esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### IGR of the Conditioning Factors  
After completing the acquisition of negative samples for debris flows, the complete modeling datasets could be established. The IGR values of the conditioning factors are shown in Table 1. The same conditioning factors showed different predictive abilities in different datasets. The negative-sample acquisition strategy has a significant effect on the predictive ability of the conditioning factors. For single-grid and multi-grid datasets, the spy technique is most effective in improving the predictive abilities of the conditioning factors, while the IF algorithm plays the most important role in determining watershed unit datasets. On the other hand, altitude, slope, TWI, rainfall, and DTR showed a strong predictive ability under different sample representation forms and negative-sample acquisition strategies, demonstrating the roles of topography, rainfall, and human engineering activities in controlling debris flow formation in the study area.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Representation Forms** & **Conditioning Factors** & **SVM-Based** & **Spy-Based** & **IF-Based** \\ \hline \multirow{8}{*}{Single grid} & Rainfall & 0.092661 & 0.096309 & 0.035283 \\  & Altitude & 0.064700 & 0.072970 & 0.044267 \\  & Slope & 0.052486 & 0.164884 & 0.050151 \\  & Plane curvature & 0.009772 & 0.011732 & 0.002229 \\  & Profile curvature & 0.007298 & 0.010095 & 0.003221 \\  & TWI & 0.102360 & 0.221782 & 0.021809 \\  & NDVI & 0.029817 & 0.030426 & 0.011323 \\  & DTF & 0.021958 & 0.069592 & 0.014625 \\  & DTR & 0.188112 & 0.217063 & 0.172086 \\ \hline \hline \end{tabular}
\end{table}
Table 1: IGR values of conditioning factors.  
Figure 5: The negative-sample acquisition results: (**a**) single-grid-SVM dataset; (**b**) single-grid spy dataset; (**c**) single-grid-IF dataset; (**d**) multi-grid-SVM dataset; (**e**) multi-grid spy dataset; (**f**) multi-grid-IF dataset; (**g**) watershed-unit-SVM dataset; (**h**) watershed-unit spy dataset; (**i**) watershed-unit-IF dataset.
## 3 Approach
### Programming Interface
#### 3.4.6 Data Security  
There can be many concerns around data security when it comes to IoT devices, whether it be about remote access to the devices, interception of data upload, or direct access to data storage cards. Most of these concerns are outside the scope of REIP SDK, as addressing them requires OS-level handling. Many can be circumvented though by thoughtful system design, such as protecting outside connections to the devices using firewalls, SSH keys, VPNs for secure remote access and using HTTPS for data upload.  
A harder to remedy issue around IoT (or any unaccompanied computer system for that matter) is that a hard drive is non-trivial to secure. For IoT devices, they often need the ability to reboot themselves in the case of system failure or power interruptions. This poses problems when trying to fully encrypt the hard drives, because a password login would be required whenever there was a reboot. The other option is physical security, i.e., making the SD card more difficult to access; however, it also makes it more difficult, and potentially costly, to repair the sensors. Therefore, encryption of sensitive data using a two-sided encryption key is important to ensure that the data cannot be directly accessed from the hard drive itself and can only be decrypted by the main server where the decryption key is stored securely. REIP SDK provides blocks for this type of encryption, including a two-stage encryption technique that reduces network bandwidth for decryption on the server by only needing to transmit a small payload instead of the full data.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
###### Abstract  
Wireless sensor networks are used to monitor the operating status of the microgrids, which can effectively improve the stability of power supplies. The topology control is a critical issue of wireless sensor networks, which affects monitoring data transmission reliability and lifetime of wireless sensor networks. Meanwhile, the data acquisition accuracy of wireless sensor networks has a great impact on the quality of monitoring. Therefore, this paper focuses on improving wireless sensor networks data acquisition satisfaction and energy efficiency. A joint acquisition time design and sensor association optimization algorithm is proposed to prolong the lifetime of wireless sensor networks and enhance the stability of monitoring, which considers the cluster heads selection, data collection satisfaction and sensor association. First, a multi-constrained mixed-integer programming problem, which combines acquisition time design and sensor association, is formulated to maximize data acquisition satisfaction and minimize energy consumption. To solve this problem, we propose an iterative algorithm based on block coordinate descent technology. In each iteration, the acquisition time is obtained by Lagrangian duality. After that, the sensor association is modeled as a 0-1 knapsack problem, and the three different methods are proposed to solve it. Finally, the simulations are provided to demonstrate the efficiency of the algorithm proposed in this paper.  
