esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Overview of Previous Research
To solve this problem, there is need for triangulation from an autonomous navigation system. NASA and ESA are working on the development of the Lunar navigation network, which will be the main system helping all upcoming Lunar missions [32]. The Moonlight Initiative ESA's program includes a series of missions which will maintain sustainable Lunar exploration. A part of the mission is the establishment of a navigation constellation of three to four Lunar Pathfinder satellites orbiting the Moon in Elliptical Lunar Frozen Orbit (ELFO), mainly covering the Lunar South Pole [18]. Launch is planned for a period between 2025 and 2026. The mission objective is to demonstrate high-bandwidth communication and navigation capabilities for the future Lunar missions. The Lunar Pathfinder will also support the Lunar Polar Sample Return mission, which aims to retrieve samples from the Moon's South Pole region. A mission developed by ESA and the satellites built by Surrey Satellite Technology LTD (SSTL) will have a NaviMoon receiver onboard [4]. The receiver's capacity will be to harness the navigation signals (strong and weak as well) from the terrestrial navigation systems (GPS and Galileo), determining the Pathfinder position with approximately 60 m accuracy. The Lunar lander Argonaut, as a part of the Moonlight Initiative mission together with the Pathfinder satellites, will help the rovers in positioning on the Lunar surface [32]. The Lunar GNSS Receiver Experiment (LuGRE) is developed in partnership between NASA and the Italian Space Agency (ASI) based on laser retroreflector technology. Mirrors on the Pathfinder's array will aim to enhance Lunar navigation and to validate operational capabilities, which will also be critical for Artemis and the future missions [33]. The review of many proposals, simulation models, and feedback from landers' and rovers' experiences can be summarized in a general conclusion that all of them are based on autonomous navigation without the possibility to connect and to collaborate under the same system. The model proposed in this paper is based on pseudolite transceiver field tests introduced by ARL from Stanford University and applied for open-pit mining and for a Mars navigation system. With a similar Moon scenario in the Shackleton Crater, our stand-alone navigation system will serve all users from the same Lunar region. In the next stage of development, when the Pathfinder constellation from the Moonlight Initiative program is operational, it will be used for referencing all local coordinates obtained by the system elaborated in this article to the Lunar reference system.
## 4 Discussion
### Scalability  
The proposed blockchain-based traceability solution suffers several scalability concerns that need to be addressed for effective implementation in real-world applications [55]. One of the primary challenges is the inherent limitation of blockchain networks in handling a high volume of transactions. As the number of participants and transactions increases, the network can become congested, leading to slower processing times and higher transaction fees (cf. Section 4.3 below). This is particularly critical in supply chains where timely updates and real-time data access are basic requirements for maintaining operational efficiency [56].  
Another scalability issue arises from the actual balance between off-chain and on-chain data storage in practical implementations. While the solution proposes a hybrid model that combines on-chain and off-chain storage, the on-chain component still requires careful consideration. As previously discussed, storing large amounts of data directly on the blockchain can lead to increased costs and slower transaction speeds, as each piece of data must be replicated across all nodes in the network. This replication not only consumes significant storage resources but also impacts the overall performance of the blockchain, making it less efficient for applications that require rapid data retrieval and processing.  
Moreover, the integration of off-chain data storage solutions introduces additional complexity [57]. While off-chain storage can alleviate some of the on-chain data burdens, it requires robust mechanisms to ensure data integrity and synchronisation between on-chain and off-chain components. If not managed properly, discrepancies between these data sources could undermine the reliability of the traceability system, leading to potential trust issues among stakeholders.  
Lastly, the scalability of the proposed solution depends on the actual infrastructure and technology stack. As the system scales, it may require more sophisticated hardware and network capabilities to support increased data loads and transaction volume [58]. This could pose challenges for smaller organisations or those in regions with limited technological resources, potentially widening the gap between larger enterprises and smaller players in a context where supply chains are in many cases global, where suppliers of raw materials, transformation industries and final customers may be placed in different continents.
