esa-sceva/satcom-chunk-collection huggingface dataset:
# Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite
## 3 Ground Assessment of the Navigation Augmentation Signals
\begin{split}\epsilon_{Pi}=\frac{1}{2}\Delta P_{i}-c(\Delta \delta t^{S}-\Delta\delta t^{R})\\ \epsilon_{\phi i}=\frac{1}{2}\Delta L_{i}-c(\Delta\delta t^{S}- \Delta\delta t^{R})\end{split}  \tag{2}\]  
where \(\Delta\) is the time difference operator. \(\Delta\delta t^{S}\) and \(\Delta\delta t^{R}\) are the differentiated clock biases, which correspond the frequency offset of the transmitter and the receiver. If the oscillator frequency is stable enough, the estimated measurements should be around a constant. If the oscillators between the transmitter and the receiver are perfectly synchronized, the differentiated clock bias \(\Delta\delta t^{S}\) and \(\Delta\delta t^{R}\) should be zero. The measurement noise of the navigation augmentation system is presented in the right panel of Figure 3. The figure indicates that the pseudorange measurements of the navigation augmentation  
Figure 2: Ground assessment of Luojia-1A H1 (**a**) and H2 (**b**) signal phase noise. system are stable over time, which is benefit from the high stable oscillators. The constant bias on the carrier phase is caused by the inaccurate receiver or transmitter frequency. Assuming the autocorrelation in the pseudorange and carrier phase is negligible, the standard deviation of the pseudorange and carrier phase measurements is 2.66 m and 0.013 m, respectively. The pseudorange measurement precision is comparable with current GNSS signals, while the carrier phase noise is slightly larger than GNSS signals but still promising. Due to the experiment employs direct ranging measurement, the clock jitter at the transmitter and the receiver may affect the carrier phase measurement accuracy. Moreover, the signals are transmitted horizontally along the ground, so the roughness of the ground will reflect the signals and introduce complex multipath effect for the ground test. Overall, the ground test verified that the OCXO equipped on Luojia-1A performs well enough for the precise ranging signal generation.
## 2 Results and Discussion
### Sources and Influencing Factors of T/O Compounds in Freshwater Bodies of the World
#### 2.5.2 Physical Factors  
The occurrence of T/O compounds in water bodies exhibited a nuanced relationship with various physical indicators, as evidenced by diverse findings in multiple studies (Table 4). Temperature, a key factor, was shown to have both positive and negative correlations with the four T/O compounds. While positive correlations were observedin studies like Lake Taihu (for GSM, CYC, and particle-bound MIB and ION [64]) and seven important reservoirs in eastern China [21], instances of negative correlations, notably in Eagle Creek Reservoir [49] and Lake Taihu (for dissolved MIB [64]), challenged the generalization of these associations. Dissolved oxygen further complicated the picture, with instances of positive and negative correlations with T/O compounds across different studies, emphasizing the multifaceted nature of the interactions. Conductivity, water depth, turbidity, and total dissolved solids contributed additional dimensions to the complexity. For example, positive correlations between GSM and MIB with conductivity in a Chinese lake (DECL, this study) differed from findings in Eagle Creek Reservoir [49] and Feng-Shen Reservoir [57]. The negative correlation between MIB and water depth in East Taihu Lake introduced further variability [50]. The lack of correlation between GSM and turbidity in Paldang Lake underscored the site-specific nature of these relationships [15]. The inclusion of oxidation-reduction potential as a contributing factor in Eagle Creek Reservoir highlighted the need for a comprehensive understanding of redox conditions in T/O dynamics [49].  
For other physical indicators, in DECL in China, GSM and MIB exhibit a positive correlation with conductivity (this study). Conversely, in Eagle Creek Reservoir in the US [49] and Feng-Shen Reservoir [57], GSM and MIB show no correlation with conductivity. Moreover, in DECL in China, CYC and ION are unrelated to conductivity. In Taihu Lake, MIB was negatively correlated with water depth [50]. In Paldang Lake in Korea, GSM was unrelated to turbidity [15]. Furthermore, in Eagle Creek Reservoir in the US, GSM and MIB displayed a positive correlation with total dissolved solids and showed no correlation with oxidation-reduction potential [49]. The intricate interplay between T/O compounds and physical indicators emphasized the importance of context-specific studies, recognizing the site-specific nature of these interactions for effective water quality management and odor control strategies.
