-bit fixed-point operation is adopted in this design.  
Figure 5: 16-tap parallel FFE structure.
# State-Sensitive Black-Box Web Application Scanning for Cross-Site Scripting Vulnerability Detection
## 5 Conclusions and Future Work  
In this work, we present a state-sensitive black-box web application scanning method, including a filtering method for excluding similar states and a heuristic ranking method for optimizing the fuzzing order of forms. The filtering method reduces repetitive visits to similar states in the scanning process, especially for the state of HTML forms, which further improves the code coverage of the black-box web scanner within a limited scanning time. The ranking method helps to prioritize the fuzzing order of forms with more injection points, which further improves the efficiency of the black-box web scanner in detecting XSS vulnerabilities. We implemented our method in our black-box scanner SSBW and evaluated it on a set of eight real-world web applications. Compared with Black Widow, the state-of-the-art black-box scanner, SSBW had an average increase of about 19% in code coverage, and the maximum increase was about 57% on PrestaShop. In terms of XSS vulnerabilities detection, SSBW detected a total of 81 more XSS vulnerabilities than Black Widow within eight hours on four applications.  
This paper introduces four types of states: GET requests, form submissions, iframes, and Javascript events. It provides a detailed introduction to the similar state filtering method for GET requests and form submissions. The method of judging similar states between iframes and GET requests is also similar. However, due to the dynamic characteristic of Javascript events, determining the similarity of their states without executing events is currently challenging. Exploring methods to avoid repeated visits to similar states of Javascript events is a topic worthy of future study. Additionally, current black box scanners still extensively rely on prepared payload dictionaries for fuzzing. The utilization of artificial intelligence techniques, such as reinforcement learning, is also worth exploring as a way to efficiently generate payloads based on the information detected by black box scanners.  
Conceptualization, T.Z.; Data curation, T.Z., H.H. and K.Z.; Investigation, T.Z.; Methodology, T.Z., K.Z. and J.Z.; Project administration, Y.L.; Resources, T.Z.; Software, T.Z. and J.Z.; Supervision, Y.L.; Validation, T.Z. and H.H.; Writing--original draft, T.Z. and H.H.; Writing--review & editing, T.Z., H.H., Y.L. and K.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available on request to the authors.  
**Acknowledgments:** The authors sincerely appreciate [PERSON] et al. for their open-source black-box scanner Black Widow, which has helped us a lot.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 4 Implementation and Experiments
### Experiment 2  
Experiment 2 basically follows the design of Experiment 1. However, the difference is that we focus more on the multi-hop relay function of the Mesh Beacons and measure the response time. Specifically, in this experiment, if we stand at location \(D\), then only Mesh Beacon \(D\) is configured to receive the presence message from the Mobile App (i.e., Type 2 message), while Mesh Beacons \(A\), \(B\), and \(C\) are forced to ignore Type 2 messages. This ensures that when the user is physically located at location \(D\), its presence message is only received by Mesh Beacon \(D\) rather than by the nearby Mesh Beacon \(C\), which has less number of hops to the Mesh Router. The same idea applies to all other cases, in which only the Mesh Beacon closest to the user is able to receive the presence message from the Mobile App. Again, at each of the four locations, we measure the response time for five times and show the result in Figure 10.  
As expected, the above experimental results are almost identical with that of Experiment 1, with the exception of the measurement results at location \(D\). Specifically, here the majority of the response times at location \(D\) are around 2.7 s, while in Experiment 1 only two measurements exceed 2.7 s. This reveals the fact that although the user is physically closer to Mesh Beacon \(D\), in Experiment 1 some of the presence message from the Mobile App must have been received and then forwarded by the nearby Mesh Beacon \(C\) so a better performance (fewer packet losses) can be observed. Moreover, if we compare the measurement results at location \(C\) of the two experiments, we can find that the average response time in Experiment 2 is slightly higher than that in Experiment 1. We speculate  
Figure 10: Time taken for the Mobile App to receive the acknowledgement message in Experiment 2 (s).  
that this is because in Experiment 1, when the user is standing beside Mesh Beacon \(C\), some of the presence messages may have been received and then forwarded by either Mesh Beacon \(A\) or Mesh Beacon \(B\). Therefore, some of the measurement results are on the 2-hop distance (round-trip). However, in Experiment 2 the round-trip distance from Mesh Beacon \(C\) to the Mesh Router is 4 hops in every measurement. This is the reason why the average response time becomes higher than that in Experiment 1.
