esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Metrics  
In this section, we briefly discuss the mostly commonly used metrics for all the protocols in the surveyed papers and the potential to identify new metrics specific to an OppNet.  
_Delivery ratio_ is the ratio of total number of packets delivered to the respective destinations to the total number of generated packets in the network. _Delivery delay_ is the time difference between generation of packets and the reception of the packets at the destination. _Delivery cost_ mainly attempts to capture the impact of replication on the performance of the OppNet. Hence, the delivery cost is usually measured in terms of the overhead such as number of copies or number of transmissions.  
Specific analysis with respect to the metrics are not presented here owing to the fact that all the protocols in Table 1 utilize all three of the above mentioned metrics. The enhancements in the protocols always involve a trade-off in the metadata exchanged between nodes prior to the actual message transfer. The metadata comprises all the data stored in each node for choosing relays such as rate of encounters, mobility patterns, profiles of nodes for predicting future contacts and daily schedules. As this certainly involves the necessary information exchange between nodes upon contact, it can be considered as pre-processing overhead and should be accounted for in evaluations. Moreover, the pre-processing overhead depends on the operation of the protocols themselves. Hence, it is essential for the authors to identify all such possible overhead in evaluating their respective OppNet protocols.
## 4 Experiments Analysis and Discussion
### Performance of Long-Term Forecasting  
TSNN-ESTD applies an integrated strategy and strives to use short-term dependencies to correct long-term bias. Figure 2 further discusses the performance under different prediction step sizes. In detail, we verify the MAE and MSE metric of TNN-ESTD and three mainstream frontier models (iTransformer, PathchTST, and Autoformer) under 48 to 336 time steps. Since PathchTST and Autoformer are inherently encoder-decoder architectures, we only integrate the short term and long term in the hope of reducing the long-term prediction bias.  
For short-term forecasting, there is little difference between the prediction errors of these models. Compared with the other approaches, TSNN-ESTD explores short-term dependence to bias the growth period, and the prediction error is relatively low. As the time step lengthens, the prediction errors of all models show a gradually increasing trend. However, the curve of TSNN-ESTD is more gentle, which also proves the superiority of the model in long-term prediction.
# A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration
## 2 Modeling and Implementation
### Null Frequency Magnitude  
The second input of the fuzzy system is the magnitude of the extracted null frequencies. This is extracted from the normalized log-power spectrum of \(g\) and has a dynamic range of 0 to 1 dB, demonstrated in Figure 1.  
As explained, \(h\) is most likely to have rapid spatial transitions compared to \(f\). This implies that \(H\) is likely to have the nulls with higher attenuation in \(G\) (nulls with lower magnitude). As a result, two fuzzy sets (\(B_{High}\) & \(B_{Low}\)) with membership functions of \(\mu^{\prime}_{o}\) and \(\mu_{o}\) are defined to assign a higher membership value to the nulls with more attenuation (or lower magnitude), whereas a lower degree of membership is assigned to less attenuated (higher magnitude) nulls. Membership functions are defined with zmf and sfm. \(B_{Low}\) is formulated in Equation (3), where \(G_{N}\) is the normalized frequency spectrum of the degraded signal \(G\) and \(a_{o}\) and \(c_{o}\) are the membership function parameters. \(A_{Far}\) membership function definition is similar to \(B_{Low}\) as they are both defined by smf.  
\[\begin{split} B_{Low}=\{(\hat{L}_{i},\mu^{\prime}_{o(\hat{L}_{i}) })|& 0<\hat{L}_{i}<\infty,o(\hat{L}_{i})=\log{(|G_{N}(\hat{L}_{i})+1|)} \},\\ \mu^{\prime}_{o}=\begin{cases}0&o\leq a_{o}\\ 2\Big{(}\frac{o-a_{o}}{c_{o}-a_{o}}\Big{)}^{2}&a_{o}<o\leq\frac{a_{o}+c_{o}}{ 2}\\ 1-2\Big{(}\frac{o-c_{o}}{c_{o}-a_{o}}\Big{)}^{2}&\frac{a_{o}+c_{o}}{2}<o\leq c _{o}\\ 1&o>c_{o}\end{cases},\\ \end{split} \tag{3}\]
## 6 Automated Accuracy Evaluation
### Quantitative Results  
The results of our automated evaluation show poor lexical similarity. As seen in Figure 4, BERTScore's F1 scores ranged from \(-0.05\) to 0.497 with a mean of 0.246, suggesting moderate token-level alignment. By contrast, Semantic Textual Similarity scores ranged from 0.439 to 0.934 with a mean of 0.736. The Semantic Textual Similarity is more important for DietNerd, because our primary goal is to deliver a semantically similar result in layman's terms to those of expert researchers. As noted by [39], similarity scores exceeding 0.7 indicate strong similarity, and our results show a concentration of scores in the upper ranges of 0.75 to 0.85. Similarity scores above 0.5 indicate moderate agreement.  
