esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.9 SigFox  
Sigfox is a low-power wide area network (LPWAN) technology designed for the Internet of Things. It uses ultra-narrowband technology to transmit data with a very low power consumption over long distances [39]. It operates in the 862-928 MHz frequency band and has a channel bandwidth of 100 Hz [40]. With a range of up to 50 km in rural areas and up to 10 km in urban areas, Sigfox can work well in applications that require long-range communication with battery-powered devices. Its data rate ranges from 100 to 600 Bps, depending on the region [30; 41].
## 2 Materials and Methods
### Multivariable Model Selection  
We analyzed the data using generalized linear (GLM) and zero-inflated negative binomial models (ZINB). These techniques can adjust for confounding and effect modification with our data because they are designed to handle non-normal distributions, zero-inflation, and count data. Any predictor variable with significant Pearson's \(\chi^{2}\) results would be tested using GLM and ZINB models. The diagnostic methods utilized for selecting the most appropriate modeling technique (GLM or ZINB) were dispersion tests, deviance residual plots, fitted vs. observed plots, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the likelihood ratio test (LRT). Linear and logistic regression models could not be employed because the goose counts had skewed distributions, and the outcome of interest was comprised of counts that contained a high number of zeroes, and a violation of the normality assumption.  
After selecting the most suitable modeling technique for the dataset, covariates were systematically evaluated to construct a final model with strong interpretability. Covariate inclusion was guided by reductions in AIC and BIC values, with lower scores indicating better model fit and stability. Each covariate's impact was assessed by sequentially adding it to the model and observing changes in these criteria. The final model included covariates that consistently contributed to minimizing the AIC and BIC.
# Speed Optimization in DEVS-Based Simulations: A Memoization Approach
## 5 Memoization Technique to Reduce the Search Cost in the DEVS Coupled Model
### Model Transition Behaviors Using the Memoization Technique  
The internal transition (coupled model) transition algorithm shown in Algorithm 1 and the external transition (coupled model) algorithm shown in Algorithm 2 represents the pseudocode of external and internal transitions. In the case of the time shift operation, it was omitted because it simply performs a function that subtracts the time advance of the model. time_advance() performs the function of receiving the TA of the model. self.select() describes the ability to receive priority target models at the current simulation time.  
Internal_transition() returns the output Y of the target model and receives the current model and the coupled model. In this process, models with X_in connected to Y_out are assigned to the cyx_group, and models with X_in connected to X_in of the coupled model are assigned to the cxx_group.  
Any other group is placed in oth_group. After traversing the connection model cyx_group and calling the external_transition() function, min_ta is compared with the time advance of the models in cyx_group that have completed the transition, and the smallest value is stored in min_ta. It traverses the remaining models oth_group and reduces oth_group.ta by the ta of the coupled model, compares the time advance of the reduced models, and stores the smallest value in min_ta.  
```
Input: Event event. Result: Update min_ta of coupled model with the value of self.ta_cache.
1. all_group = self.children();
2. cox_group = self.cox_group(event);
3. oth_group = all_group--cxx_group;
4. min_ta = self.time_advanced()--shift;
5. min_ta = cox_group.external_transition(shift, event).reduce(min_ta);
6. min_ta = oth_group.time_shift(shift).reduce(min_ta);
7. self.ta_cache = min_ta;
```  
**Algorithm 1**Internal Transition (Coupled Model)  
The ta_cache of the coupled_model becomes the smallest min_ta value. Figure 7 describes the situation in which the lower lever DEVS models {M'1, M'2, W1, W2} of the Coupled Model M1 all belong to the cyx_group, putting the ta of {W1, W2} in the Min_ta. Similarly, for Coupled Model M0 with {M1, M2} as the lower DEVS model, we have the ta of M1 as Min_ta.
