esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 1 Introduction  
Conventional manufacturing consists of formative (molds) or subtractive (machining) techniques that require multiple steps and costly infrastructure, which limits capability for timely implementation of modifications to the final product [1]. Furthermore, these conventional techniques do not allow for intricate and complex geometries that are often required in biomedical engineering applications [1]. 3D printing, or additive manufacturing (AM), however, has emerged over the past four decades as a robust tool for fabricating geometrically complex objects in a cost-effective and timely manner [1; 2; 3]. Developed in the 1980s, the core concept of 3D printing involves layered deposition of material in three-dimensional space guided by a computer-generated model [4]. This allows for the creation of very complex designs that would be extremely difficult or even impossible to produce using conventional manufacturing techniques, which has resulted in its increased adoption by various industries [1]. Its role in healthcare is becoming increasingly important through its utilization in therapeutic delivery [5; 6; 7], surgical planning [8], implant design [9] and tissue engineering [10; 11; 12]. A unique but rapidly growing application of AM is bioprinting, which allows for the seeding of cells in a spatially defined manner in 3D space [13]. This enables the production of in vitro models for drug screening and disease modeling as well as biofabrication of implantable tissues such as skin [14], cartilage [15] or bone [16]. In this review, we will outline common AM fabrication methods, biomedically relevant printing materials and their use in healthcare applications.
## 6 Speech Quality and Intelligibility Evaluation
### Objective Evaluation
#### 6.1.1 Speech Quality Perception Evaluation (PESQ)  
The standard PESQ [36] uses an auditory model, which includes an auditory filter, a spectrum, time masking, and is defined in ITU-T Recommendation p.862 ITU. Hearing loss is not included in the auditory model. In its basic version, the index measures 11 signal features compared with the reference signal and combines these signal features to give the quality value. An index developed to evaluate the high-quality codec system of the NH audience. Index prediction designed for a small amount of distortion is not accurate for larger distortion associated with low data rate codecs [35; 36]. They are  
Figure 10: The block diagram of objective evaluation used for the study. NR, noise reduction; HA, hearing aid; PESQ, speech quality perception evaluation; HASQI, hearing aid speech quality index; HASPI, hearing aid speech perception index.  
Figure 9: The spectrograms result for (**a**) clean speech, (**b**) noisy speech, and (**c**) enhanced speech by the proposed method using street noise (SNR = 15 dB).  
not expected to be accurate for the larger range of signal attenuation found in hearing aids. The noisy speech reduction and the signal distortion are measured by Equations (13) and (14), respectively:  
\[\mathit{Reduct}\triangleq\frac{1}{N*d}\sum_{i=1}^{N}\bigl{|}\hat{X}_{i}-y_{i} \bigr{|} \tag{13}\]  
\[\mathit{Dist}\triangleq\frac{1}{N*d}\sum_{i=1}^{N}\bigl{|}\hat{X}_{i}-x_{i} \bigr{|} \tag{14}\]  
where \(N\) is the total number of testing samples, and \(d\) is the size (dimension) of the input data of the first layer of the DDAE. Based on the noise reduction criterion (denoted as \"Reduct\" in the experiments), the larger the value, the better the restored speech. However, reducing much noise inevitably causes speech distortion. Based on the speech distortion measurement (denoted as \"Dist\" in the experiments), the lower the value, the better the restored speech.
# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 4 Experimental Evaluation
### Evaluation Setup
#### 4.1.2 Performance Metrics  
We use the _Accuracy_ to evaluate the effect of the proposed network embedding framework. In addition, we also use \(F1\) score that combines _Precision_ and _Recall_ to evaluate the overall performance of AHNEAP since we consider both _Precision_ and _Recall_ are important for any prediction mechanism. The two measurements _Precision_ and _Recall_ are adopted for measuring the proportion of actual positive samples in the positive examples calculated by AHNEAP, and the proportion of positive samples determined by our framework AHNEAP in all examples:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}, \tag{7}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{8}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{9}\]  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}, \tag{10}\]  
where \(TP\), \(TN\), \(FP\) and \(FN\) represent _TruePositive_, _TrueNegative_, _FalsePositive_, and _FalseNegative_, respectively.  
In this section, we mainly measure the accuracy of various methods and AHNEAP, and all evaluations calculate the hit ratio of Apps corresponding to the \(top-k\) scores. As long as the score of real used App is within the predicted \(top-k\) scores, we count it as one hit. Then, _Accuracy@k_[7] indicates the accuracy when predicting the \(k-th\) candidate applications. We mostly choose _Accuracy@4_ for comparison.
