esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 3 Underwater Energy Consumption Model  
The underwater communication is influenced by many factors like propagation delay, operating frequency, bandwidth, path loss, and transmission loss. Thus, energy consumption model designed for acoustic communication must consider all these factors. The attenuation at distance \(d\) for a signal with frequency \(f\) in an underwater acoustic channel is calculated as,  
\[A(d,f)=d^{k}v^{d} \tag{1}\]  
where \(k\) denotes the spreading factor and \(v\) is the absorption coefficient expressed in dB/km. The spreading factor \(k\) specifies the signal's propagation geometry and its value varies for different energy spreading types. The value of \(k\) for spherical spreading is 2 while cylindrical spreading  
Figure 2: Data transmission in BTM and UWDAR.  
commonly uses \(k=1\) and its practical value is 1.5. The absorption coefficient is based on the signal frequency measured in kHz. We can calculate absorption coefficient as [20],  
\[v=10^{\alpha(f)/10} \tag{2}\]  
\[\alpha(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100}+f^{2}+2.75\times 10^{-4 }f^{2}+0.003 \tag{3}\]  
The above formula is valid for frequencies above few hundred Hertz. Whereas, the following formula is valid for lower frequency ranges.  
\[\alpha(f)=0.002+\frac{0.11f^{2}}{1+f^{2}}+0.011f^{2} \tag{4}\]  
The power consumption \(P_{t}\) to transmit data over distance \(d\) is given below,  
\[P_{t}=P_{o}A(d,f) \tag{5}\]  
A sensor node consumes energy (denoted as \(R_{Tx}(x,d)\)) to transmit \(x\) bits packet over distance \(d\).  
\[R_{Tx}(x,d)=xP_{o}d^{k}v^{d}t \tag{6}\]  
where \(t\) is the transmission duration measured in seconds. Similarly, to receive a data packet a node consumes energy calculated as follows,  
\[R_{rx}(x)=xP_{r}t \tag{7}\]  
where \(P_{r}\) depends on the receiver and is a constant term.
## 7 Conclusions  
Two-dimensional Cross fluid past a stretchable plate under the effect of Buongiorno nanofluid has been considered. The nonlinear PDEs with the inclusion of various effects were converted into ODEs and tackled numerically with the utilization of the Lobatto IIIA scheme. Some conclusive remarks from the present study are mentioned below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(We\) & \(\theta_{r}\) & \(G\) & \(N\) & **Skin Friction** \\ \hline
1.1 & & & & & **0.747311** \\
1.3 & & & & & 0.675125 \\
1.5 & & & & & 0.531447 \\  & 0.1 & & & & 0.511412 \\  & 0.3 & & & & 0.340778 \\  & 0.5 & & & & 0.293925 \\  & & 0.5 & & & 0.191472 \\  & & 0.8 & & & 0.193867 \\  & & 1 & & & 0.197345 \\  & & & & 0.1 & 0.318987 \\  & & & & 0.4 & 0.383099 \\  & & & & & 0.6 & 0.421181 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tabulation of skin friction Cross fluid.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\theta_{w}\) & \(\theta_{r}\) & \(N_{t}\) & \(N_{b}\) & \(N\) & \(Pr\) & \(\varepsilon_{1}\) & \(\varepsilon_{2}\) & **Nusselt Number** \\ \hline
0.1 & 0.5 & 0.3 & 0.3 & 0.1 & 0.7 & 0.1 & 0.2 & 0.189769 \\
0.4 & & & & & & & & 0.1958574 \\
0.7 & & & & & & & & 0.1967127 \\  & 0.8 & & & & & & & 0.1955081 \\  & 1.1 & & & & & & 0.1954922 \\  & 1.4 & & & & & & & 0.1954817 \\  & & 0.7 & & & & & 0.19555544 \\  & 0.9 & & & & & & 0.1955731 \\  & 1.4 & & & & & & 0.1956091 \\  & & 0.2 & & & & & 0.1965355 \\  & & 0.4 & & & & & 0.1985558 \\  & & 0.6 & & & & & 0.1995958 \\  & & & & & & & 0.3642232 \\  & & & & & & & 0.3460589 \\  & & & & & & & 0.3278681 \\  & & & & & & & 0.1959707 \\  & & & & & & 1.1 & 0.1930041 \\  & & & & & & 1.2 & 0.1956082 \\  & & & & & & 0.3 & 0.1954585 \\  & & & & & & 0.4 & 0.1955415 \\  & & & & & & 0.5 & 0.1957222 \\  & & & & & & 0.5 & 0.1955347 \\  & & & & & & 0.6 & 0.1955344 \\  & & & & & & 0.7 & 0.1955346 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tabulation of Nusselt number Cross fluid.  
