esa-sceva/satcom-chunk-collection huggingface dataset:
# The Extended Information Systems Success Measurement Model: e-Learning Perspective
## 6 Discussion
In the context of the environment in which the proposed model and all associated hypotheses were tested and based on other statistical analyses that demonstrated the model's significance and strength, it is possible to draw the following conclusion. Likewise, in hypothesis H4, respondents most likely do not associate proactivity characteristics only with the observed IS. In addition, the results show respondents significantly consider proactivity as an indicator of the overall success of the IS, being used as a result of a specific change. Factor adaptability positively predicted behavioral intention to use the system at the UNS and the PICB. The significance of this relationship at UNS was evidenced with a path coefficient \(\beta=0.03\) and a significance level of \(p<0.05\). At the PICB, the strength and significance were even higher (\(\beta=0.37\); \(p<0.01\) ). Therefore, the relationship between adaptability and behavioral intention was vindicated according to both auxiliary hypotheses. The relationship between resilience and behavioral intention to use the system was statistically significant and positive. At UNS, the significance of this relationship was evidenced with a path coefficient \(\beta=0.25\), \(p<0.05\), and at the PICB with \(\beta=0.12\), \(p<0.05\). The resilience factor is evidenced to significantly predict behavioral intention to use the system in both cases. After analyzing the auxiliary relations among all three workforce agility constituents with the behavioral intention to use the IS in the teaching process at the UNS and the PICB, we conclude this relationship is confirmed and vital for IS success. The second quested factor from this study, use behavior, was observed through WA influence. The results show that for the IS used in the teaching process at UNS, the impact of proactivity on the use behavior is not statistically significant (\(\beta=-0.33\)). However, in another case at the PICB, this relationship was found to be statistically significant (\(\beta\) = 0.02; \(p<0.05\)). The influence of adaptability on use behavior was evidenced in both settings. At the UNS, adaptability was the most powerful of all three workforce agility constituents concerning use behavior (\(\beta\) = 0.18; \(p<0.05\)). The last WA component, resilience, demonstrated a positive impact on use behavior in both questioned institutions. Finalizing findings from all observed auxiliary hypotheses tested to question the importance of workforce agility in IS success, the authors of this paper demonstrated its significance. The proposed EISSMM model, combining essential factors from UTAUT and WA, can be used as an IS success measurement instrument in an e-learning context.
## 4 Industry 4.0 Concept in Railway Transportation
### Smart Grid Paradigm in Railway Transport
Since such a railway energy management system (REM-S) interacts with the main power grid and energy markets, its operation needs to be optimised [238] on a daily basis (day-ahead optimisation), which can take into account the daily train schedules, and on a short time scale (with schedules that are 15 min ahead being typical), which needs to fulfil the day-ahead power profile while taking into account the excesses and restrictions of local power production by coordinating between sub-stations and DERs. Real-time operators subsequently fulfil the commanded 15 min power profiles while also taking into consideration the real-time information and power flows from the local power network [238]. Obviously, the type of distributed energy resources (DG and ESS) and the means of their control would play crucial roles both in the day-ahead optimisation and local power flow control based on short time scale schedules. To this end, reference [242] considers a railway-to-grid smart energy management system based on advanced power converter topologies with electrical energy storages for a DC railway network, which can effectively re-route the energy between the power grid and the railway electrical network and increase their flexibility. Different power converter topologies for the integration of PV energy sources into the railway power grid are assessed in [243], whereas the authors of [244] discuss the use of energy storage systems on-board railway trains and propose a two-level hierarchical energy management system using AI in the form of a fuzzy logic system. It reports between 22.3% and 28.7% of improvement in energy efficiency with on-board ESS. The integration of a regenerative braking energy recuperation system with energy storage and PV systems into the railway power grid was analysed in [245]. Power flow optimisation has been performed by means of MILP and has shown the potential to reduce the cost of operation by about 30% compared to the conventional railway grid scenario. The expected evolution of the information integration of the modern railway traffic management system and energy management towards the future smart railway, which is characterised by a wide use of wireless communication networks, independent power sources, and wide data processing capabilities of individual transport entities and infrastructure, opens up space for new research and areas that are not covered by this work. Figure 20: Concept of smart grid-based railway energy management system [238]. Examples of those are traffic regulations, technological problems of timetable management, the issues of maintaining new technological systems that are not directly included in the railway system, and technological systems that have direct consequences on the safety of railway traffic.
