esa-sceva/satcom-chunk-collection huggingface dataset:
# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 2 Related Works
### Ontology-Based Data Access  
Ontology-based data access (OBDA) is proposed by [PERSON] to address the heterogeneity of data, facilitating a structured and semantically rich data access framework [25]. The concept of ontology, first introduced into the field of computer science by [PERSON] [26], then refined by [PERSON] [27] and [PERSON] [28] in turn, provides a clear formal specifications for shared conceptual models of ontologies. OBDA can leverage these ontologies to enable a more nuanced and context-aware access control mechanism. Various OBDA systems, both open-source like Ontop [29] and Morph [30] and commercially licensed like Stardog and Mastro, exemplify the practical application of these concepts, underlining the evolving landscape of access control strategies enriched by semantic technologies and ontology frameworks.  
We use Ontop as the basic model. Ontop is an open-source virtual knowledge graph tool that maps data from relational databases to RDF graphs and provides a SPARQL [31] query interface, allowing users to query data from relational databases using the SPARQL query language. Its flexibility, high performance, and ease of use make it an ideal tool for integrating relational databases with ontology models. Ontop supports multiple databases, such as Oracle, PostgreSQL, MySQL, etc. In addition, it also provides an editing plugin for the Protege ontology software, enabling efficient integration of the pattern layer and data layer of the knowledge graph. The system structure of Ontop is shown in Figure 1, which is divided into four levels: an input layer, core layer, API layer, and application layer.
[PERSON]  
1 State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, and Center for Quantum Information Technology, Peking University, Beijing 100871, China; [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
1 State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, and Center for Quantum Information Technology, Peking University, Beijing 100871, China; [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS]. (light intensity, gas discharge power, environmental temperature and vapor cell pressure) of reference cells are generally not the same as those of atomic sensors, thus the central frequency of the laser is difficult to lock to the required one. These issues will lead to systematic errors. Apart from using an additional reference cell, other alternative methods can also be exploited to stabilize the laser frequency. For example, the atomic alignment to the orientation-conversion (AOC) effect [19] has been used to suppress light shift in laser pumped \({}^{4}\)He magnetometer [9]. The saturated absorption spectroscopy and the magnetic resonance using circularly polarized light also can be implemented in the same cell [20]. But these methods all need an additional lock-in amplifier, which makes the atomic magnetometer system more complicated. In this paper, we propose a compact \({}^{4}\)He magnetometer system, which measures ambient magnetic field in the magneto-optical double-resonance (MODR) configuration and simultaneously stabilizes the laser frequency by using the inhomogeneous light shifts in the same cell. As the laser beam is absorbed by the \({}^{4}\)He atoms, it generates light-intensity spatial gradient along the propagation direction, resulting in inhomogeneous light shifts at different positions [21]. The inhomogeneous distribution of light shifts in the laser pumped \({}^{4}\)He magnetometer is analyzed theoretically and verified experimentally. The characteristics of inhomogeneous light shifts show that the variation of amplitude of the in-phase component is approximately proportional to the laser-frequency detuning, which can be used for laser-frequency stabilization. This method can also be applied to other atomic magnetometers. The compact and portable atomic magnetometers are well suited to measure geomagnetic field and space magnetic field. The rest of this paper is organized as follows. In Section 2, we describe the experimental setup. In Section 3, we give the theoretical analysis and simulation on inhomogeneous light shifts, which cause the the asymmetric line shape of the MODR signal. In Section 4, we discuss the experimental results. Section 5 is the conclusion.
