esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Bloom Filter Architecture
## 5 Performance Evaluation
### Performance in Off-Chip Memory Accesses  
The search performance of an FIB lookup algorithm mainly depends on the number of off-chip memory accesses because the time to access an off-chip memory is much slower than that to access an on-chip memory. Figures 4 and 5 show the average number and worst-case number of off-chip memory accesses, respectively, for each algorithm when the size factor of a Bloom filter is fixed at 8. For our proposed algorithm shown in Figures 4 and 5, note the numbers, since bars are barely seen. The proposed algorithm does not need to access the off-chip memory, except for when the \(p\)-BF produces _indeterminable_ results. Hence, the proposed algorithm involves much fewer off-chip accesses compared to the other algorithms. As shown in Figure 4, the proposed algorithm can terminate the search without any off-chip hash table accesses for the 10k set, and only terminates the search with hash table accesses from 0.00002 to 0.00003 in the average for other sets. In other words, the name lookups for more than 99.99% of the inputs can be completed without accessing the off-chip hash table in our proposed algorithm.  
Figure 4: Average number of off-chip memory accesses.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Routing Set**} & \multirow{3}{*}{\(\alpha\)} & \multirow{3}{*}{**NPT-BF**} & \multirow{3}{*}{**NPT-BF-Chaining**} & \multicolumn{6}{c}{**Proposed Algorithm**} \\ \cline{3-11}  & & & & & \multicolumn{2}{c}{_I-BF_} & \multicolumn{2}{c}{_p-BF_} & \multicolumn{2}{c}{**Total**} \\ \cline{3-11}  & & \(A_{b}\) & \(W_{b}\) & \(A_{b}\) & \(W_{b}\) & \(A_{b}\) & \(W_{b}\) & \(A_{b}\) & \(W_{b}\) & \(A_{b}\) & \(W_{b}\) \\ \hline \multirow{3}{*}{\(10\)k} & 2 & & & 3.136 & & 1.838 & & 1.345 & 17 & 3.182 & 19 \\  & 4 & 2.876 & 23 & 2.947 & 23 & 2.149 & 4 & 0.470 & 14 & 2.619 & 15 \\  & 8 & & & 2.884 & & 2.179 & & 0.378 & 6 & 2.557 & 8 \\ \hline \multirow{3}{*}{\(50\)k} & 2 & & & 3.074 & & 1.840 & & 1.538 & 24 & 3.379 & 25 \\  & 4 & 2.926 & 32 & 2.948 & 32 & 2.204 & 5 & 0.517 & 14 & 2.721 & 16 \\  & 8 & & & 2.927 & & 2.244 & & 0.386 & 16 & 2.630 & 19 \\ \hline \multirow{3}{*}{\(100\)k} & 2 & & & 3.116 & & 2.053 & & 1.314 & 31 & 3.367 & 32 \\  & 4 & 2.971 & 32 & 2.991 & 32 & 2.240 & 5 & 0.541 & 22 & 2.781 & 23 \\ \cline{1-1}  & 8 & & & 2.971 & & 2.287 & & 0.400 & 8 & 2.687 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of on-chip search performance.
