esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 4 Numerical Simulations
### Performance with MMSE Equalizer  
In order to evaluate the performance of UP-OCDM over doubly selective channels, we compare the performance of the UP-OCDM with OCDM and OFDM methods with the MMSE equalizer proposed in Section 3.1. The BER performance of UP-OCDM, OCDM, and OFDM systems with MMSE block equalizer is proposed over channels with the normalized Doppler frequency selected as 0.1 and 0.3, respectively. Note that these values represent high Doppler spread conditions. For example, for an OFDM system whose carrier frequency is \(f_{c}=10\) GHz and the subcarrier spacing is \(\Delta f=20\) kHz, the mobile speed would be \(v=216\) km/h and \(v=648\) km/h, respectively. We compare the performance in M-ary QAM with M from 4 to 64.  
The simulation results are shown in Figure 1(a),b. The red curves represent the performance of UP-OCDM, while the blue curves and green curves represent the performance of OCDM and OFDM, respectively. We observe that UP-OCDM and OCDM with MMSE equalizer have similar performance under doubly selective channels. In the meantime, both of them outperform OFDM due to the contributions of diversity gain via using chirped subcarriers. However, it should be noted that, with the modulation level increasing from 4 to 64, the performance of UP-OCDM and OCDM will suffer severer degradation than OFDM. For instance, for the 4-QAM case, the curves of UP-OCDM and OCDM systems intersect the curve of OFDM at about SNR = 5 dB, which means UP-OCDM and OCDM will outperform OFDM when SNR is more than 5 dB, whereas, for the 16-QAM, the intersection is at SNR = 10 dB. This is because high-level modulation formats in UP-OCDM and OCDM are more sensitive to noise. Nonetheless, the BER performance still gets better than OFDM for SNR > 22 dB in the worst scenario, that is, 64-QAM mapping and the normalized Doppler 0.3.  
Therefore, with reduced storage requirements, the proposed UP-OCDM shows similar performance as OCDM over doubly selective channels. Meanwhile, due to the exploitation of diversity gain, the performance of both UP-OCDM and OCDM is shown to outperform OFDM under high Doppler spread channels. It should be noted that the performance of OCDM and UP-OCDM might be comparable to the OFDM-FFT spreading system proposed in Reference [26], which is an OFDM based system and also achieves performance improvement via obtaining diversity gain.
## 4 Experimental Results and Evaluation
### Experiment B
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Volume Utilization (\%)**} \\ \hline  & & **[PERSON] et al. [37]** & **[PERSON] et al. [38]** & \begin{tabular}{c} **[PERSON] and** \\ **[PERSON] [39]** \\ \end{tabular} & \
\begin{tabular}{c} **[PERSON] and** \\ **[PERSON] [40]** \\ \end{tabular} &
\begin{tabular}{c} **[PERSON] and** \\ **[PERSON] [41]** \\ \end{tabular} & \multicolumn{2}{c}{**This Work**} \\ \cline{2-13}
**Problem** & \multicolumn{2}{c}{\begin{tabular}{c} **Spatial** \\ **Representation** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Layering** \\ **Algorithm** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Development** \\ **Heuristics** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **GA** \\ **Heuristic** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **ABC** \\ **with Pareto** \\ \end{tabular} } \\ \cline{2-13}  & **BL** & **VU** & **BL** & **VU** & **BL** & **VU** & **BL** & **VU** & **BL** & **VU** \\ \hline LN01 & 0 & 62.5 & 0 & 62.5 & 0 & 62.5 & 0 & 62.5 & 0 & 62.5 & 0 & 62.5 \\ LN02 & 54 & 80.7 & 23 & 89.7 & 35 & 90 & 39 & 89.5 & 28 & 96.6 & 35 & 90 \\ LN03 & 0 & 53.4 & 0 & 53.4 & 0 & 53.4 & 0 & 53.4 & 0 & 53.4 \\ LN04 & 0 & 55 & 0 & 55 & 0 & 55 & 0 & 55 & 0 & 55 & 0 & 55 \\ LN05 & 0 & 77.2 & 0 & 77.2 & 0 & 77.2 & 0 & 77.2 & 0 & 77.2 & 0 & 77.2 \\ LN06 & 48 & 88.7 & 24 & 89.5 & 77 & 83.1 & 32 & 91.1 & 49 & 91.2 & 20 & 92.2 \\ LN07 & 10 & 81.8 & 1 & 83.9 & 18 & 78.7 & 7 & 83.3 & 0 & 84.7 & 0 & 84.7 \\ LN08 & 0 & 59.4 & 0 & 59.4 & 0 & 59.4 & 0 & 59.4 & 0 & 59.4 & 0 & 59.4 \\ LN09 & 0 & 61.9 & 0 & 61.9 & 0 & 61.9 & 0 & 61.9 & 0 & 61.9 & 0 & 61.9 \\ LN10 & 0 & 67.3 & 0 & 67.3 & 0 & 67.3 & 0 & 67.3 & 0 & 67.3 & 0 & 67.3 \\ LN11 & 0 & 62.2 & 0 & 62.2 & 0 & 62.2 & 0 & 62.2 & 0 & 62.2 & 0 & 62.2 \\ LN12 & 0 & 78.5 & 3 & 78.5 & 0 & 78.5 & 0 & 78.5 & 0 & 78.5 & 0 & 78.5 \\ LN13 & 2 & 84.1 & 5 & 82.3 & 20 & 78.1 & 0 & 85.6 & 4 & 84.3 & 0 & 85.6 \\ LN14 & 0 & 62.8 & 0 & 62.8 & 0 & 62.8 & 0 & 62.8 & 0 & 62.8 & 0 & 62.8 \\ LN15 & 0 & 59.5 & 0 & 59.5 & 0 & 59.5 & 0 & 59.5 & 0 & 59.5 & 0 & 59.5 \\ \hline Average & 7.6 & 69.0 & 3.7 & 69.7 & 10.0 & 68.6 & 5.2 & 69.9 & 5.4 & 70.4 & 3.7 & 70.1 \\ \hline \hline \end{tabular} BL = no. of boxes left, and VU = volume utilization.  
\end{table} 
