esa-sceva/satcom-chunk-collection huggingface dataset:
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Ionic Liquids
#### 3.6.1 Ionic Liquids in Biomass Dissolution
During this process, lignin was also dissolved [91]. Another popular ionic liquid [emim][CH\({}_{3}\)COO] was applied in the production of composite fibers from hybrid poplar, where 6.5 wt. % of biomass could be dissolved [92]. The acetate anion has moderate basicity of hydrogen bonds (\(\beta\)), which enhances binding with lignin [85,91]. The addition of water to IL can increase lignin's dissolution level due to the formation of a higher number of hydrogen bonds [93,94]. The interactions between IL and hemicelluloses are also based on hydrogen bonds. Xylose, as a representative component of the hemicellulose monomer, can create fewer linkages with the solvent compared to glucose, due to the lower number of hydroxyl groups. Because of that, the hemicellulose solubility in IL is not as effective and might be a limiting factor in whole biomass dissolution [84].
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
## 3 The Proposed DSME MAC
### Network Model  
Let us consider a beacon enabled star topology of IEEE 802.15.4e based WSNs with \(N\) numbers of devices as \(S_{1},S_{2},...,S_{N}\), which are connected to a PAN coordinator. It is assumed that each device follows the DSME superframe structure of IEEE 802.15.4e as shown in Figure 1.  
The CSMA/CA scheme is the key component in IEEE 802.15.4e MAC. It is adopted to arrange the devices in the network with an appropriate order when they access the channel. All devices use CSMA/CA channel access scheme in the CAP period to transmit data frames to the coordinator. Every device will go for the power saving mode after packet transmission. When a device access the channel in the CAP period, it may defer transmission, if the remaining slots are not enough to transmit the data.
## 3 Results
### System Performance Evaluation
#### 3.1.1 Fire Detection Responsiveness
This result confirms that the proposed hybrid detection system maintains consistent detection performance regardless of the fire source material, reinforcing its applicability in diverse indoor fire scenarios. A comprehensive summary of these experimental results--including suppression time, peak temperature, visibility recovery, and resistance to false alarms--is provided in Appendix B (Table A1). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Paper (s)** & **Wood (s)** & **Polyurethane Foam (s)** & **Electrical Cable (s)** \\ \hline Paper (s) & - & 0.8569 & 0.8451 & 0.7077 \\ Wood (s) & 0.8569 & - & 1 & 0.8569 \\ Polyurethane Foam (s) & 0.8451 & 1 & - & 0.8451 \\ Electrical Cable (s) & 0.7077 & 0.8569 & 0.8451 & - \\ \hline \end{tabular}
\end{table} 
Table 4: \(p\)-value matrix (\(t\)-test between materials). Figure 6: Box plot of fire detection times for four ignition materials. This figure presents a box-and-whisker plot summarizing detection times for paper, wood, polyurethane foam, and electrical cable based on ten repeated fire ignition trials per material. The boxes represent the interquartile range (IQR), with median lines indicating the central tendency. Whiskers extend to the minimum and maximum values within 1.5\(\times\) IQR, illustrating total spread. The consistent clustering around the median across all materials demonstrates the proposed edge AI system’s stable and rapid detection capability, with all responses occurring well under 6 s.
