esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Autonomous Shipping Projects
This ambitious plan, created by a collaboration from the lead partners of IBM and ProMare, will bring a fully self-navigating, solar powered trimaran across the Atlantic. Using edge computing technology, IBM's Operational Decision Manager (ODM), multiple onboard sensors, weather input, and a Safety Manager function among other technologies, the _MAYELOWER_ can fully sense its environment with no humans onboard. It will navigate in compliance with the international collision avoidance regulations (COLREGs) [28]. The Belgian company Seafar is currently running trials with its vessel DESEO, a 110 m 5500 dwt containership, between Antwerp and Zebrugge. This vessel, using 4G and 5G networks, has the capability of being controlled from the shore via its RCC, located in Antwerp. There are still crew present on board, as legislation does not currently permit the controlling of a vessel from the shore, hence Seafar has applied for a permit from the Flemish government and other responsible parties to conduct its trials under strict conditions [29]. This project, focused on succeeding at semi-autonomous inland shipping, is motivated by reducing the amount of crew needed onboard from five persons to three [30].
## 3 Methodology
### Data Preparation
#### 3.3.3 Feature Selection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & \(r_{pb}\) & \(p\)**-Value** \\ \hline Distance & 0.14 & \(5.80\times 10^{-28}\) \\ Temperature\_Delivery\_Date & 0.12 & \(1.77\times 10^{-19}\) \\ Delivery Date\_Picking Date & 0.02 & 0.15 \\ Warehousing\_Time & \(-0.002\) & 0.88 \\ Foil\_thickness & 0.14 & \(3.36\times 10^{-27}\) \\ Larger\_Side & \(-0.03\) & 0.04 \\ Smaller\_side & \(-0.05\) & \(1.41\times 10^{-4}\) \\ Container\_Diameter & \(-0.11\) & \(4.65\times 10^{-19}\) \\ Container\_Bearing\_Diameter & \(-0.08\) & \(1.85\times 10^{-9}\) \\ Container\_Weight & \(-0.01\) & 0.42 \\ Container\_Height & 0.06 & \(4.89\times 10^{-7}\) \\ Pallet\_Height & \(-0.02\) & 0.23 \\ Truck\_Weight & 0.01 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Point-biserial correlation coefficient test summary: independent variables vs. “Complaint (Y/N)”. Figure 3: Point-biserial scatter plot “Temperature_Delivery_Date” vs. “Complaint (Y/N)”. Based on the findings, one can conclude that there is insufficient statistical evidence to establish a significant correlation between the variables \"Delivery Date - Picking Date\", \"Warehousing_Time\", \"Container_Weight\", \"Pallet_Height\", and \"Truck_Weight\", and the target variable. Consequently, these variables were excluded from further analysis due to the lack of significant evidence.
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 5 Experimental Result
### Research Questions  
The experimental evaluation of the proposed bug localization method was guided by the following research questions:  
**RQ1:**_Does the proposed bug localization method perform adequately?_  
This question assesses the overall effectiveness and accuracy of the proposed model in predicting buggy source code files. By examining performance metrics such as accuracy, Mean Reciprocal Rank (MRR), and Mean Average Precision (MAP), we aim to determine whether the model achieves competitive or superior results compared with established baselines.  
**RQ2:**_Can the proposed method be reliably applied to bug localization tasks?_  
This question evaluates the practical applicability and robustness of the proposed method across diverse datasets and real-world scenarios. It considers whether the method can consistently deliver reliable results in bug localization tasks involving varying software projects with different complexities and domains.  
These research questions are central to validating the model's suitability and effectiveness for real-world applications. To answer these questions, the experimental results were analyzed in comparison with established baselines, including DNNLoc, Naive Bayes (NB), learn-to-rank (LR), and BugLocator (BL). Statistical verification was performed to confirm whether the observed performance differences were significant.  
The statistical verification process involved normality testing using the Shapiro-Wilk test [43] and subsequent hypothesis testing. When the normality assumption was satisfied (\(p\)-value \(\geq\) 0.05), a \(t\)-test was applied to evaluate the significance of performance differences. For cases where the normality assumption was violated (\(p\)-value \(<\) 0.05), the Wilcoxon signed-rank test [44] was used as a non-parametric alternative.  
