esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Chemicals  
Phosphate buffer saline (PBS) tablets, AChE from _Electrophorus electricus_, acetylthiocholine iodide (AThI, purity \(>\)99%), 5,5'-dithio bis-2-nitrobenzoic acid (DTNB, purity \(>\)99%) indoxyl acetate (IDA, purity \(>\)95%), a-naphthyl acetate (\(\alpha\)-NAc, purity \(>\)98%), fast blue B salt (FBBS, purity 95%), z-sep sorbent, 0.22 \(\upmu\)m syringe filters, 5 mL syringes were from Sigma-Aldrich (Prague, Czech Republic). 96-microwell plates were supplied by Gama Group (Ceske Budejovice, Czech Republic). Acetylcholinesterase stock solution in PBS (2 U \(\upmu\)L\({}^{-1}\)) was stored in the fridge at 4 \({}^{\circ}\)C. Ethanol (EtOH), methanol, dimethyl sulfoxide, acetonitrile and ethyl acetate were supplied as high purity solvents from Merck (Darmstadt, Germany) and Supelco (Bellefonte, PA, USA). Triphenyl phosphate (TPP), ammonium formate (\(\geq\)99%) and formic acid and were from Sigma-Aldrich (Taufkirchen, Germany). Anhydrous magnesium sulphate (MgSO4) was obtained from Fluka (Buchs, Germany), and sodium chloride (NaCl) from Penta (Chrudim, Czech Republic). Sorbent primary-secondary amine (PSA, Bondesil, 40 \(\upmu\)m) was purchased from Agilent Technologies (USA). Deionized water was purified using a Milli-Q system (Millipore; Bedford, MA, USA). Aldicarb, carbofuran, carbofuran-3-hydroxy, carbaryl, dichlorvos, chlorpyrifos and paraoxon standards were of analytical standard grade and purchased by Sigma Aldrich (Taufkirchen, Germany). Malathion (purity 99%), malaoxon (purity 99%) and parathion (purity 99%) were bought by Dr. Ehrenstorfer GmbH (Augsburg, Germany). Chlorpyrifos-oxon was of analytical standard grade and supplied by Labicom (Olomouc, Czech Republic). Pesticides stock solutions (3.0 mg mL\({}^{-1}\)) were prepared in acetonitrile and kept in a freezer at \(-\)20 \({}^{\circ}\)C protected from ambient light. Pesticides working solutions were daily prepared in PBS-EtOH (7:3, v/v).
## 4 Policy/Intent-Based Security in SDN and NFV Networks
### Policy-Based Networking Development
Both the NMS and EMS can extend their functionalities dynamically to support new services, and the following illustration (Figure 2) depicts how this architecture was defined. As it is feasible to see, within the architecture, it can be found components such as Policy Definition Points (PDPs), Policy Enforcement Points (PEPs), and Resource Enforcement Points (REP) which are also replicated in Virtual Environments (VEs) within the Active Network (AN) nodes. The roles of PDPs and PEPs are crucial for the policy-based management of these networks, the PDPs are responsible for making decisions based on management policies present within the REPs enforced by PEPs at various levels and points within the network itself. These PEPs translate the decisions into actionable configurations on the network elements. The architecture utilises an AN node that can execute dynamically deployed services, enhancing the network's flexibility and responsiveness. This translation process is a recurrent theme within policy-based networking, and in the case of this precursor architecture, the NMS handles the higher-level policies, and only after a translation process can they be used by the EMS, which focuses on element-specific configurations.
