esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Evaluation Indicators  
Target detection algorithms can be evaluated by a set of generalized evaluation metrics to assess how good the model is. The evaluation metrics used in this paper include precision (P), recall (R), mean average precision mean (mAP50, mAP50:95), number of parameters (Params), and number of billion floating point operations per second (GFLOPs). The specific meaning of each indicator and how it is calculated are set out below.  
(1) Precision (P):  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{4}\]  
where TP refers to the number of target objects successfully recognized by the algorithm, and FP refers to the number of cases in which the algorithm incorrectly recognizes non-target objects as target objects. Precision indicates the proportion of correct predictions among all prediction frames detected as targets and reflects the reliability of the model's prediction results, with a high accuracy indicating fewer false positives for the model.  
(2) Recall (R):  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{5}\]  
where FN refers to the number of instances where the algorithm incorrectly recognizes a target object as a non-target object. Recall indicates the proportion of all real targets that are correctly detected and is used to measure the model's ability to detect real targets, with a high recall indicating that the model misses fewer detections.  
(3) Mean Average Precision Mean (mAP):  
\[\text{AP}=\int_{0}^{1}\text{P}(\text{r})\text{d}_{\text{r}}, \tag{6}\]  
\[\text{mAP}=\frac{1}{\text{n}}{\sum_{\text{i}=1}^{n}\text{AP}_{\text{i}}} \tag{7}\]  
where AP denotes the average precision (P) of the model at different levels of recall (R). mAP denotes the average AP of the model at different IOU thresholds. Since different IOU thresholds reflect different detection accuracy requirements, mAP can comprehensively evaluate the performance of the model at different accuracies.
# Two-Dimensional Mesoscale Finite Element Modeling of Concrete Damage and Failure
## 1 Introduction
Furthermore, this work develops a holistic method for analyzing the effects of morphological mesostructural properties on the structural response of concrete materials using cohesive zone modeling (CZM)-based finite element (FE) models. In this paper, a two-dimensional (2D) simulation of crack initiation and propagation is performed using the continuum FEM approach with the implementation of cohesive interface elements (CIEs). Additionally, the presence of voids in the concrete microstructure significantly influences its mechanical behavior, contributing to increased cracking, reduced durability, and diminished strength and stiffness. Thus, two essential tools are developed: the generation of a 2D concrete mesostructure and the insertion of CIEs within the interface of solid elements after discretizing the concrete mesostructure generated in the first step. Firstly, an in-house developed Python code CMG (concrete microstructure generator) generates the mesostructure of concrete with different aggregate shapes and circular voids. CMG is capable of generating randomly distributed and varying size circular, elliptical, and polyhedral aggregates and circular voids within the mortar matrix. Second, CIEs are inserted at the interfaces of the continuum elements constituting the mortar matrix, the aggregates, and the ITZ zone. This task is performed by a self-developed Python code CIEsIn (cohesive interface element insertion). It should be mentioned that the interface element insertion is performed before the finite element simulations. Finally, the performance of the developed code is shown using numerical examples. The outline of this paper is as follows: Section 2 comprehensively describes the procedure for generating concrete mesostructures with different aggregate shapes. Section 3 details the algorithms for the automatic generation and insertion of CIEs into an initial FE mesh. Section 4 explains the details of the numerical model, such as material properties, element types, and boundary conditions. In Section 5, the morphological parameter studies are performed, and the main results are given in the Conclusions section.
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 2 Problem Formulation
### Nonlinear Feedforward ANC System  
This paper expresses the nonlinear process using the well-known Volterra structure [11], where the Volterra series representation of \(y(n)\) can be given by  
\[y(n)=\sum_{p=0}^{\infty}y_{p}(n), \tag{3}\]  
where  
\[y_{p}(n)=\sum_{0\leq l_{1},\cdots,l_{p}\leq L}h_{p}(l_{1},\cdots,l_{p})\times u (n-l_{1})\times\cdots\times u(n-l_{p}), \tag{4}\]  
\(h_{p}(l_{1},\cdots,l_{p})\) is the \(p\)-th-order Volterra kernel, and \(L+1\) is the length of the memory. Equation (4) can be further expressed in the vector form as below:  
\[y_{p}(n)=\mathbf{U}_{p}^{T}(n)\mathbf{H}_{p}(n), \tag{5}\]  
where \(T\) denotes transportation operation, and \(\mathbf{U}_{p}(n)\) is the \(p\)-th-order reference vector with the length \((L+1)^{p}\), which is:  
\[\mathbf{U}_{p}(n)=[u(n)\times\cdots\times u(n),\ \cdots,u(n-l_{1})\times\cdots \times u(n-l_{p}),\ \cdots,\ u(n-L)\times\cdots\times u(n-L)]^{T}, \tag{6}\]  
Figure 1: Block diagram of linear feedforward ANC system.  
