esa-sceva/satcom-chunk-collection huggingface dataset:
# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 2 Materials and Methods
### Preprocessing Image Data
Thus, while the fixed-size ROI approach is simple, it is suboptimal for ROI extraction and classification in mammography. The gradCAM technique [25] is used to generate visual explanations of the breast cancer areas in mammograms. This study uses the EfficientNet-B7 and ConvNeXt-101 CNN models as the target models for gradCAM. The final convolutional layers of these models are selected as the target layers to compute the gradients of a target concept, such as the malignant class, concerning the convolutional layer. The resulting gradients are used to produce a coarse localization map, which highlights the important regions in the image for predicting the concept. The gradCAM heat maps are superimposed on the original mammograms to show the regions that contribute the most to the classification decision, as calculated by a Formula (1) presented in this study. \[L_{\text{Grad-CAM}}^{c}=\text{ReLU}(\sum_{k}a_{k}^{c}A_{k}) \tag{1}\]  
where \(c\) is a malignant class, \(k\) is the index of a feature map channel, \(a_{k}^{c}\) is the weight of channel \(k\) for class \(c\), computed by global average pooling the gradients, \(A_{k}\) is the feature map of channel \(k\), and ReLU is the rectified linear unit function. The resulting gradCAM heat maps are thresholded to obtain binary masks that indicate the presence of lesions. The contours of these masks are identified using OpenCV ([[https://opencv.org/](https://opencv.org/)]([https://opencv.org/](https://opencv.org/)), accessed on 7 March 2023), and bounding boxes are drawn around them.
# A Geospatial Framework of Food Demand Mapping
## 2 Materials and Methods
### Map Creation Procedure  
The following procedure outlines the steps for creating detailed maps by integrating both spatial and data-driven elements:  
* Generate Grid:  
A structured grid system utilizing the shape file of the specific country of interest is created. This grid establishes a spatial framework crucial for subsequent stages of data interpolation. For this, \(k=70\); 112 observations were applied for model training. The prediction grid had 60 000 rows and 2 columns, with 0.21 \(\times\) 0.21 km and 1.04 \(\times\) 1.04 km spacing covering the study area that is Lithuania and Vilnius region.
* Input into Shepard's Interpolator:  
The measurements have been incorporated into the pre-existing grid. Concurrently, the Shepard's exponent is presently under careful consideration for selection.
* Interpolation to Grid Points:  
[PERSON]'s operator to interpolate values across the entire grid is applied. This step results in a representation of geographic data.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 6 Discussion
### Evaluation of This Network Model  
The above neural network definition and training is based on the TLCS under normal weather conditions when the design speed is 80 km/h and traffic volume is 380 [veh/(h-ln)]. Training results show that this network model can achieve 100% color temperature recognition and a high fitting degree of luminance. Moreover, several groups of experimental data indicate that the predicted and actual values of inside luminance follow unary linear regression with high degrees of fitness.  
This training algorithm can be derived to other tunnel lighting scenarios, such as different tunnel direction categories, weather conditions, design speeds, and traffic volumes, etc. It only needs to construct corresponding data labels with the actual input and output data of different scenarios, and the training process and parameter configuration are roughly the same.  
Of course, if other influencing factors in tunnel lighting are considered, such as exhaust fumes in the tunnel, air visibility, or vehicle type, etc., only the dimension of the input layer of the neural network should be extended.  
Therefore, the aforementioned network training model has good scalability and adaptability, and can be used as a reference mode for other lighting scenarios.
# Cognitive Radio Network Technology for IoT-Enabled Devices +
## 2 Spectrum Sensing Strategy
### Machine Learning with Spectrum Sensing  
CR is an intelligent radio system with perception, learning, and reasoning [10]. Spectrum sensing lets the system perceive its radio surroundings. Classification and generalization algorithms help the system learn from data. The system uses knowledge and reasoning to achieve its goals [10]. Spectrum sensing that is based on machine learning (ML) is able to identify channels occupied in cognitive radio networks due to the intelligent architecture of the system [10; 11]. This is accomplished by addressing categorization and assessment. Figure 3 provides an illustration of the supervised and unsupervised machine learning methods that have been developed. Energy statistics, probability vectors, and temporal occupancy are just a couple of the features employed in machine learning models for spectral sensing. The selected features may obviously influence the efficiency with which primary users are identified.  
* Learning without supervision is an option that could be practically viable for cognitive radio networks that operate in unknown RF environments.
* In the case that cognitive radio already possesses previous data regarding the surroundings, it may make use of this information by employing the supervised learning technique.  
Figure 2: Spectrum sensing model.
## 2 Geographic Information Systems  
In the current context, the future belongs to smart cities that are capable of reinventing themselves, harnessing all socioeconomic and cultural resources and providing a sustainable and resilient environment for residents. Sustainable urban development and resilience are only possible through flexible and creative multidisciplinary approaches to urban problems.  
