esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Results
### The Experimental Evaluation of the Effect of Vehicle Misalignment and of Inter-Vehicle Distance
Furthermore, the results of the day tests have shown that the usage of an optical filter together with a proper signal processing algorithm enable the VLC receiver to work in the presence of strong sunlight interferences that can go up to 22,000 \(\mu\)W/cm\({}^{2}\). In such conditions, with a proper VLC system, one can conclude that the influence of the sunlight can have a limited impact over the BER variations, compared to the influence of vehicle misalignment. The experimental results also showed that the performances of the link were slightly better in day conditions, rather than in night conditions, as would be expected. Similar to a real case scenario in which roads are illuminated at night, the night experiments were conducted in a well-illuminated parking lot (please see Figure 8b). Thus, the lower BER achieved during these tests is a consequence of the disruptive effect generated by the fluorescent light sources. It should be remembered here that the daylight introduces a strong but easy-to-filter DC component, whereas the fluorescent lights introduce a modulated 100 Hz signal, which is slightly more difficult to completely eliminate. It should  
Figure 11: Summary of the V2V experimental BER results for the tests performed in night conditions under the influence of fluorescent lights: (**a**) for a \(\pm\)53\({}^{\circ}\) reception angle; and (**b**) for a \(\pm\)20\({}^{\circ}\) reception angle. be also mentioned that when the VLC receiver uses a rather narrow band-pass optical filter centered on the red wavelength, the effect of sunlight is significantly mitigated, which in turn generates a lower BER compared to a standard day scenario. A similar situation has been found in [22]. In this case, the experimental results also showed that the influence of the fluorescent lights is higher than the one of natural light.
## 4 Tether Materials
Critical cracks may be only 1-2 mmlong in steel fibers (necking and then brittle failure) and 10-20 mm long in UHMWPE ropes (creep, fibrillar, and then brittle failure). For example, let us estimate usable 10 km tether strength for two materials: 2 GPa New Japan Steel wire for suspension bridge cable [36] and Dyneema SK78 0.54 mm multifilament UHMWPE yarn [35]. The comparison is provided in Table 2.  
Due to the complex nature of derating, as much data as possible was derived from the actual testing of the ropes rather than from material specifications. Overall, although UHMWPE provide 10\(\times\)better average strength-to-weight ratio compared to steel filament, the performance of the UHMWPE is more derated because of strength variability, insufficient testing, and creep. Tapered tethers, although theoretically more mass-effective, were not considered. Although the tapered fibers are known to naturally occur in sea urchins and sea cucumbers, which condones the superior strength of their ligaments, as noted in 2000 by [PERSON] [37], engineering processes for the manufacturing of tapered fibers or ropes is absent. The only available option to imitate a tapered tether is hence a connection of the ropes of different thicknesses through the adapter. Such adapters impose additional weight and reliability penalties; therefore, adapters are practical only for large tapering ratios. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Materials** & **Akashi bridge cable wire** & **Dyneeema SK78 multi-filament yarn** \\ \hline Ultimate tensile strength & 2.0 GPa & 2.34 GPa (for a fill factor of 0.78) \\ Diameter & 5.23 mm & 0.54 mm \(\times\)10 \\ Effective density & 7700 kg/m\({}^{3}\) & 756 kg/m\({}^{3}\) (for a fill factor of 0.78) \\ Length to implement & 10 km & 10 km \\ (1) Yield derating & IIMD * & IIMD * \\ (2) Brittleness derating & 0\% (below fatigue limit) & 0\% (below creep limit) \\ (3) Degradation derating & IIMD * & n/a \\ (4) Temperature derating & IIMD * & IIMD * \\ (5) Radiation derating & 0\% (steel is radiation hard) & 30\% (estimated) \\ (6) Bundling derating & n/a (single wire) & IIMD * for 0.54 mm, 12\% for 5.4 mm \\ (7) Weaving derating & n/a (single wire) & IIMD * \\ (8) Erosion derating & 0.0007\%/year (274 km orbit) & 0.26\%/year (274 km orbit) \\ (9) Creep derating & IIMD * & IIMD * for 1 year lifetime (\(-\)70\%) * \\ (10) Length derating & 0\% (tested in 2 km pieces) & \(-\)70\% (7\% strength variability in 0.5 m test samples up-rated to 10 km length) \\ Total IIMD * derating & \(-\)68.75\% & 70\% \\ Total derating (1 year) & \(-\)68.75\% & 81.6\% \\ \(1/\)(Structural factor) & IIMD * & 33.3\% \\ Usable tether strength & 625 MPa * & 287 MPa \\ Derated tip velocity & 284 m/s & 616 m/s \\ Tether usable strength & 13.4 kHz & 15.0 kHz \\ \hline \hline \end{tabular}  
* If length derating exceeds creep derating, it is safe to apply creep derating only;
* Included in manufacturer data;
* Similar stress was applied to steel hanging wires at the Pylons of Messina (608 MPa at 3.6 km span).  
