esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Iron- and Copper-Chelating Activities of Saffron Leaf Extracts  
As shown in Figure 4a, the iron-chelating capacity of SLPF (EC\({}_{50}\) = 93 \(\mu\)g/mL) was significantly higher than that of SLE (EC\({}_{50}\) = 124 \(\mu\)g/mL), indicating a higher ability of SLPF to bind and sequester metal ions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Multivariate Model (R2)** & \multicolumn{2}{c}{**Linear Regression Model (Day 4)**} & \multicolumn{2}{c}{**EC\({}_{50}\) (Day 4)**} & \multicolumn{2}{c}{**EC\({}_{90}\) (Day 4)**} \\ \cline{3-8}  & **Multivariate Model (R2)** & **Estimated (R2) 1** & **Calculated (R2) 2** & **Estimated 1** & **Calculated & **Estimated 1** & **Calculated 2** \\ \hline \multirow{3}{*}{SLE} & y = 95.82C + 2.84t & y = 95.83x + 14.85 & y = 100.35x + 19.15 & 0.37 & 0.31 \(\pm\) 0.02 & 0.78 & 0.71 \(\pm\) 0.05 \\  & (0.67) & (1.00) & (0.92) & (0.26–0.35) & (0.26–0.35) & (0.60–0.81) \\ \cline{1-1} \cline{3-8}  & y = 124.82C + 3.45t & y = 124.82C + 21.24 & y = 135.33x + 19.87 & 0.23 & 0.22 \(\pm\) 0.01 & 0.55 & 0.52 \(\pm\) 0.03 \\ \cline{1-1}  & (0.82) & (1.00) & (0.96) & (0.20–0.24) & (0.20–0.24) & (0.46–0.57) \\ \hline \hline \end{tabular}  
* Based on the Multivariate Linear Model, 2 data was calculated using a logarithmic regression model relating time (days) to percentage of inhibition.  
\end{table}
Table 2: Multivariate Model Test and Estimated Linear Regression Model (for day 4), EC\({}_{50}\) and EC\({}_{90}\) values \(\pm\)SE (with upper and lower limits; 96% confidence intervals) for saffron extract-induced inhibition estimated from regression analyses on day 4.  
Specifically, the chelating activity of SLPF was approximately 70% of the activity observed for the positive control, gallic acid (EC\({}_{50}\) = 64 \(\upmu\)g/mL), suggesting that the phenolic compounds present in SLPF contribute substantially to its chelating properties by binding to metals. This high chelating efficiency is due to the presence of flavonoids and polyphenic compounds, known for their strong affinity for metal ions. In terms of copper-chelating activity (Figure 4b), SLPF also demonstrated a stronger effect than SLE, with EC\({}_{50}\) values of 75 \(\upmu\)g/mL and 104 \(\upmu\)g/mL, respectively. These EC\({}_{50}\) values represent approximately 50% of the chelating activity observed for gallic acid (EC\({}_{50}\) = 38 \(\upmu\)g/mL), reinforcing the important role that polyphenic compounds play in the sequestration of  
Figure 4: Iron- (**a**) and copper (**b**)-chelating activities (%) of SLE and SLPF at various concentrations (0–300 \(\upmu\)g/mL). Data are presented as mean \(\pm\) SD of three determinations.  
metal ions. The differences observed in the chelating capacities for iron and copper can probably be attributed to the different affinities of the different phenolic compounds for each metal ion. This disparity may be due to the unique structural characteristics of the polyphenols in SLPF and SLE, which could preferentially bind to certain metal ions based on their size, charge, and coordination chemistry.
