esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Experiment I: Different Weight Adjustment Methods for MSFL
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{\
\begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular} } & \multicolumn{4}{c}{**Emotion Recognition**} & \multirow{2}{*}{
\begin{tabular}{c} **Language** \\ **Recognition** \\ \end{tabular} } \\ \cline{3-4}  & UAR & 82.58\% & 82.20\% & 76.67\% & 80.77\% & 99.20\% \\  & ACC & 82.80\% & 82.86\% & 77.43\% & 81.09\% & 98.96\% \\ \hline M\_MSFL & UAR & 83.86\% & 83.08\% & 75.75\% & 80.89\% & 99.10\% \\  & ACC & 83.77\% & 82.99\% & 76.81\% & 81.19\% & 98.83\% \\ \hline G\_MSFL & UAR & 85.24\% & 84.44\% & 75.33\% & 81.66\% & 99.35\% \\  & ACC & 85.29\% & 84.41\% & 75.89\% & 81.86\% & 99.13\% \\ \hline \hline \end{tabular} Abbreviations: E: EMO-DB; C: CASIA; S: SAVEE; Avg: average performance of EMO-DB, CASIA and SAVEE.  
\end{table} 
Table 5: Experimental performance comparison of MSFL models with different weight adjustment methods. Figure 3: The performance of two tasks in the E_MSFL with different alpha values. 0.89% and 0.77% on UAR, respectively. Especially on EMO-DB and CASIA, the G_MSFL model achieved good results of 85.24% and 84.44% on UAR, with an improvement of 2.66% and 2.24% over the E_MSFL model and an improvement of 1.38% and 1.36% over the M_MSFL model. Compared with the E_MSFL model, the adaptive loss balancing approach (i.e., M_MSFL, G_MSFL) achieves better results and saves the process of brute force tuning than the time-consuming empirical tuning. Hence, this study incorporates the GradNorm adaptive loss balancing approach to optimize the objective function of the MSFL model, and the MSFL proposed in this paper in the subsequent experiments also defaults to using the GradNorm adaptive loss balancing approach.
## 3 Result
### Sensitivity of MOCAST+ to Hydrology and Glaciers in the Andes
#### 3.1.3 Sensitivity to Lakes/Reservoirs in South America  
The following Figure 11(a),b reports the yearly long-period gravity rates and the seasonal amplitude signal for the considered lakes and reservoirs in South America. Dotted circular areas show the window outlines employed for the localized spectral analysis.  
As described in the previous sections, the reservoirs generate the largest mass variations both concerning the long-period variations and the seasonal signals. The reservoirs' long-term trends are in the order of a few Gt/yr signals, while the seasonal mass changes can be an order of magnitude larger (>10 Gt). The Patagonian lakes (Argentina, Buenos Aires, and Viedma) are the only ones experiencing a systematic mass increase, which is due to the deglaciation occurring in the Patagonian glacierized region. However, the amplitude is more than two orders of magnitude lower than other natural lakes (Titicaca) or reservoirs (Figure 11(b)).  
The yearly long-period trends, expressed as the gravity variation in one year, have been again compared with the yearly error curves of the various missions (Figure 11(c)).  
We see that both the reservoirs are close to the noise level of the MOCAST+ mission and in principle with two years of observations their effect would be detectable. Titicaca, given the smaller mass rates observed, would be monitored at this spatial resolution after 5 years of observations. We did not plot the spectra of the Patagonian lakes' effects since the signal amplitude is several orders of magnitude below the sensitivity of all the curves. Even considering all the nearby smaller lakes in the Patagonian region to be subjected to the same average level rise would result in an undetectable signal. In any case, GRACE would be inadequate for monitoring the superficial water storage variations in all these reservoirs/lakes.  
Regarding the seasonal variations of lakes and reservoirs, the spectra of Figure 11(d) report the sensitivity between the amplitude signal recorded at the month when the maximum seasonal signal is observed. It appears that only the hydrologic variations in the Sobradinho would be above the detection threshold of MOCAST+.
## 3 Analysis of Failure Modes in Multiple Physical Fields
### Load Analysis under Coupled Thermomechanical Fields  
During motor operation, internal heat is generated, resulting in a rise in rotor temperature. Due to the different thermal expansion coefficients of materials and the constrained extrusion of the structure, thermal stress is generated inside the rotor. Therefore, the rotor temperature distribution obtained from the thermal field simulation was imported into the structural field, and the rotor model was loaded with the speed under test conditions to obtain the FEA results of the rotor under coupled thermomechanical stress. Figures 15 and 16 also show the simulation results for a rate of 8907 rpm.  
