esa-sceva/satcom-chunk-collection huggingface dataset:
# null means aBA, aDC are not used  
[aAB, null, sAB] = InverseGP (IonA, latA, IonB, latB)  
[aCD, null, sCD] = InverseGP (IonC, latC, IonD, latD)  
[aAC, aCA, sAC] = InverseGP (IonA, latA, IonC, latC)  
A = aAC - aAB  
C = aCD - aCA  
sAX = R * atan (sin(sAC/R)/(cos(aAC/R)*cos(A)+(1/tan(C))*sin(A)))  
sCX = R * atan (sin(sAC/R)/(cos(aAC/R)*cos(C)+(1/tan(A))*sin(C)))  
#Lon and Lat for points A, C are overwritten with the new approach  
[IonA, latA] = DirectGP (IonA, latA, aAB, sAX)  
[IonC, latC] = DirectGP (IonC, latC, aCD, sCX)  
IonX = IonA  
latX = latA  
UNTIL ( ABS(sAX) < threshold AND ABS(sCX) < threshold )  
ENDFUNCTION  
**Scheme 5. ** Pseudocode of the Geodesic Intersection algorithm. The new spatial PostGIS SQL function \"STX_GeodesicIntersection (a point, b point, c point, c point)\" calculates the intersection of the two geodesics AB-CD on the ellipsoid WGS84 (ellipsoid by default). Scheme 6 shows how a SQL user can easily get the intersection of two geodesics. SELECT ST_AsLatLonText ( STX_GeodesicIntersection  
(POINT (29 42),'POINT (-77 39),' POINT (0 6),'POINT (-22 64)), 'D\"M\"S.SSSSSSSS\" ) AS px;  
Query output: 54 deg31.306592212\"-14 deg3349.880679508\"  
**Scheme 6. ** A simple SQL sentence with a geodesic intersection. A GIS user will use the new function from a spatial table. Scheme 7 shows how to create a small spatial table with the four example cases and run a query to get the intersections. In addition, the query proves that the X point lies on both geodesics, hence the azimuth \(\alpha_{\text{AB}}=\alpha_{\text{AX}}\), and \(\alpha_{\text{CD}}=\alpha_{\text{CX}}\). ST_Azimuth is the PostGIS solution to the inverse geodesic problem and it is using the Karney approach as well. CREATE TABLE Geodesics (ncase integer, pa geography, pb geography, pc geography, pd geography); INSERT INTO Geodesics VALUES (1, POINT(14.5 54);POINT(14.6 54.2); POINT(14.4 54.1);POINT(14.7 54)); INSERT INTO Geodesics VALUES (2, POINT(5 52);POINT(6 51.4); POINT(4.5 51.5);POINT(5.5 52)); INSERT INTO Geodesics VALUES (3, POINT(29 42);POINT(-77 39); POINT(0 6);POINT(-22 64)); INSERT INTO Geodesics VALUES (4, POINT(-92 35);POINT(52 40); POINT(20 -8);POINT(-95 49)); WITH Intersections (ncase, px, pa, pb, pc, pd) AS ( SELECT ncase, STX_GeodesicIntersection (pa,pb,pc,pd), pa, pb, pc, pd FROM Geodesics ) SELECT ncase, ST_Azimuth (pa, pb) - ST_Azimuth (pa, px) as \"aAB-aAX\", ST_Azimuth (pc, pd) - ST_Azimuth (pc, px) as \"aCD-aCX\", ST_X(px:geometry) as \"lonX\", ST_Y(px:geometry) as \"latY\", ST_AsLatLonText ( px:geometry, D\"M\"S.SSSSSSSSSS\") as \"DD MM SS\" FROM Intersections ORDER BY ncase; Query output (3 first columns): ncase | aAB-aAX | aCD-aCX |  
1 | 1.06\(\times\)10-13 | 4.48\(\times\)10-14 |  
2 | 1.68\(\times\)10-14 | 6.66\(\times\)10-16 |  
3 | -3.33\(\times\)10-16 | 0  
4 | -1.11\(\times\)10-16 | 0  
**Scheme 7. ** Testing the exact intersection with the new function. Table 3 shows the coordinates of the intersection points calculated with the proposed algorithm in PostGIS (PL/SQL). The results with the java implementation are exactly the same.