## 4 Simulation Results and Comparisons
### Test Example and Results
To find the relationship between the computing effort and solution quality, four values of \(N\) were simulated in Algorithm 2: \(N\) = 20, 15, 10 and 5. The reduction factors corresponding to \(N\) = 20, 15, 10 and 5 were \(s\) = 6.07, 4.72, 3.4 and 2.08 [22], respectively. Thus, the available computing budgets were \(C_{b}\) = 32949, 31780, 29412 and 24038 for \(N\) = 20, 15, 10 and 5, respectively. Table 2 lists the excellent solution \({\bf x}^{*}\), the corresp \begin{table}
\begin{tabular}{c c c c c} \hline \hline \({\bf x}^{*}\) & \(E[f({\bf x})]\) & \(P(g({\bf x})\geq b)\) & \(F({\bf x})\) & **CPU Times (s)** \\ \hline
[19,28,28,42,42,41]\({}^{\rm T}\) & 10.72 & 0.9009 & 9.648 & 24.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The excellent solution \({\bf x}^{*}\), the corresponding \(E[f({\bf x})]\), \({\rm P}(g({\bf x})\geq b)\), \(F({\bf x})\) and the consumed CPU times of small problem. Figure 6: Variations of \(ST\) and \(\gamma\) over iterations. \(\mathrm{P}(g(\mathbf{x})\geq b)\), \(F(\mathbf{x})\) and the CPU times. When the value of \(N\) increases, the corresponding \(E[f(\mathbf{x})]\) decreases but the CPU time increases. In principle, the value of \(N\) depends on the available computing budget for various applications. From the results shown in Table 2, \(N=20\) is a suggested choice for the large problem. The CPU times in all cases were less than 100 s, which is short enough for real-time purposes. Besides, the inter-arrival times of orders and processing times of tasks can exhibit any distribution, and the dimensions of the problem can be high. There is no systematic procedure to obtain the preferable and robust values of the following control parameters, \([ST_{\min},ST_{\max}]\), \([\gamma_{\min},\gamma_{\max}]\) and \(N\). Although a proper parameter setting can help to have a good performance, such setting is very problem dependent. After analyzing the influence on convergence rate and solution accuracy, the preferable values of \([ST_{\min},ST_{\max}]\), \([\gamma_{\min},\gamma_{\max}]\) and \(N\) were obtained by a series of hand-tuning experiments for the considered problem.
## 7 Conclusions  
This paper investigates low-complexity NOMS and NSMS decoding algorithms using TT decomposition and TR decomposition, which have almost the same performance, but lower computational complexity and fewer parameters in comparison with the the NOMS and NSMS algorithms. We have also provided an analytical framework on the complexity of the TT-NOMS, TT-NSMS, TR-NOMS and TT-NSMS algorithms. We conducted experiments on the proposed TT-NOMS, TT-NSMS, TR-NOMS and TT-NSMS algorithms for BCH codes, Hamming code and LDPC code, and the experimental results show that among these codes, the TT-NOMS, TT-NSMS, TR-NOMS and TT-NSMS decoding algorithms have comparable decoding performances, while the proposed algorithms have fewer parameters and reduced multiplications.  
Figure 12: BER performance of TR-NOMS algorithms for LDPC(576,432) code with different rank.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Number of Parameters** & **Number of Multiplications** \\ \hline NOMS & \(EBT\) & 0 \\ NSMS & \(2 EBT\) & \(EB^{2}T\) \\ TT-NOMS or TR-NOMS & \(\sum_{d=1}^{D}x_{d}R_{d-1}R_{d}\) & \(\sum_{d=1}^{D-1}x_{d}x_{d+1}R_{d-1}R_{d}R_{d+1}\) \\ TT-NSMS or TR-NSMS & \(2\sum_{d=1}^{D}x_{d}R_{d-1}R_{d}\) & \(\sum_{d=1}^{D-1}x_{d}y_{d}x_{d+1}y_{d+1}R_{d-1}R_{d}R_{d+1}T+\) \\  & \(\sum_{d=1}^{D-1}x_{d}x_{d+1}R_{d-1}R_{d}R_{d+1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of parameters and multiplication in NOMS [22], NSMS, TT-NOMS, TT-NSMS, TR-NOMS and TT-NSMS algorithms.  
