esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Creep Behavior of Silty Clay Based on Fractal Derivative
## 2 Test Procedure and Results
### Test Procedure  
The triaxial consolidation drained tests were carried out by the TSZ automatic triaxial apparatus, and the diameter and height of the samples were 39.1 mm and 80 mm, respectively. The temperature was controlled to 20 \(\pm\) 1 \({}^{\circ}\)C during the test, and a series of various eccentric stress were applied to different samples separately. Compared with applying a graded loading, this method can directly obtain the soil creep test data and avoid manual processing errors to reflect the creep characteristics of soil more accurately. To obtain the failure deviatoric stress of \(q_{f}\) = (\(\sigma_{1}-\sigma_{3}\)), under different confining pressures \(\sigma_{3}\), a series of triaxial consolidated drained shear (CD) tests were carried out at four levels, the shear rate was set as 0.01%/min, and the test was performed in accordance with Chinese standards [37]. The obtained shear strength indexes (effective cohesion \(c^{\prime}\) and effective friction angle \(\varphi^{\prime}\)) are shown in Table 2, and the results are shown in Figure 5. The failure deviatoric stress \(q_{f}\) was divided into 5 levels, the loading increment of each level was \(\Delta q=q_{f}/5\), and the creep characteristics of sample soils below the failure deviatoric stress levels \(q\) were researched through a triaxial consolidated drained creep test. The samples were consolidated under the target confining pressure for 30 h prior to the test, respectively. Then, the axial loads were imposed on the samples until the target deviatoric stress q was reached. Finally, the stress was kept constant with a precision of \(\pm\)10 kPa and the creep behavior was observed. At present, there are no standardized creep stability criteria; [PERSON] [14] suggests that the general total observation time is set to 7-14 d in the case of the deformation reaching the stabilization stage or the constant strain rate stage. In this paper, the test was terminated after the observation time of the deformation stabilization stage reached 7 d, and the cumulative creep deformation of one consecutive 24 h period was lower than 0.01 mm [14]. The testing procedure was performed in accordance with [PERSON] et al. [38], and the data were automatically collected by the acquisition system of the instrument. The loading application scheme is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test No.** & \(\sigma_{3}\) **(kPa)** & \(q_{f}\) **(kPa)** & \(c^{\prime}\) **(kPa)** & \(\varphi^{\prime}\) **(\({}^{\circ}\))** \\ \hline
1 & 100 & 288 & & \\
2 & 200 & 568 & & \\
3 & 300 & 836 & 12.4 & 33.7 \\
4 & 400 & 1057 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The triaxial consolidated drained shear test program.  
Figure 4: Gradation curve of the soil sample.
## 2 Sensors and Test Methods  
In the context of this research activity based on an artificial neural network, where inputs rely on specific values acquired through sensor data, it is necessary to present a comprehensive overview of the sensor technologies used to collect the necessary data for feeding the artificial neural network (ANN). This paper focuses on various analyses for sideslip angle estimation using a high-performance reference vehicle equipped with multiple sensors placed in various locations to gather data for the ANN. Additionally, key characteristics of the reference vehicle, such as the center of gravity (CoG) position, tire specifications, and engine and suspension features, were also known. Acquisitions were made on different circuits that had varying characteristics and layouts. One of them was identified by tight, slow turns that had a high aerodynamic load. The other one had the opposite characteristics, with long straights and a low aerodynamic load. The third circuit had intermediate characteristics, with several fast corners. On all these circuits, the vehicle was sensorized in the same way. In addition, with the same tires and on each circuit, long runs and short runs were performed with the same vehicle. In the former, the driver was more focused on tire management and had more fuel load; in the latter, the driver was more focused on performance. In addition, limit situations such as tire locking under braking were also tested so that the NN could also be trained to recognize particular phenomena with different slip values.  
The data acquired from the sensors are often subject to noise, electromagnetic disturbances, and high-frequency vibrations. Therefore, a preliminary operation involved filtering the channels of interest to remove the noise from the acquired signals. Considering unavoidable misalignments between the sensors and the vehicle's reference system, it was essential to detect and eliminate these offsets. Additionally, to compare the outputs from various instruments mounted in different parts of the vehicle, all of the data were reported to the center of gravity (CoG).
