esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Final Remarks  
Theorem 7 provides a lower bound while Proposition 3 provides an upper bound to the factor of the best approximation algorithm for max-HR+SN. Can this gap be narrowed? We suspect that the answer is yes since the source of our hardness results, max-SMTI, has a number of \(3/2\)-approximation algorithms [16; 17; 18]. An important strategy in these algorithms is to come up with a stable matching \(\mu\) such that if \(\mu^{*}\) is a maximum stable matching of the instance, there is no length-\(3\)\(\mu^{*}\)-alternating path in \(\mu^{*}\bigoplus\mu\). In other words, in all \(\mu^{*}\)-alternating paths in \(\mu^{*}\bigoplus\mu\), the ratio of the number of \(\mu^{*}\)-edges to the number of \(\mu\)-edges is at most \(3:2\). We end with the theorem below, which shows that for some HR+SN instances, choosing \(\mu\) to be equal to a stable matching of the instance yields such a result.  
**Theorem 8**.: _In the HR+SN instance \((I,G)\), let \(W^{\prime}\) be the set of workers that get matched in every stable matching of \(I\). Suppose that in graph \(G\), whenever \(w_{1}\in W^{\prime}\) and \(w_{2}\in W-W^{\prime}\) have a firm in common in their preference lists, \(w_{1}\) and \(w_{2}\) are adjacent in \(G\). Let \(\mu\) be a stable matching of \(I\) and \(\hat{\mu}\) be a maximum locally stable matching of \((I,G)\). Then \(|\hat{\mu}|\leq\frac{3}{2}|\mu|\)._  
Proof.: Recall the proof of Theorem 1. For now, assume all firms in \(I\) have capacity \(1\). Consider \(\hat{\mu}\bigoplus\mu\). Suppose it has a length-\(3\)\(\hat{\mu}\)-alternating path: \(f_{1},w_{1},f_{2},w_{2}\). This means that \(f_{1}\) is unmatched in \(\mu\) so \(w_{1}\) must prefer \(f_{2}\) over \(f_{1}\). Similarly, \(w_{2}\) is unmatched in \(\mu\) so \(f_{2}\) must prefer \(w_{1}\) over \(w_{2}\). Thus, \((f_{2},w_{1})\) is a blocking pair of \(\hat{\mu}\). Furthermore, \(w_{1}\in W^{\prime}\) and \(w_{2}\in W-W^{\prime}\) are adjacent in \(G\) so \(w_{2}\) is a point of contact between \(f_{2}\) and \(w_{1}\) and \((f_{2},w_{1})\) is a _local_ blocking pair of \(\hat{\mu}\)--a contradiction. It follows that \(\hat{\mu}\bigoplus\mu\) has no length-\(3\)\(\hat{\mu}\)-alternating paths.  
Now, suppose some firms in \(I\) have capacity greater than \(1\). Once more, construct \((I^{(1)},G)\) from \((I,G)\). Let \(\tau\) be a maximum locally stable matching of \((I^{(1)},G)\). From the previous paragraph, \(|\tau|\leq\frac{3}{2}|\mu^{(1)}|\). Thus, \(|\hat{\mu}|/|\mu|=|\hat{\mu}^{(1)}|/|\mu^{(1)}|\leq|\tau|/|\mu^{(1)}|\leq 3/2\).
## 2 Materials and Methods
### Datasets  
In this work, we use two benchmark datasets for Ngly and Kgly site prediction. The two datasets have been used in previous studies. The first dataset (Ngly dataset) was obtained from the UniProt [44] database and was previously used in our PUStackNGly [18] study. Using the CD-HIT program [45], the dataset's redundancy was removed at the protein sequence and peptide levels with 30% identity. Ngly dataset contains 825 experimentally verified glycoproteins. These glycoproteins hold 2073 positive samples and 13,737 negative samples. The dataset is divided into 3 sets, including training, validation, and testing sets. For training and validation sets, a 1:1 balanced dataset that constructed, with 2027 positive samples and the same number of randomly selected negative samples, with 90% of these samples being used for training and 10% for validation. For independent testing and comparing, the same testing glycoproteins and samples in PUStackNGly were utilized, which contained 46 positive and 179 negative samples.  
