esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Study in Long Delay Regime  
### Experimental System  
The experimental setup to investigate frequency noise of mutually coupled lasers with long delay is shown in Figure 11. Two DFB lasers without internal isolators in their packages are employed to implement mutual optical injection. They have similar characteristics and are mutually coupled through two 1 \(\times\) 2 couplers (50/50), driven by ILX Lightwave 39425 current/TEC modules with 0.01 mA and 0.1 \({}^{\circ}\)C resolution. All fibers in the link, including laser pigtails, are polarization-maintaining fiber (PMF) to ensure polarization matching between the two lasers. Coupling strength is adjusted by an electrically controlled Micro-Electro-Mechanical System (MEMS) variable optical attenuator (VOA). An extra fiber cable is inserted before DFB#2 to adjust the coupling length. Frequency noise of the lasers are measured at Ports #1 and #2, where isolators (ISOs) are employed to prevent reflection from the measurement system. The delay time of the fiber link is on the order of ~10 ns, much longer than the time scale 1/\(f_{\text{FO}}\).
## 2 Materials and Methods
### Three-Dimensional Model  
To accurately model the bone in 3D in this study, a function implementing the Lewiner algorithm [26] for extracting iso-surfaces from 3D volumetric data was implemented. This algorithm is an enhanced version of the original marching cubes algorithm [27], providing faster performance and resolving ambiguities to ensure topologically correct results. Specifically, it uses a refined set of lookup tables to handle all possible configurations of surface intersections within a cube. This approach not only improves the accuracy of surface reconstruction, but also ensures the robustness of the generated meshes, making it particularly suitable for complex and high-resolution datasets. The algorithm's implementation allows for efficient processing and accurate depiction of iso-surfaces, contributing to the visualization and analysis of the 3D data generated by the segmentation process in our research.  
To evaluate the results in terms of modeling, a surface-based metric was used to calculate the distance between the obtained mesh and a reference mesh derived from the manual segmentation of bone. The Hausdorff distance is a measure of the extent to which two subsets of a metric space are close to each other. More formally, given two non-empty subsets \(A\) and \(B\) of a metric space with a distance function \(d\), the Hausdorff distance \(d_{H}(A,B)\) is defined as: \[d_{H}(A,B)=\max\left\{\sup_{a\in A}\inf_{b\in B}d(a,b),\sup_{b\in B}\inf_{a\in A }d(b,a)\right\}\] (3) where the following applies: \(\inf_{b\in B}d(a,b)\) measures the shortest distance from a point \(a\) in set \(A\) to any point in set \(B\). \(\sup_{a\in A}\inf_{b\in B}d(a,b)\) then considers the farthest distance of these shortest distances for all points in \(A\). This ensures that every point in \(A\) is close to some point in \(B\). \(\sup_{b\in B}\inf_{a\in A}d(b,a)\) ensures that every point in \(B\) is close to some point in \(A\). The maximum of these two quantities is taken to make the distance symmetric and to reflect the greatest extent to which one set can be far from the other. In brief, the Hausdorff distance \(d_{H}(A,B)\) is the greatest of all the distances from a point in one set to the closest point in the other set, ensuring that both sets are close to each other in a symmetrical sense. Smaller values of the Hausdorff distance indicate better performance.
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 2 Materials and Methods
### Feature Extraction
#### 2.3.1 Mel Spectrogram  
This is a 2-dimensional signal where its variables are time and frequency. To compute this spectrogram, first, the short-term Fourier transform is computed over a series of overlapping windows of the audio signal, obtaining a representation that shows the frequency content of the signal as it changes over time. Then, frequencies are scaled according to the Mel's scale [19]. This scale tries to resemble human's perception of pitches and can be computed using the following equation.  
\[F_{mel}=\frac{1000}{\log(2)}\log\left(1+\frac{F_{hz}}{1000}\right) \tag{1}\]  
The Mel spectrogram was obtained from the input audio data that we collected in our database for each class: background and three types of calls for help. Figure 3 illustrates the computation of the Mel spectrogram from a sample of each category. Note that this 2-dimensional representation of the audio signals evidences distinguishing patterns for each one of the proposed classes.