## 3 Cognitive Packet Network
Benefiting from their inherent intelligence, SPs can self-determine to transmit to the cloud when any of the two following conditions is fulfilled:  
* If all of the neighbour portable devices of the current smart handset in the near-field communication range have already been visited by checking the cognitive map of SPs. * If the predicted path availability (calculated by Formula 3) of conveying a payload from the current discovered path is larger than the estimated path availability of transmitting directly from the source smart phone. Furthermore, emergencies caused by terrorist attacks can also be accompanied with network attacks, such as node capture or denial of sleep in the client side and denial of service in the server side [50]. CPN can naturally benefit the process of defending malicious acts, because SPs and ACKs store all of the intermediate nodes from the source to the destination. Therefore, the potential malicious nodes can be located easily. All of the nodes between a malicious origin and the target node can drop attacking traffic to avoid useless occupancy of bandwidth. Meanwhile, as CPN is a QoS-driven protocol, packets can determine the validity by QoS, and smart phones can allocate different bandwidth for flows of different QoS. \begin{table}
\begin{tabular}{|c|c|} \hline
**Notation** & **Definition** \\ \hline \(a\) & A constant between 0 and 1, typically close to 1 \\ \hline \(n\) & The number of neurons in a RNN \\ \hline \(j\) & The neuron which is associated with the neighbour node that \\  & corresponds to the returned ACK \\ \hline \(k\) & The other neurons in the RNN except the neuron \(j\) \\ \hline \(w^{+}(i,j)\) & Rate at which neuron \(i\) emits excitation spikes to neuron \(j\) when \(i\) is excited \\ \hline \(w^{-}(i,j)\) & Rate at which neuron \(i\) emits inhibition spikes to neuron \(j\) when \(i\) is excited \\ \hline \(\Lambda_{i}\) & Rate at which external excitation spikes arrive at neuron \(i\) \\ \hline \(\lambda_{i}\) & Rate at which external inhibition spikes arrive at neuron \(i\) \\ \hline \(\lambda^{+}(i)\) & Rate at which neuron \(i\) receives excitatory spikes \\ \hline \(\lambda^{-}(i)\) & Rate at which neuron \(i\) receives inhibitory spikes \\ \hline \(q_{i}\) & The excitation probability of neuron \(i\) \\ \hline \(R_{l}\) & \(l\)-th reward determined by a goal function \\ \hline \(T_{l}\) & \(l\)-th decision threshold that keeps track of the historical value of rewards \\ \hline \end{tabular}
\end{table} 
Table 1: List of symbols used in Pseudocode 1. RNN, random neural network.
## 2 A Collaborative System Design
### Experimental Testing  
The system was tested with seven participants on 18 February 2024. One user set his location (University Guest House) as the starting point and Campus Dining Hall as the destination. While moving along the route determined by the application, the user saw a car parked in a way that prevented the use of a ramp in the Anutlar area and registered a complaint at 13:15 (Figure 5a,b). The system administrator, who received the complaint through the Firebase database system, updated the map at 13:20 and forwarded the complaint to the disability unit via e-mail (Figure 5c,d). Upon receiving the e-mail, a disability unit official visited the complaint location, contacted the car owner, had the  
Figure 4: Login (**a**), home (**b**), navigation (**c**), legend (**d**), complaint (**e**), and photo/message (**f**) screens of the mobile application.  
car removed from its location at 13:45, and sent a photo showing that the obstacle had been removed to the system administrator's e-mail address (Figure 5e,f). The system administrator, who received the e-mail sent by the disability unit official, updated the map again at around 14:00 and sent an e-mail to the user who registered a complaint (Figure 5g,h). Thus, the problem was solved in approximately 45 min and the system was found to work smoothly.  
Figure 5: The experimental testing stages: a complaint including a photo showing the obstacle was registered by the user (**a,b**), a symbol showing the obstacle was located on the map by the system administrator (**c**), the complaint registration was forwarded to the disability unit by the system administrator (**d**), the obstacle was removed and the system administrator was informed by the disability unit (**e,f**), the symbol showing the obstacle was removed from the map by the system administrator (**g**), and the user was informed by the system administrator (**h**).
# Uncertainty Analysis of Spherical Joint Three-Dimensional Rotation Angle Measurement
## 1 Introduction
A magnet was placed at the bottom of the spherical join. The magnetic field change was measured using a gauss meter as the spherical joint turned. The artificial neural network is used to establish the measurement model of the measured magnetic field value and the rotation angle. To further improve the accuracy of the angle measurement system, this study investigated its \(\alpha\), \(\beta\), and \(\gamma\) three-axis uncertainty. The minimum accuracy of the three-axis uncertainty is about \(2^{\prime}\), and the maximum is about \(1^{\circ}\). The remainder of this paper is structured as follows. The Section 2 introduces the principle of the measurement scheme and the experimental results, the Section 3 analyzes the sources of errors affecting the evaluation of uncertainty, the Section 4 shows the process of estimation uncertainty, and the Section 5 draws the conclusions.
# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 2 Materials and Methods
### DQN-Based Computation Offloading Decision Algorithm
This method breaks the correlation between past experiences so that it can quickly find the best task computation offloading decision path in the strategy space of the Space-Air-Ground Integrated Network. The specific computation offloading algorithm process is shown in Algorithm 1:  
```
0: total cost of task offloading C
1. initial the computation offloading service request of the offloading terminals; initial the offloading terminal state-action value and read the task amount;
2. calculate the resident time of the offloading terminal inside the network. \(T_{j}^{resident}=\frac{d_{x_{i}}}{v}\);
3. obtain information about offloading sites in the network;
4. calculate the maximum calculation amount that each offloading site can provide \(f_{margive}=(T_{j}^{resident}-T_{i}^{s}-T_{j}^{t})\times f_{j}\);
5. Sort \(f_{margive}\) according to the size of the value and select the optimal offloading network;
6. repeat
7. detect the performance and amount of offloading sites;
8. perform data cutting based on the performance of each offloading site;
9. repeat
10. calculate the state of each state after selecting an action;
11. select the corresponding action \(a_{t}\) from the state \(s_{t}\);
12. execute action \(a_{t}\) and return the excitation value \(r_{t}\) and state \(s_{t}\);
13. \(Q(s_{t},a_{t})\gets Q(s_{t},a_{t})+\alpha[r_{t}+\gamma\underset{a\in A}{ \text{max}}Q(s_{t+1},a)-Q(s_{t},a_{t})]\)
14. for\(t=1;t<n+1;t++\)do
15. store experience value \(<s_{t},a_{t},r_{t},s_{t+1}>\) in memory \(\Omega\). 16. train the Q network;
17. calculate the minimum value of the loss function \(L=(r+\gamma\underset{a^{\prime}}{\text{max}}(s^{\prime},a^{\prime})-Q(s,a))^{2}\)
18. endfor
19. choose the best offloading site, corresponding to state \(s_{t}\gets s_{t+1}\)
20. until end of each training cycle;
21. if the task status in \((O_{i},w_{total})\) is changed to \(x_{n+1,0}\)then
22. complete the task computation offloading;
23. Send the data to the offloading terminal;
24.endif
25. until Over task amount;
26. All task data processing completed. ```  
**Algorithm 1** Computation Offloading Algorithm Based on DQN
## 4 Experiments
### Experimental Setup
* True negative (TN): For telemetry data time series \(\mathbf{S}\), the points that are outside the true anomaly sequence \(\mathbf{S}_{a}^{t(j)}\) and are not judged to be anomalies by the anomaly detection sequence \(\mathbf{S}_{a}^{d(i)}\) are True negative. The degree of overlap between the true anomaly sequence and the detected anomaly sequence is not included in the calculation of the evaluation of the anomaly detection results. The same method of locating anomaly sequences and the criteria for determining the detection results are used for all groups of experiments. The performance of the anomaly detection model can be evaluated with the help of four metrics: precision (\(P\)), recall (\(R\)), accuracy (\(A\)), and an \(F1\) score. The four metrics are calculated as follows:  
\[P=\frac{TP}{TP+FP} \tag{7}\]  
\[R=\frac{TP}{TP+FN} \tag{8}\]  
\[A=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
\[F1=\frac{2*P*R}{P+R} \tag{10}\]  
\(P\) reflects the correctness of all detected anomalous sequences, and \(R\) reflects the degree to which anomaly detection results are found to be complete. These two metrics are of greater interest to anomaly detection tasks and are of greater reference value for practical engineering purposes. A reflects the correctness of the overall telemetry data classification task. The unbalanced data distribution is a significant feature of the anomaly detection task dataset, and therefore metric \(A\) is not a good indicator of the performance of the model. For example, for a 10% anomaly dataset, if the model determines that all data are normal, \(A\) is still 90%, while \(P\), \(R\), and \(F1\) scores are all 0. Therefore, \(A\) for the anomaly detection task can be used as a secondary reference metric but not the primary basis for judging model performance. The \(F1\) score is a harmonized average of \(P\) and \(R\), and reflects the overall performance of the anomaly detection model by taking into account both the precision and recall of the model.
