esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Setup and Results
### Downlink Use Case
#### 6.6.2 Dss1  
Observing the SINR graphs of DSS1 in Figure 8, we can conclude that DSS1 is capable of resolving almost all interference events in the satellite links, since SINR drops below  
Figure 6: SINR graphs in the downlink use case (baseline). (**a**) GSO-SAT 1. (**b**) NGSO-SAT 2. (**c**) NGSO-SAT 3. (**d**) FWA 8. (**e**) FWA 9.  
Figure 7: PER graphs for satellite links in the downlink use case. (**a**) Baseline. (**b**) DSS1.  
9 dB for short periods before reacting and moving to free spectrum slots. There are, as it seems, enough slots for the three satellite links to not only avoid direct but also possible ACI from neighboring nodes. It is also clear that the nodes react only when the SINR drops below the error threshold. FWA node 9 in DSS1 is only losing 2.2 dB SINR, dropping from 22 to 19.8 dB, whereas FWA node 8 is only showing a loss of 0.1 dB in the worst case. We observed approximately 18.7 dB SINR in the baseline, marginally gaining 1.1 dB in DSS1 compared to the baseline. Since FWAs were not impacted negatively, and no big differences were observed in the results achieved with DSS, we did not present SINR graphs for FWAs. It is worth mentioning that in scenarios where FWAs links are heavily interfered by satellite communication, their protection would be guaranteed by applying the DSS1+ technique.  
The PER graph explains even better how successfully DSS1 was able to solve the interference problems between the satellite links as shown in Figure 7b. The PER only spiked at the start of each interference event, being resolved less than a few seconds later.  
Finally, the average overhead of the CP and slot allocation protocol can be seen in Figure 9. Both CP and slot allocation protocol overheads are minimal, only increasing when actions need to be taken to resolve interference events. After the bootstrapping period, the maximum CP overhead, both wired and wireless, is 500 bytes/s in total. That proves that no periodic exchange of information happens, except in the case of mobile ESIMs where they need to update their position information. The wireless slot allocation overhead is no more than 400 bytes/s in the worst case. The related CP advertisement messages and the slot allocation messages are all designed to be event-driven and therefore scale nicely with the network density.
## Abstract  
The present study shows the potential of an extracellular polymeric substance (EPS) produced by _Bacillus licheniformis_ strain KX657843 isolated from earthworm (_Metaplire posthuman_) gut in the sorption of Cu(II) and Zn(II) and in flocculation. After harvesting bacterial cells from sucrose supplemented denittifying culture medium, the EPS was extracted following ethanolic extraction method. The Fourier Transform Infrared Spectroscopy (FTIR) and \({}^{1}\)H and \({}^{13}\)C Nuclear Magnetic Resonance (NMR) of EPS revealed its functional groups, electronegative constituents, unsaturated carbon, and carbonyl groups. The negatively charged functional groups of carbohydrates and protein moiety of the EPS endowed it with heavy metal binding capacity through electrostatic interactions. The highest flocculation activity (83%) of EPS was observed at 4 mg L\({}^{-1}\) and pH 11. The metal sorption by EPS increased with increasing pH. At pH 8, the EPS was able to remove 86 and 81% Cu(II) and Zn(II), respectively, from a 25 mg L\({}^{-1}\) metal solution. 94.8% of both the metals at 25 mg L\({}^{-1}\) metal solutions were removed by EPS at EPS concentration of 100 mg L\({}^{-1}\). From Langmuir isotherm model, the maximum sorption capacities of EPS were calculated to be 58.82 mg g\({}^{-1}\) for Cu(II) and 52.45 mg g\({}^{-1}\) for Zn(II). The bacterial EPS showed encouraging flocculating and metal sorption properties. The potential to remove Cu(II) and Zn(II) implies that the EPS obtained from the earthworm gut bacteria can be used as an effective agent for environmental remediation of heavy metals and in biofocculation.  
