esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Complexity Reactive Tabu Search Based Constellation Constraints in Signal Detection
## 3 CC-RTS Algorithm
For instance if \(\widehat{\mathbf{s}}\) is the initial value of RTS, and the soft output \(\widehat{\mathbf{s}}_{i}\) falls into the bright area of graph, a hard decision is considered to determine the output \(\widehat{\mathbf{s}}_{i}\) of the nearest constellation and, then we neither consider generating candidates nor conduct RTS algorithm for this soft output. A quantization operation \(k(\blacksquare)\) is then performed as \[\widehat{\mathbf{s}}_{i}=k(\widehat{\mathbf{s}}_{i})\] (7)
* **Unreliable**: The unreliable condition is that the detection signal falls into the shaded area of the graph. If \(\widehat{\mathbf{s}}\) is the initial value of RTS, and the soft output \(\widehat{\mathbf{s}}_{i}\) falls into the shaded area of the graph, we conduct hard decision for \(\widehat{\mathbf{s}}_{i}\) and generate candidates of \(k(\widehat{\mathbf{s}}_{i})\) for the RTS algorithm. The CC processing is invoked and a candidates set is generated as \[\textbf{Neb}(\widehat{\mathbf{s}}_{i})=[c_{1},c_{2},\cdots,c_{m},\cdots,c_{M} ]e\textbf{A}\] (8) The generated candidates are constrained by the constellation map and the candidate set is a selection of the \(M\) nearest constellation points to \(\widehat{\mathbf{s}}_{i}\). The size of \(\textbf{Neb}(\widehat{\mathbf{s}}_{i})\) can be either fixed or adaptive with the channel condition which introduces a tradeoff between the performance and the detection complexity with a selection algorithm. The pseudo code of the proposed CC-RTS algorithm is given in Table 2. In **Initialization**, we not only consider the hard output of MMSE but also include the soft output, generating the initial value of RTS algorithm; In **Constellation Constraints**, we define a vector **sit**. Through the distribution of **sit**, we divide the signals into reliable signals and unreliable signals. In **CC-RTS**, for the distribution of the **sit**, we only look for candidates of the initial value of **sit** is 1, and then execute RTS algorithm for these elements. This reduces the number of candidates in the traditional RTS algorithm, thereby reducing the complexity of the traditional RTS algorithm.
## 3 Materials and Methods
### Multi-Scale Positioning for Waypoint Navigation
Specifically, we use the initial positioning frame obtained from the cumulative gradient heat map to map the receptive field to the actual space. In the mapping process, we adaptively find the most suitable position transformation matrix to modify in each _Nk_ iterations, and subtract the irrelevant when the center of the position suggestion frame is stable. For the results obtained from multi-scale feature fusion mentioned above, we use the following scale-invariant mean square error \(L(y,y^{*})\) for fusion, where \(y\) is the further refinement for \(F_{*}(p)\). \[L(y,y^{*})=\frac{1}{2n}\underset{i=1}{\sum}\left(\log y_{i}-\log y_{i}^{*}+ \beta(y,y^{*})\right)^{2} \tag{5}\]  
where \(y\) and \(y^{*}\) respectively represent the true value and our predicted value. And the parameter \(\beta\) can be calculated as follow:  
\[\beta(y,y^{*})=\frac{1}{n}\underset{i}{\sum}\left(\log y_{i}^{*}-\log y_{i}\right) \tag{6}\]  
Finally, in order to generate fewer waypoints, thereby reducing the time required to calculate and update the gait, we perform a second screening to further remove the interference corner points that have no effect on the result. After the above process, we can obtain a marked point image with less disturbance and better performance. Throughout the training process, we use the following function to obtain the result:  
\[Loss(y,y^{*})=\frac{1}{n}\underset{i}{\sum}\,diff_{i}^{2}-\frac{\sigma}{n^{2}} \left(\underset{i}{\sum}\,diff_{i}\right)^{2} \tag{7}\]  
where \(diff_{i}^{2}\) represents the difference between the final true value and the predicted value. The \(\sigma\) parameter in disappear loss is set to 0.5 in this algorithm. The initial key pixel is determined by the cumulative gradient. Specifically, we divide each \(H\times W\) instance box predicted from the original image into a grid of \(H/m\times W/m\) cells (the size of each grid is \(m\times m\)). Then, we sum the absolute gradient values in each cell of all the instance boxes and select the top K (we set K = 5 in the experiment) cell. Since in our experiment, the grid-shaped corner area patch can successfully superimpose the confidence level of the feature point category of the receptive field, so as to efficiently perform threshold classification. Therefore, we transfer K cells containing path information to grid-shaped cells and treat the pixels on the grid lines as waypoint information. Then, we transfer K cells containing roadblock information to the annotated image and treat the pixels on the grid line as roadblock points with a large cost.