## 3 Results and Discussion
### Machine Learning (ML) Approach  
As the straightforward observational data analysis of the inter-period comparison may be skewed by variation of meteorological conditions, it is important to estimate the pollution changes using a business-as-usual scenario modeled using the ML technique [25]. Figure 3 shows observed and modeled trace gas concentrations during the study period, including three 12-day periods of pre-strike (20 September 2019-1 October 2019), during the strike (2 October 2019-13 October 2019) and post-strike (14 October 2019-25 October 2019) averaged over the whole city of Quito (black lines). The ML modeled BAU conditions (red broken line) show a very good agreement with the observed data before the strike, suggesting a good model performance (see Table 2, Appendix A). However, when the national strike takes place, the observations, as expected, are clearly lower than the BAU concentrations (Figure 3). Table 2 summarizes the all-site and overall Quito modeled percentage changes for each pollutant due to the strike activities using the machine learning approach. Some sites were more affected by the pollution changes than others, especially the city center, which was the epicenter of the protest. Overall, it can be seen that the highest reduction from the BAU scenario during the strike is for NO\({}_{2}\) (\(-31.53\)\(\pm\) 17.22%) (Table 1). SO\({}_{2}\) (\(-17.65\)\(\pm\) 27.38%) and CO (\(-15.55\)\(\pm\) 6.64%) also show a significant reduction during the strike. These statistics confirm the importance of the suspension of human activities, especially the circulation of motorized traffic. This has been previously proven globally during the COVID-19 pandemic [25; 40; 41; 42; 43]. While the primary pollutants show a significant reduction due to the reduction in anthropogenic activities during the national strike, O\({}_{3}\) shows only a slight reduction (\(-6.82\)\(\pm\) 15.11%) in its concentrations. The latter is due to a potential shift from a NO\({}_{\rm x}\)-saturated regime into a more NO\({}_{\rm x}\)-limited regime during the event of national transportation protest although more research is needed to better understand ozone.  
Furthermore, even up to 10 days after the strike, the pollution levels took time to return to BAU conditions (Figure 3), varying from \(-21.49\)\(\pm\) 14.72% for O\({}_{3}\) to \(-12.43\)\(\pm\) 6.25% for NO\({}_{2}\) and \(-9.71\)\(\pm\) 32.25% for SO\({}_{2}\) as an overall reduction (Table 2). This is an interesting observation, showing an extension of the effect of the violent strikes on anthropogenic activities even days after the event was over.
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
## 2 DFEC Ranging Principle
### Frequency Selection Analysis  
The DFEC method is of great significance in ranging, but not any combination of frequency values can meet the application requirements of this ranging method. Generally, we need to meet the following requirements when selecting a frequency:  
The first point is that the frequency we choose must meet the requirements of the Radio Regulatory Commission and be compatible with the frequency signals used today;  
Secondly, the frequency ratio relationship between the two frequency values we choose must satisfy more than 0.5 and less than 2. In this way, during the calculation of the cycle, the problem of ambiguous estimation of the cycle does not occur.  
Third, after we choose the frequency, the resulting wavelength difference must be greater than the distance difference introduced by the phase error, and the ranging error reflected in the range of the phase error can meet the future sub-meter positioning requirements.  
Fourth, we recommend that when the frequency is selected for distance estimation in this way, each time a higher frequency signal is used to compensate the cycle, the ranging value between two consecutive cycles will be several meters or more. In this way, when the phase cumulative value within the cycle spans the cycle, we can quickly estimate what the value is based on the carrier-to-noise ratio, doppler, and the position of the previous moment.
## 2 Materials and Methods
### Air Sampling and Analyses  
Sampling of fluoride in the occupational air was performed in accordance with the NIOSH method, using nitrocellulose membrane filters with a pore size of 0.45 \(\upmu\)m for particulate fluoride sampling [17]. Filters were placed inside plastic cassettes together with MILLIPORE cellulose filters impregnated with sodium formate for gaseous phase sampling. The sampling apparatus was supplemented by a calibrated pump and worn by each participant during the shift. After sampling, the exposed filters were dried in a desiccator. Subsequently, nitrocellulose membrane filters were decomposed in sodium hydroxide solution and the solid fluoride content was analyzed by potentiometric ion-selective electrode on an ORION 4 STAR analytical instrument. Gas-phase fluoride captured on impregnated cellulose filters were analyzed using the same procedure. The analytical method was controlled by certified reference material ASTASOL. The determination of the particulate concentration was performed by the gravimetric method. The weight of the collected fluoride content was determined from the difference in weight of the filters before and after the measurement. Particulate concentration was calculated by dividing the weight of particulate matter collected on the filter by the pumped air volume recalculated on standard condition (T = 293.15 K, \(p\) = 101.325 kPa).  