## 3 Results and Discussion
### Electrochemical Stability of LLZO-IL Composites  
The ternary composites of LLZO with either FSI IL or DFOB IL containing their corresponding lithium salt, namely LiFSI or LiDFOB, in ratio of IL:Li salt = 9:1 in mol, were prepared in addition to the binary composites of LLZO and ILs. Figure 7 summarizes the CV of these four composites. In the cathodic scan (OCV to 0.01 V vs. Li/Li\({}^{+}\)) of LLZO-FSI IL composites with and without LiFSI (Figure 7a), an increase in the reduction current was observed from 1.8 V vs. Li/Li\({}^{+}\) due to the decomposition of FSI anion as discussed in the literature [24]. This current was suppressed in the successive cycles (Figure S6), suggesting the formation of a passivation layer on Super P surface. The reduction current below 0.5 V vs. Li/Li\({}^{+}\) arose from the lithium intercalation to Super P. This current density decreased during successive cycling when LiFSI was absent. When LiFSI was present in the composite, however, the currents associated with lithium intercalation as well as lithium de-intercalation in the reverse scan were found to be constant from the 2 nd to the 4 th cycle. This confirms the lithium intercalation/de-intercalation is reproducible in the case of the ternary composite made of LLZO, FSI IL, and LiFSI. In the anodic scan from OCV to 6.0 V vs. Li/Li\({}^{+}\), the current increase began at 3.2 V vs. Li/Li\({}^{+}\) and current density reached 0.01 mA cm\({}^{-2}\) at 3.9 V vs. Li/Li\({}^{+}\) in the case of LLZO-FSI IL binary composites. This oxidation current was suppressed with the presence of LiFSI. In this case, the current density was less than 0.01 mA cm\({}^{-2}\) until 4.7 V.  
For the composites with DFOB IL, the reduction current was observed around 0.9 V vs. Li/Li\({}^{+}\). A current due to the reduction of DFOB was observed at 1.4 V vs. Li/Li\({}^{+}\) when a similar IL with DFOB anion, namely \(N\)-methoxyethyl-\(N\)-methylpiperidinium DFOB, was added to carbonate-based electrolyte [23], while when this anion was combined with \(N\)-alkyl-\(N\)-methylpyrrolidinium, the reduction current was observed at 0.4 V vs. Li/Li\({}^{+}\)[34]. Therefore, the peak at 0.9 V vs. Li/Li\({}^{+}\) is expected to be due to the reduction of the DFOB anions. In the case of the composites with DFOB IL, a stable lithium intercalation/de-intercalation was not observed regardless of the presence of LiDFOB (see Figure S6). Because of the better ionic conductivity and the ability of constant lithium  
Figure 7: CV voltammetry of (**a,c**) LLZO–FSI IL and (**b,d**) LLZO–DFOB IL composites with (red) and without (black) lithium salt in the ranges of (**a,b**) 3–0.01 V vs. Li/Li\({}^{+}\) and (**c,d**) 3–6 V vs. Li/Li\({}^{+}\). The arrows indicate the starting point and direction of the sweeps.  
intercalation/de-intercalation, LLZO-FSI IL doped with LiFSI was used for the galvanostatic charge-discharge analysis.
# Injection Molding of Biodegradable Deciduous Teeth Dental Post
## 3 Results and Discussion
### Characterization of PLA/PCL/ACP Material
#### 3.1.3 Contact Angle Test  
The contact angle test was performed with a distilled water droplet (approximately 12 \(\mu\)L in volume), and a manual goniometer (FTA-1000B, First Ten Angstroms, Portsmouth, VA, USA) was used to obtain the measurements. The results, shown in Figure 6, indicate a mean contact angle of 73.73\({}^{\circ}\)\(\pm\) 1.6\({}^{\circ}\) for the PLA/PCL/ACP composite material across three trials, classifying it as a hydrophilic material. This value is lower than the typical contact angle of pure PLA, which is approximately 80\({}^{\circ}\), as reported by [PERSON] et al. [33], confirming that PCL and ACP influence the composite's surface wettability.  
Studies show that incorporating PCL enhances PLA's wettability due to the polar functional groups within PCL, which interact with moisture and reduce the contact angle. [PERSON] et al. [34] reported that adding up to 50% PCL significantly lowers the contact angle, further supporting this observation. However, while higher PCL content (>20%) can improve hydrophilicity, it has been noted that mechanical properties are optimal when the PCL content remains at approximately 20%, as excessive PCL may compromise structural integrity.  
Although the dental post does not directly contact the deciduous teeth, its hydrophilic nature can enhance adhesion to root canal fillers such as Malay glue, thereby improving fixation strength. According to [PERSON] et al. [35], improved wettability--typically indicated by a contact angle below 75\({}^{\circ}\)--allows for deeper resin infiltration and the formation of a robust hybrid layer, ultimately leading to stronger adhesion between the dental post and the filling material.  
