esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Recovery via Thermal Gasification from Waste Insulation Electrical Cables (WIEC)
## 3 Results and Discussions
### Ultimate Analysis  
The carbon content in biomass varieties, usually ranging from 44.1% to 75.5% [34]. For the case of pine, the value was 48.7%, which corresponded to typical forestall biomass. For WIEC, the value was 52.3%. The chemical element C is the principal element in all combustible material and is responsible for the HHV of all fuels. Alternatively, hydrogen content in the pine was 7.5% and for WIEC it was 2.5%. Hydrogen presents a high HHV, but the percentage of this chemical element in fuels is very low and is related to the moisture of the biomass. In the case of WIEC, it is near null, because this material was dried and does not have the characteristics of forestall biomass to retain water.  
The nitrogen content in pine was 0.6% and in the WIEC it was 0.2%. The presence of nitrogen in a fuel can be harmful in a thermochemical process, namely combustion, due to the formation of NOx. Usually, lignocellulosic biomasses tend to not exceed more than 1% content of this element [35]. Therefore, a low nitrogen content in a fuel means that NOx formation during thermochemical conversion is minimal. Another important aspect, when NOx formation is addressed in the thermal gasification process, oxygen enters the reactor in an amount lower than stoichiometry, and therefore there is more formation of CO and CO\({}_{2}\) than NOx, in addition, the low gasification temperatures (below 900 \({}^{\circ}\)C) prevent the formation of thermal NOx.  
Regarding sulfur, pine biomass did not show any percentage of this element. Similar to the presence of nitrogen in the fuel, the absence of sulfur is also beneficial in its use in energy conversion, since it decreases the formation of SO\({}_{2}\).  
The oxygen content in the pine was 42.2% (dry bases) and 45% (dry bases) for WIEC. This element has a significant influence regarding the fuel HHV, hence it is important, sometimes, to pretreat the fuel and deoxidize it in order to increase the HHV.
# A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity
## 2 Proposed Compression Framework  
In the previous work, we adopted STFT as time-frequency transform to analyze the sparsity of audio signal and designed a codec based on the intra-object sparsity. From the statistical results of sparsity presented in Appendix A, we know that audio signals satisfy the approximate \(k\)-sparsity both in the STFT and MDCT domain, i.e., the energy of audio signal is almost concentrated in \(k\) time-frequency instants. In other words, audio signals have sparsity-promoting property in the MDCT domain in contrast to STFT, that is, \(k(r_{\text{FEPR}})_{\text{MDCT}}<k(r_{\text{FEPR}})_{\text{STFT}}\). By using this advantage of MDCT, a multiple audio objects compression framework is proposed in this section based on intra-object sparsity. The proposed encoding scheme consists of five modules: time-frequency transform, active object detection, psychoacoustic-based TF instants sorting, _NPTF_ allocation strategy and Scalar Quantized Vector Huffman Coding (SQVH).  
The following process is operated in a frame-wise fashion. As is shown in Figure 1, all input audio objects (Source 1 to Source \(Q\)) are converted into time-frequency domain using MDCT. After active object detection, the TF instants of all active objects will be sorted according to Psychoacoustic model in order to extract the most perceptually important time-frequency instants. Then, a _NPTF_ allocation strategy among all audio objects is proposed to counterpoise the energy of all preserved TF instants of each object. Thereafter, the extracted time-frequency instants are downmixed into a mono mixture stream plus side information via downmix processing operation. Particularly attention is that the downmix signal can be further compressed by existing audio coding methods. In this proposed method, SQVH technique is employed after de-mixing all TF instants, because it can compress audio signal at desirable bitrate. At the receiving end, Source 1 to Source \(Q\) can be decoded by exploiting the received downmix signal and the side information. The detailed contents are described below.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 2 Related Work
### Traditional Trusted Routing Schemes  
Providing a trusted routing environment is an important and difficult issue for WSNs. There are many related researches to implement a trusted routing scheme. [PERSON] et al. designed a novel trust-based routing protocol by extending the widely used AODV (ad hoc on-demand distance vector) routing protocol [15]. The protocol applies a trust model to recommend the trusted routing nodes and improve the security of the routing environment. Later, there are more researches on a trust-based routing scheme. In [16], [PERSON] et al. proposed a secure routing scheme by quantifying and recording the algorithm-compliance behaviors of the routing nodes. [PERSON] et al. proposed a QoS (quality of service) routing algorithm to evaluate the trustworthiness of the routing nodes [17]. The algorithm calculates the direct QoS trust of the 1-hop neighbor routing node, the indirect trust of the 2-hop neighbor routing node is calculated by the transitive rule (e.g., A trusts B and B trusts C then A trusts C). Some researchers embed the trust mechanism into the routing paths [18; 19], so that the trusted routing paths were scheduled. Most of these researches are based on a \"reputation system\", which evaluates the reputation of other nodes to make routing selection. However, building a reputation table requires the historical behaviors of the routing nodes which cannot guarantee the real-time security of WSNs. Meanwhile, the reputation table maintained by each routing node may be tampered with, so that the absolute credibility cannot be guaranteed.
