esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation
In order to alleviate the contradiction between stagnant battery energy storage technology and the increasing energy consumption of mobile terminals, some researchers have regarded energy consumption as the objective function in resource allocation optimization for a long time [33]. Energy consumption minimization, like EE optimization, ignores user priorities. This oversight risks depleting specific users' energy budgets prematurely, which shortens the overall network lifetime. Inspired by the above analysis, we introduce WSP as the objective function for our optimization problem. Omitting the circuit power consumption, we express the WSP of a matched link pair \(\rho_{m}\) and \(\mathfrak{s}_{n}\) as follows:  
\[w_{mn}=\mu_{m}P_{\rho,mn}+\
u_{n}P_{s,mn}, \tag{5}\]  
where \(\mu_{m}\) and \(\
u_{n}\) are the positive weights of \(\rho_{m}\) and \(\mathfrak{s}_{n}\). A value of \(\mathfrak{a}_{mn}=1\) indicates that \(\rho_{m}\) and \(\mathfrak{s}_{n}\) are matched, otherwise \(\mathfrak{a}_{mn}=0\). Denoting \(\mathbf{p}_{\rho}=\{p_{\mu,\text{mn}}\}\), \(\mathbf{p}_{s}=\{p_{s,mn}\}\), \(\mathbf{\alpha}=\{\mathfrak{a}_{mn}|\mathfrak{a}_{mn}=0,1\}\), \(\mathbf{\theta}=\{\theta_{mn}|\theta_{mn}\in(0,1)\}\), the optimization problem for the overall system WSP minimization is expressed as stated below:  
\[\underset{\{p_{\mu},\mathbf{p}_{\rho},\mathbf{\sigma},\mathbf{ \sigma},\mathbf{0}\}}{\text{min}}\sum\
olimits_{m=1}^{M}\sum\
olimits_{n=1}^{N }\mathfrak{a}_{mn}w_{mn}, \tag{6}\]  
\[\text{s.t.}\ \ \text{C1}:\ S_{\rho,mn}\geq Q_{\rho,m},\]  
\[\text{C2}:\ S_{s,mn}\geq Q_{s,n},\]  
\[\text{C3}:\ p_{min}\leq p_{\rho,mn}\leq p_{max},\]  
\[S_{\rho,mn}=\theta_{mn}log_{2}(1+p_{\rho,mn}g_{\rho,m}/n_{0}), \tag{1}\]0  
\[S_{\rho,mn}=\theta_{mn}log_{2}(1+p_{\rho,mn}g_{\rho,m}/n_{0}), \tag{1}\]1  
\[S_{\rho,mn}=\theta_{mn}log_{2}(1+p_{\rho,mn}g_{\rho,m}/n_{0}), \tag{1}\]2  
where C1 and C2 ensure the minimum SE requirements for all the links; C3 and C4 constrain their minimum transmit power (\(p_{min}\)) and maximum transmit power (\(p_{max}\)); C5 refers to the one-to-one correspondence matching between \(\mathcal{P}\) and \(\mathcal{S}\); C6 constrains the value of the spectrum-sharing factor. Equation (6) is a mixed integer nonlinear programming (MINLP) problem. Due to C5 and the structure of its objective function, we can decompose Equation (6) into a series of spectrum allocation and power control sub-problems at a lower level, along with a minimum weighted bipartite matching at a higher level. This decoupling naturally leads to a centralized solution.
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 2 Architecture
### Inertial-Aided Scene Matching Navigation (ISMN)  
SMN is an effective solution for localization in unknown environments when the GNSS is disturbed. We have developed a new localization method that uses local feature points extracted by the Accelerated Robust Feature (SURF) to match the current drone's captured map image and onboard satellite image, thereby obtaining the position information of the current UAV. In order to improve the efficiency of SMN, the inertial information is obtained for assistance. The ISMN procedure is shown in Figure 2. It uses IMU propagation to determine the scope of the reference satellite map to improve the real-time performance. Considering that the real-time images from the onboard camera and the reference images from the satellite map are not in the same frame, the larger the obliqueness, the higher the probability of mismatching. To solve this problem, we use the attitude from the IMU to correct the real-time input images. This approach enhances the accuracy of image matching in large viewing angles and, consequently, improves the precision of SMN.