A multidisciplinary approach to sustainable urban development can be achieved by using geographic information systems, as they allow researchers and professionals to use various methods and advanced computer tools for the simultaneous processing of statistical and geospatial data to determine the spatial characteristics of the urban area. Therefore, geographic information systems have become indispensable for sustainable urban planning as they provide the ability to better understand the current needs of a city and then plan to meet those needs.  
As urban populations grow and expand, the importance of GIS lies in its ability to collect a large amount of geospatial data necessary to understand the complexity of the city, set priorities, and solve complicated planning problems.  
Scientific literature emphasizes that GIS is useful to create a comprehensive spatial database with all street cadastral data and traffic information. Therefore, geographic information systems allow the integration of a wide range of spatial information such as road type, width, pavement, number of lanes, road and intersection capacity, speed limits, restrictions and limitations, periodic or semipermanent congestion, incidents, vehicles, etc. [8; 22; 23; 24].  
The applicability of geographic information systems in transportation stems not only from the distributed nature of transportation and traffic data but also from the need for various types of analysis. There is a need to organize the vast amount of transportation infrastructure information into a spatial database that can be integrated into GIS for the most efficient use in planning, design, construction, maintenance, and management of the transportation system.  
By using technology from GIS, urban planners can perform a variety of spatial studies that show not only the feasibility of the proposed investment but also how the proposed structure fits into the existing infrastructure and meets the requirements of a sustainable city. The usage of GIS also makes it easier for planners to visualize and spatially represent the relationships between various graphical and nongraphical data.  
In addition to the ability to perform spatial analysis and interact with specific decision support applications, another advantage of using GIS is the fact that the models and analysis results can be presented in the form of an easy-to-understand digital map. Spatial features can be highlighted, leading to a better perception of the problem and implicitly to optimal decision making.
# Breakthrough and Challenging Application: Mixed Reality-Assisted Intracardiac Surgery
## 1 Introduction
Similarly, the authors of [25] presented an MR visualization of the heart with a 3D model built with a 3 DSlicer, which allows users to section the model as desired. In [26], the software Mimics Innovation Suite Medical (_Materialise_, Leuven, Belgium) was used for the segmentation of the heart to subsequently 3D print it. For the same purpose, _Materialise_ was used in [27] for the processing of CT data to finally present a procedure to develop and produce a physical 3D stamp of a Left Ventricular Aneurysm. Also, in [28], _Materialise_ was used to segment the heart from multidetector computed tomography or 3D balanced steady-state free precession images with the aim of implementing 3D printing of the organ. Although 3D printing provides a tangible and realistic perception of the model [29], the holographic model can be sectioned and it can show outer and inner structures of the model, as proposed in this study. Probably, the most complete MR application for heart segmentation, including intracardiac membranes and surfaces, is ARTICOR (developed by Artiness srl, Milan, Italy), which was used in [30]. In this case, the segmentation is semi-automatic, and the MR experience is exclusively for holographic visualization. Although the MR experience increased the attention and enthusiasm of the medical staff, ref. [30] demonstrates that no significant improvement of knowledge of the medical users was found. In fact, although an accurate 3D model is fundamental for the surgical planning process, also developing a Heart Navigation MR application full of different interactions is important to assist physicians. The aim of this study is to propose an MR experience realization. The potential and limits of this methodology would be discussed, as well as the challenges to be overcome to meet the wishes of surgical teams. This project, conducted in collaboration with cardiothoracic surgeons at the Umberto I Hospital, involved reconstructing the 3D volume of a patient's heart affected by aortic stenosis, starting from CT scans. Each anatomical component of the heart, including the aortic valve, was segmented and a 3D model was built to represent the entire heart. The critical aspect lies in achieving a reproduction of the anatomical region of interest, including internal structures. This alignment was achieved through meticulous manual segmentation work and by using a professional software to segment in a distinguishable way the outer and inner surfaces of the membranes. The MR app was implemented to propose different interactions and activities with the 3D model other than the prefixed visualization. Finally, the cardiac surgery team has reviewed the model and tried the MR experience to evaluate the accuracy of the segmentation and the efficiency in supporting orientation and decision making.
# Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
## 3 The Proposed SET Algorithm
### Determination of Pareto Optimal Solution  
The primary objective of this section is to concurrently optimize EE and SE to obtain an optimal solution. Assume that adaptive coding and modulation scheme is used to attain the Shannon rate limit, the SE of the \(k^{\prime}h\) UE can be illustrated as:  
\[SE(p_{k})=\log_{2}(1+\beta_{k}) \tag{3}\]  
where \(p_{k}\) denotes the transmission power and \(\beta_{k}\) is the instantaneous signal-to-noise ratio of the \(k\)th UE, and \(k\in\{1,\cdots,K\}\). While, the SE of all UEs can be represented as:  
\[SE(P)=\sum_{k=1}^{K}\log_{2}(1+\beta_{k}) \tag{4}\]  
Figure 2: Control flow diagram of the SET algorithm.  
where \(P=\{p_{1},p_{2},\cdots,p_{K}\}\). Furthermore, EE is defined as the ratio of SE over the total power consumption, which can be described as:  
\[EE=\frac{SE(P)}{\sum\limits_{k=1}^{K}{(p_{k}+p_{k}^{\prime})}} \tag{5}\]  
where \(p_{k}^{\prime}\) represents the remaining percentage of circuit power of the \(k\)th UE.  
Hence, the problem is formulated as a MOP, which can be described as in Equation (6):  
\[MOP=max\{EE,SE\} \tag{6}\]  
subject to \(0\leq p_{k}\leq p_{max},\forall k\in K\), where \(p_{max}\) denotes the maximum transmission power allocated to each eNodeB and \(SE=SE(P)\) as in Equation (4).  
To solve the SET in the aforementioned MOP, the concept of Pareto optimality [23; 24] is used, which results in the conversion of the MOP to an SOP using weighted linear summation technique [25], as shown in Equation (7):  
\[SOP=(\theta\times SE)+((1-\theta)\times EE) \tag{7}\]  
subject to \(0\leq p_{k}\leq p_{max},\forall k\in K\), where Equation (7) represents the SET optimization problem (objective function), and \(\theta\) is the trade-off weighting parameter, such that \(0\leq\theta\leq 1\), which enables the flexibility to achieve the trade-off between EE and SE.  
```
1 Input:
2\(PPT:\) Packet processing time
3\(State:\) System State
4 Initialization:
5\(PPT=0\);
6\(State=\) Sleep-mode;
7 whileState=Sleep-modedo
8 if termination request is receivedthen
9\(State=\) Terminate;
10
11 else
12 if incoming packet is availablethen
13 ifPPT\(\geq\) 1 then
14\(SE(p_{k})=\log_{2}(1+\beta_{k})\);
15\(SE(P)=\sum\limits_{k=1}^{K}{\log_{2}(1+\beta_{k})}\);
16\(EE=\frac{SE(P)}{\sum\limits_{k=1}^{K}{(p_{k}+p_{k}^{\prime})}}\);
17\(MOP=max\{EE,SE\}\);
18\(SOP=(\theta\times SE)+((1-\theta)\times EE)\);
19 whileSOP is not within optimal rangedo
20 SelectSOP;
21 Compute test values using SOP method;
22
23 end if
24
25 end if
26
27 end if
28
29 end if
30
31 end if
32
33 end if
34
35 end if
36
37 end if
38
39 end for
```  
**Algorithm 1** SE and EE Trade-off (SET) Algorithm for Saving Energy in Down-link LTE Networks
## 2 Materials and Methods
### Sensory Analysis  
#### 2.4.1 Panel Training and Selection  
Sensory analysis was performed by a well-trained panel under standardized conditions in the sensory laboratory. All panelists (eight females and three males aged between 25 and 57 years) fulfilled the basic requirements given by DIN EN ISO 8586 and all but two had several years of experience conducting sensory evaluations. Additionally, training sessions specific to apple flavor were carried out prior to the study. This training sensitizes the panelists to the expected sensory impressions during apple analysis. On the one hand, the training focused on volatiles as well as taste stimuli present in apples. For the aroma training, solutions of relevant compounds were prepared in ethanol in adequate concentrations (1-2%). Filter strips were dipped into these solutions, dried and stored in cellophane coats until sensory evaluation. The following compounds were used for the odor training: ethyl decanoate, \(\alpha\)-ionone, \(\alpha\)-terpineol, linalool, 5-hexenyl acetate, hexyl butanoate, 2-hexenal (\(E\)), 3-hexenol (Z), ethyl butanoate, \(\beta\)-damascenone. At the time of the investigation, all compounds were registered in the European Union as flavoring compounds and authorized to be used in flavored foods according to regulation (EU) No 872/2012. The compounds were purchased from Sigma-Aldrich (Vienna, Austria) and had a purity of \(\geq\)96% (food grade quality). Apple juice samples with adjusted sweetness and sourness were prepared to train the taste evaluation of apple fruits. The panelists were asked to rank four apple juice samples according to their increasing sensation of sweetness (sucrose addition: 1.: +1 g/L, 2.: +5 g/L, 3.: +10 g/L, 4.: +15 g/L) and acidity (citric acid addition: 1.: +0.1 g/L, 2.: +0.5 g/L, 3.: +1.0 g/L, 4.: +1.5 g/L), respectively. Furthermore, apples of different varieties, origins and degrees of ripeness were presented to the panelists in two sessions for sensory evaluation. The results of the versatile evaluations were discussed among the panel members.
