esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 VVCM Evaluation
### Influence of VV on the Mechanical Properties of LSAM-50 Asphalt Mixture and Design Criteria  
#### 4.1.1 Vv Influencing Factors  
VV denotes the proportion of void volume to the total volume in compacted asphalt mixtures. This parameter is pivotal in asphalt mixture design, profoundly affecting its mechanical properties. The value of VV is contingent upon aggregate gradation, asphalt-aggregate ratios, and compaction efforts. In the case of a certain compaction effort, the VV is primarily influenced by its aggregate gradation and asphalt-aggregate ratios.  
The variation of VV with asphalt-aggregate ratios for LSAM-50 asphalt mixtures with different aggregate gradations is shown in Figure 4.  
Figure 4 illustrates that for a consistent compaction effort, both the asphalt-aggregate ratio and aggregate gradation substantially influence the VV of the compacted asphalt mixture. For LSAM-50 mixtures with different aggregate gradations, there is an approximate linear decrease in VV as the asphalt-aggregate ratio increases. This can be attributed to the limited lubrication at lower asphalt contents, resulting in increased friction between mineral particles and insufficient compaction. As asphalt content rises, it fills the mineral voids, leading to a progressive reduction in the void rate.  
Under consistent asphalt-aggregate ratios and compaction conditions, the VV for gradation B, which is finer, is notably lower than for the coarser gradation C. This indicates a pronounced filling effect by the fine aggregate within the coarse aggregate framework. As the proportion of fine aggregate increases, the overall VV of asphalt mixtures generally declines. Nonetheless, an excessive amount of fine aggregate can compromise the integrity of the coarse aggregate's skeleton-embedded structure, resulting in a higher VV; this elucidates the distinction between gradation E and gradation F.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Experimental Result and Analysis  
In this section, the four HSI data sets outlined in Section 3.1 are utilized, and several experiments are performed to examine the efficacy of the proposed SP-SSA method. Figure 10 compares the classification results obtained with varying numbers of training samples on four datasets. It can be noted that better classification performance is evident when larger numbers of labeled samples are utilized for training; after passing the percentages used in this work; however, the accuracy level mostly plateaus, and no further significant improvement is observed. Our proposed approach attains the best classification accuracy in almost all cases, regardless of the number of samples, proving its robustness. Classification results from all four data sets are provided in Tables 2-5 and quantitatively support the dominance of the proposed method. Individual classification maps generated by the proposed SP-SSA method and all the compared approaches are displayed in Figures 11-14.
# Stability Optimization of Explicit Runge-Kutta Methods with Higher-Order Derivatives
## 4 Maximization of the Integral Characteristics of RKMs with Higher-Order Derivatives
### Optimal Parameters of RKMs with Higher-Order Derivatives
#### 4.1.2 Optimized Stability Polynomials for TDRKMs  
In the case of \(s=3\), the admissible interval of values of \(c_{3}\) is obtained after computational experiments as \([0,1]\). The area of the stability domain is maximized at \(c_{35}=0.660856433674193\) and \(\mathcal{L}\) is maximized at \(c_{3L}=0.65360783088082\).  
In Table 2, the values of \(S\) and \(\mathcal{L}\) are presented. In Figure 2, the boundaries of stability domains are presented. It should be noted that, in the case of \(c_{3}=4/5\), the stability polynomials for classical fifth-order RKM and TDRKM are the same, so only the case of TDRKM is presented.  