## 2 Related Work
### Related Work on Neural Networks and Deep Learning
* For the first time in the literature, the impact of the bit-depth storage and reproduction of the original GNSS signal is evaluated in combination with CNN and the \(C/N_{0}\) artifacts for the problem of detection and classification of interferences in GNSSs. * A CNN with a multi-head attention layer is used for classification, which is more sophisticated than the CNN architectures used in the literature so far. * The authors have produced a novel (because it is based on \(C/N_{0}\) data) and comprehensive data set with different types of interference, levels of attenuation, and bit-depths, which was not made available before to the research community. This data set will be available after the publication of the manuscript.
## 8 Microencapsulation of _C. ternatea_'s Phytochemical
### Antimicrobial Activity of Microencapsulated C. ternatea Extract  
Antibiotic-resistant microbes are urgently needed by the food industry and medical field. The development of antibiotic-resistant microorganisms dramatically reduces the efficacy of currently available medications, leading to the failure to effectively treat infections. In light of this challenge, the development of alternative approaches to find new antimicrobial compounds is needed. Various methods can be used to analyze the activity of antibacterial and antifungal agents, such as disk diffusion assay, broth microdilution assay, and agar diffusion test. Several studies investigated the antibacterial activity of microencapsulated C. _ternatea_ flowers extract, although the number is still limited.  
[PERSON] et al. [71] studied the antibacterial activity in C. _ternatea_ extracts microencapsulated by maltodextrin through disk diffusion assay. Their study showed the significant exhibition of antibacterial activity on all test foodborne bacteria, both gram-positive bacteria such as _Bacillus cereus_, _Staphylococcus aureus_, _Streptococcus_ sp., and _Bacillus coagulans_, and gram-negative bacteria such as _Yesirnia_ sp., _Proteus mirabilis_, _Pseudomonas aeruginosa_, and _Escherichia coli_. Another bactericidal effect of the anthocyanin also examined through broth microdilution assay showed that gram-positive bacteria were more susceptible to anthocyanin in C. _ternatea_ extract microcapsules compared to gram-negative bacteria [71]. Generally, the gram-negative bacteria possess a double-layered membrane which is built from peptidoglycan and lipopolysaccharidesaccharide [71,101]. Hence, the layer is the cause of the resistance of bacteria, and makes it less susceptible to any bioactive compounds.  
Another study conducted an antimicrobial activity test of C. _ternatea_ extract microencapsulated with gelatin, which consisted of antibacterial and antifungal activity through an algal well diffusion test [80]. The foodborne pathogenic bacteria _Bacillus cereus_ and _Salmonella enterica_ indicated a greater inhibition zone among bacteria, while _Escherichia coli_ and _Staphylococcus aureus_ illustrated the lowest inhibition zone. Hence, this indicated that freeze-dried C. _ternatea_ flower extract is able to retard gram-positive and gram-negative bacteria [80]. In terms of food pathogenic fungi, _Apergillus niger_ was demonstrated to be the most affected fungi, followed by _Candidata albicans_. Similarly, a study by [PERSON] et al. [102] also reported that the methanol extract of C. _ternatea_ flower (100 mg/mL) has the highest activity against _Candidata albicans_. Therefore, the microcapsules of C. _ternatea_ flower extract have the advantageous ability to retard the food pathogenic bacteria and fungi, which make them a potential alternative to be used in food products. Various coating agents and their formulations, as well as the drying methods, showed different abilities to retain the active components' bioavailability and the physical properties' stability, especially the color intensity.
## 2 Related Work
### Encrypted Traffic Datasets and Construction Methods
#### 2.1.2 Private Datasets from Existing Researches  
Given that many defects of the current public datasets exist, some research institutes that can build labeled datasets begin to use private datasets to conduct research.  
Such datasets include private ones collected from top N websites published on the Alexas [17,18], self-collected datasets from CERNET [19], from ISP [20], datasets generated from experimental environments [21], and datasets generated by global mobile solution providers [22].  
However, the private datasets are not open for other researchers to use, and even some researchers do not provide the details of the datasets in their papers, which leads to the lack of credibility. At present, the construction of datasets is mainly carried out by traffic analysis tools combined with manual collecting and labeling behaviors. As a result, there is an interference of background traffic from the OS and possible manual errors. In addition, if the private datasets are not collected in a large-scale actual network environment, the datasets will have a considerable concept drift so that the trained model is not universal.