Workers in the potroom were personal respiratory protection equipped with organic vapors/acid gas cartridges and dust prefilter.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Participants** & **Work Position** \\ \hline \(n\) = 7 & Crane operator \\ \(n\) = 10 & Forklift operator \\ \(n\) = 2 & Anode transport \\ \(n\) = 1 & Casting of alloys \\ \(n\) = 8 & Preparation of smelting in the foundry, transport of dress \\ \(n\) = 11 & Cell operator \\ \(n\) = 19 & Anode replacement \\ \(n\) = 7 & Pumping wagon operator \\ \(n\) = 1 & Primary refining \\ \(n\) = 3 & Cell start \\ \(n\) = 1 & Measurement technician \\ \(n\) = 5 & Pumping and transport of liquid metal \\ \hline \hline \end{tabular}
\end{table}
Table 1: Monitored work positions in primary aluminum smelter.
# A System Dynamics Approach to Optimize Milk Production in an Industrial Ranch
## 3 Research Model  
System dynamics modeling was used in this research. System dynamics is a widely used technique for the conceptualization and analysis of many issues in science [14; 22; 23] and can analyze the issues related to system dynamics behavior under different conditions over time [24; 25; 26]. The systematic approach of system dynamics (SD) modeling in this article consisted of the following steps:  
1. Describe the system at Fereidan Ahrar ranch through a causal loop diagram;
2. Describe the system at Fereidan Ahrar ranch through the stock and flow diagram;
3. Develop and formulate the model structure and develop the related mathematical models that indicate different interactions in the system;
4. Identify the critical variables and determine their optimal values through the DOE technique;
5. Show the model's output based on optimal values;
6. Define different scenarios to maximize milk production based on the optimized values.
# Child's Target Height Prediction Evolution
## 4 Work Methodology Proposed  
### Business Understanding  
The main goal for this study is to contribute to a better growth assessment creating a new method to forecast a child's height when they are an adult, also known as the target height. The model output will be personalized to the child (person-centric) and is expected to deliver a more accurate prediction when compared with the existing methods, already described in the previous chapters.  
We began search for a \"better\" tool that provided a way to compare, for instance, a metric. We extensively researched this subject and found several methods for measure/comparing models' performance, such as: Accuracy, confusion matrix, and several associate measures such as sensitivity, specificity, precision, recall or f-measure (very useful when facing classification problems with imbalanced classes), MSE--mean square error, ROC--Receiver Operating Characteristic Curve, AUC--area under the roc curve, RMSE--root mean squared error, MAE--mean absolute error, R\({}^{2}\)--R Squared, Log-loss, Cohen's kappa, Sum squared error, Silhouette coefficient, Euclidean distance, or Manhattan distance [30; 31; 32; 33].  
The several methods/measures present different approaches for different problems (regression, classification, and clustering) with advantages and drawbacks. In this paper we will not describe or proceed with an extensive analysis on this subject, since other works, for instance, the ones mentioned as references, have already provided excellent analysis. Accuracy is a simple method (easy to compute and low complexity) able to discriminate and select the best (optimal) solution. It has limitations when dealing with classes and can result in sub-optimal solutions when classes are not balanced.  
Our problem is not a classification problem, but a regression problem, and our goal is to provide better performance on our predictions. Considering our challenge characteristics, we believe the measure that best suits our goal is accuracy.  
The performance results for the state-of-the-art height prediction when adult models were measured are presented in Table 1. The best accuracy is obtained by the Mid-Parental Method which presents an accuracy of 59.53%.
## 3 Signal Quality Analysis
### SQA Conclusions  
Most of the time, a BDS user can receive a stable and continuous signal. All BDS C/N\({}_{0}\) values are lower than those for GPS due to the transmission distances being longer for the former. However, even though the C/N\({}_{0}\) values are lower for each elevation angle, the BDS GEO and IGSO signal strengths are more stable than those of GPS satellites. A BDS user can use the results of SQA1 to remove irregular satellites. SQA2 and SQA3 show that by removing the satellite with the highest chance of a cycle slip, BDS can provide a good-continuity signal. If a BDS user follows the proposed SQA processes to remove irregular satellites, the probability of incorrect range measurements generated by the receiver will decrease and the user position solution availability will increase.
