esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Design  
This cross-sectional study was conducted following the approval of the Ethics Committee of Inonu University Faculty of Health Sciences (approval no: 2024/5530) and in accordance with the Helsinki Declaration. In the study, a free-weight 1-RM squat protocol was applied to determine the lower extremity maximal force performance of participants. The PAPE protocol was implemented with a squat exercise involving three repetitions at 80% of 1-RM. The warm-up protocol included 4 different sessions conducted 72 h apart, consisting of 5 min of cycling, dynamic stretching exercises following 5 min of cycling, static stretching exercises following 5 min of cycling, and proprioceptive neuromuscular facilitation (PNF) stretching exercises following 5 min of cycling. All warm-up protocol sessions were administered to all participants in the same sequence. After each PAPE protocol, the participants were asked to rate their perceived difficulty ranging from 6 to 20 using the Borg Scale (2018)) to determine their perceived difficulty during PAPE depending on the stretching techniques used in warm-up. Participants' explosive force performances were measured using a vertical jump performance test. Prior to data collection, introductory and trial sessions on warm-up procedures, vertical jump tests, and maximal squat force tests were conducted for each participant included in the research for two weeks. All intervention sessions took part at the same time of day. The PAPE protocol was applied approximately 3 min after the completion of warm-up procedures when the heart rate range was 110-120 bpm, and during this period, each member of the research groups was actively rested (walking). Participants were instructed to avoid engaging in different types of exercises the day before measurements, abstain from stimulating beverages such as tea, coffee, alcohol, and carbonated drinks, and eat their last meal at least 2 h before measurements. Figure 1 displays the experimental design diagram.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & \(n\) & **Minimum** & **Maximum** & **Mean \(\pm\) SD** \\ \hline Age (years) & 13 & 20.00 & 25.00 & 22.38 \(\pm\) 1.75 \\ Body height (cm) & 13 & 170.00 & 181.00 & 174.38 \(\pm\) 3.94 \\ Body mass (kg) & 13 & 67.00 & 81.00 & 72.30 \(\pm\) 4.13 \\
1-RM performance (kg) & 13 & 95.00 & 120.00 & 108.07 \(\pm\) 7.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic and anthropometric information on participants.
## 2 Methodology
### Machine Learning Model Application
#### 2.5.1 Building a Deep Learning Regression with Keras  
The deep neural network created takes all the features as input, feeds them into nine connected hidden layers, each of 50 nodes creating a dense network. The features are then connected into an output layer. The data used to evaluate these models were split into train and test data using a 20% holdout cross-validation. The train data was subsequently split into 30% for validation. The sequential constructor model is used since the network consists of a linear stack of layers. The dense layers were used for each hidden layer where the number of neurons was specified. The Rectified Linear Unit (ReLU) activation function was used for all the hidden layers in the network. The ReLU activation function is non-linear, computationally efficient. The activation function takes a derivative function related to all the input features allowing for backpropagation. The ReLU activation function is a good approximator. Another advantage of using ReLU is its ability to work with sparse neurons,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **DEN** & **GR** & **PERM** & **NEU** & **POR** & **RES\_DEP** & **RES\_MED** & **RES\_SLW** & **Sw** & **T\({}_{2}\)LM** \\ \hline count & 1529 & 1529 & 1529 & 1529 & 1529 & 1529 & 1529 & 1529 & 1529 & 1529 \\ mean & 2.51 & 100.35 & 272.23 & 0.18 & 0.1 & 8.77 & 8.27 & 4.11 & 0.84 & 55.77 \\ std & 0.16 & 39.41 & 1201.43 & 0.10 & 0.08 & 38.13 & 23.12 & 3.46 & 0.25 & 104.92 \\ min & 2.02 & 40.23 & 0.00 & 0.01 & 0.01 & 0.13 & 0.51 & 0.10 & 0.49 \\
25\% & 2.46 & 66.61 & 0.00 & 0.11 & 0.04 & 2.08 & 2.34 & 2.02 & 0.72 & 2.20 \\
50\% & 2.57 & 97.14 & 0.01 & 0.17 & 0.07 & 3.40 & 3.65 & 3.07 & 1.00 & 6.06 \\
75\% & 2.62 & 128.29 & 1.35 & 0.23 & 0.12 & 6.49 & 6.97 & 4.83 & 1.00 & 64.77 \\ max & 2.75 & 384.84 & 10974.59 & 0.54 & 0.31 & 1051.84 & 457.65 & 27.22 & 1.00 & 700.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary statistics of the data used in the machine learning modelling.  
making it much more efficient than the sigmoid and tanh activation functions. The ReLU function gives an output value if the input is positive but returns zero or no output if otherwise. The mean absolute error was used as the error metric between the predicted and actual values. The minimisation algorithm used to optimise the deep learning regression is the Adaptive Movement Estimation algorithm (Adam). The main advantage of Adam is that the learning rate for each input is automatically adapted. Adam saves time from further optimising the learning rate, which is the case when using gradient descent as an optimiser. Adam also uses an exponential decreasing moving average of the gradient to update variables.