In the case of \(s=4\) values of \(c_{2},c_{3}\) and \(c_{4}\) are localized in interval \([0,1]\). For maximization of area the following parameters are obtained: \(c_{2S}=0.110623837465505\), \(c_{3S}=0.340709171677390\), \(c_{4S}=0.744735038301271\). Function \(\mathcal{L}\) is maximized at \(c_{2L}=0.083899064101326\), \(c_{3L}=0.338628074770119\), \(c_{4L}=0.745123891969865\). In Table 3, values of \(S\) and \(\mathcal{L}\) are presented. We do not separately present cases of \(c_{2}=1/3\), \(c_{3}=1/2\), \(c_{4}=2/3\) and \(c_{2}=1/3\), \(c_{3}=(5-\sqrt{5})/10\), \(c_{4}=(5+\sqrt{5})/10\) obtained in [33], because their stability polynomials are represented by (14) and integral characteristics \(S\) and \(\mathcal{L}\) are the same as in the case of classical sixth-order RKM. In Figure 3 boundaries of stability domains are presented.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \(S\) & \(\mathcal{L}\) \\ \hline TDRKM, \(c_{2S}\), \(c_{3S}\), \(c_{4S}\) & 44.1945 & 5.9574 \\ \hline TDRKM, \(c_{2L}\), \(c_{3L}\), \(c_{4L}\) & 42.0867 & 8.0352 \\ \hline TDRKM, \(c_{2}=1/4\), \(c_{3}=2/3\), \(c_{4}=1\) & 29.8805 & 4.0651 \\ \hline cRKM & 23.3212 & 3.9599 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of \(S\) and \(\mathcal{L}\) for cases of fifth-order TDRKMs at \(s=3\) and classical fifth-order RKM at \(s=6\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \(S\) & \(\mathcal{L}\) \\ \hline TDRKM, \(c_{3S}\) & 27.0595 & 5.5488 \\ \hline TDRKM, \(c_{3L}\) & 25.6852 & 6.2713 \\ \hline TDRKM, \(c_{3}=3/4\) & 20.3109 & 3.7395 \\ \hline TDRKM, \(c_{3}=(5+\sqrt{5})/10\) & 21.5745 & 3.9048 \\ \hline TDRKM, \(c_{3}=1\) & 16.7082 & 3.3188 \\ \hline TDRKM, \(c_{3}=4/5\) & 18.8411 & 3.5592 \\ \hline TDRKM, \(c_{3}=2/3\) & 26.7718 & 5.1519 \\ \hline cRKM & 18.8411 & 3.5592 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of \(S\) and \(\mathcal{L}\) for cases of sixth-order TDRKMs at \(s=4\) and classical sixth-order RKM at \(s=7\).
# Cost-Effective Temperature Sensor for Monitoring the Setting Time of Concrete
## 3 Results  
During the first hours of setting, the most significant exothermic reactions occur and the greatest amount of heat is released, so its analysis provides us with a large amount of information. In order to be able to clearly visualize what happens during this period and how a different dosage of additions affects the process itself, the temperature probes of the specimens of the first test have been graphed in Figure 6.  
Figure 4: Final prototype design for test 1.  
Figure 5: Final prototype design for test 2.  
Two distinct trends can be observed. In the probes corresponding to sample 1, a maximum temperature of 32.88 \({}^{\circ}\)C was reached during setting, higher than that measured in the probes corresponding to sample 2, which reached a maximum temperature of 30.13 \({}^{\circ}\)C. In conclusion, zeolite-containing mixtures reach lower temperatures.  
During the second test, it can be seen in Figure 7 that the probes in sample 4 reached a peak temperature of 24.4 \({}^{\circ}\)C, higher than the probes belonging to sample 3, which reached a maximum temperature of 21.88 \({}^{\circ}\)C. This temperature difference can be attributed to the presence of recycled concrete aggregate, as its components can react in cement hydration processes generating greater hydration heat. In probe number 5 (corresponding to water), it can be seen that, unlike the sensors that measure inside the specimens, there was no increase in temperature due to the exothermic reactions of the cement hydration.  
In both cases, a sudden drop in temperature was observed over 24 h, which coincides with the introduction of the specimens into the water bath inside the wet chamber, since the curing water was at a stable temperature of 20 \(\pm\) 2 \({}^{\circ}\)C.  
Figure 6: First 72 h of setting test 1.  
Figure 7: First 72 h of setting test 2.  