**Keywords:** extracellular polymeric substances; earthworm; gut bacteria; flocculation; metal remediation; isotherm models +
Footnote †: journal: Applied Physics Letters  
_Appl. Sci._**2020**, _10_, 349; doi:10.3390/app10010349
# Topic-Specific Emotion Mining Model for Online Comments
## 4 Experiments
### Comparison of Sampling Time with Existing Models  
Retaining different word frequency affects the sampling time. To make it fair, our contrast models also retain nouns, adjectives, verbs, and adverbs in the text.  
Figure 6 presents the computation time of TSEM, TSEM_n, ASUM, and JST under different wfre, where wfre means the lower bound of word frequency of the words we retained. The wfre varies from 1 to 5. For instance, wfre = 1 means that we sample all the words. wfre = 2 means that we only sample words whose word frequency is greater than 1. Figure 6 shows the sampling time of 1000 iterations. As can be seen from Figure 6, the sampling time of TSEM under each word frequency is significantly lower than that of the other three methods. Our method implements dimensionality reduction representations of comment texts in accordance with the actual situation. When wfre = 1, the computation time of TSEM, TSEM_n, ASUM, and JST is 3190 s, 8510 s, 5340 s, and 7150 s, respectively. This corresponds to sampling time reduction of 62.51%, 40.26%, and 55.38% relative to the other three models. This is because each input item is sampled as a topic and an emotion when solving the topic model. Using a bag of aspect-opinion pairs as input not only eliminates redundant words, but also greatly reduces the input items when an aspect word and an opinion word are combined. The fewer the input items, the fewer the total sampling times.  
Figure 5: Perplexity with different values of \(\alpha\) and \(k\).  
\begin{table}
\begin{tabular}{l l} \hline \hline \multirow{3}{*}{topics} & taste, price, logistics, package, crowd, date, \\  & customer service, courier, texture, quality, volume, \\  & cost performance, activity, whole, gift, delivery speed, \\  & distribution, quality guarantee period, nutrition \\ \hline \hline \end{tabular}
\end{table}
Table 3: Topics list.  
For each of the above methods, the calculation time drastically decreases when the value of wfre changes from 1 to 2, The reason is that the number of words or aspect-opinion pairs with word frequency of 1 is large. Thus, when the words or aspect-opinion pairs with word frequency of 1 are deleted, the input items decrease greatly, which causes the sampling time to decrease sharply. The trends of computing time tend to be mild when the value of wfre continues to increase. This is due to the relative decrease in the number of words or aspect-opinion pairs with word frequency greater than 1. Therefore, with the increase of the retained word frequency, the decline rate of the time curve slows down.  
Experimental results prove that our model can substantially cut down the sampling time to speed up the operation.
## 5 Conclusions
### Practical Contributions
The model is scalable both horizontally (multiple units or assets) and vertically (integration into enterprise-level platforms), with compatibility ensured through standard protocols (e.g., MQTT, REST, OPC-UA). This facilitates deployment in hybrid environments and enhances system resilience. To ensure sustainability and broader adoption, future efforts should focus on:  
* Capacity-building initiatives to train personnel in DT operations. * Funding and incentive mechanisms to support small and medium enterprises (SMEs) and public sector entities. * Policy frameworks to promote digital innovation in circular economic ecosystems. Methodology, J.M.V. and O.D.C. ; Software, J.M.V. ; Formal analysis, J.M.V. ; Investigation, J.M.V. ; Writing--original draft, J.M.V. ; Supervision, O.D.C. and J.A.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. This article is part of the results of the doctoral thesis titled \"Methodological Proposal for the Improvement of Production and Service Systems in the Waste Industry Using DTs: Application in High Population Density Areas\", developed for the award of a Doctorate in Engineering, Industry, and Organizations. We express our gratitude to the Ministry of Science, Technology, and Innovation (MinCiencias) of Colombia for their support through Call 909 for high-level training to strengthen research capabilities in doctoral programs, which was essential for the completion of this work. The authors declare no conflicts of interest.
