esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Multi-View Detecting Framework
### Decision-Making Mechanism: Majority Voting  
For now, each pair of activity labels has three views of similarities: control-flow relations, attribute values, and label semantics. This section describes a decision-making mechanism to aggregate similarities from the above three views and generates final results, as shown in Figure 4. The decision-making mechanism is the majority voting, which is a widely adopted concept in ensemble learning [47]. For majority voting, we first need to determine the threshold for each view (i.e., \(\theta_{c}\), \(\theta_{d}\), and \(\theta_{s}\)) to decide whether each pair of activity labels is similar in the corresponding view. Equation (6) describes the voting mechanism, where \(V_{i}\) represents the result from a particular view, and \(m\) represents the total number of views (i.e., \(m=3\) in this paper). The activity labels are detected as redundant pair if more than half of the views are regarded as similar and vice versa. Using the majority voting as the decision-making mechanism has the following advantages: (1) it is fast and easy to implement, and requires no domain knowledge as input, (2) it can be easily extended if more views are proposed for redundant activity label detection, and (3) it can be easily integrated with domain knowledge (e.g., the voting weight of each view).  
\[Result=\begin{cases}{}^{*}Redundant^{\prime\prime}&\text{if }|i:V_{i}={}^{*}Similar^{ \prime\prime}|\geq m/2\\ {}^{*}Non-redundant^{\prime\prime}&\text{otherwise}\end{cases} \tag{6}\]  
The time complexity of the framework depends on the time complexity of computing each view. Suppose \(n,m,k\) are the number of activities, events, and attributes values. \(O(n\times(m+n)\), \(O(n\times m\times k)\), and \(O(n)\) for control-flow, attribute value and semantic views respectively. The final decision-making mechanism is also in linear time.
## 4 Simulation Results
### Capacity of Image Encryption  
For a multi-image encryption, the number of encryption images is an essential indicator of measurement. In this proposed encryption, the bit-wise XOR operation for two images will maintain the pixel value range of original images. That is, the pixel value of the original images is in the range of 0-255, and the pixel value of the combined image is still in the range of 0-255 after using the XOR operation. Therefore, multi-image encryption through XOR multiplexing should have no decryption crosstalk theoretically. Since the private key of each different image generated in the encryption process must be different, the encryption capacity is limited by the maximum number of possible private keys, which is the product of the maximum grayscale level and the resolution of the private key image. Therefore, the encryption capacity EC can be expressed as  
\[\mathrm{EC}\ =\ 2^{w}\times\mathrm{m}\times\mathrm{n}, \tag{26}\]  
where \(w\) represents the bit-depth of the encrypted image, \(\mathrm{m}\times\mathrm{n}\) represents the size of the encrypted image. If the size of the encrypted image is \(\mathrm{m}\times\mathrm{n}\), and the grayscale level is 8, the encryption capacity would be \(2^{8}\times\mathrm{m}\times\mathrm{n}\).  
We preprocess the images to ensure that each image is different. Figure 12 shows the decryption results of 200, 500, 1000, and 2000 different images with \(64\times 64\) pixels and their CC values, respectively. It can be seen that the decryption quality is still excellent when the number of encryption images reaches 2000. This indicates that the proposed encryption breaks through the number limitation of the conventional multi-image encryptions and greatly increases their encryption capacity.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Size** & **One Image** & **Two Images** & **Three Images** & **Four Images** \\ \hline \(256\times 256\) & 0.6393 s & 1.2354 s & 1.8577 s & 2.4489 s \\ \(512\times 512\) & 2.5149 s & 4.9587 s & 7.4733 s & 9.7806 s \\ \hline \end{tabular}
\end{table}
Table 4: The time of encrypting different numbers of images.  
\begin{table}
\begin{tabular}{c c c} \hline
**Image** & **NPCR (\%)** & **UACI (\%)** \\ \hline cameraman & 99.6046 & 33.1825 \\ peppers & 99.6046 & 33.9021 \\ baboon & 99.6046 & 33.3199 \\ coco & 99.6046 & 34.1323 \\ \hline \end{tabular}
\end{table}
Table 3: The values of the number of pixel change rate (NPCR) and unified average changing intensity (UACI).
