esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Birefringence of Cassava Starches in HPP-treated Cassava Flours
#### 3.3.1 Short-Range Order of Cassava Starches
Simple comparisons between pressures at 10 min HT (\(p<0.001\)) revealed that 600 MPa was significantly higher compared to 0.1-400 MPa but not to 500 MPa, and was also not different from other pressures at 30 min HT. Hence, holding time was only significant at 600 MPa (\(p=0.002\)), with 10 min having 0.328 units higher than 30 min  
Figure 3: FTIR spectra of untreated (0.10 MPa) and selected HPP-treated flours at the 875–1200 cm\({}^{-1}\) region; (**a**) 0.01 MPa and 300–500 MPa–10% flour concentration at both 10 and 30 mins; (**b**) 0.01 MPa and all 600 MPa combinations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pressure** & **Flour Conc.** & \multicolumn{2}{c}{**1047/1022**} & \multicolumn{2}{c}{**1022/995**} \\ \cline{3-6}
**(MPa)** & **(\% w/v)** & **10 min HT** & **30 min HT** & **10 min HT** & **30 min HT** \\ \hline
0.10 & 10 & 0.65 \(\pm\) 0.13 & & 0.95 \(\pm\) 0.03 \\ (untreated) & 20 & 0.87 \(\pm\) 0.19 & & 0.91 \(\pm\) 0.25 \\
30 & 0.80 \(\pm\) 0.21 & & 0.96 \(\pm\) 0.04 \\
300 & 10 & 0.99 \(\pm\) 0.09 & 0.79 \(\pm\) 0.08 & 0.84 \(\pm\) 0.16 & 0.94 \(\pm\) 0.15 \\  & 20 & 0.83 \(\pm\) 0.19 & 0.75 \(\pm\) 0.13 & 1.00 \(\pm\) 0.25 & 0.83 \(\pm\) 0.09 \\  & 30 & 0.88 \(\pm\) 0.06 & 0.84 \(\pm\) 0.11 & 0.87 \(\pm\) 0.13 & 0.83 \(\pm\) 0.21 \\
400 & 10 & 0.83 \(\pm\) 0.11 & 0.70 \(\pm\) 0.1 & 0.89 \(\pm\) 0.12 & 1.07 \(\pm\) 0.07 \\  & 20 & 0.79 \(\pm\) 0.16 & 0.76 \(\pm\) 0.13 & 0.98 \(\pm\) 0.06 & 0.98 \(\pm\) 0.18 \\  & 30 & 0.68 \(\pm\) 0.11 & 0.84 \(\pm\) 0.07 & 1.05 \(\pm\) 0.03 & 1.01 \(\pm\) 0.06 \\
500 & 10 & 0.61 \(\pm\) 0.05 & 0.73 \(\pm\) 0.09 & 1.21 \(\pm\) 0.24 & 1.27 \(\pm\) 0.27 \\  & 20 & 0.85 \(\pm\) 0.18 & 0.67 \(\pm\) 0.11 & 1.16 \(\pm\) 0.14 & 1.02 \(\pm\) 0.13 \\  & 30 & 0.84 \(\pm\) 0.10 & 0.87 \(\pm\) 0.11 & 0.91 \(\pm\) 0.05 & 1.20 \(\pm\) 0.14 \\
600 & 10 & 0.63 \(\pm\) 0.09 & 0.54 \(\pm\) 0.06 & 1.42 \(\pm\) 0.32 & 1.09 \(\pm\) 0.27 \\  & 20 & 0.86 \(\pm\) 0.26 & 0.53 \(\pm\) 0.05 & 1.34 \(\pm\) 0.55 & 0.98 \(\pm\) 0.21 \\  & 30 & 0.72 \(\pm\) 0.15 & 0.66 \(\pm\) 0.16 & 1.31 \(\pm\) 0.44 & 1.02 \(\pm\) 0.09 \\ \hline \hline \end{tabular} Results are presented as mean \(\pm\) standard deviation of individual HPP treatment (n = 3).  
\end{table} 
Table 3: Absorbance ratio values of untreated and HPP-treated cassava flours. HT. Although the opposite was expected, this still shows that 600 MPa could reduce the molecular order of starch double helices.
# GNSS-Based Monitoring Methods for Mining Headframes
## 3 Monitoring Data Processing and Analysis  
The BIMRWP platform receives BDS/GNSS observation data and employs carrier phase differential positioning technology for real-time monitoring of the headframe. It exports the average of the solved results every 6 h (dynamic 6 h) to obtain the corresponding deformation monitoring data. However, the accuracy of the monitoring results is affected by external factors.  
