esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensing Performance of a Vibrotactile Glove for Deaf-Blind People
## 1 Introduction
### Case Scenario  
TactileCom is aimed at deaf-blind individuals, so it is specifically helpful when somebody wants to convey a message to a person with deaf-blindness. Figure 1 presents an overview of the scenario with all the parts involved. On one side (that of the deaf-blind user), the system is composed of the vibrotactile glove to receive messages and a small keyboard to send messages. On the other side, the unimpaired interlocutor sends and receives messages by means of a computer, i.e., a tablet or smartphone, which implements a voice recognition system.  
In daily life, the interlocutor of a deaf-blind individual can send a message or idea by choosing one of the three selection modes included in the mobile application: an icon menu, typing, or voice recognition. The message is transmitted via the wireless link to the efferent HMI, i.e., the glove. The messages are coded in an abstract representation [33] and delivered on the fingers by a stimulation pattern.  
The codification strategy was suggested by potential users. The main principals for that suggested representation are: (i) the users are already familiar with these kind of communication, and (ii) the users only need a small amount of concepts to communicate. It was the latter that prompted the election of the stimulation patterns or tactons [35] analysed in the following sections. Hence, the simplest tactons were the ones chosen as a starting point to facilitate the everyday use of the HMI. Additionally, due to the abstract meaning-stimulus relationship, this set of tactons scales better than those that use a more mnemonic meaning-stimulus relationship [33]. Also, this method is faster and easier to decode than those that only transmit characters.  
The proposed system leads to an improvement in communication compared with the tactile sign language, which requires proximity between the two interlocutors. Moreover, the wireless link between the two interfaces represents a clear benefit in terms of portability.
# Comparative Study of Moodle Plugins to Facilitate the Adoption of Computer-Based Assessments
## 2 Related Research
### Moodle Plugins for Importing Questions
#### 2.2.4 Spreadsheet-Based Plugins  
Several plugins based on spreadsheets have been developed. Most of these require Microsoft(r) Excel to be installed, and were developed using the Visual Basic for Application (VBA) programming language (meaning that macOS users may experience problems). These plugins do not require an Internet connection to create the question pool:  
* CQ4M Generator  
This allows for the creation of large question pools for Moodle based on Excel ([PERSON], [PERSON] CQ4M Generator; [[https://sites.google.com/site/cq4](https://sites.google.com/site/cq4) mgenerator/home]([https://sites.google.com/site/cq4](https://sites.google.com/site/cq4) mgenerator/home) (accessed on 16 August 2022). It includes MCQs, numerical, Cloze, and binomial distribution questions. Each type of question is stored in a different Excel file, and question data are directly entered into the corresponding cells, with the exception of Cloze questions, where the lines of code must be directly entered. The spreadsheet is totally unlocked, so the user needs to be careful when entering questions as important cells may be deleted unintentionally. In addition, if the question data are not entered correctly, there is no warning message. This is also the case when generating the XML file, meaning that some errors may appear when the file is imported into Moodle. The language used in CQ4M Generator is English, but the message boxes for the macros are in Dutch. Excel functions can be used for calculation questions. Consequently, CQ4M Generator can create parameterised questions;  
* Moodle Cloze and GIFT Code Generator  
This is an Excel-based plugin that facilitates the question creation process by generating lines of code that are ready to be imported into Moodle [51]. Different types of questions such as MCQs, true/false, missing word, short answer, essay and Cloze are supported. As the Excel spreadsheet is totally unlocked, the user must enter the question data carefully into the corresponding cells (but it is not necessary for any line of code). Important cells may be deleted unintentionally, and no warning message is displayed if this occurs. Cloze questions include spelling questions. Moodle Cloze and GIFT Code Generator generates a GIFT or XML file to be imported into Moodle;  
* Tool based on VBA for the generation of question pools in Moodle  
This allows for the generation of question pools for Moodle from an Excel spreadsheet [57]. The following types of question are supported: MCQs, true/false and numerical questions. A tutorial is provided to guide the user when entering question data. This tool also allows for the introduction of images. Finally, an XML file is generated ready to be imported into Moodle.
