esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 3 TAS Selection Algorithms
### Proposed Decremental TAS Selection Algorithm
\begin{array}{ll}\lim_{SNR\to\infty}p_{MMSE,S}&=\lim_{SNR\to\infty}\frac{ \frac{1}{\mathcal{T}\left(\Psi_{MMSE,S}\right)}-\frac{1}{SNR}}{\mathcal{T} \left(\Psi_{MMSE,S}\right)-\frac{1}{SNR}}=\lim_{SNR\to\infty}\frac{\mathcal{T} \left(\Psi_{MMSE,S}\right)}{\mathcal{T}\left(\[PERSON]_{MMSE,S^{\prime}}\right)}\\ &=\lim_{SNR\to\infty}\frac{\mathcal{T}\left(\left(\mathbf{B}_{S}\mathbf{B}_{S}^ {H}+\frac{2K}{SNR}\mathbf{z}_{X}\right)^{-1}\right)}{\mathcal{T}\left(\left( \mathbf{B}_{Y}\mathbf{B}_{Y}^{H}+\frac{2K}{SNR}\mathbf{z}_{X}\right)^{-1} \right)}=\frac{\mathcal{T}\left(\[PERSON]_{Z,S}\right)}{\mathcal{T}\left(\[PERSON]_{Z,S ^{\prime}}\right)}=\frac{\mathcal{T}\left(\[PERSON]_{S^{\prime}}\right)+\mathcal{T }\left(\[PERSON]_{\bar{S}}\right)}{\mathcal{T}\left(\[PERSON]_{S^{\prime}}\right)}\end{array}  \tag{50}\]  
where \(\text{SNR}=\sigma_{x}^{2}/\sigma_{z}^{2}\). The asymptotic received SINR penalty of (50) has the same expression as that of (32) in the ZF-precoded MU-STLC systems.
# Network Theory and Switching Behaviors: A User Guide for Analyzing Electronic Records Databases
## 1 Introduction
### Getting Data from an Electronic Records Database  
When considering health electronic records databases, there are essentially two types of repositories [13]: medical records databases (information is recorded as part of clinical outpatient care) and administrative databases (information is recorded for monitoringand controlling health expenditures). Both kinds of databases contain information about prescription drugs and medical diagnoses.  
Of note, within the European Union, a researcher willing to analyze an electronic records database must take into consideration the General Data Protection Regulation (GDPR). This regulation was put into effect on 25 May 2018, and specifies responsibilities and requirements on entities that handle personal data. Needless to say, the Ethical Committee Approval for an administrative database analysis-based study must comply with GDPR. In this regard, we recommend further exploring this topic in the literature dedicated to GDPR and health research [14; 15; 16; 17].  
To satisfy storage, security and privacy constraints of medical records, recent literature has explored the possibility of using blockchain-based approaches [18; 19], also taking into account GDPR compliance [20]. Our primary source of data, though, still relies on extraction from traditional Relational Database Management Systems (e.g., [5]). More broadly speaking, it is important to clarify how information is recorded in the healthcare database under scrutiny. Depending on the country, healthcare information is stored in different ways. This may have an impact on the results of the analyses. For example, what happens when a patient changes medical doctor or hospital? The answer depends on the organization of the healthcare system of the country. The Italian healthcare system is regionally based. Usually, some fields of the administrative database records can change (medical doctor, hospitals), while the overall process is still handled within the specific region. Before carrying out the analysis of the database, it is critical to explore these issues to understand the limitations of the study.
