esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Histological Examination  
The blocks were coded, fixed in 4% paraformaldehyde solution (SecurBiop(r), Teramo, Italy) for two days, and then decalcified for four weeks using 17% EDTA/phosphate-buffered saline solution. The tissues were dehydrated in graded ethanol and embedded in paraffin wax (Leica Biosystems, Richmond, IL, USA). Serial sections were cut at a 5-micrometer thick buccolingual direction with a microtome (Leica, RM2255, Nussloch, Germany), and the defect site was reached and stained with hematoxylin-eosin (H&E) according to the conventional pathological protocols (Baso Diagnostic Inc, Zhuhai, China). The area of interest was examined by light microscope (Leitz, Laborlux S, Binocular microscope, Wetzlar, Germany) and scanned (ScanScope(r) AT, Vista, CA, USA) for the tissue assessment. The following parameters were examined:  
* The inflammation quantification by counting the ratio of inflammatory cells in 100 cells under the high-power magnification (\(\times\)400) and scored as follows [25]: 1. Score 0: \(<\)10%; 2. Score 1: 10-30%; 3. Score 2: 30-50%; 4. Score 3: \(>\)50%.
* Bone formation was marked with the presence or absence of osteoid cells;
* The osteoclastic activity was marked as absent or present;
* Foreign body reaction was marked with the presence or absence of macrophages or giant cells;
* Granulation tissue was marked as absent or present.  
The histological features were blindly evaluated by one trained pathologist.
## 4 The Proposed Model
### Local Weighted Smoothing and Representation Learning
#### 4.2.1 Neighborhood Definition and Similarity Computation  
Given a virtual relation subgraph \(\mathcal{G}_{k}=(\mathcal{E},\mathcal{V}_{k},\mathcal{T}_{k})\), we define the local neighborhood of entity \(h\) as  
\[\mathcal{N}_{k}(h)=\{t\mid(h,\bar{\mathbf{r}},t)\in\mathcal{T}_{k}\}. \tag{13}\]  
To measure local semantic coherence, we compute the pairwise similarity between \(h\) and each neighbor \(t\in\mathcal{N}_{k}(h)\) using dot-product:  
\[\omega_{ht}=\frac{\mathbf{e}_{h}^{(0)\top}\mathbf{e}_{t}^{(0)}}{\left\| \mathbf{e}_{h}^{(0)}\right\|\cdot\left\|\mathbf{e}_{t}^{(0)}\right\|}, \tag{14}\]  
where \(\mathbf{e}_{h}^{(0)}\) and \(\mathbf{e}_{t}^{(0)}\) are the initial embeddings of \(h\) and \(t\), respectively.
## 5 Conclusions  
In this study it was determined that gamma irradiation leads to the formation of a larger amount of radicals in shell chestnut seeds than in peeled seeds. The EPR signal recorded in irradiated horse chestnut can be used as unambiguous evidence that the samples have been irradiated. It is in accordance with the CEN protocol EN 1787 for detection of irradiated food containing cellulose. On the other hand, the signal is stable, and it can be found in the samples irradiated with a dose of 1 kGy, 45 days after treatment, whereas for the samples irradiated by 5 and 10 kGy it can be found even 250 days later. Regarding the evaluation of the effect of gamma-irradiation of Bulgarian horse chestnut seeds on their polyphenolic content and free radical scavenging activity, significant correspondence was established between them. Gamma treatment has a positive effect on phenolic content and antioxidant capacity of shell seeds, while the effect was negative for peeled seeds. Results showed that ionizing rays induced changes in phenolic content and the antiradical capability of PS and SS, depending on the dose. No clear tendency was found with changing the content of the different phenolic compounds by varying the dosage. However, the general trend was an increase in TPC and TFC or without any change after irradiation of horse chestnut seeds by 5 kGy.  
Since shell chestnut seeds exhibited strong antioxidant capability, the extraction of major polyphenolic compounds from this plant could be important for the development of value-added products from renewable by-products. In conclusion, gamma irradiation could be used as a potential method for disinfection of horse chestnut seeds on the one hand, and on the other hand for a partial delignification process of plants for recovering hydroxcinnamic acids as a value-added product from the lignin.  
Conceptualization, R.M.; methodology, R.M. and N.S.; formal analysis, R.M. and K.L.; investigation, R.M., Y.K., N.S. and K.L.; writing--original draft preparation, R.M. and Y.K.; writing--review and editing, R.M. and Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
Research equipment of the distributed research infrastructure INFRAMAT (part of the Bulgarian National roadmap for research infrastructures) supported by the Bulgarian Ministry of Education and Science was used in this investigation.  