The above picture shows the FEA results when the rotational speed reaches a peak value of 8907 rpm at 501 s of the working condition. It can be found that the concentrated stress of the rotor is at the center magnetic bridge of the rotor, the maximum stress value is 113.87 MPa, and the maximum radial displacement is 0.0145 mm.  
The FEA analysis results show that the maximum stress of the rotor is also located at the central magnetic bridge under the action of thermomechanical stress coupling, with a maximum stress of 114.6 MPa, which is less than a 1 MPa increase compared to the centrifugal force alone. The maximum rotor deformation is 0.049 mm, which is 3.4 times greater than that in a single mechanical field. The air gap between the rotor and the stator is 0.8 mm, which is within a reasonable range.  
Figure 14: Rotor deformation under a structural field.  
Figure 15: Rotor stress distribution under coupled thermomechanical fields.  
Figure 17 shows the comparative stress variation curves at the central magnetic bridge for the structural and thermomechanical fields. It can be seen from the figure that the mechanical stress curve and the stress curve under the thermal-mechanical coupling are almost coincident, and only under the working condition of a lower speed will the thermal-mechanical stress be immensely more significant than the mechanical stress. At low-speed operating conditions, the overall temperature of the motor will decrease, while the thermal stress at the magnetic bridge will increase due to the difference in thermal conductivity and heat generation rate between the rotor and the permanent magnet. The mechanical stress is proportional to the square of the speed and has little effect on the magnetic bridge.
## 5 Experiment III: Listening Experience Rating
### Procedure and Listeners  
After Expt. II, the audio excerpts were presented to listeners using the same setup as described in Section 4.2. The listening experience rating was conducted on a continuous quality scale ranging from 0 to 100 [51]. The scale was divided equally into five notches indicating opinions of 'Bad', 'Poor', 'Fair', 'Good' and 'Excellent' from lower to higher scores. Listeners were instructed to focus on both intelligibility and sound effect. Thus, the judgement on the overall listening experience was expected to be made based on the contributions of the two aspects. The following information was presented to listeners in the beginning of the experiment, in order to make them understand the definition of 'good listener experience' in the context of the current experiment.  
'In broadcasts when speech content is presented with background sound, an ideal mixing (i.e., speech-to-background) level usually allows the listener to understand the speech content with little effort, while leading to the well balanced background sound providing good atmosphere to the audio scene. In this experiment, your task is to rate the mixing level that is chosen by a series of systems. The rating will be performed on a scale of 0-100, with a higher score representing better listening experience. Your judgement should take into consideration both speech intelligibility and sound effect in the audio scenes to which you will be exposed.'  
The 15 excerpts were randomly presented to the listener via a MATLAB GUI. Each excerpt was presented on one screen. Within the screen, all six systems for that excerpt with different SBR levels were located side-by-side in a row. For each system, an individual vertical slider was used for rating the audio, with dedicated 'Play' and 'Stop' buttons. The positions of the systems in the row also varied stochastically from one excerpt to the next. Listeners listened to the audio over headphones; they were allowed to listen to each system as many times as necessary. Once the ratings were determined for all the six systems, the listener proceeded to the next excerpt until the end of the experiment.  
The rating was performed by twenty-three native British English speakers (ages: 18-34 year, mean age: 24 year) with normal hearing. None of the participant took part in the previous two experiments.
# COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?
## 4 Results
### Datasets  
Since there is no single sizeable dataset available for CXR images of COVID-19 patients, the dataset was curated from multiple sources to have sufficient training, testing, and validation data.  
**COVIDx** The first dataset of COVIDx [2] was made public to the research community, being a collection of four classes of CXR, namely (1) normal, (2) bacterial, (3) pneumonia (non-COVID), and (4) COVID-19. A total of 5941 posteroanterior (PA) CXR were collected from 2839 patients. Currently, the dataset contains only 68 X-rays for COVID-19 patients. There are 1203, 931, and 660 samples available for negative pneumonia, bacterial pneumonia, and non-COVID viral pneumonia, respectively.  
Figure 11: ResNeXt backbone module showing different paths for feature learning.  