## 4 Experiments
### Motion Vector Estimation
It can be seen from Table 7 that the proposed method has obvious advantages in the EPE3D error and running time when compared with the current scene flow algorithm, and the accuracy is also improved by 31% when compared with the FLOT network. At the same time, it can be seen that the traditional scene flow algorithm is time-consuming, which cannot meet the scanning speed of 10 Hz of Lidar, that is, the processing time needs to be as small as 100 ms. Most scene flow algorithms cannot meet this requirement, and the method in this paper takes only 18.27 ms on average in fast motion segmentation. The average time consumption of motion vector estimation is 60.24 ms. In terms of running time, the method in this paper has obvious advantages. The results of the motion vector estimation are illustrated in Figure 9, where the blue point represents the moving object and the red arrows denote both the direction and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Axis** & **KITTI (m)** & **nuScenes (m)** \\ \hline X & 0.0110389 & 0.0146704 \\ Y & 0.0173906 & 0.0194629 \\ Z & 0.0299285 & 0.0350560 \\ Total & 0.0363620 & 0.0426959 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The errors of the motion vectors in the three axes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **EPE3D (m) \(\downarrow\)** & **Time (ms)** \\ \hline FlowNet3D [18] & 0.173 & 434.12 \\ PointPWC-Net & 0.165 & 552.26 \\ FLOT [20] & 0.110 & 1111.12 \\ Ours & 0.076 & 78.51 (18.27 + 60.24) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of motion vector estimation with current scene flow methods. magnitude of motion, with the longer arrows indicating greater displacement. The impact of transforming the source object into the reference frame using motion vectors is depicted in Figure 10. Here, the red point corresponds to the source frame, while the green point is situated within the reference frame; additionally, the blue point represents a position in the source frame adjusted by incorporating the predicted motion vector. It is evident that there is a significant overlap between the green and blue points, suggesting that the estimated motion vector accurately captures the object's state of movement.
## 8 Conclusions  
This work summarizes a successful realization of 5G NR multiband OFH with a novel unprecedented DPD solutions for reducing RoF nonlinearities using the conventional volterra based methods and further exploiting deep learning methods to further improve results using Neural Networks. Firstly, a novel MSA DPD method has been proposed which reduces the complexity of CPWL method and reaches similar performance with reduction of complexity overheads and multiplications by 75%. The article also explains the theoretical foundations and elements required for building a Neural Network. The 5G NR multiband signals at 3 GHz and 20 GHz are employed to 22 km fiber length. The proposed MSA-DPD method results in reduction of ACPR from \(-\)28 dBc to \(-\)45 dBc while NN results in ACPR reduction to \(-\)48 dBc. Similarly, 11% of EVM is reduced to 3.5% with MSA method and NN leads to 2.7% at RF input power of 5 dBm. The results signify that proposed MSA-DPD method reduces the signal impairments in better proportions  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methodology** & **ACPR (dBc)** & **EVM (\%)** & **MSE (dB)** \\ \hline No-DPD & \(-\)28 & 11 & \(-\)27 \\ DPD-GMP & \(-\)41 & 5 & \(-\)30 \\ DPD-CPWL & \(-\)44 & 4 & \(-\)35 \\ DPD-MSA & \(-\)45 & 3.5 & \(-\)35 \\ DPD-ONN & \(-\)48 & 2.7 & \(-\)39 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results Summary for ACPR (0 dBm), EVM (5 dBm) and MSE.  
Figure 14: Conceptual Possible Realization of an Adaptive Predistortion Scheme.  
as compared to GMP method and CPWL. The estimated multiplication operations from CPWL to MSA are reduced from 880 to 220 leading to much less complexity and overheads meeting the standardization requirements set by 3 GPP Release 17. MSA with much less complexity and similar performance to NN can be employed for DPD depending on the application scenario.  
Conceptualization, M.U.H.; methodology, M.U.H. and J.S.; software, M.U.H. and M.R.; validation, M.I.A., M.A. and M.U.H.; formal analysis, M.U.H.; investigation, M.U.H. and M.A.; resources, M.I.A. and M.U.H.; data curation, J.S.; writing--original draft preparation, M.U.H. and M.R.; writing--review and editing, M.U.H., M.A., M.R., M.I.A., J.S.; visualization, J.S.; supervision, M.U.H. and M.I.A.; project administration, M.A.; funding acquisition, M.R. and M.U.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
Authors would like to thank [PERSON] and [PERSON] for their efforts in experimentation and data analysis.  
The authors declare no conflict of interest.