esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A novel RUL prediction method for hydrogen fuel cells based on the GRU network was proposed in this paper. The data were sampled by FCLAB in a relatively dynamic condition. Firstly, the data were preprocessed, which not only eliminated the outliers and noises, but also greatly reduced the calculation workload. Secondly, three neural networks were applied to predict the RUL of the hydrogen fuel cells. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our proposed prediction method based on the GRU has more advantages. Conclusions can be drawn as follows:  
1. The RNN (Recurrent Neural Network) model (including LSTM and GRU) is more accurate than the BPNN model in the RUL prediction. In fact, the accuracy of the RNN model is several times higher than that of the BPNN model when the starting point is 650 h.
2. Whether the starting point is 650 or 850, the GRU model is better than the BPNN model in RMSE (Root Mean Square Error), MAPE (Mean Absolute Percentage Error) and \(\mathbb{R}^{2}\) (Coefficient of Determination), and its prediction accuracy is close to the LSTM model, but the convergence of GRU is faster than that of the LSTM.  
Therefore, the proposed method can predict the RUL of the hydrogen fuel cells quickly and more accurately.  
For the RUL prediction of hydrogen fuel cells, the main direction of future research is to achieve accurate prediction under dynamic operating conditions, especially the accurate prediction of vehicle fuel cells, which is its largest application direction. The current research results show that most of the fuel cell predictions under dynamic conditions are short-term predictions, and very few methods can achieve accurate long-term predictions.  
For the selection of aging index of hydrogen fuel cells, this paper mainly considered the state of stack voltage, while some other factors, such as stack current, air and hydrogen pressure, were not considered. However, in fact, the factors affecting the performance of the fuel cells must be multiscale, so there must be more physical quantities that can directly or indirectly reflect the current performance of the fuel cells. It is not enough to consider the performance aging trend of fuel cells only from the current stack voltage or power; the future research direction must focus on the impact of some potential physical quantities on its performance.  
Conceptualization, B.L.; methodology, P.L.; software, P.L. and K.W.; validation, P.L. and K.W.; data curation, M.L.; writing--original draft preparation, P.L.; writing--review and editing, K.W.; supervision, B.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (No.U1830133).  
Not applicable.  
This work was supported in part by National Natural Science Foundation of China, under Grant U1830133. The authors would like to thank FCLAB for their hydrogen-fuel-cells data.  
The authors declare no conflict of interest.
## 4 Experiments
### The Effect of Different Modules on the Detection Results
#### 4.5.2 The Effect of DPN  
We performed ablation experiments on the proposed DPN module by changing the structure of the feature pyramid. From the penultimate row in Table 4, the top-down and bottom-up feature pyramids with the RASPP module achieve an mAP of 79.67%, which is better than the feature pyramids with other structures. It proves the effectiveness of the DPN module for multi-scale defect detection and model robustness. When the ASPP modules are consistent, the detection performance of top-down and bottom-up feature pyramids is better than that of top-down feature pyramids, as shown in the comparison of the first four rows and the last four rows in Table 4. It proves that the bottom-up path can enhance the flow of information, outperforming the FPN module with only one top-down path. In addition, it can be seen from the FPS that the top-down and bottom-up feature pyramids do not affect the overall detection speed of the model compared to the top-down feature pyramid, which proves its excellent cost performance.
## 4 Case Study  
Taking ZK279 + 840-860 of the Yulinzi Tunnel's left line in Zhengning County, Qingyang City, as an example, the optimized design of the FLSP was carried out. The surrounding rock was paleosol loess, the parameters of which are shown in Table 2. The buried depth of the tunnel, which is constructed by the three-bench seven-step method, was 50 m, and the excavation height of the upper bench was 3.8 m. The steel arch frame adopted an I20b beam, and the steel arch frame spacing was 75 cm. The shotcrete adopted C25 concrete, with a thickness of 24 cm, and the reserved deformation of the tunnel was 30 cm.  