Figure 6: Contact angle measurements of the PLA/PCL/ACP composite: (a) 75.47\({}^{\circ}\), (b) 73.38\({}^{\circ}\), and (c) 72.34\({}^{\circ}\), indicating moderate wettability.  
Furthermore, [PERSON] et al. [20] investigated biodegradable scaffolds, demonstrating that materials with optimized wettability contribute to better biocompatibility. While the PLA/PCL/ACP post does not promote bone cell attachment or proliferation, its surface properties may enhance the compatibility of the root canal restoration process.  
In summary, the PLA/PCL/ACP composite exhibits a favorable contact angle for dental applications, balancing hydrophilicity for improved adhesion to fillers while maintaining sufficient stability for primary tooth restorations. These results corroborate previous studies indicating that moderate wettability enhances fixation and material compatibility in biomedical applications [20; 33; 34; 35].
# Practical I-Voting on Stellar Blockchain
## 3 System Proposition
### Weighted Votes  
In a situation where the strength of the vote should represent the \"skin in the game\", weighted votes are a very beneficial feature. On Stellar, it can be easily represented by an amount of some tokens on a corresponding Stellar account. There are three possible implementations of such features.  
* The first one is the simplest. Instead of sending one vote token for each voter, we can send him multiple vote tokens. That way, the counting method does not change. The downside is that we lose the information about the number of voters in the elections since the number of vote tokens does not equal the number of eligible voters.
* Second one is to send vote tokens along with additional vote weight tokens that could represent the weight of such vote. The counting method would require an additional query of Stellar account assets (tokens), but we do not lose track of the number of vote tokens.  
Figure 3: Sequence diagram of final protocol.  
* Third one assumes that the voter already possesses the Stellar account with the tokens representing the vote's weight. That way, the voter can specify the account he wants to receive the vote token, instead of generating a new temporal account.  
It should be noted that all of those methods sacrifice privacy to some degree. If the weight distribution is publicly known, then it is easy to connect each person with the corresponding account, and so the vote option. In the third option, the user needs to reveal its Stellar account, which might also be some privacy leakage. There is also a risk that, if the weight tokens can be bought on an exchange, one could buy plenty of tokens just before the end of elections, gaining huge vote weight, and sell them afterward. To prevent such a scenario, some devotion periods should be required, where the user must hold the tokens on its account, e.g., for one month.
## 2 M2M Service Networks
### Related M2M Standardization Approaches
However, content delivery differs essentially from the discovery of services exposed from M2M asset devices. There are some service discovery systems targeted to local scale and embedded devices such as UPnP and Bluetooth SDP. However, there is lack of solutions for large-scale wireless and hybrid networks [33]. Overlay networking is based on the virtual communication layer, which is built on top of other transport media and/or physical network [34]. Overlay networking has been applied to improve the robustness and availability of Internet paths between hosts (e.g., MIT RON), enable smooth transition to the improved technology (e.g., 6 Bone) or to reduce network load by peer assisted data delivery (e.g., BitTorrent). Overlays have been used to route control messages and connect different entities (e.g., SIP and XMPP), and also to implement data forwarding and dissemination (e.g., Chord, Tapestry, and Pastry). Another motivation for overlay networking arises from security challenges. For example, Virtual Private Ad Hoc Networking (VPAN) has been developed to create virtual overlay networks between trusted IP capable devices [35]. In an M2M system, M2M asset devices, network and users can be mobile. In these conditions, information exchange with the M2M asset device need to solve such challenges as unreliable communication channels, temporal presence, limited power and computing capabilities. Solutions for wireless sensors networks in some specific domain and application have been developed [36]. Usually, these solutions have vendor dependent optimized solutions for computing platform, radio technology, communication and services. However, there is at least one potential emerging standard for low power communication between wireless sensors called as Bluetooth 4.0 [37], Bluetooth Smart, which may be applicable for multiple domains. However, there is still a need for generic standard based communication and service protocols working with limited capability embedded M2M assets and mobile gateways.