To analyze the results, a comparison was made between the various samples during the 180 days of monitoring, which can be seen in Figure 14.  
As already mentioned, the assembly of the specimens of tests 1 and 2 was carried out in different periods of time, so to make the comparison, the results have been superimposed, coinciding the period in which the specimens coexisted.  
At 28 and 180 days of curing, the compressive strength test was performed on the mortar specimens.  
In Figure 15, a large variation can be observed between the compressive strengths of the different materials, with the reference samples (sample 1 and CEMI) being the ones with the highest strength.  
Figure 14: Comparison of 180-day monitoring results of the four samples.  
Figure 13: Comparison of temperature values obtained during monitoring of sample 4.
## 4 Simulation Results and Analysis
### Differential Attack  
Two methods, namely, the number of pixel change rate (NPCR) and the unified average change intensity (UACI), are used to measure the resistance of differential attacks. The number of pixel change rate is a measure of the difference between two images. It is calculated by comparing the number of pixels that have changed between the two images and expressing the result as a percentage. An ideal NPCR value for an encrypted image is above 99%, indicating that all pixels have been significantly altered from their original positions. The UACI is calculated by comparing the pixel values of two images and quantifying the average intensity of the changes between the two images. It is expressed as a percentage and can range from 0 to 100, with higher values indicating a greater difference between the two images. To calculate the UACI, the differences between the pixel values of the two images are first determined. These differences are then averaged and expressed as a percentage by dividing the average difference by the maximum possible difference between the pixel values and multiplying by 100. The two methods are calculated using the following mathematical Equations (14) and (15).  
\[NPCR=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}D(i,j)\times 100\% \tag{14}\]  
\[UACI=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}\frac{C_{1}(i,j)-C_{2}(i,j )}{255}\times 100\% \tag{15}\]  
\[D(i,j)=\begin{cases}0 iff\ C_{1}(i,j)=C_{2}(i,j)\\ 1 iff\ C_{1}(i,j)\
eq C_{2}(i,j)\end{cases}\]  
where \(M\times N\) represent the size of the image in terms of width and height, and \(c_{1}\) and \(c_{2}\) are two cipher images after changing one pixel position of their original plain image. Table 2 presents the NPCR and UACI values for the encrypted images. To find the average NPCR or UACI, we add up the results for all the image sizes and then divide by the total number of images. Table 2 shows the average NPCR and UACI values for about 24 images of different sizes. When comparing the NPCR and UACI values of two images of the same type and the change in pixel values, Table 3 compares these values with those from other similar studies using the Lena image of size 256 \(\times\) 256. These results demonstrate the effectiveness of the proposed encryption in resisting differential attacks.  
Figure 10: Noise effect on ciphertext and recovery image in different degrees.
## 4 Experimental Results and Analysis  
### Dataset and Experimental Settings  
The SVM and MLP ML diagnostic system was used for the prediction of breast cancer. WDBC was utilized for the validation of the proposed classifiers. The data set is divided into 65% training and 35% testing and validation in these implementations. The data set has 569 instances with 32 features. The dataset has 357 benign and 212 malignant people.  
After that, we then perform feature scaling, which is an approach used to normalize the features of the data.  
The PSO feature selection algorithm is used to select attributes to be used by the classifiers for the diagnosis of breast cancer. After selecting the attributes, the identification of breast cancer cases is done by ML classifiers. Our methodology was validated with the implementation of the SVM and MLP based approach. The hyperparameters of the SVM were optimized using the Nelder-Mead (downhill simplex) method [58].  
To verify the performance of the models, various evaluation metrics, such as accuracy, sensitivity, specificity, recall, precision, and ROC are used. Additionally, all implementation outcomes are presented in tables and graphs for better understanding. All experiments are conducted using Python programming language running on Intel Pentium Core i3 CPU, 2.0 GHz processor speed, 4 GB RAM, and Windows 8 operating system.
