esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 3 Componential Classification of Blockchain Privacy Protecting Techniques
### Effect of Smart Contracts on Privacy  
Smart contracts are digital contracts consisting of rules and regulations, mutually agreed upon by all the parties in a decentralized network [77]. They are self-executing programs which run automatically and are tamper-proof. They are written in high-level programming languages and allow the developers along with the users to express complex behavioral requirements and patterns. The recent developments in the technology of blockchain networks revived the perception and enabled the formation of smart contracts that were originally envisioned by [PERSON] in 1994. Smart contracts are a significant part of the blockchains as they ensure simple business trading among two mutually distrusting parties without the intervention of any third intermediary. It allows disintermediation in the blockchains which is one of the technology's key features. Moreover, the correct use of smart contracts can ensure added security to the blockchain transactions. However, ensuring the correctness of the contracts is a challenging task because of the vulnerabilities of computer programs to the faults and failures [78].  
Fundamentally, much work on privacy protecting using smart contracts has yet not been achieved in literature. However, smart contracts coupled with one or more cryptographic techniques and to address the issue of blockchain privacy, have been witnessed. One such example is presented in [23]. Particularly, Hawk [23] will automatically compile a smart contract into a cryptographic protocol. This compiled program has two parts, the first one deals with execution of major function, whereas the later one protects the users. For transaction encryption and verification, Hawk uses zero-knowledge proofs. Another smart contract based privacy solution is presented in [79]. It offers a solution to the secrecy of smart contract execution and uses advanced cryptographic primitives to support zero-knowledge proofs. Additionally, the data in Enigma is distributed among various nodes unlike the conventional blockchain data storage schemes (i.e., maintaining the copy of ledger of every node). The study in [80] utilizes Enigma protocol for privacy preservation on hybrid blockchain platforms. It highlights the inefficacy of centralized (off-chain) and decentralized (on-chain) platforms when implementing smart contracts individually and proposes a hybrid approach. The authors in the study split the smart contracts a part of which was executed on an off-chain contract and the other part was executed on Rinkeby [81], an Ethereum test network. This concept was adopted in [82]. All the smart contract functions requiring higher computation or consisting of sensitive information are included in the off-chain part of the contract to be signed and executed by concerned participants only. All the unanimous agreements are done off-chain.
## 5 Trace Sampling Based on Type, Duration, and Errors
### Hybrid Approach Based on Types and Durations
Figure 8: The hybrid approach for a specific trace type (N2 in Figure 10). The left figure shows the plot of durations. The right figure shows the counts of traces in different bins before and after sampling corresponding to different values of \(\alpha=\beta\). Parameter optimization can be performed without the utilization of long historical data. We can take an initially random value for the parameters and, after each hour, verify the actual compression ratio. Then, by increasing or decreasing the values, we achieve the required sampling. This will especially work for dynamic applications when relying on available historical information is impossible. How, in practice, can we sample a trace? When a specified trace (with known type and duration) is detected with a known sampling rate \(r\), a random variable from the \(Bernoulli(r)\) distribution should be generated. This random variable will have a Boolean outcome with the value 1 that has probability \(r\) and value 0 with the probability \(1-r\). If the outcome is 1, we will store the specified trace; otherwise, we will ignore it. This will allow us to store the traces with the required sampling rates in the long run. Returning to the problem of histogram construction, we refer to a powerful approach known as the t-digest algorithm [75], which addresses several problems in histogram construction. The first concern corresponds to the storing of time series data with trace durations. For each trace type, we need to store the corresponding time series of durations for further histogram construction, and we need those time series with sufficient statistics. For 28 trace types, as in our experiments, we need to store the 28 time series. The second concern corresponds to the process of histogram construction. Each time we need those histograms, sorting should be applied to the corresponding duration data, and the procedure must be repeated each time in case of some new arrivals. The concern is the impact of outliers. An efficient solution to the mentioned problems is the t-digest algorithm. Instead of storing the entire time series data, it stores only the result of data cluster centroids and data counts in each cluster. The efficient merging approach allows for the combination of different t-digest histograms, streamlining the entire process. The t-digest is very precise while estimating extreme data quantiles (close to 0 and 1), making the procedure robust to outliers.