## 2 Materials and Methods
### Signal Processing Features
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature Name** & **Applied Cases** & **Unit/Variable Type** \\ \hline \multirow{2}{*}{Number of Steps} & Alcohol Usage [39], Parkinson’s & \multirow{2}{*}{Count} \\  & Disease [48; 49] & \\ \hline \multirow{2}{*}{Average Step Time} & Alcohol Usage [39; 50], Parkinson's & \multirow{2}{*}{Seconds (s)} \\  & Disease [48; 49] & \\ \hline \multirow{2}{*}{Average Cadence} & Alcohol Usage [39], Parkinson's Disease [48] & \multirow{2}{*}{Steps per minute} \\ \hline \multirow{2}{*}{Skewness} & Alcohol Usage [39], Paraspinal & \multirow{2}{*}{Dimensionless (statistical)} \\  & Assessment [51] & \\ \hline \multirow{2}{*}{Kurtosis} & Alcohol Usage [39], Neuron Discharge [52] & \multirow{2}{*}{Dimensionless (statistical)} \\ \hline \multirow{2}{*}{Coefficient of Variation of Step Time} & Parkinson’s, Peripheral Neuropathy [53] & \multirow{2}{*}{Dimensionless (ratio or \%)} \\ \hline \multirow{2}{*}{Harmonic Ratio} & Parkinson’s Disease, Peripheral & \multirow{2}{*}{Dimensionless (ratio)} \\  & Neuropathy [53] & \\ \hline \multirow{2}{*}{Average Step Length} & Alcohol Usage [39], Parkinson’s Disease [49] & \multirow{2}{*}{Meters (m)} \\ \hline Gait Velocity & Alcohol Usage [39], Parkinson’s Disease [49] & \multirow{2}{*}{Meters/second (m/s)} \\ \hline \multirow{2}{*}{Minimum and Maximum Difference} & Parkinson’s Disease [54] & \multirow{2}{*}{Acceleration (m/s\({}^{2}\))} \\ \hline \multirow{2}{*}{Standard Deviation} & Parkinson’s, Peripheral Neuropathy [53; 54], Paraspinal [51] & \multirow{2}{*}{m/s\({}^{2}\) (or unit of original signal)} \\ \hline \multirow{2}{*}{Root Mean Square} & Parkinson’s Disease [54] & \multirow{2}{*}{m/s\({}^{2}\)} \\ \hline \multirow{2}{*}{Entropy Rate} & Parkinson’s, Peripheral Neuropathy [53], Neural Control [55], Heart [56] & \multirow{2}{*}{Bits or dimensionless} \\ \hline \multirow{2}{*}{Regression Line for Local Maxima and Minima} & Parkinson’s Disease [54] & \multirow{2}{*}{Slope (dimensionless)} \\  & Alcohol Usage [39], Paraspinal & \\ \hline \multirow{2}{*}{Ratio of Spectral Peak} & Alcohol Usage [39] & \multirow{2}{*}{Power (a.u. or m\({}^{2}\)/s\({}^{3}\))} \\  & Assessment [51] & \\ \hline \multirow{2}{*}{Ratio of Spectral Peak} & Alcohol Usage [39] & \multirow{2}{*}{Dimensionless (ratio)} \\  & Alcohol Usage [39], Coronary Artery [57] & \\ \hline \multirow{2}{*}{Total Harmonic Distortion (THD)} & Alcohol Usage [39] & \multirow{2}{*}{Percentage (\%) or dB} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accelerometer gait features, their original use cases, and units or variable types.
## 2 Materials and Methods  
### Materials  
Ethiofencarb was purchased from Fluka (99.1%, Seelze, Germany) and used to prepare the stock solutions in de-ionized water (10 mg L\({}^{-1}\)) without any further purification. The purity of the organic compound was analyzed by high performance liquid chromatography (HPLC). The solutions were stored in the dark at 4 \({}^{\circ}\)C prior to usage. Stannum indium sulfide (SnIn\({}_{4}\)S\({}_{8}\)) was prepared using the following precursors: tin (IV) chloride pentahydrate (98%) and indium (III) chloride tetrahydrate (97%) from Sigma-Aldrich (St. Louis, MO, USA) in addition to L-cysteine (98%) from Alfa Aesar (Haverhill, MA, USA). The other chemicals used in this study were obtained from Merck (Darmstadt, Germany): ammonium acetate (reagent-grade), nitric acid, sodium hydroxide, and methanol (HPLC-grade). De-ionized water was prepared by purification using a Milli-Q water ion-exchange system (Millipore Co., Burlington, MA, USA) with a resistivity = 1.8 \(\times\) 10\({}^{7}\)\(\Omega\) cm.