## 4 Biomedical Applications
### Antifouling Materials  
HA is a representative antifouling material mainly due to its hydrophilicity and its unique moisture retention and lubrication abilities. The catechol moiety of HA-catechol can form a thin film on substrates (Figure 4a). These HA-catechol coatings on Au surfaces enhance hydrophilicity (11.9\({}^{\circ}\) of water contact angle) compared to that measured on bare Au surfaces (81.6\({}^{\circ}\)) [40]. In addition, HA-catechol-coated Au substrates have extremely low protein-adsorption levels (<5 ng/cm\({}^{2}\)) for single proteins (i.e., bovine serum albumin, lysozymes, and \(\beta\)-lactoglobulin). Polyimide (PI), Au, poly(methyl methacrylate) (PMMA), polytetrafluoroethylene (PTFE), and polyurethane (PU) are coated with HA-catechol to prepare antifouling substrates [60]. All substrates coated with HA-catechol exhibit improved hydrophilicity of the substrates (less than 50\({}^{\circ}\) of water contact angle) compared to the unmodified substrates, as shown in Figure 4b. The adsorption of bovine serum albumin on HA-catechol-coated substrates was dramatically reduced to 22-46% compared to that on unmodified surfaces (Figure 4c). Therefore, HA-catechol is an excellent coating agent for the antifouling treatment of biomedical devices.  
Figure 4: (**a**) Schematic illustration of HA–catechol (dopamine-conjugated HA: DA–HA) coating on various substrates (i.e., PI (polyimide), gold (Au), poly(methyl methacrylate) (PMMA), polytetrafluoroethylene (PTFE), and polyurethane (PU)) for antifouling applications. (**b**) Water contact angle measurements of various substrates before and after HA–catechol coating. (**c**) BSA (bovine serum albumin) adsorption (\(\upmu\)g/cm\({}^{2}\)) on bare and HA–catechol-coated surfaces after treatment with a BSA solution (1 mg/mL) at 37 \({}^{\circ}\)C for 6 h (* \(p<0.05\)). Reproduced with permission from [60]. Copyright © 2020 Elsevier.
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 4 Simulations
### BER Performance under Non-Ideal Synchronization Conditions with Jitter  
The following is the numerical simulation of the BER performance under non-ideal synchronization conditions with jitter. The simulation parameters are as follows: the \(Q\) of the \(Q\)-ary TH-PPM is 16; the pulse repetition period \(N_{\mathrm{s}}\) is set to 10; and the time-hopping sequence period \(N_{h}\) is 10; the jitter factor \(\varepsilon\) is set to 0, 0.1, 0.4; the \(E_{b}/N_{0}\) is set to 0-12 dB; the BER is calculated to \(1\times 10^{-4}\); the simulation channel is a Gaussian white noise channel. The simulation result is shown in Figure 12.  
The simulation result shows that as the jitter factor increases, the BER performance of the conventional TH-PPM rapidly deteriorates. However, the BER performance of the conventional TH-PPM with jitter is better than that with constant time offset when \(\varepsilon=\mu\). This is because the real jitter changes randomly from 0 to \(\varepsilon\) in time, which leads to a better average SNR. Furthermore, the simulation result shows that as the jitter factor increases, the BER performance of the new TH-PPM has also deteriorated. Even so, the BER performance of the new TH-PPM is much better than that of the conventional TH-PPM with jitter, which is consistent with theoretical analysis. According to the analysis in Section 3.4, the BER performance of change in the shape of the pulse is similar to that of the jitter, so we will not propose the numerical simulation with change in the shape of the pulse.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mu\) & **0** & **0.1** & **0.4** \\ \hline
**BER deterioration/dB** & & & \\
**(new TH-PPM)** & & & \\
**BER deterioration/dB** & & & \\
**(conventional TH-PPM)** & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: BER performance under non-ideal synchronization conditions with jitter.  