The results of these tests showed statistically significant differences between the proposed model and the baseline methods across all datasets. These findings provide strong evidence that the proposed model not only performs well in bug localization tasks but also offers meaningful improvements over existing methods. This supports the conclusion that the proposed method is both effective and applicable to practical bug localization scenarios.
# Glucose Data Classification for Diabetic Patient Monitoring
## 8 Conclusions  
As given in the paper, the real-time monitoring system for the diabetes condition is able to perform with the function of the Internet of Things (IoT). The effectiveness of the personal diabetes monitoring via a mobile application database has been demonstrated. In addition to that, an implementation of the blood glucose measurement system for tele-monitoring is presented. Additionally, after several tests, the evaluation result showed that the system could work appropriately with a delay of transmission less than 32 s. The measured data was sent and received with a valid value by SMS. Finally, the proposed prediction system was evaluated by several simulation experiments and the results demonstrated that the proposed model improved the accuracy of prediction. This model allows a preventive intervention for a diabetic patient and controls the glucose.  
The future work aims to send the blood glucose reading over the cloud so that it can be accessed from any location across the globe. Additionally, a new concept of early detection is planned in order to detect the symptoms of other diseases that need prompt intervention. In addition, the proposed system of monitoring will be enhanced with the inclusion of a content management framework to obtain additional information about physical activity, and communication with electronic health record (EHR) from the hospital information system to get relevant information such as illness, treatments, and drugs. Moreover, we will study the performance of our system and include algorithms to predict the connectivity of the devices in the wireless network [21].  
Methodology and supervision, J.L. and A.O.; formal analysis and investigation, A.R., L.P. and S.S.; writing--original draft preparation, A.R.; writing--review and editing, A.R. and J.L.  
This work has been partially supported by the \"Ministerio de Economia y Competitividad\" in the \"Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento\" within the project under Grant TIN2017-84802-C2-1-P.  
The authors declare no conflict of interest.
# Low-Temperature Plasma as an Approach for Inhibiting a Multi-Species Carigenic Biofilm
## 5 Conclusions  
Low-temperature plasma showed significant antib Biofilm effect against single-and multi-species biofilms formed by _S. mutans_, _S. gordonii_, and _S. sanguinis_, eliminating _S. gordonii_ in both single- and multi-species biofilms and _S. sanguinis_ in the multi-species biofilm. The biofilm model used in this work is representative of an oral variogenic biofilm containing two species (_S. gordonii_ and _S. sanguinis_) that are critical on initial colonization of the dental surface. Both microorganisms were inactivated (approximately 6.2 \(\log_{10}\) CFU/mL and 5.5 \(\log_{10}\) CFU/mL of reduction, respectively) by LTP treatment in exposure times as low as 30 s. Even without complete inactivation, _S. mutans_ viability was also significantly reduced in both single- and multi-species biofilms. In conclusion, low exposure times of LTP affects single- and multi-species variogenic biofilms, which makes LTP treatment a promising candidate to contribute to the development of new protocols for the treatment and control of dental caries.  