## 3 Results and Discussion
### Effective Wavelengths Selection  
Commercial applications of the HIS technique demand data reduction to minimize the processing time and maximize the robustness of the models. Therefore, the selection of an appropriate optimal method for the identification of sensitive features is crucial, especially for large input datasets. In our study, eleven characteristic wavelengths: 418, 434, 521, 644, 659, 740, 796, 994, 1338, 2245, and 2339 nm were selected from the full spectrum using the CFS algorithm with a 'first is best' approach. The CFS algorithm evaluates feature subsets based on a degree of dependence or predictability between wavelengths. This algorithm was able to reduce the number of features from 467 to 11, which accounted for 2% of the full number of wavelengths. The wavelengths in the regions of 400-500 nm and 600-750 nm are associated with some of the leaf pigments that control the plant's photosynthetic and light-use efficiency. The wavelengths closest to 520 nm are sensitive to chlorophyll content, which was indicated in the previous study [49]. The wavelength of 994 nm defined as the water absorption peak corresponded to the second overtone, the O-H stretching vibrational overtone of the water and carbohydrates [50]. Our results are similar to those found in previous studies, which indicated that green, red, and NIR regions are the most sensitive  
Figure 5: The correlation coefficient between leaf nitrogen content and the second derivative of the spectral reflectance obtained for sugar beet (**A**,**C**,**E**) and celery (**B**,**D**,**F**) plants treated with four nitrogen fertilization doses and three water supply levels. The dashed red lines in each plot represent the limit of statistical significance at 0.4. The data points located beyond these limits are significantly correlated with leaf nitrogen concentration. LW: low water supply, OW: optimum water supply, HW: high water supply.  
to N stress. [PERSON] et al. [51] selected five optimal wavelengths (520, 553, 673, 693, and 884 nm) for the discrimination of nitrogen fertilizer levels of a tea plant. Other authors found that reflectance in red-edges centered at 740 nm were highly correlated with leaf N content in sorghum and oat [52,53]. Although the possibility of application of the HIS to detect N-stress or the N combination and water stress has been described by many authors, only a few studies have considered the SWIR range. [PERSON] et al., [54] confirmed that the incorporation of the SWIR wavelengths into the models improved the prediction accuracies of both the water and nitrogen models. The wavebands in the SWIR region are associated with weak harmonic and overtone absorptions from biochemical compounds such as lignin, starch, and cellulose [55]. The N fertilization significantly affects the chemical compositions of plant tissues, including lignin and cellulose content [56,57], thus wavelengths from the SWIR region can also influence the results of discrimination in N treatment.
## 10 Future and Perspectives
### Hybrid Optimization Techniques  
As production systems grow increasingly intricate, the adoption of hybrid optimization techniques will become more prevalent. These approaches, which combine the strengths of various optimization methodologies, such as response surface methodology (RSM), metaheuristic algorithms, and evolutionary strategies, are designed to overcome the limitations of individual techniques. For example, the fusion of particle swarm optimization (PSO) with neural network-based modeling has demonstrated the potential to enhance process performance by simultaneously addressing multiple objectives, such as reducing energy consumption, improving product quality, and minimizing waste [86]. The flexibility and adaptability of these hybrid methods are expected to make them indispensable in tackling the multifaceted challenges of modern food production.
