esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Perspectives
It is also worth mentioning that BGs could have a pivotal role in nerve repair due to their proven pro-angiogenic properties [63], since some studies have recently highlighted the critical role of angiogenic signaling pathways in improving the quality of peripheral nerve repair [41]. The optimization of the glass-to-polymer ratio in composite biomaterials is also crucial for tailoring the physical, biological, and mechanical properties of the nerve guidance conduit. The use of electrically conductive biodegradable polymers [102] as matrixes for the production of BG/polymer composites also deserves to be investigated; in this way, the electrical insulation properties of BGs would be somehow compensated. Another strategy to improve the outcome of nerve guidance conduits could be the incorporation of chemicals, drugs, or exogenous neurotrophic factors to be released once the biomaterial has been implanted [103; 104]. Such drugs could be introduced into the lumen of hollow resorbable BG fibers undergoing progressive dissolution after implantation, thus allowing biomolecules to be released. Mesoporous bioactive glasses (MBGs) [105] could also be considered as versatile platforms for the controlled release of neural drugs and growth factor. For example, there is abundant literature showing that MBGs are excellent carriers for ibuprofen, which is commonly used due to its anti-inflammatory properties [106]; furthermore, this drug was reported to improve nerve functional recovery and increase the area and thickness of myelinated axons, too [107; 108], and it could therefore be encapsulated in MBG-based drug delivery systems addressing neuroregeneration. Conceptualization: S.K. ; literature search: S.K. and M.G.-K.; writing--original draft preparation: S.K. ; writing--review and editing: F.B. and M.M. ; funding acquisition: F.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest relevant to this article.
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 4 Results
### Test 2  
Figure 14 and Table 10 show the results obtained during Test 2 for the Galileo + GPS combination using smartphone A on pillar 9, over two hours of observation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & **CASC** & \multicolumn{4}{c}{**CEUT**} & \multicolumn{4}{c}{**GAIA**} & \multicolumn{4}{c}{**VIGO**} \\ \cline{2-13}  & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline AVE & \(-0.207\) & \(0.144\) & \(-0.028\) & \(-0.237\) & \(0.354\) & \(0.334\) & \(0.006\) & \(0.002\) & \(0.053\) & \(0.222\) & \(-0.019\) & \(0.236\) \\ STD & \(0.047\) & \(0.005\) & \(0.034\) & \(0.016\) & \(0.013\) & \(0.018\) & \(0.013\) & \(0.008\) & \(0.024\) & \(0.082\) & \(0.018\) & \(0.143\) \\ RMS & \(0.045\) & \(0.021\) & \(0.002\) & \(0.056\) & \(0.125\) & \(0.112\) & \(0.000\) & \(0.000\) & \(0.003\) & \(0.056\) & \(0.001\) & \(0.076\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Statistics results from the Test 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & **CASC** & \multicolumn{4}{c}{**CEUT**} & \multicolumn{4}{c}{**GAIA**} & \multicolumn{4}{c}{**VIGO**} \\ \hline
**Smartphone** & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline \multirow{3}{*}{A} & AVE & \(-0.093\) & \(-0.041\) & \(-0.159\) & \(-0.190\) & \(-0.127\) & \(-0.122\) & \(-0.070\) & \(0.015\) & \(-0.076\) & \(-0.125\) & \(0.084\) & \(-0.126\) \\  & STD & \(0.097\) & \(0.090\) & \(0.142\) & \(0.185\) & \(0.162\) & \(0.174\) & \(0.126\) & \(0.076\) & \(0.123\) & \(0.131\) & \(0.096\) & \(0.199\) \\  & RMS & \(0.131\) & \(0.095\) & \(0.208\) & \(0.259\) & \(0.200\) & \(0.205\) & \(0.138\) & \(0.074\) & \(0.139\) & \(0.176\) & \(0.124\) & \(0.227\) \\ \hline \multirow{3}{*}{B} & AVE & \(0.044\) & \(-0.077\) & \(-0.039\) & \(0.038\) & \(-0.072\) & \(-0.080\) & \(0.055\) & \(0.011\) & \(-0.012\) & \(0.041\) & \(0.069\) & \(-0.025\) \\  & STD & \(0.188\) & \(0.124\) & \(0.164\) & \(0.132\) & \(0.196\) & \(0.227\) & \(0.110\) & \(0.103\) & \(0.118\) & \(0.170\) & \(0.088\) & \(0.201\) \\ \cline{1-1}  & RMS & \(0.184\) & \(0.140\) & \(0.161\) & \(0.131\) & \(0.200\) & \(0.230\) & \(0.118\) & \(0.098\) & \(0.112\) & \(0.167\) & \(0.109\) & \(0.192\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Test 1 statistics comparing the smartphone A and smartphone B.  