On the one hand, the influence comes from the headframe itself, which is a damped compound pendulum structure. During operation, it will swing regularly because of factors such as wind force, mechanical traction, and vibration disturbance from its own equipment, which are normal phenomena of the headframe's deformation. On the other hand, the influence arises from the GNSS observation environment and measurement errors, such as missing original GNSS observation data caused by headframe shielding and large multipath effect errors [28], ionospheric and tropospheric delays, and other measurement errors.  
The monitoring data exported from the platform require preprocessing to improve the accuracy of deformation monitoring data and ensure the accuracy of subsequent risk assessments. For the deformation caused by the structure of the headframe itself, the position deformation of the headframe can be statistically accumulated as a cumula  
Figure 3: Working base map of the Liuzhuang mining west area.  
tive deformation variable to eliminate the risk misjudgment caused by normal regular swings. For the errors generated by GNSS monitoring, the original monitoring data need to be cleaned.  
This monitoring system has been operating since 16 April 2024. To prevent system instability in the early stage of operation, the monitoring data for the first 13 days (from 16 April 2024 to 28 April 2024) are not included in the monitoring data processing and analysis in this work. The BIMRWP platform exports continuous dynamic 6 h data for 66 days (from 29 April 2024 to 3 July 2024) from the upper and lower sheave wheel platform of the headframe at eight monitoring stations, with a total of 224 data points for each monitoring station. The first 56 days of data are used for monitoring data processing and analysis and establishing a risk assessment model. The last 10 days of data are utilized for accuracy verification of the risk assessment model.
## Materials and Methods
### Data Acquisition
#### 2.1.2 Spectral Measurement  
An ASD (Analytical Spectral Devices) Field Spec Pro FR (Full range) (350-2500 nm) spectrometer was utilized to collect the spectral information of winter wheat ears. The spectral resolution of the spectrometer is 3 nm in the range of 350-1000 nm, 10 nm in the range of 1000-2500 nm, and the spectral sampling interval was 1 nm. Using a piece of 1 x 1 m black cloth with a hole in the middle, we pierced through the ear from under the holes, and placed the probe of the spectrometer on top of the ear to measure the erect spectrum. When measuring the spectrum of the front and side of the ear, we placed the front or side of the ear upward, bent the ear slightly, and fixed it on black cloth with double-sided tape or other means that did not affect the spectrum. The probe was also placed above the ear for spectral measurement. Each ear was measured 10 times at each angle, and the average value of the 10 measurements was recorded. Vertical measurement was performed using a conventional remote sensing method, but the information acquisition was incomplete. Horizontal measurements can reflect the disease infection more comprehensively. In order to better reflect the lesions caused by FHB in the spectral information to improve the identification accuracy of FHB, the spectrum measurement was carried out from three angles for each ear, namely, front, side, and erect (Figure 3). Spectra of all samples were collected between 10:00 and 14:00 (Beijing local time), under clear and cloudless breezy weather conditions. A calibration panel was used to calculate reflectance before each measurement.
# An IoT-Based System for Efficient Detection of Cotton Pest
## 2 Materials and Methods
### Implementation Scenarios
#### 2.4.1 Scenario 1 Experimental Lab
For these data-collecting sessions, the least sensitivity level of sensors was used because of the rapid movement of insects, and multiple outputs were received every 10 ms.  