# Contrast Enhancement for Topographic Imaging in Confocal Laser Scanning Microscopy
## 7 Discussion
However, the laser power cannot be increased arbitrarily high, because semiconductor samples can suffer from a high carrier creation by the laser spot, resulting in local hot spots distorting the image quality. Thus, the use of low intensities is desired. For a smaller pinhole, resolution and contrast increase, along with a decrease of intensity. Also the contrast of PDM images is always higher than for their correspondent in-focus images. The contrast increase of the _PDM far_ is 37% compared to the in-focus case for the 10 AU pinhole. At the same time the resolution measured by the slope is increasing with smaller pinhole and _PDM far_. Along with the enhancement of the contrast, we see a higher slope for the _PDM far_ than in the in-focus case. The best outcome is given for 1 AU _PDM far_ with a still diffraction limited resolution, the corresponding image can be seen in Figure 10.  
Also the images of the ASIC show an increase in contrast for the _PDM far_ in Figure 14 of 24% and in the slope for 13%. In this case, the slope is the maximum slope at a line scan crossing one of the squares, taken from the normalized image. As mentioned in the introduction, there are other papers reporting the increase of contrast by dealigning the setup but by defocusing the sample [18; 20; 25; 32]. However, in contrast to these techniques, in our PDM there is no decrease in resolution as was reported in Reference [18]. In Figure 10 our system shows still a diffraction limited performance. Furthermore, the increase in contrast with the PDM is much higher than in other reports. In the best cases, a slope increase of above 100% _PDM far_ is measured while Reference [32] shows a slope improvement of 60% but with additional defocus of the sample. As can be seen in Figure 13, the increase in contrast of the _PDM far_ for the 10 AU pinhole is about 37% compared to the in-focus case. The highest contrast increase of 80% is measured for the  
Figure 14: Comparison of the image characteristics of the ASIC with the 10 AU pinhole in-focus and with _PDM far_. \(1\) AU _PDM far_ compared to the in-focus case. The PDM achieves an even higher contrast than a smaller pinhole in focus and also about the same resolution. Thus, the PDM substantially reduces the effort of exchanging the pinhole to achieve a higher contrast and is in this regard to some extent superior to smaller pinholes. For the final image acquisition, the optimal position for contrast enhancement needs to be identified at the slope of the in-focus depth scan. Then the PDM is performed, the lens (L2) is shifted to the according position and the target is scanned in the focus plane.
# Automatic Selective Encryption of DICOM Images
## 3 The Proposed Approach
### Approach I: Multi-Region Selective Encryption Approach for Mammography DICOM Images
#### 3.1.2 Multi-Region Encryption Process  
The elementary idea of the proposed approach is to split the DICOM image ROI into three regions, and then encrypt these regions using different encryption algorithms based on their importance, as it is believed that this will guarantee minimum encryption time in comparison with the Naive and two-region encryption approaches [25]. In order to achieve that, the three regions' sizes must be calculated, such that the total encryption time of all three regions in addition to the ROB encryption time must be less than the Naive encryption and the two-region encryption approach times. The reason behind segmenting ROI into three regions is to prove the concept, as two regions are not enough to investigate the efficiency of the proposed approach, and more than three regions will cost more time due to the extra operations of segmentation.  