\end{table} 
Table 2: The usable strength of the example high-tension materials.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 5 Implementation Walkthrough
### Formal Coordination Mechanisms
#### 5.4.2 Petri Net Semantics  
The Colored Petri Net engine enforces formal semantics through typed places and guarded transitions, implementing the formal models defined in Section 4. Places accept only tokens of compatible semantic types, ensuring coherence. Transitions implement threshold-based firing conditions with relevance aggregation, providing formal guarantees about network behavior, including deadlock freedom and bounded execution.
###### Abstract  
Previous studies have revealed that biochar could induce the disturbance of a microbial community above the family level. So far, very little is known about how individual bacteria are affected by biochar at genus or species levels. In this study, effects of biochar and its water-soluble compounds on the growth of individual soil bacteria were examined. Biochar derived from different feedstock showed disproportionate impacts on bacterial growth. Corncob biochar could significantly stimulate the growth of most tested strains, whereas the growth of four strains, including _Bacillus pumillus_ ACCC04306 (Agricultural Culture Collection of China, ACCC), _B. licheniformis_, _B. cereus_, and _Kitastospora viridis_, were inhibited by addition of rice husk biochar. All the biochars greatly supported the growth of _B. mucilaginous_ but inhibited that of _K. viridis_. More importantly, different strains exhibited discrepant growth response towards the same biochar sample, even when strains belong to the same species, suggesting that the effect of biochar on bacteria growth is strain-specific. Corncob biochar showed the strongest adsorption on _B. thuringiensis_ but the greatest growth promotion was observed in _B. mucilaginous_s, indicating that the porous structure of biochar is not the sole factor that influences cell growth. Due to the possible stimulation or inhibition of water-soluble compounds existing in biochar, the growth variation of tested strains decreased or increased correspondingly when the washed biochar was applied, indicating that water-soluble compounds in fresh biochar play an important role in cell growth and such effect is also strain-dependent. Biochar application could also enhance potassium-/phosphate-solubilizing activities through promoting bacterial growth. All these results suggested that biochar might influence bacterial growth under different mechanisms. Our findings should be valuable for an in-depth understanding of the potential mechanism of soil bacteria changes following biochar incorporation and for biochar application in agriculture.  
biochar; water-soluble compounds; bacterial growth; strain-specific 2019 9 3209; doi:10.3390/app9163209
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 2 Corrosion Detection Method of Substation Grounding Grid
### Electric Network Method  
The corrosion or fracture of nodes can be obtained by taking the resistance of ground network conductor as the fault parameter, combining the diagnosis theory with the network theory, adopting the non-linear optimization method, and comparing with the original data. The electric network method can be divided into port resistance method, sensitivity analysis method, and improved sensitivity analysis method.