# Efficient Construction of the Equation Automaton
## 3 Equation Automaton
### Equation Automaton as a Quotient of C-Continuation Automaton  
[PERSON] and [PERSON] [6] have proved that the equation automaton is a quotient of the \(c\)-continuation automaton. Let us consider the equivalence relation \(\equiv_{e}\) defined by  
\[(x,c_{x}(\mathrm{E}))\equiv_{e}(y,c_{y}(\mathrm{E}))\Leftrightarrow h(c_{x}( \mathrm{E}))\equiv h(c_{y}(\mathrm{E})) \tag{1}\]  
Sometimes we write \(x\equiv_{e}y\Leftrightarrow h(c_{x}(\mathrm{E}))\equiv h(c_{y}(\mathrm{E}))\).  
**Proposition 2**.: _The relation \(\equiv_{e}\) is right-invariant, i.e., for all letters a in \(A\), for all pairs of states \((x,c_{x}(\mathrm{E})),(y,c_{y}(\mathrm{E}))\) in \(Q\) such that \((x,c_{x}(\mathrm{E}))\equiv_{e}(y,c_{y}(\mathrm{E}))\), we have: \(\delta((x,c_{x}(\mathrm{E})),a)/_{\equiv_{e}}=\delta((y,c_{y}(\mathrm{E})),a)/_{ \equiv_{e}}\)._  
Moreover, if two states are equivalent w.r.t. \(\equiv_{e}\), then they are either both final or both non-final, since \((x,c_{x}(\mathrm{E}))\in F\Leftrightarrow\lambda(c_{x}(\mathrm{E}))=1\Leftrightarrow \lambda(h(c_{x}(\mathrm{E})))=1\).  
Figure 4: The \(c\)-continuation automaton \(\mathcal{C}_{\mathrm{E}}\).  
The equivalence class of the state \((x,c_{x}(\mathrm{E}))\) is represented by \(\mathrm{C}_{x}=h(c_{x}(\mathrm{E}))\). Since the relation \(\equiv_{\varepsilon}\) is right-invariant, we can define the quotient automaton \(\mathcal{C}_{\mathrm{E}}/_{\equiv_{\varepsilon}}=\langle Q_{\equiv_{ \varepsilon}}\), \(A_{\mathrm{E}},q_{0},\delta,F\rangle\) as follows:  
* \(Q_{\equiv_{\varepsilon}}=\{\mathrm{C}_{x}\ |\ x\in\mathrm{pos}(\mathrm{E}) \cup\{0\}\}\),
* \(q_{0}=\mathrm{C}_{0}\),
* \(F=\{\mathrm{C}_{x}\ |\ \lambda(c_{x}(\mathrm{E}))=1\}\),
* \(\delta(\mathrm{C}_{x},a)=\{\mathrm{C}_{y}\ |\ h(y)=a\ \mathrm{and}\ d_{y}(c_{x}( \mathrm{E}))\equiv c_{y}(\mathrm{E})\}\), \(\forall\,\mathrm{C}_{x}\in Q_{\equiv_{\varepsilon}}\ \mathrm{and}\ \forall a\in A_{ \mathrm{E}}\).  