and similarly, \(\mathbf{H}_{p}(n)\) is the \(p\)-th-order Volterra kernel vector with length \((L+1)^{p}\) given by  
\[\mathbf{H}_{p}(n)=[h_{p}(0,\cdots,0),\ \cdots,h_{p}(l_{1},\cdots,l_{p}),\ \cdots,\ h_{p}(L,\cdots,L)]^{T}. \tag{7}\]  
This Volterra expression can be regarded as a Taylor expansion of the generally nonlinear function. Generally, only the first \(P\) order Volterra kernels are used to model the system's nonlinearity, although the truncated error may be introduced inevitably.
## 2 Materials and Methods
### Pharmacological Activity and Biocompatibility  
#### 2.6.1 Antioxidant Activity on 2,2-Diphenyl-1-Picrylhydrazyl (DPPH)  
The antioxidant scavenging activity of the VitE loaded scaffolds was measured by a DPPH test [28]. VitE 0.1 and VitE 1 scaffolds were produced as described for the previous experiments. A test solution composed of 8.5 mL of acetonitrile, 1 mL of a stock solution of 1 mM DPPH in ethanol (final concentration 100 mM) and 0.5 mL of deionized water was prepared before the experiment. The test specimens (regular scaffolds) were placed in amber jars at the different concentrations (n = 3 per concentration) together with 10 mL of test solution and were stirred in the dark on an orbital shaker for 60 min at room temperature. A control sample only consisting of the test solution was measured as a control. At predetermined time intervals (each 20 min), 1 mL of each sample was collected and immediately analyzed. The absorbance of the collected samples was systematically read through a Perkin Elmer-Lambda 25 spectrophotometer measuring the absorbance at 517 nm. A solution of ethanol/UP water (95:5 \(\
icefrac{\
u}{\
u}\)) was used as blank sample during the experiment. The scavenging effect at each time point was calculated to a percentage according to following equation:  
\[\text{DPPH scavenging effect}\left(\%\right)=\frac{A_{0}-A}{A_{0}}\times 100 \tag{7}\]  
where \(A_{0}\) is the absorbance of the control sample, and \(A\) is the absorbance in the presence of the sample at any time. De-coloration and the subsequent decrease of absorbance at 517 nm indicated a proportional antioxidant charge increase of the teste samples.
# A High Capacity Watermarking Technique for the Printed Document
## 4 Experimental Results and Discussion
### Capacity and Robustness  
According to the algorithm proposed in this paper, it is possible to embed \(n\) bits (\(n=1,2,3\cdots\)) watermark information for a single character by adjusting the quantization function. Figure 5 shows the image before and after embedding 80-bit watermark information in a Chinese-English mixed document. Figure 6 shows an enlarged view of the characters after embedding the watermark information. In the figure, some punctuation and small characters are processed, leaving only the characters used for embedding the watermark. For English characters, a single letter symbol can be embedded with two bits of watermark information. For complex Chinese characters, a single character can be split into multiple parts for watermark embedding. For example, the Chinese character \"\(\sharp\)\" can be split into two character components, and the Chinese character \" \(\%\%\)\" can be split into three character components, thereby it further increases the watermark capacity.  
The increase of the watermark capacity will also lead to an increase in the bit error rate during the watermark extraction process. Since the QR code has strong error correction capability and has a large  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\delta\) = 0.05 & \(\delta\) = 0.1 & \(\delta\) = 0.2 \\ \hline PSNR & 31.17 & 27.73 & 25.05 \\ SIM & 97.24\% & 95.89\% & 94.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The peak signal to noise ratio (PSNR) and similarity (SIM) with different \(\delta\).  
