esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Results  
#### 3.1.1 PAHs and Indpcbs  
PAHs concentrations are presented in Table 1. \(\Sigma\)PAHs ranged from 1.27 to 16.5 ng m\({}^{-3}\) with a median value of 6.12 ng m\({}^{-3}\). PAHs levels were found in the same levels with a previous research in the same site [47] (\(\Sigma\)PAHs = 7.9 ng m\({}^{-3}\)) and slightly lower than our previous study in the nearby area of Aspropirgos (mean value 9.8 ng m\({}^{-3}\)) [36]. PAHs in other Greek cities were measured in lower concentrations, with mean values 3.34 and 6.46 ng m\({}^{-3}\) in the harbor of Volos [48], 3.08 ng m\({}^{-3}\) in Spata, and 3.21 ng m\({}^{-3}\) in Koropi [49], but significantly higher mean concentrations were reported in industrial sites of Istanbul (60.5 ng m\({}^{-3}\)) [39] and Aliaga (218 ng m\({}^{-3}\)) [21], Turkey.  
BaP is considered the most toxic PAHs compound, and its values ranged from below LOD to 3.04 ng m\({}^{-3}\) with an average value of 0.93 ng m\({}^{-3}\), which is close to the target value of 1 ng m\({}^{-3}\) from the 4 th Daughter Directive [50; 51]. BaP highest average concentration in Aspropirgos was 3.6 ng m\({}^{-3}\), while in Eleusis, the mean concentration was 0.71 ng m\({}^{-3}\)[47]. Similar levels of BaP were also recorded in Thessaloniki, with the average values for the cold and warm periods being 0.12 and 0.86 ng m\({}^{-3}\), respectively [42], while BaP concentration in Volos measured 0.60 ng m\({}^{-3}\)[49]. Lower BaP concentrations were found in industrial sites of Istanbul, Turkey (0.39 ng m\({}^{-3}\)) [39], Dunkirk, France (0.29 ng m\({}^{-3}\)) [31], and Jiangsu Province, China (up to 0.35 ng m\({}^{-3}\)) [16], whereas much higher concentrations were presented in the industrial site of Shanghai, with a mean value of 5.95 ng m\({}^{-3}\)[52].  
In Table 2, concentrations of indPCBs are presented with values ranging from 11.4-26.6 pg m\({}^{-3}\), with a median value of 18.1 pg m\({}^{-3}\). The most abundant compound was PCB28 (7.96 pg m\({}^{-3}\)), followed by PCB101 (2.86 pg m\({}^{-3}\)). Levels of PM\({}_{10}\)-bound indPCBs were similar to that reported in Thessaloniki (0.5-29.2 pg m\({}^{-3}\)) [53], but relatively lower than in Rome (163 pg m\({}^{-3}\)) [54] and Brescia (474 pg m\({}^{-3}\)) [23], Italy.
# Domain Knowledge Graph Question Answering Based on Semantic Analysis and Data Augmentation
## 5 Experiment and Analysis
### Event Entity Matching Experiment
#### 5.3.2 Experimental Results and Analysis  
The box plots displaying the results of the two methods are presented in Figure 12. Overall, the use of LTP to extract and reorganize semantic role phrases in method (2) improved the median, mean, maximum, and minimum event entity similarity values compared to method (1). We compared the similarity values of the two methods for the same questions and found that 56.71% of the questions showed improved similarity values with the use of method (2), while 12.74% of the questions showed no change. However, 30.55% of the questions exhibited decreased similarity values after processing with method (2).  
We analyzed the questions that exhibited decreased similarity values and found that their expressions were clearer, and had well-formed syntax and almost no colloquial expression. Therefore, such sentences may have become disordered and repeated when their syntax and semantics were reorganized using LTP, leading to a decrease in similarity values. These observations underscore that while method (2) is effective in enhancing the matching capability of event entities for questions with missing grammatical elements or ambiguous expressions, it may reduce the matching accuracy of properly structured sentences.