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 2 Mathematical Formulation
Equation (5) is one of the more complex constraints associated with the power equilibrium at each demand node at any time, which is due to its nonlinear and non-convex structure [25]. Equation (6) is also non-convex, and it represents the voltage drop in each line as a function of the current and power flows. Equation (7) corresponds to the application of [PERSON]'s second theorem, which associates the instantaneous power in an element with its voltage and current. Inequality constraints (8)-(10) determine the admissible values for the PV sizes, as well as the voltage and current magnitudes, respectively. Equation (11) limits the maximum number of PV generation units installed in the DC distribution network. Finally, Equation (12) shows the binary nature of the decision variable \(x_{j}\). **Remark 1**: _The optimization model (1)-(12) is from the MINLP family, which makes it difficult to solve via classical optimization methods, such as the Branch and Cut and interior-point techniques. This is due to the high probability of getting stuck in the local optimum solutions._  
To deal with the complexity of the solution of the MINLP model (1)-(12), this study implements a master-slave optimization methodology based on the power flow approach for multiple periods and the generalized normal distribution optimizer in the master stage. The following section presents the proposed solution methodology in detail.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Location-Based Routing Schemes
#### 3.1.1 Location-Based Routing with Mobility Consideration  
This section surveys the location-based routing schemes that consider limited or free mobility of sensor nodes, relay nodes, and/or sink nodes.
## 3 Aip Array Platform Applications
### Mmwave Channel Sounding  
Channel Sounding Measurements  
Another application of the AiP is in a mmWave channel sounder, where the 4 \(\times\) 4 AiP described in [22] is employed as the Tx. The environment and system setup of the AiPs channel sounding measurement can be seen in Figure 12. The metal plate is introduced intentionally to act as a reflector in the channel environment, generating a reflection path. Both the Tx and Rx are set at a height of 1.2 m above the floor, and the exact placement of the Tx, the Rx and metal plate is illustrated in Figure 13. Port 1 of the VNA is connected to the Tx and port 2 to the Rx. The channel frequency response between the Tx and Rx is obtained by measuring the S-parameter with the VNA. Laptop 1 is used to control the Tx, laptop 2 is responsible for controlling the Rx and the VNA.  
Figure 11: ‘Least squares’ calibration effectiveness measurement results.  
Figure 12: (**a**) Photograph and (**b**) block diagram of the AiPs channel sounder setup.  
Figure 13: Placement of the Tx, Rx and metal plate.  
The router is used as a relay for the connection between laptop 1 and laptop 2, and between laptop 2 and the VNA, which is physically connected via LAN cable. In addition, laptop 1 communicates with laptop 2 using TCP/IP and laptop 2 communicates with the VNA via the SCPI protocol. The AiPs are connected to the laptops via UART cables and communicate with the Serial Communication protocol. When the AiPs channel sounder measures the channel, laptop 1 controls the Tx AIP beam-steering, which enables the AiP to beam-steering from \(-53^{\circ}\) to \(53^{\circ}\) in the horizontal plane. For each beam steered by the Tx AiP, laptop 2 controls the Rx AiP steering beams from \(-72^{\circ}\) to \(72^{\circ}\) in the horizontal plane. Additionally, laptop 2 also controls the VNA to record the frequency response between the Tx and Rx.
## 6 Simulation Result
### Evaluation Results of the Learning Rate and the Attenuation Factor  
The learning rate is critical to maximize the speed of learning. A proper learning rate could let the objective function converge to the optimal value at the right time. Hence, the effects of the proposed model at different learning rates are studied and shown in Figure 4a. In Figure 4a, when the learning rate is \(1\times 10^{-6}\) and \(1\times 10^{-5}\), the task completion rate slowly converges with the increase in iteration numbers. However, the model cannot achieve the optimal value, but the local optimal value, and no real optimal solution is found. When the learning rate is 0.001, the model converges very fast in the earlier stage, but the successful rate fluctuates too much to converge with the iteration process. When the learning rate is 0.0001, both the optimization rate and the stability of the model are guaranteed. Therefore, the value of learning rate is chosen as 0.0001 in the process of model tuning.  
Attenuation factor is a key element of the DRL model. The value of the attenuation factor is usually between 0 and 1. This is because the current reward and all subsequent rewards have to be considered in DRL. The training effect of the proposed DRL model under different attenuation factors is shown in Figure 4b, from which we know that when the attenuation factor is 0.8, the model achieves the best performance compared to other attenuation factor values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \(B_{u,n}\) & 10 MHz & the bandwidth from UAV \(u\) to vehicle \(n\) \\ \(P_{u}\) & 10 W & the transfer power of UAV \(u\) \\ \(P_{n}\) & 1 W & the fixed transfer power of vehicle \(n\) \\ \(B_{u,u^{\prime}}\) & 50 MHz & the bandwidth between neighboring UAVs \\ \(\sigma^{2}\) & \(-95\) dBm & the power spectral density of Gaussian Noise \\ \(\beta\) & 2 & path loss exponent \\ \(f_{f}\) & 2 GHZ & carrier frequency \\ \(\tau\) & 100 & coefficient according to gradient descent \\ \(\lambda\) & 0.6 & weight value \\ _batch-size_ & 80 & the number of units manufactured in a \\  & & production run \\ \(\gamma\) & 0.8 & attenuation factor \\ _step_ & 80 & step in the DRL model \\ learning rate & 0.0001 & learning rate in training \\ \hline \hline \end{tabular}
\end{table}
Table 1: The setting of variable values.