# Effective Optical Scattering Range Determination Based on Ray Tracing
## 2 Theoretical Background
### Roughness  
Roughness is a common measure of surface topographic irregularities. Most of the topography measurements are in the form of digitized data of the surface height. Therefore, there is no unique roughness value for a surface. It depends on the length of the surface profile L, lateral resolution and the sampling distance d. The root-mean-square roughness \(\sigma_{\rm rms}\) is most used surface statistical parameter and is defined as:  
\[\sigma_{\rm rms}=\sqrt{\frac{1}{N}\cdot\sum_{i=1}^{N}\left(z_{i}(x)-\overline{ z}(x)\right)^{2}} \tag{1}\]  
where N is the number of samplings. z\({}_{i}(x)\) is the surface height of a single sampled point. \(\overline{z}(x)\) is the mean surface height. The same surface can have many different values depending on the measurements. The autocovariance function is a measure of the correlation and periodicity of the surface roughness. It is defined as the average of the deviation of mean surface height multiplied by a translated version of itself:  
\[G(\tau)=\lim_{L\to\infty}\frac{1}{L}\int_{-L/2}^{L/2}\left[z(x)-\overline{z}(x )\right]\left[z(x+\tau)-\overline{z}\right]\,dx \tag{2}\]  
where \(\tau\) is the lag length. For zero lag length, the autocovariance function becomes the squared roughness which is:  
\[G(0)=\sigma^{2}=\lim_{L\to\infty}\frac{1}{L}\int_{0}^{L}\left[z(x)-\overline{ z}(x)\right]^{2}\,dx \tag{3}\]  
The power spectral density function is the frequency spectrum of the surface roughness. It is the square of the Fourier transform of surface profile, or the Fourier transform of surface autocovariance function:  
\[PSD(f_{x},f_{y})=\lim_{L\to\infty}\frac{1}{L}\left|\int_{-L/2}^{L/2}z(x) \times\exp[-2\pi j\,f_{x}x]dx\right|^{2} \tag{4}\]where \(f_{k}\) is the spatial frequency that fulfills:  
\[\frac{1}{Nd}\leq f_{k}=\frac{k}{Nd}\leq\frac{1}{2d},\ (1\leq k\leq N/2) \tag{5}\]  
where \(1/Nd\) is usually called the fundamental frequency and \(1/2d\) is the Nyquist or the foldover frequency, the highest allowed frequency. The sampling distance \(d\) should be as small as possible. If it is much larger than the lateral resolution, aliasing occurs and leads to failure of measurement.
## Introduction  
The advances in sensor technologies, including Micro-Electro-Mechanical Systems (MEMS), and associated interfaces, signal processing and networking have made it possible to construct highly functional \"smart\" sensors and to connect a large number of sensors for distributed measurement and control applications. The networking of many smart sensors enables high quality detection/measurement networks with low cost and easy deployment, and it provides new monitoring and control capability for a wide range of applications, such as industrial process monitoring, health care, environmental oversight, safety and security.  
In a sensor network, sensors are generally connected to a microcontroller which provides built-in linearization, error correction, and the access to the network. The interface between the sensor node and the microcontroller becomes important to achieve easy deployment, low power, low cost, and high reliability. Some digital interfaces which were not originally designed for sensor networks, such as I\({}^{2}\)C and RS-232, have been widely used in the industry. However, some specific characteristics of the sensor network require more features for the interface. Energy efficiency may be of importance if thousands of nodes operate unattended for a long period of time. In some applications the configuration of the system needs to be done automatically and, therefore, plug-n-play, hot swap functions become necessary for the sensor network. The interface between the sensor and the microcontroller should be carefully chosen to meet the requirements of the application.  
To provide a guideline for choosing the appropriate bus for different applications, this paper will review the existing digital communication buses, including I\({}^{2}\)C, SPI, IEEE1451.2, and IEEE1451.3, which can be used for sensor networks. Several architectures for sensor networks will then be presented. A new sensor bus for hardwired intra-module communication will then be presented. Finally, some issues in the wireless sensor networking will be discussed.