## 7 Future Prospects of Multi-Source Data Fusion and Analysis Algorithms in Smart Cities
### Innovation Path of Algorithm Collaboration and Application Scenario Expansion  
At present, although data fusion and analysis algorithms have made certain progress in their respective fields, there are still deficiencies in algorithm collaboration and application scenario expansion. There is a lack of effective integration mechanisms between different algorithms, which makes it impossible to give full play to their respective advantages and achieve synergy and efficiency. In addition, the existing application scenarios are mainly concentrated in limited fields such as real estate management, transportation, and environment and fail to fully cover all aspects of smart city construction.  
In the future, efforts should be made to build a multi-algorithm collaborative framework to achieve seamless docking and collaborative work between different algorithms by optimizing algorithm combinations and parameter configurations [71]. For example, to jointly improve the accuracy of real estate value assessment, spatial statistical analysis algorithms can be combined with deep-learning algorithms--spatial statistical analysis algorithms can be used to process the structural characteristics of geographic spatial data and deep-learning algorithms can be used to mine data nonlinear relationships. At the same time, algorithm application scenarios should be actively expanded. For example, in the public service field, multi-source data fusion and analysis algorithms can be used to optimize the allocation of educational resources, improve the efficiency of medical services, and improve the quality of community elderly care services; in social management, they can help with social security prevention and control, urban emergency command, public opinion monitoring and guidance, etc.; in the cultural tourism industry, tourist route optimization, cultural heritage protection and development can be achieved by analyzing tourist behavior data and the distribution of urban cultural resources. By continuously expanding application scenarios, we can give full play to the all-round support role of multi-source data fusion and analysis algorithms in the construction of smart cities, improve the quality of life and happiness of urban residents, and promote sustainable urban development [72].
## 4 Congestion Control Based on the RED Algorithm
### Principle of the CRED Algorithm
The value of the dropping probability \(P\) is typically determined by two key elements, which are:  
**Step 1:** As the average length of packets gradually approaches the maximum threshold, the discarding probability \(P\) will gradually increase. When the average length of packets equals the maximum threshold, the discarding probability \(P\) immediately becomes 1. **Step 2:** In the RED algorithm, when the average length of packets falls within the critical zone between \(\max_{\text{th}}\) and \(\min_{\text{th}}\), the discarding probability is adjusted to ensure equal discarding intervals for all incoming packets. This adjustment involves modifying the original discarding probability \(P_{b}\) to obtain a new marking probability \(P_{a}\). The determination of packet discarding is based on the value of \(P_{a}\). Specifically, as the number of successive successful packets transmitted \(c\) increases, the probability of discarding the next packet also increases. The discarding probability is calculated using the following formula:  
\[P_{b}=\max_{p}(\text{avg}-\min_{\text{th}})/(\max_{\text{th}}-\min_{\text{th}}) \tag{5}\]  
\[P_{a}=P_{b}/(1-\text{c}*P_{b}) \tag{6}\]  
In the above formula, \(P_{b}\) represents a very small positive number. The term \(1-c*P_{b}\) is nearly equal to 1. It is important to note that \(P_{b}\) plays a decisive role in the formula, while \(P_{a}\) is used to make slight adjustments to \(P_{b}\). This correction is implemented to prevent routers from continuously losing packets. The discard probability \(P_{b}\) is defined as a piecewise function with different values in different intervals, as depicted in Figure 4. The specific functional form of \(P_{b}\) is as follows:  
\[P_{b}=\left\{ \begin{array}{ll}0&\text{avg}<=\min_{\text{th}}\\ \max_{p}(\text{avg}-\min_{\text{th}})/(\max_{\text{th}}-\min_{\text{th}})&\min _{\text{th}}<\text{avg}<\max_{\text{th}}\\ 1&\text{avg}>=\max_{\text{th}}\end{array} \right. \tag{7}\]  
In summary, we can conclude that as the average value avg starts from \(\min_{\text{th}}\) and gradually approaches \(\max_{\text{th}}\), the discarding probability \(P_{b}\) also increases linearly from 0 to \(\max_{p}\). Once avg reaches \(\max_{\text{th}}\), \(P_{b}\) immediately becomes 1. Throughout this process, the dropping probability \(P_{a}\) increases slowly with the increase of c. Eventually, when avg reaches \(\max_{\text{th}}\) or later, \(P_{a}\) further increases, resulting in the dropping of all newly arrived packets, regardless of their importance. The execution flow of the RED algorithm is shown in Figure 5.