Conceptualization, C.Y.K.-I. and S.D.; methodology, C.Y.K.-I. and S.D.; formal analysis, L.W.F.; investigation, B.H.D.P. and L.W.F.; resources, S.D.; writing--original draft preparation, L.W.F.; writing--review and editing, B.H.D.P, S.D. and C.Y.K.-I; supervision, S.D. and B.H.D.P.; project administration, S.D.; funding acquisition, C.Y.K.-I., L.W.F. and S.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Sao Paulo Research Foundation/FAPESP (Grants# 2019/01676-4 and 2019/05856-7). This research was partially supported by the National Institute of Health/National Institute of Dental and Craniofacial Research (NIH/NIDCR--1R21 DE028929-01).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Phase-Type Distributions of Animal Trajectories with Random Walks
## 4 Simulation Model
It was assumed that the animal is in a resting state if it moves less than 5 m, taking into account the error of the GPS device. From the simulated trajectories, the average velocity \(v_{avg}\), the resting distance \(d_{fix,sim}\), and the walking distance \(d_{mov,sim}\) were calculated. Since the random variables were obtained in polar coordinates, they were converted into rectangular coordinates to obtain the waypoint positions (\(x_{i}\), \(y_{i}\)) using the subfunction described in pseudocode Algorithm 2, which computes the simulated trajectories and the new positions \(x_{i}\), \(y_{i}\), as described in [57]. ```
1: Define the species and the their speed \(v\);
2: Define distribution;
3: Input statistical parameters: \(k\), \(\lambda_{4}\), \(\lambda_{1}\), \(\lambda_{2}\) and \(p\);
4:if\(T_{fix}\) has Erlang then
5:\(T_{fix,sim}\)=\(pdf Erlang(k,\lambda_{4})\);
6:else
7:\(T_{fix,sim}\)=\(pdfHyperExpo(p,\lambda_{1},\lambda_{2})\);
8:endif
9:if\(T_{mov,sim}\) has Erlang then
10:\(T_{mov,sim}\)=\(pdf Erlang(k,\lambda_{4})\);
11:else
12:\(T_{mov,sim}\)=\(pdfHyperExpo(p,\lambda_{1},\lambda_{2})\);
13:endif
14:if\(A_{traj,sim}\) has Erlang then
15:\(A_{traj,sim}\)=\(pdf Erlang(k,\lambda_{4})\);
16:else
17:\(A_{traj,sim}\)=\(pdfHyperExpo(p,\lambda_{1},\lambda_{2})\);
18:\(=randomValueHyperExpo\);
19:endif
20:if\((distance_{fix}=v\times T_{fix})<5\sim m\)then
21:\(dist=0\sim m\);
22:else
23:\(dist=v_{avg}\times T_{mov}\);
24:endif
25:\(angle_{Real}=A_{traj}+angle_{Real}\)
26:\(x_{i}=dist\times cos(ang_{Real})\)
27:\(y_{i}=dist\times sin(ang_{Real})\)return\(x_{i},y_{i}\)
```  
**Algorithm 2** Trajectories algorithm  
For the case of the KS test, Figure (b)b shows the hypothetical and real CDFs of each random variable. Hence, the approximations is as follows: \(T_{\text{fix}}\) and \(T_{\text{mov}}\) are characterized with a hyper-exponential distribution, and \(A_{\text{traj}}\) has an Erlang distribution.
## 4 Results and Discussion
### Area of Optimal IVO and IVC  
The energy losses during transition, the minimum in-cylinder pressure of the inactive cylinder, and the mass fraction of oxygen in the exhaust gas have been discussed above. Figure 18 shows the line of in-cylinder pressure (0.02 MPa) of the inactive cylinder and the line of mass fraction of oxygen (1%). On the premise of guaranteeing the mass fraction of oxygen under 1% and keeping the minimum in-cylinder pressure of the inactive cylinder above 0.02 MPa, the optimal areas for IVO and IVC can be determined by the variation features of energy losses during the transition process. Therefore, A1 and A2 are confirmed as the optimal area under the firing densities of 50% and 66.7%, respectively. As we can see, the energy losses in the optimal area decrease with the increase in firing density.
# Eco-Efficient Mobility in Smart City Scenarios
## 4 Proposed Solution
### Validation Tests  
In order to study the communication performance of our prototype, we have placed it aboard an electric scooter and travelled around the Espinardo Campus at the University of Murcia (Spain). These vehicles are a clear example of personal mobility device that can greatly improve urban mobility, but lack an external energy source and proper C-ITS integration. Together with e-bikes, e-scooters are flooding the urban landscape these days. In addition, the selected area for conducting our experiments presents realistic and heterogeneous conditions due to the presence of communication obstacles such as buildings, trees, etc.  
Figure 4: Implemented OBU prototype. (**a**) Main board and communication concentrator, (**b**) Unit installed in its case.  
The data were retrieved from the OBU's embedded sensors and transmitted to a Graphana cloud server by using a developed Python script. For the data transmission, one or both of the available LPWAN links established with (i) a private LoRaWAN gateway (Kerlink Wirnet Station) installed in the campus or (ii) a cellular NB-IoT infrastructure deployed in the area by a telecommunications company (Vodafone) were employed.  