## 3 Simulation of the Influence of Shield Tunneling on Existing Subway Lines
### Analysis of Influence Numerical Simulation Results
According to the above numerical simulation results, it can be concluded that the displacement of large-diameter shield tunneling has the following characteristics. Figure 6: Variation in the position of the maximum segment displacement of Line 5 with excavation depth. (**a**), (**b**), (**c**), (**d**) display the maximum displacement with \(L_{2}\) of 20 m, 22 m, 24 m, and 26 m, respectively. Figure 7: Contour map of the maximum segment displacement of Line 5. 1. The overall displacement trend of adjacent segments is affected by the vertical center distance between the tunnels \(V_{2}\), which is greater than the horizontal center distance between the tunnels \(L_{2}\). 2. The position of the maximum displacement of the adjacent segment moves up toward the vault with the increase in the vertical center distance between the tunnels \(V_{2}\) and is basically not affected by the horizontal center distance between the tunnels \(L_{2}\). 3. The vertical center distance between the tunnels \(V_{2}\) is an important factor affecting the displacement direction and deflection of adjacent segments, and the horizontal center distance between the tunnels \(L_{2}\) has little influence on it. 4. As the vertical center distance between the tunnels \(V_{2}\) increases, the overall displacement of adjacent segments increases; as the horizontal center distance between the tunnels \(L_{2}\) increases, the overall displacement of adjacent segments decreases.
## 4 Dense Point Cloud Map Construction Based on Point-Line Features
### Dense Point Cloud Loop Closure  
In the ORB-SLAM3 system, loop closure detection and pose optimization are essential for enhancing map precision. The system executes global Bundle Adjustment (BA) optimization upon detection of a loop closure. Figure 6a illustrates that the BA optimization model mitigates deviations resulting from accumulated errors by refining the positions of all keyframes and map points. The refined keyframes and map points are subsequently employed to update the dense point cloud map accurately, enhancing the precision and reliability of the three-dimensional reconstruction.  
The interrelations among keyframes are represented in the optimization process as a pose graph, as illustrated in Figure 6b. The system modifies keyframe poses and edge constraints to correct deviations caused by cumulative errors. This approach significantly improves the global pose estimation accuracy. The optimized posture graph facilitates precise map updates. At the same time, dense point cloud optimization following loop closure detection markedly improves the three-dimensional reconstruction accuracy, enhancing the system's stability and reliability in intricate situations.  
Figure 7 illustrates that during loop closure detection optimization utilizing the feed-forward model, our system captures pose data for each keyframe. Upon establishing a loop closure state, the system optimizes global bundle adjustment to revise keyframe poses and map point coordinates. The point clouds of each optimized keyframe posture are reassembled and filtered, enhancing the optimized point cloud map, which significantly increases the system's robustness, minimizes mistakes, and guarantees global consistency of the dense point cloud map. The comprehensive optimization procedure comprises the six steps illustrated in the figure.
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
## 5 BGPcoin Architecture for BGP Security
### BGPcoin Infrastructure  
RPKI requires CAs to sign Resource Certificates (RCs) and Route Origin Attestations (ROAs), the distributed repositories to store those certificates and attestations and the RPs to verify the ROAs. Since the permissionless blockchain has provided a complete platform to store/compute/trade and then enable a rapid deployment of any application, BGPcoin only relies on this platform as its infrastructure. Specifically, BGPcoin is supported by the EVM to compute and the Ethereum blockchain to store and the consensus protocol and the incentivized miners to verify and approve the transactions. Compared with the RPKI, BGPcoin need not build an exclusive infrastructure.  
**Blockchain vs. repository:** In RPKI, every RIR maintains a repository to store the cryptographic RPKI objects for the resource under its governance. Since the RPKI repository is managed without supervision, a malicious authority is able to launch a mirror world attack [24] in which it presents one view of its RPKI repository to some RPs and a different view to others. In contrast, Ethereum blockchain provides an append-only and tamper-resistant ledger, which enables anyone to retrace the history of transactions, and so every transaction is non-reputable. In this way, BGPcoin maintains the global consistent view of information and eliminates the mirror world attack.  