## 4 Series Expansions
### Error Bounds and Algorithm  
The series (16) and (25) shall be approximated by finite sums  
\[Y_{n}=\sum_{k=k_{0}}^{n-1}y_{k} \tag{30}\]  
To control the accuracy of this approximation, we need an analytical bound \(r_{n}\) for the truncation error  
\[\Delta_{\rm tr}Y_{n}:=|Y_{n}-Y|\leq r_{n} \tag{31}\]  
For the small-\(\omega\) expansion, in Appendix C the bound \(r_{n}=|y_{n}|\) is derived: The truncation error is not larger than the first neglected term. For the large-\(\omega\) expansion, a bound is derived in Appendix D:  
\[r_{n}=\left(\sin\phi\right)^{-n\beta-1}u_{n} \tag{32}\]with the auxiliary variable  
\[\phi:=\left\{\begin{array}{ll}\pi/2&\mbox{if $\beta\leq 1$}\\ \pi/(2\beta)&\mbox{if $\beta>1$}\end{array}\right. \tag{33}\]  
and with \(u_{n}\) as defined in Equation (26).  
To avoid cancellation, the sum (30) is computed using an extended floating-point precision \(\epsilon\), as described in Section 1. This ensures an upper bound for the total floating-point error of  
\[\Delta_{\rm fp}Y_{n}\leq\sum_{k=k_{0}}^{n-1}\epsilon|y_{k}|=\epsilon Z_{n} \tag{34}\]  
with the sum of absolute values  
\[Z_{n}:=\sum_{k=k_{0}}^{n-1}|y_{k}| \tag{35}\]  
\(Y_{n}\) is requested to approximate \(Y\) with a relative accuracy of \(\delta\),  
\[(\Delta_{\rm fp}+\Delta_{\rm tr})Y_{n}\leq\delta\cdot Y_{n} \tag{36}\]  
which is assured if  
\[\frac{\epsilon Z_{n}+r_{n}}{Y_{n}}\leq\delta \tag{37}\]  
This leads to the following algorithm:  
For each \(n=k_{0}+1,k_{0}+2,\ldots\), compute \(y_{n-1}\), \(Y_{n}\), \(Z_{n}\), and \(r_{n}\). Terminate and return \(Y_{n}\) if Equation (37) is fulfilled. Terminate and return an error code if one of the following conditions is met:  
(i) \(y_{k}\) is excessively large (approaching the largest floating-point number);  
(ii) \(y_{k}\) is excessively small (approaching the smallest normalized floating-point number);  
(iii) \(\epsilon Z_{n}/Y_{n}\geq\delta\): alternating terms have cancelled each other to an extent that floating-point errors may exceed \(\delta\);  
(iv) this is an asymptotic expansion and \(r_{k+1}>r_{k}\);  
(v) a preset limit \(n=n_{\rm lim}\) is reached.  
Since \(y_{k}\) and \(r_{k}\) require in part the same computations, the common factor \(u_{k}\) is computed ahead to avoid the repetition of gamma function evaluations and other costly operations. For the small-\(\omega\) expansion, we have just \(r_{k}=u_{k}\) and \(y_{k}=(-1)^{k}u_{k}\); for the large-\(\omega\) expansion, \(u_{k}\) is defined in (26).