According to the available research, the vault settlement and peripheral convergence have the fastest deformation rate in the upper bench excavation stage [18]. The FLSPs installed in the upper bench play a crucial role in controlling the vault settlement. This paper takes the upper bench excavation as an example to select and design the FLSP. According to the measured settlement data of the on-site tunnel excavation, the vault settlement after upper bench excavation by the three-bench method was 55% of the maximum settlement value, so the control deformation of the upper bench was 16.5 cm. According to the concept of NATM, the secondary lining does not bear the load in theory and acts only as a safety reserve. In fact, it needs to bear 30% to 40% of the load in the soft surrounding rock. Here, the secondary lining bore 30%, the primary lining bore 70%, and the upper bench excavation bore 30%.
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 4 Interpretable Anomaly Detection Framework for 6G NTN Protocols
### Interpretable Framework  
To design an interpretable framework and thereby obtain explainable detection results, an explainable detection process, and interpretable baseline detection results, an XAI approach was introduced to the current detection framework, as shown in Figure 6. The explainable detection results can be obtained via the current XAI method, whereas an explainable detection process and interpretable baseline detection method still need to be designed. With these explainable capacities, self-evolving classification and online identification of anomalous protocol sequences can be achieved.  
In the self-evolving classification process, an XAI method is used to explain the rule detection process and baseline detection results. The focus of the explanation of the detection results is the deviations of the attacks from the normal baseline, and these deviations are used to extract fingerprint features. The rule detection's DL model trains feature weights, assigning larger weights to fingerprint features of the covered attacks, a process referred to as the attention mechanism; features with larger weights receive more attention. To classify the uncovered protocol attacks, their fingerprints must also receive more attention. Thus, an explanation of the attention mechanism is required to support the adjustment of the weights in the self-evolving classification of uncovered protocol attacks. By combining the baseline and rule detection methods with explanations of the detection results and process, a self-evolving classification method for uncovered attacks can be developed.  
In the online identification of anomalous protocol sequences, an XAI method is used to provide inherently interpretable baseline detection results, which can be used to track protocol data and the original information throughout the entire mapping process. Furthermore, the detection results and the corresponding anomalous protocol data can be obtained simultaneously, thereby enabling effective anomaly identification.  
The designs of the self-evolving classification method for uncovered protocol attacks and the online identification method for anomalous protocol sequences used in the explainable framework are discussed in the following sections.
## 7 Conclusions
### Connectivity Maintenance Problem  
While all event-based connectivity approaches consist in the execution of regaining-connectivity actions in the presence of specific events (e.g., typically disconnections, whenever it happens or periodically after a certain amount of time), the _AAMO_ approach integrates a less restrictive connectivity strategy where the robots are motivated but not compelled to regain connectivity. When selecting their targets, the robots are always considering the opportunity cost of keeping connected or regaining connectivity, implicitly. Furthermore, in reconnection cases, the task location becomes the meeting point itself eliminating the need for rendezvous policy implementation and, maybe more important, avoiding deviations from natural paths. This way, the policy is utterly transparent to the eyes of the external observer: every time it is possible to explore and keep or enlarge connectivity level the robots will choose this option. On the contrary, when it is not, they merely behave guided by a pure path-cost exploration.  
Particularly concerning the efficiency related to both completion time and connectivity level maintenance, the approach is capable of decreasing the last of disconnection periods without a noticeable degradation of the completion exploration time, appearing as an intermediate solution that presents much better completion time performance than the most restrictive event-based connectivity approaches and better connectivity level along exploration than the approaches that do not take care about communication issues.
# An NDN Cache Management for MEC
## 2 Named-Data Networking Architecture  
### NDN Overview  
NDN uses a hierarchical data name that uniquely identifies _Data_. Based on this unique data name of _Data_, NDN selects an upstream network path through which the _Interest_ will be forwarded toward the publisher of the _Data_. Additionally, NDN can efficiently search a matched cache for the _Interest_ in the network using a data name. NDN divides the content into several segments and then generates _Interests_ for each segment. Figure 1 shows the NDN _Interest_/_Data_ forwarding procedure.  
1. Requestor-A transmits _Interest_ for a segment of data.
2. When a node (Node-b) receives the _Interest_ through the incoming network interface (Face-b1), Node-b reads a lookup table (PIT, Pending Interest Table). An entry of the PIT consists of both the data name of the _Interest_ and the incoming network interface (Face) of the _Interest_. Node-b conducts the PIT lookup to find a PIT entry matching the _Interest_. If there is a match, Node-b adds Face-b1 to the found PIT entry and then finishes handling the _Interest_. Otherwise, Node-b records a new entry on the PIT for the _Interest_.  