## 1 Introduction and Related Works
Similarly, in [16], the authors included in the DESERT Underwater network simulator the time evolution of the links of the multimodal acoustic mobile ad hoc network deployed in [9] and composed of low- and high-frequency modems. They also included the impairments caused by interference, and LUTs of the noise variability to test the adaptation of different modulation and coding schemes. Although, on the one hand, both the solutions in [6; 16] allow to reproduce the time evolution observed during sea trials, on the other hand they do not allow to test different channel realizations. During the last decade, researchers [17; 18] demonstrated that the time evolution of underwater acoustic channels can be statistically well characterized with two- and four-state Markov models and with a two-state Hidden Markov Model (HMM) [19]. In fact, the nature of the acoustic channel, whose error probability often changes during the day due to, for instance, presence of rain, changes of wind speed, and shipping activity, can be well characterized by HMM. Analyzing real channel measurements [6; 7; 8; 9; 10], in fact, it is common to observe time intervals with a low PSR alternated by time intervals with a high PSR, rather than having an almost constant error probability during the whole experiment. The evaluation study of which Markov and HMM model best fits the experimental data [18] showed that the HMM yields an accurate reproduction of the channel metrics, tracking long term channel behaviors well, and making it a good choice for modeling the channel in UANs simulators. The aim of this work is to present a statistical model based on the analysis of sea trial data, and to evaluate the effectiveness of this model with respect to already existing models. This statistical model is included in the DESERT Underwater simulator [15], which includes a wide set of protocols for best customizing the underwater network to the needs of a user. The model relies on measurements extracted from the ASUNA dataset [20], which presents a number of time series of link quality indicators (LQIs) measured during the aforementioned experiments. The main contribution of this article is to provide the research community with an open-source framework for underwater network simulations where the acoustic channel is modeled with high reliability and low computation complexity. The paper is structured as follows. First, in Section 2 we present the dataset used to infer the channel model parameters. Then, in Section 3 we provide the details of the statistical model and its implementation in the simulator. In Section 4 we evaluate the performance of our model when compared to legacy mathematical models, while in Section 5 we present the results of the simulations. Finally, in Section 6 we draw our concluding remarks.
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 7 Discussion
### Consideration to Take When Analyzing SHAP Exclusive Words
The preprocessing steps include converting the text to lowercase, removing punctuation, tokenizing the text, removing stop words, and, finally, stemming the words. These preprocessing steps ensure that the text remains standardized and that no meaning is lost due to punctuation or verb conjugation. Table 15 summarizes our approaches' accuracy and F1 results. Data coverage for all the above approaches is only 63% of the dataset, meaning that 37% of vulnerability descriptions do not contain words that are exclusively used for a certain class. Out of the covered 63% of descriptions, the \"Highest sum\" approach performed the best, with an F1 score of 52.8% and an accuracy of 53.7%, meaning that half of the descriptions in the dataset can be correctly classified using SHAP-based exclusive words. However, an accuracy of 53.7% is only marginally better than a random pick, making it less useful for real-life applications. Hence, while a word with a high SHAP score may have a strong association with a particular class, its presence alone does not guarantee classification into that class. A natural question to ask is \"What constitutes a good SHAP score?\" There is no fixed threshold or definition for a \"good\" SHAP score, as the interpretation of SHAP scores depends on the specific context and problem at hand. A high SHAP score for a particular word can indicate its importance in predicting the target class, but what qualifies as a high score will depend on the overall distribution of SHAP scores in the dataset and the specific requirements of the problem. However, some general guidelines can be used to interpret SHAP scores. For instance, a feature with a SHAP score close to zero has little to no impact on the model's output for that class, while a feature with a high positive or negative SHAP score strongly influences the model's output. Additionally, a feature with a consistently high or consistently low SHAP score across multiple instances can be considered more reliable than a feature with a varying SHAP score.
# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
###### Abstract  
Accidents involving electric wheelchairs are a growing concern, with users frequently encountering obstacles that lead to collisions, tipping, or loss of balance. These incidents underscore the need for advanced safety technologies tailored to electric wheelchair users. This research addresses this need by developing a driving assistance system to prevent accidents and enhance user safety. The system incorporates ultrasonic sensors and a front-facing camera to detect obstacles and provide real-time warnings. The proposed system operates independently of stable server communication and employs embedded hardware for fast object detection and environmental recognition, ensuring immediate guidance in various scenarios. In this research, we utilized the existing jolov8 model as is. But we attempted to improve performance by hardware acceleration of convolutional neural networks, supporting various layers such as convolution, deconvolution, pooling, batch normalization, and others. Thus, the YOLO model was accelerated during inference on the specialized hardware in our experiments. Performance was evaluated in diverse environments to assess its usability. Results demonstrated high accuracy in detecting obstacles and providing timely warnings. Leveraging hardware acceleration for YOLOvs delivers faster, scalable, and robust object detection, making it a great platform for enhancing driving safety on edge and embedded devices. These findings provide a strong foundation for future advancements in safety assistance systems for electric wheelchairs and other mobility devices. Future research will focus on enhancing system performance and integrating additional features to create a safer environment for electric wheelchair users.  