## 3 Theoretical Analysis
### Link Budget  
The link budget model describing the propagation of the EM wave is reported in Equation (27): the received power \(P_{r}\) in dBm is derived from the transmitted power \(P_{t}\) in dBm, the receiving and transmitting antenna gains \(G_{r}\) and \(G_{t}\) in dBi and the total path loss \(PL_{tot}\) in dB which, in the considered application, models the losses of the underwater to above water radio channel, hence assuming \(m_{1}\) as saline water and \(m_{2}\) as free air [35; 36; 39].  
\[P_{r}=P_{t}+G_{r}+G_{t}-PL_{tot}. \tag{27}\]  
In detail, \(PL_{tot}\) can be obtained as the summation of the underwater losses, \(L_{UW}\), the losses due to the reflections at the water-air interface, \(L_{UW-AW}\), and the free space losses, \(L_{AW}\). These contributions can be derived as follows:  
\[L_{UW}=8.69\alpha d_{UW}+20\log_{10}d_{UW}+20\log_{10}\beta+6 \tag{28}\]  
\[L_{UW-AW}=10\log_{10}(|\tau|^{2}Re\{\frac{\eta_{W}}{\eta_{A}}\})^{-1} \tag{29}\]  
\[L_{AW}=20\log_{10}d_{AW}+20\log_{10}f-147.5 \tag{30}\]  
where \(d_{UW}\) and \(d_{AW}\) are the distances (in m) travelled respectively underwater, before reaching the water-air discontinuity, and above water, \(f\) is the working frequency (in Hz), \(\alpha\) and \(\beta\) are the attenuation and the phase constants computed as in Equations (4) and (5), \(\tau\) is the transmission coefficient at the interface derived according to Equation (7) and \(\eta_{A}\) and \(\eta_{W}\) are the intrinsic impedances of air and water computed as in Equation (8).
## 4 Discussion
### Limitations and Future Work  
The future studies should consider factors such as worker's experience, time of day, i.e., moment of the shift (morning/afternoon, before/after lunch), as these may influence physiological adaptations. Furthermore, sample size should be increased, giving us a broader vision of the factory's population while increasing the statistical power. Another suggestion is the creation of a risk score that includes cardiorespiratory responses and biomechanical risk, giving way to more individual-specific workplace management.
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 4 Non-Thermal Atmospheric Plasma--NTAP
### Microbiological Quality and Safety  
NTAP has been used widely over the past ten years for the decontamination of food. However, there is a limited number of references investigating this technology in fish and seafood. Plasma technology offers high microbial inactivation in short processing times. Indeed, [PERSON] et al. (2017) [152] reported that cold atmospheric plasma generated by a dielectric barrier discharge (DBD) at 70 and 80 kV voltage resulted in significant microbial reduction after only 1-, 3-, and 5-min treatment. Particularly, the spoilage bacteria of fresh mackerel (_S. scombrus_) such as total aerobic psychrotrophic bacteria, _Pseudomonas_ spp., and lactic acid bacteria (LAB) were reduced during storage. LAB were found to be the dominant microflora. Another study for microbial decontamination with promising results was presented by [PERSON] et al. (2017) [153]. They used a corona discharge plasma jet (CDPJ) to induce microbial contaminants on the dried squid shreds' surface. CDPJ was generated using 20 kV pulsed DC voltage and at a 58 kHz frequency, applied for 0-3 min. The results of this study indicated that squid microbiota reduction was time-dependent, where total aerobic bacteria reduced more than 2-log, followed by marine bacteria, which were decreased by 1.6 log CFU/g. The NTAP treatment was evaluated as a possible method to improve the shelf-life of Asian sea bass slices (ASBS) stored at 4 \({}^{\circ}\)C treated with in-bag dielectric barrier discharge cold plasma (IB-DBD-CP) under two different gas combinations.  
They reported that the shelf life of ASBS treated with IB-DBD-CP, packaged under gas A and B prolonged to 12 and 15 days, respectively [154].  
It is encouraging that there is an increasing list of studies over the past five years demonstrating the antimicrobial properties of NTAP on fish [155; 156; 157; 158; 159; 160] and RTE seafood products [161; 162; 136; 153; 163], which can lead to the adoption of this method from the seafood industry. NTAP has a high potential to be commercially used within many fields of the seafood industry. [PERSON] and [PERSON] (2019) [163] recommend the NTAP method for processing dried fish and seafood that have been better documented than the fresh fish.