## 4 Discussion
### Reasons for the Different Performances of SIF and NDVI in GPP and Grain-Yield Monitoring  
The comparison between the NDVI and SIF results (see Sections 3.2 and 3.3) indicated that the SIF performed better than NDVI when applied to the GPP and grain-yield monitoring (Figures 4 and 5). Previous studies have analyzed various characteristics of vegetation-monitoring indicators. By estimating the performances of indicators when monitoring vegetation characteristics, [PERSON] et al. [32] reported that the SIF had a more significant linear relationship with the GPP than the NDVI did with the NCP. The significant relationship between the SIF and the GPP was also investigated by [PERSON] et al. [55] on a monthly scale in China. The results from these studies were confirmed and expanded upon by our results. [PERSON] et al. [34] pointed out that applying the SIF to urban mixed forest and cropland ecosystems would result in a better ability to indicate the growing season's end date than the application of the NDVI. [PERSON] et al. [56] analyzed the ability of vegetation-monitoring indicators to reveal phenology characterizations in high-latitude and snow-covered forests (including North America and North Eurasia), and the results indicated that GPP and SIF had a high level of consistency in determining the start of the growing season. [PERSON] et al. [21] found that the SIF had a higher level of performance than the NDVI when monitoring crop characteristics during weekly seasonal campaigns. This result agreed with our consistency results obtained from the NDVI, SIF, and per-unit grain yield (Figure 7). Although [PERSON] et al. [33] found that the SIF and the GPP indicated similar vegetation phenology characterizations in different seasons, the NDVI usually overestimated the GPP compared to the observations, especially during spring and autumn, over high-latitude areas. [PERSON] et al. [33] also advised that the simulations of the phenological characteristics of vegetation physiology (by SIF) and structure (by NDVI) should be separated to reduce biases in the simulated GPP results. Our results showed that the correlation between the SIF and the GPP was greater than that between the NDVI and the GPP in different seasons; this may have been due to the vegetation producing organic substances via photosynthesis [4; 57]. Due to the physiological processes of plants, organic substances can be converted into vegetation biomass (GPP or grain yields). In particular,  
Figure 8: (**a**) The correlation coefficients obtained between the SIF and the NDVI at different NDVI intervals for the whole of China; (**b**) the relationships between the NDVI and the SIF obtained in high-coverage-level forest vegetation areas.  
the key to cropland vegetation simulations might be considering the amount of organic matter present rather than the level of vegetation cover [58; 59]. Therefore, based on the principle of physiological vegetation characteristics, SIF might be more suitable than NDVI for describing GPP [28] and grain yields [8].
## 5 Conclusions  
The temperature, pressure, and turbulent kinetic energy in the cylinder were investigated with the change in the crank angle under supercritical and subcritical fuel injection conditions. The numerical simulation method was used. The flow characteristics of fuel and the combustion and emission performance (e.g., heat release rate, NO mass fraction, and soot quality fraction) were studied. The operating characteristics of the engine under supercritical and subcritical conditions were analyzed, and the following conclusions were obtained.  
1. The ignition time is shorter and the peak temperature in the cylinder is achieved earlier in the supercritical fuel injection condition compared with the subcritical fuel injection condition. The rate of pressure increase and the peak value of the average pressure of the supercritical state fuel are significantly higher than those of the subcritical fuel.
2. The time to release heat in the supercritical fuel injection condition is shorter than that in the subcritical condition. The rate of heat release in the premixed combustion stage decreases, whereas that in the diffusion combustion stage increases with the increase in the fuel temperature in both conditions.
3. The velocity of the fluid is high in the supercritical state fuel injection condition. Hence, the eddy current of high-velocity flow is formed in the local area of the cylinder, resulting in a significant increase in turbulent energy after the fuel is injected into the cylinder.
4. NO emission increases and soot emission decreases under the supercritical condition compared with the subcritical condition.  
Conceptualization, Z.Z.; Validation, Y.S.; Writing--original, draft, Y.S.; Writing--review & editing, Y.S.; Funding acquisition, Z.Z.; Investigation Z.Z.; Methodology Z.Z.; Project administration Z.Z.; Resources, Z.Z.; Supervision, W.X.; Software, Y.S., T.P., Z.Y. and Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Programs (grant no. 51776024).  
The authors declare no conflict of interest.
## 4 Performance Analysis
### Simulation Results
#### 4.3.7 Network Throughput  
Figure 8 demonstrates the impact of the proposed algorithms on average network throughput in a static network. The results obtained show that FSACO had better performance when compared with EEABR, IACO and SensorAnt. In the initial stage of route discovery, due to the probabilistic route discovery, the throughput was low. One may notice that the performance tended to be stable after several iterations. For example, FSACO would take about 60 s to reach stability. Moreover, the route establishing time of these algorithms was also very different. The results in the first route setup time and routing algorithm convergence time (Table 2) indicated this phenomenon.  