Figure 5: (**A**) A fully assembled wireless node consists of Arduino Uno, Zigbee, sensors, and battery. (**B**) Conceptual diagram of a node. Several tests were performed in a controlled environment as well as in the lab using two flying insect species. Among them, one was the Angoumois grain moth (Sitotroga Cerealella) and the other was the Guava fruit fly (Bactrocera Correcta (Bezzi)). For instance, shown in Figure 3B, some flying grain moths and fruit flies were tested within a transparent acrylic box with this prototype at Sind Agriculture Research Lab. Sharp distance measuring sensor units (two different models GP2Y0A-41 SK0F/02 YK0F) were used. Each sensor had a distance-measuring sensor unit, composed of a signal processing circuit, an infrared emitting diode (IR-LED), and an integrated combination of a position-sensitive detector (PSD). The reflectivity variety of the object, the operating duration, and the environmental temperature did not easily influence distance detection due to the adaptation of the triangulation method. Voltage was the output of this device that corresponded to distance detection. Therefore, this sensor could be used as a proximity sensor. The key feature included the distance-measuring sensor united with infrared LED, signal processing circuit, and PSD; a short measuring cycle (16.5 ms); the distance measuring range was up to 4 to 30 cm; and the output type was analog. The measuring range of these sensors was up to 150 cm; they were used with the combination of the AT Mega 2560 Arduino board and Zigbee module for transmitting data from the field to a remote location. Figure 5A shows a prototype made for initial tests while Figure 5B shows a conceptual diagram of the sensing node. Primarily, two different sizes of experimental prototype boxes were used with different ranges of sensors, Type A and Type B; see Figure 4 and Table 2 for further technical details. The first setup was made with acrylic boxes, where sensors were mounted at the top of the box and the remaining circuit was placed inside the box with a lithium polymer battery. Some experiments were performed in a controlled environment with houseflies and mosquitos as discussed earlier.
## 5 Experimental Test
### Test in Simulated Environment
#### 5.1.3 Comparison of Algorithms  
In IGA algorithm, in order to solve the requirement of non-negative probability in roulette selection, the calculation formula of adaptive value is revised as follows:  
\[\mathrm{Fit}(\mathrm{f}(\mathrm{x}))=\left\{\begin{matrix}0,\mathrm{else}\\ \mathrm{c}_{\mathrm{max}}-\mathrm{f}(\mathrm{x}),\mathrm{f}(\mathrm{x})< \mathrm{c}_{\mathrm{max}}\end{matrix}\right. \tag{20}\]  
where \(\mathrm{c}_{\mathrm{max}}=\sqrt{10}\).  
Figure 11 shows the average convergence curve variation of the three algorithms in finding the target value in ten simulation experiments. When \(t=20\), there is no significant difference between the three algorithms in the initial stage of iteration. In the later stage of iteration, IGA converges in advance, and IDE and IDE-BO have better effects. When \(t=60\), the convergence speed and convergence accuracy of IDE-BO are obviously better than the other two algorithms. In order to obtain accurate comparison results, the number of iterations in the simulation experiment is \(t=150\). In the real human-computer interaction environment, in order to avoid user fatigue, \(t\leq 30\) is generally appropriate.  
Table 3 shows the number of iterations required to reach convergence for IDE-BO and the other two traditional algorithms in 10 simulation experiments, and it can be seen that IDE-BO has reached the convergence condition most of the time at around \(t=125\), while the number of iterations required to reach around 133 in IDE. Although IGA can converge significantly faster than IDE-BO, its convergence is not good as can be seen in Figure 11. To statistically compare the performance difference in convergence speed between IDE-BO and its competitors, a Wilcoxon signed-ranks test with a 0.05 significance level is conducted. Table 4 shows the \(p\)-values of the results when comparing IDE-BO with the other two algorithms. From the table, it can be seen that the \(p\)-value of IDE-BO is much lower than  
Figure 10: Simulation experiment of testing \(p\) value.  
0.05 for IDE which has a small difference in convergence and has a much better convergence than IGA which has a faster convergence. In general, the performance difference between IDE-BO and the other two algorithms is significant.
## 3 Results  
### Characterization of 4 sPLGA-mPEG Block Copolymers  
The chemical structure of prepared 4 sPLGA-mPEG block copolymers was characterized by \({}^{1}\)H NMR spectra as shown in Figure 1b. The signal at 3.38 ppm was assigned as the terminal methoxy proton of mPEG, signals at 3.65 and 4.30 ppm were due to the methylene proton. The methoxy proton signal of LA units in PLGA segments at \(\delta\) = 1.55 ppm, and the methyne proton signal at \(\delta\) = 5.20 ppm, and for GA units, the signal at 4.82 ppm was assigned to methylene proton. In addition, proton signal at 2.66 ppm was due to the methylene proton of SA units and signal at 4.21 ppm was in correspondence with the methylene proton of PTOL units. The molecule weight (M\({}_{n}\)) of CP-1, CP-2, and CP-3 measured by GPC were 7610, 7690, and 7640 Da respectively, which were close to the theoretical molecule weight of block copolymer (8010 Da) (Figure S1). The prepared block copolymer was dissolved in SVF solution at a concentration of 30 wt%, the sol-gel transition was measured by using viscosity test as a function of temperature, as shown in Figure 1c. For the three block copolymer solutions (CP-1, CP-2, and CP-3), the viscosities increased sharply with increasing temperature, confirmed the sol-gel phase transition. The sol-gel transition temperature ranges from about 25 to 30 \({}^{\circ}\)C. The sol-gel transition temperature of 4 sPLGA-mPEG block copolymer solution depended on the LA/GA mol ratio, and with the increase of LA/GA mol ratio, the gel transition temperature increased, and the body temperature was in the gel phase for all three copolymers. With further increasing temperature, the viscosities of the copolymer solutions dropped abruptly, indicating the gel-sol phase transition.