###### Abstract  
This study introduces an innovative smart impact detection system designed for real-time monitoring of shipment status and path integrity. Leveraging the advanced capabilities of the ESPRESSIF ESP32-S3-MINI-1U-N8 microcontroller, which integrates Wi-Fi, a display, a memory card slot, and accelerometers, this detector represents a significant advancement in shipment tracking technology. The device is engineered to continuously measure impact magnitudes in terms of g-force, and records data when predefined impact thresholds are exceeded. These data are then wirelessly transmitted to a remote server, providing users with the ability to track shipment status and path via a dedicated application. The performance testing revealed impact measurements ranging from \(-0.5\) to \(2\) g, with occasional peaks reaching approximately \(4.5\) g, demonstrating the system's sensitivity and reliability in diverse conditions. This smart impact detector not only facilitates continuous monitoring, but also enhances the ability to respond swiftly to potential shipment violations, thus providing a novel solution for ensuring shipment integrity. This research contributes to the field by presenting a comprehensive real-time impact detection system that integrates modern microcontroller technology with effective monitoring capabilities, setting a new benchmark for shipment tracking systems.  
impact detector; microcontroller; impact strength; shipment status Article
## 3 Simulation of Sand Plane Strain Test
### Introduction to Experiment  
[PERSON] (1977) and [PERSON] (1978) discovered that anisotropy has a greater effect on the mechanical properties of sand under plane strain conditions than under triaxial compression conditions [27; 45]. In [PERSON]'s experiments, the friction angle \(\varphi\) reached its minimum value when the confining pressure was 2.0 or 4.0 kgf/cm\({}^{2}\) (corresponding to 200 and 400 kPa) and the deposition angle \(\delta\) was between 24\({}^{\circ}\) and 30\({}^{\circ}\). (In this paper, the deposition angle \(\delta\) is defined as the angle between the bedding plane and the direction of the major principal stress, as shown in Figure 5a.) This has been confirmed by [PERSON] (1987) [46]. However, [PERSON]'s experimental data are not clear under lower confining pressures, such as 50 and 100 kPa. Therefore, [PERSON] and [PERSON] (1986) conducted a series of plane strain tests under drained conditions to study the variation of shear strength of saturated Toyoura sand with different \(\delta\) under low confining pressures [31]. The range of confining pressure was 0.05-4.0 kgf/cm\({}^{2}\) (5-400 kPa), and the sand samples were saturated Toyoura sand. \(\delta\) varied from 0\({}^{\circ}\) to 90\({}^{\circ}\), and the corresponding stress paths for \(\delta\) of 0\({}^{\circ}\) and 90\({}^{\circ}\) are shown in Figure 5b.  
The deposition angle described in the aforementioned experiments is consistent with the rotation angle of the principal stress axis in the constitutive model, both of which describe the angle between the principal stress and fabric.  
The test scheme for this study is presented in Table 1. In the following table, the void ratios \(e\approx 0.7\) and \(e\approx 0.8\) correspond to dense sand and loose sand, respectively.  
Figure 5: Schematic diagram of the angle between the direction of principal stress and the bedding plane [31]: (**a**) major principal stress; (**b**) stress path when deposition angle is 0\({}^{\circ}\) and 90\({}^{\circ}\).
## 4 Machine Learning Model
### Experimental Results  
Figure 1(a),b illustrate the prediction accuracy for each of the MLP models through the training process. In the validation phase, the overall prediction accuracy was measured using the validation dataset. MLP model A achieved 80% accuracy, while MLP model B achieved approximately 78% accuracy. Figure 2(a),b depict the confusion matrixes of the two MLP models. It can be observed that MLP model A achieved 92% and 72% prediction accuracies for patients diagnosed with COVID-19 with a low and a high level of severity, respectively. Model B achieved a prediction accuracy of 90%, 74%, and 77% for patients diagnosed with COVID-19 with a low, medium, and high level of severity, respectively.  
The RF models produced 80.0% prediction accuracy for RF model A and 65% for RRF model B. Figure 2(c),d present the confusion matrixes of the RF models. RF model A achieved 75% and 85% prediction accuracies for patients with a low and a high level of illness severity, respectively. RF Model B achieved a prediction accuracy of 92%, 33%, and 71% for patients diagnosed with COVID-19 with a low, medium, and high level of severity, respectively.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Bootstrap & True \\ Minimum samples leaf & 3 \\ Number of trees in the forest & 50 \\ Minimum number of samples required to split & 10 \\ Number of features considered when looking for the best split & SQRT \\ Maximum depth of a tree & 6 \\ Limited leaf nodes & False \\ \hline \hline \end{tabular}
\end{table}
Table 5: Random Forest model hyperparameters.  
regression model is 76.3%, 3.7% lower than the machine learning model. For low severity illness, the binary regression model accuracy is 79.3%, whereas the machine learning model achieves 92% accuracy. For high severity illness, the binary regression model accuracy is 2.2% lower than the machine learning model accuracy. The prediction accuracy of all the models examined is summarized in Table 6. The prediction accuracy results presented in Table 5 do not include model cross-validation, thereby it is highly encouraged for applications to perform cross-validation before deployment in the field.  