Keywords:personal mobility vehicle; obstacle detection; embedded system; driving assistance; ultrasonic sensors
## 1 Introduction
The impedance matrix is derived from an analytical equation in the frequency domain, so the correctness of this solution is very high, especially in high-frequency ranges. To develop a fast and accurate power flow analysis method for a truss structure, the present paper is organized as follows. In Section 2, the mechanical impedance matrix of an individual truss beam for all three types of vibration is derived. In addition, with the combined force balance of the connecting point and coordinate transfer, the complex truss structure in practical engineering can be built up. In Section 3, vibration power flows of simple and periodically two-dimensional truss structures are verified and analyzed. Finally, transfer path analysis (TPA) is used to investigate the transfer path of different kinds of power flow.
## 4 Data Analysis and Interpretation  
This research investigated the implementation and performance of an advanced method for smoothing power fluctuations, specifically the Moving Average (MA) filter, by adjusting the data acquisition time window using an FLC. A comparative analysis was carried out with two popular power smoothing solutions previously tested by the authors in [12], the Ramp-Rate (R-R) and MA filters, to assess the effectiveness of the proposal. Two realistic scenarios comprise our research:  
* **Study Scenario 1-Li-Ion Storage System with Moderate SoC level**: aimed to maintain the Li-Ion BESS's SoC within the range of 20% to 80% initially.
* **Study Scenario 2-Li-Ion Storage System with Low SoC level**: In this undercharging scenario, the Li-Ion BESS began with an SoC below 20%, prioritizing EV charging.
## 2 Antenna System Configuration
### Antenna System Configuration  
For an antenna mounted on manned or unmanned airborne vehicles as part of an aerial communication network, beam formation and steering are necessary in all directions based on azimuth. To achieve this goal, the radiating elements must be adaptively arranged in a cylindrical shape based on the azimuth. However, the cylindrical-shaped adaptive array structure has many disadvantages with regard to radiation pattern prediction, RF (Radio Frequency) circuit configuration, antenna system mounting fixture manufacturing, and antenna system maintenance. To solve this problem, this study presented an array of polygonal shapes. Moreover, one side of the polygon was equipped with a planar array antenna (hereinafter referred to as a tile).  
When constructing an antenna system with a polygonal tile array, beam steering must be performed using a single tile in response to the polygonal shape to carry out omnidirectional beam steering based on the azimuth. During a beam-steering operation based on an active phased array antenna, antenna gain deviation occurs as a result of the radiation characteristics of the radiating element and the influence of beam steering. Also, gain deviation due to beam steering causes the quality of communication to deteriorate. In this paper, considering such deviations and antenna system implementation (maintenance, implementation, complexity, etc.), the antenna system structure for aviation communication network only was set to resemble a 10-tile cylindrical array. In terms of antenna system requirements for wireless communication, the Ku-band was denoted as the operating frequency band, and the antenna gain based on a single beam was set to 20 dBi or higher. To satisfy the required gain performance, in this paper, radiating elements arranged in an eight (based on azimuth angle) by four (based on elevation angle) pattern in a single tile were arranged via uniform spacing in a horizontal line. For the aviation communication network, we assumed that the operating altitudes of aircraft required for establishing the main communication network were similar. Accordingly, we assumed that the antenna system did not perform beam steering based on elevation angle. Therefore, only four radiating elements were arranged based on the elevation angle. Figure 1 shows a conceptual diagram of the antenna system according to the above-mentioned settings.
## 3 Results and Discussion
### Effect of Poly (MMA-BA-ST) on Drilling Fluids Properties
#### 3.4.2 Effect of Poly (MMA-BA-ST) on PV  
Figure 5 shows the variation curve of PV of drilling fluids after poly (MMA-BA-ST) was added. As can be seen from Figure 5, the variation trend of PV is similar to the AV, and the plastic viscosity of the basic drilling fluids is 51.0 mPa-s. When the addition amount is 1.0%, the PV of the drilling fluids is 57.0 mPa-s, which increases by 11.76% compared with the basic drilling fluids, with small variation. It indicates that poly (MMA-BA-ST) has little effect on the PV of drilling fluids.