## 4 Results and Discussion
### Impact of Overlapping Data and Sensor Count  
The inclusion of overlapping data and additional sensors generally enhances model performance on the test set:* **Overlapping Data**: Provides additional temporal context, improving the models' ability to capture dependencies. Deep learning models like TCN and LSTM benefit significantly, as evidenced by their higher test set metrics in overlapping configurations.
* **Increased Sensor Count**: Offers more comprehensive data, allowing models to learn from richer features and improving classification capabilities on unseen data. TCN and LSTM benefit greatly from additional sensors, capturing complex spatiotemporal patterns more effectively.  
However, in configurations with four sensors and overlapping data, the performance of TCN slightly decreases, suggesting that excessive redundancy may not always contribute to better generalization and might introduce overfitting.
## 2 Materials and Methods
### Analysis of Organic Compounds  
Humic acid (HA) and sodium alginate (SA) were purchased from Sigma-Aldrich (St. Louis, MO, USA) and used as single-feed water solutions for the filtration experiment. To examine the applicability of the membrane for water treatment under actual conditions, the feed concentrations of both the solutions with natural organic matter (NOM) used in this experiment were 5 mg/L. To achieve the required feed concentration of the NOM, stock solutions were prepared and diluted using deionized ultrapure water and then filtered using 0.45 \(\upmu\)m filters (Millipore Inc., Burlington, MA, USA).  
An Ultraviolet-Visible (UV-Vis, absorbance at 254 nm) spectrophotometer (DR5000, HACH, Loveland, CO, USA) was used to measure the concentration of humic substances, and a total organic carbon (TOC) analyzer (Schimadzu 5000A, Kyoto, Japan) was used to measure SA concentration for removal efficiency. The concentration of humic substances was determined according to the modified Lowry method [12] by using humic acids (Sigma Inc., St. Louis, MO, USA) as standards. The concentration of total organic carbon (TOC) was determined using a TOC analyzer (TOC-V, Shimadzu, Japan). The pH was adjusted to 7 using 0.1 M HCl and 0.1 M NaOH. The conductivity was adjusted to 300 \(\upmu\)S/cm with 0.1 M NaCl.
## 3 System Design
### IoT Nodes
Although ZigBee does not provide a direct connection to TCP/IP networks and cannot be used directly with MQTT, there are mechanisms for its integration into the system. As explained in Section 2.2.3, there is also an MQTT variant called MQTT-SN that allows for implementing MQTT in non-TCP/IP devices, but this requires the ability to implement the protocol in the ZigBee microcontroller, which is not possible in non-PRO Xbee Series 2 modules (therefore, it would require the use of an external microcontroller). To avoid the mentioned problems, ZiWi makes use of a Master node that connects ZigBee devices to the WiFi network. The hardware of the Master node is simple, but its software is not, since it has to act like a network bridge, mapping the multiple communications between the ZigBee and the WiFi network. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**ESP-01**} & \multirow{2}{*}{**ESP-12**} & \multirow{2}{*}{**ESP-201**} & \multirow{2}{*}{**NodeMCU**} & \multirow{2}{*}{**Sparkfun**} & \multirow{2}{*}{**Adafruit**} & \multirow{2}{*}{**WeMos D1**} \\  & & & & & & & \\ \hline ESP Version & ESP-01 & ESP-12 & ESP-201 & ESP-12E & ESP-12E & ESP-12E & ESP-12E \\ \hline Number of GPIO & \multirow{2}{*}{2} & 11 & 11 & 11 & 11 & 11 & 11 \\ pins & & & & & & & \\ Memory & 512 KB & 512 KB & 512 KB & 4 MB & 4 MB & 4 MB & 4 MB \\ \begin{tabular}{c} Ease of \\ integration in \\ prototypes \\ \end{tabular} & Medium & No & High & High & High & High & High & High \\ \hline Power voltage & 3.3 V & 3.3 V & 3.3 V & 3.3 V-6 V & 3.3 V-6 V & 3.3 V-6 V \\ \hline Form factor & Small & Medium & Large & Large & Large & Medium & Small \\ Price & \multirow{2}{*}{\(\mathbf{\hat{s}}\)} & \multirow{2}{*}{\(\mathbf{\hat{s}}\)} & \multirow{2}{*}{\(\mathbf{\hat{s}}\)} & \multirow{2}{*}{\(\mathbf{\hat{s}}\)} & \multirow{2}{*}{\(\mathbf{\hat{s}}\)} & \multirow{2}{*}{\(\mathbf{\hat{s}}\)} \\  & & & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Compatible with \\ Arduino IDE \\ \end{tabular} } & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Serial comms. \\ \end{tabular} } & It needs a USB & It needs a USB & It needs a USB & It needs a USB & \multirow{2}{*}{micro-USB} & It needs a USB & \multirow{2}{*}{micro-USB} & \multirow{2}{*}{micro-USB} \\  & adapter & adapter & adapter & adapter & adapter & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Main characteristics of ESP8266-based boards.