# Research and Development Status of Prepared Foods in China: A Review
## 5 Production Process of Prepared Foods
### Pretreatment of Raw Materials of Prepared Foods at the \"Front End\"  
First of all, the pretreatment of prepared food ingredients is at the \"Front end\", and the pretreatment processes of prepared food ingredients in central kitchen processing (The central kitchen concept is a new trend in the food industry, where centralized preparation and processing of fresh foods and the distribution of finished or semi-finished products to catering chains or related units take place) include cleaning, slitting, enzyme inactivation, sterilization [37], blanching, superheated steam treatment, removal of fishy odour, and acid drainage. Among them, the slitting, cleaning, and enzyme inactivation process is mainly used for the pretreatment of fruits and vegetables' prepared food materials. After the raw materials are harvested, they need to be cleaned, slit, and enzyme inactivated immediately to prevent the fruits and vegetables from missing the processing maturity due to the ripening effect of their own enzymes, while achieving the purpose of colour protection, crispness retention, reducing nutrient loss, and extending the shelf life. The process of slitting and washing is carried out not only by using fruit and vegetable sorting machines and fruit and vegetable washing machines, but also by manual processing. Enzyme inactivation is mainly achieved through non-thermal techniques [55]. At the same time, the processes of blanching, superheated steam, acid drainage, and removal of fishy odour are mainly used for the pretreatment of raw materials for animal prepared foods. Similarly, to the production of pickled beef chops, the raw meat needs to be drained, then blanched or subjected to superheated steam treatment. The purpose is to remove the residual blood and surface oil in the meat, and at the same time remove the odour of the meat [56]. The number of blanching and the duration of blanching times can affect the taste, colour, flavour, and nutritional quality of meat products [57]. The most suitable number of blanching times and the duration of blanching times differ for different meats; for example, chicken has the lowest concentration of odour substances after blanching twice. In addition, studies have shown that superheated steam can also be used to remove odour substances from meat and reduce the loss of juices associated with blanching compared to traditional blanching treatments [58]. The removal process of fishy odour is mainly used for pretreatment of raw materials for aquatic prepared foods [59]. Due to the high moisture content and active tissue enzymes in aquatic raw materials, it is easy to produce a fishy taste that is not conducive to sensory acceptance, which seriously restricts the development of their fresh marketing and processing. The means of removing fishy odour usually include physical methods (masking, adsorption, embedding, and irradiation), chemical methods (organic solvent extraction, acid and alkali salt method, antioxidant method, and ozone method), biological methods, and composite methods.
###### Abstract  
The recent developments in nanotechnology have driven researchers towards the application of latex extracts for the green synthesis of silver nanoparticles (AgNPs). In this study, AgNPs were biologically synthesized using latex extracts from _Tabernaemontana ventricosa_, characterized, and their respective biological activities were assessed. Our results showed prominent silver (Ag) peaks at 410 nm confirmed by UV-vis while the elemental percentage composition (\(3.89\pm 0.16\%\)) of the AgNPs was verified by EDX. The SEM and HRTEM analysis revealed spherical, ovate, and triangular AgNPs, with diameters ranging from 5.00 nm-17.50 nm; however, larger hydrodynamic diameters were revealed by NTA analysis. The FTIR spectra results displayed several peaks of bending and stretching associated with various functional groups such as alcohols, alkanes, amines, proteins, enzymes, and other biomolecules possibly responsible for the capping, reduction, and functionalization of AgNPs. In addition, the AgNPs showed strong antibacterial activity (diameter of the zone of inhibition) against _Escherichia coli_ (\(12.67\pm 1.15\) mm), _Staphylococcus aureus_ (\(11.67\pm 0.58\) mm), and _Pseudomonas aeruginosa_ (\(11.33\pm 0.58\) mm), with significant cytotoxic activity noted in the HeLa cells (\(10.52\)\(\mu\)g/mL). The study confirmed the successful production of AgNPs and recommends _T. ventricosa_ latex extracts as effective capping agents of nanoparticles (NPs).  
AgNPs; biosynthesis; antibacterial activity; cytotoxicity; latex; _Tabernaemontana_
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Experiment Design
#### 3.3.5 Cross-Validation and Parameters' Optimization  
For the model fine-tuning and model performance evaluation, nested cross-validation [43; 44] was used. In the inner loop, we performed a randomized grid-search [45] with 5-fold cross-validation to optimize the hyperparameters of the used models. The AP score was used as a target function for optimization. The performance of the best model identified during the inner loop of cross-validation was evaluated on the \"hold-out\" test set, generated during the outer loop of cross-validation. The above-mentioned procedure was repeated five times, generating five independent training and test sets. An illustration of the applied cross-validation scheme can be found in Figure 10. The search space of the hyperparameters for each of the analyzed multivariate models is provided in Appendix A. The optimal parameters selected for each model by the randomized grid search can be found in Appendix B.
