esa-sceva/satcom-chunk-collection huggingface dataset:
# Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection
## 3 Validation Experiment
### Case 1: Simulated Signal  
In this section, the simulated signal is used to evaluate the performance and robustness of the LMWT method in high-noise environments. Using the following mathematical models [15] to generate the simulated signal, and process and analyze these signals to evaluate the effectiveness of the LMWT method.  
\[\begin{split}& X(t)=\sum_{i=1}^{M}A_{i}e^{-\alpha_{i}t^{\prime }}\sin(2\pi f_{i}t)+0.2\sin(2\pi f_{r}t)+0.1\sin(2\pi  
Typically, a signal with noise can affect its frequency domain characteristics and structure, which in turn affects subsequent signal analysis and processing. Based on sampling theory [33], needing enough sampling points can effectively represent the feature of the signal, but more sampling points lead to high computational cost. Consequently, the 4000 sample points of the noisy signal are applied to LMWT. To better understand the characteristics and structure of signal with noise, the proposed method in this paper and the traditional classic method EWT are applied to the noisy signal. The signal is decomposed into multiple scales and frequency signal components. The signal components obtained by applying a one decomposition level of the signal using the LMWT method are shown in Figure 3, and the empirical modes obtained using EWT applied to the simulated noise described above are shown in Figure 4.  
Figure 2: (**a**) Simulated signal with fault impulses, (**b**) added noise, (**c**) simulated signal with added noise, (**d**) envelope spectrum of the noisy signal.  
spectrum of the most sensitive component obtained by EWT, the corresponding harmonics of the fault frequency cannot be observed. However, within the envelope spectrum of the most sensitive component acquired through LMWT, distinct fault characteristic frequency \(f_{ch}\) and its harmonics prominently emerge, serving as clear indicators of bearing damage. As shown in Figure 6, by calculating the relative energy ratio of all signal components, it can be observed that the relative energy ratio of the components obtained by using EWT decomposition is significantly smaller than that obtained by using LMWT, indicating that signal components obtained by the LMWT method can more accurately detect bearing faults than those obtained by EWT.
# Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
The outage probabilities in cooperative spectrum sharing networks were investigated under non-identical Rayleigh Fading channels [13; 14] and Nakagami-m fading channels [15]. The authors in [16] presented an asynchronous collaborative sensing framework in which the tagged vehicle collects energy measurements labeled with time and location information from collaborative SVUs and assigns weights based on their storing time and location. The authors in [17] proposed a distributed collaborative sensing scheme based on adaptive decision threshold for sensing and voting scheme for connected vehicles. An integrated overview of spectrum sensing in cognitive vehicular environments can be discovered in [18]. None of this previous research considered the effect of temporal correlation due to vehicle motion and multi-path propagation in a mobile vehicular environment. Moreover, the reporting channels between SVUs and the fusion center (FC) were assumed to be ideal. In this paper, our attention is centered on the large-scale fixed PU detection in an infrastructure-based CVN. Each SVU periodically performs sensing and reports its sensing information via the dedicated reporting channel to the nearby FC. The FC fuses the received information to make a global decision for the current cell. At this moment, some vehicles taking part in cooperative sensing may have left this cell. Based on mutual benefit, we allow the back vehicles to utilize the spectrum availability information after the front vehicles sensed. We investigate the sensing performance using hard fusion [19]. Although soft fusion can gather improved performance more than hard fusion, the burden of reporting overhead impedes its applicability [20]. The main contribution of our work is the following:  
1. investigate the effect of fading correlation on spectrum sensing performance over temporally correlated Rayleigh sensing channel;
2. make clear how dramatically the reporting channel conditions could influence the reliability of a local/global decision, when made by the FC. For local and cooperative sensing, we evaluate the sensing performances by means of theoretical calculations or Monte Carlo simulations. Our results show that poor channel condition harms the detection performance. On the other hand, we will demonstrate that, if utilized properly, the fast time-varying fading caused by the Doppler spread can be used to enhance the detection performance by taking advantage of temporal diversity. The rest of the paper is organized as follows. In Section 2, we build up the system model and make some of the assumptions about spectrum sensing in vehicular environments. In Section 3, the local sensing performance over correlated Rayleigh fading is analyzed. In Section 4, we consider the cooperative sensing performance with the fading reporting channels. This is followed by the numerical and simulated results in Section 5. Lastly, we summarize the conclusions in Section 6.