Table 13: Comparative results of the Loh and Nee (LN) test cases with other approaches.
## 3 Experimental Results and Discussion
### Pushover Tests on Structural Frames
#### 3.2.1 Diagram of Horizontal Force versus Drift  
Figure 16 shows the diagram of horizontal load versus lateral drift (hysteresis loops).  
Some interesting findings can be observed from Figure 16. First, it is highlighted that a linear-elastic behavior is observed until the critical drift \(\varphi_{y}\). This result agrees with the American standard ACI374.2R-13 [34]. Once the critical drift is reached, a progressive plasti  
Figure 16: Diagram of load vs drift. Pushover test on structural frame.  
Figure 15: Diagram of bending moment vs. rotation. Pushover test on isolated structural wall.  
occurs. The area under the hysteresis loop progressively increases, which denotes that an energy dissipation process occurs.  
The maximum load is reached for a drift of 3%, i.e., six times the critical draft. Beyond this value, the load does not increase or decrease, but it remains almost constant. However, the area under the hysteresis loops significantly increases. The structural solution shows a huge capacity of energy dissipation without losing structural capacity.
## 3 Results and Discussion
### Numerical Simulation of the Acoustic Wave
The real discharge pulse has randomness, exhibiting different characteristics according to different applied voltage, which in turn affects the formation of acoustic pulse. In addition, environmental factors such as air temperature, humidity, and pressure can also influence the final result. The electrosound-combined simulation model proposed in this paper adopts some ideal assumptions, ignoring the effects of the magnetic field and heat conduction issue in the corona field, and considering that all plasma energy is converted into explosive energy in the explosion field. Therefore, if we want to better replicate the experimental results through simulation, we need to adjust the model parameters according to the real conditions.
## 5 Performance Analyses
### Bit Error Probability Comparison
Moreover, Figure 14 shows the curves of \(\mathbb{P}_{\rm e}\times P_{\rm T}\) (dBm) and \(\mathbb{P}_{\rm e}^{\rm LB}\) while considering the system operating under ACGN and following the subfigure structure of Figure 13. For these numerical simulations considering ACGN, all data communication schemes behave similarly to the simulations with AWGN as \(L_{\rm CP}\), \(\Delta\), and \(P_{\rm T}\) vary. However, all data communication schemes when operating over PLC channels corrupted by ACGN require greater total transmission power than when the PLC channel is corrupted by AWGN to yield the same \(\mathbb{P}_{\rm e}\). For instance, with \(L_{\rm CP}=18\) and \(\Delta=3\), to achieve \(\mathbb{P}_{\rm e}=10^{-6}\), the OCDM scheme requires \(P_{\rm T}\) equal to 27 and 30 dBm for AWGN and ACGN, respectively. Meanwhile, the HS-OFDM scheme requires 37 and 40 dBm for AWGN and ACGN. To summarize, the OCDM scheme displays the best results when there is no interference, requiring almost 10 dBm less of the total power transmission to reach \(\mathbb{P}_{\rm e}=10^{-6}\) than the HS-OFDM scheme and 3 dBm less than the OTFDM scheme. Moreover, the SCCP scheme and OCDM attain the same curve of \(\mathbb{P}_{\rm e}\times P_{\rm T}\) (dBm) without interference; however, the performance of the former degrades significantly with increasing interference. Re-lying on the numerical results, we can assert that the OCDM scheme is the best choice. Indeed, it shows better performance in terms of \(\mathbb{P}_{\rm e}\times P_{\rm T}\) (dBm), even in the presence of interference due to the CP length violation and the STO. However, if the computational complexity is one of the biggest concerns and the interference is absent, the SCCP scheme is better suited.