# A New Approach to Fall Detection Based on the Human Torso Motion Model
## 3 Our Approach for Fall Detection
### The Key Concepts and Definitions in HTMM
#### 3.1.2 Gravity Vector and Torso Angle  
As we all know, the gravity line is always vertical to the ground. Therefore, all lines parallel with the \(y\)-axis can be seen as the gravity lines. Every two points on the gravity line can form a vector, which is called the gravity vector in our approach.  
According to vector translation theory in solid geometry, the start point of the gravity vector and the start point of the torso vector can be moved to origin coordinates. We take a middle frame of a fall video for example. Figure 3 shows the process of forming the torso angle.  
Figure 1: Joints of a person’s skeleton provided by Kinect. (**a**) Twenty joints of the skeleton that can be tracked when the person is standing; (**b**) 10 joints and two estimated joints of the skeleton when the person is sitting, where spin and hip center joints marked with two circles of dotted lines are estimated.  
Figure 2: Depth image space created by the Kinect sensor.  
According to the cosine law, the angle between these two vectors, named the torso angle, can be calculated by:  
\[\cos(\alpha)=\cos(\overrightarrow{HS},\overrightarrow{GH})=\frac{ \overrightarrow{HS}\bullet\overrightarrow{GH}}{\left|\overrightarrow{HS} \right|\times\left|\overrightarrow{GH}\right|}, \tag{2}\]  
where \(\overrightarrow{GH}\) means the vector from any point on the \(y\)-axis to the hip center joint. Since the point on the gravity line is self-defined and the hip center is moved to the origin coordinates, so \(\overrightarrow{GH}=(0,\ -Y_{h},\ 0)\). Equation (2) can be also represented by:  
\[\cos(\alpha)=\cos(\overrightarrow{GH},\overrightarrow{HS})=\frac{Yh-Ys}{\sqrt {\left(Xs-Xh\right)^{2}+\left(Ys-Yh\right)^{2}+\left(Zs-Zh\right)^{2}}}, \tag{3}\]  
Figure 4 shows the torso angles in three common daily activities, standing, walking and sitting. Figure 4a-c shows the RGB images of each video and Figure 4d-f are their depth images marked with the torso line and the gravity line.  
Figure 4: The torso angles in three common daily activities. (**a**–**c**) the RGB images of standing, walking and sitting; (**d**–**f**) their depth images, where the solid line is the gravity line and the dotted line is the torso line.  
Figure 3: The process of extracting torso angle from a depth image. (**a**) A depth image fetched from a middle frame in a fall video; (**b**) torso vector represented in 3D coordinates; (**c**) torso angle represented in a 2D plane after vector translation.
## 3 Experiments and Discussion
### Experiments on Vertical Acceleration and EDR Estimation
#### 3.2.1 Analysis of Vertical Acceleration Response  
A time-history of a control-fixed B737-800 aircraft flying through severe turbulence (TC3) was studied. The vertical acceleration response was obtained according to the computation flow shown in Figure 6, instead of transfer function-based method. As shown in Figure 11, aerodynamic force, pressure center and incremental aerodynamic derivatives changed significantly in turbulence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Turbulence Case (TC)** & \(\sigma_{w}\)**(_m/s_)** & **L(m)** & \(\varepsilon^{1/3}\)**(_m\({}^{2/3}\)_s\({}^{-1}\)_)** \\ \hline TC1 & 1 & 300 & 0.1291 \\ TC2 & 3 & 300 & 0.3874 \\ TC3 & 5 & 300 & 0.6457 \\ TC4 & 10 & 700 & 0.9737 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Theoretical input turbulence cases.  