## 3 Results and Discussion
### Comparative Determination of VOCs Profile from \"Sabrina\" and \"Elide\" Strawberry Samples

Table 1: Volatile metabolites detected in Sabrina and Elide strawberry cultivars and their identification codes. In Sabrina fruit grown on N5 muld, the ester mainly present was trans-2-hexen-1-ol acetate (E20), followed by ethyl hexanoate (E15) and ethyl butyrate (E6) (25.0%, 7.5%, and 5.3%, respectively), while under PE conditions, the most abundant esters were ethyl hexanoate (E15), E20, and ethyl butyrate (E6) (18.3%, 10.0%, and 8.9%, respectively) and in N18 samples were _cis_-3-hexen-1-ol acetate (E19), methyl hexanoate (E14), and ethyl butyrate (E6) (16.1%, 14.2%, and 8.8%, respectively) (Table S2). In Elide, the second-ranking class of volatiles was terpen (9.7%, 9.7%, and 13.6% for PE, N5, and N18, respectively), with linaolol (T4) as the most abundant component for all mulding conditions (7.0%, 5.3%, and 6.4% for PE, N5, and N18, respectively) (Table S1). On the other hand, the second major components of Sabrina cv. were acids, with a percentage of 34.6%, 21.3%, and 23.3% for PE, N5, and N18, respectively, with nonanoic acid (Ac8) representing the predominant component of the group for all treatments (27.6%, 18.5%, and 19.0% in PE, N5, and N18, respectively) (Table S2). Most of the volatiles listed in Table S2 have been already observed in Sabrina strawberries [15; 16] even though the present study catalogued a larger number of VOCs compared to the previous ones, presumably owing to the different pedo-climatic environment, agronomic practices, and analytical method.
## 3 Statistical Analysis  
The data were calculated, as already mentioned, by a second assessor--a trained mathematician specialized in biostatistics (W.L.). Sample size calculation was performed for the reaction time in laterality detection as the primary outcome. Equal variances were assumed in the headache group and the control group. Estimates of effect size (ES) and standard deviation (SD) were obtained by averaging the corresponding measures from [PERSON] (2012) [31], where individuals with carpal tunnel syndrome were compared with healthy controls on laterality ratings of images of hands. The estimated ES and SD were 122 ms and 207 ms, respectively. Similar values were found in a comparison between patients with a history of back pain and those with a history or the presence of back pain [52]. Using the formula to calculate the sample size for the two-tailed \(t\)-test for two independent samples, assuming alpha = 0.05, beta = 0.2, and equal-sized groups, the total sample size was n = 92 (i.e., 46 persons per group). With unequal group sizes, the total sample size would increase to n = 102 and n = 122 if the ratio of group sizes were 2:1 and 3:1, respectively. Assuming a dropout rate of 10% (cf. [31]), it was originally planned to recruit 102 subjects (i.e., 51 subjects per group).  
To describe the differences in outcome variables between the migraine and control groups, five-number summaries were visualized using box plots for descriptive analysis. Non-parametric ANOVA tests with a two-sided significance level of alpha = 0.05 were applied [53]. To adjust for multiple comparisons, the Bonferroni-Holm method was used. For the statistical analysis, we considered whether to calculate correlations using Pearson or Spearman coefficients. Given the ordinal scale level of many variables, Spearman coefficients were used to capture monotonic correlations. All analyses were conducted using the R statistical software package (Version 4.4.1) [54].