## 2 Model Construction for an SCCNMT
### Time Reliability  
After considering the multi-state travel time, the travel time for each arc will follow its own probability distribution, unlike in previous research where it was treated as a constant value. Therefore, it is possible to calculate the probability of arriving at a specific destination within a certain time for a given transportation path based on the multi-state travel times for each arc. Considering all time factors, we can obtain the feasible travel time vector \(V\) using the following Equations (7) and (8),  
\[v_{q}^{\min}\leq v_{q}\leq v_{q}^{\max}\text{ for }q=1,\;2,\;\ldots,\;z, \tag{7}\]  
\[\Big{\{}v_{b}+v_{c}\Big{|}a_{b},\;a_{c}\in P_{o}^{k}\Big{\}}\leq I^{*},\;\text {for }b=1,\;2,\;\ldots,\;s;\;c\;=\;s+1,\;s+2,\;\ldots,\;z. \tag{8}\]  
Through constraint (7) and (8), we can derive travel time upper bound vector \(S\) in \(V\). In order to avoid complex and inefficient calculations, similar to the last subsection, we also need to find a travel time upper bound as the same as MCVs. However, since time and capacity are different, we are seeking an upper bound solution, and thus, we need to use Table 2 to obtain the travel time upper bound vector. Each travel time upper bound vector represents a feasible configuration that can meet the current travel time. Therefore, if we obtain \(\rho\) travel time upper bound vectors, we can use Equation (9) to calculate time reliability \(R_{T}\), which represents that an SCCNMT can deliver within the time threshold; this can be developed as follows:  
\[R_{T}=\Pr\biggl{\{}\mathop{\cup}\limits_{h=1}^{\rho}\{S|S\leq S_{h}\}\biggr{\}}. \tag{9}\]
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 2 Materials and Methods
### SE-ResNet + FPN Network  
Using convolutional neural networks (CNNs) for semantic image attributes requires a strategic approach. CNNs, a vital tool in deep learning methodologies, follow a standardized structural composition for feature extraction. This architecture involves convolutional and pooling layers, resulting in a plain network. Examples of this genre include AlexNet and VGG. CNNs are known for their depth and intricate calculations, making them a powerful tool [77; 78]. A deep-network structure for the plain network can be used to obtain a high-level feature map of each image. After downsampling, the minor details shrink or even vanish. There needs to be more data available, though, about high-resolution feature maps without downsampling. It does not allow for the easy extraction and characterization of the intricate tiny characteristics needed for our dataset. Feature pyramid network's (FPN) concept of feature pyramids served as inspiration [70]. Backbone branch: higher layers of representation amplify input characteristics crucial for discriminating and reducing unimportant variations to extract better features for the classification task [79]. Hence, we adopted SE-ResNet as our backbone to secure ample depth for rich semantic extraction due to its shortcut-connected, gradient-efficient residual mapping. The FPN framework focuses on creating a comprehensive feature pyramid with high semantic content, using a multi-pronged approach with bottom-up and top-down pathways interlinked through lateral connections.
# Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts
## 5 Conclusions  
The main two problems we have to deal with in computer vision in an industrial context is the complexity of the objects, that is, their unusual shape and texture-less objects, and the lack of large datasets to train CNNs that can handle the previous problem. To manage this situation, we proposed in this paper an approach for selecting the best combination of descriptor that, together, provides a better classification. Even if more than one descriptors has to been calculated, this method is still fast enough for real-time applications.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Method** & **F1** \\ \hline Xception & 0.35 \\ Siamese & 0.89 \\ Our proposal (\(FSS1\times 1\)) & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between standard DL methods and our proposal.  