## 4 Discussion
Regarding the plasma proteins, the oxidative status of a plasma compartment could be an important factor in altering the fixation of \({}^{99\text{m}}\)Tc on plasma proteins. Indeed, studies have demonstrated that different stressful situations could induce alterations to the plasma antioxidant systems in animals and human beings [50; 51; 52; 53; 54]. Our findings suggest that the binding of \({}^{99\text{m}}\)Tc to plasma proteins from rats might be altered after FS (Figure 4). Moreover, the data indicate that the swimming-induced alteration to the fixation of \({}^{99\text{m}}\)Tc on plasma proteins is not time-dependent, returning to control level after 5 or 20 min of recovery. These data suggest that this assay could be used to assess the effects of stress on the blood constituents. Stressful situations such as the hemodialysis procedure have been related to increases of oxidative stress and the alteration/damage of the antioxidant defense [54]. Other authors have demonstrated that the amount of a reduced form of human albumin and glutathione peroxidase are increased after hemodialysis [55; 56], while catalase, superoxide dismutase, glutathione-S-transferase, and glutathione peroxidase experimentally induce myocardial infarction (rats) [57]. [PERSON] et al. [58] reported that the negative consequences of exercise are generally related to the difference between the number of antioxidants (antioxidant enzymes and low-molecular-weight antioxidants) and the free radicals (reactive nitrogen and oxygen species) during physical exercise. In plasma, oxidative stress could lead to the excessive production of free radicals and/or a reduction in the antioxidant defenses, with an uncontrolled redox status of the cell. This imbalance could be by-passed with the production or activation of these antioxidant enzymes, as well as endogenous substances such as glutamine. These defense mechanisms could require some time to appear and block the oxidative stress effects, explaining the data obtained with the fixation of \({}^{99\text{m}}\)Tc on plasma proteins from rats submitted to swimming after recovery (Figure 7). Moreover, some oxidative stress markers in plasma and RBCs could also sustain and justify this finding. The limitations of the current research were related to the number of animals utilized in this work and the time used for the recovery from FS. Moreover, the scarcity of publications evaluating the effect of stress on blood constituent labeling limited the discussion of the results. Although there were some limitations in the current investigation, some important findings were observed.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 4 Discussion
### Sentinel-2 Bands and GPR Modelling for Parameter Retrievals
The traditional approaches for retrieving biophysical parameters from Earth observation data have involved VIs, whereas our GPR machine learning approach allows a more thorough exploration of the sensitivity of each band to the measured variable without making assumptions. From applying the GPR model within the band analysis framework, the most informative Sentinel-2 bands that we identified were also situated around spectral regions of known reflectance and absorption and had a reasonable relationship to the ground measurements when evaluated individually (Figure 4). The sensitivity of these spectra to the biophysical variables is also broadly in agreement with past research [11; 25; 32; 33]. We acknowledge, however, that our calibrated GPR modelling approaches are based on near-Earth (i.e., UAV) observations and, when applied to satellite-based observations, the retrieval accuracy would be subject to atmospheric effects. Research in [PERSON] et al. [24] has demonstrated a good agreement between atmospherically-corrected Sentinel-2 reflectance and ground-based radiometer measurements for potato crops. Nonetheless, we recommend additional testing of the robustness and a quantification of the uncertainty of the retrieval algorithms when applied to data acquired from the Sentinel-2 platform. Using an independent dataset for calibration and validation and the most informative bands, we also demonstrated that a simpler model approach of multivariate linear regression could provide generally comparable results to the GPR model (Figures 5 and 6). [PERSON] et al. [57] discusses the computation limitations of applying GPR models to large datasets and, therefore, the simpler regression approach may be more practical when retrieving pixel-level estimates of the variables across regional and country scales.
# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 2 Conventional Sound Event Detection
### Weakly Supervised Sound Event Detection Framework  
Figure 2 shows the basic architecture of the weakly supervised learning framework for sound event detection. The input audio features are passed through a number of convolution layers to generate segmentation maps in 1D or 2D space. There are two outputs for audio tagging and sound event detection, respectively. In audio tagging, true labels are given for audio class indices, so a pooling is applied to convert the segmentation maps to probabilistic predictions, and the loss is computed with respect to the given true labels. In sound event detection, the output is interpreted as a segmentation map in 1D or 2D space. Because the onset and lengths of the sound events are not given in the training dataset, sound event timings cannot be the direct training targets. The audio tagging information--the class labels of the audio events--are used to infer the sound events. A convolutional neural network (CNN) model has been proposed for the weakly supervised learning [16]. It resembles the conventional VGG network [29] with modifications. In order to ensure for the output of the model is the same size as the input of the model, there are no downsampling layers such as max or average pooling, but only nine convolutional layers. However, this makes the receptive field smaller and computational cost very expensive.  
Figure 2: Weakly supervised sound event detection framework. The outputs of convolutional layers are segmentation maps and interpreted as audio tagging and sound event detection results. Because no ground truth for detection is provided, detection loss is not used in training the network. Only classification loss is computed.
# Improving Convolutional Neural Networks' Accuracy in Noisy Environments Using k-Nearest Neighbors
## 3 Experiments
### Comparison with Other Classifiers  
One must also address the question of whether the improvement in the results is due to either the good robustness of the kNN against noise or the replacement of the softmax layer by another supervised classifier. That is why in this section, we analyze the general results of replacing the softmax with other schemes. In particular, we consider the following representative algorithms for supervised classification:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Test**} \\ \cline{2-5}
**Noise** & **CNN + softmax \(>\)** & **CNN + kNN \(>\)** & **CNN + \(\ell_{2}\) + kNN \(>\)** & **CNN + \(\ell_{2}\) + kNN \(>\)** \\  & **kNN** & **CNN + softmax** & **CNN + softmax** & **CNN + kNN** \\ \hline
0 & \(\checkmark\) & & & \(\checkmark\) \\ \hline
10 & \(\checkmark\) & & \(\checkmark\) & \(\checkmark\) \\ \hline
20 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline
30 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline
40 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained for the statistical significance tests with respect to the synthetic noise induced in the data. The symbol \(\checkmark\) states the test is significantly accepted, whereas \(\checkmark\) states that the test is accepted in the other way. No symbol is depicted otherwise. Significance has been set to \(p<\) 0.05.  
* Support Vector Machine (SVM) [36]: This learns a hyperplane that tries to maximize the distance to the nearest samples (support vectors) of each class. It makes use of kernel functions to handle non-linear decision boundaries. In our case, a radial basis function (or Gaussian) kernel is considered. Typically, SVM also considers a parameter that measures the cost of learning a non-optimal hyperplane, which is usually referred to as parameter \(c\). During preliminary experiments, we tuned this parameter in the range \(c\in[1,9]\).
* Random Forest (RaF) [37]: This builds an ensemble classifier by generating several random decision trees at the training stage. The final output is taken by combining the individual decisions of each tree. The number of random trees has been established experimenting in the range \(t\in[10,500]\).  
For the sake of clarity, we directly provide in Figure 4 the average \(F_{1}\) of these classifiers over all datasets, in comparison to the CNN + softmax, CNN + kNN and CNN + \(\ell_{2}\) + kNN schemes. It can be observed that the approach proposed in this work (CNN + \(\ell_{2}\) + kNN) attains the best results in all cases, increasing its margin over other schemes as the noise level is higher. Note also that, on average, SVM and RaF also outperform both CNN + softmax and CNN + kNN in the presence of noise, yet they remain noticeably below CNN + \(\ell_{2}\) + kNN in terms of accuracy.