The second dataset (Kgly dataset) is collected in PredGly by [PERSON] et al. [8] and is also used in BERT-Kgly [23]. It was collected from the PLMD database ([[http://plmd.biocuckoo.org/](http://plmd.biocuckoo.org/)]([http://plmd.biocuckoo.org/](http://plmd.biocuckoo.org/)) (accessed on 4 February 2022)). After redundancy removal by CD-HIT, the dataset contains 3969 positive and 82,270 negative samples. A balanced dataset consisting of 3573 positive and the same number of randomly selected negative samples for training and validation with 90% of these samples being used for training and 10% for validation. For independent testing and comparing, we utilized the same testing samples were used in BERT-Kgly, which contained 200 positive and 200 negative samples.  
The samples in Ngly or Kgly are represented as fragments by using a sliding window approach in which the PTM site is in the middle and surrounded by w-residues from the downstream and upstream as shown in Figure 2. The optimal size for the fragment used with the Ngly dataset in the PUStackNGly study was \(n=25\) (that means \(w=12\) downstream and upstream residues). With the Kgly dataset in the PredGly study, the optimal size for the fragment was \(n=31\) (that means \(w=15\) downstream and upstream residues). The number of surrounding residues (\(w\)) can be set to a different value in PTG-PLM.  
Figure 1: The general flowchart of PTG-PLM.
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 6 Industrial Applications of Halophilic Biocompounds  
Due to their unique features, biocompatibility obtained from halophiles might have great industrial potential. Halophilic fungi, unlike halophilic prokaryotes, show the ability to grow both in salt-free conditions and in a wide range of salinity [33; 46]. In turn, many representatives of halophilic archaea and bacteria, such as Halobacteriaceae, can grow at higher salt concentrations, but the lack of salt in the environment can lead to cell damage. Halophilic microorganisms can produce many different bioproducts, such as biopolymers, carotenoids, or enzymes, which are used in various industries, including food processing, as shown in the Table 6[57]. However, there are significant differences between halophilic bacteria and halophilic fungi. Intracellular enzymes produced by haloarchae, due to their osmotic equilibrium strategy associated with the accumulation of high concentrations of KCl inside the cell, may be more tolerant to salt and alkaline pH, but lose their activity in the absence of salt [13; 124]. In turn, the enzymes of halophiles producing substances protecting against osmotic stress, i.e., fungi and some bacteria, can adapt to a wider range of extreme conditions, such as xerotolerance or thermostability and be a source of extracellular enzymes of better quality and quantity in biotechnological applications [25; 78; 125]. Production of numerous hydrolases and oxidoreductases resistant to salt presence and low water activity can be used in bioremediation processes and wastewater treatment. Fungi from saline environments are also a source of numerous ligninolytic enzymes, which could be useful in biomass conversion processes with the use of difficult to solubilize lignin materials [85]. The \(\alpha\)-amylase from _Aspergillus gracilis_ TISTR 3638 isolated from a solar saltern has shown higher activity in increasing salinity compared to commercial amylases, when used for waste water remediation, and it may find applications especially for industrial effluents contaminated with metallic ions [79]. Furthermore, other studies also investigated marine fungi for their ability to degrade polycyclic aromatic hydrocarbon (PAHs) in which _A. scleroorum_ CBMAI 849 showed nearly 100% deletion of pyrene and over 76% of benzo[a]pyrene, making it attractive for bioremediation in saline conditions [126]. The halophilics _A. sydowii_ EXF-12860 and _A. destruens_ EXF-10411 have shown great results in removing 100% of xenobiotics--PAHs and pharmaceutical compounds (PhC) in wastewaters under salty conditions (>1M NaCl), so they could be used for biotechnological downstream processing of various industrial wastewaters [71]. This enzyme obtained from another halophilic fungi _A. pencillioides_ TISTR3639 has potential application as an additive in the laundry detergent industry [78].