microgrids; wireless sensor network; topology control; cluster members association; acquisition time design 2021147756. [[https://doi.org/10.3390/en14227756](https://doi.org/10.3390/en14227756)]([https://doi.org/10.3390/en14227756](https://doi.org/10.3390/en14227756))
## 3 Weather Component Analysis during the \"Oriental Star\" Accident
### Extended Study: Ensemble Forecast  
As one of the renowned forecasting institutes, ECMWF developed its Ensemble Prediction System (EPS) in 1992, consisting of a global atmospheric general circulation model, a data assimilation system, a land surface model, an ocean wave model, and an ensemble forecasting system. In contrast to the deterministic product, which provides only one dynamic model result per grid point, the ECMWF EPS produces multiple outcomes per grid point. Forecasts such as these are designed to provide a measure of forecast uncertainty and probability, from which alternative scenarios and strategies can be developed. The ECMWF EPS generates a total of 51 forecasts (ensemble members), which are used to represent the initial analysis error (by perturbing the initial analysis) and model error (by using stochastic processes to represent the errors in the model's physics framework). The probability of occurrence of an event (e.g., rainfall above or below some threshold) can be characterized  
Figure 6: Predicted CP (solid) and LSP (dash) value following the last route of the “Oriental Star” ship with a 1-day (red), 2-day (gray), and 3-day (green) lead time.  
by the number of ensemble members predicting the event divided by the total number of members [20; 21].  
Since its first distribution, ensemble forecast has been a powerful method to minimize uncertainty and systematic bias and was applied frequently in multi-weather-related hazard early warning, e.g., flooding [22], agriculture [23]; and typhoon [24]. In marine navigation, [PERSON] et al. [25] compared deterministic and ensemble results and concluded that route optimization using the latter has the potential to reduce the risk of late arrival for voyages under adverse weather.  
This section examined the CP performance in the ECMWF EPS product. In Figure 7a, most ensemble members of the regional average (defined in the blue rectangle in Figure 7b) CP time series showed a sudden increase during the accident period. Noting that members with high values perform better, we chose the average CP prediction of the highest one-tenth of all 51 ensemble members to draw the horizontal distribution (Figure 7b). It was observed that the maximum CP value was much closer to the final route of the \"Oriental Star\" ship. Figure 7c indicates that in the ensemble run, the CP's warning effect can extend from a 24 h lead to a 48 h lead, but more cases should be considered.  
Figure 7: (**a**) Time series of regional average (blue box) ensemble CP forecast, with each colored line representing each ensemble member; (**b**) mean of the highest one-tenth of 51-member ensemble CP forecast at a 24 h lead; (**c**) similar to Figure 6 but for the mean of one-tenth of the all-ensemble CP forecasts.
## 3. Strategical Approach
### Stress Loading Scheme  
The constant-stress loading method involves subjecting each test unit to a constant-stress level. Normally, the optimal design of a classical constant-stress ADT generally has two different accelerated stress levels (Figure 1a), while three stress levels are applied to check the validity of the parameter-stress relationship (for details, see [PERSON] and [PERSON] [2]). To implement this scheme, an allocation ratio for the test unit to each stress level and a corresponding test time for each stress level are required.  
While in the step-stress loading scheme, the applied stress level is changed during the test. Therefore, a mathematical model to describe the effect of changing the stress level during the test is required. The representative model is the cumulative exposure (CE) model, which assumes that the cumulative damage depends only on the current stress  
Figure 3: Test period for each stress level under the constant-stress loading method.  
Figure 2: Usage of prototypes: a strategic approach.  
level (for details, see [PERSON] [8]). Figure 4 shows the degradation path model based on the CE model. There are three different levels of stress: normal use condition, maximum, and intermediate stress levels, as shown in Figure 4a. The normal use condition is applied to the multipurpose general tests, the maximum stress level is used for environment tests such as the HTOL, and the intermediate stress level is used to check the model's validity. For the case of the step-stress loading scheme, the mathematical model for the test plans of [PERSON] and [PERSON] [5] are used to develop the strategic approach. All test units are located in the same chamber, if possible, and the test starts at the normal use condition. After, the stress level increases to the maximum stress level; finally, the intermediate stress level is loaded at the end of the test. An advantage of step-stress loading over constant-stress loading is that it can further reduce the test time or resources. For example, let us assume that there is only one test chamber that is large enough to load all test units. In this case, the experimenter would need to perform at least two consecutive tests using the constant stress loading method. However, if the step-stress loading method is applied, it would take half the time, because the test is finished with a single test. In addition, in this paper, the proportion of the prototypes for each stress level was not required, such as in the constant-stress loading method. By adopting step-stress loading, all prototypes were tested under the same stress pattern, eliminating the need for prior information.