Under the influence of turbulence, the total aerodynamic force changes between 398-810 kN and the vertical acceleration value changes between 0.66 and 1.35 g. The position of the pressure center, as the acting point of aerodynamic force, changes with the angle of attack. The increment aerodynamic derivative \(c_{L_{W}}\) and \(c_{D_{W}}\) also change rapidly. According to the small perturbation linearization theory, the transfer function model could only be effective near the equilibrium state. The serious deviation of dynamic model from the equilibrium state brought by the transient turbulence will lead to an increase in the computing error of acceleration response. However, compared with the transfer function-based method, the fluctuation of aerodynamic force was more intense and there were more peak points.  
One of the prominent features of the algorithm is that the acceleration on different positions of the fuselage can be obtained. Different locations were identified for investigation. Point A was chosen at the gravity center and point B located at cockpit position, about five meters from point A. Point C located at the aircraft's tailgate--about 20 m away from the gravity center. The Welch spectral density estimation with the Hamming window function was used [31]. Compared with common spectrum estimation, Welch estimation performs better in frequency resolution and estimation variance [36]. The spectra of acceleration at three locations under TC1-TC4 are shown in Figure 12. For each curve, a 640 s acceleration series was analyzed. This resulted in a total of 10,240 points at a sampling rate of 16 Hz. The length of Hamming window was set as 160; the overlapping rate was selected as 50%.  
Figure 11: Time-series of vertical wind and acceleration response.  
It was found that the bumpiness at the tail of the fuselage was always stronger than that at the gravity center, while the bumpiness at the nose was the least. This was consistent with the analysis resulted in ref [14]. In moderated turbulence, there was little difference in magnitudes of vertical acceleration between locations A, B and C. However, with the increase of intensity, the difference of acceleration magnitudes increased.
# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art
### Challenges and Opportunities for Future Work
The ideal utilization of cobots likely stems from the amalgamation of human intuitive guidance with the robot's precision and computational capabilities. An operator can employ a smartwatch to roughly position a robotic arm, following which the robot's vision system accurately fine-tunes the alignment for insertion. This cooperative control, sometimes referred to as shared control, can significantly enhance efficiency and precision [13]. The future may witness robots capable of seamlessly transitioning between autonomous operation and operator-directed activity through wearables. In some situations, the robot may proactively solicit input through the wearable device--e.g., the watch vibrates and displays \"Need confirmation to proceed with the next step,\" which the user can authorize with a gesture or tap. Challenges such as recognition latency, sensor noise, and gesture ambiguity pose a threat to reliable control. To mitigate these issues, the system integrates the pre-filtering of raw IMU data and uses a finite set of gestures with minimal motion overlap. Gestures were selected and positioned to reduce confusion between similar movements (e.g., side swing vs. tap). Latency was further reduced by prioritizing short, distinct gestures such as taps and flicks. These strategies contributed to improved recognition precision and real-time responsiveness, as confirmed by the evaluation results presented in \"Results and Discussion\". In conclusion, utilizing smartwatches to operate industrial robots represents a significant advancement in improving the accessibility and responsiveness of robotics within the production sector. These interfaces utilize humans' intrinsic communication methods (motion and gesture), reducing the gap between intention and action in human-robot interaction (HRI). As collaborative robots 'advance and wearable technology evolves, we can anticipate a deeper convergence of these technologies. The anticipated result is a new generation of wearable-enabled collaborative robots that operators can effortlessly \"wear and direct\" robots that resemble colleagues rather than devices to be programmed, capable of comprehending our gestures and intents. This perspective augurs positively for the future of human-centered automation, enhancing both productivity and the work experience of individuals in industrial settings. To address battery limitations caused by continuous IMU data streaming, the system minimizes data transmission intervals through low-latency gesture segmentation and avoids redundant sampling. Additionally, future implementations will explore event-driven data acquisition, adaptive sampling rates based on motion activity, and energy-efficient Bluetooth protocols (e.g., BLE 5.0) to extend operational time in practical deployments.
## 5 2-Chain Based Routing
### Protocol Operations  
We divide the operation of protocol in two main phases. In the first phase, nodes broadcast their location information towards the sink and their neighbors. Sensor nodes find their regions on the basis of \(\theta\). In the second phase, chains are created by selecting nearest nodes. Chains are then interconnected and finally the data is transmitted towards the sink through global optimum paths.  
Figure 5: Regions formation in 2-chain based scheme.