## 5 Discussion  
This study shows the RBTO analysis for different evolutionary optimization methods, in particular, the implementation of SESO and SERA for this type of analysis. The numerical examples show that the procedure has several advantages:  
1. Insertion of the preconditioner for solving the linear system provided efficiency in computational memory, allowing large-scale problems to be solved.
2. The probabilistic analysis loop is fully decoupled from the optimization loop for easy code implementation, making the computational cost much lower than DTO analysis.
3. The compliance constraints, displacement, performance index, and limit state functions, when used, are inserted into the OT loop in a simple way.  
To highlight the importance of the reliability analysis, that is, the insertion of uncertainties into the TO procedure, the elasticity modulus and the inertia moment were inserted into the algorithm as uncertain variables, considering them normal distributions with standard deviation equal to 10% of the average value, taking the reliability analysis more realistically, for which it is possible to highlight the following:  
1. The MBB beam's DTO analysis has a reliability index, \(\beta_{t}=0.5\), which means that the optimal structure has a failure probability equal to 30.85%, achieved when the displacement constraint is violated.
2. The RBTO analysis produces optimal topologies with lower volume and more rigidity than those proposed by DTO, as already described in this study.
3. The results showed that a change in the target reliability index changes the optimized topology in two ways: the division of the members and the members' spacing in the structure. These trends are observed when the reliability index \(\beta_{t}>4.5\).
## 3 Survey Results
### Sensor Mote
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**N** & **Codename** & **Mote** & **Ready or custom** & **Mote motivation** & **Radio chip** & **Radio protocol** \\ \hline
25 & BikeNet & TMote Invent & adapted & off-the-shelf mote providing required connectivity & Chipcon CC2420 & 802.15.4 \\
26 & BriMon & Tmote Sky & adapted & off the shelf & Chipcon CC2420 & 802.15.4 \\
27 & IP net & Scatterweb ESB & adapted & Necessary sensors on board & TR1001 &? \\
28 & Smart home & ZigbeX & custom & specific sensor, size and power constraints & Chipcon CC2420 & 802.15.4 \\
29 & SVATS & Mica2 & ready & off-the-shelf & Chipcon CC1000 & SmartRF \\
30 & Hitchhiker & TinyNode & adapted & long-range communication & Semtech XE1205 &? \\
31 & Daily morning & MicaZ & ready & off-the-shelf & Chipcon CC2420 & 802.15.4 \\
32 & Heritage & 3 Mate! & adapted & TinyOS supported mote with custom sensors & Chipcon CC2420 & 802.15.4 \\
33 & AC meter & ACme (Epic core) & adapted & modular; convenient prototyping & Chipcon CC2420 & 802.15.4 \\
34 & Coal mine & Mica2 & ready & off-the-shelf & Chipcon CC1000 & SmartRF \\
35 & ITS & Custom & custom & specific sensing needs & Chipcon CC2420 & 802.15.4 \\
36 & Underwater & AquaNode & custom & specific packaging, sensor and actuator needs & custom & - \\
37 & PipeProbe & Eco mote & adapted & size and energy constraints & Nordic nRF24E1 &? \\
38 & Badgers & V1: Tmote Sky + external board; V2: custom & v1: adapted; v2: custom & v1: off-the-shelf v2: optimizations & Atmel & 802.15.4 \\
39 & Helens volcano & custom & custom & specific computational, sensing and packaging needs & Chipcon CC2420 & 802.15.4 \\
40 & Tunnels & TRITon mote [62] : TelosB-like & custom & reuse and custom packaging & Chipcon CC2420 & 802.15.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._The most popular reason for building a customized mote is specific sensing and packaging constraints. The application range is very wide; there will always be applications with specific requirements. On the other hand, part of the sensor network users are beginners in the field and do not have resources to develop a new platform to assess a certain idea in real-world settings. Off-the-shelf commercial platforms, a simple programming interface, default settings and demo applications are required for this user class. Chipcon CC1000 [66] radio was popular for early deployments; however, Chipcon CC2420 [67] is the most popular in recent years. IEEE 802.15.4 is the most popular radio transmission protocol (used in CC2420 and other radio chips) at the moment. _Design rule 11_: Driver support for CC2420 radio is essential. More radio chips and system-on-chip solutions using the IEEE 802.15.4 protocol can be expected in the coming years.
## 3 Results
### Ejection Temperature  
Through maintaining the ejection pressure at 2 bar, the atomization pressure at 15 bar, and the diameter of the ejection nozzle at 200 \(\upmu\)m, atomized powders were prepared, for which the melt was superheated by 10, 20, and 50 \({}^{\circ}\)C, respectively, above the melting temperature of Fe (1538 \({}^{\circ}\)C). Figure 2 shows SEM images of the atomization results of the powders for the constant atomization parameters specified above and different ejection temperatures.  