## 1 Introduction and Related Works
The strain hardening behavior of the three stainless steel sheets used in this study shows that martensite and grain size strengthening contributed to the case of the two 304 stainless steel sheets. In contrast, in the case of the 316 stainless steel sheets, only grain size contribution was found. Ref. [26] evaluated the effect of plastic deformation on austenitic steel using electromagnetic nondestructive evaluation (eNDE) techniques. The goal was to differentiate various levels of plastic deformation in conductive biomaterials. Two instruments were used: the GF708 magnetic field sensor (Sensitec GmbH, Mainz, Germany) and the Magnet Physik device (Dr. Steingroever GmbH, Magnet-Physik, Cologne, Germany), which measures remanence and coercivity up to 200 \({}^{\circ}\)C. A light microscope aided in deformation analysis. The results showed that plastic deformation below 5% was undetectable. Three-dimensional scanning provided new insights into the magnetic behavior of specimens. The study confirmed that mechanical and electromagnetic properties co-vary, and detecting plastic deformation in AISI 304 depends on the appropriate sensing method. This article explores changes in the mechanical and magnetic properties of biocompatible steel AISI 304 that have been mechanically processed and subjected to various thermal treatments. It presents measurements evaluating the selected mechanical parameters and intrinsic magnetic fields for electrically conductive material. The results are compared and critically evaluated, and new insight is gained in the quantitative nondestructive evaluation of the investigated structure. The novelty of this article can be considered the systematic investigation of the influence of heat treatment on the mechanical and magnetic properties of AISI 304. There is a focus on a comprehensive analysis of the relationship between heat treatment and material properties after strain hardening, which is not usually covered in detail. Several analytical procedures are used in the solution: the research includes mechanical (microhardness, strength, tensile strength) and magnetic (internal magnetic field) measurements, thus providing a more comprehensive view of the behavior of the steel at different temperatures. At the same time, a detailed analysis of the influence of temperature on magnetic properties is presented. In contrast to conventional studies, which often use wider temperature ranges, in this study, the samples were analyzed with acceptable resolution (25 \({}^{\circ}\)C steps from 50 \({}^{\circ}\)C to 1200 \({}^{\circ}\)C), which allows for a more detailed identification of changes in structure and properties. The results can be used to optimize the heat treatment of austenitic steels in technical and biomedical applications, where monitoring their mechanical and magnetic properties is essential.
## 4 Agent Training with Markov Decision Process (MDP)
### DRL-Based Solution
#### 4.2.5 Overall Training and Evaluation Process  
The collected epoch set \(\mathcal{E}\) is partitioned into subsets of length \(M\), yielding \(\lfloor N/M\rfloor\) epoch subsets. For each subset, the first \(M_{1}\) epochs are used for initial positioning, while the remaining epochs form an episode of length \(M_{2}\) for training the agent (see Algorithm 2). This structured approach maximizes sample utilization efficiency and learning frequency while ensuring stable and uniform training progression. By optimizing both the injection and utilization processes of the experience replay buffer, it enables the learning process to closely follow the pace of agent-environment interactions.  
```
Input: Online network \(Q(s,a;\theta)\), target network \(Q(s,a;\theta^{\prime})\), replay buffer \(\mathcal{D}\), exploration rate \(\varepsilon\). Output: The updated parameters \(\theta\) and \(\theta^{\prime}\).
1 for timestep \(t\in[1,T]\)do
2 for episode \(k\in[1,\lfloor N/M\rfloor]\)do
3 Agent observes state \(s_{k}(t)\) from AWR-PF.
4 If\(t\
eq 1\)then
5\(s_{k}^{\prime}(t-1)=s_{k}(t)\).
6 endif
7\(d_{k}(t)=0\).
8 If\(t=T\)then
9\(s_{k}^{\prime}(t)=\text{zeros}(9)\), \(d_{k}(t)=1\).
10 endif
11 With probability \(\varepsilon\), agent randomly select an action from \(A(t)\) as \(a_{k}(t)\),
12 otherwise \(a_{k}(t)=\text{argmax}_{i}Q(s_{k}(t),a_{i};\theta)\).
13\(\varepsilon=\text{max}(\varepsilon_{\text{min}},\lambda\varepsilon)\).
14 Obtain reward \(r_{k}(t)\) from AWR-PF.
15 Store tuple \(\big{(}s_{k}(t),a_{k}(t),r_{k}(t),s_{k}^{\prime}(t),d_{k}(t)\big{)}\) in \(\mathcal{D}\).
16 Sample random mini-batch \(B\) form \(\mathcal{D}\).
17 Calculate estimated Q-value: \(y=r+\gamma\cdot(1-d)\cdot Q(s^{\prime},\text{argmax}_{a^{\prime}}Q(s^{\prime},a^{\prime};\theta);\theta^{\prime})\).