The results of Test 2 show a better behavior than those obtained for Test 1, due to the longer duration of observations (2 h), as we see in Tests 4, 5, and 6, also with a long time of observation, to be presented later. The CEUT station shows RMS values below 15 cm, while the CASC and VIGO stations show values below 10 cm and GAIA, the shortest baseline, below 1 cm.
## 1 Introduction
The different finite element simulations can be validated by laboratory experiments using full-field optical measuring systems, which work based on the digital image correlation (DIC) method. These systems create pictures during the measurement and compare the changes of the required stochastic pattern to calculate various measurements, such as deformation and strain. There are two-dimensional and three-dimensional DIC measuring systems. This equipment is widely used in material and structure testing, because of the ease of setting up a measurement and because of its ability to measure along the whole surface instead of only between two points [23; 24]. In material testing, it is widely used to evaluate the different local strains and deformations on the surface of the specimen [25; 26; 27]. The results from these experiments can be directly compared to the results of accurately created finite element simulations [5; 11; 28; 29; 30; 31]. By this comparison, the different material models can also be validated, and the most suitable ones can be selected [32; 33]. The comparison between the simulations and the optical measurements not only provides information about the most suitable material model, but when tested at both lower and higher, but still elastic strains, it also outlines the limits and capabilities of each material model. This work is aimed to find out the effectiveness of the linear elastic material models when modeling PA6 by comparing them to a nonlinear elastic material model and validating all results using advanced full-field measurement systems. This material was chosen because its stress-strain curves have a significant amount of linearity and nonlinearity as well, and it is widely used in many fields of the industry. During the measurements, only the elastic properties were tested, the measurement method was set up in a way to avoid any other factors, such as testing rate dependence, water content dependence, and viscous properties, to simulate the effect of a quasi-static load. By finding out the limits of the linear elastic material models when modeling nonlinear elastic materials under quasi-static loading, the possibilities of these material models' usage in the industry can be determined, thus shortening the time and effort required to set up a finite element simulation for such materials. During the product development phase, only the elastic region of the material is in focus because any kind of failure has to be avoided.
###### Abstract  
This paper skillfully incorporates the memristor model into a chaotic system, creating a two-dimensional (2D) hyperchaotic map. The system's exceptional chaotic performance is verified through methods such as phase diagrams, bifurcation diagrams, and Lyapunov exponential spectrum. Additionally, a universal framework corresponding to the chaotic system is proposed. To enhance encryption security, the pixel values of the image are preprocessed, and a hash function is used to generate a hash value, which is then incorporated into the secret keys generation process. Existing algorithms typically encrypt the three channels of a color image separately or perform encryption only at the pixel level, resulting in certain limitations in encryption effectiveness. To address this, this paper proposes a novel encryption algorithm based on 2D hyperchaotic maps that extends from single-channel encryption to multi-channel encryption (SEME-TDHM). The SEME-TDHM algorithm combines single-channel and multi-channel random scrambling, followed by local cross-diffusion of pixel values across different planes. By integrating both pixel-level and bit-level diffusion, the randomness of the image information distribution is significantly increased. Finally, the diffusion matrix is decomposed and restored to generate the encrypted color image. Simulation results and comparative analyses demonstrate that the SEME-TDHM algorithm outperforms existing algorithms in terms of encryption effectiveness. The encrypted image maintains a stable information entropy around 7.999, with average NPCR and UACI values close to the ideal benchmarks of 99.6169% and 33.4623%, respectively, further affirming its outstanding encryption effectiveness. Additionally, the histogram of the encrypted image shows a uniform distribution, and the correlation coefficient is nearly zero. These findings indicate that the SEME-TDHM algorithm successfully encrypts color images, providing strong security and practical utility.  
color image; image encryption; memristor model; hybrid transforms 2020
# An Intelligent Collaborative Charging System for Open-Pit Mines
## 2 Current Development Status of Key Technologies and Equipment in Open-Pit Mining
### Current Status of Charging/Blasting Technology and Equipment in Open-Pit Mines  
Charging and blasting constitute a critical workflow in open-pit mining operations, involving explosive loading, circuit connection, and detonation processes. Prior to the development of explosive loading trucks, manual filling of explosives and manual circuit initiation were the sole methods for blasting operations. These approaches not only required high labor intensity but also suffered from low efficiency and inconsistent charging quality, directly impacting mine productivity and economic performance. To mechanize the charging process, on-site mixed explosive loading trucks (hereinafter referred to as \"explosive trucks\") have been developed and widely implemented. These integrated systems combine explosive raw material transportation, on-site mixing, and automated charging functions, offering significantly higher efficiency and safety compared to manual operations [47]. The adoption of explosive trucks has substantially reduced time and operational costs in blasting workflows. By the end of 2021, China's permitted production capacity of on-site mixed explosives reached nearly 3 million tons, accounting for nearly 50% of total explosive production capacity, with the market for explosive truck applications experiencing rapid growth [48; 49]. However, with the rapid advancement of next-generation information technologies--particularly the emergence of \"smart mines\" and \"smart blasting\" concepts--existing explosive truck technologies can no longer fully meet the demands of intelligent mine construction [50].  