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 3 MedChain Model
### Blockchain Service
\begin{array}{l}Event_{SC}\big{<}Content,PK_{pat},Digest,Sign_{pat},\ Type \big{>}\\ ContentE_{PK_{pat}}\big{(}\{DID(,st,\ et)\},PK_{req}\big{)}\\ DigestH\big{(}\{DID(,st,\ et)\}\parallel PK_{req}\parallel PK_{pat}\big{)}\\ Type\leftarrow\textit{``CreateSession''}\end{array}  \tag{2}\]  
The blockchain servers run the blockchain service on all super peers, which collectively provide a consortium blockchain network [38]. Each blockchain server maintains a complete blockchain and they run a distributed consensus algorithm to collectively determine the content of the next block. MedChain is not coupled with a particular consensus protocol. The current implementation adopts BFT-SMaRt [39] for its simplicity. It can be easily changed to another protocol with minimal adaptation, such as Proof-of-Stake [32]. To efficiently verify the data stream, the digest generation algorithm is modified. For a data stream, the digest of the \(i\)th chunk is created by hashing (\(H(\cdot)\)) the content, which consists of the chunk data (\(Chunk(i)\)) and the digest of the last chunk (\(Digest_{i-1}\)), which was formulated by the \(H(Chunk(i)\parallel Digest_{i-1}\)). Then, multiple digests of the chunks covering a continuous sampling time period forms a digest chain. As illustrated in Figure 4, the chunks of a digest chain could be distributed on different blocks and they are connected with the reference \(Ref_{L}\) in the \(Event_{DG}\). Thus, to generate a new block, only the block containing the digest of the last chunk needs to be retrieved through \(Ref_{L}\) for querying the digest of the last chunk. It is important that, with a digest chain, downloading and verifying the digest of one chunk can check the integrity of all the previous chunks. If the last chunk is valid, the integrity check can stop. Otherwise, it will repeat until a valid chunk is found. This mechanism allows early termination in the data integrity check, which increases system responsiveness in data stream access. Note that a digest chain does not need to be verified by the blockchain servers in new block generation. It can be left to data access, which is efficient especially for using less data.
# IDSMatch: A Novel Deployment Method for IDS Chains in SDNs +
## 5 Evaluation
### Network Delay under Different Scale of Incoming Traffic  
In this analysis, we delve into the impact of incoming traffic scale on network delay. Our investigation encompasses three distinct scales of incoming traffic--small, medium, and large--classified based on the production of the number of flows and the weight assigned to each flow. The traffic scenarios are categorized into three sizes: small (500 flows), medium (2000 flows), and large (4000 flows). These scenarios are tailored for different network topologies, each characterized by unique sparsity levels. Sparsity, defined as the ratio of the number of direct connections between servers to the total number of possible direct connections, is a crucial parameter influencing network behavior. Figures 6-8 serve as visual representations of the network delay under varying scales of incoming traffic and different sparsity. For our experiments, we consider three specific scenarios:  
* One IDS in the Chain: A single intrusion detection system (IDS) integrated into the service chain.
* Three Fixed IDSs in the Chain: Three fixed IDSs strategically placed within the service chain.
* Five IDSs in the Chain: A more extensive deployment with five IDSs integrated into the service chain.  
Figure 6: Detection time for topology 1 (Sparsity = 15%). **(a)** Delay with one IDS. **(b)** Delay with fixed two IDS. **(c)** Delay with long IDS chain.  
Figure 6 (sparsity 15%): The results reveal that even for a large scale of traffic, perfect matching mitigates network delay. However, despite the potential for a higher detection rate with a longer IDS chain, Figure 6c demonstrates an increase in network delay. This suggests a trade-off between detection efficiency and network performance. Figure 7 (sparsity 35%): Similar trends are observed for a topology with increased sparsity. The impact of sparsity on network delay is apparent, and the trade-off between detection capabilities and network efficiency persists. Figure 8 (sparsity 55%): In a topology with higher sparsity, the network delay is notably higher compared to a topology with lower sparsity (15%). This emphasizes the influence of network topology on delay, with denser connections resulting in improved performance.  