## 3 Results and Discussion  
### Characterization of Different Functional Group Density Resins  
As presented in Figure 1, all the resins were estimated to possess relatively wide pore size distributions and be rich in the macro and mesoporous region, which could enable adsorbate to reach the internal functional groups easily [28]. The basic parameters of the five resins are listed in Table 1. The BET surface area and pore volume decreased with the increase of functional group density, which was in line with the general findings from the preparation of porous anion exchangers by surface functionalization [29; 30].  
Figure 2 represents the FT-IR spectra of the prepared resins and CMPS. In the CMPS spectra, the strong absorption peak at 673 cm\({}^{-1}\) indicated that the CMPS contained quite a few C-Cl groups [31]. However, after amination reaction, this band gradually weakened or disappeared with the increase of the functional group densities of resins, which suggested that the chlorine methyl was involved in the reaction. Furthermore, all the synthetic resins showed strong C-N [32] and nitrogen methyl group [33] absorption bands at 1018 and 2776 cm\({}^{-1}\), respectively [32]. Both the characteristic bands gradually  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Resins** & **SD1** & **SD2** & **SD3** & **SD4** & **SD5** \\ \hline BET surface area (m\({}^{2}\)/g) & 50.43 & 49.01 & 47.45 & 42.17 & 37.96 \\ Pore volume (cm\({}^{3}\)/g) & 0.45 & 0.43 & 0.39 & 0.36 & 0.22 \\ Mean pore diameter (nm) & 34.66 & 35.97 & 32.70 & 34.47 & 23.64 \\ Total exchange capacity (mmol/g) & 1.38 & 1.81 & 2.12 & 2.57 & 3.27 \\ Weakly basic exchange capacity (mmol/g) & 1.31 & 1.74 & 2.03 & 2.42 & 3.08 \\ Strongly basic exchange capacity (mmol/g) & 0.07 & 0.07 & 0.09 & 0.15 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of different functional group density resins.  
Figure 1: Pore size distribution of different resins.  
increased with the increase of functional group density, implying that the dimethylamine functional group had been successfully grafted on the CMPS.
## 2 Materials and Methods
### Data Processing  
#### 2.3.1 Image Mosaicking  
Image mosaics of RGB images were first acquired using PhotoScan Software 1.4.5 (Agisoft LLC, St. Petersburg, Russia), and the images were saved in Tagged Image Format (TIF) files. The main processing workflow included image alignment, feature point matching, density point cloud generation, digital surface modeling (DSM), and orthophoto generation. Georeferencing was carried out in ArcGIS to unify the geographic reference system for all orthomosaic maps.
# RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets
## 1 Introduction  
Most autonomous vehicles currently rely on light detection and ranging (LiDAR) and camera technology to build their perception systems [1; 2; 3; 4]. However, one of the main drawbacks of optical technology is failure under severe weather conditions such as fog, rain and snow. On the other hand, 24 GHz and 79 GHz radar systems have been widely adopted for adaptive cruise control and obstacle avoidance. Radar systems can penetrate bad weather [5], but the main drawback is their low resolution (e.g., \(\sim\)4 meter azimuth resolution at a 15 meter range [6]), which makes object classification and recognition very challenging.  
