esa-sceva/satcom-chunk-collection huggingface dataset:
# Prototyping the Symmetry-Based Chaotic Communication System Using Microcontroller Unit
## 5 Conclusions  
Developing physical prototypes of chaos-based communication systems is of great importance for better understanding the physics of chaotic signals transmission, their noise resistivity, and attenuation features. Nevertheless, not many such prototypes are known from the literature. In this paper, we proposed an Arduino-based implementation of a chaotic communication system prototype. The developed system is able to operate using two types of modulation: a broadly known parameter modulation and novel symmetry coefficient modulation. In this study, we used these types of modulation to transmit binary messages through the noised wire. To design the system, we derived the finite-difference models of the continuous Lorenz system using a symmetric VSCD numerical integration method, which belongs to a family of semi-implicit methods, and constructed a pair of unidirectionally synchronized chaotic oscillators. Then, we developed the Arduino-based testbench with a noise-affected wired communication path. The proposed hardware implementation was tested for noise resistivity and secrecy (covertness) of messaging. To implement the secrecy analysis, we performed the quantified return map analysis technique and obtained some numerical estimations of the difference between '0' and '1' symbols. For the noise resistivity analysis, we calculated bit-error rates for various signal-to-noise ratios.  
The efficiency of the novel modulation method in comparison with the parameter modulation was found as follows: it was 10-20% more secure than parameter modulation while having a similar noise immunity. The data transfer rate of a proposed chaotic communication system is approximately 6 bps; however, even with such a relatively low rate, the proposed approach can find practical application.  
Further directions of research will include the improvement of the presented communication system in terms of the data transfer rate, implementing the system in different hardware, and developing its applications in specific domains.  
Conceptualization, D.B.; data curation, V.R., D.K. and G.K.; formal analysis, G.K., K.S. and D.B.; funding acquisition, T.K.; investigation, V.R., O.B., I.B. and K.S.; methodology, V.R.; project administration, T.K. and D.B.; resources, T.K., O.B. and G.K.; software, V.R., O.B. and K.S.; supervision, D.B.; validation, D.K. and I.B.; visualization, D.K.; writing--original draft, V.R. and T.K.; writing--review and editing, I.B., G.K. and D.B. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the grant of the Russian Science Foundation (RSF), project 20-79-10334.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 IRS-Aided Wireless Network Performance and Deployment
### IRS as a Wireless Network Receiver  
An IRS can be used as a signal receiver by deploying antenna arrays in the form of an IRS to improve the overall coverage of a wireless network [56]. An IRS was employed as a signal receiver for communication beyond mMIMO in [96]. In this paper [96], the BS was designed as a radiating surface instead of the traditional MIMO BS with active antenna arrays. This approach will help reduce the power and hardware impairments.
## 4 Reconfigurability Judgment Analysis and Autonomous Control Method
### Alignment Controllability Judgment Model  
From the analysis in Section 2, output torque and additional force cannot be offset at the same time. It cannot simultaneously control the horizontal offset, the self-rotation angle, and the vertical deformation of the air spring within a high given control precision range. However, to ensure the safe and stable operation of the shaft system, it is acceptable to relax  
Figure 2: Controllability analysis based on geometric methods: (**a**) The three convex polyhedrons have a nonempty intersection, and the alignment is controllable; (**b**) Only \(P(\delta P)\) and \(PY(\delta P)\) intersect, and the alignment is uncontrollable; (**c**) Only \(P(\delta P)\) and \(PZ(\delta P)\) intersect, and the alignment is not controllable; (**d**) Only \(PY(\delta P)\) and \(PZ(\delta P)\) intersect, and the alignment is uncontrollable.  