## 1 Introduction
[19] conducted laboratory-scale experiments to change the calcium oxide content of an industrial slag systematically. The results showed that the strength of cement composites improved with higher calcium oxide contents, which is consistent with results on the dissolution kinetics of other SCMs [27]. Considering the compositions presented in Table 1, variations beyond the calcium oxide content are apparent when comparing slags from different smelters. Therefore, the effect of changes in concentrations of other constituents than calcium oxide on the reactivity is of interest. The literature data on other systems can be used to indicate the effect of other constituents. Studies have shown that the degree of polymerization affects the dissolution behavior of SCMs [27; 28]. In iron silicate copper slags, the FeO/SiO\({}_{2}\) ratio plays a significant role in deciding the polymerization of the silicate network. Thus, since the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Ref. ** & **FeO/SiO\({}_{2}\)** & **FeO 1** & **SiO\({}_{2}\)** & ** CaO** & **Al\({}_{2}\)O\({}_{3}\)** & **MgO** & **ZnO 1** & **Cu 1** \\ \hline
[17] & 1.12 & 37.9 & 33.9 & 12.7 & 4.7 & 0.8 & / & / \\
[19] & 1.14 & 37.9 & 33.3 & 12.3 & 4.6 & 1.1 & 1.3 & 1.4 \\
[19] & 1.14 & 36.9 & 32.3 & 19.5 & 3.9 & 0.9 & 1.2 & 1.2 \\
[16; 21] & 1.14 & 36.8 & 32.3 & 3.9 & 11.0 & / & /role of iron in supplementary cementitious reactions is not fully understood [31; 32], the effect of polymerization in iron silicate slags is, most likely, not straightforward. Furthermore, the effect of alumina content has been presented for other systems [9; 27], but, as apparent from Table 1, the range of the alumina content, and differences in concentrations of cations available for charge-balancing aluminum in the silicate network, indicate an intricate relationship. In conclusion, the literature suggests that iron silicate copper slags are suitable to incorporate in SCM applications. However, data on how components beyond calcium oxide affect the performance of the iron silicate slag as an SCM is lacking. This observation highlights the need for controlled studies on the effect of changes in chemical composition on the reactivity of iron silicate slags. [PERSON] et al. [19] used an industrial slag as the base material for changing the chemical composition. Ideally, synthetic slags generated in laboratory scale experiments should be aimed at since this approach would allow the composition to be extended beyond the operational window of a specific smelter. Studies on synthesizing iron silicate copper slags on a laboratory scale for subsequent evaluation of performance as SCM could not be found in the literature. Therefore, the present paper aims to develop a method for synthesizing iron silicate slags, which can be employed to produce relevant and repeatable data and, consequently, open the field to study the effect of chemical composition on reactivity.
# Recent Advances in Optical Injection Locking for Visible Light Communication Applications
## 3 Enhancement Effects of OIL on the Characteristics of Different Devices
The measured output power at low, medium, and high injection currents for both high-reflection and low-reflection system configurations and the optical power of a free-running one are presented in Table 2.  
Additionally, in recent years, researchers have managed to utilize VCSELs as injected slave lasers and have found some interesting VCSEL characteristics in the infrared regime. These devices have two orthogonal polarizations of the fundamental transverse mode: the parallelly polarized mode and the orthogonally polarized mode. The mode competition means that one of the modes is the main mode and the other is its subsidiary. However, the situation can reverse under different conditions of the injection signals, which means that the polarizations can be switched by changing the intensity of injections or detuning frequencies [22]. Spikes can be generated by utilizing OIL in slave lasers, which can be further applied in optical spike neural networks. [PERSON] et al. proposed an approach to generate neuron-like spikes for self-injection-locked VCSELs (\(\sim\)1.56 \(\upmu\)m) using multifrequency switching [23]. A controllable spiking coding scheme that utilized switching was designed, which reached speeds of up to 1 Gbps experimentally. The affiliation of [24] used optical inputs extracted from digital images as signals that were injected into VCSELs (\(\sim\)1.30 \(\upmu\)m), thus achieving all-optical binary convolution. In summary, the typical implementation of OIL systems is based on packaged LDs and external cavities with reflective setups. These compact systems can obtain considerable decreases in linewidth and increases in output power and modulation bandwidth whilst \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Injection Current**} & \multicolumn{3}{c}{**Working Power (mW)**} \\ \cline{2-4}  & **Free Running** & **High-Reflection** & **Low-Reflection** \\  & **System** & **System** & **System** \\ \hline
130 & 3.4 & 3 & 14 \\
260 & 26 & 13 & 23 \\
390 & 186 & 93 & 180 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The measured output power at low, medium and high injection currents for both high-reflection and low-reflection system configurations as well as the optical power of a free-running LD system [21]. Figure 3: (**a**) The enhancement in the power of free-running (black) and self-injection-locked (green) systems for green LDs [18]; the normalized multiwavelength spectra of self-injection-locked external cavity systems with simultaneous (**b**) dual-and (**c**) four-mode generation, with an injection current (temperature) of 50 mA (40 \({}^{\circ}\)C) from a multiwavelength laser system [20]. maintaining a high quality of stability, which is embodied by a satisfactory side-mode suppression ratio (SMSR). Hence, OIL-based optical transmitters could function competently as vital light sources in a variety of other applications, and have shown great potential for implementation in high-bitrate VLC schemes.
