esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Compositional Analysis by X-Ray Fluorescence Spectroscopy (XRF)  
The inorganic elements present in natural bentonite (BN) and acid-treated bentonite (BA1) are listed in Table 1. Silicon (Si) was the predominant element in both samples, with a notable increase from 52.35% in BN to 62.51% in BA1. This increase is attributed to the removal of impurities and the dissolution of cations during acid treatment, which enriches the bentonite structure with silica [14]. Iron (Fe) content decreased from 18.57% to 13.13% after acidification, indicating the partial leaching of iron oxides from the structure. This leaching is associated with the removal of impurities, such as hematite and goethite, which are commonly found in natural bentonite [15]. Aluminum (Al) and silicon (Si) are the primary components of bentonite, with the high Al content expected due to its montmorillonite composition. Even after acid treatment, the Al content remained unchanged, demonstrating the stability of montmorillonite--the primary clay mineral in bentonite--and the preservation of its crystalline structure [14; 15]. Cristobalite impurities are associated with silica (SiO\({}_{2}\)) [9]. The concentrations of exchangeable cations, including potassium (K), calcium (Ca), sodium (Na), and magnesium (Mg), decreased following acid treatment. The calcium content declined from 2.73% to 1.23%, while sodium was completely removed (0.87% to 0.00%), confirming that the analyzed bentonite was predominantly calcium-type (Ca-bentonite). The extraction of these cations highlights the effect of acid treatment on ion exchange and the dissolution of secondary mineral phases, such as feldspars and carbonates [11].  
\begin{table}
\begin{tabular}{c c c} \hline
**Element** & **BN—Composition (a\%)** & **BA1—Composition (a\%)** \\ \hline Si & 52.35 & 62.51 \\ Fe & 18.57 & 13.13 \\ Al & 15.18 & 15.83 \\ K & 3.42 & 3.28 \\ Ca & 2.73 & 1.23 \\ \hline \end{tabular}
\end{table}
Table 1: XRF results for BN and BA1 samples.  
Figure 1: Influence of pH on tetracycline adsorption with BN.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 4 Additional Research
### Electricity Market Design
#### 4.2.2 Electricity Prices and Investment Signals  
Increasing VRE proportions in several markets have created increasingly longer periods of zero or negative energy prices because of excessive energy supply, including the substantial amounts of hydroelectric energy that have been generated for decades. Time-varying and inflexible energy generation can also create periods of high energy prices because energy or grid services is/ are related [93]. Therefore, operators of zero-carbon power grids should be prepared for, and robust against, high price volatility. In parallel, the volume of price-responsive loads will increase because of electrification and sector coupling, leading to a paradigm shift in power commitment, dispatch, and balancing. Due to volatile energy prices, a large volume of flexible, price-responsive demand is likely to move toward periods when flexible generators are commonly available, inverting the objective of optimal operational scheduling of generators and loads. The accelerated dynamics of supply and demand are attributable to the increased uncertainty level, requiring a change from deterministic methods for risk assessment to probabilistic methods. This will assist in mitigating flexibility requirements and increasing reliability benefits. Although several methods are already capable of probabilistic assessment, the industry take-up remains low [2].  
Traditionally, such price volatility encourages investment in flexible energy resources, but it is unclear whether the current market structures and clearing mechanisms will continue to produce supportive investment signals as we proceed toward 100% renewable electricity [94]. In other words, carbon neutrality can render it difficult to ensure that grid investment signals are correct; this is a perennial problem of electricity market design and operation. Some grid and market operators have begun to experiment with various energy sources and ancillary services to establish the required flexibility, and others have introduced forward markets for generation capacity to provide a revenue stream for resources that are required but as yet not paid for because of, for example, the imposition of price caps [95].
# Augmented Reality in Surgical Navigation: A Review of Evaluation and Validation Metrics
## 4 Discussion
### Limitations
Both pre-operative planning and intra-operative guidance for surgery are aided by improved visualization, especially when combined with intra-operative imaging for real-time visualization. The incorporation of supplementary technologies to assist the incorporation of AR systems into clinical use may address technical and clinical limitations [75]. AR technology provides a novel visualization paradigm with exciting clinical navigation and guidance potential. However, user acceptance of novel interfaces is always slower, and it is unclear if this is due to a history of slow adoption of new technology in the medical field, regulatory roadblocks, or inherent usability challenges rooted in problems with human perceptual system restrictions [105]. To summarize, when creating the user interface of AR systems in medicine, significant caution must be exercised. An otherwise reliable system may become useless if the representation loses its credibility or becomes unclear.