## 5 Conclusions  
In this study, we investigated grape and wine aroma compounds and the terrain of four plots on the same slope so as to find the deep relationship between the two and thus provide a new way to improve wine aroma quality in this region. One-way ANOVA, two-way ANOVA, and Pearson's correlation analysis were used to find the elements that influenced the aroma profiles. In summary, the variation in aroma compounds was greater between vintages than between plots. Based on two-way ANOVA, the most plot-variant aroma compounds were identified, including 25 free and 8 bound components in grapes and 21 and 10 components in AF and MLF wines, respectively. Of these, most of the free norisoprenoids, monoterpenoids, and C6 compounds in grapes varied between plots. The concentration of free and bound (\(E\))-2-hexen-1-ol was higher in CS1 than in CS2, which may be related to the location of CS1 at the top of the slope with better water permeability. Higher alcohols and esters in AF and MLF wines were most affected by the plots, including 1-heptanol, 2-heptanol, 1-octen-3-ol, ethyl 3-methylbutanoate, isoamyl acetate, etc. Wines from the four plots showed a significant difference in floral and fruity aroma attributes, which were mainly related to ethyl esters with high odor activity values. In addition, the variation of these compounds between the plots was more pronounced in the 2021 vintage with more rainfall compared to the 2020 vintage. Pearson's correlation analysis showed that concentrations of (\(E\))-3-hexen-1-ol in grapes and ethyl 3-methylbutanoate, ethyl hexanoate, isoamyl acetate, isopentanoic acid, and phenethyl acetate in wines were strongly positively correlated with the concentrations of N, P, K, Fe, and EC in the soil, but negatively correlated with soil pH. And the result showed the esters contributing to floral and fruity odor were positively correlated with N, P, K, Fe, and EC in soil and negatively correlated with soil pH, while the situation was exactly reversed for higher alcohols contributing to off-odor in wines. Interestingly, the MLF wines from CS1, with its higher soil N, P, and Fe contents, showed higher aroma quality in 2020 but lower aroma quality in 2021, which may be related to the negative effect of fertility loss caused by more rainfall in 2021. The \(\alpha\)-diversity of aroma compounds was also calculated, and it was speculated that the variations in \(\alpha\)-diversity between vintages and between plots were caused by the differences in rainfall and soil organic matter. This study was the first to dissect the influence of vineyard terrain on the aroma profile of grapes and wines in the Jieshi Mountain region with a temperate monsoon climate and provides some guidance for improving vineyard management and the quality of grapes and wines. The research can be complemented in the future by expanding the scale of wine making, increasing the number of grape varieties, and monitoring the changes in grapevine ageing.
## 3 Test and Verification  
### Corona Cage Test System  
In order to verify the validity of the numerical simulation model, a small corona cage test system was built, as shown in Figure 4. The test system consists of three parts, in which the corona occurrence is composed of a DC power supply, protection resistance, corona cage cavity, sample, etc.; the acquisition part of the discharge signal is composed of a current sampling box, data acquisition card, and computer; and the adjustment part of atmospheric parameters is composed of a vacuum pump and so on.  
The maximum radius of the corona cage cavity was 200 mm; the wire sample was simulated with a solid copper rod, and the M2 thread was set in the wire's middle to assemble a conical spike. A realistic picture of the electrode configuration with the wire and spike is shown in Figure 5. By changing the connection height between the conical spike and the wire surface and changing the curvature radius at the top of the spike, a typical burr defect with different geometric structures can be obtained.  
Figure 4: Experimental device and wiring. DC—DC power supply; R—Protection resistance; K—Corona cage cavity; L—Sample; F—Cold fog generator; M—Vacuum pump; Ra—Current sampling box; C—Data acquisition card; PC—Computer; P—Video recorder; UV—UV imager; Ca—SLR camera.  
Figure 3: Schematic diagram of mesh generation.
# Effect of Heat-Treatment Remediation on the Mechanical Behavior of Oil-Contaminated Soil
## 3 Results and Discussion
### Effect of Pollutant Removal Percentage on Friction Angle  
To determine the influence of different heat-treatment procedures on the mechanical properties of soil, Figure 4 shows the relationship between the friction angle of the soil and the removal percentage. As presented in Figure 4, regardless of whether the pollutant is gasoline or diesel, the friction angle increases with the removal percentage at different pollutant concentrations and under various treatment conditions. A higher heating temperature yields a larger friction angle. An increase in the friction angle (\(\phi\)) reflects an increase in the strength of the soil, indicating a tendency of the engineering properties of the soil to improve as the treatment efficiency increases. Figure 5 compares the friction angles of soil to which gasoline or diesel was added at a particular pollution concentration after heat treatment. Under the same heat-treatment conditions, the \(\phi\) value of diesel-contaminated soil is greater than that of gasoline-contaminated soil.  
Figure 3: Removal percentage of 10,000 ppm of pollutant with different treatment parameters. (**a**) Gasoline; (**b**) Diesel.  
Figure 4: Relationship between pollutant removal percentage and frictional angle. (**a**) Gasoline; (**b**) Diesel. Note: C: original soil; G: soil with gasoline pollutant; D: soil with diesel pollutant. L: heating temperature is 320 \({}^{\circ}\)C; M: heating temperature is 560 \({}^{\circ}\)C; H: heating temperature is 900 \({}^{\circ}\)C.