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 3 Preliminaries
### Security for LBS: The BL0K Protocol  
First, the component protocols are depicted. The BL0K method is in the form of a framework. Within ZKP + BF, the ZKP in the LBS scenario grants the user the role of the verifier V, while the LBS server plays the role of the prover P. The ZKP has two phases of execution: an initialization phase and a challenge/response phase. This method makes use of a BF, wherein LBS has a BF encoded with location information by bit vectors, BF includes multiple hash functions, and the hash functions show the bits of a member x within bits that are set in the BF to create a security mechanism between the LBS and SNS, protecting each other's privacy. The user produces q pairs (query, assertion) and ZKP creates a security technique between the user and LBS prover. This ensures privacy without leakage. The queries are then transmitted to LBS, resulting in bit vectors of q values. The ith position of the vector is 0, Qi and Ai form an inconsistent pair. If the ith position, however, is 1, then the pair is probabilistically consistent. Thus, if the LBS server is trusted, each subsequent evaluation will be accurate within the limits of the BF algorithm. It follows that only some evaluations will be accurate when the LBS server is malicious.
## 3 Methodology and Data Preparation
### Operational Monitoring Data  
The large data set from the operational monitoring was subjected to post-processing, whereby feature extraction was implemented in both the time and frequency domains. The continuous time series were segmented into lengths of 10 ms and processed further. A bandpass filter from 0.5 to 45 kHz was applied to each segment before calculating the peak amplitude, energy, and spectral centroid. The post-processing of the data took about three weeks on a workstation.  
The selected segment length led to a very high number of data points for this data set, which complicated its visualization. Hence, an aggregation was applied to 15-min periods by synchronizing the sampling rate of the acoustic emission data with the _SCADA_ data. Each period was characterized by statistical parameters, including minimum, maximum, mean, etc. In the figures shown in this article (see Sections 4.1 and 4.2), mainly the minimum and maximum values of the different acoustic features are presented, as this representation provides a good understanding of the particular distribution and intensity of the background noise. Redundant and highly correlated sensors were excluded during data preparation to reduce the data size for further analysis. Consequently, only the aggregated data from sensor 3 at the transition piece and sensor 7 at the foundation formed the basis for determining the threshold values for background noise.  
The tower _SCADA_ data were merged with the acoustic emission data to analyze their interdependencies and correlations. It is well known that wind turbines operate under the influence of constantly changing environmental conditions, and, due to the dynamic spectrum of wind speeds, rotor speeds and blade pitch angles vary. These factors have previously been identified as the most important parameters expected to significantly affect the acoustic emission characteristics of operational noise in wind turbines [44]. Wind and nacelle direction were excluded from further analysis as they were only randomly related to the other parameters.