In summary, DietNerd, when given primary literature, generates responses that are semantically similar to high-quality systematic review medical literature.
## 5 Dynamic Compressive Strength Model of FCC  
### General  
There are many existing theoretical models for predicting the quasi-static compressive behavior of FCC [55; 56; 57; 58; 59], but only [PERSON] et al. [31] and [PERSON] and [PERSON] [52] proposed the stress-strain model of FCC under dynamic compression. [PERSON] et al. [32] studied the dynamic compressive mechanical properties of CFRP-confined concrete under a high strain rate, but did not propose a dynamic compressive strength model for FCC. A mature dynamic compressive stress-strain model for FCC needs to include a number of parameters, such as the FRP thickness, FRP type, strain rate, dynamic compressive strength of unconfined concrete, and the specimen diameter. The model proposed by [PERSON] et al. [31] is incapable of accounting for the FRP type and the high-strength concrete, but the high strength is more attractive for utilization in structures that are more likely to be attacked by high strain rate loadings. The model proposed by [PERSON] and [PERSON] [52] cannot predict the performance of FRP-confined high-strength concrete. In addition, the model [52] adopts separate equations for FCC with different FRP types. In the following section, a new model considering the effects of the FRP types, concrete strength, and confining stiffness is proposed for FCHC based on the following three steps: (1) A versatile dynamic compressive strength model for unconfined concrete; (2) a reasonable expression considering the effects of FRP-confinement; and (3) determination of the constant in the equation via regression analysis.
# Attenuation of Lightning-Induced Effects on Overhead Distribution Systems in Urban Areas
## 3 Results
### EM Fields  
For each set of simulations, the effect of the presence of the buildings was analyzed calculating the following quantities:  
* Calculation of the ratio (\(k_{E}\)) between the maximum value of the electric field amplitude in the presence of buildings (\(E_{max,Building}\)) with respect to the case without (\(E_{max,noBuilding}\)), shown in Figures 3-5. The electric field was measured at the point corresponding to the middle of the line. \[k_{E}=\frac{|E_{max,Building}|}{|E_{max,noBuilding}|}\] (2)
* Comparison between the electric field measured with and without the building along the whole line (Figures 6-8), for a instant \(t^{*}\) defined as \[t^{*}=5+\frac{d}{c_{0}}[\mu s]\] (3) where \(c_{0}\) is the light speed.  
Figure 3 shows that the presence of buildings became almost negligible (the difference was less than 5%) when the distance between the line and the building was more than 100 m. For lower values, the effect was consistent and led to an attenuation of 50% when the line was 10 m from the building.  
On the other hand, when we dealt with a building that was lower than the line (i.e., \(H=5\) m), the attenuation was substantially negligible, as can be observed in Figure 4. In this case, it is important to notice an attenuation greater than one (\(k_{E}>1\)) when the line was extremely close to the building, which means an enhancement of the maximum electric field due to the presence of the building.  
Figure 5 shows how the height of the building influences the attenuation of the electric field on the conductor and confirmed that the higher the building the higher the \(k_{E}\), leading to ratios between the maximum electric field with and without the building up to 0.2 when the height was more than 40 m.  
Figure 4: Ratio between the maximum value of the electric field with and without the building. (B) \(H=5\) m, and \(d\) varies from 5 to 200 m.  
Figure 3: Ratio between the maximum value of the electric field with and without the building. (A) \(H=20\) m, and \(d\) varies from 5 to 200 m.  