The authors declare no conflicts of interest.
## 3 Intelligent Cluster-Based Routing Approach
### Intelligent Path Prediction
It can handle a larger number of nodes in the network without a significant increase in control overhead. * The main advantage of this AODV modification is that it enhances the proposed system's performance. Reduced latency: The improved route discovery and maintenance mechanisms reduce the latency in establishing and maintaining routes. This is crucial for real-time applications like video streaming and online gaming. * Enhanced reliability: The classification parameter helps select more reliable and stable routes, reducing packet losses and improving communication quality. * Adaptability: The protocol can quickly adapt to changes in the network, such as node mobility or link failures, making it suitable for dynamic ad hoc networks. * Efficient Resource Utilization: Load balancing and scalability improvements ensure that network resources are utilized efficiently, leading to better network performance and longevity. * Improved quality of service support: The modifications enhance the support for quality of service (QoS) by allowing for a better selection of routes based on specific application requirements. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & & Vehicle 1 & & & & & & & & & & & & \\
192.168.1.100 & 192.168.1.105 & 2 & Vehicle 1 & RSU1, & Learning & & & & & & & & \\  & & & (Sedan) & RSU3 & Rate: 0.01 & & & & & & & & \\
192.168.1.201 & 192.168.1.202 & 1 & Vehicle 2 & RSU2, & Learning & & & & & & & & \\  & & & (Truck) & RSU4 & Rate: 0.02 & & & & & & & & \\
192.168.1.305 & 192.168.1.308 & 3 & Vehicle 3 & RSU1, & Learning & & & & & & & \\  & & & (Motorcycle) & RSU2 & Rate: 0.01 & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Modified AODV routing protocol and its implementation.
# Biochemical Methane Potential of Cork Boiling Wastewater at Different Inoculum to Substrate Ratios
## 1 Introduction
Another study investigated the influence of different Ino/S ratios (0.5:1 to 4:1) during anaerobic codigestion under mesophilic conditions of spent coffee grounds and cow manure. Methane yields gradually increased from the Ino/S ratio of 0.5:1 to 3:1, and the highest methane yield (225 mLCH4 gVS\({}^{-1}\)) was achieved in the IS ratio of 3:1. Lower methane percentage was obtained in the ratios of 3.5:1 and 4:1. Unstable process conditions were established in the lowest examined Ino/S ratio (0.5:1), which caused the accumulation of volatile fatty acids [26]. [PERSON] et al. (2018) [27] studied the yield of methane production from swine wastewater, using sewage sludge as an inoculum in three S/Ino ratios, called A (1:1), B (3:1), and C (6:1). The highest biogas yield that the author obtained in treatment was with the A ratio (554 \(\pm\) 75 mL/g volatile solid (VS)). Cumulative methane production decreased from 382 \(\pm\) 22 to 232 \(\pm\) 5 mL/g VS when S/Ino increased from 1:1 to 6:1. The best performance in terms of kinetic parameters was obtained for Treatment A; however, Treatment B could still guarantee a stable process. The use of a higher concentration of inoculum generated a 463.1% higher methane production rate and required a 77.3% shorter adaptation time (latency phase) in the studied S/Ino range. [PERSON] et al. (2020) [28] also studied the importance of the ratio S/Ino in the anaerobic digestion of liquid fractions of mixed biomass treated cohydrothermally and the organic fraction of solid urban residues. The study showed that the S/Ino ratio had a significant impact on CH\({}_{4}\) production yield, with the 1:3 ratio reducing methane production and the 1:2 ratio achieving the best results. Cork effluent is very problematic due to the acidic character and constituents of low degradability, thus making it difficult to treat. Currently, few studies of cork boiling wastewater treatment by AD have been carried out, and information is very limited. The authors are only aware of a previous study that evaluated the AD of this effluent in order to enhance the reduction of organic pollutants [20]. Based on the considerations above, the present work aims to investigate the effect of two different ratios of Ino/CBW (1:1 and 2:1), Ino/CBW:FW (2/0.7:0.3 and 2/0.5:0.5), and Ino/CBW:CM (2/0.7:0.3 and 2/0.5:0.5) on the yield of accumulated methane production and the degradation of VSs.