## 7 Fault Location Method Based on Similarity of Voltage Signals
### Post Fault Traveling Waves
#### 8.2.2 Post Double Pole to Ground Fault  
Positive and zero sequence components of voltage are observed before and after the faults. Difference between sequence components are used to find out the impact of fault resistance on system under fault condition.  
\[\left\{\begin{array}{c}\Delta u_{fa0}=\ u_{fa0}=\frac{\sqrt{2}U_{d_{+}}(R_{ gb}-R_{gp})Z_{0\Sigma_{\kappa}}}{Z_{\Sigma}}\\ \Delta u_{fa1}=\ u_{fa1}-u_{a1}=\frac{\sqrt{2}U_{d_{+}}([R_{gp}+R_{gb}+2Z_{0 \Sigma})Z_{1\Sigma}}{Z_{\Sigma}}\end{array}\right. \tag{74}\]  
and  
\[\frac{\Delta u_{fa0}}{\Delta u_{fa1}}<\frac{Z_{0\Sigma}}{Z_{1\Sigma}} \tag{75}\]
## 3 Results
### Simulation Stage  
At the simulation stage, the validity of the DNVP is evaluated for each WW\({}_{\rm eq}\)R against the SNVP, from 0.23% to 2.16% WW\({}_{\rm eq}\)R.  
The first set of simulations were run in _DesignBuilderTM_ with a constant wind speed of 2.6 m/s, direction 260\({}^{\circ}\), and a constant WW\({}_{\rm eq}\)R. These simulations are plotted in Figure 7. Additionally, the figure includes the occupancy level and the recommended CO\({}_{2}\) concentration limit defined by the World Health Organization and the RITE [29]. The occupancy defined is 21 people and the CO\({}_{2}\) limit is 800 ppm.  
The results shown in Figure 7 describe how a 2.16% WW\({}_{\rm eq}\)R is the only simulation remaining below the established CO\({}_{2}\) limit of 800 ppm. All the other configurations of WW\({}_{\rm eq}\)R provide simulation results between 800 and 2600 CO\({}_{2}\) ppm. In all run simulations, the CO\({}_{2}\) increases exponentially until it reaches a flat line, which remains constant, except in the simulations with the WW\({}_{\rm eq}\)R under 0.45%. In these simulations, the CO\({}_{2}\) is still rising until the occupancy is zero, between 12:05 and 12:30. The simulations show the WW\({}_{\rm eq}\)Rmax for this case study, which is 4.10%. For the WW\({}_{\rm eq}\)Rmax the average CO\({}_{2}\) is circa 544 ppm.  
For the next set of simulations run in _DesignBuilderTM_, all the parameters remain the same except the WW\({}_{\rm eq}\)R, which is controlled by the DVNP EMS script, as explained in Appendix B. The WW\({}_{\rm eq}\)Rs that provided a CO\({}_{2}\) concentration below 800 ppm are not considered in this simulation, following the DNVP algorithm. The results of the CO\({}_{2}\) concentration obtained through the calculations via Equation (2), explained in Appendix A, are shown in Figure 8. CO\({}_{2}\) limits and wind conditions remain the same as those in Section 3.1.  
The results shown in Figure 8 describe the running loops of the different WW\({}_{\rm eq}\)Rs. Each of the WW\({}_{\rm eq}\)R loops rises until 800 ppm, then they change to 4.10% WW\({}_{\rm eq}\)R until the CO\({}_{2}\) concentration decrease to 400 ppm or as low as possible. As expected, for those opening configurations with a lower WW\({}_{\rm eq}\)R, the number of loops is higher.  
Figure 9 summarize the results of simulations for 0.23% WW\({}_{\rm eq}\)R, but comparing both the SNVP and the DNVP. Regarding the selected 0.23% WW\({}_{\rm eq}\)R, the results of CO\({}_{2}\) concentration, when the DNVP is on, are below 800 ppm, while they achieve circa 2500 when the SNVP is on.  
Figure 8: CO\({}_{2}\) concentration for different WW\({}_{\rm eq}\)Rs with DNVP activated.  
Figure 9: CO\({}_{2}\) concentration at 0.23% WW\({}_{\rm eq}\)R for simulation with DNVP vs. SNVP.  
The results shown in Table 5 confirm that all WW\({}_{\rm eq}\)R configurations maintain the CO\({}_{2}\) concentration under the limit when the DNVP is active, while only a 2.16% WW\({}_{\rm eq}\)R configuration maintains the CO\({}_{2}\) concentration objective when the SNVP is active.
## 4 Experimental Evaluation
### Results and Analysis
#### 4.3.6 Case Study  
To intuitively illustrate the effectiveness and robustness of the GeoCWS in CWS for complex geological words, two example sentences were selected from the GEO dataset and given in Figure 6.  
Figure 4: The effect of unlabeled data weight (\(\lambda\)) on results.  