* An incremental change in variable temperature \(\varepsilon_{1}\) and molecular diffusivities \(\varepsilon_{2}\) diminishes the heat transfer rate.
* An amplification in unsteady parameter \(A\) improves the velocity field.
* The fluid velocity diminishes by escalating the Weissenberg number \(We\) and power-law index \(n\).
* A positive variation in the Grashoff number \(G\) and thermal radiation \(N\) amplifies the temperature field.
* The temperature of the fluid escalates by improving \(N\), \(\theta_{w}\) and \(\varepsilon_{1}\).
* Both \(N\) and \(Pr\) deteriorate the Nusselt number phenomenon.
* The dimensionless parameters \(G\) and \(n\) magnify the surface drag coefficient.
## 3 Materials and Methods  
### Dataset  
The dataset comprises surgical site RGB images from 34 cardiothoracic surgery patients of Hospital de Santa Marta. The photographs correspond to the evolution of each patient's surgical wounds during a 30-day follow-up. Initially, there were 1443 images collected by the front or back camera of a Xiaomi Mi A2 Lite smartphone. As such, the images' resolution varied among samples, but the majority had a 1920 \(\times\) 1080 pixel resolution.  
The image acquisition protocol was not defined to keep the procedure as simple as possible for the patients and to emulate collection in different places. This resulted in images with several different conditions, such as differences in illumination, patient position, orientation, and background. For this reason, images with poor illumination conditions or heavily blurred with unrelated images taken by accident were eliminated from the dataset. After the image removal process, the final dataset had 1337 images.  
The acquired images could have three types of wounds: chest wound (CW), drainage wound (DW), and leg wound (LW). An example of each wound type is illustrated in Figure 1. Regarding the type of wounds that are shown in the images, 77.5% of images had CW, 67.0% DW, and 20.8% LW. The sum of these three types is higher than 100% because each image could have more than one wound type. In addition, the wounds can be categorized into binary labels, where zero indicates the wound does not have any concerning alterations, and one means that an alteration is present in the wound. Only 10.7% of such images had displayed wounds with alterations, resulting in a rather imbalanced dataset.  
Data Annotation  
For image segmentation, manual annotation was performed on the full dataset with the recourse to a graphical image annotation tool _Labelme_[21]. The annotation consists of categorizing the wound type and delineating the surgical site. For each image annotation, a ground-truth mask was created with an established pixel intensity for each wound type.  
An infected wound presents specific local signs, such as redness, disunion, pus, and exudation. These signs indicate that the wound has a risk of being infected, but the evaluation of infection in the wound must be supported by microbiological data, so it cannot be visually detected because it needs the confirmation of a positive culture of bacteria. As such, the images with a risk of infection were labeled with the help of clinical experts. Every wound was classified with one of two options: _YES_ or _NO_, corresponding to one or zero. The _YES_ label meant that the wound had an alarmist alteration that could later lead to wound infection. In contrast, the _NO_ label meant there were no alarmist signs or characteristics in the wound that could compromise its healing process. An example of each wound category is illustrated in Figure 2.  
Figure 1: Different types of wounds: (**a**) example of chest wound labelled as CW; (**b**) example of drainage wound labelled as DW; (**c**) example of leg wound labelled as LW.
## 3 Results
### Temperature Variation and \(\Delta\)AIRT
#### 3.3.2 Results in 'Ef2'  
Figure 6a,b illustrates the ambient temperature curve and \(\Delta\)AIRT change curve of HG-4 in 'EF2'. As the experiment was conducted during the cloudy daytime with sunlight on the sample surface (Figure 3b), the intermittent sunshine caused by discontinuous clouds led to the decrease-increase-decrease trend on the curve with a maximum change of up to 0.19 \({}^{\circ}\)C.  