Figure 12: BER performance under non-ideal synchronization conditions with jitter.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 7 Discussion
### Strengths and Weaknesses  
As outlined through the text, the main advantage of the proposed approach is that it can automatically design DRs for various variants of the VRP and different user defined criteria, which can be nonstandard. This is important, since there is only a limited number of manual DRs proposed in the literature for VRPs, such as the nearest neighbour (NN) rule, earliest-first (EF) and urgent-first (UF) rule that were used as a baseline. Additional heuristics are available for different VRP variants, such as the Clarke-Wright algorithm that merges routes based on the saving distance [41], but which still needs to be adapted for use in dynamic conditions with a given number of vehicles. It is also not uncommon to use search-based methods in dynamic environments, but their utilisation depends on the rescheduling time complexity and adaptation is usually not trivial for different components of the schedule that are subject to change. Additionally, existing heuristics are tailored to satisfy specific commonly optimised criteria and they might not be efficient for a specific problem instance.  
Figure 6: Analysis of stopping criteria—test set performance by number of function evaluations.  
Figure 7: Analysis of learning set size—test set performance.  
The experimental results demonstrated that the proposed method was able to generate DRs that perform better than existing rules. This is important as it shows limitations of existing rules, and outlines the need to evolve rules specialised for certain VRP flavors to obtain a better performance. Furthermore, the evolved DRs can be used both in static but also in dynamic conditions as well, which gives them a larger flexibility over search based methods (such as genetic algorithms and similar), which are usually mostly applied for static problem variants. Finally, the execution time of the proposed methods is quite small, even for larger problems, as opposed to other more sophisticated search-based methods.  
However, the proposed methodology does not come without its issues. First of all, it is required to specify the terminal nodes used to evolve the rule, which does require certain domain knowledge, although it was demonstrated that already using simple operators leads to satisfactory results. Furthermore, the evolutionary process, i.e., the generation of new DRs is a time consuming process, which can take certain time to complete (up to a few hours). However, it must be stressed out that the idea of this method is to perform the generation of DRs prior to solving the problem, which then eliminates this long execution times when directly solving the problem. Finally, the performance of DRs when considering static problem variants is evidently worse than that of standard search-based methods. However, this is expected due to the difference in which they search for solutions. However, this problem can be amended by combining the proposed methodology with other improvement methods, some of which are mentioned in the next section as possibilities for future work.