Analyzing the electric field measured at \(t=t^{*}\), it can be observed that the behavior along the whole line was similar to the behavior at the central point. In other words, changing the observation point along the line, the attenuation was kept almost constant. Figures 6 and 7 are plotted assuming \(d=10\) m, while Figure 8 is relevant to the case of \(H=20\) m. The analysis of Figure 6 shows that the presence of buildings also reduced the electric field at the line terminations from 2.5 kV/m to 1.7 kV/m. The reduction cannot be considered constant along the whole line, as at the central point, it varied from 11.2 kV/m to 6.1 kV/m, which was a more consistent percentage reduction. The same analysis can be performed for Figure 8.  
Figure 6: Comparison between the electric field measured along the whole line. (A) \(H=20\) m, and \(d\) varies from 5 to 200 m.  
Figure 7: Comparison between the electric field measured along the whole line. (B) \(H=5\) m, and \(d\) varies from 5 to 200 m.
###### Abstract  
Three-dimensional numerical simulation platform for silicon carbide crystal growth furnace was established using C programing language, where a physical model of the furnace was built based on cylindrical coordinates; governing equations for electromagnetic and temperature fields were discretized by finite volume method; radiation characteristics were studied with the help of S2S model (surface to surface radiation model); and the least distance method was proposed to check radiation faces visibility efficiently. LU decomposition algorithm based on graphic processing unit (GPU) technology was developed to accelerate the solving process of surface to surface radiation. Then the radiation heat transfer in silicon carbide crystal (SiC) growth chamber and temperature field of silicon carbide growth furnace were studied quantificationally at \(I=1250\) A and \(F=16\) kHz. The effects of coil structures (axisymmetric and spiral) on temperature field and its gradient distributions were investigated by standard deviation method. The simulation results demonstrate that spiral electromagnetic coil generates non-axisymmetric temperature field easily; the radiation heat flux is \(10^{2}\)-\(10^{3}\) times more than conduction heat flux, radiation heat transfer is helpful to increase temperature evenness; the spiral temperature field on the SiC crystal cross-section reduces the poor homogeneity of temperature gradient, which will cause crystal to generate large defects.  
SiC crystal; numerical simulation; temperature field; three-dimensional; finite volume method +
Footnote †: journal: Applied Physics Letters
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 2 Contrail Formation and Environmental Impact
### Persistence and Evolution of Contrails  
Contrails form as narrow trails of ice crystals but can persist and evolve into broader cirrus clouds under specific atmospheric conditions, such as ice supersaturation [20; 21]. These persistent contrails significantly contribute to radiative forcing by trapping longwave radiation, creating a warming effect that amplifies aviation's non-CO\({}_{2}\) climate impact [22]. Unlike CO\({}_{2}\) emissions, contrails and their induced cirrus clouds have an immediate warming effect, particularly over regions of heavy air traffic such as the North Atlantic Corridor. Factors like low temperatures, high humidity, and wind patterns contribute to their persistence and spread.  
As shown in Figure 1, contrails are most prominent in heavily trafficked air routes, highlighting their localized and cumulative environmental effects. Studies have demonstrated that contrail-induced cirrus clouds can contribute radiative forcing comparable to or exceeding that of aviation's CO\({}_{2}\) emissions. This highlights the urgency of addressing these non-CO\({}_{2}\) effects, as they compound the industry's overall climate impact [23; 24]. Targeted monitoring and mitigation strategies, such as real-time atmospheric monitoring, are essential to minimizing contrail formation and persistence, particularly in high-traffic areas [25; 26]. For example, research has shown that contrail-induced cloudiness can lead to warmer nights, as the clouds trap heat that would otherwise be lost to space [27]. This can have downstream effects on weather patterns, agricultural practices, and even human health.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 3 Motivation of the Proposed Work
### Design of Hybrid Trust Model  
#### 4.3.1 Hybrid Trust Model Combining the Beta Trust Model with the New Trust Model  
To show that trust evaluation using consecutive drops can effectively improve the early detection ability of a trust model, we propose a hybrid trust model that adaptively uses the popular the beta trust model and our new trust model based on consecutive drops. As shown in Figure 3, on each report of the behavior of a node B (that is, node A wants to evaluate the trust of node B), our hybrid trust model in node A works as follows:  
* If the new behavior is a success, use beta trust model.