## 3 Results
### Thermoluminescence (TL) Characteristics of Irradiated Glasses
#### 3.2.2 Electron Accelerator, Pulse Duration 17 ns  
For verification of the behavior of the investigated glasses for the lower dose ranges, samples were irradiated with radiation doses up to 1000 Gy. During this experiment, samples were irradiated by electron accelerator with pulse duration of 17 ns.  
As shown in Figure 4a, the raw phosphate glass possesses maximum of TL signal at 125 \({}^{\circ}\)C. For radiation dose of ca. 2 kGy, one can easily find additional maximum, which appears at ca. 210 \({}^{\circ}\)C; however, after closer analysis this signal is also observable for the lower doses. For the glass samples containing terbium (Figure 4b-d), one can observe wide TL signals from 100 to 250 \({}^{\circ}\)C, whose shapes suggest more complex structure. Probably next to the main signals at 125 \({}^{\circ}\)C (signal from glass matrix) and at 200 \({}^{\circ}\)C, other TL peaks also contribute to the overall emission from terbium-doped glass. This will be further discussed in Section 3.3.  
As one can see in Figure 5, all samples exhibit linear dependencies of TL signal intensity vs. dose in the dose range from 50 to 2000 Gy. Analyzing data included in Table 3, one can see that yield of TL emission is directly linked with amount of dopant in the glass, analogical as in case of higher dose ranges, for which the results were presented  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **PPG** & **PPG\_Tb01** & **PPG\_Tb05** & **PPG\_Tb30** \\ \hline N\({}_{\text{inf}}\) & \((5.4\pm 0.3)\times 10^{6}\) & \((2.3\pm 1.0)\times 10^{6}\) & \((4.0\pm 0.5)\times 10^{6}\) & \((5.3\pm 0.4)\times 10^{6}\) \\ k & \((6.7\pm 0.8)\times 10^{-2}\) & \((7.7\pm 0.6)\times 10^{-2}\) & \((5.7\pm 1.3)\times 10^{-2}\) & \((12.0\pm 2.0)\times 10^{-2}\) \\ Adj. R-Square & 0.992 & 0.997 & 0.980 & 0.971 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of fitted linear functions for glass samples (dose 1–30 kGy).  
Figure 4: TL curves for samples irradiated using beta radiation (17 ns pulse duration), PPG (**a**), PPG_Tb01 (**b**), PPG_Tb05 (**c**), and PPG_Tb30 (**d**). PPG samples were read without shutter.  
in Section 3.2.1. With increasing concentration of rare earth oxide, the total TL emission increases significantly.