## 5 Conclusions and Future Work  
In this paper, we proposed the Trust-CH framework as simultaneous security and power-saving scheme for a wireless sensor network. In the proposed scheme, a mechanism for building trust between the WSN nodes was proposed, depending on the behavior of the nodes and monitoring the messages they send and receive. These messages are represented by Hello, ACK and lost packets. After the trust is built between nodes, the clustering mechanism is activated based on trust technology to reduce communication distances between nodes and save energy. After building trust between nodes and selecting CH, the scheme proposes to reduce cryptographic limitations by using a lightweight algorithm and automating coding parameters based on the level of trust between nodes. Trusted nodes also use a shared key and lightweight authentication, while untrusted nodes use other encryption and authentication algorithms.  
The Cooja simulator was used to simulate the Trust-CH algorithm and analyzed for several metrics such as power consumption, network lifetime and degree of impact on malicious nodes. The achieved results of the proposed scheme show that it can effectively save power consumption by 28% compared to Speck128 static encryption. Furthermore, it can enhance the network lifetime by 53%. Moreover, it extends the network lifetime by 40% and 42% compared to the FlexCrypt and TMFOGA, respectively. The proposal supports clustering and dynamic encryption with the same technology.  
In future work, we are planning to study the viability of extending the proposed method to deploy and test it for a variety of practical IoT applications due to the rapid growth of IoT technology which has common security problems with wireless networks.  
Conceptualization, R.A., R.W., T.A.-A. and T.A.A.; investigation, R.A. and T.A.-A.; data duration, R.A. and R.W.; writing--original draft, T.A.-A., T.A.A. and R.W.; visualization, R.A. and T.A.A.; supervision, R.A.; writing--review and editing, R.A., R.W. and T.A.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### High-Pass Flat Negative Group Delay Circuit  
The circuit structure is shown in Figure 1. When the values of the components in the circuit are as shown in Table 1, the circuit is a circuit with a high-pass negative group delay function. The results are shown by the red lines in Figures 3 and 4.
## 6 Results
### Energy Reduction
#### 6.3.1 DTLS Handshake  
Figure 11 shows the energy consumption of a device for a successful DTLS handshake in different scenarios and radio conditions. The figure shows that the energy consumption increases with the degradation of radio conditions, reaching a very high value in radio condition 4. As an example, the measured energy consumption of the DTLS handshake in radio condition 4 is more than 18 times that observed in radio condition 0 in the case of the IP. This is due to repetitions, MCS and possible retransmissions occurring in radio condition 4. We notice from the figure that the energy consumption in the IP is always more than that in NIDD for all the radio conditions. Using NIDD requires 10% to 32% less energy than using IP. When using SCHC, the energy demand is reduced in both cases, and this is evident especially in bad radio conditions. In radio condition 4, SCHC reduces the energy consumption of a successful handshake from 9.4 mWh to 5.6 mWh and 8.5 mWh to 4.4 mWh in IP transportation and NIDD, respectively.  
This translates to a 40% reduction in energy consumption for the IP and a 47% reduction for NIDD. In fact, when using SCHC over the IP, the energy consumption is less than that with NIDD (without SCHC). Obviously, using SCHC with NIDD is the most energy-efficient scenario out of them all.
## 3 Experimental Verification
### Experimental Results and Analysis  
#### 3.2.1 Parameter Settings and Training Process  
According to the Figure 1 in Section 2, the network structure and parameter setup of SRMANet are presented in Table 3, where F represents the number of filters; KS represents filter width; DR represents the dropout rate; and R represents the gating size in the SE-Res block. SR is the compression ratio of Squeeze-Attention; GS is the gating size of External-Attention, which controls the computational complexity. The [32@256 \(\times\) 1] denotes the 32 feature maps of size 256 \(\times\) 1. It should be pointed out that the SE-Res block enhances the training stability, which is used together with dropout to reduce the risk of overfitting.  
The data with 40 Hz speed, 1 A load and 2048 sample length were selected as the benchmark dataset and the following part of the study was carried out using this benchmark dataset. The training process of SRMANet is presented in Figure 8. We found that SRMANet converges quite rapidly. After testing, it was found that SRMANet could identify almost all samples at an epoch of about 60 times. This illustrated SRMANet's capacity to identify universal features from vibration signals.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 5 Testbed Deployment and Experimentations
### Experiment Settings  
The source node generates photos periodically (every 30 s). In our experiments, we vary the value of \(\alpha\) (\(\alpha=0\): no duty cycle is applied) and the data redundancy \(\beta=m\), that is the number of repair packets sent by the source. We mainly focus on the impact of the duty cycle and the source coding in the performance of the protocol. Thus, we vary the value of \(\alpha\) while \(\beta\) is a constant and vice versa. For Figures 4-11, the source coding is not a variable. While in Figure 12, \(\alpha\) is set to 10. For the INFR scenario, the source generates data packets representative of infrequent traffic, with a random inter-packet delay ranging from 5 to 10 s. The other experiment parameters are summarized in Table 1. Results presented in this section are the average of 2 to 8 images.  