It is noted that the sunlight could lead to the temperature and \(\Delta\)AIRT change in two aspects: one is the ambient temperature change directly; the other one is the heat conduction from the left irradiated side (with sunlight in Figure 3b) to the central calculated area. Therefore, the \(\Delta\)AIRT fluctuated with the similar trend of temperature change. The maximum amplitude of \(\Delta\)AIRT was 0.29 K and there was no visible cyclic characteristic. The weak stress-induced infrared radiation signal was completely submerged by the ambient temperature change.  
The decomposed results of \(\Delta\)AIRT in each frequency component are plotted in Figure 7a-c, respectively, as well. The \(\Delta\)AIRT\({}_{\text{low}}\) shows a similar trend with that of the ambient temperature. The trend of \(\Delta\)AIRT\({}_{\text{mid}}\) was consistent with the stress curve in period as well with the maximum amplitude of 0.076 K in Figure 7b, which is approximately 40% of the amplitude of the ambient temperature change (0.19 K). Figure 7c shows that the \(\Delta\)AIRT\({}_{\text{high}}\) changed with large fluctuations due to instrumental noise, wind, and sunlight in the range of 0.1 K. The standard error of \(\Delta\)AIRT\({}_{\text{high}}\) is about 15 mK, indicating that the experimental condition was relatively more complex than that in 'EM1'.  
Figure 5: Results of decomposed \(\Delta\)AIRT using wavelet analysis in the cyclic loading process of sample HG-1: (**a**) \(\Delta\)AIRT\({}_{\text{low}}\); (**b**) \(\Delta\)AIRT\({}_{\text{mid}}\); (**c**) \(\Delta\)AIRT\({}_{\text{high}}\).  
Figure 6: The measured results of sample HG-4 in the cyclic loading process: (**a**) The ambient temperature change; (**b**) \(\Delta\)AIRT change.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
### Novelty and Contributions  
Motivated by the aforementioned issues, this paper proposes a partial offloading strategy for the GEO/LEO hybrid satellite networks scenario. To avoid the problem of excessive energy consumption caused by too much pursuit of minimizing mission processing delay, the goal of this paper is to minimize the total energy consumption of the satellite networks while ensuring missions QoS constraints. Specifically, we first construct a framework for GEO/LEO hybrid satellite networks and formulate the mission processing delay and energy consumption in detail, modeling the computation offloading problem as a mixed-integer nonlinear programming (MINLP) problem. Then, we transform the above MINLP problem into the minimum cost maximum flow (MCMF) problem and propose the successive shortest path-based computation offloading (SSPCO) method, which can obtain the offloading decision in polynomial time. Simulation experiments verify the effectiveness of the proposed SSPCO method. The main contributions are as follows:  
* Constructing a partial offloading model for GEO/LEO hybrid satellite networks, which aims to minimize the total energy consumption of the networks while guaranteeing QoS constraints of satellite missions.
* Transforming the partial computation offloading problem into the MCMF problem and proposing the SSPCO method to obtain the computation offloading decision in polynomial time.
* Performing extensive numerical experiments to demonstrate the effectiveness of the proposed SSPCO method. The results show that SSPCO achieves significant performance superiority compared with other benchmark strategies in the total energy consumption of the satellite networks, QoS violation degree, and algorithm running time.
## 5 Implications of the Study
### Key Findings  
* Current NAS for GANs systems primarily focus on automated architecture generation for both Generator and Discriminator networks or exclusively for Generator networks. However, an approach that concentrates on searching for superior Discriminator networks has yet to be introduced. Dedicated Discriminator architecture search methods could lead to enhanced overall GAN performance.
* The reliance on CIFAR-10 and STL-10 datasets for evaluating GAN architectures limits the generalizability of findings. While these datasets enable direct comparisons, they are insufficient for evaluating performance across diverse applications. Broader dataset evaluations, including CelebA, LSUN, and COCO, are necessary.
* Most existing NAS-GAN systems are limited to unconditional image generation tasks. Expanding research to other types of image generation, such as conditional image generation and image-to-image translation, would broaden the scope and impact of NAS in GAN applications.