Node-b conducts the CS (Content Store) lookup using the data name of the _Interest_ to find a matched cache to the _Interest_. If there is a match, Node-b forwards the found cache to Requestor-A through Face-b1 and then deletes the relative entry from the PIT. It then finishes dealing with the _Interest_.
* Otherwise, Node-b forwards the _Interest_ using the information from the FIB (Forwarding Information based) table.
* If a node having the relevant _Data_ receives the _Interest_, the node forwards the _Data_ along the reverse of the path through which the _Interest_ passed.
* When receiving the _Data_, Node-b conducts the PIT lookup to find a PIT entry matching the _Data_. If there is no matching entry, Node-b discards the _Data_.
* Otherwise, Node-b caches the _Data_ in its CS, and then forwards the _Data_ through the network interfaces that are recorded on the found PIT entry.
* Finally, Requestor-A receives the _Data_.
* Thereafter, Requestor-B generates _Interest_ for the _Data_ that has been previously transmitted to Requestor-A.
* If Node-b receives the _Interest_ from Requestor-B, Node-b forwards the cached _Data_ to Requestor-B and then accomplishes the operation of the _Interest_.
## 4 Performance Evaluation and Discussion
### Example 2: Abrupt Dynamics and Signal Fading Scenario
Figure 8: The trajectory of the receiver in the simulation for Example 2. (The numbers with circles corresponds to the numbers in Table 2, and the red arrows denotes the direction of the receiver’s movement). Figure 9: The \(C/N_{0}\) and Doppler estimated results of the four algorithms after 60 s for Example 2. (**a,c**) are the results for the PRN 14 satellite signal; (**b,d**) are the results for PRN 20 satellite signal. Therefore, for the first two abrupt turns under relatively high \(C/N_{0}\), all four tracking algorithms can track well. When the third turn is under relatively low \(C/N_{0}\), the KF and PLL tracking cannot adapt to the signal variation. The serious strength fading and abrupt motion cause the KF and PLL tracking cannot match the actual harsh situations. However, because the proposed adaptive factor can adjust the noise statistics to satisfy the real variable noise environments, the proposed AKF tracking can compensate for the modeling error under harsh environments. Accordingly, in comparison with other tracking methods, the proposed AKF approach can adapt to the variable signal and show more robust tracking performance when the serious strength fading and sharp turns coincide. The proposed tracking approach can enhance the navigation performance of GPS receivers in complex environments with higher-order dynamic and signal attenuation.
## 4 The Proposed Use Case: Data Model and Scalability Analysis
### The Smart Campus Use Case
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Information** & **Range** & **Resolution** & **Size (B)** \\ \hline Identification code of the generator & - & - & 2 \\ Timestamp of the set of information & - & 1 s & 5 \\ Three-phase voltage (RMS value) & 0 \(\div\) 500 V & \(<\) 0.01 V & 2 \\ Three-phase current (RMS value) & 0 \(\div\) 300 A & \(<\) 0.01 A & 2 \\ Three-phase active power & 0 \(\div\) 200 kW & 0.01 W & 3 \\ Three-phase reactive power & \(-\)200 \(\div\) 200 kvar & 0.02 var & 3 \\ Three-phase Power Factor (PF) & 0 \(\div\) 1 & \(<\) 0.01 & 1 \\ Grid Frequency & 0 \(\div\) 100 Hz & \(<\) 0.01 Hz & 2 \\ MPPT 1 voltage & 0 \(\div\) 1000 V & 0.015 V & 2 \\ MPPT 1 current & 0 \(\div\) 200 A & \(<\) 0.01 A & 2 \\ MPPT 2 voltage & 0 \(\div\) 1000 V & 0.015 V & 2 \\ MPPT 2 current & 0 \(\div\) 200 A & \(<\) 0.01 A & 2 \\ Alarm Code & - & - & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of information packed into each data sample transferred by a single photovoltaic inverter. Each information is computed as the average of all of the measured samples over the given monitoring time interval. For reactive power measurements, positive values represent inductive loads, while negative values represent capacitive loads. MPPT: Maximum Power Point Tracker. is composed by a single device (e.g., a heat pump) with a nominal power consumption of 200 kVA, the maximum size of each set of information transferred by the system is on the order of 28 B every 15 min, leading to an expected data throughput of about 0.031 B/s. Finally, concerning the transmission of information from supervisory entities to independent equipment, a more generalized scenario can be considered, i.e., by considering the provisioning of job schedules to single controllers, such as smart thermostats, smart plugs, or other simple equipment controllers. From the communication point of view, those devices can behave both as single sensors and as controllers. Acting as sensors, they communicate the state of the related controlled parameter (e.g., the room temperature) every 15 min, according to the data model described in Table 2. Acting as controllers, they receive a set of control instructions (e.g., room temperature set-points) every 3 h, by using a confirmed LoRaWAN message. As detailed in Table 6, the set of control instructions includes: the identification code of the device, the time reference signal (for synchronization purposes), a set-point profile (with a time resolution of 15 min), and the initial timestamp of the job schedule. Assuming that a 16 bit register is adequate for representing any possible set-point value, a 36 B message is sent to each smart controller every 3 h, with a corresponding data throughput of about 0.003 B/s.