## 3 Results and Discussions
### The Results of the Stress-Strain Curves
#### 3.2.2 The Influence of Different Forming Directions on Dynamic Impact Shear Properties
As shown in Figures 13c and 14c, when the strain rate was 4.5 \(\times\) 10\({}^{4}\) s\({}^{-1}\), the small angle grain boundary had a large proportion, indicating that the grains division was more serious and the material had been recrystallized. Also, it can be seen that the deformation of each grain structure was not uniform. The deformation mechanism of aluminum alloy 7050-T7451 was twinning and sliding together under high speed dynamic impact. In the first stage, the deformation was dominated by twins, and the second stage of deformation was dominated by slippage after the twins were consumed [28]. The twin will selectively appear in the grain of favorable orientation during plastic deformation, and twin grain boundaries can reduce the average free path of dislocation and play a hardening role after the formation of twin grain. At the same time, the long axial grain shaped the total area of grain boundary area became larger, the dislocation barrier became more and the need to coordinate with different grain orientation became more, made the metal deformation resistance enhancement [29]. Therefore, combined with Figures 10 and 14, the specimens in ND and RD of aluminum alloy 7050-T7451 were more prone to shear fracture failure, and the dynamic shear mechanical properties in ND and RD of aluminum alloy 7050-T7451 were better than that in TD.
## 8 Conclusions  
In this paper, we have designed and implemented an omni-directional acoustic micro-modem based on a Cortex-M3 for use in UWSNs. The size of the developed cylindrical modem is only 70 mm in diameter and 35 mm in height. The modem is also equipped with a spherical transducer and operated  
Figure 16: Experimental environment for the tests in a river.  
Figure 17: Output voltage with respect to distance and input voltage in a river.  
by an external 14.8 volts lithium-ion battery. Physical layer functions including the issues of frame format and symbol structure were investigated and implemented fully in software and hardware. For the verification of the functionality and performance of the modem, indoor and outdoor experiments were executed in a water tank, a pond and a river. Generally, experimental results matched expected results by analysis but in some cases of river tests there were small deviations between them. According to the experiments in the river, it was possible for the modem to send data wirelessly up to 70 m with a data rate of 0.2 kbps while consuming a power of 4.5 watts. The maximum communication distance will increase if we use a higher supply voltage or a transducer having lower resonant frequency. Except for the transducer, the modem hardware is made of general purpose off-the-shelf digital and analog devices which make it possible to curtail component expenditures. Since the developed micro-modem possesses the characteristics of low-power consumption, micro size, low cost, omni-directivity and so on, it is expected that the modem can be used for various UWSN applications requiring acoustic communication.  
The main contribution of this work is the development of the cylindrically shaped micro-modem guaranteeing an omni-directional beam pattern in three dimensions. From these characteristics, the modem could be utilized for underwater moving vehicles such as ROVs and AUVs, devices requiring omnidirectional property such as UWSN sink nodes, and biomimetic fish robots. As our next work, we will address the following two topics: one is to improve the performance of our modem in terms of data rate and communication distance by loading state-of-art transmission technologies such as equalization, multi-level and multi-carrier modulation and channel coding into a digital signal processor. The other is to optimize analog circuitry to enhance the hardware efficiency.