**COVIDCT:** The University of San Diego collected a CT dataset of 349 CT images called COVID-CT [1]. The COVID-CT dataset contains clinical information on 216 COVID-19 patients. The CT images were extracted using PyMuPDF4 from 760 preprints such as medRxiv2, bioRxiv3, NEJM, and JAMA between 19 January 2020 and 25 March 2020.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
###### Abstract  
The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial's communication, using multi-input multi-output (MIMO) technologies to overcome the bandwidth limitation problem is highly restricted in underwater acoustic communication by high inter-channel interference (ICI) and the channel multipath effect. Recently, the spatial modulation techniques (SMTs) have been presented as an alternative solution to overcome these issues by transmitting more data bits using the spatial index of antennas transmission. This paper proposes a new scheme of SMT called spread-spectrum fully generalized spatial modulation (SS-FGSM) carrying the information bits not only using the postulated data symbols and index of active antennas as in conventional SMTs, but also transmitting the information bits by using the index of predefined spreading codes. Consequently, most of the information bits are transmitted in the index of the transmitter antenna, and the index of spreading codes. In the proposed scheme, only a few information bits are transmitted physically. By this way, consumed power transmission can be reduced, and we can save the energy of underwater nodes, as well as enhancing the channel utilization. To relax the receiver computational complexity, a low complexity deep learning (DL) detector is proposed for the SS-FGSM scheme as the first attempt in the underwater SMTs-based communication. The simulation results show that the proposed deep learning detector-based SS-FGSM (DLSS-FGSM), compared to the conventional SMTs, can significantly improve the system data rate, average bit error rate, energy efficiency, and receiver's computational complexity.  
MIMO; underwater acoustic communications; energy efficiency and spectral efficiency; SM; FGSM; deep learning; spread spectrum +
Footnote †: journal: Sensors
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 2 Fundamentals of Dense Video Captioning
### Challenges in DVC  
Event Recognition and Overlapping Events: A primary challenge in DVC is accurately recognizing and differentiating between distinct events, especially when they occur simultaneously or overlap in time. This necessitates models with a robust ability to distinguish between visually similar events and manage temporal ambiguities.  
Event Localization Accuracy: Precise event localization is vital for generating meaningful captions. However, achieving high localization accuracy is challenging due to the complexity and variability of video content. Factors like camera motion, occlusions, and varying event durations can all contribute to localization errors.  
Multimodal Integration and Natural Language Generation: DVC requires the integration of multiple modalities, encompassing visual, auditory, and temporal information. Effective multimodal fusion is crucial for generating accurate and informative captions. Furthermore, the generated captions must be natural and fluent, which mandates models with a profound understanding of language structure and semantics.
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 3 Results
### ROM Precision Evaluation  
Measuring range of motion (ROM) is usually one of the first steps in an evaluation in musculoskeletal rehabilitation [48]. This measure is used to evaluate and classify joint deficiencies in patients and to indicate the results of rehabilitation programs. For ROM measurements for clinical practice, doctors and researchers mainly use goniometers, inclinometers and motion analysis systems based on markers in a controlled environment under the instructions of the medical staff [49].  
The main objective of this study was to analyze the accuracy of the ROM measurement through the application in task mode. For the analysis of the precision of the ROM measurement, an experiment based on the measurement of the ROM of the following more representative tasks (movements) was performed: (1) left shoulder abduction (the results of this experiment are shown in Table 3), (2) left elbow flexion (the results of this experiment are shown in Table 4), (3) abduction of left hip (the results of this experiment are shown in Table 5) and (4) right knee flexion (the results of this experiment are shown in Table 6). To achieve this, the ExerCam task mode and manual ROM calculation through a PVC goniometer were used. Measures were taken for a series of 3 repetitions per task (S1, S2, S3), parameterizing the tasks with the initial and final grades indicated in Table 1 and using a ten-degree increment between targets.
### New Technologies in Traffic: A Socially Challenging Issue?  
In general, emerging technologies in mobility are well-valued by most of the existing studies on traffic safety [16], with young men being the population group expressing the most positive opinions [17]. Specifically, the most highly valued aspects are safety improvements [18], reduced traffic congestion and travel time [19], amenities for the elderly and/or physically challenged [20; 21], environment and fuel savings [22; 23], and other miscellaneous benefits [24; 25].  
However, several studies have pointed out that the geographic provenance of their users could be the main factor affecting the assessment of ITS systems [26; 27]. Namely, people living in highly industrialized countries with high rates of economic wealth are favorable to the inclusion of traffic-related technology. In contrast, people living in emerging countries, where the evidence is scarce, have been implied to remain more reluctant [26]. Enhanced by many social disparities, one of the factors influencing these gaps might be the current unequal penetration of information and communication technologies (ICTs) among different countries.  
Precisely, one of the regions that seems more affected by these issues is the Caribbean, whose countries are in the lower-middle zone of the world ranking, which reflects the number of households with internet access [28]. In contrast, this list is headed mainly by European and Asian countries [29]. Conversely, almost all the existing studies conducted in low and middle-income countries (LMICs) show a lower predisposition to accept and use advanced support technologies in traffic [27; 30]. Some of the main constraints commonly expressed by users from LMICs are the fear of potential cybersecurity issues or system failures [31], personal data treatment [32], a feeling of excessive monitoring [33; 34], and environmental concerns [35].  