Figure 8: Times to classify an image with the different Subset selection methods on each level. The times on this Figure correspond to the average time that the classifier needs to classify an image using each descriptor on that level. The times of the \(BSS8\times 8\) are not shown since its values are around 200 ms and distorts the plot.  
The proposed method is a greedy approach that iteratively adds (Forward Subset Selection) or removes (Backward Subset Selection) descriptors to the solution until performance is not improved. The resulting descriptor set always improves the quality of the classification comparing to the best descriptor by its own. This selection techniques can be extended to different datasets and contexts as proved within this paper and previous ones [34; 35; 36].  
The used dataset for the experiments is composed by seven typical industrial texture-less objects. The proposed method achieves a state-of-the-art classification quality for that given dataset. Our method achieves a F1 of 0.971, 3% more than the best descriptor alone. Description and classification of a new image can be achieved in real-time applications, given its low processing time (between 10 and 50 ms).  
The next steps will include a larger set of descriptors and DL networks in order to mix both classical and Neural Network methods. As this particular application is within a bigger industry 4.0 set-up, the following works will include not only a visual approach, but the application as a whole.  
Conceptualization, I.M., J.A. A.R., and B.S.; methodology, B.S.; software, I.M.; validation, J.A., A.R., and B.S.; formal analysis, I.M.; investigation, I.M.; resources, I.M.; data curation, I.M.; writing--original draft preparation, I.M.; writing--review and editing, I.M., J.A., A.R., and B.S.; visualization, I.M.; supervision, B.S.; project administration, J.A.; funding acquisition, J.A. All authors have read and agreed to the published version of the manuscript.  
This paper has been supported by the project SHERLOCK under the European Union's Horizon 2020 Research & Innovation programme, grant agreement No. 820689.  
The authors declare no conflicts of interest.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 4 Proposed Method
### Softmax Optimization  
In addition to the inference part of FPGA, the result of segmentation also needs to be accelerated on CPU in the implementation of the semantic segmentation thread. Softmax operation, as the last layer of the SegNet network, is used to realize multi classification. It maps the output of multiple neurons to the (0\(\sim\)1) range. The sum of the mapped values is 1, which can be regarded as the probability value. In the calculation of softmax, the maximum probability is taken as the final prediction result, as shown in Figure 6. This operation only normalizes the result and does not change the numerical relationship, so the operation can be omitted. Softmax is relatively complex because of its exponential operation, which affects the real-time performance of the system. After removing softmax, the speed of semantic segmentation is improved by nearly 77 ms.
# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 1 Introduction  
Authorship attribution is the process of attributing a given text to its rightful author based on linguistic and stylistic characteristics. It involves extracting and analyzing distinctive patterns in writing styles, ranging from syntactic structures to lexical choices [1]. The significance of authorship attribution extends across various domains, namely, plagiarism or fake news detection [2], forensic linguistics [3], or literary studies [4]. Beyond cybersecurity or fake news detection, authorship attribution holds considerable value in the educational domain, particularly in digital libraries [5; 6; 7]. Moreover, identifying the authorship of academic works, research papers, and literary contributions can assist in organizing and categorizing content, as it can facilitate tracking academic contributions and ensuring proper acknowledgment of one's work.  
Within the domain of authorship attribution, [PERSON] et al. [8] identify three distinct tasks, namely, (1) authorship identification [9; 10], which aims to identify the author of a given work; (2) authorship profiling or characterization [11; 12; 13], which explores the demographic traits such as age, gender, or educational level associated with the author;and (3) authorship verification or similarity detection [14], which seeks to establish whether the presumed author aligns with the actual writer of a given document. All these tasks can be formulated as detection problems, where the goal is to determine the degree of similarity between texts by comparing their writing styles. Understanding the unique features that distinguish one author from another aids in solving practical problems and highlights the complexity of human writing. The methodology for authorship attribution typically involves a combination of computational and statistical techniques.  