## 3 Reinforcement and Q-Learning in Routing
 \tag{6}\]  
where the immediate return \(R(d,x)\) is an indicator function which returns 1 if the destination \(d\) is directly reachable through \(x\), and otherwise 0. \(FuzzTO\) is a fuzzy _transfer opportunity_ metric that combines the social characteristics of a node and its estimated ability to complete message forwarding. When a node becomes disconnected, the associated Q-value begins to decay exponentially. In these protocols, the Q-learning process is decoupled from the data delivery, respecting the classical separation between routing and forwarding. These depend on exchanging routing data, and in this regard, they inherit the _Q-routing_ property of being a combination of Q-learning and a conventional routing algorithm. As a side effect, routes with associated messaging are maintained for all nodes in the network, whether there is actual traffic through them or not. Furthermore, they are only usable with destination-based routing. Other algorithms take a more direct approach to Q-learning. In FQLRP [20], each message is considered an agent, and the set of possible states is the set of network nodes. Therefore, the set of actions available to an agent is the set of neighboring nodes at a given moment. Then, the reward is computed as a function of the current node and its neighboring nodes' attributes, such as available buffer space and remaining energy. The idea is that the rewards express the likelihood of a successful forwarding. A modification of the basic Q-learning scheme is that the candidate nodes are first filtered through a _Fuzzy Logic-Based Instant Decision Evaluation_. All these protocols share in common that Q-learning is used to select a candidate node to replicate data to, but there is no explicit action to _not_ copy. Q-values are computed for actions, and in the usual representation, the only actions considered are _forwardings_. Furthermore, no explicit notion of time or sequencing is defined, using the simple exponential decay of Q-values to express the passing of time. This precludes the system from learning that it is better to avoid copying because there is probably a better opportunity in the future. It is important because many ONs have marked temporal patterns, such as the schedules of public transport systems, the workday and weekly mobility cycles of city inhabitants, or the migration patterns of wild animals. Integrating the notion of time and sequence in the learning system would allow capitalizing on those patterns. We will evaluate the gains of this representation in Section 6. Finally, we want to mention a related but different concept to ON, the _Opportunistic Routing_. This term usually refers to the ability to take advantage of overheard messages in a broadcast medium. The _Opportunistic Routing_ application is not restricted to ONs in the sense of networks whose devices encounter sporadically. In fact, most Opportunistic Routing research concentrates on ad hoc and mesh networks, which are connected networks, though with a potentially dynamic topology.
## 3 Problem and Methods  
### Medical Image Edge Detection Problem  
The problem to solve is the edge detection problem. The current work improves solutions of particular medical images due to their complex edges based on X-rays and tomographic images.  
Image edge detection involves the detection of discontinuities in brightness while processing the image in order to find the boundaries of objects.
# Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds
## 2 Related Work
### Video Matting  
We can perform video matting based on image matting, but it can lead to inefficiency, poor matting quality, time uncorrelation, and other issues. [PERSON] et al. [7] also provided an additional background image for real-time portrait video matting. This extra background is a vital prerequisite for improving video matting performance, as demonstrated by [PERSON] et al. However, two significant drawbacks exist to using a captured background image as a priori, dramatically limiting the application of background matting in practice. First, a captured background image is used. In that case, the background must be static, which means that even small disturbing ground behind it (e.g., light variations, slight jitters, and complex backgrounds) will affect the matting results. Second, the background image requires extra effort from the user to capture, and this process must be performed carefully to ensure that the obtained background image is aligned with the video sequence. Previous trimap-based video matting methods consider the frames' temporal relationship and generate coherent trimaps or matting by performing spatiotemporal optimization of the video sequence. [PERSON] et al. [27] proposed video instance matting (VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence. It incorporates temporal mask and temporal feature guidance to improve the temporal consistency of alpha matte predictions. Some techniques [2] encode temporal coherence using nonlocal extinction Laplacian operators over multiple frames. Recently, video matting methods without trimap have attracted much attention. [PERSON] et al. [28] introduced a trimap-free, high-quality neural matte extraction approach that specifically targets the assumptions of visual effects production. [PERSON] et al. [29] divided the matting target into three subtargets and learned the consistency of each subtarget in real-world portrait matting. However, the method needs interframe prediction consistency due to the lack of comprehensive consideration of temporal relationships. To eliminate the above problems, in this paper, we use the BRM module that recovers the background from historical frames, obtains the prior from current frames, and then uses it as the matting prior for the current or future frames.