**BGPcoin miner vs. relying party:** In RPKI, Relying Parties (RPs) play the role of trust anchors with three functions for BGP border routers: (1) through the sync protocol [34], they collect the RPKI objects from the distributed RPKI repositories; (2) by using a validation tool, they verify cryptographically ROAs; (3) at the end, RPs push origin validation results to the BGP border routers for routing decisions. In this case, only if the RPs are honest and trustworthy, the validation results can be ensured to be reliable.  
In BGPcoin, the smart contracts have defined the restriction logic to issue an ROA. Only if a transaction to issue the ROA meets the restriction, the ROA will be appended to the blockchain. Once an ROA_add transaction is submitted to issue an ROA, the miners compete to verify and confirm the transaction for the fee. Any cheating of one miner leads to its potential bookkeeping rights being lost. Without the RPs, BGPcoin allows a BGP border router to use the Ethereum client in its own AS to directly request ROAs from the blockchain.
## 3 Results and Discussion
### Strength Characteristics  
Strength characterization of the UBC was undertaken by measuring the CBR. As presented in Table 2, similar results were obtained for both the compaction methods used. Therefore, again the high comparability of the test results can be observed, but, as indicated earlier, due to variation in the OMC, the compaction method for moisture-susceptible materials should be chosen carefully.  
Strength characterization of CBC was undertaken by measuring the 28-day compressive strength and the results are presented in Table 2 and Figure 7. Small differences can be observed between the two compaction methods for natural aggregate mixtures. As expected, an increase in f\({}_{c,28}\) is observed for the cement content due to the formation of calcium silicate hydrates (C-S-H gel), which is mostly responsible for the development of compressive strength. A decrease in compressive strength is observed with the addition of waste rubber, which is in accordance with some previous research [44]. For mixtures with waste rubber, a higher compressive strength is recorded for the Proctor compaction method. The differences between the two compaction methods used are 39.62%, 46.4% and 47.9% for 20%, 30% and 60% sand replacement by rubber respectively. To analyse this high compressive strength difference between the two compaction methods used, the correlation of f\({}_{c,28}\) and compaction characteristics (MDD and density) is presented in Figure 7.  
There is a high correlation between the compressive strength and MDD regardless of the compaction method. Also, the same conclusion can be drawn for the correlation between f\({}_{c,28}\) and \(\rho\). Since the MDD and \(\rho\) are influenced by the lower density of rubber, that is one reason for the lower f\({}_{c,28}\) of rubber mixtures and the high difference between the compaction methods. However, for mixtures with waste rubber, the difference between MDD and \(\rho\) is much smaller compared to the f\({}_{c,28}\) difference obtained by the two compaction methods. The reason could be the great difference in the behaviour of the rubber mixtures when different compaction methods are used but also when the compression test is conducted. This finding presents a good basis for further research. Finally, the optimal waste rubber content is expected to be up to 30% sand replacement, since a higher rubber content could lead to a drop in the 28-day compressive strength under 2 MPa, as is usually required for most pavement purposes.
## 5 Experimental Evaluation
### Evaluation Metrics  
We evaluate all methods from four perspectives: accuracy, robustness, fairness, and detection ability. Final Accuracy (Acc) refers to the test accuracy achieved by the global model after convergence. Robustness Drop (RD) measures the performance degradation caused by adversarial clients. The Gini coefficient is used to quantify inequality in the reward distribution among clients. Recall and False-Positive Rate (FPR) are computed based on the identification of malicious clients. In addition, we report the total Training Time and Communication Volume to assess the system-level overhead and scalability.  
To quantify the fairness of incentive distribution, the Gini coefficient \(G\) is calculated as follows:  
\[G=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}|r_{i}-r_{j}|}{2n\sum_{i=1}^{n}r_{i}}, \tag{16}\]  
where \(r_{i}\) is the cumulative reward received by client \(i\), and \(n\) is the total number of clients. A lower Gini value indicates a more balanced and equitable incentive distribution.  
This metric enables the assessment of whether TAIM's trust-guided reward allocation contributes to reducing reward centralization and maintaining fairness among heterogeneous clients.