**Author Contributions:** Conceptualization, Y.L. and Q.W.; Methodology, Y.L.; Validation, Q.W.; Formal analysis, Q.W.; Investigation, Q.W.; Resources, Q.W. and Y.L.; Data curation, Q.W.; Writing--original draft, Q.W. and Y.L.; Writing--review and editing, B.K.N., C.-T.L. and Y.M.; Visualization, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the research funding of the Macao Polytechnic University, Macao SAR, China (Project no. RP/ESCA-02/2021).  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Model and Methodology
### Kalman Filter  
The Kalman filter is an estimation algorithm named after [PERSON], which operates recursively on streams of input data (containing random variations) to produce a statistically optimal estimate of the underlying system state. It is over 50 years old but is still one of the most essential and standard data fusion algorithms in use nowadays. The great success of the Kalman filter is due to its adaptive, recursive, optimal characteristics and small computational requirement. Therefore, it is widely used in various fields from radar and computer vision to meteorological purposes. Similar to our previous study [23], the specific set of mathematical equations, which are used to estimate and remove the errors in the solar radiation forecasts, are described as follows.  
In this work, the forecasting bias, rather than the variable itself as used in [28; 29], is chosen as the state variable in the Kalman filter equations. The forecasting bias \(x_{t}\) at time \(t\) is related to the state at previous time step \(t-\delta t\):  
\[x_{t|t-\delta t}=x_{t-\delta t|t-2\delta t}+\eta_{t-\delta t}, \tag{1}\]  
where \(\delta t\) is a time lag, \(t|t-\delta t\) indicates that the value of a variable at time \(t\) depends on values at time \(t-\delta t\), and \(\eta\) is the white noise that has the variance (\(\sigma_{\eta,t}^{2}\)) being uncorrected in time. Due to some of the unavoidable factors including numerical noises, the lack of accuracy in the description of sub-grid physical processes and complex geographic conditions, the Kalman filter algorithm further assumes that the forecast error \(y_{t}\) could be stated as follows:  
\[y_{t}=x_{t}+e_{t}=x_{t|t-\delta t}+\eta_{t}+\epsilon_{t}, \tag{2}\]  
where \(\epsilon_{t}\) is normally distributed with zero-mean and variance \(\sigma_{\epsilon,t}^{2}\).  
Following the idea of Kalman [30], \(x_{t}\) can be written as a combination of the previous predicted bias and the previous forecast error with a parameter of Kalman gain (\(G_{t}\)):  
\[\hat{x}_{t+\delta t|t}=\hat{x}_{t|t-\delta t}+G_{t}(y_{t}-\hat{x}_{t|t-\delta t }), \tag{3}\]  
\[G_{t}=\frac{p_{t-\delta t}+\sigma_{\eta,t}^{2}}{p_{t-\delta t}+\sigma_{\eta,t} ^{2}+\sigma_{\epsilon,t}^{2}}, \tag{4}\]  
\[p_{t}=(p_{t-\delta t}+\sigma_{\eta,t}^{2})(1-G_{t}), \tag{5}\]where the hat (-) notation indicates the estimation of the variable, and \(p\) is the expected mean square error. Given a reasonable initial guess of \(p_{0}\) and \(G_{0}\), this Kalman filter will quickly converge. The time lag \(\delta t\) of 24-h is used in this study. In addition, the estimation of two white noises \(\sigma_{\eta,t}^{2}\) and \(\sigma_{e,t}^{2}\) is crucial to the implementation of the Kalman filter procedure. The detailed information can be found in Appendix A of [PERSON] et al. [31].
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 3 Materials and Methods
### Datasets Description
#### 3.1.2 Pavia University  
The ROSIS sensor uses a flight above Pavia, Italy, to capture the scene at Pavia University. The scene resolution was originally \(610\times 610\times 115\), however, some areas of the image were removed since they were missing information, leaving \(610\times 340\times 103\) as the final resolution. As illustrated in Figure 3, it comprises nine distinct classes and has a geometric resolution of 1.3 m. Radiometric adjustments involve the removal of 12 bands.