It should be noted that the prediction accuracy achieved by our proposed machine learning models is highly dependent on the quality of the dataset. Although the dataset used is limited, the results that have been achieved are superior to similar works on this dataset ([[https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset?select=covid.csv](https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset?select=covid.csv)]([https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset?select=covid.csv](https://www.kaggle.com/tanmoyx/covid19-patient-precondition-dataset?select=covid.csv)), accessed on 28 October 2021).
## Materials and Methods
### Experiments
#### 4.2.2 Photothermal Measurement in Water  
The sample (Fe\({}_{3}\)O\({}_{4}\) NPs in 0.5 mL of deionized water) contained in a 1.5 mL Eppendorf tube was irradiated by a NIR continuous laser at 810 nm (Photon Soft Tissue Diode Laser, Zolar Technology & MFG, Canada) with an external adjustable power, \(P_{0}\) (0-3 W). The distance between the sample and the laser was 1-2 cm and the laser spot size was about 1 mm. The laser powers that were used was 0.5 and 1.0 W. Each sample was identically irradiated for 5 min. The resulting temperature rise was recorded by thermocouples (J-type, National Instrument, Austin, TX, USA) connected to a portable data acquisition system (NI USB-9222A, National Instruments, Austin, TX, USA) and recorded every 30 s with NI-DAQmx (National Instruments, Austin, TX, USA) and software (LabVIEW 8.6, National Instruments, Austin, TX, USA). All measurements were obtained in triplicate except stated otherwise.  
The experimental photothermal conversion efficiency (\(\eta_{\text{exp}}\)) of the NPs was calculated directly from steady-state temperature increase as follows:  
\[\eta_{\text{exp}}=\frac{Q_{\text{exp}}}{P(1-10^{-A_{\text{exp}}})} \tag{1}\]  
where \(Q_{\text{exp}}\) (W) was calculated with previously reported expression [47]: 16.855\(\Delta T\) (mW), \(\Delta T\) is the temperature change, \(P\) is the incident laser power and \(A_{\text{exp}}\) is the absorbance of the Fe\({}_{3}\)O\({}_{4}\) nanoparticles at 810 nm.
## 4 Experimental Setup
### Experimental Design  
In this study, we conducted experiments to find answers to the research question set in Section 4.1. To accomplish this, we compared three proposed models (MulDIC\({}_{TI}\), MulDIC\({}_{TC}\), and MulDIC\({}_{TIC}\)) with the baseline model (Text Only). We conducted all experiments on a per-project basis.  
First, we performed modality-specific pre-processing on the sampled data to train the baseline and proposed models (see Section 3.2). We used the pre-processed data as input for each model, and the data used by each model are consistent across modalities. After inputting the data into the model, we extracted features for each modality (refer to Section 3.3). In the case of the three proposed multimodal models, we combined the extracted features from each modality (see Section 3.4). Next, we classified the received feature vectors into their respective classes and measured their performance by comparingthe results to the ground truth (see Section 3.5). We performed this process for all models and compared the results obtained to evaluate the effectiveness of the proposed models.  
To evaluate the value of multimodal models utilizing image and code data, we compared the classification performance of three proposed models with the baseline model. In the MulDIC\({}_{TI}\) and MulDIC\({}_{TC}\) models, we assessed the individual contributions of image and code data. In the MulDIC\({}_{TIC}\) model, we examined the value of using image and code together. Furthermore, we compared the MulDIC\({}_{TIC}\) model with the MulDIC\({}_{TI}\) and MulDIC\({}_{TC}\) models to evaluate the effectiveness of leveraging heterogeneous modalities.