* If the new behavior is a failure, use new trust model based on the number of consecutive drops (or failures).
## 4 Discussion
### Task Performance
[23], subjects issued 68.73 \(\pm\) 19.38 commands on average, which is a much greater amount compared to the one reported in our study (9.4 \(\pm\) 1.2). [PERSON] et al. [37] proposed an MI-based BCI system and performed a variety of tasks using a robotic arm, such as moving fixed targets to a shelf. Subjects who could theoretically grasp a maximum of six targets per run managed to grasp an average of 4.6 \(\pm\) 0.9 targets. Moving a block to the shelf took on average 63.8 \(\pm\) 5.1 s, while making direct comparisons between the two systems is difficult, it is clear that, compared to our BCI, theirs allowed users to complete the tasks very quickly. However, their interface only allowed for movement in a two-dimensional plane, meaning the reach-and-grasp tasks were divided into four sequential steps. The robot also performed some of the movements automatically, such as returning to the starting position after grasping the object on the table. In [PERSON] et al. [52], a similar study with an EOG-controlled robotic arm, subjects obtained slightly faster average trial completion times (357.5 s). However, the system proposed in [PERSON] et al. [52] utilized rapid blinking as a stop command, which could be more fatiguing for individuals with ALS who have difficulty blinking. The proposed BCI provides users with flexible manual end-point control of an ARM; yet, compared to interfaces with shared control [29; 43] or goal selection control [39; 41; 44], its task throughput is much lower. Depending on the specificity of the actions provided, goal selection strategies will offer vastly different experiences to the user. Goal selection strategies that present users with specific actions, such as drinking from a glass of water, are the least flexible, as they require every operation to be set previously. In contrast, by giving users control over gross movements, while automation is used for fine adjustments, shared control interfaces provide users with a more engaging experience.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 3 Prediction of Long-Term Settlement  
### Empirical Method  
The empirical method first proposed by [PERSON] (1969) [5] is the most widely used approach in predicting tunneling-induced surface settlements. As proposed by [PERSON] (1969), an invert Gaussian distribution curve was advocated to describe the surface settlement trough caused by a single tunnel:  
\[S(x)=S_{\text{max}}\exp(-\frac{x^{2}}{2i^{2}}) \tag{1}\]  
where \(S(x)\) denotes the surface settlement at a distance x from the tunnel centerline, \(S_{\text{max}}\) denotes the maximum surface settlement above the tunnel centerline, and \(i\) is called the trough width, which denotes the horizontal distance from the inflection point of the  
Figure 3: Soil profiles and geotechnical parameters of water-rich soft strata in Changzhou.  
settlement trough curve to the tunnel centerline. Because of the undrained condition in a typical EPB shield tunneling, the \(S_{\text{max}}\) can be written as:  
\[S_{\text{max}}=\frac{0.313V_{l}D^{2}}{i} \tag{2}\]  
where \(V_{l}\) denotes the volume loss defined as the amount of lost material caused by shield construction, and \(D\) denotes the diameter of the tunnel.  
As described in Equation (2), two key parameters--volume loss \(V_{l}\) and trough width \(i\)--are required when applying the empirical method in engineering practice. According to [PERSON] (2002) [6], it is difficult to predict the volume loss \(V_{l}\) before tunnel construction since it is dependent on a number of unknown factors. The nature of the geological and geotechnical conditions is the most decisive factor that governs the \(V_{l}\). As reported by [PERSON] and [PERSON] (1982) [17], the trough width \(i\) is mostly connected to the buried depth, as well as the geological conditions, and is generally independent of the tunnel diameter or work quality. For the purpose of practical prediction, a linear relationship between the trough width \(i\) and the buried depth \(H\) was suggested by [PERSON] and [PERSON] (1982):  
\[i=KH \tag{3}\]  
where \(K\) is called the trough width parameter. Since the volume loss \(V_{l}\) and the trough width parameter \(K\) are both closely related to local geological conditions, establishing a statistical relationship between them is of great importance in the surface settlement empirical prediction.