## 2 Materials and Methods  
### Sampling Campaign  
The sampling campaign was carried out in an organic winery (Azienda olivitivinicola Giulia Fiorentini, located in the town of Cupramontana, Ancona, Italy, geographical coordinates: 43\({}^{\circ}\)26\({}^{\prime}\)48\({}^{\prime\prime}\) N, 13\({}^{\circ}\)6\({}^{\prime}\)59\({}^{\prime\prime}\) E; 505 m altitude) that has never used commercial starter strains.  
Thirty-four samples were collected in the 2016 vintage: (i) 10 samples of the Montepulciano grape variety and 10 samples after autoenrichment; (ii) 12 items from the winery environment and pieces of equipment (3 walls, 1 floor, 2 wooden barrels; 4 destemmers, and 2 steel fermentation tanks); and (iii) 2 Verdicchio spontaneous fermentation.  
Twenty-four samples were collected in the 2019 vintage: (i) 6 samples of the Verdicchio grape variety and 6 samples after autoenrichment; (ii) 9 items from the winery environment and pieces of equipment (3 walls, 2 floors, 2 destemmers, 2 steel fermentation tanks); and (iii) 2 Verdicchio spontaneous fermentation. Details are reported in Table S1.  
The two vineyards (Montepulciano and Verdicchio) each have two hectares and are located close to the winery. The grapes were randomly collected from randomized blocks.  
Grape samples (about 1 Kg of undamaged ripe grape bunches) were picked in sterile plastic bags. The related vineyards are located close to each other and close to the cellar (distance of about 300 m). Both vineyards were subjected to the same fungicide treatment used for organic wine production (copper and sulfur compounds). Winery environment and equipment samples were sampled using sterile swabs streaked on random environmental surfaces (10 cm\({}^{2}\)). Furthermore, samples of spontaneous fermentation were collected through the middle valve of the vat under sterile conditions. All the samples were stored on ice during sampling and immediately transported to the laboratory and processed.
###### Abstract  
To overcome the limitations of existing maritime radar identification analysis methods, which are only applicable to sea-skimming aircraft and fail to quantitatively calculate the probability of radar correctly identifying the target under electromagnetic influence from marine geographical features (MGFs), an advanced method is proposed for calculating the radar identification probability in marine areas using 3D MGF models. The method first established the radar identification criteria in 3D space, considering radar line of sight (LOS), radar target adhesion (RTA), and radar resolutions in range, azimuth angle, and elevation angle. It then comprehensively analyzed errors from both the aircraft and MGFs. Finally, the probability of a target at a specific marine location being correctly identified by radar was calculated using the Monte Carlo method. Theoretical derivations and simulation results demonstrated that: (1) Unlike existing methods limited to sea-skimming aircraft, the proposed method is applicable to aircraft at any altitude, better aligning with current aircraft performance and requirements; (2) While existing methods provide only a binary result of \"identified\" or \"unidentified,\" the proposed method offers a probability value. For the same marine location point \(T_{a}\), the proposed method yields radar identification probabilities of 0.0877 for sea-skimming aircraft and 0.5887 for high-altitude aircraft, providing more precise and intuitive decision-making support for mission planners.  
m aritime radar; line of sight; radar target adhesion; marine geographical feature; 3D model; radar resolution; mission planning
# ZnO@MoS2 Core-Shell Heterostructures Enabling Improved Photocatalytic Performance
## 2 Experimental Section
### Characterization of the ZnO Micro/Nanorods and ZnO@MoS\({}_{2}\) Core-Shell Heterostructures  
X-ray diffraction (XRD, X'Pert Pro MFD, PANalytical, Almelo, Netherlands) determined the structure of the as-received ZnO micro/nanorods and ZnO@MoS\({}_{2}\) core-shell heterostructures samples. Scanning electron microscopy (SEM, ZEISS Sigma 500, Jena, Germany, working voltage of 15 kV) was used to characterize the morphology of the as-received samples. An energy dispersive spectrometer (EDS) was employed to confirm the elements of the samples. Raman spectra (LabRAM HR UV-NIR, Paris, France) laser wavelength of 488 nm) and ultraviolet-visible spectra (Shimadzu, UV3600 Plus, Kyoto, Japan) were conducted to detect the optical properties of the samples. A photochemical reactor (OCRS-K, mercury lamp as the light source, working power of 500 W) was utilized to investigate the photocatalytic properties of the as-received nano-structures.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 2 Background  
### Cache Memories  
Caches are small memories with very fast access times that are situated between the cores and the main memory. Their purpose is to keep frequently used data and instructions very close to the core to speed up memory access. In this work, we consider set-associative caches. A cache is made of _sets_, each containing a fixed number of _ways_ (see Figure 1a). We denote the number of sets in the cache as \(S\) and the number of ways per set as \(W\). A _memory block_ is the memory representation of a cache line. It can be stored in any of the \(W\) ways of a cache set; thus, the cache can store up to \(S\times W\) memory blocks. When a set is full and a line has to be deleted, a _replacement policy_ determines which line is to be removed from the set.  