Figure 5 depicts the overall multi-region encryption approach. Firstly, DICOM image regions' sizes are calculated based on the ROI size, where the ROI is determined using extracted thresholds based on the approach described in [25]. Thus, if the ROI size with respect to image size is (-\(\geq\)55%), pixel data will be segmented into three ROI regions (R1, R2, R3) and ROB. After that, the plain text of all regions is compressed using LZ4 lossless compression. Next, the compressed data are encrypted using AES 256, AES 128, and XXTEA based on their importance. Finally, the ciphertext of all regions is stored in the tag (07 FE0, 0010). It is worthwhile to mention that AES is a sophisticated encryption algorithm, and it is adopted by the DICOM standard with an efficient processing time. Therefore, AES was utilized to encrypt the ROI due to its robustness and the importance of that region. While XXTEA is a lightweight encryption algorithm, it is selected to encrypt less important regions such as the ROB to demonstrate the proposed approach. However, there are several lightweight encryption methods that would be suitable to encrypt the ROB.  
Figure 4: Estimated encryption time of AES 256.
# Improved Electrochemical-Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
## 2 Materials and Methods  
Optimization is an engineering challenge based on identifying the set of parameters \(\mathbf{\theta^{*}}=\{\theta_{1},\ldots,\theta_{i},\ldots,\theta_{n}\}\) that minimize an objective function \(f(\mathbf{\theta})\) while ensuring the constraints of the physical phenomenon (inequality \(g_{j}(\mathbf{\theta})\) and equality \(h_{k}(\mathbf{\theta})\) constraints). Each parameter \(\theta_{i}\) can vary in a range defined between a lower bound \(a_{i}\) and an upper bound \(b_{i}\) in accordance with the parameter constraints. Therefore, an optimization problem is generally expressed with the mathematical formulation shown in Equation (1). The subscript \(i\) denotes the \(i\)-th parameter, \(j\) is used to indicate the \(j\)-th inequality constraint and \(k\) for the \(k\)-th equality constraint. The bold symbol \(\mathbf{\theta}\) denotes a parameter set, being a vector.  
\[\begin{cases}\text{Find }\mathbf{\theta}^{\bullet}:\\ f(\mathbf{\theta}^{\bullet})\leq f(\mathbf{\theta})\ \forall\mathbf{\theta}\\ \text{S}_{j}(\mathbf{\theta})\leq 0,\quad j=1,\dots,m\\ h_{k}(\mathbf{\theta})=0,\quad k=1,\dots,p\\ a_{i}\leq\theta_{i}\leq b_{i},\quad i=1,\dots,n\end{cases} \tag{1}\]  
The aim of this work is the introduction of a novel battery parameter estimation approach based on a multi-objective function that also integrates the reversible mechanical deformation of the battery during its operating conditions. To achieve this goal, it is necessary to develop an appropriate model that accurately describes the battery behavior and properly asses the optimization problem defining objective functions, parameters, and constraints as deeply described in this work. Optimization is performed using the BOBYQA algorithm [21], which is especially useful when the first derivative of the objective function is not available and each simulation is computationally expensive, requiring an efficient optimization strategy. A more detailed overview of the electrochemical-mechanical model and the optimization algorithm is provided in the following sections.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 5 Performance Evaluation  
### Experiment Setup  
The evaluation of execution of Fuzzy based Vertical Handover Decision Algorithm (FVHDA) is done by Network simulator (NS-2) [34]. Consider a zone of 1000 \(\times\) 1000 m of irregular network conveyed. The administration demand is fluctuated as 50 Kb/s, 100 Kb/s, 150 Kb/s, 200 Kb/s and 250 Kb/s. IEEE-Distributed Coordination Function (DCF) is utilized as the MAC layer convention for wireless LANs. The reenacted traffic is CBR with UDP source and sink. Figure 10 shows the simulation topology and Table 5 summarizes the simulation parameters used. In the topology, BS1 to BS4 belongs to the WiMAX network and BS5 and BS6 belongs to UMTS networks.