The melt ejection temperature is primarily reflected in the melt viscosity. A high melt temperature leads to a low viscosity, while a low temperature leads to a high melt viscosity. Atomization of the high viscosity melt (low temperature, 1548 \({}^{\circ}\)C) generally results in a large amount of fibers/ligaments and less spherical particles, as can be seen in Figure 2a. With the increase in temperature to 1568 \({}^{\circ}\)C, the viscosity drops; thus, it is possible to disintegrate the melt flow into particles, mostly spherical ones (Figure 2b). The further increase in the ejection temperature to 1588 \({}^{\circ}\)C leads to uniformity in the morphology of the formed particles; thus, for the parameters specified above, we obtained mostly spherical powders with a diameter between 20 and 120 \(\upmu\)m (Figure 2c). Therefore, we can state that a further increase in melting/ejection temperatures would lead to finer and more uniform particles. However, such an increase in temperature is not possible due to technological limitations related to the fact that the melting temperature of iron is close to the softening temperature of the quartz tube in which it is heated, meaning that further melt heating would lead to the flow of the quartz, prohibiting the atomization process.
## 3 Results
### Proteome Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Protein ID**} & \multirow{2}{*}{**Protein Name**} & \multirow{2}{*}{**Petiple Sequence**} & \multicolumn{2}{c}{**Log2 Protein Abundance**} \\ \cline{3-6}  & & & **WT** & **RI1-2** & **RI1-3** \\ \hline Q53 LQ0 & Protein disulfide isomerase-like 1-1 & FLIGDIEASQGAFQYFGLREDQ & 15.67 & 0 & 0 \\ Q0 JJZ6 & Anoctamin-like protein & VPLIIIIQDGESKK & 14.95 & 0 & 0 \\ O56834 & Minor outer capsid protein P2 & NLFSLLQKRK & 13.89 & 0 & 0 \\ Q8W1L6 & Peroxisomal fatty acid beta-oxidation & \multirow{2}{*}{MNKAMSLLKGALDYSDFK} & 13.66 & 0 & 0 \\  & multifunctional protein & & & \\ Q5 ON75 & Mitogen-activated protein kinase & RGKKPPK & 13.59 & 0 & 0 \\ Q84 UP7 & Mixed-linked glucan synthase 6 & SHPYMGRAQEEFVNDRR & 12.38 & 0 & 0 \\ C7J0A2 & DNA topoisomerase 3-alpha & ASRYRMSEHTMK & 12.03 & 0 & 0 \\ Q9 SQ9 & NAC domain containing protein 50 & NASGQAS & 0 & 14.88 & 14.80 \\ Q6 ETL8 & Very-long-chain & LAAMRLPK & 0 & 13.19 & 13.43 \\ Q0 JMH0 & 2-hydroxyacyl-CoA lyase & ARDNVLKMEAQLAK & 0 & 15.58 & 15.05 \\ B9 FMJ3 & Kinesin-like protein KIN-13A & LARFQHRLK & 0 & 13.85 & 14.66 \\ Q0 IZQ2 & Cysteine-tRNA ligase & QYEKSDEIR & 0 & 12.47 & 14.73 \\ Q9 AV50 & Double-stranded RNA-binding & \multirow{2}{*}{ILPLFRPKSNSR} & 0 & 16.49 & 14.17 \\  & protein 6 & & & \\ Q6K4V3 & Zinc finger CCCH domain-containing & \multirow{2}{*}{APSSTSK} & 0 & 15.22 & 14.92 \\ Q9 SP32 & Endoribonuclease dicer homolog 1 & AEENKSKPEER & 0 & 14.70 & 14.53 \\ Q7 FAD5 & Synaptonemal complex protein ZEP1 & ARLLYVDSRELECMEQELK & 12.49 & 15.82 & 15.43 \\ Q62 CZ2 & Brassinosteroid LRR receptor & NFARQSVFLAVTVLSVILIF & 11.70 & 14.56 & 14.02 \\  & kinase BRL3 & SLLIHYKLWK & \multirow{2}{*}{14.21} & 14.26 & 12.17 \\ Q0 DV66 & Pheeophorbide a oxygenase & AWWQLYPR & 14.21 & 14.26 & 12.17 \\ Q0D5P3 & Formin-like protein 11 & EASKVAPVR & 11.62 & 14.11 & 14.05 \\ Q7 XD96 & Endoribonuclease & ASLCLHMSYFK & 12.20 & 13.78 & 14.07 \\ Q67W65 & Transcription initiation factor TFIID & EDELQKAK & 13.86 & 12.72 & 13.59 \\ Q6 YUL8 & Kinesin-like protein KIN-4A & ARNIQNKFIVNR & 14.76 & 0 & 11.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Differentially expressed proteins in WT and _OsVTC1-1_ RNAi lines. Figure 5: Venn diagram of wild type (green) proteome overlapped with RI1-2 (blue) and RI1-3 (yellow) lines.