This study focuses on enhancing authorship attribution, specifically for the Romanian language. While existing methodologies have shown promising results in English and other widely studied languages, the linguistic complexity of Romanian presents unique challenges for which out-of-the-box solutions do not work. Even though the immediate focus is on Romanian, the methodologies applied in this study can easily be extended and applied to other limited or less-resourced languages. This study proposes a hybrid Transformer strategy to address these challenges, combining handcrafted linguistic features with contextualized embeddings. This approach is compared with a baseline ensemble of seven machine learning classifiers considering majority soft voting. In addition to proposing an enhanced authorship attribution method, this research includes a comparative evaluation against existing studies for Romanian and performs an analysis of the top discriminative features and authors' writing styles. The aim is to assess the efficiency of the proposed strategies when compared to previously established methodologies.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 3 Security Model  
Because the cost of data replication is very low relative to the cost of production, the data can easily be copied and disseminated, which results in damage to the rights and interests of the owners of data assets. Therefore, it is necessary to ensure that the ownership, right to use and control of data and data assets are clearly defined and confirmed in the full business chain and life cycle of data asset sharing and circulation, and are effectively guaranteed by technical means and management system.
# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 2 Materials and Methods
### Machine Learning Techniques for Comparative Analysis of Single Cell Annotation
Transformers, however, read through the whole set of gene expressions at once; it is just that the weights provided by the self-attention mechanisms identify the importance of one gene over another. The model can thus learn the complicated interdependence across the sets of genes relevant to cell type identification. As a result of training on different scRNA-seq datasets, transformers carry the knowledge of contextual relationships between the various gene expressions, thus helping to correctly infer cell type. Transformers classify cell type based on the patterns in expression data across the whole dataset; with learned weights, it makes a prediction on the most likely cell type for a cell. The model gives its output as probabilities for each cell type, upon which the highest probability gives the classification result. Continued adaptation of transformer architectures for genomic data is expected to further enhance their utility in single-cell analysis.
## 2 Theory of Video SAR
### Image Size  
This section describes the image size of ViSAR. The image size is limited by the Doppler bandwidth of the antenna beamwidth. With spotlight SAR, the image size is determined by the beamwidth. On the other hand, in the case of stripmap SAR, image size is determined by the pulse repetition interval (PRI) in pulsed SAR operation, or by the bandwidth of the dechirp signal in FMCW SAR  
Figure 3: Frame rate of video SAR on a circular path: (**a**) varying resolution at \(v=20\) m/s; (**b**) varying platform velocity at \(\rho_{a}=0.08\) m.  
operation. In the case of a ViSAR mode that is operated using spotlight SAR on a circular path, the received signal is the dechirped azimuth signal. The Doppler bandwidth is limited by scene size or antenna beamwidth as follows ([2], p. 44):  
\[B_{a}\approx\frac{2 vW_{a}\sin\alpha_{dc}}{\lambda R_{a}} \tag{5}\]  
where \(\theta_{a}\approx\frac{W_{a}}{R_{a}}\) approximately corresponds to the antenna beamwidth in azimuth and \(W_{a}\) is the azimuth size of the scene area. The Doppler bandwidth is therefore proportional to both the radar velocity and the image size in the azimuth. According to the Nyquist theorem, the PRF of conventional SAR should be greater than the Doppler bandwidth. Figure 4 shows an example of the relationship between the image size and the Doppler bandwidth described by Equation (5), with \(v\) = 20 m/s, and \(R_{a}\) = 1000 m. Note that, for example, the Doppler frequency of a scatterer located at the radius of 30 m is 376 (=752/2) Hz in this case. In order to maximize the effective image area, the antenna beamwidths in the azimuth and elevation directions should be of the same order [23].  
When the SAR image is synthesized using the PFA, the image size is limited by wave front curvature, since the algorithm uses the plane-wave approximation. Therefore, these characteristics should be taken into consideration in the design process. The maximum image size can be increased by using wave front curvature compensation methods, as in [33; 34; 35]. In PFA, the scene size is limited by range curvature, and residual video phase ([2], Equation (3.130), [36], Equation (B.23)).  
1. [label=()]
2. Scene size (diameter) is limited by range curvature as follows: \[S<2\rho_{a}\sqrt{\frac{2R_{a}}{\lambda}}\] (6)
3. Scene size is limited by residual video phase (RVP) ([36] (Equation (B.26)), when the chirp rate is very high, as follows: \[S<2\rho_{a}\frac{f_{c}}{\sqrt{\frac{f_{c}}{\pi}}}\] (7) where \(k_{r}\) is the chirp rate in range.  
Figure 5 illustrates an example of the scene sizes generated using the PFA, with \(R_{a}\) = 1000 m and \(f_{c}\) = 94 GHz, constrained by wave front curvature according to Equation (6). Therefore, the antenna  
Figure 4: Doppler bandwidth limited by scene size.  
beamwidth should be determined prior to operation so that it corresponds to the required image size. The scene size can be converted to beamwidth limit.