Figure 5: Test in a Chinese–English mixed document.  
Figure 6: Character used for watermark embedding.  
information capacity, it is a good choice to embed the QR code as watermark information in the text picture. On the one hand, the watermark capacity is increased, on the other hand, the robustness of the watermark information is also improved. In addition, the algorithm proposed in this paper also performs denoising processing before watermark extraction, thus reducing the impact of small noise. Figure 7 shows the comparison between the embedded QR code and the final extracted QR code in the text of Figure 1. It can be seen that although Figure 7b has a bit error, it can still scan the information carried in the QR code. In Section 4.3 we compare the robustness of our algorithm with the existing algorithms from the perspective of the bit error rate.
## 2 Related Work
AMRBC offers an adaptive, range-based MAC protocol tailored for VANETs, prioritizing safety messages to reduce safety message delay. However, its applicability to DTNs is limited as it primarily focuses on VANET-specific challenges and lacks comparison with other state-of-the-art MAC protocols designed for DTNs. The work by [PERSON] et al. [21] introduces CBVRP, a cluster-based VANET routing protocol tailored for non-populated areas like deserts. CBVRP achieves high communication efficiency and reliable information delivery by implementing a cluster structure and cluster head election suitable for desert environments. However, its effectiveness may be limited to specific network conditions with sparse node distribution. Its performance in more dynamic or dense DTN environments might require further investigation. [PERSON] et al. [22] introduced fuzzy logic into the Ad Hoc On-Demand Distance Vector Routing AODV routing protocol for VANETs to enhance route selection. This integration enables more flexible decision-making by considering factors like vehicle speed, distance, and traffic conditions. Fuzzy logic facilitates stable route selection, enhancing network efficiency. However, implementing the fuzzy system may demand additional computational resources, potentially increasing overhead; whereas this approach focuses on VANETs, it highlights the potential benefits of incorporating fuzzy logic for dynamic routing decisions in DTNs. However, the trade-off between routing efficiency and overhead requires careful consideration in resource-constrained DTN environments. This section reviewed various cluster-based routing protocols proposed for DTNs or related network types, and these studies offer valuable insights and techniques. However, limitations exist, including scalability concerns, increased overhead due to complex clustering algorithms, and challenges in achieving high delivery ratios and low end-to-end delays inherent to DTNs. Our proposed Adaptive Clustering-based Routing Protocol (ACRP) addresses these shortcomings by focusing on dynamic cluster formation, efficient cluster head selection, and a lightweight routing mechanism to improve network performance in OppNets.
## 4 Correlations between Spatial Indicators and Lighting Parameters  
The correlations have been obtained adopting the \(I\) index of the axial analysis (see also Sections 2.2 and 2.3). The choice of this spatial indicator has been made because it shows the best goodness-of-fit with respect to the observed traffic data, as demonstrated in Section 2.5. This spatial indicator has been correlated with the main lighting parameters: the average maintained luminance in the carriageways (\(L_{uv}\)), the average maintained illuminance (\(E_{h,uv}\)) and the minimum maintained illuminance (\(E_{h,min}\)) on the sidewalks. These lighting parameters have been measured and calculated for the 12 analysed roads (see Section 3). The correlations between \(I\) index and lighting parameters are found by means a linear regression analysis performed using the least squares method. The used lighting parameters are: those measured at the present status (in this Section named Scenario 1), those calculated at the design status (in this Section named Scenario 2) and those given by the European technical standards as minimum lighting requirements values (ideal case, in this Section named Scenario 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Road ID**} & \multirow{2}{*}{**Carriageway**} & \multicolumn{2}{c}{**Sidewalk**} & \multicolumn{2}{c}{**Sidewalk**} \\  & & **(on Luminaire Side)** & **(on the Opposite Luminaire Side)** \\ \cline{2-6}  & \(L_{uv}\) [cd/m\({}^{2}\)]** & \(E_{h,uv}\) [lx] & \(E_{h,min}\) [lx] & \(E_{h,uv}\) [lx] & \(E_{h,min}\) [lx] \\ \hline
1 & 1.03 & 21.4 & 13.2 & – & – \\
2 & 1.24 & 17.5 & 10.7 & 13.2 & 5.75 \\
3 & 1.08 & 15.7 & 3.93 & – & – \\
4 & 1.06 & 12.4 & 9.02 & 9.34 & 8.76 \\
5 & 1.07 & 13.9 & 7.11 & 12.4 & 10.2 \\
6 & 1.02 & 14.0 & 9.49 & 8.66 & 6.93 \\
7 & 1.02 & 11.8 & 5.07 & 9.51 & 8.50 \\
8 & 1.00 & 12.3 & 11.5 & 9.56 & 10.5 \\
9 & 0.77 & 11.2 & 5.16 & 3.69 & 3.05 \\
10 & 1.00 & 9.82 & 4.45 & 9.82 & 4.45 \\
11 & 0.60 & 15.4 & 4.66 & 4.49 & 2.90 \\
12 & 0.86 & 8.62 & 3.05 & 8.62 & 3.05 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of the software simulation at the design status for the considered lighting parameters. The luminance and illuminance values are expressed with three digits.  