# Perspective of Using Vertically Oriented Graphene as an Electrochemical Biosensing Platform
## 2 Materials and Methods  
### Synthesis of VG  
We obtained VG using specialized equipment for carbon material growth known as the PlasmaPro100 model Nanofab 1000 (Oxford Instruments, Abingdon, UK). This equipment operates in plasma-assisted (PECVD) and thermal (CVD) modes. For the growth process of VG, we used the PECVD mode using capacitively coupled plasma in the range of 200 to 900 \({}^{\circ}\)C between two electrodes. VG was grown directly on the working electrodes of the electrochemical sensor using the following parameters: temperature of 700 \({}^{\circ}\)C, growth time of 1 h, 300 W RF power, and 300 mTorr pressure. The gas mixture for obtaining VG was Ar:CH\({}_{4}\) at a ratio of 190:10 sccm (standard cubic centimeter per minute). In a previous study, we analyzed the growth conditions of VG in order to find the best characteristics of VG for use in modifying the surface of an electrochemical sensor for different cell identification and characterization. The study's conclusion [20] was that VG grown at a temperature of 700 \({}^{\circ}\)C has the best characteristics.  
The functionality of the VG-based electrochemical sensor was demonstrated by testing it on two human colon adenocarcinoma cell lines: SW403, which is a cell line with a high level of invasiveness, and HT29, which is a cell line with a low level of invasiveness. The cell lines were cultured using the protocol [19] in the mediums RPMI-1640 and Mc-Coy's 5A. We suspended the cells in potassium ferrocyanide solution [Fe(CN)\({}_{6}\)]\({}^{3-/4-}\) and prepared two concentrations of each cell line: 10\({}^{4}\) and 10\({}^{6}\) cells/mL.
## 5 Experimental Results and Analysis
### Modal Analysis of Test Model  
The modal parameters of the structure were identified via the 5-200 Hz sweep excitation and the top acceleration response signal of the structure was extracted, as shown in Figure 5a. The frequency domain analysis of the top acceleration response is shown in Figure 5b, and the self-vibration frequency of the structure was obtained via the peak picking method. The high-order modal information of the structure cannot be well recognized due to the wide sweep frequency.  
To accurately obtain the second-order modal parameters of the structure, the sweep range was reduced to 90-140 Hz. A frequency domain analysis was conducted to obtain the structure self-vibration frequency of 111.3 Hz (Figure 6).
## 3 A Prototype System for Pedestrian Positioning
### Electronic Module
#### 3.1.2 GPS Receiver
This is a very important point. There are periods of e.g., 30 s when GPS readouts stray away by 40 m in one direction. A dead reckoning system, weighting GPS coordinates with inertial sensors' readouts to produce a better position estimate, will succumb to this bias error sooner or later. When the GPS receiver regains good visibility with satellites, the estimated position will be all of a sudden in a quite different location. The dead reckoning filter will have to take some time to adapt to a new, accurate GPS position. During this time the filter output will be encumbered with large errors. A way out is to recognize the situation and reinitialize the filter. To account for these random bias errors another source of absolute positioning should be introduced. Maps, video data are examples thereof. Another problem associated with GPS readouts is the correspondence of the HDOP parameter with the actual accuracy of latitude and longitude. Equation (6) shows that the value of HDOP is interpreted as the distance error. Figure 3 sheds light on the problem. It was noticed that the interpretation of the HDOP parameter depends on a GPS receiver at hand. The correlation coefficient (12) shows a poor linear dependence between distance errors \(r_{e}(t)\) and the values of HDOP. The number of observed satellites and position error is not correlated. This confirms the statement about the necessity of additional positioning data. \[\rho(r_{e}(t),\mathrm{HDOP}(t))=\frac{\mathrm{Cov}(r_{e}(t),\mathrm{HDOP}(t))} {\sqrt{\mathrm{Var}(r_{e}(t))\mathrm{Var}(\mathrm{HDOP}(t))}}=0.19 \tag{12}\]  
Figure 3: The relation between the values of the HDOP parameter and the distance errors \(r_{e}(t)\).