esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 4 Experiments and Discussion
### Datasets
#### 4.1.1 The Toronto Emotional Speech Set (TESS)  
TESS is a publicly available dataset designed to support research in emotion recognition from speech signals. It contains recordings of multiple emotions expressed by actors and is specifically curated to aid in the development and validation of emotion recognition systems. The dataset comprises a total of 2800 utterances, with each of the 200 target words spoken in all seven emotional states: anger, disgust, fear, happiness, pleasant surprise, sadness, and neutral. The dataset is uniformly distributed across all seven emotions (Figure 3), with each emotion having an equal number of utterances. The recordings are performed by two actresses representing two age groups (young and older adults), which adds diversity in terms of age variation to the dataset. All recordings in TESS are available in the high-quality WAV format, ensuring minimal loss of information and making the dataset suitable for various signal processing and machine learning tasks.
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 5 Phase Estimation of Noisy Coherent State
### Robustness of M-estimators  
We will now analyze the robustness of the two M-estimators, the sample mean, and the median. First, we analyzed the FBP (14) for phase estimation, as shown in Figure 4. To produce this figure, we randomly replaced data with artificial data, which were generated by the normal distribution \(\mathcal{N}(1000,0.1)\). The step size for increasing the number of replacing outliers (outlier counts) is 250 in Figure 4. In this setting, an estimator is regarded as being outside the parameter space when it returns the value \(\arctan(1)\simeq 0.785\). This corresponds to \(\hat{\alpha}_{R}\) and \(\hat{\alpha}_{I}\) being close to 1000.  
Figure 3: Performances of M-estimators for phase estimation of a coherent state in the presence of a single outlier quantum state.  
Figure 2: Performances of M-estimators (bisquare and gamma) and the standard estimators (mean and median) as functions of the sample size.  
Based on this figure, FBPs for four estimators were roughly estimated as follows: mean = 1500/5000 = 0.3, bisquare = 1750/5000 = 0.35, gamma and median = 2750/5000 = 0.55. From a first glance at this figure, we might conclude that the median and gamma M-estimators are robust estimators. However, we already know from Figure 3 that the median does not show consistency as an estimator. This discrepancy is easily resolved by realizing that the definition of the FBP is not related to the actual estimate at all. From this simple example, we conclude that the FBP should not be used as a measure of robustness a priori.  
Next, we plotted the \(\varepsilon\)-curve proposed at the end of Section 3. This is to plot behaviors of an M-estimator as a function of the contamination parameter \(\varepsilon\) for a fixed sample size. In Figure 5, we show the \(\varepsilon\)-curve for phase estimation for the sample size \(n=5000\). From this figure, we find that gamma M-estimators were the most robust estimators for small \(\varepsilon\). By contrast, the sample mean and the median smoothly deviated from the true value. However, it is noted that the bisquare and gamma M-estimators behaved in uncontrollable manners after some threshold: \(\varepsilon\simeq 0.25\) for bisquare and \(\varepsilon\simeq 0.35\) for gamma.  
Before we move to the next subsection, we report the average number of iterations of the M-estimator algorithm in Tables 1 and 2. From the two tables, we see that the number of iterations needed to get a robust estimate was relatively small. This shows that the M-estimator can be implemented efficiently.  
Figure 4: The finite breakdown point (FBP) for estimating phase of a coherent state.  
Figure 5: \(\varepsilon\)-curve for phase estimation for the sample size \(n=5000\).
###### Abstract  
The usage of images in different fields has increased dramatically, especially in medical image analysis and social media. Many risks can threaten the integrity and confidentiality of digital images transmitted through the internet. As such, the preservation of the contents of these images is of the utmost importance for sensitive healthcare systems. In this paper, the researchers propose a block-based approach to protect the integrity of digital images by detecting and localizing forgeries. It employs a visual cryptography-based watermarking approach to provide the capabilities of forgery detection and localization. In this watermarking scheme, features and key and secret shares are generated. The feature share is constructed by extracting features from equal-sized blocks of the image by using a Walsh transform, a local binary pattern and a discrete wavelet transform. Then, the key share is generated randomly from each image block, and the secret share is constructed by applying the XOR operation between the watermark, feature share and key share. The CASIA V 1.0 and SIPI datasets were used to check the performance and robustness of the proposed method. The experimental results from these datasets revealed that the percentages of the precision, recall and F1 score classification indicators were approximately 97% for these indicators, while the percentages of the TAF and NC image quality indicators were approximately 97% and 96% after applying several known image processing and geometric attacks. Furthermore, the comparative experimental results with the state-of-art approaches proved the robustness and noticeable improvement in the proposed approach for the detection and localization of image forgeries in terms of classification and quality measures.  
image forgery detection; image forgery localization; Walsh Hadmard transform; visual cryptography; watermarking +
Footnote †: journal: Electronics Electronics
## Case Studies
### Case Study 1: Edmodo
#### 5.2.3 Analysis and Findings  
The operationalization of the Data-Driven Personalized Learning Model (DDPLM) inside Edmodo presented interesting data concerning the personalization of language learning processes. The analyzed data show significant improvement in student engagement and comprehension, especially in what were previously known as challenge areas. Figure 3 tracks the progression of learner engagement over a six-month period on the Edmodo platform, illustrating the effectiveness of personalized learning enhancements.  