# Study on the Bearing Characteristics of Composite Foundations with Permeable Concrete Piles
## 4 Results and Discussions
### Influence of the Parameters of Cushion
Figure 24 shows the influence of load on the settlement of composite foundations with different inter-pile soil moduli. The figure indicates that the settlement of composite foundations increases with increasing load for all inter-pile soil moduli. Additionally, under the same load, the settlement decreases as the inter-pile soil modulus increases. Further analysis of the settlement at a load of 300 kPa shows that as the inter-pile soil modulus increases from 20 MPa to 40 MPa, the settlement decreases from 21.42 mm to 9.62 mm, a reduction of approximately 55.17%. This demonstrates that as the inter-pile soil modulus increases, the stress concentration in the pile is alleviated, the stiffness of the foundation is enhanced, the compressibility of the soil is effectively reduced, and the bearing capacity is increased, resulting in a decrease in foundation settlement. Figure 25 shows the influence of load on the pile-soil stress ratio for different inter-pile soil moduli. The figure indicates that the pile-soil stress ratio increases and decreases with increasing load. Under the same load, the pile-soil stress ratio decreases as the inter-pile soil modulus increases. When the load is less than 150 kPa, the differences in the pile-soil stress ratio among different inter-pile soil moduli are minimal, and the pile-soil stress ratio increases linearly with increasing load.
## 2 Results  
### Cloning and Sequence Characterization of HpGGPPS Genes  
Using three reported _GGPPS_ genes of _H. pluvialis_ retrieved from Genbank (accession nos. KX236181, KX236182, and KX236183), eight candidates were carried out from the Iso-Seq data by BlastN analysis with e-value of 0. Using designated gene-specific primers, the full-length coding sequences (CDS) of eight putative _HpGGPPS_ genes (_HpGGPPS1-1_, _HpGGPPS1-2_, _HpGGPPS2-1_, _HpGGPPS2-1_, _HpGGPPS3-1_, _HpGGPPS3-2_, _HpGGPPS3-3_, and _HpGGPPS3-4_) were successfully isolated. Sequences were submitted to Genbank with accession numbers MN689792-MN689799.  
The nucleotide sequence analysis revealed that eight putative _HpGGPPS_ genes are ranging 1011-1455 bp in length and varied from 960 bp to 1272 bp in CDS region (Supplemental Table S1). Their deduced amino acids are ranging 319-423 aa with putative molecular weight of 36.27-45.75 kDa, respectively (Supplemental Table S1). The alignment of nucleotide sequences of eight _HpGGPPS_ genes suggested 39.9-100% similarity in the CDS region (Supplemental Figure S1). Clearly, three classes were presented according to the similarity. High nucleotide sequence similarity was found within classes, including 98.6% identity between _HpGGPPS1-1_ and _HpGGPPS1-2_, 97.4% identity between _HpGGPPS2-1_ and _HpGGPPS2-2_, and 99.1-100% identity among _HpGGPPS3-1_, _HpGGPPS3-2_, _HpGGPPS3-3_, and HpGGPPS3-4._ It is noted that In-Del variation was found between sequences of _HpGGPPS3-1_ and _HpGGPPS3-2_ (sharing 100% similarity with each other), and between sequences of _HpGGPPS3-3_ and _HpGGPPS3-4_ (sharing 100% similarity with each other). By contrast, low percentage of nucleotides identity was found among classes, which was lower than 49%.  
By the local BlastN against _H. pluvialis_ genome sequences, _HpGGPPS1-1, HpGGPPS1-2, HpGGPPS2-1_, and _HpGGPPS2-2_ hit different scaffolds, revealing different genome region. Differently, _HpGGPPS3-1_ and _HpGGPPS3-2_ hit the same scaffold, as well as _HpGGPPS3-3_ and _HpGGPPS3-4_. By considering their 100% sequence identity, they might be isoforms from alternative splicing. Further sequences alignment of _HpGGPPS_ genes with corresponding genome scaffolds confirmed that _HpGGPP3-1_ and _HpGGPP3-2_ were isoforms from modified splicing of intron, while _HpGGPPS3-3_ and _HpGGPPS3-4_ were isoforms from exon exclusion (Figure 1). Gene structure analysis suggested that _HpGGPPS1-1_ and _HpGGPPS1-2_ were composed from nine exons, as well as _HpGGPPS3-1_, and _HpGGPPS3-2_. Diversely, _HpGGPPS2-1_ and _HpGGPPS2-2_ were from seven and eight exons, respectively (Figure 1). Due to the missing nucleotide information in the scaffold, the gene structure of _HpGGPPS3-3_ and _HpGGPPS3-4_ is not complete.
## 4 System Design and Analysis  
In this section, we will detail and analysis Sonum from two parts: The synergetic software-defined sampling and detection approach for long flow and the optimal network utilization mechanism for long flow scheduling.  
Sonum has four modules: Flow Collector, Flow Counter, Long Flow Detection and Long Flow Scheduling. The function of the first three modules is discovering long flow. The function of the last module is re-routing the discovered long flow.  