Currently, two types of explosive loading trucks are employed in open-pit mine charging operations: side-rotating loading trucks and high-mast spiral robotic arm loading trucks. These trucks feature borehole searching and positioning systems to locate blast holes, followed by inserting explosive-filled charging pipes into the holes for completion. Side-rotating trucks, with shorter rotating arms, require frequent back-and-forth travel on the bench platform to complete borehole searching and charging for all holes. High-mast spiral robotic arm trucks, equipped with longer arms, reduce travel frequency and distance on the bench. However, their extended arms lead to difficulties in borehole searching and repeated alignment issues caused by inaccurate hole positioning [51; 52].  
Field practice has proven that while existing explosive loading trucks have replaced significant manual charging operations, technical challenges persist in the charging process, including inaccurate borehole searching, difficult borehole locating, imprecise blast hole positioning, and prolonged vehicle travel time. These issues often necessitate manual intervention. Current trucks exhibit critical shortcomings such as insufficient collaborative operations, low charging efficiency, and inadequate intelligence, failing to fully achieve automated, intelligent, and highly coordinated charging workflows.
## 3 Results
### The Spatiotemporal Characteristics of the Impact of Environmental Factors on GPP
#### 3.3.2 The Spatial Characteristics of the Impact of Environmental Factors on GPP
In desert vegetation areas, the expression patterns of the comprehensive impact of various environmental factors on GPP are the same as those in grassland areas; PAR and VPD also have good expression effects. In addition, in order to clearly express the relationship expression patterns of the comprehensive impact of five environmental factors on GPP in different ecosystems, we summarized and listed them in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environmental Factor** & **Ecosystem Type** & **Expression Pattern** & **Equation** \\ \hline \multirow{4}{*}{PAR} & Farmland area & Linear & Y = 0.00010X + 0.01 \\  & Grassland area & Non-linear & Y = 0.0016 \(\times\) exp\((-\)\(\times\)/\(-\)191.960\()\) + 0.0043 \(\times\) exp\((-\)\(\times\)/\(-\)191.8636\()-0.0172\) \\  & Desert vegetation area & Non-linear & Y = 0.0350 \(\times\) exp\((-\)\(\times\)/\(-\)191.2646\()\) \\ \hline \multirow{4}{*}{VPD} & Farmland area & Linear & Y = 0.04X + 0.02 \\  & Grassland area & Linear & Y = 0.16X \(-\) 0.007 \\  & Desert vegetation area & Linear & Y = 0.46X \(-\) 0.089 \\ \hline \multirow{4}{*}{Tair} & Farmland area & Linear & Y = 0.00055 \(+\) 0.04 \\  & Grassland area & Non-linear & Y = 0.0013 \(+\) 0.051 \(\times\) exp\((-\)\(\times\)/\(-\)10.5252\()\) \\  & Desert vegetation area & Non-linear & Y = 0.0402 \(\times\) exp\((-\)\(\times\)/\(-\)9.0609\()-0.0224\) \\ \hline \multirow{4}{*}{Tsoil} & Farmland area & Linear & Y = 0.00098X + 0.03 \\  & Grassland area & Non-linear & Y = 0.1634 \(-\) 0.1502 \(\times\) (11.4502 \(\times\) exp\((-\)/\(-\)11.8658\()-0.0407\) \\ \hline \multirow{4}{*}{SWC} & Farmland area & Linear & Y = 0.2906 \(+\) 0.03 \\  & Grassland area & Non-linear & Y = 1.5471 \(-\) 34.5865 \(\times\) x + 247.6533 \(\times\) x\({}_{\rm{mean}}\) \\ \cline{1-1}  & Desert vegetation area & Non-linear & Y = \(-\)0.0116 \(+\) 0.6203 \(\times\) exp\((-\)\(\times\) (\(-\)0.1598)/0.0203\()^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Specific information on the expression patterns of the comprehensive impact of five environmental factors on GPP in different ecosystems. Figure 10: The expression pattern of the comprehensive impact of five environmental factors (PAR, VPD, SWC, Tair, and Tooil) on GPP in different ecosystems. **(a)** Farmland areas. **(b)** Grassland areas. **(c)** Desert vegetation areas.