some constraints and even reduce some of the alignment control precision. Considering the mechanical properties of HPDME-ASVIS, the parameters of air pressure, horizontal offset, self-rotation angle, and vertical deformation can be regarded as soft constraints, of which the constraint boundaries can be expanded. The rest of the parameters are regarded as hard constraints, of which the constraint boundaries are not allowed to adjust. By expanding the boundary of some constraints, the alignment system can be controlled by decreasing control precision. At this time, the control objectives are  
\[\begin{split}&\mathbf{P_{min}}-\mathbf{P_{0}}-\left[\varepsilon_{p_{ min}},\varepsilon_{p_{min}},\ldots,\varepsilon_{p_{min}}\right]^{T}\leq\mathbf{ \delta p}\leq\mathbf{P_{max}}-\mathbf{P_{0}}+\left[\varepsilon_{p_{max}}, \varepsilon_{p_{max}},\ldots,\varepsilon_{p_{max}}\right]^{T}\\ &\mathbf{z_{min}}-\left[\varepsilon_{z},\varepsilon_{z}, \varepsilon_{z},\varepsilon_{z}\right]^{T}\leq\mathbf{z}\leq\mathbf{z_{max}}+ \left[\varepsilon_{z},\varepsilon_{z},\varepsilon_{z},\varepsilon_{z}\right]^ {T}\\ &\mathbf{y_{min}}-\left[\varepsilon_{x_{c}},0,0,\varepsilon_{p_{ 0}},0\right]^{T}\leq\mathbf{y}\leq\mathbf{y_{max}}+\left[\varepsilon_{x_{c}},0,0,\varepsilon_{\beta},0\right]^{T}\\ & s.t.\Bigg{\{}\begin{array}{l}0\leq\varepsilon_{p_{min}}\leq \varepsilon_{pmin}^{\max};0\leq\varepsilon_{p_{max}}\leq\varepsilon_{pmax}^{ \max}\\ 0\leq\varepsilon_{z}\leq\varepsilon_{z}^{\max},0\leq\varepsilon_{x_{c}}\leq \varepsilon_{x_{c}}^{\max};0\leq\varepsilon_{\beta}\leq\varepsilon_{\beta}^{ \max}\end{array} \tag{24}\]  
where \(\varepsilon_{p_{min}}\), \(\varepsilon_{p_{max}}\), \(\varepsilon_{x_{c}}\), \(\varepsilon_{\beta}\), \(\varepsilon_{z}\) is the adjustment amount of the boundary of each soft constraint, \(\varepsilon_{pmin}^{\max}\), \(\varepsilon_{pmax}^{\max}\), \(\varepsilon_{x_{c}}^{\max}\), \(\varepsilon_{\beta}^{\max}\), \(\varepsilon_{z}^{\max}\) is the maximum value of each corresponding acceptable adjustment amount.
## 4 Energy Model for Cooperative Communication Using LDPC Code
\begin{array}{ll}C_{0}+C_{1}\times L+C_{2}\times L^{2}& \mbox{for}\;O(n^{2})\\ C_{0}+C_{1}\times L&\mbox{for}\;O(n)\end{array} \right., \tag{13}\]where L is the number of transmission bits and \(C_{0}\), \(C_{1}\) and \(C_{2}\) are coefficients depending on the software and CPU parameters. \(E_{enc}\) is the encoding energy per bit and is taken 1 \(\mu J\)[26]. \(E_{i}^{t0}\) denotes the local transmission energy cost per bit for transferring the aggregated data to the remaining active sensors, \(\gamma\) is the percentage of remaining data after aggregation and it reflects the correlation between data amongst different sensors. \(r\) is the rate of LDPC encoding. Since the use of a rate \(r=1/2\) makes the size of the data after encoding, \(2\) times the original data size, the \(\frac{L_{i}}{r}\) term is used to represent the data size after encoding a message size of \(L_{i}\) with rate \(r\). The same energy per bit \(E_{i}^{t0}\) is needed to transmit a command signal from the cluster head to the selected sensors. After receiving all the bits, the nodes encode the transmission sequence according to some diversity scheme, such as the STBC. \(E_{M}^{l}\) denotes the energy cost per bit for the long haul MIMO transmission [4]. \(\sum_{i=1}^{N_{t}}\frac{L_{i}}{r}\gamma_{i}\) is divided by the optimal bit size of the long haul transmission \(b_{mimo}\) to find the number of symbols present in the received signal. The number of symbols is then multiplied by the optimal bit size of the local transmission \(b_{l_{r}}\) to find the total bit length. \(E_{j}^{t}\) is the energy per bit required to transmit the data from a sensor to the cluster head at the receiver side. \(N_{r}\) is the number of sensors at the receiving cluster. For the SISO approach, sensors transmit their data to the cluster head and as there is no burden for channel estimation, the cluster head will transmit all the aggregated data directly to the destination node without any cooperation. So the total energy consumption becomes  
\[E_{SISO}=\sum_{i=1}^{N_{t}-1}L_{i}E_{i}^{t}+E_{da}\sum_{i=1}^{N_{t}}L_{i}+E_{ enc}\sum_{i=1}^{N_{t}}\frac{L_{i}}{r}\gamma_{i}+E_{S}^{l}\sum_{i=1}^{N_{t}} \frac{L_{i}}{r}\gamma_{i}, \tag{14}\]  
where \(E_{S}^{l}\) denotes the SISO long haul transmission and can be calculated as a special case of MIMO transmission with \(N_{t}=1\) and \(N_{r}=1\). In both SISO and C-MIMO case, fixed constellation size is used. Since the encoding energy using Richardson scheme is same for both C-MIMO and SISO approach, it is not considered in the equation for C-MIMO and SISO.