As shown in Figure 3, A packet of a certain flow enters the openflow switch and performs normal forwarding according to flow table. Based on the sampling rate \(v_{s}\), the Flow Collector Module determines whether to send the protocol header of the packet to the Flow Counter Module for processing.  
In step 1, The Flow Counter matches the header of the protocol submitted by the Flow Collector with the established entry. If there is a match and the counter does not overflow, the counter increments. If there is no match, a new count entry is created according to the protocol header data. As shown in Figure 4, in order to facilitate lookup and counting, each flow is identified by 5-tuple: Source/destination IP, source/destination port number and transport protocol. Flow Counter Module perform a hash calculation on the 5-tuple of each flow, then record the flow information and count result plus 1.  
In step 2, the controller broadcasts the detection parameters according to the network status, including the sampling rate \(v_{s}\) and the long flow threshold \(T_{l}\). OpenFlow switch sends all the flow information whose counter value is greater than \(T_{l}\) to the Long Flow Detection Module of the controller.  
In step 3, the controller caches the flow information and determines whether the flow is a potential long flow or not. If yes, the Long Flow Detection Module sends the flow information to the Long  
Figure 4: Count operation of flows.  
Figure 3: The system model of Sonum.  
Flow Scheduling Module. Long Flow Scheduling Module generates flow tables according to optimal network resource utilization mechanism and send flow tables to all switches.  
Steps 1-3 can detect and schedule long flows. Specifically, they can be realized through interaction information between different switches.
## Materials and Methods
### Microbiological Analysis  
Ground beef samples (10 g) were aseptically taken by sampling transversely sections from the packaged ground beef, added to 90 mL of sterile 1/4 strength Ringer's solution (Lab M, Lancashire, UK), and homogenized in a stomacher (Interscience, Paris, France) for 60 s. Following homogenization, decimal dilutions in 1/4 strength Ringer's solution were prepared and 1 or 0.1 mL of the appropriate dilutions were poured or spread respectively, on selective and non-selective culture media. Total Viable Counts (TVC) were enumerated on Plate Count Agar (PCA; Lab M, Lancashire, UK) after incubation at 30 \({}^{\circ}\)C for 48 h; lactic acid bacteria (LAB) on double layer of De Man, Rogosa and Sharp agar (MRS; Lab Lancashire, UK) (pH 5.8) at 30 \({}^{\circ}\)C for 72 h; _Pseudomonas_ sp. on Pseudomonas Selective Agar (Lab M, Lancashire, UK) with Cetrimide-Fucidin-Cephaloridine supplement (CFC; Lab M, Lancashire, UK) agar incubated at 25 \({}^{\circ}\)C for 48 h; _B. thermosphacta_ on STAA Agar Baser (Biolife Italiana Srl, Monza MI, Italy) with STAA Selective Supplement (Biolife Italiana Srl, Monza MI, Italy) incubated at 25 \({}^{\circ}\)C for 48 h; yeasts and moulds on Rose Bengal Chloramphenicol Agar (RBC; Lab M, Lancashire, UK) incubated at 25 \({}^{\circ}\)C for 96 h; Enterobacteriaceae on a double layer of Violet Red Bile Glucose Agar (VRBGA; Lab M, Lancashire, UK) incubated at 37 \({}^{\circ}\)C for 24 h.  
The microbial shelf-life of ground beef was assessed by the following equation [21]:  
\[SL_{microbial}=t_{lag}+(\ln 10)\times\frac{\log N_{s}-\log N_{0}}{\mu_{max}} \tag{1}\]  
where, \(SL_{microbial}\) is the estimated shelf-life (day) based on the microbiological data of _B. thermosphacta_ which was the specific spoilage organism, \(t_{lag}\) is the lag time (day), \(\mu_{max}\) the maximum specific growth rate (day\({}^{-1}\)), \(N_{0}\) is the initial enumerated population (log CFU/g), and \(N_{s}\) the minimal spoilage level set at 7.0 log CFU/g according to [PERSON] et al. (2011) [22]. Parameters of \(t_{lag}\) and \(\mu_{max}\) were calculated by the Baranyi model as described below in Section 2.6.
# A Cloud-Based Real-Time Mechanism to Protect End Hosts against Malware
## 4 Evaluation
### Scan Registration Time  
A scan task begins when Image Handle receives the path name of an executable from Image Controller. A scan task ends when Image Handler receives a preliminary report from VirusTotal. A preliminary scan report contains lots of important information, including file size, scan ID, scan date, and so on.  