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Design of the Status-Byte-Assisted RDMA Transmission Mechanism
#### 3.2.4 Data-Sending Module  
The data-sending module first checks the status byte stored in the second status byte memory block to determine whether there are available receiving memory blocks. Next, it checks the first status byte memory block to determine whether there are available sending memory blocks, and then executes the data-sending process.  
Figure 6 shows the flowchart of the data-sending process. Specifically, the steps of the data-sending process are as follows:  
1. Check the status of the receiving memory blocks: The sending thread examines the second status byte memory block to determine the status of the receiving memory blocks. If the \(j\)-th receiving memory block contains data (status is 1), it means there are no available memory blocks, and synchronization operations must be performed until the \(j\)-th receiving memory block is empty (status is 0);
2. Check the status of the sending memory blocks: The sending thread verifies the first status byte memory block to determine the status of the \(j\)-th sending memory block. If the \(j\)-th sending memory block is empty (status is 0), it waits for the filling thread to fill the data. If the memory block contains data (status is 1), the sending thread initiates RDMA Write requests to transmit the data stored in the sending memory block to the \(j\)-th receiving memory block on the receiver end;
3. Update the status byte: The sending thread uses inline operations to update the status byte in the third status byte memory block, reflecting that the \(j\)-th receiving memory block now contains data. After the data sending is complete, the sending thread updates the corresponding status byte of the sending memory block in the first status byte memory block to indicate that it is empty. Additionally, the sending thread updates the status byte in the second status byte memory block to reflect that the \(j\)-th receiving memory block now contains data and waits for the receiver to process the data.  
Figure 5: Flowchart of the data-filling process.
# Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting
## 3 Proposed Method
### Patch Extraction  
To overcome the characteristic problems of AUS images, we developed an image-patch voting system. Patch-based voting is also useful for augmenting training data. Image-patch voting makes our system robust to noise and prevents it from being affected by unrelated anatomical structures such as the bladder (see Figure 1) by generating a joint solution to the decisions made for patches of many different locations and scales. However, a patch-based system can only extract local information, which may be insufficient for AUS images due to their low SNR values. Therefore, we propose to create multiple patches of different sizes with matching centers to extract information from different scales. As shown in Figure 3, we decided to use four concentric patches, which we call quadruplet patches. The sizes of the patches in our system were \(64\times 64\), \(128\times 128\), \(256\times 256\) and \(512\times 512\) pixels. All patches were downsized to \(64\times 64\) pixels, except for the smallest scale, which was already \(64\times 64\) pixels. The resulting quadruplet patches cast votes for the endpoints of the ellipsoid diameters. For the voting process, we trained a novel neural model explained in Section 3.2.  