## 2 Material and Methods
### Assessment Criteria  
In the literature, different performance metrics are used to determine the accuracy of classification models. In most scientific domains, the area under the receiver operating characteristic curve (ROC AUC) has become the accepted standard metric for assessing binary classifications. Binary classification is a typical activity that makes use of machine learning and computational statistics [33]. In this study, seven measures based on true positive (TP), true negative (TN), false positive (FP), and false negative (FN) were utilized. The formulas of measures are briefly defined as follows [13].  
Figure 7: Proposed deep learning model for fault classification.  
Accuracy (\(A_{cc}\)) is the ratio of the sum of correct predictions to the total samples of dataset.  
\[A_{cc}=\frac{TP+TN}{TP+TN+FP+FN}\times 100\% \tag{2}\]  
Precision (\(P\)) is the ratio of correct predictions of the positive class to the total positive predictions.  
\[P=\frac{TP}{TP+FP} \tag{3}\]  
Recall (\(R\)), also called sensitivity, is the ratio of correct predictions of the positive class to the sum of correct predictions of the positive class and incorrect predictions of the negative class.  
\[R=\frac{TP}{TP+FN} \tag{4}\]  
The F1 score is a weighted average of precision and recall. The F1 score is usually more valuable than accuracy, particularly if the underlying dataset has an uneven class distribution.  
\[F1-score=\frac{2\times(\text{Precision}\times\text{Recall})}{\text{Precision}+ \text{Recall}} \tag{5}\]  
The confusion matrix summarizes the accuracy, specificity, recall, and precision and is used to measure the performance of a machine learning classification problem. In addition, it is an efficient tool for drawing receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUC). The ROC curve is a graphical plot that demonstrates the characteristic capability of a binary classifier.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 2 System Model
### Energy Model  
Assuming that all the nodes transmit with constant power \(P_{t}\), the transmission energy \(E_{tx}(i,s_{i})\) of CM (indexed by \(s_{i}\) of cluster \(i\)) can be written as:  
\[E_{tx}(i,s_{i})=\frac{LP_{t}}{r_{s_{i}i}}. \tag{7}\]  
Assuming, a CM has an initial energy \(E_{initial}(i,s_{i})\), then the residual energy of the node after \(T\) transmissions becomes:  
\[E_{res}(i,s_{i})=E_{initial}(i,s_{i})-\sum\limits_{T}E_{tx}(i,s_{i}). \tag{8}\]  
Since a CH forwards data of all the CMs, the transmission energy of \(i\)th CH becomes:  
\[E_{res(i)}=E_{initial}(i)-\sum\limits_{T}\sum\limits_{s_{i}}E_{tx}(i), \tag{9}\]  
where, \(E_{initial}(i)\) is the initial energy of CH, and  
\[E_{tx}(i)=\sum\limits_{n}\frac{LP_{t}h_{in}}{r_{in}}. \tag{10}\]  
The total energy consumption in the networks after \(T\) transmissions can now be written as:  
\[E_{tot}=\sum\limits_{i\in I}\sum\limits_{s_{i}}E_{tx}(i,s_{i})+\sum\limits_{i \in I}E_{tx}(i). \tag{11}\]
## 2 Related Work
### Histogram Shifting  
Histogram shifting (HS) [8] works on histograms of features; it creates space for embedding by selecting an histogram bin and shifting to the right (resp., to the left) by one position all features in the bins corresponding to values higher (resp., lower) than the one corresponding to the selected bin. In this way, the selected bin can be used, together with the emptied neighboring one, for embedding. As the embedding potential is related to the number of elements in the selected bin, features leading to peaked histograms were sought. Among these, residuals from prediction [9,10] provided good performance. [PERSON] _et al._[11] proposed the use for embedding of the smallest bins (at each tail of the histogram) that could provide the required capacity. In this way, distortion is kept under control. All reversible watermarking methods need, in fact, to strike a balance between high payload and low distortion. From the theoretical point of view, [PERSON] _et al._[12] provided a recursive code construction for binary covers that can achieve the upper bound of the embedding rate under a given distortion constraint. As the title of their paper suggestively reports, they studied the relationship between reversible data hiding and lossless compression, a theme that has been extensively analyzed in literature [13,14]. Generally, the payload of DE-based methods is higher than the one achievable with HS-based ones. Conversely, image quality is, normally, better with HS-based methods than it is with DE-based ones.  