Different cache replacement policies exist, such as the least recently used (LRU), pseudo-LRU, first in first out (FIFO), least frequently used (LFU) and pseudo-random replacement policies. In modern micro-architectures, replacement policies are often undocumented. However, the replacement policies of some micro-architectures have been reverse-engineered; for example, Sandy Bridge and Ivy Bridge use a pseudo-LRU replacement policy [23]. Moreover, Ivy Bridge, Skylake and Haswell use adaptive cache replacement policies, which only work as pseudo-LRU policies in some situations [24].  
As we have seen so far, a fixed mapping is used to determine whether a memory address is present in the cache. Typically, the input address is decomposed into three parts: a set index, an address tag and a line offset (see Figure 1a). A successful cache access is called a _cache kit_; in contrast, a failed cache access is called a _cache miss_. A cache miss results in a longer access time and can be detected with precise timing measurements.  
A key feature of the LLCs in some modern processors is that they are divided into different cache slices (see Figure 1b). As well as the set and way indices, a slice index must also be determined in order to place a line into this sort of partitioned cache. Although microprocessor designers do not disclose the mapping that is used between memory blocks and cache slices, it has been reverse-engineered in previous work on Intel architectures [25].
# Permuted Pattern Matching Algorithms on Multi-Track Strings
## 3 KMP-Based Permuted Pattern Matching Algorithms
### Multi-Track KMP Algorithm
The sorted pattern \(\mathbb{P}_{sort}\) and the array \(SI\) save the sorted indices of suffixes of text used to perform permuted-matching between the pattern and a substring of the text. If symbols on the text and the pattern are mismatched, we shift the matching position of the pattern using _Border_. If the pattern matches a substring of the text, then the MTKMP algorithm outputs the occurrence position at the text and shifts the pattern according to _Border_. ```
1 Function\(\mathsf{constructMTBorderArray}(\mathbb{P})\)
2 compute \(SI[i]\leftarrow\mathsf{SI}(\mathbb{P}[i:])\) for \(1\leq i\leq m\);
3\(i\gets 1\); \(j\gets 0\); Border\([0]\leftarrow-1\);
4 while\(i\leq m\)do
5 while\(j\geq 0\) and \(\mathbb{P}[j+1]\langle SI[1]\rangle\
eq\mathbb{P}[i]\langle SI[i-j]\rangle\)do
6\(j\leftarrow\) Border\([j]\);
7\(i\gets i+1\); \(j\gets j+1\);
8\(\mathsf{Border}[i]\gets j\);
9
10 return\(\mathsf{Border}[i]\);
```  
**Algorithm 1**Multi-track border array construction algorithm. ```
1 Function\(\mathsf{MTKMP}(\mathbb{T},\mathbb{P})\)
2 compute \(SI[i]\leftarrow\mathsf{SI}(\mathbb{T}[i:])\) for \(1\leq i\leq n\);
3 compute \(\mathbb{P}_{sort}\leftarrow\mathsf{SI}(\mathbb{P})\);
4\(\mathsf{Border}[i]\leftarrow\mathsf{constructMTBorderArray}(\mathbb{P})\);
5\(i\gets 1\); \(j\gets 0\);
6 while\(i\leq n\)do
7 while\(j\geq 0\) and \(\mathbb{T}[i]\langle SI[i-j]\rangle\
eq\mathbb{P}_{sort}[j+1]\)do
8\(j\gets\) Border\([j]\);
9\(i\gets i+1\); \(j\gets j+1\);
10 if\(j=m\)then
11 output\((i-j)\);
12\(j\leftarrow\) Border\([j]\);
13
14
15
```  
**Algorithm 2**The multi-track KMP algorithm. **Theorem 1**. : _Given a text \(\mathbb{T}\) and a pattern \(\mathbb{P}\), the MTKMP algorithm computes all occurrence positions of \(\mathbb{P}\) in \(\mathbb{T}\) in \(O(nk+mk)\) time._  
Proof. : According to the above arguments, we can shift \(\mathbb{P}\) by \(x\), where \((j-x+1)\) is the length of the longest proper border of \(\mathbb{P}[1:j+1]\) if \(\mathbb{T}[i:i+j]\bowtie\mathbb{P}[1:j+1]\) and \(\mathbb{T}[i:i+j]\bowtie\mathbb{P}[1:j+1]\). Since \(\mathsf{Border}[i]\) is the longest proper border of \(\mathbb{P}[:i]\), the algorithm finds the occurrence position