###### Abstract  
Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for early and accurate detection methods. Traditional mammography, although widely used, has limitations, including radiation exposure and challenges in detecting early-stage lesions. Electrical Impedance Mammography (EIM) has emerged as a non-invasive and radiation-free alternative that assesses the density and electrical conductivity of breast tissue. EIM images consist of seven layers, each representing different tissue depths, offering a detailed representation of the breast structure. However, analyzing these layers individually can be redundant and complex, making it difficult to identify relevant features for lesion classification. To address this issue, advanced computational techniques are employed for image integration, such as the Root Mean Square (\(\mathcal{C}_{\text{RMS}}\)) Contrast and Contrast-Limited Adaptive Histogram Equalization (CLAHE), combined with the Coefficient of Variation (CV), CLAHE-based fusion, weighted average fusion, Gaussian pyramid fusion, and Wavelet-PCA fusion. Each method enhances the representation of tissue features, optimizing the image quality and diagnostic utility. This study evaluated the impact of these integration techniques on EIM image analysis, aiming to improve the accuracy and reliability of computational diagnostic models for breast cancer detection. According to the obtained results, the best performance was achieved using Wavelet-PCA fusion in combination with XGBoost as a classifier, yielding an accuracy rate of 89.5% and an F1-score of 81.5%. These results are highly encouraging for the further investigation of this topic.  
breast cancer; electrical impedance mammography; automatic classification Article
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 1 Introduction
The design of the frame for each of the above four situations will be described and evaluated in detail and the type of the signal and modulation to be transmitted will be discussed. This paper is organized as follows. Section 2 describes all the measurements. The communications hardware of the modem is specified in Section 3. Section 4 presents the channel study, synchronization and modulation tests performed to design the physical layer. Section 5 details the physical layer proposal for the four cases concluded from the previous tests. Finally, Section 6 explains the conclusions of our research group in the moment of closing the design of this project.
## 5 Models Based on Canonical Piece Wise Linearization
### Estimation Algorithm  
Estimation is initiated by the collection of coefficients \(c_{klq}\), \(d_{kqr}\) and \(e_{kqr}\), into a \(\text{R}\times 1\) vector \(v\). here, the total number of coefficients is represented by \(\text{R}\) and \(v\) is related with a signal whose time is sampled over the same period. Coefficients \(c_{21}\) are associated to the signal \(x(n-1)\abs{x(n-1)}^{2}\). \(Z\) a \(\text{N}\times\text{R}\) matrix represents the collection of all such vectors. The predistorter training block output upon convergence will be \(z_{p}(n)=x(n)\) hence, \(z(n)=u(n)\).  
For N number of samples, output is:  
\[z_{p}=Zv \tag{18}\]  
where \(Z=[Z(1),\ldots,\ Z(N)]^{T}\) and \(z_{p}=[z_{p}(1),\ldots,z_{p}(N)]^{T}\) and \(v\) is as mentioned earlier, a \(\text{R}\times 1\) vector containing set of coefficients \(c_{klq}\), \(d_{kqr}\) and \(e_{kqr}\).  