## 4 Experiments
### Quantitative Experimental Results  
Our quantitative results, as presented in Tables 2 and 3, provide comparisons of AUROC and F1-scores at both the image and pixel levels. These results confirm significant improvements over baseline algorithms.  
On the MVTec-wood dataset, both baseline models achieve perfect image-level AUROC scores. This implies that, under reasonable image-level thresholds, they can correctly classify all images. However, from an image-level perspective, their predictions exhibit bias due to the default 0.5 threshold. According to the image-level post-processing strategy for determined false alarms, the filtered predictions of both trackers achieve perfect predictions for the two image-level metrics. Pixel-level metrics also validate the effectiveness of this paper's approach. Specifically, the two metrics for Fastflow improve by 1.34% and 4.32%, respectively, compared to the unfiltered results. For Cflow, the corresponding improvements are 1.25% and 6.83%.  
The TFDS-RP dataset also validates the effectiveness of the proposed method in removing false alarms and improving overall performance. Given the higher complexity of this scenario, baseline predictions perform inferiorly compared to the wood examination application. None of the methods achieve correct image-level predictions. Specifically, Fastflow's image-level F1-score is improved by 40% through our method, as it correctly eliminates 24 image-level false positives. For Cflow, our method improves the image-level F1-score by 69.58%, as it correctly eliminates 16 image-level false positives. Despite the challenging lighting and complex background in this scenario, this paper's method still makes significant progress in pixel-level metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Image-Level Metrics**} & \multicolumn{2}{c}{**Pixel-Level Metrics**} \\  & **AUROC** & **F1-Score** & **AUROC** & **F1-Score** \\ \hline Fastflow & 100.00\% & 95.65\% & 96.98\% & 56.49\% \\ Filtered Fastflow & 100.00\% & 100.00\% & 98.28\% & 58.93\% \\ Cflow & 100.00\% & 91.67\% & 97.41\% & 58.11\% \\ Filtered Cflow & 100.00\% & 100.00\% & 98.63\% & 62.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative performances on MVTec-wood dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Image-Level Metrics**} & \multicolumn{2}{c}{**Pixel-Level Metrics**} \\  & **AUROC** & **F1-Score** & **AUROC** & **F1-Score** \\ \hline Fastflow & 72.36\% & 28.57\% & 91.13\% & 7.85\% \\ Filtered Fastflow & 94.74\% & 40.00\% & 91.36\% & 15.24\% \\ Cflow & 65.78\% & 46.15\% & 88.64\% & 6.84\% \\ Filtered Cflow & 81.05\% & 78.26\% & 93.58\% & 13.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative performances on TFDS-RP dataset.
###### Abstract
Surveys of surface water salinities of the coastal lakes within ENP indicated brackish to hypersaline salinities that varied according to season (higher in the summer following the dry season), and proximity of upstream freshwater sources and exchange with Florida Bay marine waters downstream [22; 23]. This paper aims to present the relationship between the freshwater input and hydrochemistry of surface waters and lake-bottom groundwater in shallow (1-2 m) brackish lakes within the southern Everglades known as the Mangrove Lakes (Figure 1). It explores the spatial and temporal changes in groundwater chemistry using electrical resistivity, EM methods, surface water, and groundwater sampling. The EM surveys were conducted from 2016 to 2017 using a GSSI EMP-400 Profiler multi-frequency [24] EM conductivity meter integrated with a GPS receiver deployed in a flat-bottomed plastic kayak towed behind a motorized ion boat. In addition, electrical resistivity soundings were conducted at spot locations in July 2019 using a floating electrode array connected to an Advanced Geosciences, Inc. (AGI) Super Sting resistivity meter [25]. During the survey, at various spot locations, the surface water conductivity, temperature, pH, and salinity were recorded using a YSI water quality data sonde. Water depths were also measured. Groundwater-specific conductivity was continuously monitored at four shallow wells on the shorelines of the lakes and was compared to nearby inverted lake bottom resistivity to calculate a formation factor for the Lakes region. This formation factor was used to convert the inverted formation resistivity to groundwater resistivity and produce a regional map of groundwater salinity. This study investigated the spatial and temporal changes in the surface water and groundwater salinity in the Mangrove Lakes of ENP using electrical resistivity and EM methods. Understanding the hydrochemistry of the aquifer can help to establish sustainable water resources management, and regularly monitoring the hydrological conditions is required for proper water management practice and conservation actions. This study showed that floating electrical resistivity arrays and EM ground conductivity meters can effectively characterize the lake bottom salinity and can be used regularly in monitoring the surface water and groundwater salinity in shallow water bodies.