## 3 Results and Discussion  
### Influence of Initial pH  
The solution pH is crucial in determining the efficiency of Fenton oxidation in degrading organic pollutants. The effect of pH within the range of 3.0 and 7.0 on DAP removal by Fenton oxidation is shown in Figure 2. This figure indicates that independent of initial pH, the DAP concentration started to decrease at the beginning of Fenton oxidation. As it can be seen, an increasing pH from 3.2 to 4.0, 5.1, 6.2 and then to 7 decreased the DAP removal from 100 mg/L to 1.1 mg/L, 14.7 mg/L, 24.7 mg/L, 44.7 mg/L and 63.7 mg/L, respectively. This result indicated that more than 98% of DAP removal was obtained at pH 3.2, while the percentage of DAP removal was between 36% and 85% for other initial pH values after 120 min of treatment. Therefore, the degradation of DAP was significantly affected by the initial pH. The highest DAP degradation yield was obtained at pH 3.2. This can be correlated to the generation of hydroxyl radicals by Fenton's reaction. In fact, a greater generation of hydroxyl radicals at acidic conditions enhances the oxidation of organics. However, at a pH higher than 3.2, the formation of ferric hydroxide complexes Fe(OH)\({}_{3}\) and the auto-decomposition of H\({}_{2}\)O\({}_{2}\) to oxygen and water reduces the generation of hydroxyl radicals, which decreases the degradation efficiency of DAP. Additionally, the formation of Fe(III)-complexes at high pH values limits the regeneration of ferrous ion (Fe\({}^{2+}\)) catalyst and then reduces the regeneration of hydroxyl radicals [24; 25]. Several studies have reported that pH can dramatically affect the degradation of organic pollutants such as phenols, dyes, and petrochemicals by Fenton oxidation. Higher degradation yields were measured at pH values in the range 3.0-4.0 [26; 27; 28; 29; 30].
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### Unmanned Aerial Vehicles  
A UAV system can be remotely controlled by experienced personnel or operate automatically and autonomously through programmed software. UAVs have been known for decades. Their first applications were in the late 19 th century, during the Spanish-American War, where they were used for military reconnaissance to take aerial photographs. Over time, these UAVs have evolved significantly; for example, the Tadiran Mastiff UAV was developed in 1973 for the Yom Kippur War. This system was designed with an advanced system of data links and electronics that allowed it to transmit real-time, high-resolution video to operators on the ground. This made obtaining critical information about the target areas easier, significantly improving surveillance and reconnaissance capabilities during the conflict [16]. This and other events evidenced the advantages of UAVs, highlighting the ability to perform high-risk missions and their potential to attack enemy territory.  
Thanks to advances in science and technology, UAVs can now integrate a wide range of modules, such as high-resolution cameras, communication systems, and multispectral and thermal sensors, among others, as shown in Figure 1. This capability makes them much more functional and versatile tools for civilian applications. Additionally, the wireless communication systems used by UAVs are easier to implement due to their compact size and the ability to be quickly configured. They have good mobility and are inexpensive, making them suitable for use in various situations. Their rapid deployment makes them ideal for responding to unexpected events.  
Despite their wide applications, systems designed with UAVs present a significant challenge in terms of energy consumption. The energy consumption of a UAV comprises two main components: (i) propulsion power, which is required for the UAV to fly and stay airborne, and (ii) payload power, which powers the communication and processing devices during flight. An obvious solution would be to use higher-capacity batteries, but this would result in a heavier power system, which would reduce the payload capacity of the UAV [17]. Several methods of powering the UAV are currently being studied, including wireless power, which offers advantages in extended flight applications and achieves a transmission efficiency of approximately 20 percent. The wireless transfer methods being explored include electromagnetic induction and magnetic resonance, which are suitable for charging when the UAV is near a fixed platform. Other methods include microwave or laser power transfer, which allow power delivery over longer distances and sometimes while the UAV is in flight. However, these methods still require high transmission accuracy and face challenges related to efficiency and safety, especially in environments with obstacles or variable atmospheric conditions [18; 19]. Therefore, this paper proposes a solution based on the design and implementation of a T-UAV.  
Figure 1: UAV structure, based on [20].
## 3 Information Leakage Analysis of Merkle Tree-Based Authentication Schemes  
In this section, we investigate the vulnerabilities against side-channel attacks of Merkle tree-based authentication method (Section 3.1). Then, we demonstrate the previous authentication schemes are not secure against the side-channel attacks we found (Section 3.2). For the rest of this paper, we assume that there is an adversary eavesdropping communication between the prover \(\mathcal{P}\) and verifier \(\mathcal{V}\).  
Exposure of structural information in Merkle tree-based authentication and its potential risks were previously analyzed by [PERSON] et al. [22] and [PERSON] [23]. [PERSON] et al. [22], especially, developed a notion called secure name to prevent information leakage about the correlation between nodes in the tree and the graph. However, to the best of our knowledge, detailed diagnosis of information leakage has not yet been conducted in the research.
## 2 Methods
### Description of the Problem
#### 2.1.3 Passive AHT Study  
A base case solution was made for comparison of results. Table 5 provides a summary of flow specifications applicable for both the base case and AHT methods while Figure 6 shows the orientation of hot and fluid flow.  