18 Calculate \(L(\theta)=\mathbb{E}_{(s,\lambda\varepsilon,\gamma)\sim B}\Big{[}\big{(}y-Q(s,a;\theta)\big{)}^{2}\Big{]}\).
19 Update online network: \(\theta\leftarrow\theta-\alpha\cdot\
abla_{\theta}L(\theta)\).
20 Update target network: \(\theta^{\prime}\leftarrow\tau\cdot\theta+(1-\tau)\cdot\theta^{\prime}\).
21 endfor
22 endfor
```  
**Algorithm 2** Training Iteration of the DDQN-EW Algorithm  
Upon completion of agent training, we evaluate its generalization capability using a separately collected epoch set \(\mathcal{E}^{\prime}=\{E_{n}\}_{n=1,2,\ldots,N^{\prime}}\). Following the same protocol as the training phase, we partition \(\mathcal{E}\prime\) into \(\lfloor N^{\prime}/M\rfloor\) independent evaluation episodes. The trained agent then executes a pure exploitation policy (see Algorithm 3) to perform epoch-specific weighting and positioning throughout these episodes.
## 4 Communication Standards for Wireless Sensor Networks
### Wavenis  
Wavenis is an emergent wireless communication technology for low power machine to machine communication applications. It can be used for distances up to 200 m for various indoor applications. This technology can be used in various metering applications in smart grid. It can be used in automatic meter reading, advanced metering infrastructure and remote communication applications. Wavenis operates in the bands of 868 MHz, 915 MHz, and 433 MHz. The data throughput of Wavenis ranges from 4.8 Kbps to 100 Kbps [59]. Table 2 shows the comparative analysis of various communication standards for WSN.  
Wireless sensor network (WSN) uses various short distance communication technologies as they are suitable for power efficiency and wide spread availability. These communication standards use unlicensed ISM band for their operation which makes WSN effective for communication but vulnerable to attacks.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Protocol/Standard** & \begin{tabular}{c} **Spectrum** \\ **Type** \\ \end{tabular} & **Frequency Band** & \begin{tabular}{c} **Maximum Data** \\ **Throughput** \\ \end{tabular} & \
\begin{tabular}{c} **Coverage** \\ **Range** \\ \end{tabular} & **Advantages** & **Disadvantages** &
\begin{tabular}{c} **Market** \\ **Espousal** \\ \end{tabular} \\ \hline Zigbee & Unlicensed & 868 MHz, 915 MHz, 2.4 MHz & 250 Kbps & Up to 100 m & Low cost, Low power usage, Less complex & Low data rate, Short range, Interference with other technologies using ISM band, Low battery power supply & Very High \\ \hline Bluetooth & Unlicensed & 2.4 GHz & 21 Kbps & Up to 100 m & Low power usage & Low data rates, Very short range, Less secured, Interference with other technologies using ISM band & Very High \\ \hline Wi-Fi & Unlicensed & 2.4 GHz, 5.8 GHz & 2 Mbps to 54 Mbps & Up to 250 m & High data rates, Robust, Point to point and point to multipoint communication, Low cost, IP support and network scalability & Complex design, Prone to interference, data rates may deteriorate due to interference or co-existence problems & Very high \\ \hline Z-Wave & Unlicensed & 868 MHz, 908 MHz & 9.6 Kbps to 40 Kbps & Up to 30 m & Low power usage & Very Low data rates, Short range & Medium \\ \hline  & & & & Simple and low cost solutions, Allows co-existence of multiple networks, Keeps the black and white list of devices, Self-organizing standard, More secured & All the devices operating on WirelessHART must have routing capability, No manager & Very high for industrial control \\ \hline
6 LoWPAN & Unlicensed & 868 MHz, 915 MHz, 2.4 MHz & Up to 250 Kbps & Up to 100 m & Low power usage & Low data rates, Short range & Medium \\ \hline Wavenis & Unlicensed & 868 MHz, 915 MHz, and 433 MHz & 4.8 Kbps to 100 Kbps & Up to 200 m & Low power usage & Very Low data rates, Short range & Very low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of various communication standards for WSN (Wireless Sensor Network).