To get the scan registration time of various executables with different sizes, we sent each test file individually to VirusTotal 100 times and recorded the average scan registration time. Figure 4 shows the results. The file sizes of the test files vary from 0.018 MB to 17.405 MB. The average scan registration time changes from 2.34 s to 43.12 s. Figure 4 shows that the scan registration time highly depends upon the size of an executable. The larger a file size is, the longer its scan registration time takes. After all, it takes longer time to transmit and scan a larger file.  
\begin{table}
\begin{tabular}{c c} \hline
**Processor** & **Intel® CoreTM** **i5-4430 CPU 3.00G HZ/3.00G HZ** \\ \hline
**Memory** & DDR 1 GB \\
**Disk** & 60 GB \\ \hline \end{tabular}
\end{table}
Table 2: Hardware specification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **File Name** & **Size (MB)** \\ \hline
1. & Rgui.exe & 0.018 \\
2. & Aero Color Show.exe & 0.300 \\
3. & Magnifier v2.4 & 0.245 \\
4. & Fiddler.exe & 1.099 \\
5. & Win RAR.exe & 1.375 \\
6. & RandPass & 1.442 \\
7. & Enigeo v4.1.0 & 3.381 \\
8. & Spotify.exe & 6.400 \\
9. & Cmake-gui.exe & 9.359 \\
10. & Filezilla & 12.128 \\
11. & Line.exe & 13.167 \\
12. & Evernote.exe & 17.405 \\ \hline \end{tabular}
\end{table}
Table 3: Information of test files.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 3 Operator's Dilemma in the Unlicensed Spectrum
### Finite Repeated Game in the Unlicensed Spectrum  
Now we investigate whether two LTE operators have any incentive to take LTE with the LBT strategy, especially when the game is played a known finite number of times. We assume that two LTE operators are aware of the end of the game and monitor the other operator's action perfectly.  
Note that there is significant difference between the one-shot game and repeated game. The repeated game can induce punishment, retaliation, collusion, and cooperation that are not possible choices for operators in the one-shot game. First, we focus on two LTE operators repeating the previous one-shot game a finite number of times \(T\). To solve a finite horizon game, backward induction is useful to verify the behavior of operators. Intuitively, if two LTE operators encounter the last stage of the game, they choose the dominant LTE-itself strategy because there is no future on which to have an influence themselves. This means that two LTE operators still have no incentive to harmoniously coexist with a known finite number of times. Based on the knowledge of backward induction, by going back through the stages of the game, two LTE operators select the dominant LTE-itself strategy in _every_ sequence of the game. However, this conclusion would not apply if operators can play an infinite number of times. In reality, since cellular networks constantly operate to provide communications services, it is not possible for everlasting operators to know the last moment of the game.
## 4 Basic Concepts about Applied Cryptography Mechanisms
### Data Leakage Detection Technique  
Data leakage detection (DLD) is a well-known process designed to identify and prevent the unauthorized disclosure of sensitive information. DLD systems are specialized tools that monitor and safeguard personal data, identifying instances of data misuse and identifying the source of the leak. Data leakage can occur both intentionally and accidentally, where private or confidential information is shared with unauthorized parties. Figure 1 illustrates the data leakage process in e-commerce applications. In the context of business operations, an entity's sensitive data may need to be shared with various stakeholders such as clients, employees, and business partners, whether they are inside or outside the organization's premises. However, there is a risk that the recipients may misuse or unintentionally disclose this information to unauthorized third parties [31]. Data leakage presents a significant challenge in the modern business landscape as safeguarding data from unauthorized access is crucial [1]. The uncontrolled leakage of data can expose businesses to various risks, and once sensitive information leaves the organization's domain, it puts the company in serious jeopardy. Even a single attack on a company can impact a large number of customers, particularly merchants, along with their distinct data [32]. Data leakage has become a major problem for organizations, and it often goes unnoticed as it originates from diverse sources that are difficult for most individuals to identify. The expanding global nature of businesses has further complicated the situation, as sensitive information can be electronically transferred using various technological devices like USB keys, spreadsheets, and web pages [1]. This underscores the increasing importance of addressing data security, especially when one client needs to transmit information from one nation to another through intermediaries. Failure to address data security could lead to severe financial losses for the company if the data are released. The process for detecting data leaks is  
1. The distributor enters their login information.
2. The distributor enters the data (for instance, text files) into the database.
3. After logging into the system, the agent requests a specific file, or the distributor uploads all files for the agents appropriately, along with the private key.
4. The distributor delivers the desired file to the requested agents, who then add some fictitious objects.  
According to his/her demands (explicit requests or sample requests), agents will download the files.
* The distributor will search for the leaked data and locate the file if any agents (fake agents) release the information to a third party [32].