The LS solution is the solution for the equation represented by:  
\[[Z^{H}Z]\vartheta=Z^{H}x \tag{19}\]  
Solution minimizing the cost function is represented by:  
\[C=\sum_{n=1}^{N}\abs{z_{p}(n)-x(n)}^{2} \tag{20}\]  
Figure 10: Block diagram representing ILA architecture for Multi Band DPD utilizing Generalized Memory Polynomial (GMP), Canonical Piecewise Linearization (CPWL) and Magnitude Selective Affine (MSA) methodology.
# Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
## 3 Results
### Comparison with Other Segmentation Methods  
As no previous attempts to the segmentation of acral melanoma have been described in literature, we would like to compare our method with other well-known segmentation algorithms and summarize how our work differs from them. We compared the proposed RAG-based segmentation approach with three popular low-level image segmentation methods including region growing (RG), Otsu's method (Otsu), and active contour model (ACM). The RG algorithm is a seeded region-growing algorithm which is designed to segment healthy skin around skin lesion in dermoscopy image. For the segmentation of skin lesion, we select one seed which is located in the left corner on the healthy skin. It gives us the certainty that we will separate the homogeneous background from the skin mole [7; 40]. We use the Chan-Vese (CV) active contour algorithm with the following parameters: mask function--large with a general CV method [41]. For the RAG algorithm, two parameters have to be adjusted. Firstly, the amount of superpixels has been set to 400. The more regions, the longer the merging process lasts. The second parameter is the threshold value for the merging algorithm. Setting a very low threshold will not merge any regions and will give us back the original image. A very large threshold on the other hand would merge all regions and return the image as just one big blob.  
Figure 8: Results obtained with a RAG-based segmentation approach: (**a**) image pre-segmented with the SLIC algorithm; (**b**) visualization of the RAG for oversegmented regions; (**c**) hierarchical merging outcome; (**d**) final contour of the RAG-based segmentation method.  
While the difference between the health skin and the skin lesion is still significant, the parameter could be adjusted during a few experiments. As it is difficult to obtain good segmentation outcome, and the definition of 'good' often depends on the application, these methods are usually used for obtaining the first impression of the arose problem before building a more sophisticated detection system. The results presented in Table 1 indicate that the proposed approach is very efficient in segmenting the acral melanomas. Furthermore, we observed that the implemented algorithm has been more robust to varying lighting conditions and permanent changes to the foreground (an acral lesion) than the remaining algorithms. It can be established that, for most of the image cases, the RAG-based segmentation procedure offered better results than the RG, Otsu, and ACM. For the evaluation of the advantages and correctness of the implemented algorithm, we present a few outcomes after applying the described solution and other segmentation methods (Figure 9).
## 3 Results and Discussions
### Effect of Vacuum Impregnation on Physicochemical Parameters
[9] reported that vacuum impregnation of calcium lactate did not alter the water activity values (0.991-0.996) of apple slices. Conversely, [PERSON] et al. [43] reported that apple slices exhibited a slight decrease in water activity (from 0.980 to 0.978) after vacuum impregnation of fructose syrup at different concentrations. Similar trends were observed by [PERSON] et al. [19] in apple slices vacuum-impregnated with grape juice (decrease from 0.984 to 0.975); furthermore, these effects are dependent on experimental conditions. Additionally, [PERSON] et al. [12] reported an increase in water content in apple tissues (85.91 to 91%) vacuum impregnated with hibiscus extract and noted that the gained water is associated with the vacuum process that transferred water from the outside to the inside of the apple tissues; however, this effect is influenced by the vacuum pressure and processing time. Similar trends were observed in apple slices vacuum impregnated with calcium lactate and quercetin derivatives, which exhibited an increase in moisture content of up to 30% [9; 40]. Table 6 shows the effect of vacuum impregnation of the HS extract on the color of apple slices. In general, all vacuum impregnated samples exhibited significant differences (\(p<0.05\)) between the treated and untreated samples for all color parameters (luminosity, a\({}^{\star}\), and b\({}^{\star}\)). After VI apple slices showed luminosity values ranged from 34.44 to 48.25, a significant reduction compared to the untreated apple slices (72.22). Regarding the a\({}^{\star}\) (14.17-17.28) and b\({}^{\star}\) (15.60-28.38) parameters, the VI samples exhibited changes compared to the control (a\({}^{\star}=-3.41\), b\({}^{\star}=21.30\)). In this context, vacuum impregnated apple slices showed appreciable color changes (TCD of 30.59 to 42.34) compared to the untreated sample. These results are in agree with those reported in vacuum-impregnated papaya cubes with HS extracts, which describe a color change from orange to red [4]. Similar trends have been reported in cranberries vacuum-impregnated with sucrose, ascorbic acid, and citric acid; moreover, the highest TCD values were observed in treatments with a higher degree of impregnation [17]. Additionally, it has been reported that color parameters (TCD of 44.20 to 58.80) of apple tissues impregnated with grape juice are significantly affected by vacuum impregnation conditions, particularly vacuum pressure [19].