###### Abstract  
In recent decades, researchers have been focused on developing noise-robust methods in order to compensate for noise effects in automatic speech recognition (ASR) systems and enhance their performance. In this paper, we propose a feature-based noise-robust method that employs a novel data analysis technique--robust principal component analysis (RPCA). In the proposed scenario, RPCA is employed to process a noise-corrupted speech feature matrix, and the obtained sparse partition is shown to reveal speech-dominant characteristics. One apparent advantage of using RPCA for enhancing noise robustness is that no prior knowledge about the noise is required. The proposed RPCA-based method is evaluated with the Aurora-4 database and a task using a state-of-the-art deep neural network (DNN) architecture as the acoustic models. The evaluation results indicate that the newly proposed method can provide the original speech feature with significant recognition accuracy improvement, and can be cascaded with mean normalization (MN), mean and variance normalization (MVN), and relative spectral (RASTA)--three well-known and widely used feature robustness algorithms--to achieve better performance compared with the individual component method.  
robust principal component analysis; noise robustness; filter-bank features; mel-frequency cepstral coefficients; deep neural network +
Footnote †: journal:
## 4 Conclusions  
We developed a Kapton film with patterned 20 \(\upmu\)m-sized thru-holes by ion milling for use as a novel, efficient, flexible, recyclable, and active PM2.5 air filter. The single-layer Kapton air filter thru-holes effectively captured PM of various sizes (0.3-10.0 \(\upmu\)m) through its micro-sized pores and strong electrostatic force (arising from its high work function). Although the diameter of the thru-holes is 15 \(\upmu\)m, the Kapton air filter captures PMs with a diameter that is smaller than 10 \(\upmu\)m. The single-pass filtering efficiency of 0.3-0.5 \(\upmu\)m and 5.0-10.0 \(\upmu\)m sized PMs are 27.9% and 78.2%, respectively, and these values will be significantly enhanced in repeated air circulation. Furthermore, the filter was reusable after multiple and simple washing cycles even with tap water and it was stable when heated at 350 \({}^{\circ}\)C for 10 min. Therefore, the proposed filter is a promising candidate for use as a highly efficient and economical recyclable air filter for various applications, including home appliances and industrial filtration systems.  
Conceptualization, S.-J.C., K.-K.K. and S.C.; methodology, S.-J.C., K.-K.K. and S.C.; validation, M.j.L., K.-S.C., U.-D.S., J.L., J.J. and J.J.; formal analysis, M.j.L., K.-S.C., U.-D.S., J.L., J.J. and J.J.; resources, K.h.K., H.j.K., J.S.Y.; writing--original draft preparation, S.-J.C. and S.C.; writing--review and editing, S.-J.C. and S.C.; visualization, W.-T.P.; project administration, S.-J.C.  
This work was supported by the Technology Innovation Program (Grant No. 10052274) of the Ministry of Trade, industry & Energy (Korea).  
Authors thank [PERSON] at Yonsei University (Wonju, South Korea) for thermogravimetric and differential scanning calorimetry analyses.  
The authors declare no conflict of interest.
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 3 Results and Discussions  
This section represents the results of the methods defined in the previous section. The first part shows the assessment of the positional quality of low-cost GNSS receivers, whereas the second part represents displacement detection. In both cases, results will be presented for G-baseline as well as for L-baseline.
# Application of Compact Folded-Arms Square Open-Loop Resonator to Bandpass Filter Design
## 3 Microstrip Filter Design  
This section reports on the fifth-order filter implementation using the proposed microstrip folded-arms square open-loop resonators. The section is divided into two subsections (resonator dimension and coupling coefficient and external quality factor). Each subsection discusses a stage in the microstrip filter design in detail and captures the sub-circuit parameters that are later put together to achieve the complete bandpass filter circuit results.
## 4 Blockchain Technology for Security
### Structure of Blockchain  
Organizations and corporations can use blockchain infrastructure for the specified objectives:  
**Cost Saving**: A lot of money is spent keeping centrally maintained databases, such as those used by banks and other governmental agencies, up to date and secure from cybercrime and other types of corruption.  