Figure 5: The overall performance over iterations.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 4 Experimental Results
### Train Results  
Based on the wargaming dataset, we trained three network models: CNN-GRU, CNN-MSA-GRU, and ELP-Net. Figure 5 illustrates the comparison of their training results, including the loss and accuracy curves recorded after each mini-batch. In the left panel, the loss values of all models are shown per training step, while the right panel presents accuracy values, which are calculated every 10 time steps as one episode. From the figure, it is evident that the loss curves for all models converge over time, demonstrating stable training. The ELP-Net model exhibits a slightly higher loss during training, likely due to the integration of the GNN and transformer modules. These components significantly improve model capacity but at the cost of increased complexity. Consequently, ELP-Net achieves the highest accuracy among the three models, benefiting from its ability to capture both spatial and temporal dependencies effectively.  
During the training process, validation on the test dataset revealed some degree of overfitting in all models. To ensure better generalization and optimal test results, we selected the model parameters corresponding to the highest top 1 average accuracy on the validation set as the final parameters for testing.
## 3 Materials and Methods
### Volatile Compound Analyses  
Volatile compound extraction and analysis followed a published method [15]. Briefly, 5 mL wine sample, 1 g sodium chloride, and 10 \(\upmu\)L internal standard (IS, 4-methyl-2-pentanol, 1.0100 g/L in ethanol) were mixed into a 15 mL vial containing a magnetic stirrer. The vial was then sealed using a screw cap with a PTFE-silicon septum. Subsequently, the mixture in the vial was agitated on a magnetic plate (PC-400, Supelco, Bellefonte, PA, USA) at 40 \({}^{\circ}\)C for 30 min. Afterwards, a pre-conditioned DVB/CAR/PDMS solid phase microextraction (SPME) fiber (2 cm, 50/30 \(\upmu\)m, Supelco, Munich, Germany) was inserted into the head space of the vial and the volatiles were absorbed for another 30 min under the same agitation condition. The desorption of the volatiles from the fiber was achieved by injecting the fiber into the GC injection port for 8 min at 250 \({}^{\circ}\)C. An Agilent 7890A gas chromatography (GC) coupled with an Agilent 5975C mass selective detector (MSD) was used for the analysis of the volatile compounds. An HP-INNOWAX capillary column (60 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness, JandW Scientific, Folsom, CA, USA) was used for the separation of the volatiles. The carrier gas was helium and the flow rate was set at 1 mL/min. The oven temperature was programmed as follows: 50 \({}^{\circ}\)C kept for 1 min; 50-220 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min; 220 \({}^{\circ}\)C maintained for 5 min. The temperature of the detector and transfer line were set at 230 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. The ion source was in electron impact (EI) mode at 70 eV. Full scan mode with the \(m/z\) range of 30 to 350 was used. Volatile compounds were identified and quantified on an off-line Agilent ChemStation Software (Agilent Technologies, Inc., Santa Clara, CA, USA). Identification was conducted by comparing their mass spectrum and retention indices (RIs) with the reference standards in the NIST11 library and further confirmed with their external standard. The volatile compounds without the external standards available were tentatively identified by comparing their RIs with the known pattern in the NIST11 database. For quantitation, the peak ratios of the target compound to the IS against the concentration were plotted to generate standard calibration curves. Compounds that had no standards were approximately quantified by the standards with the same functional group and/or similar amounts of carbon atoms [60].
# DNA Barcoding as a Plant Identification Method
## 4 Chloroplast DNA Barcodes
### _matK_  
_matK_ (maturas K) is one of the most rapidly evolving chloroplast genes, which has been used for identification at the family, the genus, and even the species level. _matK_ exhibits interspecific divergence and a low transition/transversion rate. It is approximately 1550 bp long and encodes maturase K, an enzyme involved in the splicing of type-II introns [35; 39; 44; 45]. However, its use as a universal DNA barcode is hampered by technical problems, mainly the design of the universal primer sets, due to the high substitution rate [37; 46; 47]. However, _matK_ constitutes a suitable marker for angiosperm, flowering plant, hypophyte, lycophbyte, gymnosperm, and monilophyte identification [45; 47].
## 10 Future Directions
### Federated Learning with Unreliable Participants  
In real-world scenarios, not all the participants can be assumed to be reliable or have stable connectivity. Research should explore mechanisms for FL that can handle intermittent participation and varying levels of reliability among clients, ensuring that the global model remains robust despite these challenges.