One of the main results was the increased efficiency of the students in mastering complex grammatical structures and vocabulary compared to traditional approaches. The adaptive algorithms of DDPLM guaranteed that every student was provided with learning materials relative to their degree of competence and learning speed; hence, individualized learning.  
DDPLM real-time feedback helped students detect errors on the spot; thus, learning was reinforced, and learning strategies were adjusted faster. The instant correction and feedback mechanism helped keep the students motivated, as it promoted regular interaction with the learning material.  
The teachers also gained from the practice of DDPLM. They were provided with critical student performance analytics to track patterns and identify hot spots in student performance. Users could easily provide more focused assistance and adapt their teaching methods to better serve the students, thereby improving the overall effectiveness of the instructional methods.  
The merging of DDPLM with Edmodo showed an obvious positive result on language learning, confirming that data-driven personalized learning systems could change educational practices to become more sensitive to the individual learner's profile and needs.
## 4 Discussion  
The model proposed in this paper is based on statistical methods and only considers displacement values as input variables. Some explicitly influential factors, such as temperature, water level, etc., which are well-established in physics, are not directly considered input factors in the proposed model. Incorporating these factors directly into the model as inputs using existing analysis methods may lead to a decrease in predictive accuracy due to accumulated measurement errors in the environmental factor measurement devices. Therefore, in future research, how to integrate these environmental factors and propose efficient and accurate predictive modeling approaches will further enhance the physical interpretability of the model and improve its applicability.
# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 6 Discussion
### Constraints  
Notwithstanding the promising outcomes, certain restrictions warrant consideration:  
* Data Representativeness and Imbalance: The eICU DB, while comprehensive, originates from particular critical care contexts and may not adequately reflect the diversity of patient groups found in wider clinical situations. The significant class imbalance--characterized by a markedly lower number of stroke cases compared to non-stroke instances--continues to pose a difficulty that impacts model sensitivity.
* Image Data Quality: The imaging data utilized in our trials were pseudo-randomly allocated to patient records instead of being sourced from clinically validated stroke imaging modalities. This method, although beneficial for preliminary model construction, may restrict the applicability of our results to actual diagnostic situations.
* Computational Limitations: Our studies were performed on a CPU-based workstation, notwithstanding the presence of a high-memory gear. The lack of GPU acceleration may have constrained the scale and velocity of deep learning trials, potentially affecting the optimization of CNN models.
* Trade-Offs of Ensemble Methods: While ensemble techniques enhanced the overall accuracy, the stacking ensemble notably exhibited a substantial decline in stroke recall. This indicates that the approach to amalgamating predictions necessitates more refinement to prevent undermining the detection of the minority class.  
The analysis utilizes retrospective data from the eICU database, which, although extensive, may not accurately represent the dynamic and developing aspects of clinical practice. Real-time clinical situations require prospective validation to evaluate the operational feasibility of our models.
## 2 Materials and Methods  
### Materials  
PTE (\(>\)98%), collagen (type I), bovine serum albumin (BSA), EDTA, paraformaldehyde, and heparin were purchased from Sigma (St. Louis, MO, USA). Anti-I\(\kappa\)B\(\alpha\) (44D), anti-phospho-I\(\kappa\)B\(\alpha\) (Ser\({}^{32}\)/\({}^{36}\)) (SA5) and phospho-IKK\(\alpha\)/\(\beta\) (Ser\({}^{176}\)/\({}^{180}\)) (16A6) monoclonal antibodies (mAbs), as well as the anti-phospho-NF-\(\kappa\)B p65 (Ser\({}^{536}\)) polyclonal antibody (pAb) were purchased from Cell Signaling (Beverly, MA, USA). Protein assay dye reagent concentrate was purchased from Bio-Rad Laboratories Inc. (Hercules, CA, USA). Anti-phospho-Akt (Ser\({}^{473}\)) pAb was purchased from BioVision Inc. (Mountain View, CA, USA). The 6-(phenylsulfinyl)-tetrazolotetrazolo-[1,5-b] (Ro106-992), (E)-3-(4-methylphenylsulfonyl)-2-propenenitrile (BAY11-7082), and 8-[4-(1-aminocyclobutyl)phenyl-9-phenyl-1,2,4-triazoleolo [3,4-f][1,6]naphthyridin-3(2H)-one, dihydrochloride (MK-2206) were obtained from Cayman (Ann Arbor, MI, USA). CFTM488A Dye and CFTM405M Dye were obtained from Biotium (Hayward, CA, USA). The anti-\(\alpha\)-tubulin mAb was obtained from NeoMarkers (Fremont, CA, USA), and the Hybond-P polyvinylidene difluoride (PVDF) membrane, enhanced chemiluminescence (ECL) Western blotting detection reagent and analysis system, horseradish peroxidase (HRP)-conjugated donkey anti-rabbit immunoglobulin G (IgG), and sheep anti-mouse IgG were obtained from Amersham (Buckinghamshire, UK). PTE was dissolved in 0.1% DMSO.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 4 A Proposed New Secured Unclonable Medical Watermarking Scheme
### The Proposed Medical Image System Architecture  
The proposed MIS allows a doctor/user to receive securely a medical image through a TA server. The doctor does not communicate directly with a medical image generator. Here, the TA server plays a mediator role in the proposed system. In Figure 7, the proposed system architecture comprises three main components: First, the TA server hosts a secure database (DB). Second, the medical device as an example of the clone-resistant medical generator A. Third: A doctor D as an eligible user with an embedded SUC in his or her own device such as a computer or mobile/token.  