## 3 System Model
Therefore, the above assumption still holds when those features are not identical, while the \(n_{i}\) devices are the closest to \(B\!S_{i}\) in terms of the virtual distance (corresponding to the \(n_{i}\) devices with the highest tendency to \(B\!S_{i}\)). The objective of this study is to identify \(n_{i}\) corresponding to the PSNE of the game below:  
\[\mathcal{G}=\{\mathbf{DV}_{All},(p_{j})_{j\in\{1,...,N_{0}\}},(U_{j})_{j\in\{1,...,N_{0}\}}\} \tag{1}\]  
where \(\mathbf{DV}_{All}\) are the players, \(\mathbf{p}=\{p_{j}|j=1,...,N_{0}\}\) are the pure strategies used by the players, each player's strategy is chosen from the pure strategy set \(\{B\!S_{i}|i=0,1,...,M\}\) and \((U_{j})_{j\in\{1,...,N_{0}\}}\) are the players' payoff functions, which are assumed to be identical and related to the number of devices selecting the same cell, the positions of devices and ICI. Equation (1) is a class of non-cooperative game, called the congestion game, as long as \(\forall j\in\{1,...,N_{0}\}\); \(U_{j}\) is a utility function of the number of devices selecting the same BS with a monotonically non-increasing feature (or non-decreasing feature if congestion \(C_{j}\) is used as a cost function). Given pure strategies \(\mathbf{p}\), PSNE is defined as [70]:  
**Definition 1**. : _A set of pure strategies, \(\mathbf{p}^{*}\) is called a PSNE if \(\forall j\in\{1,...,N_{0}\}\) and \(\forall p_{j}\in\mathbf{p}\),_  
\[U_{j}(p_{j}^{*},p_{-j}^{*})\geq U_{j}(p_{j},p_{-j}^{*}) \tag{2}\]  
Based on the assumptions above, given a set \(\mathbf{n}^{*}=\{n_{i}|i=1,...,M\}\), \(\mathbf{p}^{*}\) is fixed, so the definition of PSNE could be changed into the following:  
**Definition 2**. : \(\mathbf{n}^{*}\) _is said to be a PSNE if its corresponding \(\mathbf{p}^{*}\) satisfies Definition 1._  
Figure 2: System model. Each picocell holds a circular boundary (_i.e._, the azure dashed circle) indicating the PSNE. Device A is inside the boundary, so it transmits to the picocell and interferes with the macrocell. Although Device B is within the edge of the picocell, it is outside of the dashed circle, so it selects the macrocell and interferes with the picocell.
## 4 Discussion
### Strengths and Limitations of This Study  
To the best of our knowledge, no study to date has qualitatively and quantitatively examined the efficacy and efficiency of the different VL thresholds for improving strength and athletic adaptations. Importantly, all investigations included were monitored using velocity, which reinforces the isolation of the independent variable under study (intra-set VL, and so intra-set fatigue). Specifically, this methodology ensures that all participants included in the different VL groups: (i) trained at an identical relative load (i.e., %1 RM) throughout the RT program regardless of the changes in neuromuscular performance or motivation state [38], and (ii) incurred the programmed intra-set fatigue even if they performed a different number of repetitions within the set [26]. Moreover, the fact that no between-or within-group analyses showed a significant (_p_ < 0.05) heterogeneity would strengthen the findings of the present research.  
On the other hand, this research is not exempt from limitations. Firstly, all the studies included male-only samples, so the knowledge on the strength and athletic adaptations produced by the different VL thresholds should be extended to the female population. Secondly, a reduced range of exercises was examined. On this matter, although squat and bench press represent two of the main multi-joint exercises used in RT routines, it would be of great practical value to amplify these analyses to other upper- (e.g., prone bench pull or shoulder press) and lower-limb (e.g., hip thrust or deadlift) exercises. Thirdly, the similarities between studies in terms of the subject's RSR (range 0.9-1.5 kg\(\cdot\)kg\({}^{-1}\)), duration (range 5-8 weeks), or training frequency (2-3 sessions/week) limited the implementation of meta-regression analyses to examine the possible influence of these parameters on our findings. Finally, only sprint and jump abilities were examined as parameters of athletic performance, so future studies are encouraged to include other sports abilities such as throws, changes of direction, or other specific tests (e.g., Wingate test).