###### Abstract  
In this study, an alternative debiteriting technique for olives, invented and patented by Prof. [PERSON], was employed. Olive fruits (_Olea europana_ cv. Megaritiki) were stored under CO2 atmosphere immediately after harvest for a period of 15 days. After the treatment, a sensory evaluation between the olives stored under CO2 and those stored under regular atmospheric conditions (control) was performed. Additionally, the CO2-treated olives were used for the cold press of olive oil production. The volatile profile of the olive oil produced was analyzed using headspace solid-phase microextraction (HS-SPME) and gas chromatography coupled to mass spectrometry (GC-MS). A total of thirty different volatile compounds were detected. The volatile characteristics of olive oil are attributed, among others, to aldehydes, alcohols, esters, hydrocarbons, alkanes, and terpenes. The volatile compounds' analysis showed many differences between the two treatments. In order to compare the volatile profile, commercial olive oil was also used (produced from olives from the same olive grove with a conventional process in an industrial olive mill). The antioxidant activity, the content of bioactive compounds (polyphenols, \(\alpha\)-tocopherol, carotenoids, and chlorophylls), and the fatty acids' profile were also determined. The results showed that the oil produced from CO2-treated olives contains different volatile components, which bestow a unique flavor and aroma to the oil. Moreover, this oil was found comparable to extra virgin olive oil, according to its physicochemical characteristics. Finally, the enhanced content in antioxidant compounds (i.e., polyphenols) not only rendered the oil more stable against oxidation but also better for human health. The overall quality of the olive oil was enhanced and, as such, this procedure holds great promise for future developments.  
Keywords:antioxidants; carbon dioxide; cold pressed; extra virgin olive oil; fatty acids; HS-SPME/GC-MS; modified atmospheres; olives; sensory evaluation; volatile and bioactive compounds +
Footnote †: journal: _antioxidants_
# Proxy Re-Encryption Scheme for Decentralized Storage Networks
## 1 Introduction  
Blockchain technology has been actively developing in recent years. A decentralized storage network [1] based on the blockchain is a promising application direction. The decentralized storage network would redefine data ownership, privacy, and accessibility. Taking the example of the traffic surveillance cameras, the data may be stored on a decentralized storage network. Therefore, the public can verify that the data exist, but only authorized parties can access it. Multiple institutions (such as insurance companies) to access data require an encryption scheme with access control. Traditional symmetric or asymmetric cryptography cannot meet this requirement, as these schemes require specifying who can decrypt before encrypting. The proxy re-encryption (PRE) is a suitable scheme for data sharing.  
PRE allows a user to grant access permission in a cryptographic method. [PERSON] would allow [PERSON] to visit her data under [PERSON]'s authorization. However, the ciphertext must be transferred to the new one (Figure 1). In a decentralized storage network, data integration suffers from the challenge of the outsourcing attack. Blockchain consists of many semi-trusted resource providers. When asked for proof, the malicious provider would downloadthe data content from other honest providers on the fly. Proof-of-replication (PoRep) brings the concept against the outsourcing attack. The idea is to encode the user data with a unique key, e.g., the provider's public key. Meanwhile, the encoding algorithm should be expensive, and decoding is cheap, so the resource provider would not drop the replicated data, as regenerating the replication would cost more. Since everyone tends to reduce the cost, the data would lose redundancy without PoRep. PoRep is the mandatory algorithm to convenience the verifiers that the dedicating storage resource is spending.  
Existing PRE is not ideal for a decentralized storage network because the extra ciphertext would trigger an expensive replicating operation (Figure 2). Combined with PoRep, the cost of PRE sharing is too high.  
We propose a CCA (chosen ciphertext attack)-secure and collusion-resilience (collusion safe) proxy re-encryption scheme for the decentralized storage network (Figure 3).  
1. No new ciphertext is generated for the permission grant in a decentralized storage network. It brings down the cost for proof-of-replication in a permission-less decentralized storage network.
2. The collusion-resilience scheme in group algebra requires only one key pair.  
The rest of the paper is organized as follows. In Section 2, we talk about the detail of decentralized storage networks and proxy re-encryption. In Section 3, we dive into the knowledge, terms, and formula used in this work. The proposed scheme and security analysis are presented in Section 4. In Section 5 we give a practical implementation and show the experiment result. The various extended questions are talked about in the evaluation, Section 6. Finally, Sections 7 and 8 provide the conclusion and future work.