## 4 Discussion
The H\({}_{2}\)O\({}_{2}\) generated is highly toxic and CAT catalyzes its transformation into H\({}_{2}\)O [67; 68]. Regarding the _psbA_ gene, it was up-regulated under Cr and Pb exposures, which illustrates the role played by the D1 protein of the photosystem II (PSII) in combatting oxidative stress [69; 70]. For _HSP70_ gene expression, we observed a slight increase after Cr and Pb exposures. Similar results have been observed previously in _Tetraselmis suecica_ exposed separately to 5 mg/L of CdCl\({}_{2}\), CuCl\({}_{2}\) and PbCl\({}_{2}\) during 24 h [71]. This trend may be explained by the nature of HSP70 responsiveness to environmental stress. Indeed, HSP70 has been considered a good biomarker of toxicity only after long-term exposure to environmental pollutants [72; 73; 74]. Concerning _C. dorsiventrale_,removal capacities differed according to the metal. For the lowest concentration of Cd, the removal reached a maximum of 22.1%. Therefore, _C. dorsiventrale_ did not seem to represent a promising candidate for Cd removal despite its tolerance observed towards this metal. Conversely, Cr removal efficiency attained 89% and is equally distributed between intracellular and extracellular compartments. The most spectacular results, however, were obtained with Pb since its removal rate reached 95%. This result was higher than those of previous studies on the capacity of the microalgae _Chroococcus minutus_, _Chlorococcus aegyptiacum_ and _Chlorella vulgaris_ in removing heavy metals, including Pb [74, 75, 76, 77]. Interestingly, 86% of removed Pb was adsorbed in microalgae cell surfaces. This opens up some new research avenues that could track the regeneration of algal biomass and the release of intact metals. Despite these promising results, the study's use of artificial seawater and batch cultures might limit the applicability of _C. dorsiventrale_ to real-world conditions. Future research should address these limitations by exploring large-scale cultivation and metal recycling to improve scalability and practical application.
## 4 Conclusions  
In summary, a high precision and high speed TDC implemented with online calibration based on the ZYNO-7020 was designed and tested. A ones-counter encoder was employed to convert the thermometer code with bubble errors into binary code. The dead time was further decreased by taking advantage of the pipelined adder tree structure. Bin-by-bin calibration was implemented in the FPGA to minimize the DNL values. In addition, an innovative online calibration based on a frequency counter for temperature compensation is proposed. Experimental results demonstrated the sampling rate was 170 M and the LSB was 17.4 ps. The DNL reached [\(-0.97\), \(4.12\)] LSB before calibration, and reached [\(-0.90\), \(1.67\)] LSB after bin-by-bin calibration. The INL reached [\(-2.45\), \(9.54\)] LSB before calibration, and reached [\(-1.90\), \(3.31\)] LSB after bin-by-bin calibration. Finally, the online calibration based on a ring oscillator is tested at different temperatures, showing the precision of the TDC can still be maintained at 19 ps from 30 \({}^{\circ}\)C to 70 \({}^{\circ}\)C. Compared with previously published works, the sample rate and precision has been significantly improved thanks to our ones-counter encoder circuit and online calibration circuit (shown in Table 6). These outstanding performances demonstrate the potential for application in a high counting rate and a high time resolution detector.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & **Used** & \multirow{2}{*}{**FPGA**} & **LSB** & **Precision** & **DNL** & **INL** & **Sample** & \multirow{2}{*}{**Resources**} & \multirow{2}{*}{**Power**} \\  & **Method** & & **[ps]** & **[ps]** & **[LSB]** & **[LSB]** & **[LSB]** & & **[Ms/s]** & \\ \hline
2018 [30] & TDL & Cyclone-IV & 45 & 18 & [\(-0.5\), \(0.13\)] & [\(-0.48\), \(0.37\)] & 75 & - & - \\
2018 [3] & TDL & Kintex-7 & 3 & 5.76 & - & [\(-9\), 9] & 45.5 & - & - \\