## 3 Results and Discussion
### Optimization of UE
#### 3.1.3 Effect of Extraction Time  
The extraction time was also optimised to ensure sufficient extraction without degradation of the target analyte; the investigated times were 30 min, 60 min and 90 min. It was found that percentage recovery increased from 30 min to 60 min with a significant difference (\(p\) = 0.02) in recovery, while a further increase of extraction time from 60 min to 90 min showed a slight decrease in the percentage recovery (Figure 4) with no significant difference (\(p\) = 0.37) in recovery, a pattern followed by 30 min recoveries when compared to that of 90 min (\(p\) = 0.05), as shown in Table S3. The decrease in recoveries could indicate the possible degradation of the analytes by radiation; therefore, 60 min was used as the optimal extraction time. The results obtained were comparable to those obtained by [PERSON] et al., who optimised extraction times of 15-60 min and obtained higher recoveries (70 to 112%) at 45 min [26].  
Figure 4: Effect of extraction time on the PAH’s %recovery using UE (\(n\) = 5).  
Figure 3: Effect of the extraction solvent volume on the PAH’s %recovery using UE (\(n\) = 5).
## 1 Introduction
The new features include the capability to generate reusable connection-disconnection traces per node. Trace reusability is the key to experiment reproducibility. Second, we proposed an easy-to-implement \"reliability\" score that gives E-SEAS and BPA network condition awareness and boosts the performance achieved in dew environments with nodes' mobility. Such integration derives into new heuristics that we call RelegSEAS and RelBPA. Third, we propose an RL approach with awareness of network conditions, and we demonstrate that using this approach can outperform human-designed heuristics. Finally, as a summary of our empirical findings, we discovered that giving awareness of node connection activity to the scheduler gives better endurance and adaptability when the network conditions are dynamic. Also, we show that providing awareness of the policies improves the job completion rate by up to 95% without giving up performance. This paper is organized as follows: In Section 2, we introduce the necessary concepts of dew computing in the context of our work; in Section 3, we make a review of the state of the art related to scheduling algorithms. In Section 4, we describe the necessary changes to extend the simulation capabilities to represent dynamic network-related features as an effect of nodes' mobility. Section 5 describes the mobility models used in this work. In Section 6, we show the definitions of the proposed algorithms for this job. In Section 7, we describe the experimentation methodology and the metrics used to compare the performance of our defined algorithms, followed by our results with their respective discussion. Finally, in Section 8, we show our findings and propose the next steps toward improving this field of study.
## 2 Materials and Methods
### RNA Extraction and Illumina Sequencing  
Total RNA from frozen samples was extracted using the RNAprep Pure Plant Plus Kit (Tiangen, China), following the manufacturer's instructions. Then, contaminated genomic DNA in the extracted RNA was removed using DNase I (Takara, Dalian, China). RNA purity and quality were analyzed using a NanoPhotometer spectrophotometer (IMPLEN, Westlake Village, CA, USA) and an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Samples with RIN (RNA integrity number) values \(\geq\) 8.0 were used for sequencing library construction. The sequencing libraries were prepared with an NEBNext Ultra RNA Library Prep Kit (NEB, MA, USA). In total, 9 libraries were prepared with different light spectra treatments, and 150 bp paired-end reads were sequenced using an Illumina HiSeq 4000 platform (Illumina, San Diego, CA, USA).
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.8 Efficient Code Dissemination (ECD)  
ECD [34] uses three techniques to improve the efficiency and reduce the latency for disseminating updates: variable packet sizes, selection of the highest impact senders, and impact-weighted backoff timers. By measuring the link quality to its 1-hop neighbours, a node can select the most efficient packet size (taking retransmissions into account). The impact of a sender is determined by the number of neighbouring nodes it can reach--weighted by the link quality, so that the sender with the lowest number of required transmissions is selected. A transmission backoff timer is weighted by the impact factor, so that the node with the highest impact is likely to transmit first, providing quick selection of the highest impact sender. Nodes have 5 associated states, extending the Deluge state model: IDLE (advertise available pages) and listen to received advertisements, 'RX' (having requested newer pages), ESTIMATION (used to estimate the impact from hearing further requests), CONTENTION (backoff state before transmission), TX (send requested packets, and return to IDLE). Nodes that overhear update packets being sent when in the CONTENTION state return to the IDLE state--only the _winner_ sends the update packets.  
The key focus of ECD is improving efficiency, and it has no specific features supporting autonomic operation.