In [15], the image is partitioned into two regions such that they correspond to separated areas in the interpolation-error histogram. [PERSON] and [PERSON] combined multiple predictors to leverage upon the changes in statistical properties from one portion of an image to another [16]. [PERSON] _et al._[17] used the Electronic Code Book (ECB) mode of the Advanced Encryption Standard (AES) algorithm and embedded 3 bits per block. Patches were used instead of single pixels, as an improvement of a reversible and separable watermarking scheme for encrypted images, in [18]. In [19], [PERSON] _et al._, proposed a lossless data hiding scheme for public-key-encrypted images, leveraging upon the homomorphic properties of some cryptosystems. They also proposed a distinction between the terms \"lossless\" and \"reversible\", with the latter indicating that the cover medium can be perfectly recovered, while the former referred to the coincidence of the displayed data with the cover (even though the cover was altered to some extent).
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## Abbreviations  
UAV unmanned aerial vehicle DDQNdeep double Q-learning network GPSRgreedy perimeter stateless routing DDQN-MTGPSRmulti-objective optimized GPSR routing protocol DSDVdestination-sequenced distance-vector OLSRoptimized link state routing DSRdynamic source routing
# Minimum Viable Products for Internet of Things Applications: Common Pitfalls and Practices
## 4 Results
### RQ1: How Do Internet of Things Start-Ups Build Their MVPs in the Early Stage?
#### 4.1.1 Key MVPs from Internet of Things Start-Ups
Most of the hardware designs are done in-house but the activity might also involve outsourcing or contractors. This type is found in Case A, B, D and F, as illustrated in the quote below:  
_\"The development is close to the hardware, so you need to know the entire structure of the hardware. When you are on engine control, you need to understand how the electrical circuit is built to write the code structure\"_ (Case B)  
Functional testing is an important activity for this type of MVP. Testing can be done at the unit level, component level, system level and user level. _\"Q: you make finished physical products every time you prototype? A: Yes. Then they are ready so that a non-technical user can test them. We run some tests ourselves, just watching if it works or not... it is important that it functions all together so that actual users can provide feedback.\"_ (Case B)  
Figure 4: Iterative constructions of MVPs in Internet of Things start-ups. _\"It helped us a lot in proper testing in a live environment and getting an idea of daily usage by a customer._ (Case F)__  
MVP type 2 would be functionally complete and operable with actual data:  
_\"what could be done in-house testing and then there was some field testing ourselves before the shipment. Then of course, the most interesting data came from the real shipment\"_ (Case D)__  
The transitions from MVP type 2 to type 3 involves the shift of effort from hardware development to software development. When a hardware unit as a core value proposition is complete and validated, the added value components, such as, data visualization and processing is more focused. Quality attributes are the focus in MVP type 3. MVP type 3 involves the design and optimization of current MVPs to achieve the best acceptable quality products. The acceptable quality MVPs is reached at a so-called Minimum Acceptable Quality to enable the rapid prototyping. Non-functional testing is the focus for MVP type 3. The integration of software and hardware elements was done and tested regressively, as shown in Case 3:  
_\"They [software components] are at the same time so tightly depending on our hardware that it is not possible to understand the stuff without a deep understanding of the whole.\"_ (Case C)__  
At this step, the focus is on the integration of hardware and software elements to provide the best values:  
_\"Different people work on different parts, so they are easy to distinguish. The electronic boys alternate between the code and developing the product itself.\"_ (Case C)__
## 4 Results  
The dual-phase images in LEVIR-CD [31] were sourced from 20 distinct areas across various cities in Texas, USA. This dataset comprises 637 pairs of very high-resolution (VHR, 0.5 m/pixel) Google Earth (GE) image patches, each measuring 1024 \(\times\) 1024 pixels. These bitemporal images with a period of 5 to 14 years have significant land-use changes, especially the construction growth. LEVIR-CD covers various types of buildings, such as villa residences, tall apartments, small garages, and large warehouses. These bitemporal images are annotated by remote sensing image interpretation experts using binary labels (1 for change and 0 for unchanged). Some examples of these images are shown in Figure 10.  