## 5 Simulation Experiments  
### Experimental Design  
In order to verify the effectiveness of the TLISOA for solving the MCMUAVCTAP, simulation experiments are conducted for two experimental scenarios to verify the superiority of the algorithm for solving the MCMUAVCTAP. In experimental scenario 1, the UAVs are set to be six, the total number of agricultural tasks to be performed is 20, and the agricultural tasks are performed in a 2 km \(\times\) 2 km farmland. The initial position of the UAVs is (0, 0), and the position range of each agricultural task is from (100, 100) to (2000, 2000) (unit m); 20 points are randomly selected as the positions of the 20 agricultural tasks within this range. The length of time required for each agricultural task is in the range of (5, 15) (unit min), and the speed of all UAVs is constant at 300 m/min. Six UAVs are set up in experimental scenario 2, and the total number of agricultural tasks to be performed is 50, and the rest of the conditions are consistent with experimental scenario 1. The multi-UAV cooperative task allocation algorithms based on CAM-GA [23], A-QCDPSO [24], SOA [25], PSO-AWOA [26], CESMA [27], and TLISOA are selected for comparison, and each algorithm's population size is \(population=30\), and the maximum iteration is \(Max_{iteration}=500\); in order to reduce the impact of randomness in the algorithm, each algorithm is run independently 20 times, and the simulation environment is shown in Table 1.
# Periodontal Condition and Subgingival Microbiota Characterization in Subjects with Down Syndrome
## 4 Discussion  
In the present study, a large cohort of 124 subjects with DS showed significantly higher PCR counts of _T. forysthia_, culture counts of _E. corrodens_ and total anaerobic counts, when comparing periodontitis with periodontal health or gingivitis. Subjects with periodontitis were also significantly older and the prevalence of hypothyroidism and levothyroxine intake significantly lower, as compared with the periodontal health and gingivitis groups. Studies that have previously assessed the subgigivil microbiota in DS subjects, have included a limited number of subjects and applied culture-independent techniques [19; 20; 23; 37]. In the present study, a combination of culture and molecular techniques were selected, since they can be considered as complementary methodologies [32; 38]. Using these techniques, higher counts and frequencies of detection of relevant periodontal pathogens, such as _P. gingialis_, _A. actinomycetemcomitans_, _P. intermedia_, _T. forysthia_ and _E. corrodens_, were identified in periodontitis, when compared with gingivitis and periodontal healthy DS subjects, as it has been previously reported in periodontitis patients without DS [39]. These quantitative and qualitative differences in the subgingival microbiota of periodontitis subjects may, in part, explain the early onset and progression of periodontitis in DS subjects, as it has been highlighted in previous studies [20; 37]. However, in the present study, statistically significant differences among groups were only observed for culture counts of _E. corrodens_ and total anaerobic counts, between periodontal health and periodontitis patients, and for qPCR counts of _T. forysthia_, when comparing periodontitis with periodontal health or gingivitis patients. This microbiological profile is also in agreement with the results obtained using Next Generation Sequencing (NGS), reported in a subset of this study population, which showed significantly higher levels of the genera _Tannerella_, _Porphyomonas_ and _Aggregatibacter_ in periodontitis, as compared with the periodontal health group [36]. In the present study, when using qPCR, _T. forysthia_ was the most prevalent bacterial species in periodontitis patients (67.9%) but also in the other two groups. A similar trend has been reported in previous studies: [PERSON] et al. [20] reported that _T. forysthia_ was the most prevalent species in periodontitis DS patients (95.5%), being significantly higher when compared with periodontal health; [PERSON] et al. [37] reported a high frequency of detection for _T. forysthia_ (89.7%) in periodontitis DS patients, also significantly higher than in gingivitis patients; and _T. forysthia_ was the only bacterial species with significantly higher presence and counts in DS subjects, when compared with healthy controls and individuals with cerebral palsy [23].
## 4 Experimental Results and Analysis
### Experimental Results  
In this subsection, we give two different schemes to show the visual quality of the recovered images with different pixel expansion.  