## 2 Methods
### The Design of the Hardware
#### 2.1.2 BDM Circuit and Antenna  
The BTTT uses the BDM910 module, integrating RDSS radio frequency transceiver chips, power amplifier chips, and dedicated RDSS baseband circuits. The BDM910 module is made of aluminum. And the module supports Beidou RDSS and RNSS functions and can achieve RDSS positioning, communication, and RNSS navigation positioning [18]. The module has a small size, high integration, low power consumption, and simple interfaces, with a transmission power consumption of 36 W and a standby power consumption of 0.9 W. The BDM910 module power interface is divided into VCC and VCCPA, with input voltages of 5 V and 12 V, respectively. The BDM910 module connects to the main control chip's two UARTs through a connector and processes communication and positioning data through the RNSS and RDSS ports. The module does not require an external LNA and can be used with a passive antenna.  
The antenna model TA-011 and GPS are utilized, with a size specification of 52.8 mm by 23.3 m and fabricated using dielectric ceramic material. The frequency ranges prescribed for the Beidou antenna are limited to the L-band at 1615.68 MHz \(\pm\) 4.08 MHz and the S-band at 2491.75 MHz \(\pm\) 4.08 MHz. It is defined electrically by a standing wave ratio (SWR) that is less than or equal to 2.0, an impedance value of 50 ohms, a normal axial ratio that does not exceed 3 dB, an LS isolation of at least 20 dB, an L-bandwidth exceeding 20 MHz, and an S-bandwidth exceeding 100 MHz. Regarding its gain, the antenna's normal polarization gain is measured at 2.0 dBic in the L-band and at 4.5 dBic in the S-band. The Beidou module and antenna are produced by Beijing BDStar Navigation Technology Co., Ltd. (Beijing, China).
## 10 Bacteria-Activated Self-Healing Cementitious Materials
### Effect of Bacteria on Concrete Performance
In comparison with standard concrete aged for 7, 28, and 56 days, the bacterial concrete samples experienced a 55.8, 49.9, and 48.4% reduction in the total charge passed through them, respectively [134]. Rapid chloride penetration of 380 Coulomb was resisted effectively when 10% of the silica fume concrete was combined with the best bacterial concentration of Sparcious Pasteurii [130]. Despite a mere 762 Coulomb penetration for the concrete containing 30% of FA, a drastic decrease in the chloride ions was found in every FA-based concrete when Sporoscarina Pasteurii bacteria of concentration \(10^{5}\) cells/mL was added. It was concluded that the capability of concrete to repel the penetration of the chloride ions determines the lifespan of the concrete structures that interact with the de-icing salts or marine environments. The SEM analysis revealed the existence of calcite precipitation in the concrete and mortar, wherein rod-shaped bacteria were found to be linked-in with the calcite crystals. The ingress of the deleterious substances was blocked by the deposits that served as an obstacle, resulting in a reduction in the permeability of the concrete [64]. Mineral precipitation led to the enhancement of the concrete's microstructure due the incorporation of bacteria, which was confirmed using the XRD, EDS, and SEM analysis. Compared to the concrete devoid of bacteria, concrete with different volumes of added bacteria achieved a maximum weight of calcium formation of 38.76% when the Bacillus Megaterium concentration was 30 \(\times\) 10\({}^{5}\) cfu/mL [117]. Bacteria embedded with distinct and diverse calcite crystals were identified and visualized by the SEM analysis. XRD and EDS analysis verified the presence of high quantities of calcium in the specimen, where the calcite existed as CaCO\({}_{3}\). Consequently, the durability of the concrete was enhanced [136; 137]. Figure 8 displays the SEM micrographs of the bacteria-activated specimen and control sample, wherein the calcite crystals were found to exist within the bacterial concrete [130]. It also affirmed the filling of the pores by the deposition of calcium carbonate due to the incorporation of bacteria in RHA concrete, indicating an enhanced strength of the concrete. Clearly, calcite was plugged in the voids of the bacterial concrete [134]. Microstructures analyses verified that the formed cracks in the test samples contained the calcium carbonate deposits. Hence, acid ingress, chloride permeability, and water absorption were diminished together with the increase of the UPV readings [138].