# Effects of Glyoxylic Acid on Metabolism and Ripening of 'Rocha' Pears Treated with 1-MCP
## 1 Introduction
So far, no documented attempts have been made to assess whether exogenous GLA can restore the ripening capacity of 1-MCP-treated 'Rocha' pears. This study aimed to investigate whether GLA treatment could significantly enhance the ripening process following 1-MCP application. Our hypothesis was that GLA could influence metabolic pathways linked to ripening, thereby mitigating the ripening delay caused by 1-MCP. To test this, we analyzed key ripening indicators, including fruit firmness, total soluble solids (TSS), volatile organic compounds (VOCs), respiration rate, and ethylene production during the ripening phase. Additionally, we examined the activity of essential enzymes related to ethylene biosynthesis, particularly assessing whether GLA treatment could induce 1-aminocyclopropane-1-carboxylic acid (ACC) synthase (ACS) and ACC oxidase (ACO) activity in 'Rocha' pears.
# Challenge of Utilization Vegetal Extracts as Natural Plant Protection Products
## 4 Plant Extracts as Natural Plant Protection Products  
Plants can be considered as a perfect laboratory with potential to supply organic and inorganic substances without which human and animal life would be impossible [15; 27; 32; 40; 41]. These substances synthesized in the plant kingdom may be of primary (proteins, carbohydrates, and fats) or secondary (terpene, steroids, anthocyanins, anthraquinones, phenols and polyphenols, etc.) metabolites classes [3; 16; 32; 42; 43; 44; 39; 42; 45; 46; 47; 48; 49; 42; 43; 41; 42; 44].  
Secondary metabolites reported till now include the following classes: nicotine, caffeine, flavorings (vanillin and cinnamic aldehyde), pigments (indigo and kaki), pyrethrum (natural insecticide obtained from marigolds and chrysanthemum varieties), rubber (natural latex obtained from the bark  
Figure 1: The action mechanism of essential oils on insects (adapted from [PERSON] 2016 [21]). (Notes: _EO_s—essential oils; _ACHE_—acetylcholinesteraserase; _GABA_ receptor—\(\gamma\)-aminobutyric acid receptor; _octopamine_—a neurotransmitter, neurohormone and circulating neurohormone-neuromodulator working on nervous system in insects).  
of the rubber tree), dyes, odorants, flavorants and alkaloids [3; 16; 39; 42; 45]. Although essential oils have demonstrated high efficacy, multiple mechanisms of action, low toxicity on vertebrates, and potential use of byproducts as reducing agents, the number of commercially available natural plant protection products based on essential oils still remains low.  
Practical use of vegetal extract as natural plant protection products is limited to individual producers and small crops. Herbal extracts used as biopesticides have a few advantages such as:  
* High efficacy against a wide range of pests and diseases of agricultural and medical importance.
* Multiple action mechanisms due to the large number of active ingredients in each mix.
* Low toxicity against non-target organisms, including humans.
* Relatively simple and cheap production processes.
* Reducing health risks during application due to low residue toxicity.  
Analyzing the main strengths and weaknesses that result from the use of plant extracts as natural plant protection products, the main challenges for future research include: (1) developing effective stabilization processes (e.g., microencapsulation); (2) simplifying complex and costly authorization requirements for natural plant protection products use, and (3) optimizing plant growth conditions and extraction processes leading to a homogeneous chemical composition [39].
# Transitioning Broadcast to Cloud
## 3 Technologies Needed to Support Convergence
### Cloud Playout  
The last, and most important technology that is needed to enable convergence is a high-quality, cloud-based implementation of a playout system.  
The design of such a system is a non-trivial task. As we discussed earlier, current cloud-based video workflows typically use HLS/DASH-type segmented media formats, causing them to operate with significant delays and random-access limitations. One cannot build a broadcast-grade playout system based on such architecture. Even so-called ultra-low-delay versions of HLS, DASH, or CMAF (1998; 1999; 2000) are inadequate. For most master control operations, such as previews, non-linear editing, switching, etc., frame-level access accuracy is an essential requirement.  
In Figure 8, we show one possible cloud playout system architecture that can be suggested. To enable frame-level random access this system uses an internal Intra-only mezzanine format. Such a format could use any image or video codec operating in Intra-coding mode, along with PCM (Pulse-Code Modulation) audio, and index enabling access to each frame. Both input live feeds and pre-recorded content are then converted in such internal mezzanine format and placed on cloud storage. All subsequent operations, such as previews, non-linear editing, as well as selection and mix of content producing channel outputs, are done by accessing media in such mezzanine format. The final stream selections, addition of logos, transitions, etc. are done by \"stream processor\" elements.  