These findings underscore the intricate relationship between the incoming traffic scale, IDS deployment strategy, and network delay across diverse sparsity levels. The trade-offs revealed in the experiments provide valuable insights for optimizing network configurations based on specific traffic characteristics and security requirements.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 4 QoS-Aware Security Framework: Proposed Scheme
### Motivation for Framework Design  
The key motivation behind the design of the framework and hence this paper, is the rise of new problems in MANETs that includes the possibility of opportunistic communication by malicious devices flooding a network to consume resources thereby causing denial of services to legitimate nodes, stealing information for malicious purposes and diverting network resource flow against the wish of administration. This latter part can be likened to taking over control of a network. This research work introduces new interfaces, such as the lightweight IDS in the cross-layer framework, which is something that would have been otherwise close to impossible within the traditional layered architecture. Another motivation is the introduction of an upward-downward and downward-upward flow within layers as seen in Figure 2. The key design features of the proposed framework are geared towards fostering a method in which the layers can communicate with each other without compromising the quality and integrity of the data. According to [PERSON] [18], the cross-layer design is known for its substantial gain in QoS, output, and efficiency. The traditional layered architecture does not allow direct communication between layers, but with this design, adjacent layers can communicate, and every layer works on its variables. The cross-layer design of the framework also allows new abstractions within the communication infrastructure between layers. [PERSON] et al. [19], in a work-in-progress paper, proposed the QoS framework below and this work evaluates its efficacy.
# Space Habitat Data Centers--For Future Computing
## 4 Proposed Solution
### SHDC--Supplying Asteroid Water  
The SHDC requires access to asteroid water for cooling. The supply of asteroid water involves three entities i.e., space water reservoir entity (SWRE), asteroid water mining entity (AWME), and the computing platform service provider. The SWRE and AWME can also supply water to other outer space applications that require access to water and products that can be directly derived from water. In the consideration here, the SWRE is located in a low temperature location in Earth orbit but at a significantly higher altitude than that of the SHDC. The SWRE is far from the sun because the maximum low Earth orbit altitude is significantly lower than the Sun-Earth distance. This large separating distance implies that heat from the sun does not significantly increase the asteroid water temperature. In addition, the SWRE enables the execution of other space applications. Examples of such are space applications requiring the provision of hydrogen fuel for space-based applications as identified by [PERSON] et al. (2018). The SWRE receives information on SHDC location and stores and supplies asteroid water to the SHDC  
Figure 3: Relations between provider-specific interfaces (PSIs), space computing nodes (SCNs), and entities in the international computing space station.  
via water supply vehicles. The water supply vehicles ensure the supply of low-temperature asteroid water (accessed from the SWRE) to a given SHDC. They incorporate cooling systems to maintain asteroid water at low temperature. In addition, the use of a water supply vehicle reduces the asteroid water supply delay in comparison to the case where asteroid water is mined and directly supplied to the SHDC. Relations between the AWME, SWRE, and SHDC are shown in Figure 4. The flowchart showing SWRE and SWR functionality is shown in Figure 5.  
Figure 4: Relations between asteroid water mining entity (AWME), water storage, and access by platform service providers.  
Figure 5: Relations between the AWME, space water reservoir entity (SWRE), and space water reservoir (SWR) in the supply of asteroid water for the space habitat data centre (SHDC).
## 2 Materials and Methods
### Multicollinearity Assessment of Predictor Variables  
#### 2.6.1 Pearson Correlation Matrix  
The Pearson correlation matrix constitutes a tabular representation that delineates the correlation coefficients among various sets of variables. Each coefficient spans a range from \(-1\) to \(1\), signifying the strength and direction of the linear association between two variables. The correlation coefficient elucidates the degree of relationship between two variables, where \(+1\) denotes a perfect positive correlation, \(0\) signifies no correlation, and \(-1\) indicates a perfect negative correlation [38]. The matrix shown in Figure 10 illustrates that the input variables designated for training ML models are devoid of multicollinearity.