The locations of the training patches were chosen randomly from a normal distribution around diameter endpoints, while evenly spaced patches were created from test images in a sliding window manner with a stride of 10 pixels. The system extracts 200 patches from each transverse and sagittal image of the training set, which can be considered as an augmentation  
Figure 3: Patch-extraction process. For a given image location \(T^{j}(x,y)\) of patient \(j\), patches from four different scales centering \((x,y)\) were extracted. All of them were downsized to the smallest scale, and the quadruplet patch \(PQ(T^{j}(x,y))\) was obtained.  
method that increases size of the training data set. The number of patches extracted from test images changes according to the image size. Sample training patch locations are represented on Figure 4a,b, and test patch locations are represented on Figure 4c,d.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 4 Experimental Evaluation & Results
### Classification on Real Data
However, the evaluation also returned a few false positives. The examination of these false positives will allow us to understand the reasons that evoked classifiers errors and we are currently in the process of examining more features that can be added to the model to increase its accuracy in real data. A typical mis-classification error that has been found in this process is depicted in Figure 9 where the creeping line pattern of SIEM PILOT has falsely detected as Steps with a higher probability. A reason behind this error, and in general behind the confusion of the Steps pattern with all other patterns, is probably hidden in the shapelet transformation step. In that step, the time-series is converted to a vector in the shapelets' space using the distance of the time-series from each shapelet. This transformation ignores the number or frequency of occurrence of simple shapelets (e.g., step) in the time-series and keeps only the minimum distance (i.e., from the part of the time-series that mostly resembles to the shapelet). If GENDIS fails to extract more complex shapelets (e.g., consecutive steps, or U-turns) then the transformation will fail. In order to avoid this, either more and longer training samples (time-series) must be used or the transformation method has to be redesigned and this is part of our future work. Figure 8: A creeping line pattern detected in a small section of a long trajectory (**left**) and a zoom on the selected section (**right**). Figure 9: An example of a mis-classification of a creeping line pattern as steps.
# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 3 Ethical Considerations
### Patient Privacy  
As ML technologies continue to permeate healthcare, safeguarding patient privacy has emerged as a paramount concern. The integration of artificial intelligence in clinical settings offers substantial opportunities for enhancing patient outcomes, but it also raises significant ethical, legal, and technological challenges related to the management of sensitive patient data. A review by [PERSON] and [PERSON] [16] emphasizes the need for robust frameworks that ensure patient privacy while enabling data-driven insights.  
When leveraging ML algorithms to predict hospital LoS, patient privacy is a vital concern. These predictive models can use various types of sensitive patient data, including medical records, demographic information, and lifestyle factors, which raises several important privacy issues.  
* Data security and unauthorized access: \"94% of hospitals had at least one security breach in the past two years\" were mentioned in [17]. Increased risk of data breaches and unauthorized access to sensitive patient information due to complex big data systems;
* Privacy risks of data aggregation: Combining data from multiple sources can lead to the re-identification of individuals;
* Challenges of data anonymization: Anonymization techniques can be flawed, risking patient re-identification;
* Data governance and compliance issues: Ensuring data management practices meet legal standards is challenging [18];
* Informed consent and ethical considerations: Patients must be clearly informed and provide consent for their data to be used;
* Data integrity and accuracy: Accurate and complete data are essential for both privacy and effective predictions [19].
## 1 Introduction
Using the tools within Google Earth Engine, it was found that global dryland tree cover was significantly underrated and recent estimates exceeded previous ones by over 40% [41]. The goal of this paper is to evaluate hydrologic and land cover changes in global arid/semi-arid regions over the last decade (2010-2019), through time series and trends analysis, correlation and interactions of multiple hydrological indicators and vegetation indices. Another key point of this research is to evaluate two major process-based land surface modeling and data assimilation models (GLDAS and FLDAS) by satellite-based hydrological products. Additional analysis displayed an overestimation of surface soil moisture (SSM) and an underestimation of evapotranspiration (ET), as well as a discovery of a strong discrepancy observed in Egypt by the aforementioned models. To mitigate this discrepancy, machine learning models using extra data from satellites (Moderate Resolution Imaging Spectroradiometer (MODIS)) were developed which increased the process-based model performance significantly and provided results with lower RMSE and bias. This optimization effort highlights the promising applications of the synergistic approach of data-driven and process-based modeling for hydrological studies. This paper is organized into four sections. The information regarding the study area, data sources and methods are described in Section 2. Section 3.1 illustrates the time series boxplots of multiple hydrological parameters used over arid regions. In Section 3.2, the trends of ET, soil moisture and precipitation during the period of 2010-2019 over global drylands are presented. Section 3.3 shows the landcover and water storage changes of selected areas. In Section 3.4, the comparison between FLDAS and GLDAS surface soil moisture and ET products is shown by correlation maps, followed by the approach of data-driven modelling of the aforementioned products. Finally, the results are discussed in detail in Section 4 and a conclusion in Section 5 is provided for the paper.