**Experiment 1:** This experiment is about a \((3,5)_{4}\)-XVC scheme. It is constructed by a \((3,5)\)-OVC scheme with basis matrices  
\[B_{0}=\left[\begin{array}{cccccccc}0&1&1&1&1&0&0&0\\ 1&0&1&1&1&0&0&0\\ 1&1&0&1&1&0&0&0\\ 1&1&1&0&1&0&0&0\\ 1&1&1&1&0&0&0&0\\ \end{array}\right]\]\[B_{1}=\left[\begin{array}{cccccccccccc}1&0&0&0&0&1&1&1\\ 0&1&0&0&0&1&1&1\\ 0&0&1&0&0&1&1&1\\ 0&0&0&1&0&1&1&1\\ 0&0&0&0&1&1&1&1\\ \end{array}\right].\]  
Its pixel expansion is \(m=8\) and the contrast is \(\alpha=1/8\) in a \((3,5)\)-OVC scheme. To decrease the pixel expansion, we take \(m^{\prime}=4\) to construct a \((3,5)_{4}\)-XVC scheme. The contrast is \(\alpha^{\prime}=1/2\). Its shadow images and reconstructed secret images are shown in Figure 1. It is easy to verify the security condition is satisfied. Superimposing two shadows leaks no information about the secret image, which is shown in Figure 1g-i. The secret image can be revealed by superimposing any three shadows, which is shown in Figure 1j-l. The contrast condition is satisfied.  
It can be seen that the proposed \((3,5)_{4}\)-XVC scheme keeps the same contrast with that of the \((3,5)\)-XVC scheme, and furthermore reduces the pixel expansion. Relative to a \((3,5)\)-OVC scheme, the visual quality of the recovered image is greatly improved.  
**Experiment 2:** The other experiment is regarding a \((4,5)_{6}\)-XVC scheme. This example aims to show that the proposed scheme with different pixel expansion still shows good visual quality of the recovered image. It is constructed by a \((4,5)\)-OVC scheme with basis matrices  
\[B_{0}=\left[\begin{array}{cccccccccccccccc}0&1&1&1&1&0&0&0&0&0&0&1&0&1&0\\ 0&1&0&0&0&1&1&1&0&0&0&1&0&1&0\\ 0&0&1&0&0&1&0&0&1&1&0&1&0&1&0\\ 0&0&0&1&0&0&1&0&1&0&1&1&0&1&0\\ 0&0&0&0&1&0&0&1&0&1&1&1&0&1&0\\ \end{array}\right]\]  
and  
\[B_{1}=\left[\begin{array}{cccccccccccccccc}1&0&0&0&0&1&1&1&1&0&0&0&0&0\\ 0&1&0&0&0&1&0&1&1&1&0&1&0&0&0\\ 0&0&1&0&0&1&1&0&1&1&0&0&1&0&0\\ 0&0&0&1&0&1&1&1&0&1&0&0&1&0\\ 0&0&0&0&1&1&1&1&1&0&0&0&0&1&0\\ 0&0&0&0&1&1&1&1&1&0&0&0&0&0&1\\ \end{array}\right].\]  
Its pixel expansion is \(m=15\), and the contrast is \(\alpha=1/15\) in a \((4,5)\)-OVC scheme. To decrease the pixel expansion, we take \(m^{\prime}=6\) to construct a \((4,5)_{6}\)-XVC scheme. The contrast is \(\alpha^{\prime}=8/15\). Its shadows and reconstructed secret images are shown in Figure 2. It is easy to verify that the security condition is satisfied. Superimposing two shadows or three shadows leaks no information about the secret image, which is shown in Figure 2g-l. The secret image can be revealed by superimposing any four shadows, which is shown in Figure 2m-o. The contrast condition is satisfied.  
It can be seen that the proposed \((4,5)_{6}\)-XVC scheme keeps the same contrast with that of a \((4,5)\)-XVC scheme, and furthermore reduces the pixel expansion. Relative to the \((4,5)\)-OVC scheme, the visual quality of the recovered image is greatly improved.
# Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
### Origin  
Using representations of an object under investigation to perform tests and thereby gain knowledge goes back to the Apollo program of the National Aeronautics and Space Administration (NASA) [9]. In the 1960s, NASA pioneered the development of a second, identical spacecraft that remained on Earth during the mission. This \"twin\", which at that time did not exist digitally but in reality, was used extensively by astronauts for training during the pre-flight phase, while one spacecraft was in space, the second spacecraft (the physical twin) was used to simulate alternatives using available flight data to recreate flight conditions as realistically and accurately as possible. The origin of the idea to perform such twinning with a DT is attributed to [PERSON] and [PERSON] of NASA, with [PERSON] presenting the concept in a lecture on product life cycle management (PLM) in 2003 [10]. Initially, the idea was referred to as the \"mirrored spaces model\" and not as the \"digital twin\" [11]. However, it already had the three essential elements that are still used to identify DTs, (i) a physical product in real space, (ii) a virtual product in virtual space, and (iii) the connection of data and information which ties together these two spaces [12]. In summary, this core concept of the DT envisaged a system that ties physical entities to virtual counterparts, leveraging the benefits of both the virtual and physical environments to the advantage of the entire system by exchanging valuable information about the products and processes [13]. In 2012, the concept of DT was revisited by NASA, which defined the DT as a \"multiphysics, multiscale, probabilistic, ultra fidelity simulation that reflects, promptly, the state of a corresponding twin based on the historical data, real-time sensor data, and physical model\" [14]. Many academic publications refer to this definition of a DT, e.g., [6; 11; 15; 16; 17; 18; 19; 20; 21; 22].