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 3 Results
### RF-OK Model-Based Interpolation
For concentrations greater than 200 mg/kg, the RF-OK method demonstrated significantly smaller errors than the RF and OK methods. Figure 10: Results of the optimal RF–OK model-based prediction. (**a**) Prediction map, and (**b**) comparison of actual and predicted heavy metal concentration by RF–OK.
###### Abstract  
The main objectives of this work are the development of fundamental extensions to existing scanning microwave microscopy (SMM) technology to achieve quantitative complex impedance measurements at the nanoscale. We developed a SMM operating up to 67 GHz inside a scanning electron microscope, providing unique advantages to tackle issues commonly found in open-air SMMs. Operating in the millimeter-wave frequency range induces high collimation of the evanescent electrical fields in the vicinity of the probe apex, resulting in high spatial resolution and enhanced sensitivity. Operating in a vacuum allows for eliminating the water meniscus on the tip apex, which remains a critical issue to address modeling and quantitative analysis at the nanoscale. In addition, a microstrip probing structure was developed to ensure a transverse electromagnetic mode as close as possible to the tip apex, drastically reducing radiation effects and parasitic apex-to-ground capacitances with available SMM probes. As a demonstration, we describe a standard operating procedure for instrumentation configuration, measurements and data analysis. Measurement performance is exemplarily shown on a staircase microp capacitor sample at 30 GHz.  
a 2021 11 2788  
governed by geometry. SMM has received a growing interest from the research community to address a wide range of applications, including semiconductor materials such as 1D and 2D materials [14; 15; 16; 17], biology [18; 19; 20; 21; 22; 23; 24; 25], quantum physics [26; 27; 28; 29; 30] or energy materials [31; 32; 33]. There is an urgent need to develop SMM traceability to yield quantitative and calibrated data. In this effort, we developed a SMM operating inside a scanning electron microscope (SEM) using a microstrip probe structure operating up to 67 GHz [34; 35; 36; 37; 38]. Our previous works were completed by first presenting quantitative data performed at 30 GHz on microsized metal oxide semiconductor (MOS) capacitors. The modeling, measurement configuration, experimental part, data analysis and discussion proposed in this manuscript demonstrate the ability of the new instrument to simultaneously provide electronic, topography and calibrated complex impedance images.
## 6 Results and Discussions
### Network Performance Behaviour under DDoS Attacks with SECOD Activated
Note that this is just a high-level comparison, as the detection time may vary based on the types of attack, test scenarios or testbed hardware. If we reduce the monitoring period of SECOD to 3 s, the average detection time becomes 9.65 s. Therefore, we find SECOD usually requires triple times of monitoring period in this test bed and threshold combination. This could come from the dynamic update of the threshold per port, and the stuck state of switch from time to time during monitoring period. Thus, the longer monitoring period in the SECOD, the more fluctuation in the detection time. However, the detection time for SECOM is always less than 2 s and quite stable as well. If reduce the monitoring period of SECOD further to 1 or 2 s, the attack frequency cannot hit the threshold trigger due to the hardware limit of Zodiac-FX switches. Both SECOD and SECOM provide lightweight countermeasures against DDoS attacks, they are easy to implement and maintain, and no extra device is required. The dynamic threshold allows auto-adjustments to the network behaviours, so that legitimate burst traffic can pass through without triggering alerts. Once a DDoS attack is detected, the root cause of attack can also be localised so as to give tips to the administrator at once. As SECOM runs more checks than SECOD in anomaly detection, which means a higher resource consumption, the network operator can deploy SECOM if early detection has a high priority, and resources, such as power, is not a problem. Moreover, SECOD is preferred if the long lifespan of device outweighs detection time. Figure 12: Comparison of detection time using both SECOD (with different monitoring durations) and SECOM algorithms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methodology** & **Average Detection Time (s)** \\ \hline ESESID [27] & 6.02 \\ \hline Flowjustifier [28] & \textless{}20 \\ \hline SDIoT-DDoS-DA [55] & 5 \\ \hline [PERSON] et al. [56] & 3 \\ \hline SECOD & 9.65 \\ \hline SECOM & 1.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Average Detection Time.