Recent work on automotive radar has been aimed at improving azimuthal and range resolution at the 300 GHz frequency [5; 7]. In previous work [8], we studied the performance of a number of neural network architectures applied to 300 GHz data for object detection and classification using a prototypical object set of six different objects in both isolated and multiple object settings. Our results were very much dependent on the different scenarios and objects, achieving accuracy ratings in excess of 90% in the easier cases, but much lower success rates in a detection and classification pipeline with many confusing objects in the same scene. We do not rely on Doppler signatures [9; 10; 11] as we cannot consider only moving objects, nor do we use multiple views [12] to build a more detailed radar power map, as these are not wholly suited to the automotive requirement to classify both static and moving actors from a single view.  
In our work--and indeed in the general literature--radar datasets tend to be small due to the difficulty of data collection and human annotation. While we endeavour to collect and label larger datasets in the wild, this is challenging due to the lack of advanced, high resolution automotive radars for high frame rate imaging, and the difficulty of radar image labelling in comparison with video sequences. Therefore, to create larger datasets to train deep models for such tasks, in this paper, we present a novel radar data augmentation technique which can be used for both classification and detection (Figure 1). Our experimental hypothesis is that such augmentation will improve the accuracyover standard camera-based data augmentation source data. Thus, the main contributions of this paper are as follows:  
* We present a novel radar data augmentation technique (RADIO) based on the measured properties of the radar signal. This models signal attenuation and resolution change over range, speckle noise and background shift for radar image generation.
* We demonstrate that such data augmentation can boost the accuracy and generalizability of deep models for object classification and detection, trained only with a small amount of source radar data. This verifies the effectiveness of the RADIO approach.
## 4 Numerical Simulation
### Simulation Technique  
Three-dimensional finite element models of the tested ECC specimens were constructed in ABAQUS/Explicit solver [35]. The cylindrical ECC specimen, spherical steel ball, hollow steel ring holder, and steel drop weight were modeled using their actual dimensions in the experimental test setup. The drop weight was modeled as a prismatic bar with a cylindrical head, as shown in Figure 12. The different ECC specimens were modeled using 8-noded hexagonal solid elements (C3D8R), which is commonly used in the simulation of impact behavior in the ABAQUS [35] library. A sensitivity analysis was conducted to determine an optimum number of elements. The problem was solved with increasingly finer mesh size, and the analysis was assumed to be optimum when the numerical response nearly matched the corresponding experimental data.
## 4 Findings from the Literature Review
### Topic 4--Intelligent Diagnosis  
The last identified topic is now analyzed. The first highlighted content is photovoltaic (PV) systems. Considering the energy context, power generation appears as descriptive of the topic, as in Topic 3. The currents generated and present in PVs have non-stationary characteristics. In this case, the LOF (local outlier factor) technique is used not only to identify the fault, but also to assess its degree of severity [61]. It is interesting that this technique appears to be applied only in the context of photovoltaic installations, despite being an established technique in the study of anomaly identification. In this context, clustering is proposed to apply a wavelet cluster-based bandpass filtering in the data pre-processing phase to obtain the fault-sensitive envelope signal for hydraulic pumps [62]. This topic considers ageing gearboxes, already presented in topic 1. As with the bearings, some differences have led to their separate analysis. In this topic, different techniques are used for the detection and diagnosis of gearbox faults, such as series wavelet analysis and fuzzy clustering method [63]. Analyses always start with vibration analysis, but then evolve with different techniques. This deviates from the characteristics outlined in the contribution assigned to topic 1. In addition, bearings and gears are explicitly considered separately in this contribution, which was not made explicit in the previous contributions allocated in other topics. In this topic, the analyses of gearboxes are closely linked tothose of conveyor belts. Clustering is here applied considering not only the most common vibration analyses [64], but also some temperature analyses [65]. Taking up one of the terms highlighted as representative of this topic, namely the concept of decision making, this is explicitly stressed in the context of conveyor belts [64; 65]. Another recurring theme in topic 4 is bearings. In this case, the commonality in this subgroup of articles is that local mean decomposition is applied to the analysis of bearing health [66; 67; 68]. The cluster takes on the role of a tool for fault diagnosis [66] or a technique to support fractional lower order p-function optimization [67; 68]. The topic 4 applications of clustering in fault diagnosis focus on the analysis of inrush current and internal faults [69]. The latter relates to the concept of \"internal faults\", which is considered representative of this topic. Techniques that characterized topic 4 are curvilinear component analysis (CCA), applied in the field of bearings [70] and PCA, which is used for dimensionality reduction and elimination of collinearity, where clustering is instead used to identify known or new events [71].