To optimize GPU memory usage and avoid overfitting, we divided the image into multiple patches each sized 256 \(\times\) 256. Specifically, 80% of the patches are allocated for training, 10% for validation, and the remaining 10% for testing.  
Figure 10: Examples of the training samples used in this paper from the LEVIR-CD dataset.
## 3 Results and Discussion
### Commercial Oil  
Figure 7b compares the IR spectra obtained before and after the aging of the oil-based commercial paint sample. In this case, it was only possible to acquire the spectrum on the grey part as the entire surface was affected by the color change. As for the Oil_V sample, the semi-quantitative evaluation highlights an increase in the region from 3600 to 2600 cm\({}^{-1}\) due to hydrolysis and oxidation reactions [80] (see Supplementary Table S12). A splitting of the band related to the carbonyl group is evident with also the formation of a peak at 1711 cm\({}^{-1}\) associated with fatty acids formed after aging [48]. Also for this sample, there is a decrease in the main binder peaks (Supplementary Table S12) and the appearance of a shoulder at 1630 cm\({}^{-1}\) ascribable to the C=O _stretching_ of diketones [76]. Finally, as in the previous sample (Oil_V), there is a band at 2346 cm\({}^{-1}\), probably related to sulfonic acid [69].  
Concerning the results obtained by THM-Py-GC/MS, unlike what was observed in the stand oil sample, there is an increase in saturated dicarboxylic acids, as can be shown in Table 3. The increase in dicarboxylic acids %D (in particular, azelaic acid) brings to the consequent increase in the ratio A/P and D/P and the decrease in the A/Sub ratio if compared to the value obtained before aging. At the same time, the content of oleic acid decreases and, consequently, the O/S value, as expected. All these considerations confirm the formation of dicarboxylic acids as a result of oxidation, as already hypothesized with ATR-FTIR spectroscopy [48].
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 2 Related Works
However, the mentioned methods are all used for classification tasks of a natural-image domain; they cannot be directly applied to segmentation tasks, especially for 3D medical image segmentation tasks. In fact, the research on medical image segmentation tasks with noisy labels is in its infancy. For example, [PERSON] et al. [12] proposed deploying a meta-learning framework for automatically learning the weights of pixels based on gradient directions, which was an extension of the \"learning to re-weight\" [13] method, from classification tasks to segmentation tasks. However, this method requires extra, trusted subsets of samples for training. [PERSON] et al. introduced a pick-and-learn method with label-quality evaluation andan over-fitting control module [14]. Then, important weights were assigned for dealing with 2D noise-labeled biomedical segmentation. [PERSON] et al. proposed a tri-network [15] by extending co-teaching [16], where they maintained three networks simultaneously for sample selection. This method can be regarded as hard-weighting. However, both the pick-and-learn and tri-network methods have been developed for 2D medical images, and they cannot be directly applied for resource-intensive 3D medical volumetric image segmentation. From the perspective of sample selection, [PERSON] et al. [33] proposed their two-stream networks, based on pixel-wise sample selection. They selected samples from the disagreement area, where two predictions coming from two networks were different [34]. However, this method selected pixel-wise samples and was not suitable for estimating the quality of every slice. Presently, the related investigations for 3D biomedical segmentation with low-quality noisy labels has not aroused enough attention. In this paper, we target the problem of noisy labeled 3D medical organ segmentation. In particular, we focus on the inconsistent noisy labeled slices among the whole volume. After obtaining the quality-awareness scores of slices, we further propose our hybrid noise-tolerance architecture with the help ofa re-weighting strategy, which is different from previous works.