## 4 Experiment and Discussion
### Experiment on Environmental Sound Recognition
#### 4.4.2 Discussion  
Compared with the two tests, some interesting observations are made. Firstly, on the whole, the average recognition rate of the environment test is lower than that of the speech test. The reason might be that training of the environmental sounds is not sufficient and the professional music is difficult to recognition. Secondly, the average recognition rates of the environmental sounds for patient S6 are higher than those of the speeches. After the analysis, his recognition rate for sounds is higher than that for other patients. His experience in music might show some effects. Finally, the recognition rate of the baseline algorithm decrease 5.5% for the prescription formula is designed for the speech intelligibility. However, the recognition rates for the GP algorithm and the IEC algorithm increase slightly. The reason comes from two aspects. Firstly, the average recognition rates for speeches are not very high and the self-fitting algorithm can improve the recognition rates for environmental sounds. Secondly, higher recognition rates of some patients for environmental sounds increase the average value, such as patient S6. In addition, compared with the IEC algorithm, the average recognition rate of the GP algorithm is slightly decreased, mainly due to the low recognition rates of patients S4 and S6, as shown in the figure.
## 2 Materials and Methods
### Dosage Criteria  
Pre-cast concrete uses different types of concrete according to the specific requirements of the elements to be manufactured. The strength requirements (\(f_{c}\)) are typically between 25 and 45 MPa, and usually require plastic workability (Abrams cone) [49]. A further important aspect for the dosage is the characteristic of the steel reinforcements in order to use reinforced (RC) or pre-stressed (PC) concrete. For these experiments, two main concrete types were selected to analyse the effect of the different variables, since they were considered those most representative for this proposal: (a) PC-45 designed with 420 kg/m\({}^{3}\) cement volume, and (b) RC-30 with 360 kg/m\({}^{3}\) cement volume. Both were configured with similar aggregate graining but using different W\({}_{\text{eff}}\)/C ratio and additive rates.  
Firstly, the control batches were accurately manufactured, controlling all the parameters related to the water movements in the mixture and estimating the exact effective water volume (W\({}_{\text{eff}}\)) of each one, since companies tend to have insufficient control over this. Based on these two reference batches, different mixtures with similar functional characteristics were configured in order to analyse: (a) the effect on the cement volume of a reduction, (b) the influence of the additives, and (c) the influence of the recycled aggregates, as follows:  
* Strength: The following mixtures were performed in order to analyse the influence of the cement reduction on the final compressive strength: PC-45 with cement volume of 420, 360 and 300 kg/m\({}^{3}\) and RC-30 with 360, 320 and 250 kg/m\({}^{3}\), maintaining the additive volume constant across the batches of each type. Furthermore, the PC-45 mix with 300 kg/m\({}^{3}\) was analysed using twice the volume of plasticiser additive as the reference (_plast_x2_), which optimises the effective W\({}_{\text{eff}}\)/C ratio for the same consistency up to the limits allowed by standards.
* Hardening rate: To study the impact on the hardening effect of the concrete, the RC-30 mix was analysed in two accelerator additives in two further configurations: one without accelerator (RC-30-Accl_x0) and the other with twice the volume of the control (RC-30-Accl_x2). Additionally, the PC-45 mix with 360 kg/m\({}^{3}\) cement volume, was performed using a new-generation additive based on nanocomposites, to study the benefits for the hardening effect at early stages (24 h) (PC-45-360-1%nano).
* Recycled concrete (RAC) behaviour: We analysed the effect of the RCA on the RC-30 mix, using recycled replacement rates (r) of 50% and 100% of the coarse conventional aggregate volume. The total mixing water was offset by the absorbed water volume due to the recycled aggregate fractions achieving similar free or effective water volumes in the three batches. This allows for a true comparison of strength criteria for the different mixtures, following Laserna and Montero [18].
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 3 Graph-Enhanced Multimodal Recommendation Method: GEMRec
### Multimodal Feature Preprocessing  
The main goal of text feature extraction is to obtain meaningful linguistic information from course descriptions, titles, subtitles, and other textual data. Initially, a pretrained BERT model is used to extract contextual definitions of words, and these are combined with bag-of-words (BoW) and TF-IDF features. A multilayer perceptron (MLP) network is designed to integrate BERT outputs with BoW and TF-IDF features, thereby capturing both semantic depth and statistical information, as follows:  
\[h_{\text{BERT}}=H_{0}h_{\text{text}}=MLP\;([h_{\text{BERT}};v_{BoW};v_{TF-IDF}]) \tag{1}\]  
The goal of video feature extraction is to capture the visual and temporal information within course videos. We use a combined approach, first applying a 3D Convolutional Neural Network (C3D) to capture spatial-temporal features, \(\mathbf{f}_{\text{C3D}}\), and incorporating 2D CNN with LSTM for sequential relationships. The video frames are uniformly sampled, extracting a series of key frames \(\{\mathbf{l}_{1},\mathbf{l}_{2},\dots,\mathbf{l}_{m}\}\), where each frame \(\text{lil\_ili}\) undergoes 2D CNN processing to derive spatial features \(f_{i}=CNN\;2D(I_{i}),i\in[1,m]\). These spatial features are then fed into an LSTM network to capture temporal dependencies, with the hidden state of the final synchronized layer \(\text{hmh\_mhm}\) representing the sequence.  