## 6 Evaluation  
The proposed protocols have been evaluated using the model described in Section 5. The evaluation conditions used and evaluation results are discussed below. The proposed protocols have been compared with the ones that rely on CSMA/CA only for collision avoidance. The latter is referred to as a \"compared protocol\" in this paper. While the proposed protocols stagger message transmission timing at the application layer, the compared protocols make no attempt to avoid a collision. With a compared protocol based on autonomous response-type stray node detection, all nodes send a REPLY message simultaneously at T-second intervals. With a compared protocol based on request-response-type stray node detection, all nodes simultaneously send a REPLY message and forward a REQUEST message for detection of stray nodes. Table 3 lists the proposed and compared stray node detection protocols evaluated and their respective abbreviations. These abbreviations are used hereafter.
# Raman Spectroscopy as Noninvasive Method of Diagnosis of Pediatric Onset Inflammatory Bowel Disease
## 4 Discussion  
The analysis of the amide I Raman band of calprotectin, deconvoluted in different sub-bands directly correlated with various protein secondary structures, revealed an excellent tool to look at their spectral variations as a consequence of two inflammatory bowel diseases, [PERSON]'s disease and ulcerative colitis, in childhood.  
Starting from this, the purpose of this study was furnishing an evaluation method whose peculiarity lies in its ability of detecting an inflammatory disease without any invasive procedure, such as in vivo analysis and/or biopsy, in an automated and highly efficient way.  
This method, deeply described in the previous section, furnished at the same time some information regarding the quality of the protein network induced by the inflammatory state. As a matter of fact, the observed enhancement of the nonreducible (trivalent)/reducible (divalent) crosslinking ratio, as accounted by the parameter \(R\), is indicative of an increasing, upon inflammation, of the quantity of trivalent, nonreducible cross-links at the expenses of the divalent reducible ones, and/or as a consequence of their reduced formation. As a consequence, a more strongly interconnected protein network is conceivable, that will clearly result in altered mechanical and functional properties [57].  
One could argue that an increase in the amide I band could be also ascribed to protein conformational changes induced by factors different from inflammatory state, including aging,  
Figure 6: ROC curve (**a**), Youden index and optimal cutoff-point (**b**) and distribution graph (**c**) for UC and CD in IBD subjects.  
dehydration and radiological conditions [58; 59; 60]. Nevertheless, these factors don't affect the reported data, as the groups of subjects were very similar to each other, differing only regarding intestinal inflammation.  
The obtained results demonstrated the feasibility of Raman spectroscopy as diagnostic modality for rapid detection of IBD and, among IBD subjects, for a clear and unequivocal distinction between CD and UC. Beyond noninvasiveness, the proposed method offers significant advantages, including ease of use, experimental repeatability and speed of execution. In addition, it is extremely low-cost, compared to the currently used diagnostic techniques requiring special diagnostic kits, and nondestructive, since the sample can be stored and analyzed several times after some time, being the only limit of analysis given by the degradation of the sample itself. Encouraged by the obtained results, the next step of the research will concern the possibility to apply the proposed Raman-based method for an early categorization of disease for patients with indeterminate colitis diagnoses.