2018 [22] & TDL & Spartan-6 & 25.6 & 37 & [\(-0.90\), \(1.23\)] & [\(-0.04\), \(2.96\)] & 115 & 415 Slices & 0.131 W \\
2021 [25] & TDL & Artix-7 & 22.2 & 26.04 & [\(-0.95\), \(1.19\)] & [\(-2.75\), \(1.24\)] & 125 & 216 LUTs & 0.164 W \\ This work & TDL & ZYNQ-7020 & 17.4 & 19 & [\(-0.90\), \(1.67\)] & [\(-1.90\), \(3.31\)] & 170 & 522 LUTs & 0.340 W \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with the FPGA-based TDL-TDCs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Power** \\ \hline Total dynamic & 1.601 W \\ Clocks dynamic & 1.399 W \\ Logic dynamic & 0.015 W \\ BRAM dynamic & 0.014 W \\ MMCM dynamic & 0.123 W \\ PS & 1.399 W \\ Total static & 0.138 W \\ \hline \hline \end{tabular}
\end{table}
Table 5: The power of the TDC system.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Properties of an Antibiotic-Eluting Scaffold
### Manufacturing Properties  
The ideal bone scaffold must also be cost-effective, easy-to-use and scalable. One way the production of scaffolds can be maximised, is through the use of three-dimensional printing. Three-dimensional printers are thought to decrease long-term purchase costs once the high set up and maintenance expenses have been recompensed. Antibiotic-eluting scaffolds can be produced using this method if a thermostable drug such as tobramycin is used [43]. Many of these materials have been shown to be efficacious in both _in vitro_ and _in vivo_ models of osteomyelitisitis [43; 44]. In the latter study, 3D-printed resorbable calcium phosphate scaffolds containing sitafloxacin and rifampicin outperformed gentamicin-laden PMMA when bacterial colonization outcomes and bone growth were assessed [44]. Another benefit of 3D-printing is that it allows for the fabrication of patient-specific designs which completely fill the bone void. An alternative approach to achieve this is to manufacture injectable scaffolds that harden _in vivo_[45].
## 3 Model for Security Resource Scheduling Based on MHALSTM-MPC  
To address the aforementioned issues, this study proposes an integrated model that combines a Multi-Head Attention Long Short-Term Memory Network (MHALSTM) with Model Predictive Control (MPC). This model is referred to as MHALSTM-MPC. This model aims to achieve the real-time and dynamic management of security resources by accurately predicting passenger flow and optimizing resource scheduling. The overall structure of the proposed model is illustrated in Figure 1.  
Among various Recurrent Neural Network (RNN) models, Long Short-Term Memory (LSTM) networks are chosen as the primary predictive model due to their proven ability to effectively capture long-term dependencies and mitigate the vanishing gradient problem often encountered in traditional RNNs. These characteristics make LSTM particularly suitable for processing complex time-series data, such as passenger flow data. This involves both short-term fluctuations and long-term trends. For instance, [PERSON] and others have demonstrated the effectiveness of using optimized LSTM models in predicting passenger flows across different traffic areas, highlighting their potential in traffic flow management [16; 17; 18]. However, further optimization is necessary for the highly dynamic passenger inflow at high-speed train stations.  
Furthermore, the application of MPC theory in this model further enhances the control process. MPC leverages the predicted passenger flow from the MHALSTM model to dynamically adjust security resources in response to traffic fluctuations. Its core strength lies in its ability to optimize resource allocation over a finite time horizon while respecting operational constraints, such as maximum waiting time or minimum staffing requirements. Unlike reactive or heuristic methods, MPC proactively balances immediate efficiency and long-term cost objectives, ensuring a structured and scalable approach to decision-making. The implementation of this integrated model provides a more refined and responsive approach to security resource management at railway stations.