In our proposed hybrid FSACO routing algorithm, the data transmission preferred the route with the shorter path as described in Section 3.5. Hence, the source node could effectively transmit more sensing data to the sink node.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 5 Performance Evaluation
In Figure 6, we compare the overall system throughput of the proposed cognitive HTS system architecture, referred to as FFR with CR, with those of two reference architectures: (1) Conventional 4 FR configurations in satellite communications without CR with terrestrial communications, referred to as 4 FR without CR, and (2) FFR employed in satellite communications without CR with terrestrial communications, referred to as FFR without CR. Since the interference between satellite and terrestrial systems varies in different schemes, the throughput of terrestrial system also varies in different schemes. Moreover, for a fair comparison, we use the same clustering method SCUG and user scheduling method GREEDY. For a given user size, the throughput gaps of different schemes demonstrate that the overall system throughput increases dramatically by incorporating the FFR pattern and CR into satellite communications. For example, when \(K\) = 150, we can achieve approximately 11.2% gain in the overall system throughput by employing FFR, and 39.7% gain in the overall system throughput by employing CR, which results in approximately 27% loss in the throughput of terrestrial networks. Figure 5: Satellite throughput with different numbers of antennas and gateways (under FFR pattern without CR, \(\beta=10\)). Figure 7 presents the impacts of BS density on the throughputs of both satellite and terrestrial systems with different numbers of users. As the number of satellite users \(K\) increases, the throughput of the satellite system increases, while the throughput of terrestrial system remains constant. This phenomenon can be explained by two aspects. First, the transmit power of satellite remains the same. Second, the number of user groups in the approximated BD algorithm is constant and thus, the interference at the terrestrial BSs is effectively controlled through the first precoder and remains unchanged. In addition, for a given user size, as the number of BSs \(\beta\) increases, the throughput of the terrestrial system increases, while the throughput of the satellite system decreases. This is because more BSs results in severer interference at the satellite users. Figure 6: System throughput of different schemes with varying satellite user numbers. The lower and upper parts of each bar correspond to the throughputs of terrestrial and satellite communications, respectively. (\(N_{t}=256\), \(N_{g}=10\), and \(\beta=10\)). Figure 7: Throughput of different systems with different numbers of terrestrial base stations (BSs) versus satellite user numbers. The lower part and upper part of each bar correspond to the throughputs of terrestrial and satellite communications, respectively (under the FFR pattern with CR, \(N_{t}=256\) and \(N_{g}=10\)).
## 4 Natural Antioxidants and Their Role in Cosmetics
### Lichen-Derived Antioxidants  
Lichens are an important group of fungi that produce bioactive substances widely used in medicine and food. These complex organisms are composed of a fungal partner (mycobiont), primarily from Ascomycota, and a photosynthetic partner (photobiont), which may be green algae, cyanobacteria, or both. Additionally, bacteria and viruses are often present in the lichen symbiosis. Lichens are a rich source of secondary metabolites, including amino acid derivatives, sugar alcohols, aliphatic acids, minerals, macrocyclic lactones, monocyclic aromatic compounds, quinones, chromones, xanthones, dibenzofurans, depsides, depsidones, depsones, terpenoids, steroids, carotenoids, and diphenyl ethers. These organisms appear to be a promising resource for antioxidant compounds, particularly polyphenols such as depsides, depsidones, and dibenzofurans [173; 174].  
Figure 6: Chemical structures of some antioxidant compounds specific for various organisms: (**20**) kojic acid (Fungi, _Aspergillus_ sp.), (**21**) atranorin (Fungi, lichens), (**22**) mycosporine–glycine (mycosporine-like amino acid—MAA) (red algae, Rhodophyceae), (**23**) porphyra-334 (MAA) (red algae, Rhodophyceae), (**24**) epigallocatechin gallate (Plantae, _Camellia sinensis_), and (**25**) aloesin (Plantae, _Aloe_ sp.).  
Atranorin, a compound with the depside structure, is one of the most common lichen secondary metabolites, and is found in various species of lichens, such as _Stereocaulon capsitosum_, _Stereocaulon alpinum_, _Parmelia sulcata_, _Evernia prunastri_, _Cladina kalbi_, and _Lethariella canariensis_. After usnic acid, atranorin (Figure 6) is the second most studied secondary metabolite with anti-inflammatory, analgesic, wound healing, antibacterial, antifungal, antitumor, cytotoxic, antioxidant, antinociceptive, antiviral, and immunomodulatory activities [175; 176]. Usnic acid is the active compound present in _Ustnea_ sp. extracts, with high antibacterial and cytotoxic properties f.e. _Ustnea barbuta_ is a species that has been employed in modern-day cosmetic and pharmaceutical products [177].  