## 3 Results and Discussion
### Effect of Voltage on Ammonia Synthesis  
The intensity of the reaction electric field, the plasma discharge power, and the intensity of the plasma-liquid reaction can be influenced by adjusting the applied voltage, and the effect of voltage on ammonia synthesis under sealed gas flow is investigated here. Figure 5 demonstrates the effect of applying different peak-peak voltages on NH\({}_{4}\)\({}^{+}\) and NOx in the liquid phase product. From Figure 5a, the concentration of NO\({}_{3}\)\({}^{-}\) in the product initially increases and then decreases with increasing voltage, while the concentration of NO\({}_{2}\)\({}^{-}\) continuously decreases. Consequently, the synthesis rate reaches a maximum of 12.69 mmol-min\({}^{-1}\) at a peak-peak voltage of 33 kV, with an energy consumption of 491.86 MJ\(\cdot\)mol\({}^{-1}\). The reason for this may be that increasing the peak-peak voltage of high-voltage alternating current generates more energetic electrons in the electric field, thereby increasing the probability of exciting or dissociating gas molecules, and that excessive voltages may also lead to the decomposition of ammonia and nitrite. However, while increasing the voltage improves the yield, energy consumption also increases. As shown in Figure 5b, the selectivity of NH\({}_{4}\)\({}^{+}\) increases with voltage, reaching 80.7%.
## 5 Conclusions  
In order to address the problem whereby non-IT personnel are not familiar with the code when experts from multiple fields jointly develop SCs, this paper proposes a reuse-oriented visual SC generator to support efficient SC development, especially suitable for complex multi-party interaction scenarios. This allows people from different fields to be involved in the SC development process, not merely programmers. Our approach enables SC designers to construct business models from business requirements, automatically generate code from business models, and provide security checks and deployment tests. We propose a model based on BPMN to model complex interactions in multi-party SCs. At the same time, the transformation rules from the BPMN model to the reusable code base are defined and maintained to support the automatic generation of SC codes. In addition, we propose multi-layer reusable libraries and implement the maintenance of reusable libraries and the visual development of the Solidity language using a visual web tool based on Google Blockly. The visualization method based on Google Blockly has also been used for the automatic generation of SC codes. The experimental results of five cases show that the proposed method and generator help to improve the reusability of SC development. The qualitative comparison with other advanced methods and tools shows that the proposed automatic SC generation method has certain advantages.  
One of our ongoing works aims to gradually explore the requirements of different domains and continuously add new reuse units to the reuse library. Another work seeks to realize the functions of monitoring the execution of SCs in the blockchain and carrying out correctness verification.  
Conceptualization, X.S. and W.L.; Methodology, X.S. and W.L.; Software, W.L.; Validation, X.S.; Resources, H.X. and Z.W.; Writing--original draft, X.S. and W.L.; Writing--review and editing, H.X., X.W. and Z.W.; Project administration, X.W. All authors have read and agreed to the published version of the manuscript.  
The research in this paper is partially supported by the National Key Research and Development Program of China (No. 2021 YFF0900900).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Model**} & **Business** & **Interactive** & **Code** & **Visual** & **Security** & **Complete** \\  & & **Processes** & **Relationship** & **Reuse** & **Interface** & **Checking** & **Smart** \\ \hline Proposed Method & BPMN + Blockly & YES & YES & YES & YES & YES & YES \\ Skotnica [11] & BPMN & YES & NO & NO & YES & NO & NO \\ Qin [12] & IDLC & NO & NO & NO & NO & NO & NO \\ Guida [16] & SC Descriptor Model & NO & NO & YES & YES & NO & YES \\ Wöhrer [13] & CML & NO & NO & NO & NO & NO & YES \\ Mao [17] & Char-RNN + Blockly & NO & NO & YES & YES & NO & YES \\ Ingo [10] & BPMN & YES & NO & YES & YES & NO & NO \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between SC generation methods.
## 4 Conclusions  
In this paper, a simple approach using circularly polarized light to enhance the imaging effect of PSOCT was proposed. This approach uses the inverse Fourier transform of the interference fringe to obtain a complex field and numerically calibrates it to make the incident light perfectly circularly polarized. Reflectivity and birefringence images were obtained using the Hilbert transform-based method. The effects of improving the image  
Figure 6: Depth profiles of a single reflector (blue curves: corrected, orange: uncorrected). (**a**) Chicken breast; (**b**) pork; (**c**) beef.  
Figure 7: (**a**) A 3D rendering of a beef sample; (**b**) a 3D rendering of a pork sample. Magnified views of the green boxes represent the 150 th and 350 th PSOCT birefringence images that compose them. (A 3D rotation video of pork sample reconstruction is detailed in Supplementary Materials).  
of birefringence in chicken breast, pork, and beef tissue were demonstrated. The findings of this study indicate a significant improvement in the imaging of birefringence. This approach provides a practical solution for enhancing PSOCT without requiring significant system modifications, making it a cost-effective and accessible approach for improving imaging capabilities.  