Figure 8: Example architecture of cloud playout system.  
In addition to enabling frame-level-accurate processing operations, the use of Intra-only mezzanine format also minimizes impacts of possible failures in the system. All signal processing blocks shown in Figure 8 can be run in a redundant fashion, with checks and switches added to ensure frame-accurate fault-tolerance.
# CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway
## 2 System Model
### Platoon Physical Access Control  
To protect mobile platoons from distant intruders, we investigate the issue of physical access control. The primary goal is to limit the number of vehicles in a platoon to those platooning, preventing distant opponents from introducing phony navigation signals. Figure 1 depicts the system model we have developed. An AV platoon is made up of the vehicles AV1 through AV3. To join the platoon, candidate vehicle C claims to be within platooning distance of AV3. The vehicle AV3 verifies [PERSON]'s trajectory.  
**Candidate (C):** Candidate C sends a join request to seek admission to a battalion. An established certificate authority (CA) has granted the candidate a public/private key pair (\(pk_{C}\), \(sk_{C}\)) and a certificate (\(cert_{C}\)). It has adaptive cruise control (ACC), which allows him to maintain the following distance on his own without any human assistance. Receiving V2V communications enables the ACC to cooperate. The platoon does not accept a candidate unless it has successfully completed a proof-of-concept challenge from the verifier.  
**Verifier (V):** The platoon's last vehicle functions as a checkpoint. When a candidate's digital identity is linked to his or her actual location, it is known as a proof of fact (PoF). A trustworthy certificate authority issues a public/private key pair (\(pk_{V}\), \(sk_{V}\)) and certificate (\(cert_{V}\)) to the verifier, just as it does to all other vehicles. In addition, the verifier is capable of accurately determining the distance to any car in the same lane as it.  
**The Proof-of-Following Primitive:** Following distance \(d_{\textit{ref}}\) and the verifier's lane must be maintained by the candidate. We define PoF in a more rigorous manner than the one first proposed in [22].  
**Definition 1. Route:**_Time-ordered locations are used to depict a vehicle's path \(L_{X}\). \(L_{X}\) = (\(\ell_{X}\) (1), t (1)), (\(\ell_{X}\) (2), (t (2)),..., (\(\ell_{X}\) (n), t (n)), \(\ell_{X}\) (\(i\)) is the geospatial location of the vehicle (\(x_{X}\)(\(i\)), \(y_{X}\)(\(i\))) with \(1\leq i\leq n\), and t (\(i\)) < t (\(j\)) for \(i<j\)._  
**Definition 2. Proof-of-Following:**_We specify a path for the verifier and a way for the candidate. The candidate should follow the verifying driver's lead in the same lane if Euclidean distance between \(V\) and \(C\) is satisfied:_  
\[\|\ell_{V}(I)-\ell_{C}(I)\|=d_{re\bar{z}_{2}}\forall i\]  
_A desired following distance is defined as \(d_{n\!f}\) in this case. If this is not the case, the verifier will REJECT._
## 2 Background and Related Work
### NDN Caching Policies
in [22] proposed a cooperative caching (LCC) decision scheme based on the lifetime of IoT data. Depending on the lifetime of the data and the request rate, the caching decision is made. Further, the nodes have been categorized into three types: edge nodes, which reside next to consumers, mid-tier nodes, and root nodes, which are next to producers. Each node type has different dynamic caching thresholds that change based on the request rate, so long-lived data packets are cached near their producers. In [23], the authors proposed the Caching Fresh and Popular Content (CFPC) approach. It essentially aimed to implicitly cache the most recent and popular data at intermediate nodes. CFPC is based on how many requests are received for each piece of data content during each period and at each router. Both popularity and freshness thresholds are dynamically updated over time. The computed thresholds are only used when the content store is full, to decide whether or not to cache the incoming content. As such, fresh data content with lower popularity can still be cached when there are still free places in the CS. More importantly, the CFPC may require an exorbitantly large data structure to maintain the popularity of requested content, especially when there is a mix of popular and unpopular traffic flows. This poses a scalability issue. Our proposed PF-ClusterCache, on the contrary, operates with a reduced fixed-size popularity table at each router. CFPC will serve as a benchmarking approach for our proposed PF-ClusterCache. The same authors proposed DANTE [25], a revised version of CFPC that is adapted to vehicular named data networks. They leveraged the broadcast nature of the wireless support to cache different content at nearby nodes and consequently improve the caching diversity. The broadcast nature of the wireless medium allows each vehicle to infer what content is available in its vicinity. Diversifying the caching of different content among nearby vehicles limits the caching redundancy and results in better network performance. DANTE necessitates a wireless network and is tailored and dedicated to vehicular NDN. PF-ClusterCache, on the contrary, is a more general caching approach that works regardless of the nature of the underlying communication technology.