Theorem 3.1: _(Theorem 10 in [6]). Let \(\mathrm{E}\) be a regular expression. The automaton \(\mathcal{C}_{\mathrm{E}}/_{\equiv_{\varepsilon}}\) deduced from the \(c\)-continuation automaton is isomorphic to the equation automaton \(\mathcal{E}_{\mathrm{E}}\)._  
We note that the number of states of \(\mathcal{E}_{\mathrm{E}}\) is majorized by \(||\,\mathrm{E}\,||+1\).  
Example 4: _Let us consider the regular expression \(\mathrm{E}=(a^{*}+ba^{*}+b^{*})^{*}\) from Example 1. There are three \(\equiv_{\varepsilon}\)-equivalence classes when applying the function \(h\) that remove indices from letters for different \(c\)-continuations of \(\mathrm{E}\):_  
\[h(c_{0}(\mathrm{E})) =(a^{*}+ba^{*}+b^{*})^{*}\] \[h(c_{a_{1}}(\mathrm{E})) =h(c_{b_{2}}(\mathrm{E}))=h(c_{a_{3}}(\mathrm{E}))=a^{*}(a^{*}+ba^ {*}+b^{*})^{*}\] \[h(c_{b_{4}}(\mathrm{E})) =b^{*}(a^{*}+ba^{*}+b^{*})^{*}\]  
_The \(c\)-continuation automaton \(\mathcal{C}_{\mathrm{E}}\) and the quotient automaton \(\mathcal{C}_{\mathrm{E}}/_{\equiv_{\varepsilon}}\) which is isomorphic to the equation automaton \(\mathcal{E}_{\mathrm{E}}\) are schematized in Figure \(5\):_
# How Does the Built Environment Affect Drunk-Driving Crashes? A Spatial Heterogeneity Analysis
## 2 Literature Review
### Spatial Analysis for Traffic Crashes  
Commonly used crash models in the existing studies include ordinary least squares regression models [20], Poisson and Poisson-extended models [21], negative binomial regression models [22], zero-inflated models [23], hurdle models [24], logistic regression models [1], Bayesian models [25], and the conditional autoregressive prior (CAR) model [26].  
However, the above-mentioned studies assume that the influence of explanatory variables on the explained variable does not change with the spatial position, which means that the models used for analysis do not take spatial heterogeneity into consideration [19].  
[PERSON]'s first law of geography laid the foundation of spatial analysis for traffic crashes. It indicates that the more adjacent things are, the more related they are to each other. Since the approaches of spatial analysis were introduced to the field of traffic safety, researchers kept working on uncovering the spatial autocorrelation and heterogeneity of traffic crashes and related variables. In this regard, the spatial lag model, spatial error model, and spatial Durbin model were applied to explore the spatial autocorrelation between residential and demographic factors as explanatory variables and the traffic crashes as the explained variable based on different spatial scales, including states, countries, segments, regions, traffic analysis zones (TAZ), zip codes, and census tracts [4]. However, compared with spatial autocorrelation, the spatial heterogeneity of alcohol-related crashes is rarely studied, whereas for megacities with complex and diverse build environments, spatial heterogeneity should not be excluded. [PERSON] et al. proved that the social context, rural cultural values, and the legal and physical environment promote the frequency of drunk driving more significantly in rural areas rather than in the downtown [27]. Similarly, by analyzing breath-testing data, [PERSON] et al. found that the detection rate in rural areas was higher than that in urban areas [28].  
Regarding this matter, the geographically weighted regression (GWR) model has been employed to analyze traffic crashes to recognize the spatial heterogeneity of different explanatory variables. [PERSON] et al. used the GWR model to examine the relationships between crash counts and various contributing factors based on Twitter-based human activity information data [29]. [PERSON] et al. proposed a GWR model and revealed the spatially non-stationary between the built environment and traffic crashes [19]. Nevertheless, to our knowledge, little research has been conducted on the spatial heterogeneity of the built environment factors to the drunk driving crashes. This manuscript provides Chinese case studies for researchers from other countries and regions around the world.