# Track Modulus Assessment of Engineered Interspersed Concrete Sleepers in Ballasted Track
## 3 Field Experimentation Methods
### Field Site and Instrumentation  
A field test zone was selected in a tangent track in which the timber sleepers had recently been replaced with EIC sleepers. A visual assessment showed evidence of ballast fouling in the test zone location, although drainage still appeared to function properly, as there was no standing water, and appropriate shoulders were still present. This selection of a single zone limited the variability in data due to substructure differences, while capturing various installation conditions, including different permutations of interspersed timber and EIC sleepers (Figure 6). The instrumentation was deployed to quantify both vertical wheel-rail input loads and vertical track displacements.  
Vertical wheel-rail forces were quantified using strain-gauge-based industry-standard circuits as described by [PERSON] (1986). The installation of strain gauges on the rail required the welding of gauges to the rail using a portable strain-gauge welding unit. This process involved grinding the web and base of the rail to remove rust and expose pure metal, clamping a ground wire to the base of the rail, and placing the strain gauge and using the welding electrode to send current through the material, which welded the strain gauge to the rail. These gauges were subsequently calibrated using a loading frame with calibrated load cell, allowing strains to be used to quantify vertical wheel-rail loads. During the calibration, vertical loads of up to 200 kN (45,000 lbf) were applied to the rail while the voltage from the strain bridge was simultaneously recorded.  
Figure 5: A comparison of (**a**) an EIC sleeper; (**b**) a typical North American freight HAL concrete sleeper; (**c**) a typical North American light rail transit concrete sleeper.  
Vertical track displacements were measured to quantify the track modulus on both EIC sleepers and timber sleepers. Displacement transducers, which are linear potentiometers (Figure 7), were used to measure the track displacement at the rail base relative to the ground. This was accomplished by driving support rods 40 mm (5/8 in.) in diameter to refusal in the field-side rib between the sleepers. The potentiometers had a maximum stroke length of 25 mm (1 in.), and were accurate to \(\pm\)0.002 mm (7.9 \(\times 10^{-5}\) in.). The calibration factors were provided by the manufacturer and further checked before installation using a calibration height gauge.  
All data were recorded using a National Instruments (NI) Compact Data Acquisition System (cDAQ) via a LabVIEW virtual instrument (VI) developed for the instrumentation used in this experiment. The data were collected at a sampling rate of 2.5 kHz to ensure the maximum wheel-rail load was captured.
# A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load
## 4 Model Updating Test  
A simply supported concrete beam under static load is shown in Figure 7a. The section of the reinforced concrete beam is 150 mm \(\times\) 250 mm, and the length of the span is 1900 mm. For the distributive girder of static load, the length of the span is 950 mm. The grade of concrete strength is C25. This test was implemented according to a test standard as illustrated in literature [31].  
The tested simply supported beam is divided into eight elements with nine nodes, as shown in Figure 7b. The concentrated load P is 15 kN. The deflections of seven nodes are measured several times so that the statistic values of the deflections are obtained. The statistics of the measured deflections at seven nodes are listed in Table 1 where the node deflections are assumed to be of Beta distributions. Based on the measured data, the proposed HP-SMUM is applied to update the concrete beam.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Node** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Mean (mm) & 0.192 & 0.357 & 0.454 & 0.488 & 0.455 & 0.358 & 0.196 \\ Standard deviation (\(\times 10^{-2}\) mm) & 0.576 & 1.071 & 1.362 & 1.464 & 1.365 & 1.074 & 0.588 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of measured deflections of seven nodes.  
Figure 6: The statistics of deflections of the continuous beam based on the updated finite element model. (**a**) Means of deflections; (**b**) Standard deviations of deflections; (**c**) Probability density functions of the deflection at the 4 th node.  
Figure 7: Bending test of a simply supported concrete beam. (**a**) Bending test; (**b**) Mechanical model of the simply supported beam.  
The means and standard deviations of the element updated factors are plotted in Figure 8 which shows that some degree of damage exists in the middle of the beam, and this result confirms the true case where small cracking happens in the concrete beam under the load.  