## 2 Related Work
### Lexicon-Based Approach  
The lexicon-based approach is based on the unsupervised learning technique where a sentiment lexicon is constructed to determine the polarity of the given text via the predefined function of the indicator of positive and negative [15]. The popular lexicons include the Opinion Lexicon [16], AFINN Lexicon [17], and NRC Emotion Lexicon [18]. The value averaging the polarities derived from each word of the text in the lexicon serves as the sentiment score, which reflects the orientation of the polarity of the text [19]. The existing studies generally utilize the polarity of the previous sentence as a tie-breaker when the classification of tested sentences cannot be derived from the scoring function. Therefore, the generation of a meaningful lexicon based on the unsupervised labeling corpus is crucial with the lexicon-based approach [15]. As the creation of a lexicon is a time-consuming process, and a predesigned lexicon is not necessarily directly applicable to different domains and languages, the conventional lexicon-based methods are dedicated to extracting domain-specific words [20].  
Extensive researches have been made with the lexicon-based approach. In [21], a visual framework, _EmoWeb_, was proposed to dynamically analyze the sentiment of textual content relying on a well-constructed lexicon. Experiments were conducted by collecting data from websites to show the applicability of the proposed scheme. A Neural Network (NN) technique was used to overcome the shortcoming of the lexicon-based approach, where some words are ignored by the lexicon [22]. A Portuguese context-sensitive lexicon [23] was constructed for sentiment polarity identification. It employed LexReLi as methodology to build the proposed lexicon, and the efficiency of different combinations of the techniques was also investigated. In [24], an Arabic senti-lexicon was created for the sentiment analysis of Arabic language, where a Multi-domain Arabic Sentiment Corpus (MASC) was built.
# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 2 Durability Prediction Approach for Carbonated Concrete Structures
### One-Dimensional Concrete Carbonation Model  
In general, [PERSON]'s first law has been used as a model to determine the movement of material in porous media such as concrete. In order to express the carbonation speed in concrete structures, the fluid flow in stationary flow using [PERSON]'s first law has been utilized by several researchers [19; 20]. Equation (1) exhibits a carbonation velocity model of concrete structures using [PERSON]'s first law:  
\[x=\frac{2D}{a}(C_{1}-C_{2})\cdot t \tag{1}\]  
where \(x\) is the carbonation depth(cm); \(C_{1}\) denotes CO2 (g/cm3) concentration introduced from outside; \(C_{2}\) is the CO2 concentration (g/cm3) of the boundary between carbonated and uncarbonated parts in concrete; \(D\) is CO2 diffusion coefficient; \(t\) is time elapsed (in seconds); and \(a\) is the amount of CO2 (g/cm3) per unit volume required for concrete carbonation.  
If the distance from the carbonation boundary to the concrete surface is regarded as the carbonation depth and CO2 concentration introduced from outside is substituted with atmospheric CO2 concentration, Equation (2) can be obtained [21]:  
\[C_{x}=\sqrt{\frac{2\cdot D_{CO_{2}}}{a}\cdot C_{CO_{2}}\cdot t}=A\cdot\sqrt{t} \tag{2}\]  
where \(A\) denotes the carbonation velocity coefficient (cm2/s), and \(t\) is elapsed time after concrete carbonation (seconds). \(C_{x}\) is the carbonation depth (cm) at elapsed time; \(D_{CO_{2}}\) is CO2 diffusion coefficient (cm2/s); \(C_{CO_{2}}\) is the atmospheric CO2 concentration (g/cm3); and \(a\) is CO2 (g/cm3) uptake.
## 3 Case Study  
### Study Area  
The study area was located in Saskatchewan, Canada, as shown in Figure 4, and had a dimension of 28 by 40 km, as well as an image size of 1400 by 2000 with 20 m spatial resolution. The landscape of the study area was in an agricultural environment. In an attempt to monitor land cover changes from pasture to cropland, we generated a daily time-series synthetic Sentinel-2 images for the vegetation growing season from 26 April 2018 to 10 October 2018 from clear-sky Sentinel-2 observations and MODIS images (MCD43A4). The image time-series was used to demonstrate our proposed methods in this study for cloud-free time-series image reconstruction.  
Figure 4: The study area (\(28\times 40\) km) in Saskatchewan, Canada.
## 4 Application in Tunnel Smooth Blasting
### Damage Influenced by Bedded joints  
In the quality system for evaluating smooth blasting tunnels, it is necessary to focus on studying the overbreak/underbreak and damage of the surrounding rock mass [63; 64; 65; 66]--especially for the tunnel investigated in the present study, which has a bedded rock mass structure--while the influence induced by joints should be also considered. Six representative positions of the 3D damage contour are chosen to be amplified and labeled from '1' to '6' as shown in Figure 12. It can be classified into four situations to discuss: Situation 1 (e.g., Location 1) shows that the joint nearly parallel to the part  
Figure 14: Damage contour in red obtained from the numerical study.  