of \(\mathbb{P}\) in \(\mathbb{T}\) correctly. Using Lemma 1, \(SI\) can be computed in \(O(nk)\). From Lemma 5, \(\mathsf{constructMTBorderArray}(\mathbb{P})\) runs in \(O(mk)\). Both **while** loops are called \(O(n)\) times at most, since \(i-j\leq i\leq n+1\), and the value of \(i\) always increases each time the outer loop is called, while the value of \(i-j\) always increases each time the inner loop is called. Each comparison of \(\mathbb{P}[j]\langle\mathsf{SI}(\mathbb{P}[1:])\rangle\) and \(\mathbb{T}[i]\langle\mathsf{SI}(\mathbb{T}[i-j:])\rangle\) consumes \(O(k)\) time. Hence, Algorithm 2 runs in \(O(nk+mk)\) time.
## 3 Mathematical Solution of the Problem
### Regression Model Substituting Numerical Solution of Transcedental Equation  
Although the presented method for numerical solutions of a transcendental equation returns accurate values of the substitution \(t\) and consequently the precise vehicle position where the maximum transient offfracking is reached, its practical application is inefficient and requires advanced mathematical and programming skills. To address this issue, it is necessary to create a more efficient and simplified calculation procedure which is able to instantly return the required parameters determining the maximum transient offfracking position with the desired accuracy. Moreover, a newly designed calculation tool should eliminate the need to perform any additional vehicle movement simulation or swept path analysis in order to determine the critical vehicle position in an active transient offfracking state. Regarding a relatively big dataset of generated input parameters and corresponding calculation results, the selection of an appropriate regression model which can predict the substitution \(t\) with the desired level of accuracy turned out to be the most practical solution. Several widely used nonlinear regression models were tested and compared by applying available Python functions. The benchmark for the desired level of accuracy of the tested regression models applied for the prediction of the steering angle \(\beta_{d}\) is set as \(1/10\) of the \(1^{\circ}\) angle, or in decimal form \(0.10^{\circ}\).
## 2 Materials and Methods
### Evaluation  
We used the following five metrics to measure model performance [61]: ACC, Sn, Sp, Recall, Precision, MCC, AUC and AP, representing accuracy, sensitivity, specificity (recall reate), precision rate, [PERSON]'s correlation coefficient, area under receiver operating characteristic (ROC) curve, and average of precision, respectively.  
\[ACC=\frac{TP+TN}{TP+FP+TN+FN} \tag{3}\]  
\[Sn=Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[Sp=\frac{TN}{TN+FP} \tag{5}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FN)(TN+FN)(TP+FP)(TN+FP)}} \tag{6}\]  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[AP=\sum_{k=1}^{n}(Recall_{n}-Recall_{n-1})\times Precision_{n} \tag{8}\]  
where \(TP\), \(FP\), \(FN\) and \(TN\) mean number of samples for true positive, false positive, false negative and true negeative.  
ROC curve is drawn according to probalibity threshold, with the true positive rate (sensitivity) as the vertical coordinate and the false positive rate (1-specificity) as thehorizontal coordinate. AUC is defined as the area under the ROC curve. The value of AUC can range from 0.5 to 1. The closer the AUC is to 1.0, the higher the accuracy of the detection method. In contrast, a value of 0.5 indicates a random prediction.  
For _AP_ metrics, it is a more effective metric for an imbalanced dataset test. These metrics are calculated by \(Recall_{n}\) and \(Precision_{n}\) at the \(n\)th probability threshold. Generally, AUC is enough to evaluate the model test by a balanced dataset, while for an imbalanced dataset test, it is better to use _AP_ as scoring.