# Autonomous Wireless Sensor Networks in
## 1 Introduciton
### Impact on Integrated Pest Management
The operation range for the temperature sensor should be \(-\)20 to +70 \({}^{\circ}\)C; (d) A sustainable power supply for long-term field operation: The low power aspect of WSNs can allow for exploring energy-efficient options that would eliminate the requirement for a battery on the sensory nodes such as embedded energy harvesting with supercapacitors; and (e) Harsh environment and enclosure: To ensure reliable data collection over longer periods, both the node hardware and sensors must perform well under harsh conditions. Basic data cleaning and aggregation algorithms need to be employed to generate forcing for all the sites in parallel and combine on different scales. Quality Control (QC) of the incoming data must be done in near-real time or shortly after the measurements to minimize data loss and shorten the time to identify and fix problems. Pest population dynamics are driven by environmental factors at the global and local scale. The El Nino/Southern Oscillation Index (ENSO), for example, is a global phenomenon with an impact on human infectious disease risk worldwide through droughts, flood, and other weather extremes [23; 24; 25; 26]. On the other side of the time spectrum, locally measured rainfall/temperature with very high temporal frequency will also drive the population dynamics more directly [27; 28]. Capturing the micro-meteorological conditions that are specific to pest resting sites could significantly increase the accuracy of currently used population dynamics models as well as disease transmission models since the processes pertaining to disease incubation rates are also temperature dependent [29]. In this paper, we examine the application of the \"PentaSense\" wireless network on a specific vector, _Ae. albopictus_, best known as the Asian tiger mosquito. It was first introduced in Europe around the end of the 20 th century and has since spread to most of the countries on the continent. This mosquito has vector competence for a wide range of disease such as Dengue, Yellow fever, Chikungunya and Zika [30; 31; 32; 33]. The main environmental factors influencing the establishment of this mosquito and driving the inter-annual population dynamics are temperature and precipitation. Climate normals and long-term averages can be used to describe the niche regional suitability for the establishment of the vector, however daily and sub-daily values of the meteorological variables are needed to drive the population dynamics. Autonomous Wireless Sensor Networks (WSN) can provide precise, site-specific information of the most important variables in remote areas and allow us to model the vector population. The paper describes the developed WSN-SDSS framework and the results of two model applications for the simulation of climatic suitability and micro-meteorological driven dynamics of _Ae. albopictus_.
# Theoretical Modelling of Ion Exchange Processes in Glass: Advances and Challenges
## 4 Future Challenges
### Formation/Dissolution of Silver Nanoparticles  
Metal nanocluster formation following ion exchange can arise if both suitable dopant cations and post-exchange processing techniques are used. Among all dopants, silver stands out for its great diffusivity in glass and its high tendency to form metal clusters [33]. As for the post-exchange techniques, heat treatments (annealing) under different atmospheres [100; 101] and (pulsed) laser irradiation [102] are commonly applied. The formation of metal structures can be regarded as a drawback and an advantage. For example, in light waveguiding, metal inhomogeneities must be avoided because they cause a high light absorption. On the contrary, some investigations have found these structures useful for sensing applications, through the surface enhancement of Raman scattering (SERS) [103], for fabricating photonic crystals [104] or, in general, for plasmonic optics [105; 106].  
Modelling of the formation and dissolution of silver nanoparticles is a very challenging task because the two complex processes compete dynamically. On the one hand, annealing or irradiation promotes silver aggregation and the formation of nanometre-sized clusters while, at the same time, the diffusion and dissolution of silver cations are produced. Therefore, at least three species must be included in the modelling: both exchanging cations and metallic silver. Moreover, this process is often done in gaseous atmospheres, which provide other cations that diffuse into the glass, increasing the number of species to be included in the model. On the other hand, this kind of process is strongly dependent on the cation concentration, so that the MIE should be considered. Finally, not enough experimental studies have been realized until now to characterize the great variety of mechanisms involved in these processes. Some theoretical descriptions of nanoparticle formation under specific post-exchange processing techniques (purely thermal annealing [107] and annealing in hydrogen atmosphere [108]) have been given. Both authors assumed the charge neutrality approximation that, under these processing techniques, is probably fulfilled. However, they did not assess the introduction of the MIE in their models and if that would lead to a better description of the cluster formation. Considerable work, both theoretical and experimental, must be done before having a complete theoretical model of this promising technique.
# Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link
###### Abstract  
To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, the centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In the present paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with the whole-constellation centralized extended Kalman filter, the iterative cascade extended Kalman filter, and the increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power; however, it yields results with a relatively good accuracy.  
inter-satellite link; whole-constellation centralized extended Kalman filter; distributed orbit determination; iterative cascade extended Kalman filter; increased measurement covariance extended Kalman filter; balanced extended Kalman filter