## 2 Materials and Methods
### The Adsorption Experiments of Cimetidine onto Bricks  
Batch experiments were used in the adsorption process and the dark condition was maintained in all experiments to inhibit the potential photo-degradation of cimetidine. The prepared WRB, GWRB, and HWRB were used in the adsorption behavior of cimetidine onto waste bricks. The kinetics adsorption processes were conducted as follows: WRB, GWRB, and HWRB were added into the three solutions with the initial cimetidine concentration of 5 mg/L and a dosage of 2 g/L. All the mixed solution was stirred with a speed of 150 rpm. The solution pH values were maintained at about 7.0 \(\pm\) 0.1 by using 0.01 mol/L HCl and NaOH solution in all experiments. At different time intervals, the sampleswere collected and filtrated through a 0.22 \(\upmu\)m polytetrafluoroethylene membrane filter (Jinteng, Tianjin, China). The concentrations of cimetidine in the solutions were analyzed. The equilibrium adsorption amount of cimetidine onto bricks was calculated by Equation (1).  
\[q_{e}=V(C_{0}-C_{e})/m, \tag{1}\]  
where \(C_{0}\) and \(C_{e}\) are the initial and equilibrium concentration of cimetidine (mg/L), \(q_{e}\) (ug/g) is the adsorption capacity at equilibrium, \(V\) is the volume of the solution (L), and \(m\) is the mass of the adsorbents (g).  
Adsorption isotherms of cimetidine onto waste bricks were conducted by adding 60 mg WRB, GWRB and HWRB into 30 mL solution with the cimetidine initial concentrations were 1, 2, 5, 8, 12, 16, 20, and 40 mg/L. The solution pH values were maintained at 7.0 \(\pm\) 0.1 by using 0.01 mol/L HCl and NaOH solution. The suspensions were shaken at a speed of 150 rpm for 24 h. Finally, the solutions were filtered through a 0.22 \(\upmu\)m polytetrafluoroethylene membrane filter and the concentrations of cimetidine were analyzed.  
Furthermore, the influences of pH values and ionic strength were investigated to gain insight into the adsorption properties of cimetidine onto WRB, GWRB, and HWRB. To determine the influence of pH values on the adsorption of cimetidine, a series of 5 mg/L cimetidine solutions were prepared with the pH values range from 4.0 to 10.0. The solution pH values were maintained at desired values by using 0.01 mol/L HCl and NaOH solution in all experiments. Other experimental conditions were the same as the isotherm experiments.  
The ionic strength effect on the adsorption of cimetidine onto WRB, GWRB, and HWRB was studied. A series of solutions were prepared with different ionic strength (NaCl, 0.001-0.2 mol/L). The solution pH was maintained at 7.0 \(\pm\) 0.1 by using 0.01 mol/L HCl and NaOH solution in all experiments. Other experimental conditions were the same as the isotherm experiments.  
The concentration of cimetidine was analyzed by ultra performance liquid chromatography (UPLC, Waters) at a wavelength of 225 nm. The mobile phase was a mixture of 0.01% CH\({}_{3}\)COOH solutions-methanol (65:35, \(\psi\)/\(\psi\)) and the flow rate was 0.4 mL/min.
## 3 Hardware Design  
The primary driving force behind the Marmote SDR hardware design was to create a general and flexible WSN research platform that enables experimentation with various power saving techniques, such as energy harvesting, and various analog sensor and radio front-ends. Thus, the Marmote SDR platform follows a modular three-layer approach, where the radio front-end, the mixed-signal flash FPGA-basedprocessing unit and the power supply are separated into three different modules, as shown in Figure 1. The stacked architecture makes it possible to seamlessly replace the top-layer radio front-end and the bottom-layer power supply modules, while keeping the same mixed-signal processing module intact.  
The Marmote SDR configuration presented in this paper includes a 2.4 GHz radio front-end, a flash FPGA SoC-based mixed-signal processing module and a rechargeable battery-based power management module. The photo and block diagram of these modules are presented in Figure 1.