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 6 Typical Fault Diagnosis of EHA
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Method**} \\ \hline
**Optimization Method** & **Basic Structure of CNN** & _Acc (\%)_ & _P (\%)_ & _F\({}_{1}\) (\%)_ \\ \hline \multirow{5}{*}{PSO} & 1 DCNN & 79.51 & 79.78 & 79.57 \\  & 2 DCNN & 84.60 & 83.41 & 83.93 \\  & FCNN & 89.20 & 84.69 & 86.82 \\  & 1 DCNN & 83.14 & 91.64 & 87.14 \\  & 2 DCNN & 91.64 & 89.30 & 90.39 \\  & FCNN & 96.86 & 96.95 & 96.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Diagnosis results of different models. Figure 15: The confusion matrix of different models: (**a**) The confusion matrix of PSO-1 DCNN; (**b**) The confusion matrix of PSO-2 DCNN; (**c**) The confusion matrix of PSO-FCNN; (**d**) The confusion matrix of MSPSO-1 DCNN; (**e**) The confusion matrix of MSPSO-2 DCNN; (**f**) The confusion matrix of MSPSO-FCNN. average accuracy rate improved by 10.2% compared to 1 DCNN. This is because, on the one-dimensional scale, the original fault signal cannot visually represent the fault characteristics of EHA, and some faults with close fault characteristics can be easily confused. However, the representation of two-dimensional image features is more abundant, and the GADF encoded image can eliminate part of the redundant information, making 2 DCNN have certain robustness. The FCNN method proposed in this paper improves the accuracy by another 5.7% compared to 2 DCNN, reaching 96.86%, and it can effectively identify several types of faults, and the recognition rate of fault 2 is even 100%. This is because although 2 DCNN can extract richer fault features from the GADF graph, it will lose some essential features that only exist in the one-dimensional original fault data. Nevertheless, FCNN can take advantage of the 2 DCNN while retaining the original features in the 1D fault data, thus improving the fault diagnosis rate of aircraft EHA. Furthermore, we tested the MSPSO-1 DCNN, MSPSO-2 DCNN and MSPSO-FCNN, respectively, with the original data without noise signal and obtained the results as shown in Table 7. Case 1 is the case without noise. It is easy to see from Table 7 that MSPSO-1 DCNN is the most sensitive to noise, while benefiting from the sparsity of the GADF graph, MSPSO-2 DCNN and MSPSO-FCNN have good anti-noise robustness.
# Fast Conflict Detection for Multi-Dimensional Packet Filters
###### Abstract  
To support advanced network services, Internet routers must perform packet classification based on a set of rules called packet filters. If two or more filters overlap, a filter conflict will occur and lead to ambiguity in packet classification. Further, it may affect network security or even the correctness of packet routing. Hence, it is necessary to detect conflicts to avoid the above problems. In recent years, many conflict detection algorithms have been proposed, but most of them detect conflicts for only prefix fields (i.e., source/destination IP address fields) of filters. For greater practicality, conflict detection must include non-prefix fields such as source/destination IP port fields and the protocol field. In this study, we propose an efficient conflict detection algorithm for five-dimensional filters, which include both prefix and non-prefix fields. In the proposed algorithm, a tiny lookup table is created for quickly filtering out a large portion of non-conflicting filter pairs, thereby reducing the overall conflict detection time. Experimental results show that our algorithm reduces the detection time by 10% to 28% compared with other conflict detection algorithms for 20 K filter databases. More importantly, our algorithm can be used to extend any existing conflict detection algorithms for two-dimensional filters to support fast conflict detection for five-dimensional filters.  
conflict detection; firewall policy; packet classification; packet filters; network security Article  
Faster conflict detection; 2022
## 2 Materials and Methods
### RNA Sequencing and Data Analysis  
Hepatic monocytes of _E. histolytica_- or _L. monocytogenes_-infected C57 BL/6 mice were sorted on the indicated days p.i., with a purity of 80-85% and surface staining was performed using CD11b (APC-Cy7; Ly6C (FITC) and Ly6G (APC). Cells were then sorted into collection tubes containing 2 mL of RNAprotect cell reagent (Qiagen). RNA was isolated using the RNeasy Plus Micro Kit (Qiagen) and RNA integrity was analyzed using an Agilent 6000 Pico Kit and an Agilent 2100 Bioanalyzer (Agilent). Samples used for transcriptome sequencing fulfilled the following criteria: total RNA \(\geq\) 200 ng (\(\geq\)20 ng/\(\upmu\)L); RNA integrity number (RIN) \(\geq\)7.0, 285/18S \(\geq\) 1.0. RNA library preparation and sequencing were performed by BGI Genomics, China. The data comprised paired-end short reads. All raw data were aligned to the mouse reference genome GRCm38 Ensembl 85 and the corresponding Gencode annotation using STAR [20] (version 2.5.2a). Differential expression analysis was performed in R (version 3.3.3; R Foundation for Statistical Computing, Austria) using DESeq2 (version 1.13.8) [21]. Reads from different lanes per replicate were combined after checking for the absence of batch effects on a PCA plot. We performed differential gene expression analysis between both infection models within the Ly6C\({}^{\mathrm{hi}}\) and Ly6C\({}^{\mathrm{lo}}\) monocytes respectively using the day after infection as additional co-variate in the DESeq design. To test for differential expression across all three time points (naive, d3 p.i, d5 p.i.), we used a likelihood ratio test for each monocyte group using time point, infection model, and an interaction term for both variables as full design and compared it against the reduced model with the interaction term removed. A threshold of 0.05 for Benjamini-Hochberg corrected \(p\) values was used to determine significance by the \"statistical overrepresentation test\". Gene set analysis was performed using PANTHER GO-slim (Version 16) [22], with \(p\) adj! 0.05. Heatmaps were created using http://heatmapperca (accessed on 1 July 2019) combining time points 3 and 5. Volcano plots were made using GraphPad Prism V8.4.3.