## 7 Performance Evaluation of the Recommended Stack Configurations in Alternative WHAN Scenarios
As a consequence, the average amount of hops between two nodes is much lower. Further, we observe that the overall PDR is almost 100% for all topologies, independent of the amount of traffic. Therefore, the achievable performance improvements in terms of PDR are rather low. The improvement of delay and energy efficiency are higher, since the recommended protocol stack configurations reduce the amount of packet losses and, thus, the APL layer RTO expirations, which has a noticeable positive effect on the delay and the energy efficiency. Overall, it has been shown that the recommended configurations for WHANs that have been derived in Section 6 achieve higher performance than the default one in a total of six different network scenarios for all considered traffic conditions.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
### Our Contributions  
To apply the DPA attack, we have selected an open-source core of AES from Open-cores [32]. Therefore, the contributions to this work are given as follows:  
* **Setting to apply DPA attack.** To execute the DPA attack on modern low-power FPGA devices, a large number of power traces are required. This results in an increase in the correlation calculation time. Therefore, to reduce this calculation time, we proposed our custom correlation technique (details are given in Section 3.1).
* **Secret key identification using DPA attack.** We applied the DPA attack (our custom correlation technique) on our FPGA implementation to obtain the secret key by measuring the power traces of the computations involved in the AES algorithm.
* **Countermeasure to mitigate the DPA attacks.** To provide resistance against DPA attacks, we provided a countermeasure using Boolean and multiplicative masking for the linear (i.e., ShiftRows, MixColumns, and AddRoundkey) and non-linear functions (i.e., SubBytes) of the AES, respectively. The descriptions are given in Section 3.2.  
We synthesized two different Verilog implemented designs, i.e., (i) without countermeasure (we termed this as DESIGN-I), and (ii) the inclusion of countermeasure (we named this as DESIGN-II), using the Vivado IDE tool. The implementation results for DESIGN-I and DESIGN-II are given on a Zynq 7020 FPGA device. Our DESIGN-I utilizes only the 424 FPGA slices. On the other hand, our DESIGN-II takes 714 FPGA slices. Subtraction of FPGA slices of DESIGN-I from DESIGN-II determines the FPGA slices (i.e., 290) required for the implementation of additional logic (i.e., our proposed countermeasure). With the utilization of these hardware resources, we have shown the extraction and prevention of a 128-bit secret key of AES. Consequently, our DESIGN-II provides the acceptability of this work for applications that require prevention against DPA attacks.  
This paper is organized as follows: Section 2 provides the required mathematical background about the implementation of AES. Section 3 describes the proposed method to provide resistance against the DPA attack on FPGA. The implementation results are described in Section 4. Finally, this work is concluded in Section 6.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 2 Related Works  
The issue of battery and network life span is not new. Researchers have tried to develop some models for data flow, power management scheme, and power harvesting means targeting to prolong the network lifetime. [PERSON] [3] and his teams proposed a software-based approach to estimate both the state of charge and the voltage of batteries in WSN nodes based on the use of a temperature-dependent analytical battery model. In the work of [7], they presented a routing technique that enhances the lifetime of a wireless sensor network. As the power consumption, a wireless  
Figure 1: GSM sensor node, in a GSM cell.  