## 3 Results and Discussion
### Optimization of SPME Parameters
Figure 2: HS extraction time profiles obtained with both selected fibers at 37 \({}^{\circ}\)C.  
The influence of pH and temperature on the extraction efficiency were also examined performing extractions by using different pH buffers and temperatures, even if they did not show a positive effect. Figure 4 reports, for instance, the results obtained working at room temperature and at 50 \({}^{\circ}\)C, clearly showing how the temperature increase produced a decrease of the peak area. As can be easily inferred from the results obtained so far, the CAR/DVB/PDMS fiber showed the best extraction efficiencies for almost all the analytes and was then selected for the prosecution of the experiments. Figure 5 reports the extraction time profiles obtained with the CAR/DVB/PDMS fiber at room temperature in the presence of 30% NaCl. Equilibrium was reached after 45 min for toluene and ethylbenzene, while longer sampling times were required for the other analytes. However, considering that non-equilibrium extractions can be performed working under reproducible conditions, 60 min were selected as the best compromise between time and sensitivity. Figure 4: Effect of the temperature on the DI extraction efficiency obtained with both selected fibers.
# A Heterogeneous Machine Learning Ensemble Framework for Malicious Webpage Detection
## 2 Related Work  
### Heuristic-Based Malicious Website Detection  
Heuristic-based techniques use an algorithm to generate matches from a database after scanning suspicious webpages. In this approach, blacklisting is the most widely employed practice. The database contains profiles of known malicious websites such as URL, IP addresses, and domains. If a newly added URL matches the known malicious URL listed on the blacklist, it is deemed malicious [12]. The heuristic method is implemented using a webpage execution dynamics analysis, identifying any signature of malicious activities, such as abnormal process generation and repetitive redirection. However, this tedious mechanism requires everyday access to and analysis of each website. Because a large number of new URLs are generated each day, it is impracticable to maintain a valid blacklist. Nevertheless, its simplicity and efficiency are sufficient to overcome its inherent limitations, and the heuristic method is widely used in antivirus systems.  
Phishing websites often include branding that appears legitimate and may even use the same logo as the actual company. It is very difficult to determine which page belongs to each website, but this can be partially supplemented by heuristic approaches. For this purpose, a tentative blacklist is generated in XML format by analyzing all webpages under the same hostname. When a particular business name is typed on Google, it shows a valid URL at the top of the search result page; if this URL is blocked, it is deemed as phishing, and the address is automatically updated and included in a blacklist [13].  
Heuristic-based approaches have the advantages of simplicity, high efficiency, and general performance; however, there are nondeterministic polynomial (NP)-hard problems [14], computational complexity owing to several iterations, and local optimization problems [15].
## 2 Related Work and Theory
### Theoretical Analysis
#### 2.3.2 Working Mechanism of Attentional Mechanisms  
1. Channel and spatial attention mechanisms: the channel attention mechanism adaptively adjusts the weights of feature channels to focus on important rain streak information while suppressing irrelevant ones. It captures global information through average pooling and generates weights via convolution, prioritizing channels with stronger rain streak features and adjusting to different rain conditions. The spatial attention mechanism weights the significance of different image regions, creating an attention map that highlights areas with concentrated rain streaks. It combines global and local information through max-pooling and average-pooling, ensuring that rain streaks are processed in key regions while preserving details in others.