## 3 SSGLR-Based Monitoring
The SSGLR statistic can be then expressed as:  
\[R_{k,l,m}=\max_{max}\frac{N_{1,k,l,\tau}}{2\delta_{0}^{2}}(\hat{\mu}_{1,k,l, \tau}-\mu_{0})^{2} \tag{16}\]  
The SSGLR control chart decision rule calls for specifying two limits, g and \(h\), which satisfy the condition \(0<\text{g}<h\), where g is the limit that tells when it is time to stop sampling at the present point of sampling while h is the control limit. This is how the decision rule is defined. Taking \(k\) to be the current sampling point and l as the present sum of observations at the specified sampling point:  
1. Evaluate \(R_{mkl}\). 2. If \(R_{mkl}\leq\text{g}\), it means that at the sampling point \(k\), you will stop sampling and then wait until you reach sampling point \(k\)+1 to re-sample. Therefore, the sample size at the \(k\) sampling point here is now \(n_{k}=l\). 3. If \(\text{g}<R_{mkl}<h\), we have two options that we need to follow, which include: 1. Taking the subsequent observation at the sampling point \(k\); 2. Moving to step A, which is given above with the present sum of observations at the k sampling point provided as \(l=l+1\). 4. In case \(R_{mkl}>h\), it signals that the process vector mean \(\mu\) has experienced a change. You can choose the control chart parameters h and g to meet the in-control performance requirements that have been specified. If \(\hat{R}_{k,m,l}\) falls outside the interval \(\left[\hat{h}_{GLR}\delta_{0},\delta_{GLR}\delta_{0}\right]\), then the chart will trigger a signal. The KDE control limit can be obtained by using the equation below:  
\[\int_{-\infty}^{\hat{R}_{GLR}\delta_{0}}g\left(\hat{R}_{k,m,l}\right)d\hat{R} _{k,m,l}=\alpha\text{ and }\int_{-\infty}^{\hat{S}_{GLR}\delta_{0}}g\left(\hat{R}_{k,m,l}\right)d\hat{R} _{k,m,l}=\alpha \tag{17}\]
# Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay
## 5 Conclusions  
In this paper, SEM images of seeping and soaking municipal waste-contaminated clay samples were processed and studied in IPP and PCSA. The main conclusions are as follows:  
1. The threshold was determined by binarization and morphological processing of the SEM images using IPP and PACS. IPP was used to preprocess and binarize the SEM images to eliminate uneven brightness. The particles that came into contact and overlapped with each other were identified and separated in each image by using PACS.
2. Based on the relationship between the threshold value and apparent void ratio and the relationship between the threshold value and particle fractional dimension, the range of the pixel threshold value of municipal waste-contaminated clay was determined as 110-140.
3. The pixel threshold value range of 110-140 of municipal waste-contaminated clay was validated based on the relationship between the pixel threshold value and apparent void ratio, the variation in pore blockage with seepage depth, and the compaction factor value variation with seepage depth and contaminant concentration.  
This study proposes a simple and accurate method for determining effective threshold range of image pixel values for municipal waste-contaminated clay, thus improving the reliability of the extraction of the microstructure parameters of contaminated soil. Because of the abundance of soil pores, most types of soil can use this method to establish the threshold value. However, this method is only applicable to soil, and further research on the microstructure of rocks is required.  
All authors contributed to the study conception and design. The methodology, investigation and data curation were performed by R.Z. The funding acquisition, methodology and supervision were performed by L.C. The formal analysis and resources were performed by Y.G. The  
Figure 10: Relationship between compaction factor and seepage depths.  
first draft of the manuscript was written by [PERSON] and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Natural Science Foundation of China (No. 42372309, 41972281).  
Not applicable.  
All relevant data are within the paper.  
We are grateful to [PERSON], [PERSON], and [PERSON] for preparing the experiments, drawing figures and testing the software for threshold determination. We also thank the editor and three anonymous reviewers for their thoughtful comments, which led to a greatly improved manuscript.  
The authors declare no conflicts of interest.
# Positioning Using IRIDIUM Satellite Signals of Opportunity in Weak Signal Environment
## 2 Iridium Satellite Orbit and Signal Characteristics
The simplex frequency accesses are only used for downlink signals and they are the only frequencies that may be transmitted during the simplex timeslot. Four messaging channels and one ring alert channel are available during the simplex timeslot. The simplex signal and duplex signal are both bursts. Each burst has an un-modulated tone, unique word (BPSK) and information data (QPSK) [13]. The simplex bursts (The ring alert signal and the primary message signal) are always transmitted from Iridium satellite and can be passive received by the receiver, which means they can be used for Iridium SOPs positioning. Actually, the IRIDIUM NEXT burst signals of simplex channels that are collected by us are from 6.5 to 20.32 ms.