PSOCT can enhance image contrast and show more of the samples' internal microscopic structure. PSOCT has been applied in many clinical fields and has good development prospects. The proposed calibration algorithm for circularly polarized light has low complexity and a negligible effect on imaging time. The improvement of the imaging SNR may be favorable for the imaging of clinical PSOCT systems. In the proposed setup, the circularly polarized light of PSOCT is created in free space through a QWP, which requires that the sample be in vitro. Nevertheless, the circularly polarized light calibration method proposed in this paper can be applied to all conventional PSOCT systems.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app14062525/s1](https://www.mdpi.com/article/10.3390/app14062525/s1)]([https://www.mdpi.com/article/10.3390/app14062525/s1](https://www.mdpi.com/article/10.3390/app14062525/s1)): Video S1: A 3D rotation video of pork sample reconstruction.  
Methodology, S.L.; software, L.H.; validation, S.L.; writing--original draft, S.L.; writing--review and editing, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62205093, and Hainan Province Science and Technology Special Fund, grant number ZDYF2023 SHFZ135.  
Not applicable.  
The data used to support the findings of this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
# Applying Game Theory to Teach the PERT Scheduling Method
## 3 Use of the PERT Method in Construction Planning and Scheduling
### Scheduling in Construction  
According to [PERSON] et al. [72], the successful performance of a project depends on proper organization, scheduling, and control. The project schedule results from project planning and describes all the tasks necessary to complete the project within the planned timeframe, including durations, start and end times for each activity, and the resources and costs assigned. In project scheduling, the critical path is the set of activities that determines the project execution time. A delay in an activity on the critical path will delay the project timeframe, so these activities must receive special attention [73].  
Construction planning and scheduling are essential in any construction process since, without proper scheduling, it is impossible to make precise budgets or exact control of the activities. Scheduling must include exhaustive control of costs and deadlines and manage resources efficiently [74]. Several scheduling methods are found in construction, such as the Gantt Chart, the CPM (Critical Path Method), the Fondhal-LPU method (LinearPoint Union), and the PERT method (Program Evaluation and Review Technique). These methods are used to control the timing and sequence of activities in a project [75].  
In this sense, technological advances have significantly improved the teaching of construction scheduling, making computer knowledge necessary to reduce time in data interpretation and variable control [76]. Scheduling methods are generally based on network systems that allow planning and controlling projects [77]. However, construction scheduling is only the beginning of the construction process; the biggest challenge is to control the schedule by constantly updating the information to make the best decisions and use resources efficiently [78], where the use of Game Theory for teaching a scheduling method such as PERT can bring multiple benefits.
## 7 SensorNC Applications
### Node Placement Strategies
#### 7.2.2 Results  
The primary factors for all experiments are: the number of nodes, the number of sinks and the sensing range. For each deployment, nodes are distributed over a circular field shape, and sinks are placed at the center of gravity of a sector of a circle (CGSC). In the random deployment, we generated 10 scenarios and took the average value for the analysis of the worst-case delay in the sensor field. The routing topology we use here is based on [PERSON]'s shortest path algorithm, which produces the shortest hop distance from a source to a sink. We also assume that \(r_{tx}\) is twice \(r_{sense}\) in all strategies. All of the selected values for the experiments are based on a realistic model of an MICAz more running under TinyOS.  
The analytical results of the worst-case delay comparison among the three strategies are shown in Figure 14. For SensorNC computations, the token-bucket arrival curve and rate-latency service curves are considered. In particular, for the service curve, we use a rate-latency function, which corresponds to a duty cycle of 1%. For a 1% duty cycle, it takes 5 ms of time-on-duty with a 500-ms cycle length, which results in a latency of 0.495 s (the values are calculated based on CC2420 AckLpl.h and CC2420 AckLplP.nc.) The corresponding forwarding rate is 2500 bps. In all scenarios, THT outperforms the other strategies. In a 100-node network, the worst-case delay improves from 2.04 s to 1.52 s to 1.5 s for the 2-, 3- and 4-sink scenarios, respectively. In fact, the more sinks, the lower the worst-case delay should be. However, the sink placement at CGSC does not perform so well for a larger number of sinks. Another interesting observation is that the random deployment can have a lower worst-case delay than the square grid deployment, e.g., for a 1000-node network with 20 sinks scenario. It seems that a random deployment is more or less comparable to a square grid for a large-scale network.  
Figure 14: Worst-case delay comparison of three-node placement strategies in different scenarios.