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 4 Results
### Visualising Local and Global Heatmaps of Features Using LRP
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model Name** & **Train/Test** & **Acc** & **Preci** & **Recall** & **Speci** & **F1-Score** & **Cross-V** \\ \hline
**M-1D-CNN-1-28 \({}^{\star}\)** & **80/20** & **0.8264** & **0.7011** & **0.6189** & **0.9028** & **0.657** & **Yes** \\ \hline M-1D-CNN-2-28 \({}^{\star}\) & 80/20 & 0.8041 & 0.6727 & 0.5497 & 0.8985 & 0.5978 & Yes \\ \hline Logistic Regression-28 \({}^{\star}\) & 80/20 & 0.7993 & 0.6616 & 0.5221 & 0.9015 & 0.583 & Yes \\ \hline Decision Tree-28 \({}^{\star}\) & 80/20 & 0.7864 & 0.6875 & 0.3794 & 0.9363 & 0.4881 & Yes \\ \hline Random Forest-28 \({}^{\star}\) & 80/20 & 0.7773 & 0.7172 & 0.2842 & 0.9588 & 0.4066 & Yes \\ \hline SVM Linear-28 \({}^{\star}\) & 80/20 & 0.7973 & 0.656 & 0.5209 & 0.8991 & 0.5802 & Yes \\ \hline SVM RBF-28 \({}^{\star}\) & 80/20 & 0.7785 & 0.6358 & 0.4159 & 0.9122 & 0.5023 & Yes \\ \hline XG Boost-28 \({}^{\star}\) & 80/20 & 0.7649 & 0.5727 & 0.4956 & 0.8641 & 0.5311 & Yes \\ \hline
**Results in Literature** & – & – & – & – & – & – & – \\ \hline Logistic Regression [62] & 75/25 & 0.8003 & 0.6796 & 0.5367 & – & 0.5998 & No \\ \hline Random Forest [62] & 75/25 & 0.7975 & 0.6694 & 0.4796 & – & 0.569 & No \\ \hline SVM RBF [62] & 75/25 & 0.7622 & 0.5837 & 0.5122 & – & 0.5457 & No \\ \hline Logistic Regression [56] & 70/30 & 0.8075 & – & – & – & – & No \\ \hline Random Forest [56] & 80/20 & 0.8088 & – & – & – & No \\ \hline SVM & 80/20 & 0.8201 & – & – & – & – & No \\ \hline ADA Boost [56] & 80/20 & 0.8153 & – & – & – & – & No \\ \hline XG Boost-28 [56] & 80/20 & 0.8294 & – & – & – & – & No \\ \hline Logistic Regression [63] & 80/20 & 0.8005 & – & – & – & – & No \\ \hline
**Reduced Features Results** & – & – & – & – & – & – & – \\ \hline M-1D-CNN-4-16 \({}^{\star}\) & 80/20 & 0.8462 & 0.7339 & 0.6718 & 0.9104 & 0.7014 & Yes \\ \hline
**M-1D-CNN-5-16 \({}^{\star}\)** & 80/20 & **0.8554** & **0.7399** & **0.713** & 0.908 & **0.726** & Yes \\ \hline Logistic Regression-16 \({}^{\star}\) & 80/20 & 0.7996 & 0.6633 & 0.52 & 0.9026 & 0.5823 & Yes \\ \hline Decision Tree-16 \({}^{\star}\) & 80/20 & 0.7871 & 0.6911 & 0.3788 & 0.9374 & 0.4886 & Yes \\ \hline Random Forest-16 \({}^{\star}\) & 80/20 & 0.7807 & 0.7058 & 0.3139 & 0.9521 & 0.4337 & Yes \\ \hline SVM-16 \({}^{\star}\) & 80/20 & 0.7986 & 0.6571 & 0.5278 & 0.8984 & 0.5849 & Yes \\ \hline XG Boost-16 \({}^{\star}\) & 80/20 & 0.7618 & 0.5645 & 0.5035 & 0.8569 & 0.532 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of ours (with \({}^{\star}\)) vs. other models on TCCDD. Accuracy, precision, specificity, and cross-validation are represented by Acc, Preci, Speci, and Cross-V, respectively.