## 3 Evaluation Methods
### Measure Back Muscle Electromyography Signal  
The purpose of the EMG measurement Experiment was to evaluate the reduced activity of the exoskeleton on the muscles of the lower back and analyze the effectiveness of the exoskeleton from another perspective. The areas where the electrical signals of the muscles of the lower back change significantly during semi-squat are mainly located in the erector spines and multifidus muscle on the left and right sides of the spine [29]. The erector spines are far away from the spine and produce a greater torque when the lumbar spine is stretched. The multifid muscle, which is usually attached to the lumbar spine, accounts for a quarter of the energy contribution of the trunk extensor during maximum trunk stretching [30]. As the handling quality becomes heavier, the amplitudes of EMG signals of the erector spines and multifidus muscle increase [31,32].  
The specific experimental methods are as follows: Before the experiment began, Ag/AgCl surface electrodes were installed at the left thorax lumbar erector spinae, the right thorax lumbar erector spinae, the left thorax thoracic erector spinae, and the right thorax thoracic erector spinae after rubbing with alcohol at the test point of the subject's back. The test point is based on [PERSON] et al. [11]; EMG signals were first measured at the test point in the standing position. The subjects were required to relax their back muscles before the experiment began. After it was detected that the back muscles EMG signals remained stable and the amplitude was near 0 for 10 s, a complete carrying movement experiment started. The EMG signal was not recorded during the bending of the subject. While the subject bent down to reach the established semi-squat posture, the left and right bending angle of the hip was between 130\({}^{\circ}\) and 140\({}^{\circ}\) (130\({}^{\circ}<\theta_{left\_angle}<140^{\circ},130^{\circ}<\theta_{right\_angle }<140^{\circ}\)), and the angle of the back from vertical was between 45\({}^{\circ}\) and 55\({}^{\circ}\) (45\({}^{\circ}<\theta_{back\_angle}<55^{\circ}\)). The posture capture system would indicate the wearer. After receiving the cue, the wearer began to lift the object, and another participant used the NORAXON EMG software to record the EMG signal data of the wearer.  
In this experiment, seven subjects were asked to stand in a designated position and carry an 8 kg weight to a 0.5 m platform at a fixed frequency. In another set of experiments, subjects were asked to lift the same weight with the help of hip exoskeleton; the starting position and frequency of the semi-squat were the same as those without the exoskeleton. The only difference between the experiments is whether the subjects wore an exoskeleton or not.
## 7 Risk Analysis
### Emergency Response Service
#### 7.3.1 Tampering  
1. **Component**  
_Security risk_: ERS tampering leads to a deliberate delay or obstruction of responses to emergencies, dissemination of inaccurate information leading to incorrect decisions, misallocation of resources, and compromise of operational efficiency. The attacker engages in activities such as manipulating data, modifying processes, or changing communication channels within the emergency response team, that is, compromising the integrity of the ERS system.  
_Security measure_: Measures like implementing strong access controls to limit who can interact with the system, user authentication and authorization mechanisms, database integrity verification using cryptographic hashes and digital signatures, system logging and auditing which records every database modification, and network segmentation to shrink the attack surface by isolating critical system components can be used.
2. **Ports and Communication**  
_Security risk_: Tampering with ERS ports introduces both safety and privacy risks. If ERS is not able to correctly receive data, it will not be able to help the driver when needed. Moreover, considering the connection to the internet of such a component, many checks must be performed in order to avoid possible port tampering.  
_Security measure_: Like in the case of component analysis, in this case, possible countermeasures relate to authentication and traffic control. Securing the entire communication can prevent port tampering and reduce the overall risk.