Figure 15: Section profiles measured from the Zigaojian Tunnel site. (**a**) Close to the tunnel face, (**b**) 1.5 m in front of the tunnel face, (**c**) 3 m in front of the tunnel face, and (**d**) 4.5 m in front of the tunnel face.  
of the peripheral holes enhanced the overbreak at individual boreholes and the degree of damage along the joint, which indicates that strong reflection of stress wave occurred at the joint and the damage increased due to the overlap of the stress waves. In Situation 2 (e.g., Locations 2 and 5) the boreholes are located exactly on the joint, which seems to more easily cause overbreak and large-scale damage due to the weak mechanical properties of joints that facilitate the generation of cracks along the joints. In Situation 3 (e.g., Location 3), the joint located between the two boreholes impeded the generation of penetrating cracks which could help form a smooth profile for the tunnel. Therefore, the underbreak formed near to the joint. Moreover, damage was caused at a further position along the joint, which may be the result of the enhanced effect of stress waves close to the joint. Situation 4 (e.g., Locations 4 and 6) is similar to Situation 3, however the joint is closer to one of the boreholes, which caused more damage along the joint and affected the thorough connection between the boreholes (short of red damage contour). In summary, the joint position could change the direction of damage expansion and distinctly influence the degree of damage in the surrounding rock mass. Referring to the four situations discussed above, measures such as increasing or decreasing charge quantity could be adopted to decrease the overbreak and damage for the surrounding rock mass according to the different relative positions between boreholes and joints.
## 2 Engineering Background  
Figure 1 shows the location of Mine L in the southeast of Zhaoyuan City, Shandong Province, as well as a 3D model of the mine surface, ore body and main tunnels. The main ore vein is 2600 m long and more than 300 m wide, strikes NE 60-70\({}^{\circ}\), dips SE, and dips 35-45\({}^{\circ}\). The ore is mainly pyrite-sericite and pyrite-servicates cataclastic, and the ore body occurs in thick and large faults. In the alteration zone, the surrounding rocks are a tectonic fracture alteration zone and L and Wendeng super unit granites. The granite rock has a dense structure and is a hard-highly hard rock with good stability. The rocks in the fractured alteration zone are broken, with strong alteration and developed fissures, but the degree of cementation is good, and the integrity and stability are relatively good. Mine L adopts an upward horizontal layered point column (route) backfilling mining method.  
Research on medium and fine tailings paste filling technology has been conducted beginning in November 2020. On the basis of the original full tailings cementation filling technology, the filling slurry preparation process was modified, and the filling slurry cementitious material ratio experiment was carried out. In January 2021, the filling station of L Mine changed the filling tailings from full tailings to medium-fine tailings. During the filling process, the filling slurry is not easy to solidify and the strength of the filling body is low which caused the problem of layered subsidence, as shown in the Figure 2 shown. At the same time, there are problems such as loss rate, production cost and labor intensity which directly affect the safety and normal production of underground operators, increases the filling cost and the dilution rate of the stop, and makes the original filling cementitious material unable to meet the filling requirements. In order to improve the strength of the filling body and change the state of the filling body, Dongfeng Branch Mine has carried out research on the filling technology of medium-fine tailings paste by introducing new cementitious materials and improving the filling process flow, as well as research on the proportion of the filling slurry cementitious material, etc. Based on the results, the filling technology of medium-fine tailings paste in Dongfeng Branch Mine is proposed.  
Figure 1: Schematic diagram of the location of the L mining area in Shandong Province.  
Figure 2: The layered subsidence problem was observed from the adjacent mine room.