## 7 Conclusions and Perspectives  
We have proposed several protocols to revoke a set of nodes, to renew symmetric and asymmetric keys and to establish a shared key between two authenticated nodes of the network. Our solutions use the ECC, which allows nodes to easily construct shared keys without interaction. Moreover, all of our protocols have been automatically verified as secure using Scyther. This ensures the security of our solutions. We also have implemented and tested all of our protocols on TelosB nodes in order to evaluate their execution time relative to one another. These results show that according to the load of the network and to the topology, one protocol might be more efficient than another. Then, according to the context (size of the network, size of the battery, type of mote, energy consumption for communication, computation resources of the motes), one solution might be better than another one. All of these parameters should be taken into account before choosing one real solution.  
In this version of our key renewal mechanism, we did not ensure message integrity. Ensuring message integrity implies the addition of MAC generated using a different key than the one used for encryption. Thus, response messages will take more time to be generated and will be 16 bytes bigger. We plan on proposing an extension that includes integrity mechanisms in our future works. Notice that the key establishment protocols DJS and IJS both ensure integrity, because they use the ECIES scheme, which includes a MAC operation on the cipher text.  
As a long-term perspective, we plan on testing our protocols in more realistic platforms, such as the IoT-LAB platform [47]. This would give us an idea about how our different protocols would perform in a large-scale network and how long it would take to revoke and renew distributed keys and to establish the required secured links.
## 2 System Model of the TR Acoustic Communication Using FMT
### Receive Structure  
#### 2.2.1 FMT Demodulation  
For the convenient expression, the symbol interval \(T_{c}\), which has been explained in Section 2.1, is omitted in the following sections. After \(x(k)\) is transmitted through the acoustic channel \(H\left(\omega\right)\), the received FMT signal \(r\left(k\right)\) can be expressed as:  
\[r\left(k\right)=\sum_{p\rightarrow\infty}^{+\infty}x\left(k-p\right)h\left(p \right)+w\left(k\right) \tag{2}\]  
where \(h\left(k\right)\) denotes the time domain response of the acoustic channel \(H\left(\omega\right)\), and \(w\left(k\right)\) denotes the channel noise.  
Figure 3 shows the block diagram for the receive structure based on baseband model. Referring to Figure 3, it is observed that the FMT demodulator is composed of subcarrier demodulators, receive filters and \(K\)-times down-samplers. The \(m\)-th output signal of the FMT demodulator can be expressed as:  
\[\begin{split}& y_{m}\left(nK\right)=\sum_{i=1}^{M}\sum_{l=\infty}^{+ \infty}\sum_{k=\infty}^{+\infty}\sum_{p=\infty}^{+\infty}g\left(k-p-lK\right)h \left(p\right)e^{-\frac{2\pi}{M}up}g^{\prime}\left(nK-k\right)e^{i\frac{2\pi }{M}\left(l-m\right)k}a_{{}_{i}}\left(lK\right)\\ &+\sum_{k=\infty}^{+\infty}w\left(k\right)g^{\prime}\left(nK-k \right)e^{-i\frac{2\pi}{M}nk}\\ &=\sum_{i=1}^{M}\sum_{l=\infty}^{+\infty}f^{\left(m,i\right)} \left(l,n\right)a_{{}_{i}}\left(lK\right)+\eta_{{}_{m}}\left(nK\right)\end{split} \tag{3}\]  
where \(g^{\prime}\left(k\right)\) denotes the time domain response of the receive filter \(G^{{}^{*}}\left(\omega\right)\), \(f^{\left(m,i\right)}\left(l,n\right)\) denotes the time domain response of the composite channel between the \(i\)-th input signal of FMT modulator  
Figure 3: Block diagram for the receive structure of the proposed method.  
\(a_{{}_{i}}\left(nK\right)\) and the \(m\)-th output signal of FMT demodulator \(y_{{}_{m}}\left(nK\right),\) and \(\eta_{{}_{m}}\left(nK\right)\) denotes the output noise in the \(m\)-th output signal of FMT demodulator. The corresponding frequency domain expressions of \(f^{\left(m,i\right)}\left(l,n\right)\) and \(\eta_{{}_{m}}\left(nK\right)\) are:  
\[F^{\left(m,i\right)}\left(\omega\right) =G\left(\omega\right)H\left(\omega+\frac{2\pi}{M}i\right)G^{*} \left(\omega+\frac{2\pi i}{M}-\frac{2\pi m}{M}\right) \tag{4}\] \[\eta_{{}_{m}}\left(\omega\right) =W\left(\omega\right)G^{*}\left(\omega-\frac{2\pi}{M}m\right) \tag{5}\]