Figure 7: A proposed system operation scenario.  
All medical devices should be registered in TA DB. The proposed MIS attains the following security features:  
1. Medical images are not repeatable and are non-replaceable.
2. Provably unique medical images.
3. Selective authentication privilege.  
Figure 8 illustrates the TA DB structure that stores patient records. Each patient record contains patient's medical images, and some information about their watermarked images.  
Figure 8 shows an example about a patient's record consisting of the basic information of the patient, the patient's watermarked images, medical devices IDs and the used tickets for signing watermarks, and the data. Note that the clone-resistant watermarked image is transmitted and stored in TA DB. Therefore, each user/doctor should send a request to the TA server to get a patient's medical image. In this proposed system architecture, the user/doctor cannot communicate directly with the medical device. The communication is only done through the TA server and the communication with the TA server is performed over insecure channels.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 8 Verification and Validation of the Simulation Model  
To gauge the efficacy of the model and to build trust in its functionalities, verification and validation are required. In the verification process, it is determined whether model implementation and associated data accurately represent the conceptual description and specifications. In the validation process, it is determined how accurately the simulation model and its associated data represent the real world from the perspective of the end-users [22]. In other words, verification answers the question: \"Is the model right?\", while validation answers the question: \"Is this the right model?\". The verification and validation of the model is carried out using accepted techniques from the simulation literature [23]. The methods are both subjective and objective. There are three approaches commonly used in deciding the validity of the simulation model, as described by [23]. In the first approach, the development team conducts evaluations during the model development process. The second author has helped in this evaluation. In the second approach, the users (field experts) of the model determine whether the model is satisfactory. The fourth author in this paper has helped in this process. In the third approach, a third party, independently of the development (of the simulation model) and user teams but with thorough understanding of the intended purposes of the model, evaluates the validity. The third author has helped in this evaluation. The following subsections describe the verification and validation in detail.
###### Abstract  
The paper assessed the impact of the heat treatment of spruce wood, the (radial and tangential) side of the specimens exposed to fire, and the type of material (prism--higher density, floor--lower density) on the combustion process and the rate of fire spread. Five groups of specimens were used--untreated spruce wood specimens, two groups of heat-treated spruce wood specimens from the prism (higher density specimens), and two groups of heat-treated spruce wood specimens from the floor (lower density specimens). In one group, the flame was applied to the radial side, and in the other group to the tangential side of the specimens. The effect on the combustion process was assessed based on the parameters of mass loss and mass loss rate over time. The effect on the rate of fire spread across the specimens was assessed by the parameter fire spread rate. These parameters were determined using a simple test method where the specimens were exposed to a direct flame at an angle of 45\({}^{\circ}\). To complement the results and to assess the processes involved, the temperatures at the specimen surfaces were also measured during the experiment. The main achieved results of the study are the findings on how the heat treatment, the density, and the side of the wood along which the fire spreads affect the burning process of the wood. The results indicated a significant effect of the density of the spruce thermowood on its combustion process. The higher density radial specimens exhibited a higher mass loss rate, and the overall average mass loss of the higher density samples was 27% of the original mass higher than that of the lower density samples. Additionally, the results suggested that the heat treatment of lower-density spruce wood (floor) does not significantly affect the mass loss and the mass loss rate. The difference in the overall average mass loss of the thermowood of floor and untreated wood samples was less than 2%, which is statistically insignificant. It was also found that for thermowood, fire will spread faster on the tangential side, where the fire spread rate is 29% higher compared with the radial side (for the floor samples). Based on the findings of other authors in a similar field, the results confirm that heat-treated spruce wood is more easily ignitable than untreated wood, which was proven by the spontaneous combustion of most of the thermowood samples during the experiment compared with the untreated wood samples.  
mass loss; mass loss rate; thermowood; fire resistance; density; exposed side +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Transportation faces various challenges nowadays. There are numerous efforts to enhance traffic safety and efficiency. C-ITS is a key technology in this field that aims to address these challenges. It connects the traffic participants and the infrastructure in a cooperative network. Thanks to the communication, the actors can exchange essential information to realize a wide range of safety improvements, efficiency enhancement, and convenience applications.  