To merge features from C3D and LSTM, both short-term and long-term information is integrated to enhance the video feature representation, as follows:  
\[\alpha=softmax\;(W[f_{\text{C3D}};h_{m}]+b) \tag{2}\]  
\[h_{\text{video}}=\alpha\cdot f_{\text{C3D}}+(1-\alpha)\cdot h_{m} \tag{3}\]  
Audio feature extraction aims to capture acoustic and speech information in course audio. We use a Convolutional Recurrent Neural Network (CRNN) as the main feature extractor to extract local acoustic features F through convolutional layers, capture temporal dependencies H through recurrent layers, and then aggregate the features through a self-attention mechanism to generate a comprehensive audio representation, as follows:  
\[A=softmax\;\left(W_{a}\cdot tanh\;\left(W_{h}H^{\top}\right)\right) \tag{4}\]  
where \(W_{a}\) and \(W_{h}\) are trainable parameter matrices, and A is the attention weight matrix.  
To supplement the CRNN output with spectral information, we use Mel-frequency cepstral coefficients (MFCC) features, integrating them through a fully connected layer to further enhance fusion. This enables the model to fully leverage both deep learning and traditional audio features, as follows:  
\[h_{\text{audio}}=ReLU\;(W_{1}h_{\text{CRNN}}+b_{1})+ReLU\;(W_{2}m_{MFCC}+b_{ 2}) \tag{5}\]
# Combining Virtual Reality and Organizational Neuroscience for Leadership Assessment
## Literature Review  
### Evidence-Centered Design for Serious Games  
With the introduction of computer games in the 1970s, which produced a rapid increase in playing time among children as well as adults, and thanks to their appeal and playability, researchers began developing \"serious\" games (SGs) with specific purposes that went beyond common entertainment [11]. Serious games (SGs) are currently applied as an assessment methodology in training for a wide range of skills in various fields, including education, health, and the military, and have shown positive results [12; 13; 14]. In organizational leadership research, few studies have investigated the effectiveness of SGs as educational training tools to improve leadership skills [15; 16]. Moreover, leadership training studies have mainly used two-dimensional SGs, characterized by flat graphics and lower realism, as opposed to using three-dimensional SGs (3 DSGs), which include stereographic depth graphics and a higher sense of realism [17]. 3 DSG design features, based on fantasy or simulated worlds, storytelling, challenges and rewards, and advanced technologies that allow developing interactive three-dimensional environments, can stimulate user involvement and motivational engagement, provide more ecologically valid assessments and training, and reduce test anxiety and biases as compared to traditional approaches. Furthermore, the 3 DSG approach is a stealthy and non-verbal way to assess various behaviors (e.g., decision making, time taken to perform a task) during gameplay, is capable of measuring a wide range of skills, and can capture a vast amount of data over time. Because serious games involve achieving a set of goals while navigating complex situations, they can be used in companies for employee training purposes to develop problem-solving skills.  
Evidence-centered design (ECD) has been proposed as a valid and reliable framework of reference for the development of automated stealth assessment test designs [16; 18]. ECD is built on the premise that a test is a measurement instrument with which specific claims about the test scores are associated, and that a good test is a good match of the test items and the test takers' skills. Hence, ECD was conceived in the educational field to improve the validity and reliability of test measures for learners. The ECD framework defines three interconnected core models, namely the competency model(s), structural model(s), and task model(s). The competency model refers to the theoretical skills and abilities (unobservable indicators) that researchers want to measure using the game; the structural model identifies the multifaceted behaviors (observable indicators) that can reveal theoretical skills and abilities; and the task model refers to the tasks or situations that can activate behaviors related to the skills and abilities that researchers intend to measure. Therefore, ECD and 3 DSGs allow for the development of various contextual situations, thereby providing greater predictive validity and less bias than traditional assessments [16; 19; 20; 21].