# Multi-Classifier Pipeline for Olive Groves Detection
## 2 Materials and Methods
### Satellite Imagery Classification
#### 2.3.3 Evaluation Method  
To evaluate the performance of the multi-classifier pipeline and the accuracy of the resulting maps, various methods are applied. First, we visually compared the three land cover maps produced by each pipeline and to highlight the differences between them, we performed raster subtraction focusing on Olive class. To realize this, the classes that are not olive were regrouped into one Non-olive class and we compared the olive class spatial distribution for the baseline classification and both MLP pipeline and OCSVM pipeline classification maps. The change in area was quantified from the subtraction raster to obtain the surface loss of each class.  
To evaluate the performance of the new multi-classifier pipeline, we calculated the True Positive Rate (TPR) and the False Positive Rate (FPR) to check if there is an improvement in the olive trees detection with the multi-classifier pipeline compared to the baseline pipeline. Moreover, the comparison of MLP and OCSVM is also observed. TPR was calculated based on the SCO-Live project citizen field observations using the following equations:  
\[\textit{True Positive Rate}=\frac{\textit{Correctly predicted points}}{\textit{Total points}}\times 100\% \tag{1}\]  
A point is considered correct if there is at least one pixel of predicted Olive class in the point's location and its eight neighboring pixels as the spatial accuracy of GPS is considered.  
On the other hand, the calculation of FPR was based on the non-olive vegetation polygons vector of the RPG dataset that are located inside the area of interest. We defined the incorrect pixels as the Olive class pixels inside those polygons, and computed FPR using following equations:  
\[\textit{False Positive Rate}=\frac{\textit{Incorrectly predicted pixels}}{\textit{Total pixels}}\times 100\% \tag{2}\]
## 3 Recent Advances on IEEE 802.15.4-Based WMSN Approaches
This type of design through a central node implies a clear increase in the traffic overhead (additional traffic, mainly control messages and retransmissions, generated to send data messages in multi-hop networks) in the network and, consequently, in the energy consumption, particularly on those nodes close to the Network Manager. Furthermore, the NM centralizes the decision making in cases such as when communication errors occur because of interferences, high traffic load or topology changes (e.g., node failures), eliminating this functionality for the remaining network nodes. A weakness of WirelessHART refers to its lack of interoperability with other WMSN based on IEEE 802.15.4, mainly because the MAC layer designed by WirelessHART is incompatible with the IEEE 802.15.4 MAC layer. Finally, it should be noted that WirelessHART components require more hardware complexity to support the full functionality of the standard, which means an extra cost. On the other hand, ISA SP100.11a [6; 17; 26; 27; 28] offers the industrial sector efficient communications in an interfering environment by means of the implementation of specific software modules that minimize the number of collisions among messages and provide security and deterministic latencies on the data transmission. As WirelessHART, it also uses DSSS/FHSS at PHY layer, and TDMA at MAC layer. Unlike WirelessHART, ISA SP100.11a includes the CSMA-CA protocol at MAC level to further enhance the protection against collisions in the network. Additionally, routing paths are also configured by a Network Manager, but, in comparison with WirelessHART, ISA increases the number of redundant paths between a same source-destination pair. As it can be seen, WirelessHART and ISA SP100.11a share a significant number of features at PHY and MAC levels [27], what has contributed to the recent establishment of a work group, denoted as ISA 100.12 [6], aimed at integrating WirelessHART and ISA SP100.11a standards in a single recommendation. ISA SP100.11a supports both 16-bit-based and 64-bit-based addressing schemes. Furthermore, ISA headers can exploit a 128-bit addressing scheme to enable IP connectivity inside the mesh network. This is possible through the addition of 6 LoWPAN standard (described in the next subsection) within the ISA network layer. The main drawbacks of this standard, as well as in the case of WirelessHART, are, on the one hand, the high implementation complexity of the entire protocol stack, what requires more advanced and costly devices and, on the other hand, the lack of interoperability with other WMSNs based on IEEE 802.15.4 standard. From the developers point of view, this fact might be incompatible with the deployment of low-rate and low-cost WPANs, therefore restricting its use and future penetration in the market. Finally, WirelessHART and ISA SP100.11a are proprietary standards, what means that both specifications and implementations are only accessible by payment.