Figure 11: False-colour renderings of the horizontal illuminance mapping for some analysed roads at the design status (one for each group of Table 1).
## 3 Results and Discussion
Consequently, the moment at the edges (M\({}_{\text{edge}}\)) of the single-lap adhesive joint can be expressed in terms of the maximum moment (M\({}_{\text{max}}\)), the distance between the applied load (F) location and the outer support (S), and the overlap length (OL), as follows [38]:  
\[\text{M}_{\text{edge}}=\text{M}_{\text{max}}(1-\frac{\text{OL}}{2\text{S}}) \quad\text{where}\quad\text{M}_{\text{max}}=\frac{\text{F}\times\text{S}}{2} \tag{1}\]  
The bending moments at the edges of the overlap are provided in Table 4 by inserting the experimental failure load into Equation (1). This reveals that the bending moments at the edges of the overlap are directly proportional to the peak loads sustained by the joints (Figure 11). Consequently, the CFRP/CFRP single-lap adhesive joints experience the highest bending moments before failure, while the aluminum/aluminum single-lap adhesive joints exhibit the lowest bending strength at the edge points. In simpler terms, the bending moment at the edge of the overlap is dictated by the load carried by the joint as well as the types of adherends, which encompasses various adherend combinations, such as CFRP/CFRP, aluminum/aluminum, and two scenarios involving CFRP/aluminum and aluminum/CFRP. Hence, it can be concluded that a larger bending moment at the edges of the overlap results in a greater force at the midpoint of the overlap length. It should be noted that some other factors, such as environmental conditions and loading rate, can be investigated as a subject of future research.
# Multiplexing Perfect Optical Vortex for Holographic Data Storage
## 1 Introduction
The use of POV beams in data storage enables a higher storage density by designing more storage cells within the same storage area. This advancement contributes to the improvement in data capacity and efficiency, making it a valuable solution for modern data storage requirements. Secondly, the storage error is decreased. The utilization of POV beams in data storage results in a decrease in storage errors and crosstalk among POVs within a data page. This contributes to improved data integrity, accuracy, and reliability in holographic data storage systems.
# Spectrum Sharing Opportunities for 6G Terrestrial and Non-Terrestrial Networks +
## 3 Coexistence Analysis Results  
In this section, the results of the coexistence conditions between the FSS system and the FS WLL P-MP system are shown, considering the impact of the FSS Earth station transmission on both the receiving TS and the receiving CS. Since, in a P-MP link, the position of the TS is not always known, the coexistence analysis has been performed assuming a TS receiver located on all of the pixels of the WLL P-PM link coverage area. The calculation was performed considering only one TS receiver. The same evaluation has been repeated on all of the TS receivers present in the P-MP link coverage area.  
The following results of the coexistence analyses are presented:  
1. Regarding separation distances between FS P-MP CS and FSS Earth station as a function of FSS Earth station elevation angle for different shielding effects, the calculations take into account that for antennas located on the roof of a building, shielding up to 30 dB can be provided when a roof parquet or safety wall at the building borders exists or when the antenna is mounted on the side of a roof or building.