## 2 Related Work
### Security in Communication Systems
#### Threats  
On the other hand, there are mainly 4 threats to which a communication system might be exposed:  
* Destruction of information and/or other resources
* Corruption or modification of information
* Theft, removal or loss of information and/or other resources
* Disclosure of information.
## 3 Theoretical Part
### Digitalization and 3D Modeling of Cultural Heritage
#### 3.3.1 Digital Preservation of Cultural Heritage  
Digital preservation of cultural heritage refers to the use of digital technologies to preserve, protect, and disseminate knowledge about cultural assets. This approach allows for the creation of permanent digital records of cultural objects that can be used for restoration, research, education, and presentation. Digital records also provide the possibility of virtual restoration, where damaged or lost parts of artifacts can be reconstructed using historical data and technologies such as 3D modeling and Virtual Reality [28].  
One of the key aspects of digital preservation is its role in preventing the loss of cultural heritage due to natural disasters, wars, or deterioration from age. Digitization enables cultural artifacts to be preserved in their original condition and made available for future generations, even if the original items become inaccessible or destroyed [29]. Examples of successful initiatives in digital preservation include projects like CyArk, which uses3D laser scanning to document world heritage, and Europeana, which digitizes cultural heritage across Europe [30].  
Digitization also contributes to the democratization of culture, allowing broader access to cultural content through digital platforms. This means that people around the world can access information about cultural heritage regardless of geographic location, promoting global awareness and education about cultural values [31].
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
###### Abstract  
The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability.  
Keywords:wireless sensor networks; IEEE 802.15.4e; performance analysis; LLDN; star topology +
Footnote †: journal: Sensors
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 4 Experiment and Analysis
### Experiments and Analysis of Results
#### 4.3.4 Comparative Experiments on Open-Source Datasets  
To further validate the diversity of the dataset used in the predictive model, the proposed method was tested on the open-source ETTh1 dataset associated with the original Informer model. Comparative experiments were conducted using both single models and ensemble models. The effectiveness of the proposed method was assessed through visualizations of the fitting results between the predicted and actual values from the test set. The visualization results for the test set fittings are presented in Figures 11 and 12, where  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Target** & **Car-num** & **Etth1** \\ \hline \multirow{2}{*}{Informer Model} & MSE & 2.79 & 1.39 \\  & MAE & 1.30 & 0.60 \\ \hline \multirow{2}{*}{ARIMA + BPNN Model} & MSE & 11.11 & 2.90 \\  & MAE & 2.67 & 1.61 \\ \hline \multirow{2}{*}{ARIMA + GRU Model} & MSE & 9.81 & 3.04 \\  & MAE & 2.60 & 1.64 \\ \hline \multirow{2}{*}{ARIMA + LSTM Model} & MSE & 10.76 & 2.90 \\  & MAE & 2.71 & 1.61 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative experimental results of integrated prediction models.  
Figure 10: Experimental results of integrated model comparison. (**a**) ARIMA + BPNN model; (**b**) ARIMA + GRU model; and (**c**) ARIMA + LSTM model.  
\"TRUE\" denotes the actual values and the labels for other models represent the predicted values of both the proposed model and the comparison models for the corresponding time series in the test set.  
In the comparative experiments conducted using the open-source dataset, the enhanced predictive model presented in this paper yields conclusions consistent with the supply volume prediction data. On the ETTh1 dataset, the ensemble models demonstrate overall better fitting performance compared to the single models, exhibiting improved model accuracy and robustness. However, the proposed enhanced Informer model outperforms the ensemble prediction models, further indicating that the results from the  
Figure 11: Results of single model comparison experiment (ETTh1). (**a**) SVR model; (**b**) LSTM model; (**c**) GRU model; (**d**) BPNN model; (**e**) Informer model; and (**f**) InformerStack model.  
Figure 12: Experimental results of integrated model comparison (ETTH1). (**a**) ARIMA + BPNN model; (**b**) ARIMA + GRU model; and (**c**) ARIMA + LSTM model.  
ETTh1 dataset experiments suggest the superior fitting capability of the proposed model for long-sequence time series data.  
In summary, through two sets of comparative experiments using empirical data on logistics transportation supply volume and the open-source ETTh1 dataset, the results consistently show that the proposed enhanced Informer model achieves high prediction accuracy for long-sequence time series forecasting. Additionally, the visual fitting charts indicate a better ability to capture local changes and fluctuations, thereby demonstrating overall improved predictive performance.