Our result analysis originates from detailed logs collected through the serial output of Arduino devices. Python tools retrieve, store, parse, and analyse the logs (collected logs represent a volume of 26 GBytes). The physical and radio topology are described in [55].  
A video illustrating a demo for ODYSSE is available online [56].
## 2 Materials and Methods
### Data  
#### 2.2.1 Ground Data  
Ground truth samples were taken at 20 points distributed over the reservoir of Hassan Addakhil dam (Figure 2), where nitrate, dissolved oxygen and Chl-a were measured by chemical process at the laboratory of the Guir-Ziz-Rheris Hydraulic Basin Agency in Errachidia and Gaya Laboratory in Rabat, Morocco, according to the Morocca law adopted for aquatic waters [41] (Table 1). The Chl-a indicates the stage of eutrophication in the reservoir, while the nitrates' concentration is directly related to the agricultural practices upstream of the reservoir, as well as to wastewater discharge.  
Dissolved oxygen was measured in situ using a portable dissolved oxygen meter (BANTE Instruments 821). For the determination of Chl-a, a volume of samples between 0.1 and 2 L was first filtered under vacuum through a glass fiber filter without organic binder with a diameter greater than 1 \(\upmu\)m, depending on the algal content, after shaking. Then we proceed to the extraction step by pouring a small volume of acetone (20 mL to 30 mL) into the tube containing the filtered pieces. This step was followed by shaking the extract contained in the extraction tubes for at least 3 min. Finally, we proceeded to the reading of a part of the clear extract by UV-Vis spectrophotometry (Lovibond), which provides double-beam operation with a scattered light rate of 0.01%, wavelength accuracy of +/\(-\)0.1 nm and stability of 0.00015. The measurements were made at two wavelengths, A1 = 665 nm and A2 = 750 nm, by comparison with a reference cell filled with acetone.  
The determination of nitrates was done by UV-Vis spectrophotometric calibration (Lovibond). In fact, after the preparation of the solution noted, it was smothered by dissolving 129 mg of ammonium nitrate (of raw formula NH4 NO3) in 1.0 L of distilled water. A solution of mass concentration (or content) equal to 100 mg L\({}^{-1}\) was then obtained. Then, we subtracted the absorbance of the blank from the absorbance of each standard solution and plotted the calibration curve showing absorbance versus mass of nitrate, in milligrams per liter. Finally, the nitrate concentration C was determined from the UV-visible calibration curve, established following the Beer-Lambert law.  
The analyses of the samples were measured in three replicates, and the average was calculated. The Table 1 shows the average of the three measured values.
## 3 Results and Discussion
### MD Simulation  
The best-docked poses of BarCS and BarS that were obtained from molecular docking analysis through AutoDockVina were taken as the initial structure and were subjected to 100 ns of MD simulation [50]. Data from MD simulations were processed by computing the RMSD in order to look into the structural stability.  
Average protein (considering only heavy backbone atoms) RMSD value calculated over the last 50 ns of MD simulations for BarCS and BarS is 2.412 and 2.875 A, respectively.  
Both BarCS and BarS formed stable formations after 40 and 50 ns, respectively (Figure 6). Several reports state that an RMSD value of less than 3.0 A is the most acceptable [51; 52].  
Figure 5: Two-dimensional illustrations of a serotonin receptor docked with (**a**) (Bar–CLA) complex and (**b**) Bar alone.  
Figure 6: The RMSD over 100 ns. The black color represents serotonin receptor alone, orange color represents the BarS complex, and green color represents the BarCS complex.  
The lower RMSD values of the BarCS complex indicate smaller deviation from the initial protein structure. This result demonstrates that BarCS creates a more stable complex. The protein structures before and after the MD simulation were compared by studying superimposed structures using Chimera 1.15 software, using tool-structure comparison followed by the MatchMaker feature (Figure 7). MatchMaker performs a fit after automatically identifying which residues should be paired. The findings support the view that ligand-receptor interaction increases the compactness of the protein structure (Figure 7) [53].  
The radius of gyration data (Rg) for BarS and BarCS for over the last 20 ns of MD simulations were 26.42 and 26.07 A, respectively (Figure 8). The Rg data for BarCS and BarS were decreased along with the time of the simulation, indicating that structures became more compact (Figure 8).  
Figure 8: The Rg data over 100 ns simulation time. Black color represents serotonin receptor alone, orange color represents the BarS complex, and green color represents the BarCS complex.  
Figure 7: Superimposed structures of (**a**) unbounded serotonin (pink) and serotonin after simulation (orange) for BarS and (**b**) serotonin after simulation (green) for BarCS.