2. Synergistic effect of channel and spatial attention: the spatial and channel attention mechanisms apply weighting to the feature map from different dimensions, with channel attention focusing on feature channels and spatial attention on regions. Combining these mechanisms creates a synergistic effect in deraining tasks. Channel attention prioritizes rain-related information, while spatial attention focuses on important regions to ensure accurate rain streak removal and preserve image details. In a multi-branch network, both attention mechanisms work together to enhance feature gathering, improving feature map learning and ultimately enhancing the quality of the derained image.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 3 Identity-Based Encryption Scheme
### Security Analysis
When \(\mathcal{O}=\mathcal{O}_{\mathcal{S}}\), \(\mathbf{v}_{0}\in\mathbb{Z}_{Q}\) and \(\mathbf{v}^{*}\in\mathbb{Z}_{Q}^{m_{1}\times 1}\) are uniformly sampled at random. According to Lemma 3, the matrix \(-\mathbf{R}^{T}\mathbf{v}^{*}\) adheres to a discrete random distribution, the expression \(-\mathbf{R}^{T}\mathbf{v}^{*}+\mathbf{e}\) similarly conforms to a discrete random distribution. Consequently, the distribution of the challenge ciphertext \(c^{*}\) in Game 2 is indistinguishable from its distribution in Game 3. **Queries 2:** The adversary \(\mathcal{A}\) may proceed to make private key queries in the same manner described in Queries 1. **Guess:** The adversary \(\mathcal{A}\) attempts to determine whether the ciphertext is a random vector in the ciphertext space \(\mathbb{Z}_{Q}^{m^{\prime}}\) or the actual ciphertext of the plaintext bit message \(msg^{*}\). The challenger \(\mathcal{C}\) determines whether the sampled samples from the oracle \(\mathcal{O}\) are \(LWE_{\lambda,\mathcal{Q},\mathcal{X}}\) samples from \(\mathcal{O}_{s}\) or random samples from \(\mathcal{O}_{s}\) based on the guess. In summary, when \(\mathcal{O}=\mathcal{O}_{s}\), the adversary \(\mathcal{A}\) experiences the scenario as in Game 2; when \(\mathcal{O}=\mathcal{O}_{s}\), the scenario is perceived as in Game 3. Given that the simulator \(\mathcal{S}\)'s capability to resolve the \(LWE_{n,\mathcal{Q},\mathcal{X}}\) assumption is equivalent to the adversary \(\mathcal{A}\)'s ability to differentiate between Game 2 and Game 3, and considering that no PPT simulator \(\mathcal{S}\) can effectively solve the \(LWE_{n,\mathcal{Q},\mathcal{X}}\) assumption, it follows that the IBE scheme discussed in this paper is secure under the IND-sID-CPA model. This completes the proof.
# Research on Seismic Phase Recognition Method Based on Bi-LSTM Network
## 5 Conclusions  
In this paper, the application of the Bi-LSTM network in automatic seismic phase recognition was studied, and a seismic phase recognition model based on the Bi-LSTM network was designed to solve the problems existing in the current phase recognition methods, with the STEAD dataset used for training and testing. At the same time, the proposed model based on the Bi-LSTM network was compared with the traditional STA/LTA and AIC methods. The experimental results show that when the Bi-LSTM network is applied to phase automatic recognition, compared with the STA/LTA and AIC methods, the misdetection rate is reduced by about 8-15%, and the precision and RMSE are slightly improved. This shows that the automatic seismic phase recognition model based on the Bi-LSTM network can reduce the misdetection rate of automatic phase recognition.  
Conceptualization, L.W. and J.C.; methodology, L.W.; software, H.C.; validation, L.W., L.D. and L.G.; formal analysis, X.S.; investigation, L.D.; resources, L.W.; data curation, J.C.; writing--original draft preparation, L.W.; writing--review and editing, L.W.; visualization, L.W.; supervision, X.S.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research Project Item of Hebei Province Education Department, grant number ZC2023104; Science and Technology Innovation Program for Postgraduate students in IDP subsidized by Fundamental Research Funds for the Central Universities, grant number ZY20230322; the Key Laboratory Open Fund Project of Hebei Provincial, grant number FZ224105; and the College Students' Innovation and Entrepreneurship Training Program Project of Institute of Disaster Prevention, grant number 202311775011.  
Not applicable.  
The Stanford Earthquake Dataset can be downloaded from the official website of Stanford Earthquake Dataset (STEAD). This dataset is a global seismic signal dataset for artificial intelligence, supporting the Python programming language.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l} STA & Short-term average \\ LTA & Long-term average \\ AIC & Akaike information criterion \\ RNN & Recurrent neural network \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**A**} & \multicolumn{1}{c}{**M**} \\ \hline \multirow{2}{*}{Bi-LSTM} & P & 0.48 & 72.13\% & 23.18\% \\  & S & 0.49 & 73.89\% & 25.34\% \\ \hline \multirow{2}{*}{STA/LTA} & P & 0.49 & 73.45\% & 37.52\% \\  & S & 0.62 & 76.23\% & 35.87\% \\ \hline \multirow{2}{*}{AIC} & P & 0.43 & 75.13\% & 32.81\% \\  & S & 0.58 & 78.92\% & 30.72\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experimental results of the automatic seismic phase recognition model based on the Bi-LSTM network with STA/LTA and AIC methods.