sensor network became an issue. [PERSON] et al. [8] proposed that power may be harvested by using different technologies such as solar to keep the network alive. [PERSON] et al. [13] have described the conditions of distribution of network nodes that determine coefficients that affect the network lifetime. [PERSON] et al. [14] have proposed an approach for reducing energy consumption in a WSN based on an enhanced cluster head selection method. For optimizing the energy consumption in WSN, routing protocols can also be adjusted. It is within this regard that [PERSON] et al. [15] tried to modify LEACH [16] protocol to optimally route data in the network while providing a low power consumption. Apart from the issue of battery life span, researchers also tackled the relationship between the battery drainage and RSSI. [PERSON] et al. [17], worked on GPS as a location application for a mobile phone and tried to demonstrate that GPS signal with a higher signal to noise ratio SNR means with high RSSI consume less energy while less SNR signals consume a lot of energy. The works in [17; 18] demonstrated that smartphones express a quick battery drains when they are in locations with low signal quality (RSSI) for GSM or WIFI. However, the team was not able to quantitatively bring a relationship between the battery drainage and the signal quality. To the best of our knowledge, this is the only work that brings a mathematical relationship between RSSI and current consumption of a GPRS sensor node which will help during network planning.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 2 Related Work
### Siamese Network-Based Tracking  
An end-to-end network is utilized with the intention of improving tracking performance. The approach gains excellent tracking performance through offline pre-training on mass data. [PERSON] et al. [14] proposed to predict object locations through a Region Proposal Network (RPN) [15]. The whole structure is made up of a Siamese network and RPN, while the model is trained end-to-end. [PERSON] et al. [16] pointed out that correlation filters can be regarded as a special layer in the Siamese framework, which is able to respond to environmental changes by the means of continuously regenerating the filter. [PERSON] et al. [17] put forward a novel algorithm on the foundation of the Siamese network, where one branch forecasts the confidence of the target location by the means of forecasting each pixel, while the other branch regresses the four edges between the specimens and the earth truth distance [18]. [PERSON] et al. [19] proposed a Siamese attention key point network and obtained bounding boxes by the means of forecasting the coordinates of the upper left, center and lower right corners of the target.
## 2 Overview of the GA-BP Algorithm  
BP is a widely used nonlinear artificial neural network that does not require mathematical modeling of the system. It relies solely on sample data to perform nonlinear mapping from the input space (quantity of input neurons) to the output space (quantity of output neurons) [19]. For parameter design optimization, BP can be utilized in place of finite element calculations to map the input-output relationship of the optimization objective. Figure 1 shows the BP neural network model's organizational layout. Figure 1: Diagram of the BP model. Where \(\{X_{1},\ldots X_{n}\}\) represent the input values and \(\{Y_{1},\ldots Y_{m}\}\) represent the output values. In addition, \(w_{ij}\) denotes the weights within the hidden layer and the input layer, and \(w_{jk}\) denotes the weights separating the output layer from the hidden layer. Empirical equations can be used to calculate the number of nodes in the hidden layer; in general, the number of layers and nodes in the hidden layer is chosen depending on the outcomes of the model training process. Equation (1) is the empirical formula used to calculate the number of hidden layer nodes [19]:  
\[n_{1}=\sqrt{n+m}+a \tag{1}\]  
Here, \(n_{1}\) represents the hidden layer number. The variables \(n\) and \(m\) denote the number of input and output units, respectively, and \(a\) is a constant within the interval of 1 to 10. BP uses the mean square error (MSE) as the criterion for evaluating network training. Network training ceases when the MSE of the network falls below a predetermined threshold. The learning steps are as follows:  
(1) Variable Definitions  
Input: \(x=(x_{1},x_{2},\ldots,x_{n})\)  
Input vector of the hidden layer: \(hi=(hi_{1},hi_{2},\ldots,hi_{p})\)  
Output vector of the hidden layer: \(ho=(ho_{1},ho_{2},\ldots,ho_{p})\)  
Input vector of the output layer: \(yi=(yi_{1},yi_{2},\ldots,yi_{q})\)  
Output vector of the output layer: \(yo=(yo_{1},yo_{2},\ldots,yo_{q})\)  
Desired output: \(d_{o}=(d_{1},d_{2},\ldots,d_{q})\)  
Connection weights between the input and the hidden layer: \(w_{ij}\)  
Connection weights between the hidden and the output layer: \(w_{jk}\)  
Thresholds of the hidden layer neurons: \(b_{h}\)  
Thresholds of the output layer neurons: \(b_{0}\)  
Number of sample data: \(k=1,2,\ldots,m\)  
Activation formulation: \(f(\bullet)\)  
Error formulation: \(e=\frac{1}{2}\sum_{o=1}^{q}{(d_{o}(k)-yo_{o}(k))^{2}}\)  
(2) Network Initialization: Set the error function \(e\), specify the accuracy \(\varepsilon\) and the maximum number of learning iterations M, and assign random numbers between \(-1\) and \(1\) to each connection weight.