The received data were stored in a database and showed in real time to the user by means of the dashboard presented in Figure 5. As observed, different temporal series are provided; hence, the user may access this information by using her smart device or a regular computer to check the current status or a historical record of the device parameters. In the specific example depicted in Figure 5, different plots representing network metrics such as throughput or packet loss can be seen, in this case extracted from the NB-IoT link. This information is relevant to evaluate the performance of the communications and detect disconnections or low coverage areas. Data extracted from some of the equipped sensors and transmitted over the LoRaWAN connection are also shown, namely, a plot representing the scooter's speed and another one showing the evolution of the detected level of CO\({}_{2}\) during a ride. In this case, the unit was suddenly exposed to the exhaust pipe of a combustion motorbike to check the good operation of the sensor. In addition, other parameters such as GPS position, inertial sensor measurements, etc. are also transmitted to the cloud. All these raw data are stored in the server, so they are available to be employed by final services involving data processing or analytics. In this line, several applications like those identified in Section 3 may be developed. For example, we are currently working on an online vehicle tracking service to avoid thefts and the use of Machine Learning (ML) techniques for the detection of falling events by analyzing accelerometer data [39].  
Figure 5: Data dashboard with results from validation tests.
## 4 Conclusions  
It was shown that high recording density is possible using a compound multiplexing method that combines shift multiplexing with peristrophic multiplexing. Shift multiplexing with spherical reference waves has been previously studied, and it was determined that the \(y\)-axis selectivity is inferior, thereby making high-density recording difficult. It was shown in the present study that \(y\)-axis selectivity was substantially improved by adding a peristrophic multiplexing method to enable such high recording density. In this paper, we provided fundamental proof of the effectiveness of shift-peristrophic multiplexing and the possibility of high recording density. If shift multiplexing with a 10 \(\mu\)m pitch is combined with a rotation of 10\({}^{\circ}\), it is possible to easily achieve 1 Tbits/inch\({}^{2}\) recording density. This possibility has been experimentally proven and will be further examined by analysis of a more practical system in the future.
###### Abstract  
In this study, we investigate the usefulness of the spectral whitening procedure, devised by one of the authors as a preprocessing stage of envelope-detected single-look synthetic aperture radar (SAR) images, in application contexts where phase information is relevant. In the first experiment, each of the raw datasets of an interferometric pair of COSMO-SkyMed images, representing industrial buildings amidst vegetated areas, was individually (1) synthesized by the SAR processor without Fourier-domain Hamming windowing; (2) synthesized with Hamming windowing, used to improve the focalization of targets, with the drawback of spatially correlating speckle; and (3) processed for the whitening of complex speckle, using the data obtained in (2). The interferograms were produced in the three cases, and interferometric coherence and phase maps were calculated through 3 \(\times\) 3 boxcar filtering. In (1), coherence is low on vegetation; the presence of high sidelobes in the system's point-spread function (PSF) causes the spread of areas featuring high backscattering. In (2), point targets and buildings are better defined, thanks to the sidelobe suppression achieved by the frequency windowing, but the background coherence is abnormally increased because of the spatial correlation introduced by the Hamming window. Case (3) is the most favorable because the whitening operation results in low coherence in vegetation and high coherence in buildings, where the effects of windowing are preserved. An analysis of the phase map reveals that (3) is likely to be facilitated also in terms of unwrapping. Results are presented on a TerraSAR-X/TanDEM-X (TSX-TDX) image pair by processing the interferograms of original and whitened data using a non-local filter. The main results are as follows: (1) with autocorrelated speckle, the estimation error of coherence may attain 16% and inversely depends on the heterogeneity of the scene; and (2) the cleanness and accuracy of the phase are increased by the preliminary whitening stage, as witnessed by the number of residues, reduced by 24%. Benefits are also expected not only for differential InSAR (DInSAR) but also for any coherent analysis and processing carried out performed on SLC data.  
deconvolution; interferometric coherence; noise whitening; SAR interferometry (InSAR); single-look complex (SLC) images; synthetic aperture radar (SAR); unbiased estimation Article
## 2 Materials and Methods
### Electrochemical Tests
#### 2.3.1 Corrosion Potential  
The open-circuit potential (OCP) was monitored over 24 h using the \"Ecorr vs. Time\" technique, with data recorded every 30 s or when a potential shift of 100 mV occurred. The results were analyzed to determine whether the material exhibited stable corrosion potential, passivation behavior or progressive degradation over time.