**Data Retrospective**: You can always check the history of any transaction within a blockchain architecture. This is an ever-growing archive, as opposed to a centralized database that is more like a single data snapshot.  
**Security and Validity of Data**: It is difficult to interfere with data once published, owing to the nature of the blockchain. It takes time to validate records since they are validated in each isolated network rather than employing a pool of computational resources. This means the system must choose between efficiency, data protection, and validity [61; 62].
## 3 Downlink and Uplink Transmission Operations
When the value of the VOA is at its minimum, the minimum BER is exceptionally low, whereas the scheme offers a minimum BER of slightly below 10 \(\times\) 10\({}^{-3}\) for the high attenuation value, as shown in Figure 6b, which greatly supports the applicability of the proposed scheme. Figure 6: (**a**) The multiplexed uplink signal received from five RNs: (**b**) the BER vs. the received signal power [data rate = 10 Gbps].
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 2 System Model and Overview  
We consider a disaster area where a catastrophic event is evolving, and several evacuation routes are available within the disaster area. Let us denote their set by \(E=\{1,\ldots,e,\ldots,|E|\}\), while each human \(m\) residing in the disaster area aims at selecting an evacuation route \(e\) to escape from the area. The set of evacuees is denoted by \(M=\{1,\ldots,m,\ldots,|M|\}\). Each evacuation route is characterized by a specific capacity of evacuees \(C_{e}\), and a corresponding rate of evacuation \(\lambda_{e}\), where both depend on the physical characteristics of the evacuation route, e.g., stairs or highway. At the time that an evacuee examines to select an evacuation route, a number of humans \(|M|_{e}\), \(\forall e\in E\) are already in the specific route \(e\), \(\forall e\in E\). The distance of each evacuee \(m\) from the evacuation route \(e\) is denoted by \(d_{m,e}\). Figure 1 provides an illustrative example of the topology and model under consideration [11].
## 4 Influence of Ageing on Charge Transport Properties and Discussion
### Influence of Ageing on Carrier Mobility Properties
Figure 8: (**a**) Model of thermally activated hopping charge conduction in dielectrics, \(E_{\text{F}}\) is the Fermi level, \(E_{\text{C}}\) is the conduction band, and \(E_{\text{g}}\) is the band gap width. Charge carriers hop between traps without (**b**) and with (**c**) applying the electric field. Using Equation (4), we can obtain the influence of the trap energy and trap density on the carrier mobility and conductivity. Figure 9 shows the relationship between the carrier mobility and the density and energy of traps. As the ageing time increases, the trap level of the silicone rubber insulation material decreases as shown in Figure 7, resulting in carriers that easily hop between traps. In addition, the shallow trap density of the silicone rubber insulation material also increases with the aging time, which reduces the mean distance between traps and the hopping distance. Both the reduction in trap energy and the increase in trap density change the carrier migration characteristics, resulting in an increase of the carrier mobility with increasing aging time of the insulating materials. The conductivity of the insulating material is directly proportional to the carrier mobility, so the conductivity of the silicone rubber will increase as the aging time increases, which is consistent with the experimental results shown in Figure 5.
## 2 Materials and Methods
### Late Fusion Methodology  
Regarding the Late Fusion model as described in prior work [25], a meta-classifier was developed and trained, with the fusion of the prediction probabilities from the two separate visual and audio models (**S1.1**, **S1.2**). Initially, two single models were developed, a visual model that uses visual data to collect landmarks, detect human body stances, and predict aggressive probabilities in the said stance. One audio model that collects audio data and uses feature extraction with various techniques, such as noise filtering and MFCCs, is trained on these features and predicts probabilities of aggressive verbal instances. In the Late Fusion model development, probabilities of these two models were saved and then fused following testing on a holdout dataset, with unseen data, during training. Every row was processed over the visual data frame and compared with a corresponding row that exists in the audio data frame in the same order [39]. Data were merged based on their true and predicted labels. To handle uncertainty predictions, the Hybrid UncertaintyCalibration approach was implemented as presented in [40]. Finally, the confidence of each model was used to adjust the final predictions (**S1.6**).