## 4 Performance Analysis  
To evaluate the performance of a communication system, the BER, the outage probability, and the average capacity are employed commonly. In this section, the above criterions are used to analyze the coherent satellite-to-ground optical communication system with aperture averaging. BPSK is used as the modulation.
## 3 Results and Discussion
### Simulation Results of the Calibrated Model  
This section presents and discusses the simulation results obtained of the calibrated model. This includes an analysis of the moisture condensation risks during the off-use ventilation mode. In addition, the starting time needed for the HVAC to provide appropriate indoor conditions at the beginning of the working periods is evaluated. Finally, the different ventilation scenarios were simulated and the potential energy savings were assessed.
## 8 Routing Protocols of Multi-UAV Systems
### Topology-Based Routing Protocols
#### 8.1.1 Static  
Static-based routing protocols [73; 81; 82] are a subcategory of topology-based protocols that use a preconfigured static routing table for data transfer between UAVs referenced by their static IP addresses. To add a new UAV, the administrator must manually reconfigure the rules in the routing table to fit the scalability of the multi-UAV system. Therefore, this type of protocol is not suitable for a high-mobility network because of the lack of scalability and fault tolerance. The three variants of this sub-category of protocols are Load Carry And Deliver (LCAD) [73], Data-Centric Routing (DCR) [81], and Multi-Level Hierarchical Routing (MLHR) [82].  
The LCAD protocol [73] is based on the Store-Carry-Forward mechanism [74]. It is adapted for a centralized architecture because it allows the transport of messages between an infrastructure (e.g., ground control station) and all UAVs. The communication mechanism of this protocol consists of three stages: (1) the load stage, in which the packets are stored in the UAVs; (2) the carry stage, in which the UAVs transmit these packets via the gateway UAVs; (3) finally, in the Deliver stage, the gateways deliver the packets to the final UAVs.  
The DCR protocol [81] is based on a broadcasting mechanism. It is suitable for a single-group architecture. The transmission mechanism is based on the header attributes contained in the data. The data are sent only to the UAVs that want them, thus optimizing the network throughput. Then, the MLHR protocol [82], which is based on a node clustering paradigm, allows us to address the scalability challenge. The UAVs are hierarchically divided into clusters according to their types and roles. Each cluster contains a gateway UAV, which is responsible for sharing data with other clusters. Therefore, this protocol is well suited for a multi-group architecture.
# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
## 2 Microservices and Grayscale-Based Image Transform Features
### File Type Microservices
#### 2.1.2 Microsoft Office (MS) Documents  
According to a study conducted by Cisco from January to September 2017, Microsoft Office documents accounted for 38% of the globally identified malicious file types [27]. These documents are commonly used by end users in both corporate and personal contexts, making them susceptible to malicious activities.  
There are three distinct pathways by which malware can infect the victim's system through Microsoft Office documents. First, the victim can be infected through the exploitation of features. When new features are introduced to users as the software becomes outdated, it can inadvertently create vulnerabilities in the software, which can provide opportunities for attackers to gain unauthorized access to the systems. Secondly, malware can infiltrate systems through macros. Macros are defined by a set of rules that describe how the input instructions are executed in order to perform a particular task. Although they have advantages with regards to automation, malicious code can be embedded within them, which allows malware to infect the system. Finally, attackers also target vulnerabilities arising from the absence of software updates. As software evolves, developers often release updates for security bugs. However, not all users promptly update their software, leaving their systems susceptible to known security flaws. Attackers take advantage of this by targeting systems that have not been updated, using known vulnerabilities to gain access and introduce malware [28].