## 3 Experimental Investigation
### Experimental Apparatus  
Figure 8 shows a schematic diagram of the experimental apparatus and optical arrangement. The pump beam was a continuous-wave, diode-pumped solid-state (DPSS) source with a 532 nm wavelength laser in Gaussian distribution, owned by Sprout-G. For implementing periodic heating, a sine-wave modulated frequency was formed using a mechanical chopper of Stanford Research Systems (SRS). In addition, a New port He-Ne laser with 633 nm wavelength and 5 mW intensity was used as the probe beam. The relative position between the pump beam and the probe beam was adjusted every 100 \(\upmu\)m using a Newport M-TS50 DC.5 motorized-stage. The phase difference of the probe beam generated by the temperature gradient of the air layer was measured using a Hamamatsu Photonics C100443-01 photoelectric position sensor. The phase delay was derived using the deflection angle of the probe beam, measured in the position sensor and an AMETEK 7270 DSP lock-in amplifier with respect to the modulated frequency of the mechanical chopper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Rate (Mass\%)** \\ \hline Cu & 99.9461 \\ S & 0.0223 \\ P & 0.0316 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of analysis of the composition of light absorption thin film.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Thermal Conductivity**} & \multicolumn{2}{c}{**Specific Heat**} & \multicolumn{1}{c}{**Density**} \\  & **(W/m-K)** & & **(J/kg-\({}^{\circ}\)C)** & **(kg/m\({}^{3}\))** \\ \hline Quartz & \(1.4\) & \(710\) & \(2650\) \\ Sapphire glass & \(k_{11}\) & \(36.1\) & & \\ \(k_{22}\) & \(27.1\) & \(761\) & \(3980\) \\  & \(k_{33}\) & \(36.1\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Properties of materials used in the experiment.  
The detailed alignments of the specimen, pump beam, and probe beam are illustrated in Figure 3, and the intensity of the pump beam, the diameter of the pump beam, the height of the probe beam, the relative position between the pump and probe beam, and the modulated frequency of the pump beam were set as shown in Table 6. The thermophysical measurement technique using the photothermal effect cannot use a section between 0 mm and 0.15 mm owing to the interference between the pump and the probe beam [31]. However, because the technique used in this study has no interference owing to the characteristics of the optical alignment, it can use the entire section between 0 mm and 0.6 mm to obtain the phase delay. After obtaining approximately 300 results for 30 s in one relative position, the phase delay used an arithmetic mean of the data as a representative value with a 95% confidence level.
## 3 Results and Discussion
### Effect of Pre-Harvest YE Application on Carotenoid Content
The xanthophyll level was reduced more drastically than the caroten level, reaching values down to 60%-70% in mature grapes compared to the setting (Tables 2 and 3). Moreover, our data clearly showed that the at-harvest \(\beta\)-carotene content was greater than lutein in both varieties, which was in accordance with our previous report [7]. Geographic origin is recognized to affect the relative (all\(E\))-lutein and \(\beta\)-carotene contents [2]. In some regions (northern Spain and southern Italy), \(\beta\)-carotene prevailed in grapes at maturity [7,20] in contrast to other cultural situations (northern Italy and southern France) in which the xanthophyll level was higher than the caroten level [1,3]. The interaction between treatment and ripening was very significant for lutein variation (\(p<0.001\)) in both varieties (Figure 5a). YE-treated grapes showed an enhanced content of lutein only at veraison; then, they had lower levels in the following samples until reaching 200 ug/kg and 249 ug/kg of berries in Negro Amaro and Primitivo, respectively (Tables 2 and 3). An oscillating trend was observed in the case of \(\beta\)-carotene (Figure 5b). YE treatment conditioned the level of variation of this carotenoid less homogenously in Negro Amaro (TxR: F = 5.04, \(p\) = 0.008) and, especially, in Primitivo (TxR: F = 4.43, \(p\) = 0.013). However, a common reduction of the compound between YE (270 ug/kg and 370 ug/kg) and control samples (470 ug/kg and 426 ug/kg) was recorded in both cultivars (Tables 2 and 3). These findings were in disagreement with those of [PERSON] et al., 2018 [20], who reported that YE foliar application triggered \(\beta\)-carotene and lutein accumulation in mature grapes; however, they studied its effect on different varieties (Tempranillo, Graciano, and Garnacha). Figure 4: Total carotenoid variation in yeast extract-treated (YE) and control (C) Negro Amaro (**a**) and Primitivo (**b**) wine grapes during ripening. Finally, concerning the identified 5,6-epoxyxanthophylls, the data confirmed the difficulty of defining the border between the end of storage and the beginning of degradation, mainly in the case of 5,6-epoxylutein (Figure 6c) [7]. Nevertheless, the elicitor treatment caused a significant reduction in the concentrations of violaxanthin, (9'Z)-neoxanthin, and 5,6-epoxylutein, as particularly evident in the two last sampling dates of Primitivo grapes (Figure 6; Tables 2 and 3). Figure 5: (allE)-lutein (**a**) and \(\beta\)-carotene (**b**) contents in yeast extract-treated (YE) and control (C) Negro Amaro and Primitivo wine grapes during ripening. Figure 6: _Cont._
## 4 Results
### The Reliability of the Moonlight Remote Sensing Imagery
#### 4.1.2 Visual Examination of the ISS Nightlight Imagery  
Comparing the ISS imagery with the Landsat-7 optical remote sensing mosaic (1 January-31 March 2013, the limitations of Landsat-7 band strips and clouds) and the Google Earth imagery in low-light suburban areas of Komsomsolsk-on-Amur, Russia (ISS034-E-37257), it is very obvious that the ISS nightlight imagery with three bands well captured the ground surface in the suburban area out of the city boundaries under faint lunar illumination, in addition to the bright urban areas with emitted lights (Figure 7). However, the ground was under heavy snow cover when the ISS photo was acquired, so distinguishing more surface types in the suburbs in this imagery is very difficult. To further check whether moonlight remote sensing images are valuable under faint lunar illumination, we selected three sub-areas (A, B, and C) with different brightness and colors in the ISS image, referenced to the Google Earth image taken in the daytime in summer. Part A is mainly covered by frigidiferous forests, making it difficult to accumulate snow on tree crowns, due to the relatively high height of the trees, blowing winds, and the sunny sides of the mountains. Part B is almost fully covered by white snow, and in the ISS image it shows a bright blue color. Therefore, part B is much brighter than part A in the ISS image, even though it was under a similar faint lunar illumination. Part C is mainly located in the city of Komsomsolsk, which is the brightest and most colorful part of the ISS image.  
Figure 6: Three cases of the VIIRS/DNB moonlight remote sensing and Sentinel-2 images. 1. snowy mountains, 2. forests, farmlands, and 3. rivers.
###### Abstract
These existing studies have primarily taken account of the effects of foundation shape (e.g., strip, rectangle, square and circular), foundation embedment (e.g., surface, solid and skirted), soil-strength heterogeneity (e.g., homogeneous or non-homogeneous) and soil-foundation interaction (e.g., smooth or rough) on the capacities of a single suction foundation. However, only a few studies focused on the capacities of the multipod bucket foundation in clay, which were mainly related to tripod bucket foundation [28; 29; 30; 31]. The studies on the undrained capacities of tetrapod bucket foundations in clay remain scarce. [PERSON] et al. (2020) [32] investigated the uplift response of tetrapod bucket foundations in soft clay using centrifuge modeling. The vertical, horizontal and moment capacity factors of tetrapod bucket foundations have not been given in existing specifications, such as API [33] and DNVGL design codes [34]. Systematic studies in this study were performed by using 3D finite-element analyses to provide insights into the group effects of tetrapod bucket foundations on its capacities and failure modes with various foundation separation distances, embedment depths, soil-strength heterogeneities and load direction angles. The purpose of this study is to establish a bridge connecting the capacities of tetrapod bucket foundations and those of a single bucket foundation. Generalized formulas proposed in this study can conveniently evaluate the uniaxial capacities of tetrapod bucket foundations in clay through multiplying the uniaxial capacities of a single bucket foundation by the corresponding group effect factors, which can be used as a reference for the industrial designs of capacities of tetrapod bucket foundations. This provides a useful tool for engineers who are not good at finite-element methods to quickly design the foundation.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 2 Model Description and Problem Formulation