# Design and Development of a Wideband Planar Yagi Antenna Using Tightly Coupled Directive Element
## 2 Printed Yagi Antenna Design Principles
As the guided wavelength is smaller than free space wavelength by a factor of square root of the relative permittivity of the substrate material,the major design parameters such as dipole arm lengths, length and spacing between the directive elements are normalized accordingly. Considering the same number of directive elements, the initial length and width of the antenna without dielectric material was calculated to be 285 mm and 85 mm, respectively. It can be seen that the use of substrate material reduces the length by more than half. In summary, typical performance characteristics parameters of a printed Yagi antenna are affected by (i) reflector and feeder geometry (ii) spacing between reflector and the feeder and (iii) the total numbers of directive elements. Therefore, to achieve required performance it is essential to optimize the geometrical parameters of these structural variables.
## 2 Materials and Methods
### Total Phenolic Content (TPC) and Antioxidant Properties
#### 2.9.2 Total Phenolic Content (TPC)  
The TPC was quantified in accordance with the method described by [PERSON] (2010) and [PERSON] et al. (2016) with some modifications. The ethanolic extracts (200 \(\mu\)L) were mixed with diluted (10:1) Folin and Ciocalteu reagent (800 \(\mu\)L). After 3 min, 1250 \(\mu\)L of 7% Na\({}_{2}\)CO\({}_{3}\) was added, and the mixtures were left to stand for 0.5 h in the dark at 20 \(\pm\) 2 \({}^{\circ}\)C. The absorbance was read at 725 nm (Helios Gamma, Thermo Fisher Scientific, Waltham, MA, USA) against a blank sample. The TPC was presented as gallic acid equivalent (GAE) per 1 g of dry weight (dw).
## 4 Experimental Results and Discussions  
### Experimental Data Collection  
This study utilized point cloud data from two shield tunnel sections from Nanjing Metro Line #2 and Line #10, spanning 1400 m (Dataset 1) and 1600 m (Dataset 2), respectively. Both tunnels are constructed from standard precast concrete segments, which is a typical design for such infrastructure. The tunnel point cloud data were collected using the Track Laser Scanning and Detection (TLSD) system, as illustrated in Figure 4. The TLSD system is composed of three primary components. The first is the Faro Focus 350 laser scanner, a high-performance instrument characterized by a ranging accuracy of \(\pm\)1 mm, a scanning speed of up to 976,000 points per second, and a maximum scanning range of 350 m. Its compact dimensions (230 \(\times\) 183 \(\times\) 103 mm) and low weight (4.2 kg including battery) make it highly suitable for mobile applications within confined tunnel spaces. The second component is a track-mounted trolley, which provides stable driving power and mobility for the system along the tunnel. The third is an industrial tablet computer, which facilitates real-time monitoring and data processing during acquisition.  
The data acquisition procedure was systematically executed. First, key operational parameters were established, including a point cloud resolution of 3.1 mm at a 10 m distance, a scanning frequency of 50 Hz, and a constant trolley speed of 0.5 m/s. The scanner then performed a continuous two-dimensional spiral scan of the tunnel cross-section, recording the scan distance, incidence angle, and timestamp for each measured point. The scanning distance is inherently variable, with its range determined by the standard 5.5 m inner diameter of the tunnels and the central position of the track-mounted system.  
Finally, the system synthesized these raw measurements with the trolley's velocity data to compute the precise three-dimensional coordinates for the entire point cloud.
## 4 Experiment and Analysis
### Frequency Resolution and LED-ID Recognition Analysis  
At the same distance, with the increase of frequency, the number of stripes increases. Increasing the number of stripes also causes the stripes to become dense. Therefore, the accuracy of feature extraction will be reduced, which will affect the classifier's training and recognition. This section tests the impact of different frequency resolution on the LED-ID recognition rate, and the experimental results are as follows.  
When the minimum frequency resolution was smaller than a certain critical value, both of the recognition rates for ANN and SVM dropped sharply. We can see from Figure 16 that this critical value is 50 Hz.
# Power Quality Enhancement in a Grid-Integrated Photovoltaic System Using Hybrid Techniques
## 6 Proposed Method
### Preliminaries  
In this study, the DC/AC and DC/DC converters were controlled using the proposed BPSO-GWO algorithm. This algorithm is very easy to implement with less adjusting parameters, and it is effective for integrating PV into the grid to attain smooth power quality. Moreover, it is a superior fitness solution with good convergence speed, and the response of the controller was studied under constant temperature and changing irradiance.  
Figure 3: Structure of the 31-level MLI with the blocking voltage process.  
The results show the proficiency of the proposed controller at improving the performance of the system.