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 3 Problem Formulation  
This paper explores the problem of path planning for autonomous vehicles within urban environments, where the need for timely data transfer is as important as minimizing drive time. Specifically, we assumed that each vehicle has a certain amount of data that must be successfully transferred before reaching its destination. In previous studies, this problem was explored and tested in a simple grid world environment [82]. To advance this exploration, we constructed a detailed and dynamic graphical representation of urban road networks, utilizing real-world vehicular mobility data to create a more realistic environment.
## 2 System Architecture
### SCADA System
#### 2.4.1 VBS Functions--Integration PLC/Proceq Pundit Lab+  
The UPV test equipment \"Proceq Pundit Lab+\" [20], can be connected to a PC, via its COM port, using the serial communication with the following parameters:  
* 115,200 Baud Rate
* 8 Data Bit
* 1 Stop Bit
* No parity  
This device has a communication protocol based on a series of hexadecimal commands in a pre-defined order.  
After each measurement, the equipment sends the resulting data as well as some extra information in a sequence of hexadecimal blocks [20]. The measurement data are composed by 108 bytes added by seven more information bytes at the beginning and two more Checksum bytes at the end (assuming there is no sample curve); the equipment will send a total of 117 bytes.  
\[0\text{xEF 0x00 }\underbrace{0\text{xAA 0 xAA 0 xAA}0\text{xAB 0 xBB}}\underbrace{0\text{xBB}}\underbrace{0\text{xBB}} \underbrace{0\text{xBB}}\underbrace{0\text{xBB}}\underbrace{0\text{xCC}}\] \[\text{A--Total Data Size}\] \[\text{B--Measured Data (108 bytes)}\] \[\text{C--Checksum}\]  
Relevant information for this study is the following data [20]: (i) propagation time (bytes 29 to 32); (ii) general absolute position (bytes 36 to 39); (iii) propagation velocity (bytes 37 to 40); and (iv) general absolute position (bytes 44 to 47).  
The communication between the PLC and the Pundit equipment is assured by the SIEMENS WinCC SCADA system with custom VBS (Visual Basic Scripts) to allow serial communication (send and receive hexadecimal data). The \"Microsoft Communications Control\" Version 6.0 ActiveX control developed by Microsoft was used to perform this task.  
Four custom scripts were developed to handle the serial communication and the data flow, namely: Open_S0; Send(); Receive(); and Close_S0. A brief explanation of these scripts follows.  
\"Open_S()\"--Script that uses the _MSComm_ object to define the serial communication parameters according to the ones used by the Pundit Lab+ as well as the COM port where the device is connected and the start of the communication.  
\"Send()\"--Script that, using the _MSComm_ object, is responsible for sending the hexadecimal data blocks to trigger the measurement of the UPV device.  
\"Receive()\"--Script that reads and processes the entire buffer received from the UPV device. Starts by identifying the bytes that records the velocity and time information. After the acquisition, the data are converted to a decimal base for easier interpretation.  
\"Close_S()\"--Script whose only objective is to fully close the serial communication.
# Profile Design of the Grooved Die and Rolling Force Prediction in the Cold Pilger Rolling Process
## 3 Die Groove Shape Design in the Cold Pilger Rolling Process
### Q Factor Calculation According to the Pilger Die Groove Shape  
The Q factor controls the direction of the texture to ensure the integrity of the pipe used in the material for high-pressure vessels. The Q factor is defined as the ratio of wall reduction change to outer diameter reduction of the tube during the cold pilger rolling [18; 19; 20]. The equation for the Q factor is as follows:  
\[\begin{array}{l}\text{Q}=\frac{\ln\Big{(}\frac{\delta_{1}-\delta_{2}}{ \delta_{1}}\Big{)}}{\ln\Big{(}\frac{d_{o1}-d_{o2}}{d_{o1}}\Big{)}}\end{array} \tag{17}\]  
Here, \(\delta_{1}\) and \(\delta_{2}\) are wall thicknesses at the inlet and outlet of the material, and \(d_{o1}\) and \(d_{o2}\) denote the outer diameters at the inlet and outlet of the material, respectively.  
In general, it is preferable for the Q factor to have the appropriately same value above 1 [17]. However, if the Q factor value is increased, the shape and surface quality of the product is negatively affected because the material is significantly deformed in the wall direction. Therefore, if the wall and outer diameter reductions are similarly obtained, the deformation of the material in the wall direction is small, and the appropriate Q factor value can be obtained.  
In this study, the Q factor value was greater than 1. It was intended to be as uniform as possible to prevent defects in the tube during the pilger rolling process as well as to obtain the characteristics that can be used at high pressures.