## 2 Materials and Methods
### Statistical Methods  
Data were summarized using frequencies and percentages for categorical data and mean and standard deviations for continuous data. Analyses were conducted using multiple logistic regression modeling techniques. The following outcomes of interest were investigated: having good knowledge about COVID-19 (Model 1); fear of falling ill with COVID-19 (Model 2); practicing respiratory etiquette (Model 3); adherence to hand hygiene (Model 4). The following variables were included in all models: gender (male = 0; female = 1); age (continuous); studying medical or life sciences majors (no = 0; yes = 1); marital status (married = 0; single = 1). In Models from 2 to 4 the variable having good knowledge (poor = 0; good = 1) was also included. In Models 3 and 4 the following variables were also included: vulnerability to COVID-19 (not at all/little/moderate = 0; quite a bit/extreme = 1) and fear of falling ill with COVID-19 (not at all/little/moderate = 0; quite a bit/extreme = 1). A significance level of 5% was used for hypothesis testing. Moreover, to assess whether the perceived risk of COVID-19 (i.e., vulnerability and fear of falling ill with COVID-19) acts as a mediator of the effect of knowledge on preventive behaviors (practicing respiratory etiquette and adherence to hand hygiene), we used the Baron and Kenny method [15]. The null hypothesis of no-indirect effect was rejected if both regressions (i.e., of the knowledge on perception and of perception on behavior) showed a statistically significant effect. This procedure was carried out considering the vulnerability and severity variables separately.  
Statistical analysis was performed using the STATA software program, version 16 [16].  
The data set was deposited in the Mendeley Data repository (doi:10.17632/7 stvxrdznc.1).
# Embedded Zero-Shot Image Classification Based on Bidirectional Feature Mapping
## 2 Related Work  
### Embedded Zero-Shot Image Classification  
Embedded zero-shot image classification methods require learning a feature space mapping function on the seen class in order to achieve knowledge migration from the seen class to the unseen class and to classify the unseen class. Embedding-based methods map image features and semantic information into a certain space and subsequently perform classification based on strategies such as nearest neighbor search. In general, embedded zero-shot image classification methods can be categorized into three types: visual-semantic mapping, semantic-visual mapping, and mapping in a common space. The DeViSE model proposed by [PERSON] et al. (2013) uses a pre-trained skip-gram model and a CNN to extract the semantic and visual features of an image as inputs and chooses hinge loss as the model's loss function to construct a deep zero-shot classification model. [PERSON] et al. (2014) used a generalized dictionary model to map the visual features and labels of an image into the common space and then used a self-training strategy to incorporate reliable test samples into the model learning process to make the model performance improve. [PERSON] et al. (2015) proposed a novel ecological supervision-based approach to learn classifier weights by applying knowledge graphs and graph convolution to a comparative learning framework, which accurately exploits the hierarchical structure between target species and explores potential relationships between categories. [PERSON] et al. (2016) use visual and semantic information to assist the model in mining inter-class relationships and leveraging learned knowledge, and then use graph convolution to optimize the classifier. [PERSON] et al. (2017) design a spatial attention mechanism to extract key visual features and propose a semantic fusion approach to enrich semantic knowledge. [PERSON] et al. (2018) mitigate the modal heterogeneity and domain drift problems by decoupling visual semantic features to complement different modal information.  
Since embedding-based methods are trained using only seen class samples in the training phase, it leads to the problem that the model will be biased towards seen class samples in the generalized zero-shot image classification task. For this problem, existing methods generally mitigate the problem by designing the loss function or changing the feature mapping approach of the model. Previous embedded models only use a single feature mapping approach to learn the mapping function, which results in the model not being able to fully utilize the information of feature representations and only learn a limited number of feature relationships ([PERSON] et al., 2016). Therefore, it is important to deeply explore the intrinsic feature connections between visual and semantic features for the embedded zero-shot image classification task.