### PCL-Indexability Approach for Scalar-State Targets
If \(g(P,Z)>0\), the MP metric function can be further defined by  
\[\text{mp}(P,Z)=\frac{f(P,Z)}{g(P,Z)}. \tag{41}\]If \(g(P,P)>0\) for all \(P\), the _MP index_ is defined by  
\[\text{mp}^{*}(P)\triangleq\text{mp}(P,P). \tag{42}\]  
As in (Definition 7 in [22]), the subproblem (38) is PCL-indexable (with respect to threshold policies) if the following PCL-indexability conditions hold:  
* (PCLII) \(g(P,Z)>0\) for every state \(P\) and threshold \(Z\);
* (PCLI2) \(\text{mp}^{*}(P)\) is monotone non-decreasing, continuous, and bounded below;
* (PCLI3) for each \(P\), the metrics \(F(P,Z)\), \(G(P,Z)\), and the index \(\text{mp}^{*}(P)\) are related by: for \(-\infty<Z_{1}<Z_{2}<\infty\), \[F(P,Z_{2})-F(P,Z_{1})=\int_{(Z_{1},Z_{2}]}\text{mp}^{*}(Z)\,G(P,dZ),\] where the right-hand side is a Lebesgue-Stieltjes integral. The interest of the above PCL-indexability conditions lies in their applicability through the verification theorem in (Theorem 1 in [22]), which ensures that, for a scalar-state project, conditions (PCLII)-(PCLI3) above imply that the project is indexable, and the MP index \(\text{mp}^{*}(P)\) is its Whittle index. Ref. [40] and Theorem 1 in [22] provide the following Lemma 1 to prove indexability for the special case of a single dynamic model (\(M=1\)). Lemma 1: _If subproblem (38) is PCL-indexable, then it is indexable with Whittle index \(\text{mp}^{*}(P)\)._  
This paper conjectures that the model satisfies such conditions, and will present partial numerical evidence supporting satisfaction of such conditions.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 7 Conclusions  
This article has proposed an adaptive low-complexity filtering algorithm based on the chip design for location tracking. The coefficients of \(\alpha\) and \(\beta\) are extracted from KF tracking approach to form a proposed approach. The KG of the simulated environment is relatively small. Therefore, DKF was selected from the two algorithms, DKF and PKF, to use. The inherent coefficients are implemented by the chip design. To optimize and improve the implementation of the chip design, this algorithm used a lot of shifters instead of multipliers and dividers to overcome the problem of chip design which cannot calculate the floating-point number. Compared with software, the processing time has been greatly reduced. The results show that proposed approaches are better than KF tracking approaches in software. Moreover, the implementation of chip design significantly reduces lots of processing time and costs. The processing time of proposed filtering algorithm is 0.003007 ns with one clock on FPGA and the error distance of CDF also has high accuracy performance. The chip design which is 0.18 \(\upmu\)m CMOS Mixed Signal RF General Purpose Standard Process FSG AI 1P6M1.8&3.3 V consumes power 3.24 mW, gate count is 22.84 k, works in the frequency of 83.33 MHz, and chip area is 582.63 \(\upmu\)m \(\times\) 580.23 \(\upmu\)m.  
Conceptualization, Y.-S.C.; data curation, W.-T.C.; formal analysis, Y.-S.C. and W.-T.C.; funding acquisition, Y.-S.C. and S.-L.C.; investigation, Y.-S.C. and S.-L.C.; methodology, Y.-S.C., W.-T.C. and S.-L.C.; project administration, Y.-S.C.; resources, Y.-S.C. and S.-L.C.; software, Y.-S.C. and W.-T.C.; supervision, S.-L.C.; writing--original draft, Y.-S.C., W.-T.C. and S.-L.C.; writing--review and editing, Y.-S.C., W.-T.C. and S.-L.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology, Taiwan (R.O.C.), under Grant numbers of MOST-107-2221-E-033-017, MOST 110-2823-8-033-002, MOST 110-2121-M-033-001, and MOST 111-2121-M-033-001.  
The authors gratefully thank the financial support from the Ministry of Science and Technology, Taiwan (MOST-107-2221-E-033-017, MOST 110-2823-8-033-002, MOST 110-2121-M-033-001, MOST 111-2823-8-033-001, and MOST 111-2121-M-033-001).  
The authors declare no conflict of interest.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 6 Security and Performance Analysis  
### Scheme Comparison  
The scheme proposed in this paper is decentralized due to the introduction of the permissioned blockchain. Therefore, it can overcome the single point of failure problem and provide the main requirements of IoT access control, and because of the admission mechanism of the permissioned blockchain, it can provide higher security. Table 2 shows the comparison between the existing access control scheme and the scheme in this paper in realizing different functions.