## 4 Network Interference Probability Analysis
### Homogenous Node Deployment Probability Analysis
**_In homogeneous networks, when the transmission range is 0.85r, the probability that the network is interference-optimal will get the maximum value._  
**Proof. ** To Equation (21), when \(0<r_{1}<0.85r\), \(P_{\text{homs}}^{\prime}>0\); when \(0.85r<r_{1}<r\), \(P_{\text{homs}}^{\prime}<0\). Therefore, if \(d_{1}=0.85r\), \(P_{\text{homs}}\) can get the maximum value, i.e., if the transmission range is 0.85r, the probability that the network is interference-optimal will get the maximum value.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 4 Performance Analysis
### Communication Complexity  
Communication complexity refers to the number of bits that the reader needs to transmit to identify all tags in the system, and it also indicates the energy required by the reader. The more bits transmitted in the communication channel, the higher the energy consumption of the system.  
Let \(C(n)\) denote the communication complexity of GMQT, and \(Q(n)\) denotes the total number of slots. \(C(n)\) is expressed as:  
\[C(n)=\sum_{i=1}^{Q(n)}(L_{reader,i}+L_{tag,i}). \tag{7}\]  
Here, \(\text{L}_{reader,i}\) denotes the number of bits the reader transmits for sending command in \(\text{i}_{th}\) cycles, and \(\text{L}_{tag,i}\) denotes the number of bits the tags respond in \(\text{i}_{th}\) cycles. \(\text{L}_{reader,i}\) consists of the length of reader transfer instruction \(\text{L}_{conn,i}\), the length of query prefix \(\text{L}_{pre,i}\) and the length of query group \(\text{L}_{group,i}\). \(\text{L}_{tag,i}\) consists of the length of group mapping \(\text{L}_{mapping,i}\), and the length of remaining IDs except prefix \(\text{L}_{req,i}\). In GMQT algorithm, \(\text{L}_{group,i}\) = 2, \(\text{L}_{mapping,i}\) = 4. \(\text{L}_{pre,i}\) + \(\text{L}_{req,i}\) = \(\text{L}_{ID}\), \(\text{L}_{group,i}\) and \(\text{L}_{mapping,i}\) don't exist every time. Only when the query command does not include group number do the tags reply with the group mapping code.  
Thus, \(\text{P}_{group}\) + \(\text{P}_{mapping}\) = 1, and \(\text{P}_{group}\), \(\text{P}_{mapping}\) denote the possibility of \(\text{L}_{group,i}\) and \(\text{L}_{mapping,i}\).  
In summary, C(n) is expressed as:  
\[\begin{cases}C(n)=\sum_{i=1}^{Q(n)}(\text{L}_{conn,i}+\text{L}_{ID}+2\text{P} _{group}+4\text{P}_{mapping}),\\ \text{P}_{group}+\text{P}_{mapping}=1.\end{cases} \tag{8}\]
###### Abstract  
Transit Signal Priority (TSP) is a system designed to grant right-of-way to buses, yet it can lead to delays for private vehicles. With the rapid advancement of network technology, self-driving buses have the capability to efficiently acquire road information and optimize the coordination between vehicle arrival and signal timing. However, the complexity of arterial intersections poses challenges for conventional algorithms and models in adapting to real-time signal priority. In this paper, a novel real-time signal-priority optimization method is proposed for self-driving buses based on the CACC model and the powerful deep Q-network (DQN) algorithm. The proposed method leverages the DQN algorithm to facilitate rapid data collection, analysis, and feedback in self-driving scenarios. Based on the arrival states of both the bus and private vehicles, appropriate actions are chosen to adjust the current-phase green time or switch to the next phase while calculating the duration of the green light. In order to optimize traffic balance, the reward function incorporates an equalization reward term. Through simulation analysis using the SUMO framework with self-driving buses in Zhengzhou, the results demonstrate that the DQN-controlled self-driving TSP optimization method reduces intersection delay by 27.77% and 30.55% compared to scenarios without TSP and with traditional active transit signal priority (ATSP), respectively. Furthermore, the queue length is reduced by 33.41% and 38.21% compared to scenarios without TSP and with traditional ATSP, respectively. These findings highlight the superior control effectiveness of the proposed method, particularly during peak hours and in high-traffic volume scenarios.  