The applications are usually categorized into three generations. Day1 applications typically implement cooperative awareness, Day2 broadens the horizon via perception sharing, and Day3 introduces coordinated maneuvering. As of 2023, Day1 use cases are mass-deployed, and Day2 applications are in the final stages of standardization.  
Naturally, the C-ITS applications depend on the various network parameters. In the scope of this work, we aim to provide realistic simulation results based on real-world traffic data for different future deployment realizations of Day1 and Day2 applications. We hope this work will support the C-ITS community's decisions with realistic and accurate data.  
The rest of the paper is organized as follows. The background of the article is elaborated in Section 2. Section 3 introduces the applied simulation environment, our proposed extensions, and the used configuration. The details of the traffic modeling follow this in Section 4. The results are collected, introduced, and analyzed in Section 5. Finally, we conclude the paper and highlight our future work.
## 3 Results and Discussions
### Limitations for Practice  
1. The applied PSD method for the data of this study uses the Fast Fourier Transformation (FFT) process, so PSD plots show averaged intensity over the observed frequency range of interest along the data collected, thereby the plots do not show severe events that happen during real distribution.
2. Parcel express companies use others vehicles like minivans, mid-size or heavy trucks to deliver shipments; therefore, data from this study can have limited use for the entire parcel delivery chain, but covers the important portion related to stacked parcels. In a trailer such shipments are unitized in sacks, bundles or pallets and require cargo securement regulations so they are blocked and braced with dunnage or straps and load bars. In addition, parcels travel in heavy truck or mid-size trucks to get to their final destination and operators use bigger containers, such as roll or cage containers, to group the packages together. At this step various packages are grouped together in real life and it is very rare to have identical parcels stacked together.
3. Here has to be noted that the packages during the distribution suffer vibration events in the lateral and longitudinal excitation also, but the widespread practice in the laboratory use single degree-of-freedom (SDOF) vibration system. Thus, the package does receive a more complex input vibration in the real practice, which can cause more complex vibration response between the layers of packages.
4. Last, but not least, the theoretical dynamics of stacked parcels can be described as a non-linear free vibration of coupled mass-spring system, thereby the various geometrical sizes and weights of packages in the stacked unit load will change vibration intensity in a non-linear way.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Floor** & **1 st Layer** & **2 nd Layer** & **3 rd Layer** \\ \hline Motorway & 0.099 & 0.122 & 0.131 & 0.135 \\ Arterial road & 0.119 & 0.164 & 0.156 & 0.177 \\ Summary & 0.136 & 0.166 & 0.170 & 0.181 \\ Country road & 0.151 & 0.187 & 0.187 & 0.203 \\ Urban road & 0.154 & 0.187 & 0.197 & 0.204 \\ Laboratory & 0.427 & 0.376 & 0.387 & 0.397 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall G\({}_{\rm rms}\) of various layers and the source of data (1–200 Hz).  
Figure 10: PDF for recorded vertical RMS (g) at field measurement and in laboratory at, (**a**) Summary-Floor, (**b**) Laboratory-Floor, (**c**) Summary-1 st layer, (**d**) Laboratory-1 st layer, (**e**) Summary-2 nd layer, (**f**) Laboratory-2 nd layer, (**g**) Summary-3 rd layer, (**h**) Laboratory-3 rd layer.
## 3 Results
### No-Gap Reconstruction  
In this case, the reference dataset without any gap was reconstructed by using the two methods. Figure 5a,b, respectively, shows the sample snapshots of the noise-free and noisy reconstructions for the first sampling mask. The noisy dataset had the total PVNR of 19.0 dB and RMSE of 0.250, as depicted in the top row of Figure 5b. In Figure 5a, the top row shows the reference and the second row shows the sampling mask. The third row shows the sample noise-free snapshots as reconstructed by csDMD method resulting in an RMSE value of 0.291. The bottom row shows the same snapshots as reconstructed by DMDct method. The RMSE value of DMDct reconstruction is 0.130. In Figure 5b, the third row shows the results obtained from csDMD method by using the noisy samples. This resulted in an RMSE value of 0.182 and PVNR of 21.8 dB. The bottom row shows the results of DMDct reconstruction which resulted in an RMSE value of 0.119 and PVNR of 25.5 dB.