## 5 Simulation Results and Analysis
### Simulation 4: Performance of 2D DOA Estimation with Respect to Correlation Coefficient  
The last simulation inspects the performance against correlation between signals. We assume the first two signals are correlated and the third signal is uncorrelated with the first two. Under this assumption, the signal covariance matrix \(P\) is:  
\[\mathbf{P}=\begin{bmatrix}\sigma_{s}^{2}&\rho\sigma_{s}^{2}&0\\ \rho^{*}\sigma_{s}^{2}&\sigma_{s}^{2}&0\\ 0&0&\sigma_{s}^{2}\end{bmatrix} \tag{35}\]  
where \(\sigma_{s}^{2}\) is the power of the three signals and \(\rho\) is the correlation coefficient of the first two signals, which is defined as:  
\[\rho=\frac{\text{E}\{s_{1}(t)s_{2}^{*}(t)\}}{\sqrt{\text{E}\{[s_{1}(t)]^{2}\} \text{E}\{[s_{2}(t)]^{2}\}}}=\lim_{N\to\infty}\frac{1}{\sigma_{s}^{2}}\sum_{t= 1}^{N}s_{1}(t)s_{2}^{*}(t) \tag{36}\]  
In this simulation, the correlation coefficient of the first two signals is varied from \(0\) to \(0.9\), while SNR is fixed at \(-6\) dB, the number of sensors is fixed at \(7\) and the number of snapshots is fixed at \(500\). As shown in Figures 9 and 10, performance of the proposed method approaches to the CRB for weak and moderately correlated signals but degrades  
Figure 8: Scatter plot of 2D DOA estimates of the proposed method.  
Figure 7: RMSE of DOA estimation versus the number of snapshots from \(100\) to \(1000\). (**a**) RMSE of the elevation angle, (**b**) RMSE of the azimuth angle.  
severely at medium and strong correlated signals. More specifically, the estimation accuracy of the first two correlated signals degrade as the correlation coefficient increases, while the estimation accuracy of the third uncorrelated signal keep unchanged. The reason for this phenomenon lies in the fact that the proposed method exploits the spatial signatures matrix \(\hat{B}\) to estimate the 2D DOA. The accuracy of \(\hat{B}\) degrades along with increasing correlation, which leads to noninvertiblity for the auto-correlation matrix \(\hat{\mathbf{R}}_{yy}\). Therefore, our method is more suitable for source signals with weak or moderate correlation.
## 2 Model
### Monte Carlo Simulation for Analyzing the Annual Performance of Multi-Junction Cells  
The design, performance analysis, and optimization calculation we used is a combination of the numerical optimization calculation and the Monte Carlo method (Figure 2) [63; 92; 94; 95]. The vector of variables in numerical optimization is the set of bandgap energy at each junction. At each moment (time and date), the meteorological parameters and atmospheric parameters were given by random numbers (Monte Carlo method). The energy output across a year was summed and divided by the total irradiation, also calculated by the above meteorological and atmospheric conditions. The merit function for numerical optimization calculation is the annual average efficiency of the power conversion, directly coupled to the performance ratio. The initial value for numerical optimization calculation can be given by that of combination determined at the sun height on the winter solstice [96]. The optimized bandgap given by this method is identified to be close to the values given by the optimizing routine [96].  
Figure 1: The energy flow of multi-junction cells. Left: Normal multi-junction cell; Right: Super-multi-junction cell. ERE—external radiative efficiency [61].  
Considering that the target of this calculation is to identify the variation of output performance influenced by the different climate and spectrum in other years (Figure 2), the difference between the initial value and optimized value was not crucial; specifically, both had broad distributions [96], and difference between the initial value and optimized results were often invisible. Therefore, to save computation time, the first step of the flowchart in Figure 2 was optimized not by the annual dataset (365 days multiplied by the number of divisions of the time in the daytime) but by the representative sun height on the winter solstice.  
With an increase in the number of junctions in the simulation in Figure 2, it may be the case that the efficiency of \(i\) number of junctions is higher than that of (\(i+1\)) of the number of junctions. This case can be equivalently modeled by allowing the bandgap energy of the (\(i+1\))th junction to be equal or greater than that of the (\(i\))th junction, but not allowing the bandgap energy of the (\(i+1\))th junction to be less than that of the (\(i\))th junction.