connected environment; self-driving bus; private vehicle; real-time signal priority; DQN +
Footnote †: journal: Applied Sciences  
doi: 10.3390/accepted: 27 September 2023
## 3 Results
### Clock Generator  
Selected simulation results of the clock generator are shown in Figure 9. Figure 9a illustrates particular input and output signals. Ten top waveforms are subsequent phases generated by the clock. The following two waveforms are two phases of an external clock that controls the designed block. The reset signal, shown below the clock pulses, is used to break a given multiphase cycle. This signal causes that a new cycle starts with the following impulse of the external clock. The bottom waveform is the external \"fill\" signal that controls the width of all clock phases. Figure 9b,c presents a total supply current, \(I_{\text{DD}}\), for the supply voltage of 1.2 V.  
Measurements of the clock generator are shown in Figures 10 and 11. Figure 10 illustrates the operation of the clock generator for different numbers of phases: 10, 7 and 3, respectively. The programming sequences in particular cases (bits Bsi9-Bsi0) are as follows: (Figure 10a) \"011111111\"; (Figure 10b) \"0111111011\"; and (Figure 10c) \"011011111\". In all these cases, the first occurrence of the \"0\" value at first position starts the multiphase cycle from CLK0 phase. The second occurrence of the \"0\" value stops the cycle, thus starting a new cycle. Case (a) illustrates a full set of clock phases (10 clock phases), thus the second \"0\" is not required in this case. Figure 11 shows the clock waveforms for different widths, for an example case of five clock phases (programming sequence: \"011110111\").  
Figure 9: Selected simulation results, illustrating the performance of the designed multi-phase clock, controlled by a simple two-phase external clock: (**a**) selected pulse waveforms for an example case of 10 phases; (**b**) supply current proportional to power dissipation; and (**c**) supply current—the worst case.  
Figure 11: Measurement results of the clock generator with five phases, for different widths of the clock pulses (fill ratio).  
Figure 10: Measurement results of the implemented clock generator for selected numbers of clock phases in a single cycle: (**a**) 10; (**b**) 7; and (**c**) 3.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 3 Method, Part 1: Specular Reflection Point Calculation
### Successful Specular Reflection Point Convergence Criteria  
A simple and accurate check on the specular point algorithm convergence is to test the Snell angle agreement at each iteration. [PERSON]'s law states that the angle to the receiver and the GPS transmitter with respect to the local surface ellipsoid normal should be equal in the perfect case. As the estimated specular point converges on the final estimate, the Snell angle difference between the direct and reflected paths will approach zero. A reasonably accurate estimate can be obtained by setting a Snell success threshold at a low (but non-zero value). The Snell angle test is calculated as follows. Initially, calculate the unit vectors from the specular point to the transmitter and receiver, respectively, and the unit vector at the current estimate of the specular point,  
\[\hat{D}_{RS}=\frac{\vec{R}-\vec{S}}{\left|\vec{R}-\vec{S}\right|} \tag{11}\]  
\[\hat{D}_{TS}=\frac{\vec{T}-\vec{S}}{\left|\vec{T}-\vec{S}\right|} \tag{12}\]  
\[\hat{S}=\frac{\vec{S}}{\left|\vec{S}\right|}. \tag{13}\]  
Using these values the difference magnitude between the two local surface Snell angles can be calculated in radians as (including vector dot product operations),  
\[d\theta=\left|\cos^{-1}(\hat{D}_{RS}\cdot\hat{S})-\cos^{-1}(\hat{D}_{TS}\cdot \hat{S})\right|. \tag{14}\]  
As the solution converges, \(d\theta\) will approach zero. However, when \(d\theta\) decreases below a given tolerance, the specular point solution iteration can be exited and the final estimate of \(\vec{S}_{i}\) used as the final value for subsequent delay and Doppler calculations described in Section 4. Generally, a tolerance of 0.1 degrees is sufficient for open loop tracking reflection targeting as the analysis in Section 6 demonstrates.