From a theoretical point of view, different approaches have been developed to understand interpersonal differences in the degree of acceptance of technology. Initially, [PERSON] and [PERSON] (1985) developed the theory of planned behaviour (TPB) [36], which shows that the intention to behave, modulated by attitude and subjective norm, may be the best behavioral predictor. A few years later, [PERSON] (1989) proposed the technology acceptance model (TAM) [37], in which perceived ease of use and perceived usefulness are hypothesized to directly influence attitudes towards technology and, consequently, the intention to use it. Overall, both TPB and TAM models agree on the imperative need for users to have a positive attitude and value a certain behavior as necessary steps to increase its likelihood. However, to the best of our knowledge, these assumptions have never been tested in this region, specifically regarding support technologies for mobility.
## 3 Results
### Impact of Perceived Control on Perceived Comfort and Health
#### 3.4.2 Perceived Control vs. Perceived Overall Comfort--Regression Analysis  
The relation between the overall comfort and five perceived control variables has been examined by applying the ordinal regression analysis. The results are presented in Table 7. The maximum OR value (1.28) corresponded to the perceived control over noise. The results showed that if the perceived control over noise increases by one unit in the 1-7 point scale, there is a 1.28 time likelihood that the overall comfort will increase by one unit. Perceived noise control was the parameter with the lowest score according to the occupants' recordings, as shown in Table 2. The impact on the overall comfort of the control over noise, lighting, temperature and shading from the sun, was almost equivalent (ORs: 1.16, 1.14 and 1.12, respectively). Lastly, the perceived control over ventilation, despite the fact that it was ranked as the second worst parameter, had the lowest impact on overall comfort (OR 1.03, 95% CI: 1.00-1.06).  
Moreover, occupants were separated in two groups: those who had no or low control and those who had high control for the sum of the five control parameters. The results are presented in Table 8. The ranking remains the same as in the previous case, but two remarks can be highlighted. First, in the \"low control\" group, occupants were less sensitive to controlling parameters, with lower OR values. In addition, the perceived control over ventilation was not significant (\(p\) > 0.05). Second, in the \"high control\" group, occupants were more sensitive to all controlling parameters with increased OR values. The perceived control over lighting showed an almost equal impact on overall comfort as the perceived control over noise.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **OR CI (95\%)** & \(p\)**-Value** \\ \hline Perceived control over noise & **1.28 (1.25–1.32)** & \(p\) < 0.001 \\ Perceived control over lighting & **1.16 (1.13–1.19)** & \(p\) < 0.001 \\ Perceived control over temperature & **1.14 (1.11–1.18)** & \(p\) < 0.001 \\ Perceived control over shade & **1.12 (1.10–1.15)** & \(p\) < 0.001 \\ Perceived control over ventilation & **1.03 (1.00–1.06)** & \(p\) < 0.05 \\ \hline \end{tabular}
\end{table}
Table 7: Relations between perceived control and perceived overall comfort.
## 2 Network Structure and Fault/Event Model  
In sensor network research, faults and events are often handled separately. Hence techniques for detecting faults in a wireless sensor network might not perform well as intended when both faults and events coexist in the network. Similarly, event detection techniques might not show the expected performance if fault behavior deviates from the predefined simplified model. In order to present our event detection scheme in the presence of various types of faults, we briefly describe our grid-based sensor network structure and fault/event model to be used throughout the paper. Grid-based sensor networks have been proposed for energy efficient data aggregation and routing. Our fault-tolerant event detection scheme is thus developed to conform to the basic protocol of the hierarchical networks.
## 1 Introduction
### Aims and Novelty  
This study outlines the development process of an ML-based extension for the RD \(\alpha\) system. The primary objective was to create an ML-based system capable of accurately recognizing striking techniques and identifying the target object using data obtained from sensors embedded in the smart boxing gloves. To achieve this, ML models were implemented for the classification of various striking techniques, described in Section 1.2 and the differentiation between valid targets (e.g., punching bag, punch bag, gloves) and invalid targets (e.g., concrete wall) on which the striking techniques are executed.  
To implement the classification models, it was necessary to identify the most suitable supervised machine-learning algorithms for the sensor data derived from the aforementioned sensors. Moreover, an assessment was carried out to determine which features and feature representations of the multi-variate time-series sensor data could be utilized for training and testing the supervised machine-learning models. Additionally, datasets were constructed for training and testing the implemented models, achieved through the conduction of two experiments involving mutually exclusive participant groups. Ultimately, the objective was to evaluate whether the developed classification approach, in conjunction with the data from the RD \(\alpha\) system, could achieve a predictive accuracy of 85%. This value was chosen as a baseline since related work [19, 20, 23, 24] showed the feasibility of achieving this accuracy rate. It was estimated that reaching this value would verify the proof of concept of the ML-based classification system and that the implementation of even higher accuracy can be achieved afterward through adaptations.