# Experimental Study on Vibration Control of
###### Abstract  
Suspended piping systems often suffer from severe damages when subjected to seismic excitation. Due to the high flexibility of the piping systems, reducing their displacement is important for the prevention of damage during times of disaster. A solution to protecting piping systems during heavy excitation is the use of the emerging bounding tuned mass damper (PTMD) technology. In particular, the single-sided PTMD combines the advantages of the tuned mass damper (TMD) and the impact damper, including the benefits of a simple design and rapid, efficient energy dissipation. In this paper, two single-sided PTMDs (spring steel-type PTMD and simple pendulum-type PTMD) were designed and fabricated. The dampers were tested and compared with the traditional TMD for mitigating free vibration and forced vibration. In the free vibration experiment, both PTMDs suppressed vibrations much faster than the TMD. For the forced vibration test, the frequency response of the piping system was obtained for three conditions: without control, with TMD control, and with PTMD control. These novel results demonstrate that the single-sided PTMD is a cost-effective method for efficiently and passively mitigating the vibration of suspended piping systems. Thus, the single-sided PTMD will be an important tool for increasing the resilience of structures as well as for improving the safety of their occupants.  
vibration control; bounding tuned mass damper; impact damper; PTMD; suspended piping system +
Footnote †: journal: Applied Sciences
## 7 Conclusions and Future Work  
We consider the problem that existing LLM-based knowledge graph reasoning methods extract the in-context triples only through fixable metrics, such as triples surrounding the query entities, or triples contain the query relations. These methods cannot make full use of the triples of knowledge graphs, resulting in difficulties in ensuring the effectiveness of knowledge graph reasoning. To alleviate this problem, in this paper, a rule-and-reinforce in-context triple extraction method is proposed to enhance the in-context learning of LLMs for KG reasoning. Specifically, the encoder-decoder structure-based extractor is trained by the triples which support the reasoning of the logical rules. Furthermore, the LLM's feedback is also collected and is formulated as rewards to optimize the in-context triple extractor. Finally, given a query triple, the extractor can generate the necessary in-context triples to enhance the reasoning of the LLMs. The proposed method and its experimental results demonstrate that both logical rules and the feedback of LLMs are effective for in-context triples extraction. Furthermore, the extracted in-context triples obtained by the proposed method can improve the effectiveness of knowledge graph reasoning.  
In this study, we mainly consider the logical rules which can be represented by multiple hops on KGs. However, in practical applications, there exist many more complex types of KG reasoning. For example, the reasoning query \"who is the person who has both starred in the movie 'New Police Story' and directed the movie 'On the Run'?\" introduces a logical conjunction while reasoning. So in the future, we will expand the proposed method to cover more types of complex reasoning.  
**Funding:** This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \# & **Method** & **Ex. 1** & **Ex. 2** & **Ex. 3** & **Ex. 4** & **Ex. 5** & **Average** \\ \hline
1 & directly connected in-context triples & 2.50 & 3.44 & 3.86 & 2.46 & 1.84 & 2.82 \\
2 & in-context triples guided by logical rules & 4.64 & 4.78 & 5.56 & 5.34 & 4.64 & 4.99 \\
3 & in-context triples by logical rules and LLMs feedback & 5.82 & 5.66 & 6.22 & 6.78 & 7.02 & 6.30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Scores of manual evaluations for in-context triples by different methods.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available in [16; 38; 41; 42; 43].  
**Conflicts of Interest:** The authors declare no conflicts of interest.