## 3 Assessment of Precise MGEX Orbit and Clock Products
### SLR Validation
#### 3.3.2 Bds
Whereas COD (before DOY198 2018), GFZ, SHA and TUM were all reported to use ECOM for BDS-2 satellites, GFZ performed better during the ON period than the other three products, indicating that modifications could be applied for BDS-2 IGSO/MEO satellites during the ON period in GFZ solution. COD modified the SRP model for BDS satellites since DOY198 2018 and the modified SRP model took the ON mode into consideration [36]. With the refined SRP model, the STD values of COD during the ON period were significantly improved by 87%, 67% and 59% for C08, C10 and C11, respectively, diminishing from 37.0, 24.7 and 34.3 cm to 5.0, 8.3 and 14.0 cm. The SLR residuals of BDS-2 GEO satellite C01 are shown in Figure 9 with respect to the beta angle and the satellite-sun elongation angle, respectively. As can be seen, the SLR residuals of GFZ and SHA had a clear dependency on the satellite-sun elongation angle. The mean biases of GFZ (before DOY330 2018) and SHA residuals were about -30 cm. Compared to GFZ and SHA, C01 orbits of WUM solutions had a less obvious dependency on the satellite-sun elongation angle and showed a smaller STD of 9.8 cm. The better performance of WUM is attributable to the improved BDS-2 GEO SRP model [17]. Additionally, it is interesting to note that an improvement can be observed for the C01 orbit of GFZ beginning DOY330 2018. The STD value improved from 22.2 to 17.6 cm, and the mean bias improved from -28.1 to -12.6 cm. Figure 8: SLR residuals of ESM BDS-2 satellites with respect to the satellite–sun elongation angle \(\varepsilon\). For BDS-3 MEO satellites, the STD values of SLR residuals were slightly larger than those of the BDS-2 MEO C11 during the YS period. What is more, the SLR residuals of BDS-3 satellites showed a clear linear dependency on the satellite-sun elongation angle for both ESM and WUM solutions, as shown in Figure 10. The slope was negative for CAST satellites C20 and C21, but positive for SECM satellites C29 and C30. The clear dependency on the satellite-sun elongation angle is attributable to the deficiency of SRP models. In order to improve BDS-3 orbit performances, we performed BDS-3 POD experiments using an a priori box-wing model together with ECOM. For the other POD strategies, refer to [PERSON] et al. (2013). The SLR residuals of the experiment results are also presented in Figure 10. Obviously, the dependency of the SLR residuals on the satellite-sun elongation angle could be decreased with an a priori box-wing model for SECM satellites, and smaller STD values of 3-4 cm were achieved. Figure 10. SLR residuals of BDS-3 satellites with respect to the satellite–sun elongation angle \(\varepsilon\). Figure 9. SLR residuals of C01 with respect to the beta angle and the satellite–sun elongation angle \(\varepsilon\).
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
###### Abstract  
Pipelines function as blood vessels serving to bring life-necessities, so their safe usage is one of the foremost concerns. In our previous work, a fiber Bragg grating (FBG) hoop strain sensor with enhanced sensitivity was developed to measure the pressure drop induced by pipeline leakage. Some hoop strain information during the leakage transient process can be extracted from the amount of FBG hoop strain sensors set along the pipeline. In this paper, an integrated approach of a back-propagation (BP) neural network and hoop strain measurement is first proposed to locate the leak points of the pipeline. Five hoop strain variations are employed as input neurons to achieve pattern recognition so as to predict the leakage point. The RMS error can be as low as 1.01% when choosing appropriate hidden layer neurons. Furthermore, the influence of noise on the network's performance is investigated through superimposing Gaussian noise with a different level. The results demonstrate the feasibility and robustness of the neural network for pipeline leakage localization.  
FBG hoop strain sensor; pipeline leakage localization; transient model; BP neural network 2018 8, 146; doi:10.3390/app8020146
# Detecting the Speed Change Intention from EEG Signals:
## 2 Materials and Methods
### Pseudo-Online BMI  
Once detected the best combination of features and electrode configurations for the three types of classification models, the pseudo-online analysis was performed in order to check them in a simulated real-time scenario.  
No cross-validation was used in the pseudo-online analysis in order to simulate the real-time conditions. This way, the model was created using the first eight trials with the explained offline procedure and tested with the last two trials of each session with the following procedure.  