The passive AHT study focused on a circular insert at varied quantities (\(N\)) across the length of the CCTHE inner pipe. Figure 7 shows the circular insert at a fixed hole diameter of 8.5 mm. The quantity of insert across the inner pipe was varied at \(N\) = 3, 5, and 7, represented by Figure 8. Additionally, each insert had an offset distance of 300 mm from each other.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Cell Zone** & **Condition** \\ \hline Mass Flow (kg/s) & Cold inlet & 6 \\ Hot inlet & 1, 3, 5, 7, 9, 11, 13, and 15 \\ \hline Temperature (K) & Cold Inlet & 288.15 \\ Hot Inlet & 368.15 \\ \hline Outlet Gauge Pressure (Pa) & Cold and Hot Inlet & 0 \\ \hline \end{tabular}
\end{table}
Table 4: Initial conditions for numerical model validation.  
Figure 6: Flow configuration of CCTHE.  
After performing the simulation, \(\dot{Q}\), \(\Delta p\), \(f\), and \(Nu\) were generated. The thermal performance factor, \(\alpha\), expressed as Equation (3), was also evaluated. This parameter represented the ratio of the relative effect of change in heat transfer rate to change in friction factor and is used to measure the effectiveness of the passive AHT technique applied.  
\[\alpha=\frac{Nu/Nu_{0}}{(f/f_{0})^{1/3}}, \tag{3}\]  
where \(Nu\) and \(f\) refer to the Nusselt number and friction factor for CCTHE with inserts, respectively, while \(Nu_{0}\) and \(f_{0}\) refer to the Nusselt number and friction factor for CCTHE without heat transfer enhancement, respectively.
# Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm
## 5 Results and Discussions
### Comparison of Various Algorithms on the Real Network  
In order to test the maximum modularity of different algorithms, this paper applies PSO-GA hybrid algorithm, GN algorithm, Fastgreedy algorithm, LFM algorithm to five real network data sets of karate network, dolphin network, book network, football network and scientist cooperative network. The algorithm was tested. The experimental results obtained are shown in Figure 2. Among them, the ordinate Qmax is the maximum modularity of the network. For the calculation formula (3), see the third section. The abscissa is each data set.  
It can be seen from Figure 2 that the PSO-GA hybrid algorithm is more modular than the modularity-based community detection algorithm GN and Fastgreedy, indicating that the hybrid algorithm is effective in community detection. Because the algorithm is too random, the LFM algorithm can't get a good result in many cases. The community partitioning results obtained by the algorithm vary greatly each time, so it can't effectively find the community partitioning mode with higher module degree. Due to the huge time consumption of the GA algorithm itself, it is not suitable for large-scale complex networks and the Fastgreedy algorithm based on the GA algorithm has higher module value on most networks. The results show that the PSO-GA algorithm proposed in this paper has certain advantages in the accuracy of community detection compared with other algorithms.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis
### Classification Results and Analysis
#### 4.4.2 Comparison of the F1-Score and Confusion Matrix
As observed, confusion primarily occurred within class 0 (i.e., medium-other) and class 4 (i.e., small), or class 1 (i.e., merchant) and class 3 (i.e., sailing), or between classes 2 (i.e., medium-passenger) and 4. According to the composition of the VAIS dataset introduced in Section 4.1 and Figure 14, some small ships and medium-other ships had relatively high similarity. Similarly, there were similarities between the mast of sails down and merchant hoisting equipment. The other was that some small and medium-passengers existed a similarity. Hence, the aforementioned classes were prone to confusion. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{6}{c}{**Method**} \\ \cline{2-7}  & **ME-CNN [32]** & **Fine-VGG-16** & **Fine-ResNet-18** & **Improved** & **Proposed** \\ \hline Medium-other & 0.6364 & 0.7211 & 0.7821 & 0.8264 & 0.8402 \\ Merchant & 0.8695 & 0.7862 & 0.8429 & 0.9117 & 0.9197 \\ Medium-passenger & 0.7155 & 0.7123 & 0.8160 & 0.8943 & 0.9180 \\ Sailing & 0.9824 & 0.9440 & 0.9494 & 0.9495 & 0.9650 \\ Small & 0.8830 & 0.8966 & 0.9372 & 0.9369 & 0.9492 \\ Tug & 0.8333 & 0.9048 & 0.8837 & 0.9091 & 0.9524 \\ Avg. total & 0.8200 & 0.8275 & 0.8685 & 0.9047 & 0.9241 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: F1-score obtained using different CNN methods when applied to the VAIS dataset. Figure 15: Confusion matrix and its normalization of the proposed method using the self-built dataset. Notes: Numbers 0, 1, 2, and 3 correspond to the bulk, container, passenger, and sailing ship types; (a) confusion matrix; and (b) confusion matrix normalization. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{6}{c}{**Method**} \\ \cline{2-7}  & **CNN [29]** & **Fine-ResNet-18** & **Fine-VGG-16** & **CNN [38]** & **Improved** & **Proposed** \\  & **CNN** & **AlexNet** & **ResNet-18** & **Fine-VGG-16** & **CNN**[38] & **CNN** & **Method** \\ \hline Bulk & 0.8493 & 0.9124 & 0.9032 & 0.9284 & 0.8693 & 0.9513 & 0.9585 \\ Container & 0.8637 & 0.9260 & 0.9217 & 0.9097 & 0.8938 & 0.9654 & 0.9695 \\ Passenger & 0.8630 & 0.9377 & 0.9167 & 0.9042 & 0.9160 & 0.9621 & 0.9864 \\ Sailing & 0.9338 & 0.9503 & 0.9132 & 0.9667 & 0.9537 & 0.9777 & 0.9846 \\ Avg. total & 0.8775 & 0.9316 & 0.9137 & 0.9272 & 0.9082 & 0.9641 & 0.9748 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: F1-score obtained using different CNN methods when applied to the self-built dataset.
# Preparations for Galileo PRS in Poland
## 2 Galileo Services and Signal Plan
The E1 OS signals are modulated using the Composite Binary Offset Carrier (CBOC), E6 HAS signals (denoted as CS--Commercial Service) use the Binary Phase Shift Keying (BPSK), E5a and E5b signals use the Alternative BOC modulation (AltBOC), and E1 PRS and E6 PRS signals are modulated with the Binary Offset Carrier with cosine phasing (BOCcos) [2]. The technical characteristics of the Galileo Signals in the E1, E6 and E5 bands are presented in Table 1.
## 4 Results
### Forecasting the Share of Airline Business Models at Pula Airport
#### 4.2.1 Analysis of the Share of Airline Business Models at Pula Airport  
The following Table 5 shows the shares of airline business models by year at Pula Airport in the period 2009-2019.  
Figure 12 shows graphically (via pie charts) the shares of airline business models by year at Pula Airport in the period 2009-2019, according to Pula Airport data [56].  
Figure 11: Forecasted number of aircraft operations at Pula Airport in the period 2020–2026.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Year** & **FSC (\%)** & **LCC (\%)** & **CC (\%)** & **Other (\%)** \\ \hline
2009 & 34 & 44 & 21 & 1 \\
2010 & 27 & 40 & 32 & 1 \\
2011 & 32 & 44 & 23 & 1 \\
2012 & 29 & 45 & 25 & 1 \\
2013 & 29 & 45 & 25 & 1 \\
2014 & 23 & 49 & 27 & 1 \\
2015 & 26 & 45 & 28 & 1 \\
2016 & 20 & 54 & 24 & 2 \\
2017 & 22 & 56 & 21 & 1 \\
2018 & 22 & 57 & 20 & 1 \\
2019 & 20 & 59 & 20 & 1 \\ \hline \end{tabular}
\end{table}
Table 5: The shares of airline business models by year at Pula Airport in the period 2009–2019 [56].  
Figure 12: Structure of business models shares at Pula Airport **(a)** in 2009; **(b)** in 2010; **(c)** in 2011; **(d)** in 2012; **(e)** in 2013; **(f)** in 2014; **(g)** in 2015; **(h)** in 2016; **(i)** in 2017; **(j)** in 2018; **(k)** in 2019 [56].  
Figure 13 shows an analysis of the shares of all airline business models with linear trendlines by year at Pula Airport in the period 2009-2019. The figure clearly shows that the share of low-cost carriers (LCC) has been continuously increasing in the observed period, while the business models of traditional (FSC) and charter (CC) carriers have been declining. The dominance of low-cost carriers (LCC) is evident at Pula Airport.