Further, using the obtained element updated factors, the deflections of the concrete beam are calculated by means of Equation (18), and the results are shown in Figure 9. From Figure 9, it is found that the statistics of calculated deflections of the updated concrete beam coincide with the actual measured data very well. This result testifies the effectiveness of the proposed statistical model updating method once again.
# Effect of Biochar Application Depth on Crop Productivity Under Tropical Rainfed Conditions
## 4 Discussions  
### Biochar Research  
Maintaining appropriate levels of nutrient cycle is key for successful soil management efforts in humid tropical regions. Cover crop, mulch, compost or manure additions supply nutrients to crops, shorten nutrient cycle by increasing microbial biomass activity and hold onto applied mineral fertilizers [51; 52]. However, none of these seem to have a long-term effect like biochar. Biochar research regained momentum with the discovery of the Amazonian _terra preta_ soils in the endless search for sustainable agriculture and worsening climate change. The _terra preta_ soil is today widely studied for reproduction and application in soil amendment because of its huge potential for nutrient/carbon capture and storage [16]. The biochar-rich _terra preta_ soils in the Amazon occur in various depths and spatial extents and are also highly potent in nutrient and carbon, and can therefore support strong vegetation growth. While biochar studies have mostly focused on the production modes and application rates, little has been done on the application depth. This study was set up to determine the effect of biochar application depth on crop productivity under tropical rainfed conditions. A fixed soil-biochar volume ratio of 1:20 (5% biochar) was applied in 3 depths--TA (10 cm), TB (20 cm), and TC (30 cm)--and investigated against a control treatment (CK) with no biochar.
## 3 Registration Algorithms Based on Probability Stochastic Distribution Model  
The critical idea of probability stochastic distribution model-based algorithms is to employ the probability density function of the point cloud to complete the point cloud registration without extracting feature points in the alignment process, and there is no need to establish an explicit relationship between the corresponding points through iteration. This section presents an overview of three optimization methods: RANSAC-based, NDT-based, and GMM-based variations. Milestone methods are listed in Figure 4.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Classifications**} & \multirow{2}{*}{**Typical Algorithms**} & \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Strengthens**} & \multirow{2}{*}{**Weaknesses**} \\ \cline{5-6}  & & & & & & \\ \hline \multirow{4}{*}{Iterative Closet Point (ICP)-Based} & P2P-ICP [31] & Besl and McKay & 1992 & & \(\bullet\) & Require large \\  & Scale-ICP [39] & [PERSON] et al. & 2009 & & & overlap area; \\  & Sparsel-ICP [41] & [PERSON] et al. & 2015 & \(\bullet\) & Wide & \(\bullet\) & High requirements \\  & GF-ICP [43] & [PERSON] et al. & 2017 & & & applied range. & \(\bullet\) & For initial position; \\  & DCP [44] & Wang and Solomon & 2019 & & & & \\  & VGICP [46] & [PERSON] et al. & 2021 & & & & optimal solution. \\ \hline \multirow{4}{*}{Graph Matching (GM)-Based} & SM [50] & Leordeanu and Hebert & 2005 & & \(\bullet\) & Require \\  & TM [63] & [PERSON] et al. & 2011 & & & & significant features; \\ \cline{1-1}  & FGM [54] & Zhou and de la Torre & 2016 & \(\bullet\) & High precision; & \(\bullet\) & Difficult to \\ \cline{1-1}  & SDRSAC [59] & [PERSON] et al. & 2019 & \(\bullet\) & Robust and & & guarantee extracted \\ \cline{1-1}  & DGM [56] & [PERSON] et al. & 2021 & & & feature precision \\ \cline{1-1}  & IRON [61] & Sun & 2022 & & & and quality. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of registration algorithms based on hierarchical optimization.  
Figure 4: Chronological Overview of Registration Algorithm Based on Probability Stochastic Distribution Model [72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99].