## 2 Materials and Methods  
### Reagents  
All reagents, either analytical or HPLC grade, were purchased from Merck (Darmstadt, Germany). The phenol standards (3-hydroxytrosol, 2-(4-hydroxyphenyl) ethanol (tyrosol), p-coumaric acid, vanillin acid, vanillin, luteolin, apigenin, pinoresinol, p-hydroxyphenylacetic acid (internal standard 1), o-coumaric acid (internal standard 2) oleuropein and caffeic acid), Trolox, fluorescein, and 2,2\({}^{\prime}\)-Azobis(2-amidinopropane) dihydrochloride (AAPH) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Volatile standards 4-methyl-2-pentanol (internal standard), ethanol, ethyl propionate, 4-methyl-2 pentanone, butyl acetate, 2-methyl-1-butanol, 3-methyl-1-butanol, 3-octanone, acetic acid, propionic acid, 1-octanol, butyric acid, heptanoic acid, nonanoic acid, (\(E\))-2-hexenal, hexanol, (\(E\))-2-nonenal were purchased from Merck. Tocopherols standards were purchased from Calbiochem (Merck). All standards had a purity of 98% or higher.
# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 4 Case Study
### Correlation Analysis
#### 4.3.1 Coefficient Calculation  
The time series of traffic parameters between 400 and 650 min are shown in Figure 13b, and it can be obviously seen that the trend of index holds similar to the trend of volume and density, which is synchronization characteristics. However, such similar trend cannot be found in traffic velocity time series, due to the slight change of aircraft velocity in the cruise phrase, which is relatively stable during most of the time. In order to quantify this similarity, calculations of correlation coefficient between different time series are shown in Figure 14.  
Figure 14: Values of correlation coefficient.  
Figure 13: Traffic parameter time series. (**a**) The statistical distribution of paprameters on September 1 st (**b**) The statistical distribution of parameters between 400 min and 650 min on September 1 st.  
According to the intensity of correlation, the order ranking from high to low is traffic volume, traffic density, and traffic velocity, which is consistent with the results ranking in Section 4.3. In conclusion, the correlation between velocity and index is the weakest, while there is a significant negative correlation in volume-index and density-index.
## 5 Long-Short-Range Communication  
Standards used for communication in IoT healthcare applications are divided into two categories, i.e., long-range and short-range standards. Communication occurs among devices within a wireless area network, which are, in turn, connected to a base station and the central node of the WBAN. In this article, we consider both short- and long-range communication.
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
## 3 Data Analysis for Interferometry and Real-Time Tomography
### Tomographic Reconstruction
#### 3.2.2 Convergence and Error Studies of ML-EM  
The convergence properties of the ML-EM algorithm are summarized in Figure 10, which shows the \(L_{1}\) norm for the first 15 iterations for different numbers of measurements (number of projection angles \(N_{a}\)). Gaussian noise (standard deviation \(\sigma=0.01\)-\(0.05\)) is added to the measurement. The reconstructed distribution matches the original distribution best if noise is lowest, and more interestingly, the error is not strongly correlated to the number of projections. The downside of many projection angles is that more noise is picked up. It turns out that \(N_{a}\) around seven achieves the best convergence properties for the distribution and a noise level of \(\sigma=0.01\).