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## Appendix B Derivation of (16)  
Applying a \(z\)-transform to Equations (1)-(4), we obtain  
\[R_{\rm p}(z) = R_{\rm s}(z)/z+TV_{\rm s}(z)/z, \tag{10}\] \[V_{\rm p}(z) = V_{\rm s}(z)/z,\] (11) \[R_{\rm s}(z) = R_{\rm p}(z)+\alpha(R_{\rm o}(z)-R_{\rm p}(z)-\Delta tV_{\rm p}(z )),\] (12) \[V_{\rm s}(z) = V_{\rm p}(z)+(\beta/T)(R_{\rm o}(z)-R_{\rm p}(z)-\Delta tV_{\rm p }(z)). \tag{13}\]  
By simplifying these equations, \(R_{\rm p}(z)\) and \(R_{\rm o}(z)\) are related by  
\[R_{\rm p}(z)=\frac{(\alpha+\beta)z-\alpha}{(z^{2}+(\alpha+\beta-2)z-\alpha+1) T+\beta(z-1)\Delta t}TR_{\rm o}(z). \tag{14}\]  
Because we have assumed that the maximum acceleration of the target \(A_{\rm max}\) is constant but do not assume measurement errors, the true and measured target positions are expressed as  
\[R_{\rm t}^{\rm ca}(z) = Z[A_{\rm max}(kT)^{2}/2]=\frac{z(z+1)}{2(z-1)^{3}}A_{\rm max}T^{ 2}, \tag{15}\] \[R_{\rm o}(z) = R_{\rm t}^{\rm cr}(z)+Z[\Delta tA_{\rm max}(kT)]=R_{\rm t}^{\rm cr }(z)+\frac{z}{(z-1)^{2}}\Delta tA_{\rm max}T, \tag{16}\]  
where \(Z[\,]\) denotes the \(z\)-transform. With Equations (14)-(16), the predicted error in the \(z\)-domain, \(E_{\rm p}(z)\equiv R_{\rm t}^{\rm ca}(z)-R_{\rm p}(z)\), is  
\[E_{\rm p}(z)=\frac{z((z+1)T-(2\alpha+\beta)\Delta t)}{2(z-1)\{(z^{2}+(\alpha+ \beta-2)z-\alpha+1)T+\beta(z-1)\Delta t\}}A_{\rm max}T^{2}. \tag{17}\]  
Applying the final value theorem to Equation (17) yields Equation (16).
# Exploring the Potential of Microextraction in the Survey of Food Fruits and Vegetable Safety
## Abbreviations  
AChE: acetylcholinesterase enzymes; ACN: acetonitrile; ADI: Admissible Daily Intake; APVMA: Australian Pesticides and Veterinary Medicines Authority; BHA: terbutylhydroxylovanisol; BHT: butylhydroxylouene; CIB&RC: Central Insecticides Board and Registration Committee; Codex: Codex Alimentarius Commission; DAD: diode array detection; DDT: dichloro-diphenyl-trichloroethane; DES: deep eutectic solvents; DI: direct immersion; DLLME: dispersive liquid-liquid microextraction; DNA: deoxyribonucleic acid; dSLE: dispersive solid-liquid extraction; dSFE: dispersive solid-phase extraction; EC: European Commission; EDCs: endocrine disruption compounds; EFSA: European Food Safety Authority; EPA: Environmental Protection Agency; EU: European Union; FAO: Food and Agriculture Organization; FDA: Food and Drug Administration; FSAN: Fool Standards Australia New Zealand; FSSA: Food Safety and Standard Authority of India; GC: gas chromatography; GreETs: green extraction techniques; H\({}_{2}\)O: water; HCH: hexachlorocyclohexane; HPLC: high-performance liquid chromatography; HS: headspace; IL: ionic liquid; ILPR: imidazolium ionic liquid-modified phenolic resin; ITAB: France's Organic Food and Farming Institute; IECFA: Joint Food and Agriculture Organization; LC: liquid chromatography; ILE: liquid-liquid extraction; LLME: liquid-liquid microextraction; LOD: limit of detection; LPE: liquid-phase extraction; LTP: low temperature partition; MeOH: methanol; MEPS: microextraction packed sorbent; MRLs: maximum residue limits; MS: mass spectrometry; MS/MS: tandem mass spectrometry; MSPE: magnetic solid-phase extraction; MTMOS-CPTES: methyltrimethoxysilane-chloropropyltriethoxysilanes; NADES: natural deep eutectic solvents; OSHA: Occupational Safety and Health Administration; PG: propyl gallate; PMRA: Pest Management Regulatory Agency; PUHs: phenylurea herbicides; QuEChERS: Quick, Easy, Cheap, Effective, Rugged, and Safe; ROS: reactive oxygen species; REACH: Registration, Evaluation; Authorisation, and Restriction of Chemicals; SALE: salting-out assisted liquid-liquid extraction;SC-CO\({}_{2}\): supercritical carbon dioxide; SDME: single-drop microextraction; SFE: supercritical-fluid extraction; SLE: solid-liquid extraction; SFO: solidification of floating organic droplet; SPME: solid-phase microextraction; TBHQ: tert-butylhydroquinone; WHO: World Health Organization; UAE: ultrasound-assisted extraction; UHPSFE: ultra-high pressure supercritical fluid; UPLC: ultra-high performance liquid chromatography; USA: United States of America; UV: ultraviolet.