There are also preparations containing extracts or metabolites from various species of lichens such as _Cetraria islandica_, _Platismatia glauca_, _Parmeliopsis ambigua_, _Punctelia subratecta_, _Evernia mesomorpha_, _Anaptychya ciliaris_, _Nephroma parile_, and _Ochrolecchia tartarea_ and species from the genera _Cladonia_, _Usnea_, _Umbilicaria_, _Parmelia_, _Ramalia_, _Xanthopramelia_, and many others [166; 177; 178; 179].
## 2 Literature Review
### Passenger Flow Assignment Model  
Based on the analysis of passenger choice behavior, the passenger flow assignment method can allocate passengers to different paths based on the user equilibrium (UE) criteria and stochastic user equilibrium (SUE) criteria. The SUE model, which provides a reasonable equilibrium traffic flow pattern by considering travelers' imperfect perceptions of travel cost, is widely used [29]. [PERSON] [30] proposed a stochastic transit assignment model considering differences properties in passengers' utility function. [PERSON] [31], [PERSON] [32], and [PERSON] [33] proposed the assignment models considering the capacity constraint. [PERSON] [34] used the automatic fare collection (AFC) data to assign the passenger flow on the metro network. [PERSON] [35] assigned the passenger flow in a high-speed railway network by introducing some restricted conditions. Based on the schedule-based network, [PERSON] [36] proposed a network loading model to distribute passengers for monitoring the network performance. [PERSON] [37] proposed a dynamic assignment model to simulate the within-day and day-tO-Day learning process of passengers' route choices. Furthermore, [PERSON] [38] proposed a simulation-based dynamic passenger assignment framework, and [PERSON] [39] proposed a trip-based network representation and a set of path algorithms.  
In summary, the previous studies of passenger flow assignment concentrate on a single specific operation system, whereas this study focuses on the integrated operation scenarios between two different systems which adopt both cross-line and skip-stop modes. Previous studies focused on passenger choice between lines in the physical network; nevertheless, the choice between different types of trains becomes the research point in this study.
## 2 Related Work
### Hybrid Methods  
Character-based approaches may not harness word information to its fullest extent, whereas word-based methods are vulnerable to errors stemming from segmentation problems. In order to overcome the drawbacks of both approaches, [PERSON] et al. [31] employed the skip-gram model and bi-directional LSTM RNN model to extract word embeddings and character embeddings, respectively. These embeddings were subsequently merged to create the ultimate representations. Nonetheless, employing such a basic concatenation method can result in an excessive amount of redundant information. To address this issue, [PERSON] et al. [14] proposed the Lattice-LSTM model based on mixed characters and lexicon words for the first time. In contrast to character-based models, this model effectively exploits the semantic connections among neighboring characters within words. Furthermore, when compared to word-based models, it mitigates the adverse consequences stemming from word segmentation errors. Adversarial training avoids model overfitting by adding noise, which has been combined with the Lattice-LSTM model by successive research [32; 33]. [PERSON] et al. [15] also extended work on the Lattice-LSTM model and proposed a SoftLexicon method by integrating more lexical information without modifying the internal structure of LSTM. This approach incorporates all the words corresponding to each character within the representation layer and can be employed in different sequence labeling frameworks. In the field of agriculture, to alleviate the problems of agricultural text professionalism and uneven distribution of entity types, [PERSON] et al. [34] combined SoftLexicon with an attention mechanism and proposed the AttSofflexicon to help the model effectively utilize lexical information. Building upon prior research, we integrate the SoftLexicon approach with RoBERTa and utilize the traditional BiLSTM-CRF architecture to achieve the intelligent extraction of entities from Chinese electronic medical records.
# Reliability Optimization Design of Diesel Engine System Based on the GO Method
## 2 Diesel Engine Reliability Modeling and Distribution Based on the GO Method
### Diesel Engine System Analysis
#### 2.1.1 Input-Output Boundary Condition Analysis of the Diesel Engine System  
The system analysis suggests that the input of the diesel engine system consists of power supply, high-pressure gas cylinder, water tank, air, operation signal, and various sensor signals, while the output of the system involves the normal operation of the diesel engine, cooling system and lubrication system, and the normal discharge of reaction exhaust gas.