Trials were analyzed epoch by epoch, with a size of 625 samples (1.25 s), shifting them every 100 samples (0.2 s) producing an effective 1.05 s overlap between epochs. The preprocessing was done in the same way as in the offline model with the state variable filters. However, in the pseudo-online analysis, the whole signal was processed, extracting the features based on the electrode configuration that maximized the results (tested in Section 2.5.3) and for each of the models (Both, Increments and Decrements). Meanwhile, the classification of each window was only evaluated in the interval between points marked as the beginning of walking and the change labeled (see Figure 5). Regarding the command decision, a TP was computed if five consecutive epochs were identified as a speed change class and the detection was detected at most one second before the actual IMUs detection of the change, otherwise it would be considered as a FP. Taking into account the epoch size and shifting, the time window in which a TP can be considered valid before the actual change contains information from \([-2.25\) s,\(-1.25\) s] to \([1.25\) s,\(-0.2\) s] previous to the change.  
The number of successful changes, i.e., the TP rate of the test, could be defined as:  
\[\text{TP}=\frac{\text{Number of true event detections}}{\text{Number of true events}} \tag{10}\]  
If one of the epochs was detected within the change range, a true event was computed and the signal processing was stopped until the start of the next repetition. As a realistic way to compute FP, once one was detected, the next one could not be computed until at least two seconds passed. This helps to simulate that in a real-time scenario, as the command is sent (to perform a feedback action), it is necessary to wait a while until the next command can be sent again. As the normal walking time period has a duration of 8-10 s, the FP evaluation can be expressed as:  
\[\text{FPmin}=\frac{\text{Number of false activations}}{\text{walking time windows in minutes}} \tag{11}\]  
Moreover, a ratio of TP to FP/min was computed. The higher the value of the ratio, the better the evaluation. The value of the variable ratio is equivalent to the number of \(\frac{\text{TP}}{\text{FP/min}}\).
# CATSWoTS: Context Aware Trustworthy Social Web of Things System
## 3 Context Aware QoS Trust Management
### Trust Management
#### 3.2.1 Context Manager
It then forwards that list of services to local QoS manager for the discovery of services fulfilling the QoS criteria; otherwise, a message is sent to social relations. In case the required context criteria are neither found in local context manager of SS nor its social circle, then the request goes to the central level. The central context manager, which maintains a huge database of service providers, repeats the process, and identifies the potential services. The process for services identification based on context is described in Algorithm 1. ```
Get the values of  1. \(\delta\) and \(\alpha\)  2. \(\Omega=\Omega 1,\Omega 2,...,\Omega n\)  3. \(\Phi=\Phi 1,\Phi 2\) and \(\lambda=\lambda 1,\lambda 2\) \(i\gets 0\) foreach service 'S' do if\(S.\)\(\delta==\delta\)and \(S.\)\(\alpha==\alpha\)then for\((j\gets 1;j<N;j++)\)do if\((\Omega j\)\(i=S.\)\(\Omega\)\(j)\)breakhen elsecontinue  endif  make a list endfor if(\(\tau==S.\)\(\tau\))then \(\Delta\)\(\lambda\leftarrow\lambda 2-\lambda 1\) \(\Delta\)\(\Phi\leftarrow\Phi 2-\Phi 1\) \(a\leftarrow(sin(\Delta\Phi/2))*(sin(\Delta\Phi/2))+cos(\Phi 1)*cos(\Phi 2)*(sin(\Delta\lambda/2))*(sin(\Delta\lambda/2))\) \(c\gets 2*atan2(sqrt(a),sqrt(1-a))\) \(d\gets R*c\) \(sURL[i]\gets S.url\) \(i++\) \(sort(sURL[i])\) endif endif endfor
```  
**Algorithm 1** Services identification based on context  
The context manager generates a list of those services that have a similar context, which is required by an SS. This list is sorted by the distance between an SS (where a service is required) and an SP (the location of the service) in descending form i.e., services with minimum distance will be on the top. This list is then forwarded to QoS manager for the evaluation of QoS criteria on the basis of required criteria of an SS. The list may be either generated by the local context manager of an SS or the local context manager of the contacts in a social network or the central server. The lists, which are received from recommenders including social relations and central level, are then consolidated at the local level.