## 4 Discussion
### Amendments and Soil Properties
#### 4.1.4 Soil Biogeochemical Properties after Plant Harvest  
Growing vegetables resulted in a drawdown of soil labile N, indicative of plant uptake. This was particularly evident in soils planted to radish, which exhibited significant decreases in INN and DON uniformly across all treatments. In amended treatments, INN concentrations declined by 25-33 times compared to soils in Lab Inc and only three times in UNAM. Similarly, soil DON declined six times in BIOC and INF and 10 times in COMP compared to Lab Inc. Less of a difference was observed in soils planted to lettuce, where the overall INN and DON concentrations remained much greater compared with soils planted to radish. Greater concentrations in lettuce compared to radish could suggest soil labile N accumulation in excess of plant demand or possible plant stunting (inability to take up N) caused by other growing conditions unrelated to N, including exposure to Pb or nutrient deficiency.  
Growing plants also decreased soil DOC concentrations in almost all treatments except for radish in INF and lettuce in COMP. The declines may be associated with heterotrophic rhizosphere microorganisms, which are typically supported by the carbon rich environment created through root exudates [93]. The symbiotic relationship between plants and rhizosphere microbes often facilitates microbially-mediated processes of soil OM immobilization and mineralization [94]. The lack of a decline in DOC in COMP planted to lettuce points to the sustained presence of OM from compost. This contrasts with INF planted to radish, where the lack of significant change in DOC could reflect an increase in root exudation in the acquisition of nutrients other than N.
# Stochastic and Higher-Order Effects on Exploding Pulses
## 2 Influence of Additive and Multiplicative Noise on Exploding Dissipative Solitons
### Stochastic Equations  
The stochastic complex cubic-quintic complex Ginzburg-Landau equation (SCQGLE) under the influence of additive noise reads  
\[\partial_{t}A=\mu A+\beta|A|^{2}A+\gamma|A|^{4}A+D\partial_{xx}A+\eta\,\xi. \tag{1}\]  
Here, \(A(x,t)\) is the complex envelope, \(\beta=\beta_{r}+\mathrm{i}\,\beta_{i}\); \(\gamma=\gamma_{r}+\mathrm{i}\,\gamma_{i}\); \(D=D_{r}+\mathrm{i}\,D_{i}\); \(\mu\) is the distance from linear onset (real), and \(\eta\) the noise strength. \(\beta_{r}>0\) and \(\gamma_{r}<0\) in order to guarantee that we are in the presence of an inverted Hopf bifurcation saturating to quintic order. The complex white noise \(\xi(x,t)\) satisfies \(\langle\xi\rangle=0\) (zero mean), \(\langle\xi(x,t)\ \xi(x^{\prime},t^{\prime})\rangle=0\) and \(\langle\xi(x,t)\ \xi^{*}(x^{\prime},t^{\prime})\rangle=2\delta\left(x-x^{ \prime}\right)\delta\left(t-t^{\prime}\right)\) (delta correlated in space and time). Discretization in space and time should not affect quantitative noise features, so that \(\xi(x,t)\) becomes replaced by \((\chi_{r}+\mathrm{i}\,\chi_{i})/\sqrt{dx\,dt}\), where \(\chi_{r}\) and \(\chi_{i}\) are uncorrelated random numbers (in space and time) obeying a normal distribution with zero mean and unit variance.  
The SCQGLE with multiplicative noise that we investigate here is of the form  
\[\partial_{t}A=(\mu+\eta\,\zeta)A+\beta|A|^{2}A+\gamma|A|^{4}A+D\partial_{xx}A, \tag{2}\]  
where the white noise \(\zeta(t)\) satisfies \(\langle\xi\rangle=0\) (zero mean), and \(\langle\xi(t)\ \zeta(t^{\prime})\rangle=\delta\left(t-t^{\prime}\right)\) (delta-correlated in time but homogeneous in space), so that \(\zeta(x,t)\) is replaced by \(\chi_{r}/\sqrt{dt}\), where \(\chi_{r}\) corresponds to uncorrelated random numbers obeying a normal distribution with zero mean and unit variance. Additive noise implies perturbations on short length and time scales. However, homogeneous multiplicative noise leads to a homogeneous enhancement and suppression of the modulus as a function of time. As an example, we can mention electroconvection in nematic liquid crystals by superposing noise on the driving voltage [36].