2. The separation distances between the FS P-MP CS/TS and the FSS Earth station for different azimuth offsets of the FS system have been evaluated.
3. An evaluation of the exclusion zone around the FS WLL CS and the receiver around the FS WLL TS was performed exclusion zone is the geographical area within FSS licensees is not allowed to have FSS transmitters.
## 3 Theoretical Background  
### Preprocessing  
In signal processing, preprocessing plays an important role. Once experimental results are obtained, they are modeled to extract useful information. Generally, the data output is either too large, too small, or fragmented. As a part of preprocessing, the data are classified and processed accordingly [48]. In this work, we use the following normalization procedure to translate the signal into the range of \([-1,\ 1]\)[49]:  
\[S_{norm}^{i}=2\times\left(\frac{S_{org}^{i}-S_{min}}{S_{max}-S_{min}}\right)-1 \tag{1}\]  
Figure 2: Flow chart for greedy algorithms.  
where \(S^{i}_{org}\), \(S^{i}_{norm}\) are the \(i^{th}\) samples of the original and normalized (scaled) signal, respectively, and \(S_{max}\) and \(S_{min}\) are the maximum and minimum values achieved by the signal \(S_{org}\), respectively.
# Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients
## 4 Evaluation of the Proposed MC-FCM Algorithm
The DB, ASWC and PBM indices show the compact and separated level of the clusters, while the MA and RI indices measure the quality of the clusters for the labeled datasets, to see the compatibility between the clusters and the labeled groups. When performing the evaluation in this study, we focused on the MA and DB indices. Since the clustering algorithm is affected by the initial step, each run with the parameters set was conducted several times. We considered these scenarios:  
1. Perform FCM with \(m\) = 2 several times and record the run with the best MA index result;
2. Perform FCM with changing \(m\) and record the run with the best MA index result;
3. Perform MC-FCM with changing \(m_{L}\), \(m_{U}\) and \(\alpha\) and record the run with the best MA index result;
4. Perform MC-FCM with the same \(m_{L}\) and \(m_{U}\) as in (iii), adjust \(\alpha\) and record the run with the best DB index result;
5. Perform FCM7I several times with the same \(m_{L}\) and \(m_{U}\) as in (iii) and record the run with the best MA index result. The comparison results of various clustering algorithms on the five UCI-benchmark datasets are shown below. The results of the 5 indices for each algorithm implemented in 5 different datasets are shown in Tables 2-6, with the best index results bolded in the tables. The results show that the proposed MC-FCM algorithm gives better results for most of the indices in all five datasets. The MA index results using MC-FCM are consistently the best when compared to FCM and FCM7I. For the DB index, MC-FCM gives the significantly better results in four out of five datasets. Other indices are similar for all cases. Regarding the number of iterations in the clustering process using MC-FCM, after averaging multiple runs, the WINE and IRIS datasets have a fewer number of iterations than FCM and a similar number of iterations compared to FCM7I2I. The HEART dataset has similar numbers of iterations \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Samples** & **Attributes** & **Classes** & **Description** \\ \hline ECOLI & 336 & 7 & 8 & \begin{tabular}{c} This dataset consists of 7 characteristics of 8 \(E\). _coli_ \\ bacteria types used to identify them. \\ \end{tabular} \\ HEART & 303 & 13 & 2 & \begin{tabular}{c} This dataset consists of 13 symptoms used to \\ determine if one has heart disease. \\ \end{tabular} \\ WDBC & 569 & 32 & 2 & \begin{tabular}{c} This dataset consists of 32 metrics obtained from \\ X-ray images of breast cancer tumors used to \\ determine if one has breast cancer. \\ \end{tabular} \\ IRIS & 150 & 4 & 3 & \
\begin{tabular}{c} This dataset consists of 4 characteristics of 3 types of \\ irises used to identify them. \\ \end{tabular} \\ WINE & 178 & 13 & 3 &
\begin{tabular}{c} This dataset consists of 13 chemical constituents in 3 types of Italian wine used to identify them. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarized descriptions of the experimental datasets.